01900nas a2200169 4500008004100000022001400041245010800055210006900163260000800232300001400240490000800254520135900262100001801621700001701639700002601656856004801682 2017 eng d a1873-274700aEEG based zero-phase phase-locking value (PLV) and effects of spatial filtering during actual movement.0 aEEG based zerophase phaselocking value PLV and effects of spatia cApr a156–1640 v1303 aPhase-locking value (PLV) is a well-known feature in sensorimotor rhythm (SMR) based BCI. Zero-phase PLV has not been explored because it is generally regarded as the result of volume conduction. Because spatial filters are often used to enhance the amplitude (square root of band power (BP)) feature and attenuate volume conduction, they are frequently applied as pre-processing methods when computing PLV. However, the effects of spatial filtering on PLV are ambiguous. Therefore, this article aims to explore whether zero-phase PLV is meaningful and how this is influenced by spatial filtering. Based on archival EEG data of left and right hand movement tasks for 32 subjects, we compared BP and PLV feature using data with and without pre-processing by a large Laplacian. Results showed that using ear-referenced data, zero-phase PLV provided unique information independent of BP for task prediction which was not explained by volume conduction and was significantly decreased when a large Laplacian was applied. In other words, the large Laplacian eliminated the useful information in zero-phase PLV for task prediction suggesting that it contains effects of both amplitude and phase. Therefore, zero-phase PLV may have functional significance beyond volume conduction. The interpretation of spatial filtering may be complicated by effects of phase.1 aJian, Wenjuan1 aChen, Minyou1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2816119202493nas a2200145 4500008004100000245011400041210006900155260000800224300000800232490000700240520200500247100002102252700002602273856004802299 2017 eng d00aEEG Error Prediction as a Solution for Combining the Advantages of Retrieval Practice and Errorless Learning.0 aEEG Error Prediction as a Solution for Combining the Advantages  cMar a1400 v113 aGiven the frequency of naming errors in aphasia, a common aim of speech and language rehabilitation is the improvement of naming. Based on evidence of significant word recall improvements in patients with memory impairments, errorless learning methods have been successfully applied to naming therapy in aphasia; however, other evidence suggests that although errorless learning can lead to better performance during treatment sessions, retrieval practice may be the key to lasting improvements. Task performance may vary with brain state (e.g., level of arousal, degree of task focus), and changes in brain state can be detected using EEG. With the ultimate goal of designing a system that monitors patient brain state in real time during therapy, we sought to determine whether errors could be predicted using spectral features obtained from an analysis of EEG. Thus, this study aimed to investigate the use of individual EEG responses to predict error production in aphasia. Eight participants with aphasia each completed 900 object-naming trials across three sessions while EEG was recorded and response accuracy scored for each trial. Analysis of the EEG response for seven of the eight participants showed significant correlations between EEG features and response accuracy (correct vs. incorrect) and error correction (correct, self-corrected, incorrect). Furthermore, upon combining the training data for the first two sessions, the model generalized to predict accuracy for performance in the third session for seven participants when accuracy was used as a predictor, and for five participants when error correction category was used as a predictor. With such ability to predict errors during therapy, it may be possible to use this information to intervene with errorless learning strategies only when necessary, thereby allowing patients to benefit from both the high within-session success of errorless learning as well as the longer-term improvements associated with retrieval practice.1 aRiley, Ellyn, A.1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2839663000661nas a2200133 4500008004100000022001400041245007400055210006900129260000800198300001100206520023600217100002600453856004800479 2017 eng d a1744-414400aEvaluation of multidimensional models of WAIS-IV subtest performance.0 aEvaluation of multidimensional models of WAISIV subtest performa cApr a1–143 aThe present study examined the extent to which the covariance structure of the WAIS-IV is best accounted for by models that assume that test performance is the result of group-level factors and multiple independent general factors.1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2843003101456nas a2200121 4500008004100000022001400041245009200055210006900147260000800216520103600224100002601260856004801286 2017 eng d a0334-176300aHow neuroscience can inform the study of individual differences in cognitive abilities.0 aHow neuroscience can inform the study of individual differences  cFeb3 aTheories of human mental abilities should be consistent with what is known in neuroscience. Currently, tests of human mental abilities are modeled by cognitive constructs such as attention, working memory, and speed of information processing. These constructs are in turn related to a single general ability. However, brains are very complex systems and whether most of the variability between the operations of different brains can be ascribed to a single factor is questionable. Research in neuroscience suggests that psychological processes such as perception, attention, decision, and executive control are emergent properties of interacting distributed networks. The modules that make up these networks use similar computational processes that involve multiple forms of neural plasticity, each having different time constants. Accordingly, these networks might best be characterized in terms of the information they process rather than in terms of abstract psychological processes such as working memory and executive control.1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2819555600969nas a2200193 4500008004100000022001400041245007600055210006900131260000800200300001100208490000700219520037900226100002600605700002500631700002600656700002400682700002100706856004800727 2017 eng d a1741-255200aPrediction of subjective ratings of emotional pictures by EEG features.0 aPrediction of subjective ratings of emotional pictures by EEG fe cFeb a0160090 v143 aEmotion dysregulation is an important aspect of many psychiatric disorders. Brain-computer interface (BCI) technology could be a powerful new approach to facilitating therapeutic self-regulation of emotions. One possible BCI method would be to provide stimulus-specific feedback based on subject-specific electroencephalographic (EEG) responses to emotion-eliciting stimuli.1 aMcFarland, Dennis, J.1 aParvaz, Muhammad, A.1 aSarnacki, William, A.1 aGoldstein, Rita, Z.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/2793477601805nas a2200145 4500008004100000022001400041245013800055210006900193260000800262520128000270100001801550700001701568700002601585856004801611 2017 eng d a1741-044400aUse of phase-locking value in sensorimotor rhythm-based brain-computer interface: zero-phase coupling and effects of spatial filters.0 aUse of phaselocking value in sensorimotor rhythmbased braincompu cMar3 aPhase-locking value (PLV) is a potentially useful feature in sensorimotor rhythm-based brain-computer interface (BCI). However, volume conduction may cause spurious zero-phase coupling between two EEG signals and it is not clear whether PLV effects are independent of spectral amplitude. Volume conduction might be reduced by spatial filtering, but it is uncertain what impact this might have on PLV. Therefore, the goal of this study was to explore whether zero-phase PLV is meaningful and how it is affected by spatial filtering. Both amplitude and PLV feature were extracted in the frequency band of 10-15?Hz by classical methods using archival EEG data of 18 subjects trained on a two-target BCI task. The results show that with right ear-referenced data, there is meaningful long-range zero-phase synchronization likely involving the primary motor area and the supplementary motor area that cannot be explained by volume conduction. Another novel finding is that the large Laplacian spatial filter enhances the amplitude feature but eliminates most of the phase information seen in ear-referenced data. A bipolar channel using phase-coupled areas also includes both phase and amplitude information and has a significant practical advantage since fewer channels required.1 aJian, Wenjuan1 aChen, Minyou1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2834333301744nas a2200145 4500008004100000022001400041245009100055210006900146260000800215300001400223490000700237520128000244100002601524856004801550 2016 eng d a1552-348900aModeling General and Specific Abilities: Evaluation of Bifactor Models for the WJ-III.0 aModeling General and Specific Abilities Evaluation of Bifactor M cDec a698–7060 v233 aThe present study examined issues related to structural modeling of abilities by the use of simulated data as well as analysis of the standardization data from the Woodcock-Johnson-III. In both cases, results were evaluated with cross-validation. Simulation results showed that cross-validation with an independent data set was more successful in identifying the model that was used to generate test scores than were several fit indices. Analysis of the Woodcock-Johnson-III standardization data with cross-validation showed that bifactor models provided better fit than hierarchical or correlated factor models. This was true considering both fit indices and cross-validation. General and specific factors shared a considerable amount of variance as evaluated by using the bifactor models to partition variance. The results of the present study suggest that there is a certain degree of ambiguity in determining the exact amount of covariance in test performance accounted for by general and specific factors. This calls in to question the practice of adjusting or controlling for general abilities when evaluating measures of specific abilities. Evidence for the validity of a construct should not be limited to factor analysis of tests purported to measure that construct.1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2618790100997nas a2200181 4500008004100000022001400041245008500055210006900140260000800209300001100217490000700228520043500235653002400670100002600694700002600720700002100746856004800767 2015 eng d a1741-255200aEffects of training pre-movement sensorimotor rhythms on behavioral performance.0 aEffects of training premovement sensorimotor rhythms on behavior cDec a0660210 v123 aBrain-computer interface (BCI) technology might contribute to rehabilitation of motor function. This speculation is based on the premise that modifying the electroencephalographic (EEG) activity will modify behavior, a proposition for which there is limited empirical data. The present study asked whether learned modulation of pre-movement sensorimotor rhythm (SMR) activity can affect motor performance in normal human subjects.10asensorimotor cortex1 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/2652911903835nas a2200301 4500008004100000022001400041245015300055210006900208260001200277520274700289653005303036653004003089653003503129653003403164653003303198653003503231100002003266700002203286700002603308700002103334700001903355700002103374700001903395700002603414700002103440700002403461856004803485 2015 eng d a1872-895200aP300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls.0 aP300based braincomputer interface BCI eventrelated potentials ER c02/20153 a

OBJECTIVE: Brain-computer interfaces (BCIs) aimed at restoring communication to people with severe neuromuscular disabilities often use event-related potentials (ERPs) in scalp-recorded EEG activity. Up to the present, most research and development in this area has been done in the laboratory with young healthy control subjects. In order to facilitate the development of BCI most useful to people with disabilities, the present study set out to: (1) determine whether people with amyotrophic lateral sclerosis (ALS) and healthy, age-matched volunteers (HVs) differ in the speed and accuracy of their ERP-based BCI use; (2) compare the ERP characteristics of these two groups; and (3) identify ERP-related factors that might enable improvement in BCI performance for people with disabilities.

METHODS: Sixteen EEG channels were recorded while people with ALS or healthy age-matched volunteers (HVs) used a P300-based BCI. The subjects with ALS had little or no remaining useful motor control (mean ALS Functional Rating Scale-Revised 9.4 (±9.5SD) (range 0-25)). Each subject attended to a target item as the items in a 6×6 visual matrix flashed. The BCI used a stepwise linear discriminant function (SWLDA) to determine the item the user wished to select (i.e., the target item). Offline analyses assessed the latencies, amplitudes, and locations of ERPs to the target and non-target items for people with ALS and age-matched control subjects.

RESULTS: BCI accuracy and communication rate did not differ significantly between ALS users and HVs. Although ERP morphology was similar for the two groups, their target ERPs differed significantly in the location and amplitude of the late positivity (P300), the amplitude of the early negativity (N200), and the latency of the late negativity (LN).

CONCLUSIONS: The differences in target ERP components between people with ALS and age-matched HVs are consistent with the growing recognition that ALS may affect cortical function. The development of BCIs for use by this population may begin with studies in HVs but also needs to include studies in people with ALS. Their differences in ERP components may affect the selection of electrode montages, and might also affect the selection of presentation parameters (e.g., matrix design, stimulation rate).

SIGNIFICANCE: P300-based BCI performance in people severely disabled by ALS is similar to that of age-matched control subjects. At the same time, their ERP components differ to some degree from those of controls. Attention to these differences could contribute to the development of BCIs useful to those with ALS and possibly to others with severe neuromuscular disabilities.

10aalternative and augmentative communication (AAC)10aamyotrophic lateral sclerosis (ALS)10aBrain-computer interface (BCI)10abrain-machine interface (BMI)10aelectroencephalography (EEG)10aevent-related potentials (ERP)1 aMcCane, Lynn, M1 aHeckman, Susan, M1 aMcFarland, Dennis, J.1 aTownsend, George1 aMak, Joseph, N1 aSellers, Eric, W1 aZeitlin, Debra1 aTenteromano, Laura, M1 aWolpaw, Jonathan1 aVaughan, Theresa, M uhttp://www.ncbi.nlm.nih.gov/pubmed/2570394002134nas a2200241 4500008004100000022001400041245009300055210006900148260001200217300001100229490000700240520137400247653003001621653002501651653002001676653001701696653003501713100002601748700002401774700002601798700002001824856004801844 2015 eng d a1741-255200aWord-level language modeling for P300 spellers based on discriminative graphical models.0 aWordlevel language modeling for P300 spellers based on discrimin c04/2015 a0260070 v123 a

OBJECTIVE: In this work we propose a probabilistic graphical model framework that uses language priors at the level of words as a mechanism to increase the performance of P300-based spellers.

APPROACH: This paper is concerned with brain-computer interfaces based on P300 spellers. Motivated by P300 spelling scenarios involving communication based on a limited vocabulary, we propose a probabilistic graphical model framework and an associated classification algorithm that uses learned statistical models of language at the level of words. Exploiting such high-level contextual information helps reduce the error rate of the speller.

MAIN RESULTS: Our experimental results demonstrate that the proposed approach offers several advantages over existing methods. Most importantly, it increases the classification accuracy while reducing the number of times the letters need to be flashed, increasing the communication rate of the system.

SIGNIFICANCE: The proposed approach models all the variables in the P300 speller in a unified framework and has the capability to correct errors in previous letters in a word, given the data for the current one. The structure of the model we propose allows the use of efficient inference algorithms, which in turn makes it possible to use this approach in real-time applications.

10aBrain Computer Interfaces10ainference algorithms10alanguage models10aP300 speller10aprobabilistic graphical models1 aSaa, Jaime, F Delgado1 ade Pesters, Adriana1 aMcFarland, Dennis, J.1 aÇetin, Müjdat uhttp://www.ncbi.nlm.nih.gov/pubmed/2568629302427nas a2200349 4500008004100000022001400041245011100055210006900166260001200235300001100247490000700258520132200265653001501587653002801602653003001630653003701660653002701697653002901724653001101753653001601764653001701780653001301797653003501810653003101845653003201876653004101908653002901949100001201978700001301990700002602003856004802029 2014 eng d a1558-021000aAdaptive spatio-temporal filtering for movement related potentials in EEG-based brain-computer interfaces.0 aAdaptive spatiotemporal filtering for movement related potential c07/2014 a847-570 v223 aMovement related potentials (MRPs) are used as features in many brain-computer interfaces (BCIs) based on electroencephalogram (EEG). MRP feature extraction is challenging since EEG is noisy and varies between subjects. Previous studies used spatial and spatio-temporal filtering methods to deal with these problems. However, they did not optimize temporal information or may have been susceptible to overfitting when training data are limited and the feature space is of high dimension. Furthermore, most of these studies manually select data windows and low-pass frequencies. We propose an adaptive spatio-temporal (AST) filtering method to model MRPs more accurately in lower dimensional space. AST automatically optimizes all parameters by employing a Gaussian kernel to construct a low-pass time-frequency filter and a linear ridge regression (LRR) algorithm to compute a spatial filter. Optimal parameters are simultaneously sought by minimizing leave-one-out cross-validation error through gradient descent. Using four BCI datasets from 12 individuals, we compare the performances of AST filter to two popular methods: the discriminant spatial pattern filter and regularized spatio-temporal filter. The results demonstrate that our AST filter can make more accurate predictions and is computationally feasible.10aAlgorithms10aArtificial Intelligence10abrain-computer interfaces10aData Interpretation, Statistical10aElectroencephalography10aEvoked Potentials, Motor10aHumans10aImagination10aMotor Cortex10aMovement10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity10aSignal Processing, Computer-Assisted10aSpatio-Temporal Analysis1 aLu, Jun1 aXie, Kan1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2472363201705nas a2200157 4500008004100000022001400041245008200055210006900137260001200206520117900218653002901397653002501426653002201451100002601473856004801499 2014 eng d a1872-769700aThe advantages of the surface Laplacian in brain-computer interface research.0 aadvantages of the surface Laplacian in braincomputer interface r c08/20143 a

Brain-computer interface (BCI) systems frequently use signal processing methods, such as spatial filtering, to enhance performance. The surface Laplacian can reduce spatial noise and aid in identification of sources. In BCI research, these two functions of the surface Laplacian correspond to prediction accuracy and signal orthogonality. In the present study, an off-line analysis of data from a sensorimotor rhythm-based BCI task dissociated these functions of the surface Laplacian by comparing nearest-neighbor and next-nearest neighbor Laplacian algorithms. The nearest-neighbor Laplacian produced signals that were more orthogonal while the next-nearest Laplacian produced signals that resulted in better accuracy. Both prediction and signal identification are important for BCI research. Better prediction of user's intent produces increased speed and accuracy of communication and control. Signal identification is important for ruling out the possibility of control by artifacts. Identifying the nature of the control signal is relevant both to understanding exactly what is being studied and in terms of usability for individuals with limited motor control.

10abrain-computer interface10asensorimotor rhythms10asurface laplacian1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2509128602595nas a2200421 4500008004100000022001400041245009200055210006900147260001200216300001100228490000700239520137400246653001001620653000901630653003401639653002801673653003001701653002801731653002701759653003501786653001101821653001101832653000901843653001601852653001901868653002301887653002801910653001801938100002001956700002101976700002601997700001902023700001902042700001902061700002102080700002402101856004802125 2014 eng d a2167-922300aBrain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis.0 aBraincomputer interface BCI evaluation in people with amyotrophi c06/2014 a207-150 v153 aBrain-computer interfaces (BCIs) might restore communication to people severely disabled by amyotrophic lateral sclerosis (ALS) or other disorders. We sought to: 1) define a protocol for determining whether a person with ALS can use a visual P300-based BCI; 2) determine what proportion of this population can use the BCI; and 3) identify factors affecting BCI performance. Twenty-five individuals with ALS completed an evaluation protocol using a standard 6 × 6 matrix and parameters selected by stepwise linear discrimination. With an 8-channel EEG montage, the subjects fell into two groups in BCI accuracy (chance accuracy 3%). Seventeen averaged 92 (± 3)% (range 71-100%), which is adequate for communication (G70 group). Eight averaged 12 (± 6)% (range 0-36%), inadequate for communication (L40 subject group). Performance did not correlate with disability: 11/17 (65%) of G70 subjects were severely disabled (i.e. ALSFRS-R < 5). All L40 subjects had visual impairments (e.g. nystagmus, diplopia, ptosis). P300 was larger and more anterior in G70 subjects. A 16-channel montage did not significantly improve accuracy. In conclusion, most people severely disabled by ALS could use a visual P300-based BCI for communication. In those who could not, visual impairment was the principal obstacle. For these individuals, auditory P300-based BCIs might be effective.10aAdult10aAged10aAmyotrophic Lateral Sclerosis10aBiofeedback, Psychology10abrain-computer interfaces10aCommunication Disorders10aElectroencephalography10aEvent-Related Potentials, P30010aFemale10aHumans10aMale10aMiddle Aged10aOnline Systems10aPhotic Stimulation10aPsychomotor Performance10aReaction Time1 aMcCane, Lynn, M1 aSellers, Eric, W1 aMcFarland, Dennis, J.1 aMak, Joseph, N1 aCarmack, Steve1 aZeitlin, Debra1 aWolpaw, Jonathan1 aVaughan, Theresa, M uhttp://www.ncbi.nlm.nih.gov/pubmed/2455584300766nas a2200217 4500008004100000022001400041245013600055210006900191260001200260300001000272490000700282653002400289653003400313653004400347653001100391653002900402653002000431100002600451700002300477856004800500 2014 eng d a2157-310700aModality specificity is the preferred method for diagnosing the auditory processing disorder (APD): response to Moore and Ferguson.0 aModality specificity is the preferred method for diagnosing the  c08/2014 a698-90 v2510aAuditory Perception10aAuditory Perceptual Disorders10aEvoked Potentials, Auditory, Brain Stem10aHumans10aNeuropsychological Tests10aPsychoacoustics1 aMcFarland, Dennis, J.1 aCacace, Anthony, T uhttp://www.ncbi.nlm.nih.gov/pubmed/2536537300757nas a2200217 4500008004100000022001400041245012700055210006900182260001200251300001000263490000700273653002400280653003400304653004400338653001100382653002900393653002000422100002300442700002600465856004800491 2014 eng d a2157-310700aModality Specificity trumps other methods for diagnosing the auditory processing disorder (APD): response to Dillon et al.0 aModality Specificity trumps other methods for diagnosing the aud c08/2014 a703-50 v2510aAuditory Perception10aAuditory Perceptual Disorders10aEvoked Potentials, Auditory, Brain Stem10aHumans10aNeuropsychological Tests10aPsychoacoustics1 aCacace, Anthony, T1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2536537501901nas a2200145 4500008004100000022001400041245011500055210006900170260001200239300001200251490000700263520141100270100002601681856004801707 2014 eng d a1558-910200aSimulating the effects of common and specific abilities on test performance: an evaluation of factor analysis.0 aSimulating the effects of common and specific abilities on test  c10/2014 a1919-280 v573 a

PURPOSE: Factor analysis is a useful technique to aid in organizing multivariate data characterizing speech, language, and auditory abilities. However, knowledge of the limitations of factor analysis is essential for proper interpretation of results. The present study used simulated test scores to illustrate some characteristics of factor analysis.

METHOD: Linear models were used to simulate test scores that were determined by multiple latent variables. These simulated test scores were evaluated with principal components analysis and, in certain cases, structural equation modeling. In addition, a subset of simulated individuals characterized by poor test performance was examined.

RESULTS: The number of factors recovered and their identity do not necessarily correspond to the structure of the latent variables that generated the test scores. The first principal component may represent variance from multiple uncorrelated sources. Practices such as correction or control for general cognitive ability may produce misleading results.

CONCLUSIONS: Inferences from the results of factor analysis should be primarily about the structure of test batteries rather than the structure of human mental abilities. Researchers and clinicians should consider multiple sources of evidence to evaluate hypotheses about the processes generating test results.

1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2468643802668nas a2200241 4500008004100000022001400041245009000055210006900145260001200214300001100226490000700237520188700244653002802131653003502159653003402194653003102228653004102259653001902300100001202319700002602331700002102357856004802378 2013 eng d a1741-255200aAdaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces.0 aAdaptive Laplacian filtering for sensorimotor rhythmbased brainc c02/2013 a0160020 v103 aOBJECTIVE: Sensorimotor rhythms (SMRs) are 8-30 Hz oscillations in the electroencephalogram (EEG) recorded from the scalp over sensorimotor cortex that change with movement and/or movement imagery. Many brain-computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two or three dimensions. At the same time, if SMR-based BCIs are to be useful for people with neuromuscular disabilities, their accuracy and reliability must be improved substantially. These BCIs often use spatial filtering methods such as common average reference (CAR), Laplacian (LAP) filter or common spatial pattern (CSP) filter to enhance the signal-to-noise ratio of EEG. Here, we test the hypothesis that a new filter design, called an 'adaptive Laplacian (ALAP) filter', can provide better performance for SMR-based BCIs. APPROACH: An ALAP filter employs a Gaussian kernel to construct a smooth spatial gradient of channel weights and then simultaneously seeks the optimal kernel radius of this spatial filter and the regularization parameter of linear ridge regression. This optimization is based on minimizing the leave-one-out cross-validation error through a gradient descent method and is computationally feasible. MAIN RESULTS: Using a variety of kinds of BCI data from a total of 22 individuals, we compare the performances of ALAP filter to CAR, small LAP, large LAP and CSP filters. With a large number of channels and limited data, ALAP performs significantly better than CSP, CAR, small LAP and large LAP both in classification accuracy and in mean-squared error. Using fewer channels restricted to motor areas, ALAP is still superior to CAR, small LAP and large LAP, but equally matched to CSP. SIGNIFICANCE: Thus, ALAP may help to improve the accuracy and robustness of SMR-based BCIs.10aassistive communication10abrain computer interface (BCI)10abrain-machine interface (BMI)10aelectroencephalogram (EEG)10aleave-one-out (LOO) cross-validation10aspatial filter1 aLu, Jun1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/2322087901885nas a2200193 4500008004100000022001400041245009100055210006900146260001200215300001600227490000800243520126500251653002901516653002201545653002601567653002401593100002601617856004801643 2013 eng d a1872-895200aCharacterizing multivariate decoding models based on correlated EEG spectral features.0 aCharacterizing multivariate decoding models based on correlated  c07/2013 a1297–13020 v1243 aOBJECTIVE: Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. METHODS: Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). RESULTS: The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. CONCLUSIONS: Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. SIGNIFICANCE: While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated.10abrain-computer interface10amulticollinearity10amultivariate decoding10asensorimotor rhythm1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2346626702896nas a2200241 4500008004100000022001400041245010900055210006900164260001200233300001400245490000700259520207700266653005002343653003302393653001702426653002402443653004102467653002502508653002302533100002402556700002602580856004802606 2013 eng d a2157-310700aFactors Influencing Tests of Auditory Processing: A Perspective on Current Issues and Relevant Concerns.0 aFactors Influencing Tests of Auditory Processing A Perspective o c07/2013 a572–5890 v243 aBackground: Tests of auditory perception, such as those used in the assessment of central auditory processing disorders ([C]APDs), represent a domain in audiological assessment where measurement of this theoretical construct is often confounded by nonauditory abilities due to methodological shortcomings. These confounds include the effects of cognitive variables such as memory and attention and suboptimal testing paradigms, including the use of verbal reproduction as a form of response selection. We argue that these factors need to be controlled more carefully and/or modified so that their impact on tests of auditory and visual perception is only minimal. Purpose: To advocate for a stronger theoretical framework than currently exists and to suggest better methodological strategies to improve assessment of auditory processing disorders (APDs). Emphasis is placed on adaptive forced-choice psychophysical methods and the use of matched tasks in multiple sensory modalities to achieve these goals. Together, this approach has potential to improve the construct validity of the diagnosis, enhance and develop theory, and evolve into a preferred method of testing. Research Design: Examination of methods commonly used in studies of APDs. Where possible, currently used methodology is compared to contemporary psychophysical methods that emphasize computer-controlled forced-choice paradigms. Results: In many cases, the procedures used in studies of APD introduce confounding factors that could be minimized if computer-controlled forced-choice psychophysical methods were utilized. Conclusions: Ambiguities of interpretation, indeterminate diagnoses, and unwanted confounds can be avoided by minimizing memory and attentional demands on the input end and precluding the use of response-selection strategies that use complex motor processes on the output end. Advocated are the use of computer-controlled forced-choice psychophysical paradigms in combination with matched tasks in multiple sensory modalities to enhance the prospect of obtaining a valid diagnosis.10aadaptive forced-choice psychophysical methods10aauditory processing disorder10adissociation10adouble dissociation10aforced-choice psychophysical methods10amodality specificity10aresponse selection1 aCacace, Anthony, T.1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2404794502121nas a2200145 4500008004100000022001400041245007800055210006900133260001200202300001100214490000600225520167000231100002601901856004801927 2013 eng d a1932-620300aModeling Individual Subtests of the WAIS IV with Multiple Latent Factors.0 aModeling Individual Subtests of the WAIS IV with Multiple Latent c09/2013 ae749800 v83 aPerformance on a cognitive test can be viewed either as measuring a unitary function or as reflecting the operation of multiple factors. Individual subtests in batteries designed to measure human abilities are commonly modeled as a single latent factor. Several latent factors are then used to model groups of subtests. However these latent factors are not independent as they are related through hierarchical or oblique structures. As a result, the simple structure of subtest performance results in complex latent factors. The present study used structural equation modeling to evaluate several multidimensional models of the Wechsler Adult Intelligence Scales- fourth edition (WAIS-IV) subtests. Multidimensional models of subtest performance provided better model fit as compared to several previously proposed one dimensional models. These multidimensional models also generalized well to new samples of populations differing in age from that used to estimate the model parameters. Overall these results show that models that describe subtests as multidimensional functions of uncorrelated factors provided a better fit to the WAIS-IV correlations than models that describe subtests as one dimensional functions of correlated factors. There appears to be a trade-off in modeling subtests as one dimensional and modeling with homogeneous latent traits. More consideration should be given to models that include multiple uncorrelated latent factors as determinants of the performance on a given subtest. These results support the view that performance on any given cognitive test is potentially the result of multiple factors. Simple structure may be too simple.1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2405864302018nas a2200169 4500008004100000022001400041245012700055210006900182260001200251520141700263100002301680700002601703700002801729700001901757700002401776856004801800 2013 eng d a1558-913700aReliability of broadband middle-ear power-reflectance in younger and older adults: Application of Generalizability Theory.0 aReliability of broadband middleear powerreflectance in younger a c07/20133 aPURPOSE: To assess the reliability of broadband middle-ear power reflectance (BMEPR) and transmittance profiles for chirp and tonal stimuli using generalizability theory (GT). METHOD: In adults without a history of middle-ear disease, the authors assessed the reliability of BMEPR to chirp and tonal stimuli using a multivariate approach based on an analysis of variance model (GT). For comparisons with other published studies, Pearson's product-moment correlation coefficients (Pearson's r) also were used. RESULTS: Based on GT with chirp stimuli, overall BMEPR measures had good reliability; however, the reliability of individual profiles across frequencies and ears was less than optimal. Lower generalizability coefficients were found when transmittance was evaluated. Test-retest reliability using Pearson's r was better for right versus left ears, and mid-frequencies were generally more reliable than those at either extreme of the frequency range. In contrast, tonal stimuli had higher generalizability coefficients and Pearson's r values than chirps for all frequencies tested; Pearson's r values were also higher for right versus left ears. CONCLUSION: The authors extended the use of GT as a preferred way to evaluate reliability of BMEPR and transmittance profiles for chirps and tones because it allows for a more comprehensive evaluation compared with unidimensional pairwise correlations.1 aMahoney, Marty, J.1 aMcFarland, Dennis, J.1 aCarpenter, Michelle, S.1 aRizvi, Sabahet1 aCacace, Anthony, T. uhttp://www.ncbi.nlm.nih.gov/pubmed/2382443702062nas a2200241 4500008004100000022001400041245012200055210006900177260001200246300001100258490000600269520128800275653002801563100002001591700002601611700002401637700002101661700002401682700002301706700002201729700002101751856004801772 2012 eng d a1741-255200aEEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis.0 aEEG correlates of P300based braincomputer interface BCI performa c04/2012 a0260140 v93 aThe purpose of this study was to identify electroencephalography (EEG) features that correlate with P300-based brain-computer interface (P300 BCI) performance in people with amyotrophic lateral sclerosis (ALS). Twenty people with ALS used a P300 BCI spelling application in copy-spelling mode. Three types of EEG features were found to be good predictors of P300 BCI performance: (1) the root-mean-square amplitude and (2) the negative peak amplitude of the event-related potential to target stimuli (target ERP) at Fz, Cz, P3, Pz, and P4; and (3) EEG theta frequency (4.5-8 Hz) power at Fz, Cz, P3, Pz, P4, PO7, PO8 and Oz. A statistical prediction model that used a subset of these features accounted for >60% of the variance in copy-spelling performance (p < 0.001, mean R(2)?= 0.6175). The correlations reflected between-subject, rather than within-subject, effects. The results enhance understanding of performance differences among P300 BCI users. The predictors found in this study might help in: (1) identifying suitable candidates for long-term P300 BCI operation; (2) assessing performance online. Further work on within-subject effects needs to be done to establish whether P300 BCI user performance could be improved by optimizing one or more of these EEG features.10aUser-Computer Interface1 aMak, Joseph, N.1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aMcCane, Lynn, M.1 aTsui, Phillippa, Z.1 aZeitlin, Debra, J.1 aSellers, Eric, W.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/2235050100719nas a2200217 4500008004100000022001400041245011900055210006900174260001200243300002600255490000800281653004400289653001100333653001800344653001100362653000900373653002200382100002600404700002300430856004800453 2012 eng d a1878-589100aQuestionable reliability of the speech-evoked auditory brainstem response (sABR) in typically-developing children.0 aQuestionable reliability of the speechevoked auditory brainstem  c05/2012 a1-2; author reply 3-50 v28710aEvoked Potentials, Auditory, Brain Stem10aFemale10aHearing Tests10aHumans10aMale10aSpeech Perception1 aMcFarland, Dennis, J.1 aCacace, Anthony, T uhttp://www.ncbi.nlm.nih.gov/pubmed/2244617801970nas a2200205 4500008004100000022001400041245009800055210006900153260001200222300001100234490000700245520134700252653001301599653001401612653001101626653002401637653002901661100002601690856004801716 2012 eng d a1744-411X00aA single g factor is not necessary to simulate positive correlations between cognitive tests.0 asingle g factor is not necessary to simulate positive correlatio c01/2012 a378-840 v343 aIn the area of abilities testing, one issue of continued dissent is whether abilities are best conceptualized as manifestations of a single underlying general factor or as reflecting the combination of multiple traits that may be dissociable. The fact that diverse cognitive tests tend to be positively correlated has been taken as evidence for a single general ability or "g" factor. In the present study, simulations of test performance were run to evaluate the hypothesis that multiple independent abilities that affect test performance in a consistent manner will produce a positive manifold. Correlation matrices were simulated from models using either one or eight independent factors. The extent to which these factors operated in a consistent manner across tests (i.e., that a factor that facilitates performance on one test tends to facilitate performance on other tests) was manipulated by varying the mean value of the randomly selected weights. The tendency of both a single factor and eight independent factors to produce positive correlations increased as the randomly selected weights operated in a more consistent fashion. Thus the presence of a positive manifold in the correlations between diverse cognitive tests does not provide differential support for either single factor or multiple factor models of general abilities.10aAptitude10aCognition10aHumans10aModels, Theoretical10aNeuropsychological Tests1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2226019002099nas a2200241 4500008004100000022001400041245011200055210006900167260001200236300001000248490000700258520136500265653001501630653001001645653002701655653001101682653001701693653002801710100002401738700002601762700002101788856004801809 2012 eng d a1873-274700aValue of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface.0 aValue of amplitude phase and coherence features for a sensorimot c01/2012 a130-40 v873 aMeasures that quantify the relationship between two or more brain signals are drawing attention as neuroscientists explore the mechanisms of large-scale integration that enable coherent behavior and cognition. Traditional Fourier-based measures of coherence have been used to quantify frequency-dependent relationships between two signals. More recently, several off-line studies examined phase-locking value (PLV) as a possible feature for use in brain-computer interface (BCI) systems. However, only a few individuals have been studied and full statistical comparisons among the different classes of features and their combinations have not been conducted. The present study examines the relative BCI performance of spectral power, coherence, and PLV, alone and in combination. The results indicate that spectral power produced classification at least as good as PLV, coherence, or any possible combination of these measures. This may be due to the fact that all three measures reflect mainly the activity of a single signal source (i.e., an area of sensorimotor cortex). This possibility is supported by the finding that EEG signals from different channels generally had near-zero phase differences. Coherence, PLV, and other measures of inter-channel relationships may be more valuable for BCIs that use signals from more than one distinct cortical source.10aAlgorithms10aBrain10aElectroencephalography10aHumans10aMotor Cortex10aUser-Computer Interface1 aKrusienski, Dean, J1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/2198598400447nas a2200145 4500008004100000022001400041245006100055210005900116260001200175300001200187490000700199100002600206700002100232856004800253 2011 eng d a0001-078200aBrain-Computer Interfaces for Communication and Control.0 aBrainComputer Interfaces for Communication and Control c05/2011 a60–660 v541 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/2198482200535nas a2200157 4500008004100000022001400041245006500055210006300120260001200183300005400195490000800249653002200257100002600279700002400305856004800329 2011 eng d a1872-895200aCovariance is the proper measure of test-retest reliability.0 aCovariance is the proper measure of testretest reliability c09/2011 a1893; author reply 1893–1893; author reply 18950 v12210aSpeech Perception1 aMcFarland, Dennis, J.1 aCacace, Anthony, T. uhttp://www.ncbi.nlm.nih.gov/pubmed/2141484003784nas a2200337 4500008004100000022001400041245006200055210006100117260001200178300001100190490000700201520283200208653001503040653001003055653002403065653002903089653001103118653002603129653001103155653000903166653002903175653002103204653003103225653003403256653002203290653001603312100002103328700002603349700002303375856004803398 2011 eng d a1050-054500aDichotic and dichoptic digit perception in normal adults.0 aDichotic and dichoptic digit perception in normal adults c06/2011 a332-410 v223 aBACKGROUND: Verbally based dichotic-listening experiments and reproduction-mediated response-selection strategies have been used for over four decades to study perceptual/cognitive aspects of auditory information processing and make inferences about hemispheric asymmetries and language lateralization in the brain. Test procedures using dichotic digits have also been used to assess for disorders of auditory processing. However, with this application, limitations exist and paradigms need to be developed to improve specificity of the diagnosis. Use of matched tasks in multiple sensory modalities is a logical approach to address this issue. Herein, we use dichotic listening and dichoptic viewing of visually presented digits for making this comparison. PURPOSE: To evaluate methodological issues involved in using matched tasks of dichotic listening and dichoptic viewing in normal adults. RESEARCH DESIGN: A multivariate assessment of the effects of modality (auditory vs. visual), digit-span length (1-3 pairs), response selection (recognition vs. reproduction), and ear/visual hemifield of presentation (left vs. right) on dichotic and dichoptic digit perception. STUDY SAMPLE: Thirty adults (12 males, 18 females) ranging in age from 18 to 30 yr with normal hearing sensitivity and normal or corrected-to-normal visual acuity. DATA COLLECTION AND ANALYSIS: A computerized, custom-designed program was used for all data collection and analysis. A four-way repeated measures analysis of variance (ANOVA) evaluated the effects of modality, digit-span length, response selection, and ear/visual field of presentation. RESULTS: The ANOVA revealed that performances on dichotic listening and dichoptic viewing tasks were dependent on complex interactions between modality, digit-span length, response selection, and ear/visual hemifield of presentation. Correlation analysis suggested a common effect on overall accuracy of performance but isolated only an auditory factor for a laterality index. CONCLUSIONS: The variables used in this experiment affected performances in the auditory modality to a greater extent than in the visual modality. The right-ear advantage observed in the dichotic-digits task was most evident when reproduction mediated response selection was used in conjunction with three-digit pairs. This effect implies that factors such as "speech related output mechanisms" and digit-span length (working memory) contribute to laterality effects in dichotic listening performance with traditional paradigms. Thus, the use of multiple-digit pairs to avoid ceiling effects and the application of verbal reproduction as a means of response selection may accentuate the role of nonperceptual factors in performance. Ideally, tests of perceptual abilities should be relatively free of such effects.10aAdolescent10aAdult10aAuditory Perception10aDichotic Listening Tests10aFemale10aFunctional Laterality10aHumans10aMale10aRecognition (Psychology)10aReference Values10aReproducibility of Results10aTask Performance and Analysis10aVisual Perception10aYoung Adult1 aLawfield, Angela1 aMcFarland, Dennis, J.1 aCacace, Anthony, T uhttp://www.ncbi.nlm.nih.gov/pubmed/2186447101225nas a2200241 4500008004100000022001400041245009800055210006900153260001200222300001400234490000700248520047500255653002000730100002100750700002700771700002100798700002600819700001900845700002100864700002300885700002700908856004800935 2011 eng d a1552-684400aNeurological principles and rehabilitation of action disorders: rehabilitation interventions.0 aNeurological principles and rehabilitation of action disorders r c06/2011 a33S–43S0 v253 aThis third chapter discusses the evidence for the rehabilitation of the most common movement disorders of the upper extremity. The authors also present a framework, building on the computation, anatomy, and physiology (CAP) model, for incorporating some of the principles discussed in the 2 previous chapters by Frey et al and Sathian et al in the practice of rehabilitation and for discussing potentially helpful interventions based on emergent neuroscience principles.10aUpper Extremity1 aPomeroy, Valerie1 aAglioti, Salvatore, M.1 aMark, Victor, W.1 aMcFarland, Dennis, J.1 aStinear, Cathy1 aWolf, Steven, L.1 aCorbetta, Maurizio1 aFitzpatrick, Susan, M. uhttp://www.ncbi.nlm.nih.gov/pubmed/2161353601351nas a2200289 4500008004100000022001400041245009800055210006900153260001200222300001200234490000700246520047500253653001100728653002500739653002300764653002500787653002000812100002100832700002600853700002000879700002600899700001900925700002000944700002300964700002600987856004801013 2011 eng d a1552-684400aNeurological principles and rehabilitation of action disorders: rehabilitation interventions.0 aNeurological principles and rehabilitation of action disorders r c06/2011 a33S-43S0 v253 aThis third chapter discusses the evidence for the rehabilitation of the most common movement disorders of the upper extremity. The authors also present a framework, building on the computation, anatomy, and physiology (CAP) model, for incorporating some of the principles discussed in the 2 previous chapters by Frey et al and Sathian et al in the practice of rehabilitation and for discussing potentially helpful interventions based on emergent neuroscience principles.10aHumans10aModels, Neurological10aMovement Disorders10aRecovery of Function10aUpper Extremity1 aPomeroy, Valerie1 aAglioti, Salvatore, M1 aMark, Victor, W1 aMcFarland, Dennis, J.1 aStinear, Cathy1 aWolf, Steven, L1 aCorbetta, Maurizio1 aFitzpatrick, Susan, M uhttp://www.ncbi.nlm.nih.gov/pubmed/2161353602447nas a2200229 4500008004100000022001400041245007700055210006700132260001200199300001400211490000800225520176000233653002901993653002002022653000902042100002602051700002602077700002102103700002402124700002102148856004802169 2011 eng d a1872-895200aThe P300-based brain-computer interface (BCI): effects of stimulus rate.0 aP300based braincomputer interface BCI effects of stimulus rate c04/2011 a731–7370 v1223 aOBJECTIVE: Brain-computer interface technology can restore communication and control to people who are severely paralyzed. We have developed a non-invasive BCI based on the P300 event-related potential that uses an 8×9 matrix of 72 items that flash in groups of 6. Stimulus presentation rate (i.e., flash rate) is one of several parameters that could affect the speed and accuracy of performance. We studied performance (i.e., accuracy and characters/min) on copy spelling as a function of flash rate. METHODS: In the first study of six BCI users, stimulus-on and stimulus-off times were equal and flash rate was 4, 8, 16, or 32 Hz. In the second study of five BCI users, flash rate was varied by changing either the stimulus-on or stimulus-off time. RESULTS: For all users, lower flash rates gave higher accuracy. The flash rate that gave the highest characters/min varied across users, ranging from 8 to 32 Hz. However, variations in stimulus-on and stimulus-off times did not themselves significantly affect accuracy. Providing feedback did not affect results in either study suggesting that offline analyses should readily generalize to online performance. However there do appear to be session-specific effects that can influence the generalizability of classifier results. CONCLUSIONS: The results show that stimulus presentation (i.e., flash) rate affects the accuracy and speed of P300 BCI performance. SIGNIFICANCE: These results extend the range over which slower flash rates increase the amplitude of the P300. Considering also presentation time, the optimal rate differs among users, and thus should be set empirically for each user. Optimal flash rate might also vary with other parameters such as the number of items in the matrix.10abrain-computer interface10aneuroprosthesis10aP3001 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aTownsend, George1 aVaughan, Theresa, M1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/2106797001319nas a2200205 4500008004100000022001400041245008200055210006900137260001200206300001400218490000800232520070000240653001500940653002900955653000800984100002600992700002601018700002101044856004801065 2011 eng d a1872-678X00aShould the parameters of a BCI translation algorithm be continually adapted?.0 aShould the parameters of a BCI translation algorithm be continua c07/2011 a103–1070 v1993 aPeople with or without motor disabilities can learn to control sensorimotor rhythms (SMRs) recorded from the scalp to move a computer cursor in one or more dimensions or can use the P300 event-related potential as a control signal to make discrete selections. Data collected from individuals using an SMR-based or P300-based BCI were evaluated offline to estimate the impact on performance of continually adapting the parameters of the translation algorithm during BCI operation. The performance of the SMR-based BCI was enhanced by adaptive updating of the feature weights or adaptive normalization of the features. In contrast, P300 performance did not benefit from either of these procedures.10aadaptation10abrain-computer interface10aEEG1 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/2157100401826nas a2200217 4500008004100000022001400041245007300055210006900128260001200197300001600209490000800225520117900233653002901412653000801441653001801449100002501467700002101492700002101513700002601534856004801560 2011 eng d a1872-895200aTrained modulation of sensorimotor rhythms can affect reaction time.0 aTrained modulation of sensorimotor rhythms can affect reaction t c09/2011 a1820–18260 v1223 aOBJECTIVE: Brain-computer interface (BCI) technology might be useful for rehabilitation of motor function. This speculation is based on the premise that modifying the EEG will modify behavior, a proposition for which there is limited empirical data. The present study examined the possibility that voluntary modulation of sensorimotor rhythm (SMR) can affect motor behavior in normal human subjects. METHODS: Six individuals performed a cued-reaction task with variable warning periods. A typical variable foreperiod effect was associated with SMR desynchronization. SMR features that correlated with reaction times were then used to control a two-target cursor movement BCI task. Following successful BCI training, an uncued reaction time task was embedded within the cursor movement task. RESULTS: Voluntarily increasing SMR beta rhythms was associated with longer reaction times than decreasing SMR beta rhythms. CONCLUSIONS: Voluntary modulation of EEG SMR can affect motor behavior. SIGNIFICANCE: These results encourage studies that integrate BCI training into rehabilitation protocols and examine its capacity to augment restoration of useful motor function.10abrain-computer interface10aEEG10aReaction Time1 aBoulay, Chadwick, B.1 aSarnacki, W., A.1 aWolpaw, Jonathan1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2141136601419nas a2200181 4500008004100000022001400041245011600055210006900171260001200240300001000252490000600262520084000268653001601108100001801124700002601142700002101168856004801189 2010 eng d a1741-255200aA comparison of regression techniques for a two-dimensional sensorimotor rhythm-based brain-computer interface.0 acomparison of regression techniques for a twodimensional sensori c02/2010 a160030 v73 aPeople can learn to control electroencephalogram (EEG) features consisting of sensorimotor-rhythm amplitudes and use this control to move a cursor in one, two or three dimensions to a target on a video screen. This study evaluated several possible alternative models for translating these EEG features into two-dimensional cursor movement by building an offline simulation using data collected during online performance. In offline comparisons, support-vector regression (SVM) with a radial basis kernel produced somewhat better performance than simple multiple regression, the LASSO or a linear SVM. These results indicate that proper choice of a translation algorithm is an important factor in optimizing brain-computer interface (BCI) performance, and provide new insight into algorithm choice for multidimensional movement control.10aYoung Adult1 aFruitet, Joan1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/2007550301515nas a2200181 4500008004100000022001400041245007300055210006900128260001200197300001100209490000600220520095800226653002801184100002601212700002601238700002101264856004801285 2010 eng d a1741-255200aElectroencephalographic (EEG) control of three-dimensional movement.0 aElectroencephalographic EEG control of threedimensional movement c06/2010 a0360070 v73 aBrain-computer interfaces (BCIs) can use brain signals from the scalp (EEG), the cortical surface (ECoG), or within the cortex to restore movement control to people who are paralyzed. Like muscle-based skills, BCIs' use requires activity-dependent adaptations in the brain that maintain stable relationships between the person's intent and the signals that convey it. This study shows that humans can learn over a series of training sessions to use EEG for three-dimensional control. The responsible EEG features are focused topographically on the scalp and spectrally in specific frequency bands. People acquire simultaneous control of three independent signals (one for each dimension) and reach targets in a virtual three-dimensional space. Such BCI control in humans has not been reported previously. The results suggest that with further development noninvasive EEG-based BCIs might control the complex movements of robotic arms or neuroprostheses.10aUser-Computer Interface1 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/2046069001127nas a2200241 4500008004100000022001400041245003400055210003300089260001200122300001000134490000700144520043600151653005800587653001100645653002900656653002100685653002600706653002000732653003700752100002600789700002200815856004800837 2010 eng d a1469-182500aSymptoms as latent variables.0 aSymptoms as latent variables c06/2010 a165-60 v333 aIn the target article, Cramer et al. suggest that diagnostic classification is improved by modeling the relationship between manifest variables (i.e., symptoms) rather than modeling unobservable latent variables (i.e., diagnostic categories such as Generalized Anxiety Disorder). This commentary discusses whether symptoms represent manifest or latent variables and the implications of this distinction for diagnosis and treatment.10aDiagnostic and Statistical Manual of Mental Disorders10aHumans10aInterview, Psychological10aMental Disorders10aModels, Psychological10aSleep Disorders10aStress Disorders, Post-Traumatic1 aMcFarland, Dennis, J.1 aMalta, Loretta, S uhttp://www.ncbi.nlm.nih.gov/pubmed/2058438401741nas a2200253 4500008004100000022001400041245008200055210006900137260001200206300001400218490000700232520097500239653000801214653002901222653002201251653002401273100003201297700002601329700002001355700002401375700001901399700002101418856004801439 2009 eng d a1873-624600aA scanning protocol for a sensorimotor rhythm-based brain-computer interface.0 ascanning protocol for a sensorimotor rhythmbased braincomputer i c02/2009 a169–1750 v803 aThe scanning protocol is a novel brain-computer interface (BCI) implementation that can be controlled with sensorimotor rhythms (SMRs) of the electroencephalogram (EEG). The user views a screen that shows four choices in a linear array with one marked as target. The four choices are successively highlighted for 2.5s each. When a target is highlighted, the user can select it by modulating the SMR. An advantage of this method is the capacity to choose among multiple choices with just one learned SMR modulation. Each of 10 naive users trained for ten 30 min sessions over 5 weeks. User performance improved significantly (p<0.001) over the sessions and ranged from 30 to 80% mean accuracy of the last three sessions (chance accuracy=25%). The incidence of correct selections depended on the target position. These results suggest that, with further improvements, a scanning protocol can be effective. The ultimate goal is to expand it to a large matrix of selections.10aBCI10abrain-computer interface10ascanning protocol10asensorimotor rhythm1 aFriedrich, Elisabeth, V. C.1 aMcFarland, Dennis, J.1 aNeuper, Christa1 aVaughan, Theresa, M1 aBrunner, Peter1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1878660302112nas a2200289 4500008004100000022001400041245004800055210004100103260001200144300001200156490000800168520133600176653002201512653002901534653000801563653002001571653001501591653002401606100001901630700001901649700001601668700002301684700002601707700002101733700002001754856004801774 2008 eng d a0165-027000aAn auditory brain-computer interface (BCI).0 aauditory braincomputer interface BCI c01/2008 a43–500 v1673 aBrain-computer interfaces (BCIs) translate brain activity into signals controlling external devices. BCIs based on visual stimuli can maintain communication in severely paralyzed patients, but only if intact vision is available. Debilitating neurological disorders however, may lead to loss of intact vision. The current study explores the feasibility of an auditory BCI. Sixteen healthy volunteers participated in three training sessions consisting of 30 2-3 min runs in which they learned to increase or decrease the amplitude of sensorimotor rhythms (SMR) of the EEG. Half of the participants were presented with visual and half with auditory feedback. Mood and motivation were assessed prior to each session. Although BCI performance in the visual feedback group was superior to the auditory feedback group there was no difference in performance at the end of the third session. Participants in the auditory feedback group learned slower, but four out of eight reached an accuracy of over 70% correct in the last session comparable to the visual feedback group. Decreasing performance of some participants in the visual feedback group is related to mood and motivation. We conclude that with sufficient training time an auditory BCI may be as efficient as a visual BCI. Mood and motivation play a role in learning to use a BCI.10aauditory feedback10abrain-computer interface10aEEG10alocked-in state10amotivation10asensorimotor rhythm1 aNijboer, Femke1 aFurdea, Adrian1 aGunst, Ingo1 aMellinger, Jürgen1 aMcFarland, Dennis, J.1 aBirbaumer, Niels1 aKübler, Andrea uhttp://www.ncbi.nlm.nih.gov/pubmed/1739979702437nas a2200241 4500008004100000022001400041245006700055210006500122260001200187300001400199490000700213520176200220653000801982653000801990653002001998653001902018100002402037700002602061700001802087700001802105700002402123856004802147 2008 eng d a0301-051100aElectrophysiological markers of skill-related neuroplasticity.0 aElectrophysiological markers of skillrelated neuroplasticity c07/2008 a221–2300 v783 aNeuroplasticity involved in acquiring a new cognitive skill was investigated with standard time domain event-related potentials (ERPs) of scalp-recorded electroencephalographic (EEG) activity and frequency domain analysis of EEG oscillations looking at the event-related synchronization (ERS) and desynchronization (ERD) of neural activity. Electroencephalographic activity was recorded before and after practice, while participants performed alphabet addition (i.e., E+3=G, true or false?). Participant's performance became automated with practice through a switch in cognitive strategy from mentally counting-up in the alphabet to retrieving the answer from memory. Time domain analysis of the ERPs revealed a prominent positive peak at approximately 300 ms that was not reactive to problem attributes but was reduced with practice. A second prominent positive peak observed at approximately 500 ms was found to be larger after practice, mainly for problems presented with correct answers. Frequency domain spectral analyses yielded two distinct findings: (1) a frontal midline ERS of theta activity that was greater after practice, and (2) a beta band ERD that increased with problem difficulty before, but not after practice. Because the EEG oscillations were not phase locked to the stimulus, they were viewed as being independent of the time domain results. Consequently, use of time and frequency domain analyses provides a more comprehensive account of the underlying electrophysiological data than either method alone. When used in combination with a well-defined cognitive/behavioral paradigm, this approach serves to constrain the interpretations of EEG data and sets a new standard for studying the neuroplasticity involved in skill acquisition.10aEEG10aERP10aneuroplasticity10askill learning1 aRomero, Stephen, G.1 aMcFarland, Dennis, J.1 aFaust, Robert1 aFarrell, Lori1 aCacace, Anthony, T. uhttp://www.ncbi.nlm.nih.gov/pubmed/1845586101945nas a2200193 4500008004100000022001400041245008500055210006900140260001200209300001400221490000600235520133600241653002801577100002601605700002501631700002601656700002101682856004801703 2008 eng d a1741-256000aEmulation of computer mouse control with a noninvasive brain-computer interface.0 aEmulation of computer mouse control with a noninvasive braincomp c06/2008 a101–1100 v53 aBrain-computer interface (BCI) technology can provide nonmuscular communication and control to people who are severely paralyzed. BCIs can use noninvasive or invasive techniques for recording the brain signals that convey the user's commands. Although noninvasive BCIs are used for simple applications, it has frequently been assumed that only invasive BCIs, which use electrodes implanted in the brain, will be able to provide multidimensional sequential control of a robotic arm or a neuroprosthesis. The present study shows that a noninvasive BCI using scalp-recorded electroencephalographic (EEG) activity and an adaptive algorithm can provide people, including people with spinal cord injuries, with two-dimensional cursor movement and target selection. Multiple targets were presented around the periphery of a computer screen, with one designated as the correct target. The user's task was to use EEG to move a cursor from the center of the screen to the correct target and then to use an additional EEG feature to select the target. If the cursor reached an incorrect target, the user was instructed not to select it. Thus, this task emulated the key features of mouse operation. The results indicate that people with severe motor disabilities could use brain signals for sequential multidimensional movement and selection.10aUser-Computer Interface1 aMcFarland, Dennis, J.1 aKrusienski, Dean, J.1 aSarnacki, William, A.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1836777900704nas a2200145 4500008004100000022001400041245006800055210006600123260001200189300000800201490000700209520026800216100002600484856004800510 2008 eng d a1469-182500aPhase-alignment of delayed sensory signals by adaptive filters.0 aPhasealignment of delayed sensory signals by adaptive filters c04/2008 a2120 v313 aCorrection of sensory transmission delays is an intractable problem because there is no absolute reference for calibration. Phase alignment is a practical alternative solution and can be realized by adaptive filters that operate locally with simple error signals.1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2070655601908nas a2200169 4500008004100000022001400041245012200055210006900177260001200246300001400258490000600272520133700278653002801615100002601643700002101669856004801690 2008 eng d a1741-256000aSensorimotor rhythm-based brain-computer interface (BCI): model order selection for autoregressive spectral analysis.0 aSensorimotor rhythmbased braincomputer interface BCI model order c06/2008 a155–1620 v53 aPeople can learn to control EEG features consisting of sensorimotor rhythm amplitudes and can use this control to move a cursor in one or two dimensions to a target on a screen. Cursor movement depends on the estimate of the amplitudes of sensorimotor rhythms. Autoregressive models are often used to provide these estimates. The order of the autoregressive model has varied widely among studies. Through analyses of both simulated and actual EEG data, the present study examines the effects of model order on sensorimotor rhythm measurements and BCI performance. The results show that resolution of lower frequency signals requires higher model orders and that this requirement reflects the temporal span of the model coefficients. This is true for both simulated EEG data and actual EEG data during brain-computer interface (BCI) operation. Increasing model order, and decimating the signal were similarly effective in increasing spectral resolution. Furthermore, for BCI control of two-dimensional cursor movement, higher model orders produced better performance in each dimension and greater independence between horizontal and vertical movements. In sum, these results show that autoregressive model order selection is an important determinant of BCI performance and should be based on criteria that reflect system performance.10aUser-Computer Interface1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1843097401649nas a2200241 4500008004100000022001400041245004600055210004500101260001200146300001200158490000800170520095000178653002901128653002901157653001701186653004201203100002301245700002001268700002601288700002401314700002101338856004801359 2008 eng d a0165-027000aToward enhanced P300 speller performance.0 aToward enhanced P300 speller performance c01/2008 a15–210 v1673 aThis study examines the effects of expanding the classical P300 feature space on the classification performance of data collected from a P300 speller paradigm [Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroenceph Clin Neurophysiol 1988;70:510-23]. Using stepwise linear discriminant analysis (SWLDA) to construct a classifier, the effects of spatial channel selection, channel referencing, data decimation, and maximum number of model features are compared with the intent of establishing a baseline not only for the SWLDA classifier, but for related P300 speller classification methods in general. By supplementing the classical P300 recording locations with posterior locations, online classification performance of P300 speller responses can be significantly improved using SWLDA and the favorable parameters derived from the offline comparative analysis.10abrain-computer interface10aevent related potentials10aP300 speller10astepwise linear discriminant analysis1 aKrusienski, D., J.1 aSellers, E., W.1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1782277704357nas a2200385 4500008004100000022001400041245009700055210006900152260001200221300001200233490000800245520323100253653001503484653001003499653001403509653001003523653001803533653004203551653002703593653003003620653001103650653001103661653000903672653003203681653002303713653002203736653002803758100002503786700002603811700001903837700002003856700002603876700002103902856004803923 2008 eng d a1388-245700aTowards an independent brain-computer interface using steady state visual evoked potentials.0 aTowards an independent braincomputer interface using steady stat c02/2008 a399-4080 v1193 a

OBJECTIVE: 

Brain-computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze.

METHODS: 

Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs.

RESULTS: 

Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control.

CONCLUSIONS: 

The data demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI's dependence on muscle activity is a function of many factors, including the display, task, environment, and user.

SIGNIFICANCE: 

SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.

10aAdolescent10aAdult10aAttention10aBrain10aBrain Mapping10aDose-Response Relationship, Radiation10aElectroencephalography10aEvoked Potentials, Visual10aFemale10aHumans10aMale10aPattern Recognition, Visual10aPhotic Stimulation10aSpectrum Analysis10aUser-Computer Interface1 aAllison, Brendan, Z.1 aMcFarland, Dennis, J.1 aSchalk, Gerwin1 aZheng, Shi Dong1 aMoore-Jackson, Melody1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1807720802623nas a2200289 4500008004100000022001400041245008600055210007000141260001200211300001100223490000700234520175100241653001501992653002002007653002902027653002702056653002202083653001102105653001602116653003502132653002802167100002402195700001902219700002602238700002102264856004802285 2007 eng d a0018-929400aA µ-rhythm Matched Filter for Continuous Control of a Brain-Computer Interface.0 aµrhythm Matched Filter for Continuous Control of a BrainComputer c02/2007 a273-800 v543 a

A brain-computer interface (BCI) is a system that provides an alternate nonmuscular communication/control channel for individuals with severe neuromuscular disabilities. With proper training, individuals can learn to modulate the amplitude of specific electroencephalographic (EEG) components (e.g., the 8-12 Hz mu rhythm and 18-26 Hz beta rhythm) over the sensorimotor cortex and use them to control a cursor on a computer screen. Conventional spectral techniques for monitoring the continuousamplitude fluctuations fail to capture essential amplitude/phase relationships of the mu and beta rhythms in a compact fashion and, therefore, are suboptimal. By extracting the characteristic mu rhythm for a user, the exact morphology can be characterized and exploited as a matched filter. A simple, parameterized model for the characteristic mu rhythm is proposed and its effectiveness as a matched filter is examined online for a one-dimensional cursor control task. The results suggest that amplitude/phase coupling exists between the mu and beta bands during event-related desynchronization, and that an appropriate matched filter can provide improved performance.

10aAlgorithms10aCerebral Cortex10aCortical Synchronization10aElectroencephalography10aEvoked Potentials10aHumans10aImagination10aPattern Recognition, Automated10aUser-Computer Interface1 aKrusienski, Dean, J1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1727858401200nas a2200229 4500008004100000022001400041245009400055210007100149260001200220300001400232490000700246520047400253653003500727653001500762653002200777100002600799700002600825700002600851700002000877700002500897856004800922 2006 eng d a1534-432000aBCI Meeting 2005–workshop on BCI signal processing: feature extraction and translation.0 aBCI Meeting 2005–workshop on BCI signal processing feature extra c06/2006 a135–1380 v143 aThis paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial analysis, optimization schemes, the role of insight in analysis, adaptation, and methods for quantifying BCI feedback.10aBrain-computer interface (BCI)10aprediction10aSignal Processing1 aMcFarland, Dennis, J.1 aAnderson, Charles, W.1 aMüller, Klaus-Robert1 aSchlögl, Alois1 aKrusienski, Dean, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/1679227801505nas a2200205 4500008004100000022001400041245010600055210006900161260001200230300001400242490000800256520087000264653001501134653000801149653002201157100002601179700002501205700002101230856004801251 2006 eng d a0079-612300aBrain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms.0 aBraincomputer interface signal processing at the Wadsworth Cente c02/2006 a411–4190 v1593 aThe Wadsworth brain-computer interface (BCI), based on mu and beta sensorimotor rhythms, uses one- and two-dimensional cursor movement tasks and relies on user training. This is a real-time closed-loop system. Signal processing consists of channel selection, spatial filtering, and spectral analysis. Feature translation uses a regression approach and normalization. Adaptation occurs at several points in this process on the basis of different criteria and methods. It can use either feedforward (e.g., estimating the signal mean for normalization) or feedback control (e.g., estimating feature weights for the prediction equation). We view this process as the interaction between a dynamic user and a dynamic system that coadapt over time. Understanding the dynamics of this interaction and optimizing its performance represent a major challenge for BCI research.10aadaptation10aBCI10aSignal Processing1 aMcFarland, Dennis, J.1 aKrusienski, Dean, J.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1707124501626nas a2200229 4500008004100000022001400041245006800055210006500123260001200188300001400200490000600214520094100220653002401161100002501185700002201210700002601232700001901258700002601277700002401303700002101327856004801348 2006 eng d a1741-256000aA comparison of classification techniques for the P300 Speller.0 acomparison of classification techniques for the P300 Speller c12/2006 a299–3050 v33 aThis study assesses the relative performance characteristics of five established classification techniques on data collected using the P300 Speller paradigm, originally described by Farwell and Donchin (1988 Electroenceph. Clin. Neurophysiol. 70 510). Four linear methods: Pearson's correlation method (PCM), Fisher's linear discriminant (FLD), stepwise linear discriminant analysis (SWLDA) and a linear support vector machine (LSVM); and one nonlinear method: Gaussian kernel support vector machine (GSVM), are compared for classifying offline data from eight users. The relative performance of the classifiers is evaluated, along with the practical concerns regarding the implementation of the respective methods. The results indicate that while all methods attained acceptable performance levels, SWLDA and FLD provide the best overall performance and implementation characteristics for practical classification of P300 Speller data.10aNormal Distribution1 aKrusienski, Dean, J.1 aSellers, Eric, W.1 aCabestaing, François1 aBayoudh, Sabri1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1712433401404nas a2200181 4500008004100000022001400041245011100055210006900166260001200235300001600247490000600263520080700269653002801076100002301104700002601127700002101153856004801174 2006 eng d a1557-170X00aAn evaluation of autoregressive spectral estimation model order for brain-computer interface applications.0 aevaluation of autoregressive spectral estimation model order for c09/2006 a1323–13260 v13 aAutoregressive (AR) spectral estimation is a popular method for modeling the electroencephalogram (EEG), and therefore the frequency domain EEG phenomena that are used for control of a brain-computer interface (BCI). Several studies have been conducted to evaluate the optimal AR model order for EEG, but the criteria used in these studies does not necessarily equate to the optimal AR model order for sensorimotor rhythm (SMR)-based BCI control applications. The present study confirms this by evaluating the EEG spectra of data obtained during control of SMR-BCI using different AR model orders and model evaluation criteria. The results indicate that the AR model order that optimizes SMR-BCI control performance is generally higher than the model orders that are frequently used in SMR-BCI studies.10aUser-Computer Interface1 aKrusienski, D., J.1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1794603802016nas a2200265 4500008004100000022001400041245013800055210006900193260001200262300001400274490000700288520114400295653003401439653002901473653002501502653002901527653000901556653001901565100002201584700002501606700002601631700002401657700002101681856004801702 2006 eng d a0301-051100aA P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance.0 aP300 eventrelated potential braincomputer interface BCI the effe c10/2006 a242–2520 v733 aWe describe a study designed to assess properties of a P300 brain-computer interface (BCI). The BCI presents the user with a matrix containing letters and numbers. The user attends to a character to be communicated and the rows and columns of the matrix briefly intensify. Each time the attended character is intensified it serves as a rare event in an oddball sequence and it elicits a P300 response. The BCI works by detecting which character elicited a P300 response. We manipulated the size of the character matrix (either 3 x 3 or 6 x 6) and the duration of the inter stimulus interval (ISI) between intensifications (either 175 or 350 ms). Online accuracy was highest for the 3 x 3 matrix 175-ms ISI condition, while bit rate was highest for the 6 x 6 matrix 175-ms ISI condition. Average accuracy in the best condition for each subject was 88%. P300 amplitude was significantly greater for the attended stimulus and for the 6 x 6 matrix. This work demonstrates that matrix size and ISI are important variables to consider when optimizing a BCI system for individual users and that a P300-BCI can be used for effective communication.10aAmyotrophic Lateral Sclerosis10abrain-computer interface10aelectroencephalogram10aevent-related potentials10aP30010aRehabilitation1 aSellers, Eric, W.1 aKrusienski, Dean, J.1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1686092002977nas a2200361 4500008004100000022001400041245007400055210006800129260001200197300001100209490000700220520195300227653001202180653001002192653002702202653002202229653001102251653002702262653001302289653001302302653001602315653003102331653001702362653002802379100002402407700002602431700001902457700002502476700002402501700002102525700002102546856004802567 2006 eng d a1534-432000aThe Wadsworth BCI Research and Development Program: At Home with BCI.0 aWadsworth BCI Research and Development Program At Home with BCI c06/2006 a229-330 v143 a

The ultimate goal of brain-computer interface (BCI) technology is to provide communication and control capacities to people with severe motor disabilities. BCI research at the Wadsworth Center focuses primarily on noninvasive, electroencephalography (EEG)-based BCI methods. We have shown that people, including those with severe motor disabilities, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one or two dimensions. We have also improved P300-based BCI operation. We are now translating this laboratory-proven BCI technology into a system that can be used by severely disabled people in their homes with minimal ongoing technical oversight. To accomplish this, we have: improved our general-purpose BCI software (BCI2000); improved online adaptation and feature translation for SMR-based BCI operation; improved the accuracy and bandwidth of P300-based BCI operation; reduced the complexity of system hardware and software and begun to evaluate home system use in appropriate users. These developments have resulted in prototype systems for every day use in people's homes.

10aAnimals10aBrain10aElectroencephalography10aEvoked Potentials10aHumans10aNeuromuscular Diseases10aNew York10aResearch10aSwitzerland10aTherapy, Computer-Assisted10aUniversities10aUser-Computer Interface1 aVaughan, Theresa, M1 aMcFarland, Dennis, J.1 aSchalk, Gerwin1 aSarnacki, William, A1 aKrusienski, Dean, J1 aSellers, Eric, W1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1679230103537nas a2200253 4500008004100000022001400041245009500055210006900150260001200219300001200231490000800243520277200251653002903023653000803052653002703060653001303087653001403100653002403114100002603138700002603164700002403190700002103214856004803235 2005 eng d a1388-245700aBrain-computer interface (BCI) operation: signal and noise during early training sessions.0 aBraincomputer interface BCI operation signal and noise during ea c01/2005 a56–620 v1163 aOBJECTIVE: People can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the electroencephalogram (EEG) recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. The recorded signal may also contain electromyogram (EMG) and other non-EEG artifacts. This study examines the presence and characteristics of EMG contamination during new users' initial brain-computer interface (BCI) training sessions, as they first attempt to acquire control over mu or beta rhythm amplitude and to use that control to move a cursor to a target. METHODS: In the standard one-dimensional format, a target appears along the right edge of the screen and 1s later the cursor appears in the middle of the left edge and moves across the screen at a fixed rate with its vertical movement controlled by a linear function of mu or beta rhythm amplitude. In the basic two-choice version, the target occupies the upper or lower half of the right edge. The user's task is to move the cursor vertically so that it hits the target when it reaches the right edge. The present data comprise the first 10 sessions of BCI training from each of 7 users. Their data were selected to illustrate the variations seen in EMG contamination across users. RESULTS: Five of the 7 users learned to change rhythm amplitude appropriately, so that the cursor hit the target. Three of these 5 showed no evidence of EMG contamination. In the other two of these 5, EMG was prominent in early sessions, and tended to be associated with errors rather than with hits. As EEG control improved over the 10 sessions, this EMG contamination disappeared. In the remaining two users, who never acquired actual EEG control, EMG was prominent in initial sessions and tended to move the cursor to the target. This EMG contamination was still detectable by Session 10. CONCLUSIONS: EMG contamination arising from cranial muscles is often present early in BCI training and gradually wanes. In those users who eventually acquire EEG control, early target-related EMG contamination may be most prominent for unsuccessful trials, and may reflect user frustration. In those users who never acquire EEG control, EMG may initially serve to move the cursor toward the target. Careful and comprehensive topographical and spectral analyses throughout user training are essential for detecting EMG contamination and differentiating between cursor control provided by EEG control and cursor control provided by EMG contamination. SIGNIFICANCE: Artifacts such as EMG are common in EEG recordings. Comprehensive spectral and topographical analyses are necessary to detect them and ensure that they do not masquerade as, or interfere with acquisition of, actual EEG-based cursor control.10abrain-computer interface10aEEG10aElectroencephalography10aLearning10amu rhythm10asensorimotor cortex1 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aVaughan, Theresa, M1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1558918402110nas a2200181 4500008004100000022001400041245009500055210006900150260001200219300001400231490000700245520151200252653004101764653002501805100002401830700002601854856004801880 2005 eng d a1059-088900aThe importance of modality specificity in diagnosing central auditory processing disorder.0 aimportance of modality specificity in diagnosing central auditor c12/2005 a112–1230 v143 aPURPOSE: This article argues for the use of modality specificity as a unifying framework by which to conceptualize and diagnose central auditory processing disorder (CAPD). The intent is to generate dialogue and critical discussion in this area of study. METHOD: Research in the cognitive, behavioral, and neural sciences that relates to the concept of modality specificity was reviewed and synthesized. RESULTS: Modality specificity has a long history as an organizing construct within a diverse collection of mainstream scientific disciplines. The principle of modality specificity was contrasted with the unimodal inclusive framework, which holds that auditory tests alone are sufficient to make the CAPD diagnosis. Evidence from a large body of data demonstrated that the unimodal framework was unable to delineate modality-specific processes from more generalized dysfunction; it lacked discriminant validity and resulted in an incomplete assessment. Consequently, any hypothetical model resulting from incomplete assessments or potential therapies that are based on indeterminate diagnoses are themselves questionable, and caution should be used in their application. CONCLUSIONS: Improving specificity of diagnosis is an imperative core issue to the area of CAPD. Without specificity, the concept has little explanatory power. Because of serious flaws in concept and design, the unimodal inclusive framework should be abandoned in favor of a more valid approach that uses modality specificity.10acentral auditory processing disorder10amodality specificity1 aCacace, Anthony, T.1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/1648986801195nas a2200253 4500008004100000022001400041245009000055210006900145260001200214300001600226490000700242520044000249653002800689100001500717700001600732700001700748700002400765700001700789700001900806700002600825700002100851700002100872856004800893 2005 eng d a1526-632X00aPatients with ALS can use sensorimotor rhythms to operate a brain-computer interface.0 aPatients with ALS can use sensorimotor rhythms to operate a brai c05/2005 a1775–17770 v643 aPeople with severe motor disabilities can maintain an acceptable quality of life if they can communicate. Brain-computer interfaces (BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG rhythms recorded over sensorimotor cortex. These results suggest that a sensorimotor rhythm-based BCI could help maintain quality of life for people with ALS.10aUser-Computer Interface1 aKübler, A1 aNijboer, F.1 aMellinger, J1 aVaughan, Theresa, M1 aPawelzik, H.1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aBirbaumer, Niels1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1591180902332nas a2200457 4500008004100000022001400041245009000055210006900145260001200214300001100226490000700237520103300244653000901277653003401286653002701320653002901347653003701376653001101413653001101424653001601435653000901451653001601460653001701476653001301493653001401506653002301520653002801543653002501571653002201596653002801618100001501646700001501661700002301676700002401699700001601723700001901739700002601758700002101784700002101805856004801826 2005 eng d a1526-632X00aPatients with ALS can use sensorimotor rhythms to operate a brain-computer interface.0 aPatients with ALS can use sensorimotor rhythms to operate a brai c05/2005 a1775-70 v643 a

People with severe motor disabilities can maintain an acceptable quality of life if they can communicate. Brain-computer interfaces (BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG rhythms recorded over sensorimotor cortex. These results suggest that a sensorimotor rhythm-based BCI could help maintain quality of life for people with ALS.

10aAged10aAmyotrophic Lateral Sclerosis10aElectroencephalography10aEvoked Potentials, Motor10aEvoked Potentials, Somatosensory10aFemale10aHumans10aImagination10aMale10aMiddle Aged10aMotor Cortex10aMovement10aParalysis10aPhotic Stimulation10aProstheses and Implants10aSomatosensory Cortex10aTreatment Outcome10aUser-Computer Interface1 aKübler, A1 aNijboer, F1 aMellinger, Jürgen1 aVaughan, Theresa, M1 aPawelzik, H1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aBirbaumer, Niels1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1591180902354nas a2200157 4500008004100000022001400041245011600055210006900171260001200240300001400252490000700266520182800273100002602101700002102127856004802148 2005 eng d a1534-432000aSensorimotor rhythm-based brain-computer interface (BCI): feature selection by regression improves performance.0 aSensorimotor rhythmbased braincomputer interface BCI feature sel c09/2005 a372–3790 v133 aPeople can learn to control electroencephalogram (EEG) features consisting of sensorimotor rhythm amplitudes and can use this control to move a cursor in one or two dimensions to a target on a screen. In the standard one-dimensional application, the cursor moves horizontally from left to right at a fixed rate while vertical cursor movement is continuously controlled by sensorimotor rhythm amplitude. The right edge of the screen is divided among 2-6 targets, and the user's goal is to control vertical cursor movement so that the cursor hits the correct target when it reaches the right edge. Up to the present, vertical cursor movement has been a linear function of amplitude in a specific frequency band [i.e., 8-12 Hz (mu) or 18-26 Hz (beta)] over left and/or right sensorimotor cortex. The present study evaluated the effect of controlling cursor movement with a weighted combination of these amplitudes in which the weights were determined by an regression algorithm on the basis of the user's past performance. Analyses of data obtained from a representative set of trained users indicated that weighted combinations of sensorimotor rhythm amplitudes could support cursor control significantly superior to that provided by a single feature. Inclusion of an interaction term further improved performance. Subsequent online testing of the regression algorithm confirmed the improved performance predicted by the offline analyses. The results demonstrate the substantial value for brain-computer interface applications of simple multivariate linear algorithms. In contrast to many classification algorithms, such linear algorithms can easily incorporate multiple signal features, can readily adapt to changes in the user's control of these features, and can accommodate additional targets without major modifications.1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1620076000534nas a2200133 4500008004100000245007400041210006900115260001200184100002400196700001900220700002600239700002100265856011400286 2005 eng d00aTracking of the mu rhythm using an empirically derived matched filter0 aTracking of the mu rhythm using an empirically derived matched f c03/20051 aKrusienski, Dean, J1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttps://www.neurotechcenter.org/publications/2005/tracking-mu-rhythm-using-empirically-derived-matched-filter01351nas a2200409 4500008004100000020001800041245007500059210006900134260003300203653002700236653003000263653001900293653002900312653002500341653003300366653003400399653000800433653002700441653003900468653002100507653001700528653002000545653002900565653003000594653001500624653001800639653002300657653002600680653001100706653002300717653004000740100002400780700001900804700002600823700002100849856007100870 2005 eng d a0-7803-8710-400aTracking of the mu rhythm using an empirically derived matched filter.0 aTracking of the mu rhythm using an empirically derived matched f aArlington, VAbIEEEc03/200510abioelectric potentials10aBrain Computer Interfaces10abrain modeling10abrain-computer interface10acommunication device10acommunication system control10acortical mu rhythm modulation10aEEG10aElectroencephalography10aempirically derived matched filter10ahandicapped aids10alaboratories10amatched filters10amedical signal detection10amedical signal processing10amonitoring10amotor imagery10amu rhythm tracking10anoninvasive treatment10arhythm10asynchronous motors10atwo-dimensional cursor control data1 aKrusienski, Dean, J1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=141955902705nas a2200337 4500008004100000022001400041245007000055210006400125260001200189300001200201490000700213520166200220653001501882653001001897653001401907653003601921653002501957653002701982653002102009653003102030653002202061653001102083653002402094653002802118100001902146700002602165700002002191700002102211700002102232856011402253 2004 eng d a0018-929400aBCI2000: a general-purpose brain-computer interface (BCI) system.0 aBCI2000 a generalpurpose braincomputer interface BCI system c06/2004 a1034-430 v513 aMany laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.10aAlgorithms10aBrain10aCognition10aCommunication Aids for Disabled10aComputer Peripherals10aElectroencephalography10aEquipment Design10aEquipment Failure Analysis10aEvoked Potentials10aHumans10aSystems Integration10aUser-Computer Interface1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aHinterberger, T1 aBirbaumer, Niels1 aWolpaw, Jonathan uhttps://www.neurotechcenter.org/publications/2004/bci2000-general-purpose-brain-computer-interface-bci-system02279nas a2200205 4500008004100000022001400041245007000055210006400125260001200189300001600201490000700217520166200224653002801886100001901914700002601933700002401959700002101983700002102004856004802025 2004 eng d a0018-929400aBCI2000: a general-purpose brain-computer interface (BCI) system.0 aBCI2000 a generalpurpose braincomputer interface BCI system c06/2004 a1034–10430 v513 aMany laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.10aUser-Computer Interface1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aHinterberger, Thilo1 aBirbaumer, Niels1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1518887501886nas a2200181 4500008004100000022001400041245010200055210006900157260001200226300001800238490000800256520129000264653002801554653002701582100002101609700002601630856004801656 2004 eng d a0027-842400aControl of a two-dimensional movement signal by a noninvasive brain-computer interface in humans.0 aControl of a twodimensional movement signal by a noninvasive bra c12/2004 a17849–178540 v1013 aBrain-computer interfaces (BCIs) can provide communication and control to people who are totally paralyzed. BCIs can use noninvasive or invasive methods for recording the brain signals that convey the user's commands. Whereas noninvasive BCIs are already in use for simple applications, it has been widely assumed that only invasive BCIs, which use electrodes implanted in the brain, can provide multidimensional movement control of a robotic arm or a neuroprosthesis. We now show that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys. In movement time, precision, and accuracy, the results are comparable to those with invasive BCIs. The adaptive algorithm used in this noninvasive BCI identifies and focuses on the electroencephalographic features that the person is best able to control and encourages further improvement in that control. The results suggest that people with severe motor disabilities could use brain signals to operate a robotic arm or a neuroprosthesis without needing to have electrodes implanted in their brains.10abrain-machine interface10aElectroencephalography1 aWolpaw, Jonathan1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/1558558402353nas a2200229 4500008004100000022001400041245008900055210006900144260001200213300001400225490000700239520162900246653003101875653003501906653002701941653001301968100002301981700002602004700002102030700002402051856004802075 2004 eng d a1534-432000aConversion of EEG activity into cursor movement by a brain-computer interface (BCI).0 aConversion of EEG activity into cursor movement by a braincomput c09/2004 a331–3380 v123 aThe Wadsworth electroencephalogram (EEG)-based brain-computer interface (BCI) uses amplitude in mu or beta frequency bands over sensorimotor cortex to control cursor movement. Trained users can move the cursor in one or two dimensions. The primary goal of this research is to provide a new communication and control option for people with severe motor disabilities. Currently, cursor movements in each dimension are determined 10 times/s by an empirically derived linear function of one or two EEG features (i.e., spectral bands from different electrode locations). This study used offline analysis of data collected during system operation to explore methods for improving the accuracy of cursor movement. The data were gathered while users selected among three possible targets by controlling vertical [i.e., one-dimensional (1-D)] cursor movement. The three methods analyzed differ in the dimensionality of the cursor movement [1-D versus two-dimensional (2-D)] and in the type of the underlying function (linear versus nonlinear). We addressed two questions: Which method is best for classification (i.e., to determine from the EEG which target the user wants to hit)? How does the number of EEG features affect the performance of each method? All methods reached their optimal performance with 10-20 features. In offline simulation, the 2-D linear method and the 1-D nonlinear method improved performance significantly over the 1-D linear method. The 1-D linear method did not do so. These offline results suggest that the 1-D nonlinear or the 2-D linear cursor function will improve online operation of the BCI system.10aaugmentative communication10aBrain-computer interface (BCI)10aElectroencephalography10aFeedback1 aFabiani, Georg, E.1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan1 aPfurtscheller, Gert uhttp://www.ncbi.nlm.nih.gov/pubmed/1547319500690nas a2200181 4500008004100000245009300041210006900134100002300203700001500226700001600241700001900257700002600276700002400302700002100326700002100347700001500368856012500383 2004 eng d00aP300 for communication: Evidence from patients with amyotrophic lateral sclerosis (ALS).0 aP300 for communication Evidence from patients with amyotrophic l1 aMellinger, Jürgen1 aNijboer, F1 aPawelzik, H1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aKuebler, A uhttps://www.neurotechcenter.org/publications/2004/p300-communication-evidence-patients-amyotrophic-lateral-sclerosis-als02176nas a2200217 4500008004100000022001400041245009600055210006900151260001200220300001400232490000800246520147500254653003601729653002901765653003401794653001001828653002201838100002601860700002401886856004801910 2004 eng d a0378-595500aSeparating stimulus-locked and unlocked components of the auditory event-related potential.0 aSeparating stimuluslocked and unlocked components of the auditor c07/2004 a111–1200 v1933 aA new method is evaluated for separating stimulus-locked and unlocked components of auditory event-related EEG activity. The new method uses a regression based subtraction procedure as a way to account for latency and amplitude variability within individual trials. It was applied using the oddball paradigm under conditions of active and passive listening and analyzed as spectral correlations (normalized differences) between post-stimulus epochs of standard, target or deviant stimulus conditions and silent pre-stimulus baseline activity. The regression-subtraction procedure accounted for a greater amount of variance than a method that uses linear subtraction alone. The major component of the response to auditory stimulation was an event-related synchronization in the delta and theta (2-4 Hz) frequency range. Event-related desynchronizations were also observed in the 10 Hz (alpha/mu) and in the 20-30 Hz (beta) frequency range. The regression based subtraction procedure provides better separation of stimulus-locked and unlocked components of event-related EEG activity then linear subtraction alone. Stimulus-locked and unlocked components show different patterns and topographies of effects related to attention and active discrimination. Studying both stimulus-locked and unlocked components of event-related EEG reactivity in the frequency domain provides a more comprehensive account of dynamic brain activity subserving auditory information processing.10aevent-related desynchronization10aevent-related potentials10aevent-related synchronization10aphase10aspectral dynamics1 aMcFarland, Dennis, J.1 aCacace, Anthony, T. uhttp://www.ncbi.nlm.nih.gov/pubmed/1521932602522nas a2200277 4500008004100000022001400041245008500055210006900140260001200209300001400221490000700235520169700242653003101939653002701970653001601997653001302013653001402026653002502040653001502065653001902080653002402099100002602123700002602149700002102175856004802196 2003 eng d a0301-051100aBrain-computer interface (BCI) operation: optimizing information transfer rates.0 aBraincomputer interface BCI operation optimizing information tra c07/2003 a237–2510 v633 aPeople can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the EEG recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. In the present version of the cursor movement task, vertical cursor movement is a linear function of mu or beta rhythm amplitude. At the same time the cursor moves horizontally from left to right at a fixed rate. A target occupies 50% (2-target task) to 20% (5-target task) of the right edge of the screen. The user's task is to move the cursor vertically so that it hits the target when it reaches the right edge. The goal of the present study was to optimize system performance. To accomplish this, we evaluated the impact on system performance of number of targets (i.e. 2-5) and trial duration (i.e. horizontal movement time from 1 to 4 s). Performance was measured as accuracy (percent of targets selected correctly) and also as bit rate (bits/min) (which incorporates, in addition to accuracy, speed and the number of possible targets). Accuracy declined as target number increased. At the same time, for six of eight users, four targets yielded the maximum bit rate. Accuracy increased as movement time increased. At the same time, the movement time with the highest bit rate varied across users from 2 to 4 s. These results indicate that task parameters such as target number and trial duration can markedly affect system performance. They also indicate that optimal parameter values vary across users. Selection of parameters suited both to the specific user and the requirements of the specific application is likely to be a key factor in maximizing the success of EEG-based communication and control.10aaugmentative communication10aElectroencephalography10ainformation10aLearning10amu rhythm10aoperant conditioning10aprosthesis10aRehabilitation10asensorimotor cortex1 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1285316902255nas a2200169 4500008004100000022001400041245007100055210006700126260001200193300001400205490000700219520174400226653002001970100002601990700002102016856004802037 2003 eng d a1090-058600aEEG-based communication and control: speed-accuracy relationships.0 aEEGbased communication and control speedaccuracy relationships c09/2003 a217–2310 v283 aPeople can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the EEG recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. In our current EEG-based brain-computer interface (BCI) system, cursor movement is a linear function of mu or beta rhythm amplitude. In order to maximize the participant's control over the direction of cursor movement, the intercept in this equation is kept equal to the mean amplitude of recent performance. Selection of the optimal slope, or gain, which determines the magnitude of the individual cursor movements, is a more difficult problem. This study examined the relationship between gain and accuracy in a 1-dimensional EEG-based cursor movement task in which individuals select among 2 or more choices by holding the cursor at the desired choice for a fixed period of time (i.e., the dwell time). With 4 targets arranged in a vertical column on the screen, large gains favored the end targets whereas smaller gains favored the central targets. In addition, manipulating gain and dwell time within participants produces results that are in agreement with simulations based on a simple theoretical model of performance. Optimal performance occurs when correct selection of targets is uniform across position. Thus, it is desirable to remove any trend in the function relating accuracy to target position. We evaluated a controller that is designed to minimize the linear and quadratic trends in the accuracy with which participants hit the 4 targets. These results indicate that gain should be adjusted to the individual participants, and suggest that continual online gain adaptation could increase the speed and accuracy of EEG-based cursor control.10aVideo Recording1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1296445301629nas a2200265 4500008004100000022001400041245010300055210006900158260001200227300001200239490000800251520078600259653003101045653002901076653002801105653002701133653002401160653002001184653001901204100001901223700002601242700002601268700002101294856004801315 2003 eng d a0304-394000aElectroencephalographic(EEG)-based communication: EEG control versus system performance in humans.0 aElectroencephalographicEEGbased communication EEG control versus c07/2002 a89–920 v3453 aPeople can learn to control electroencephalographic (EEG) sensorimotor rhythm amplitude so as to move a cursor to select among choices on a computer screen. We explored the dependence of system performance on EEG control. Users moved the cursor to reach a target at one of four possible locations. EEG control was measured as the correlation (r(2)) between rhythm amplitude and target location. Performance was measured as accuracy (% of targets hit) and as information transfer rate (bits/trial). The relationship between EEG control and accuracy can be approximated by a linear function that is constant for all users. The results facilitate offline predictions of the effects on performance of using different EEG features or combinations of features to control cursor movement.10aaugmentative communication10abrain-computer interface10abrain-machine interface10aElectroencephalography10amu and beta rhythms10aneuroprosthesis10aRehabilitation1 aSheikh, Hesham1 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1282117802517nas a2200241 4500008004100000022001400041245007400055210006900129260001200198300001600210490000800226520179400234653001302028653002902041653002502070653001902095653001902114100002302133700002602156700002402182700002102206856004802227 2003 eng d a1388-245700aEMG contamination of EEG: spectral and topographical characteristics.0 aEMG contamination of EEG spectral and topographical characterist c09/2003 a1580–15930 v1143 aOBJECTIVE: Electromyogram (EMG) contamination is often a problem in electroencephalogram (EEG) recording, particularly, for those applications such as EEG-based brain-computer interfaces that rely on automated measurements of EEG features. As an essential prelude to developing methods for recognizing and eliminating EMG contamination of EEG, this study defines the spectral and topographical characteristics of frontalis and temporalis muscle EMG over the entire scalp. It describes both average data and the range of individual differences. METHODS: In 25 healthy adults, signals from 64 scalp and 4 facial locations were recorded during relaxation and during defined (15, 30, or 70% of maximum) contractions of frontalis or temporalis muscles. RESULTS: In the average data, EMG had a broad frequency distribution from 0 to >200 Hz. Amplitude was greatest at 20-30 Hz frontally and 40-80 Hz temporally. Temporalis spectra also showed a smaller peak around 20 Hz. These spectral components attenuated and broadened centrally. Even with weak (15%) contraction, EMG was detectable (P<0.001) near the vertex at frequencies >12 Hz in the average data and >8 Hz in some individuals. CONCLUSIONS: Frontalis or temporalis muscle EMG recorded from the scalp has spectral and topographical features that vary substantially across individuals. EMG spectra often have peaks in the beta frequency range that resemble EEG beta peaks. SIGNIFICANCE: While EMG contamination is greatest at the periphery of the scalp near the active muscles, even weak contractions can produce EMG that obscures or mimics EEG alpha, mu, or beta rhythms over the entire scalp. Recognition and elimination of this contamination is likely to require recording from an appropriate set of peripheral scalp locations.10aartifact10abrain-computer interface10aelectroencephalogram10aelectromyogram10aRehabilitation1 aGoncharova, I., I.1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1294878700603nas a2200157 4500008004100000022001400041245016200055210006900217260001200286300003100298490000700329653001100336100002600347700002400373856004800397 2003 eng d a1050-054500aPotential problems in the differential diagnosis of (central) auditory processing disorder (CAPD or APD) and attention-deficit hyperactivity disorder (ADHD).0 aPotential problems in the differential diagnosis of central audi c07/2003 a278–80; author reply 2800 v1410aHumans1 aMcFarland, Dennis, J.1 aCacace, Anthony, T. uhttp://www.ncbi.nlm.nih.gov/pubmed/1295631201652nas a2200217 4500008004100000022001400041245007200055210006900127260001200196300001400208490000800222520096900230653003301199653002801232653002401260653002601284653002601310100002401336700002601360856004801386 2003 eng d a0304-394000aQuantifying signal-to-noise ratio of mismatch negativity in humans.0 aQuantifying signaltonoise ratio of mismatch negativity in humans c05/2003 a251–2550 v3413 aMismatch negativity (MMN) is thought to represent a neurophysiological index of auditory information processing that is independent of attention. Because this measure does not require an overt behavioral response, MMN has potential to evaluate higher order perceptual abilities in infants, young children and difficult-to-test populations, thereby extending results obtained from more basic physiologic and electroacoustic measures (auditory brainstem responses, ABRs; otoacoustic emissions, OAEs). Whereas the basic tenet of MMN is appealing, several issues-of-contention remain to be solved before this event related potential (ERP) can be applicable for routine clinical use. These issues include the consistent identification of MMN within individuals (vs. groups), its stability over time, and its reportedly poor signal-to-noise ratio (SNR). Herein, we focus on the issue of SNR, by comparing and contrasting SNR of MMN with other long latency auditory ERPs.10aauditory brainstem responses10aevent related potential10amismatch negativity10aotoacoustic emissions10asignal-to-noise ratio1 aCacace, Anthony, T.1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/1269729502161nas a2200241 4500008004100000022001400041245009800055210006900153260001200222300001200234490000800246520137200254653002701626653003301653653003601686653003401722653001801756653002201774653002501796100002401821700002601845856004801871 2003 eng d a0378-595500aSpectral dynamics of electroencephalographic activity during auditory information processing.0 aSpectral dynamics of electroencephalographic activity during aud c02/2003 a25–410 v1763 aDynamics of electroencephalographic (EEG) activity during auditory information processing were evaluated in response to changes in stimulus complexity, stimulus discriminability and attention using the oddball paradigm. In comparison to pre-stimulus baseline conditions, auditory stimulation synchronized EEG activity in delta, theta and alpha frequency bands. Event-related synchronization (ERS) effects were greatest at approximately 3 Hz (theta frequency band), and their magnitude depended on stimulus and task demands. Event-related desynchronization (ERD) of EEG activity was observed in the beta frequency band. This effect was greatest at approximately 21 Hz but occurred only for easily discriminable stimuli in attention-related target conditions. Because active discrimination tasks also required a button-press response with the right hand, ERDs involved more complex responses that may be related to a combination of perceptual, motor and cognitive processes. These results demonstrate that oddball and attention-related EEG responses to auditory stimulation could be characterized in the frequency domain. The specific design and analysis features described herein may prove useful since they provide a simple index of the brain's response to stimulation while at the same time provide powerful information not contained in typical time domain analysis.10aElectroencephalography10aevent-related brain dynamics10aevent-related desynchronization10aevent-related synchronization10apsychophysics10aspectral analysis10atime domain analysis1 aCacace, Anthony, T.1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/1258387903064nas a2200373 4500008004100000022001400041245009000055210006900145260001200214300001000226490000700236520200500243653002902248653001002277653001502287653001402302653001002316653001802326653002702344653003002371653001302401653001102414653001602425653002802441653001302469653002002482653002802502653002202530100002102552700002602573700002402599700001902623856004802642 2003 eng d a1534-432000aThe Wadsworth Center brain-computer interface (BCI) research and development program.0 aWadsworth Center braincomputer interface BCI research and develo c06/2003 a204-70 v113 a

Brain-computer interface (BCI) research at the Wadsworth Center has focused primarily on using electroencephalogram (EEG) rhythms recorded from the scalp over sensorimotor cortex to control cursor movement in one or two dimensions. Recent and current studies seek to improve the speed and accuracy of this control by improving the selection of signal features and their translation into device commands, by incorporating additional signal features, and by optimizing the adaptive interaction between the user and system. In addition, to facilitate the evaluation, comparison, and combination of alternative BCI methods, we have developed a general-purpose BCI system called BCI-2000 and have made it available to other research groups. Finally, in collaboration with several other groups, we are developing simple BCI applications and are testing their practicality and long-term value for people with severe motor disabilities.

10aAcademic Medical Centers10aAdult10aAlgorithms10aArtifacts10aBrain10aBrain Mapping10aElectroencephalography10aEvoked Potentials, Visual10aFeedback10aHumans10aMiddle Aged10aNervous System Diseases10aResearch10aResearch Design10aUser-Computer Interface10aVisual Perception1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/1289927506596nas a2200265 4500008004100000022001400041245006100055210005900116260001200175300001100187490000800198520581800206653001906024653003606043653002106079653002706100653001106127653002806138100002106166700002106187700002606208700002406234700002406258856004806282 2002 eng d a1388-245700aBrain-computer interfaces for communication and control.0 aBraincomputer interfaces for communication and control c06/2002 a767-910 v1133 a

For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world - a brain-computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and controltechnology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or 'locked in', with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user encode commands in these signals and that the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance. Current BCIs have maximum information transfer rates up to 10-25bits/min. This limited capacity can be valuable for people whose severe disabilities prevent them from using conventional augmentative communication methods. At the same time, many possible applications of BCI technology, such as neuroprosthesis control, may require higher information transfer rates. Future progress will depend on: recognition that BCI research and development is an interdisciplinary problem, involving neurobiology, psychology, engineering, mathematics, and computer science; identification of those signals, whether evoked potentials, spontaneous rhythms, or neuronal firing rates, that users are best able to control independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that control; delineation of the best algorithms for translating these signals into device commands; attention to the identification and elimination of artifacts such as electromyographic and electro-oculographic activity; adoption of precise and objective procedures for evaluating BCI performance; recognition of the need for long-term as well as short-term assessment of BCI performance; identification of appropriate BCI applications and appropriate matching of applications and users; and attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those communication and control capacities that are most important to the user. Development of BCI technology will also benefit from greater emphasis on peer-reviewed research publications and avoidance of the hyperbolic and often misleading media attention that tends to generate unrealistic expectations in the public and skepticism in other researchers. With adequate recognition and effective engagement of all these issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.

10aBrain Diseases10aCommunication Aids for Disabled10aComputer Systems10aElectroencephalography10aHumans10aUser-Computer Interface1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aMcFarland, Dennis, J.1 aPfurtscheller, Gert1 aVaughan, Theresa, M uhttp://www.ncbi.nlm.nih.gov/pubmed/1204803800559nas a2200157 4500008004100000022001400041245010000055210006900155260001200224300002900236490000700265653003100272100002600303700002400329856004800353 2002 eng d a1059-088900aFactor analysis in CAPD and the "unimodal" test battery: do we have a model that will satisfy?.0 aFactor analysis in CAPD and the unimodal test battery do we have c06/2002 a7–9; author reply 9-120 v1110aReproducibility of Results1 aMcFarland, Dennis, J.1 aCacace, Anthony, T. uhttp://www.ncbi.nlm.nih.gov/pubmed/1222735801157nas a2200181 4500008004100000022001400041245006300055210006100118260001200179300001400191490000600205520061700211653002800828100002100856700002600877700002400903856004800927 2000 eng d a1063-652800aBrain-computer interface research at the Wadsworth Center.0 aBraincomputer interface research at the Wadsworth Center c06/2000 a222–2260 v83 aStudies at the Wadsworth Center over the past 14 years have shown that people with or without motor disabilities can learn to control the amplitude of mu or beta rhythms in electroencephalographic (EEG) activity recorded from the scalp over sensorimotor cortex and can use that control to move a cursor on a computer screen in one or two dimensions. This EEG-based brain-computer interface (BCI) could provide a new augmentative communication technology for those who are totally paralyzed or have other severe motor impairments. Present research focuses on improving the speed and accuracy of BCI communication.10aUser-Computer Interface1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M uhttp://www.ncbi.nlm.nih.gov/pubmed/1089619403091nas a2200373 4500008004100000022001400041245008600055210006900141260001200210300001100222490000600233520197800239653001502217653002002232653003602252653002102288653002702309653002202336653001102358653002702369653004102396653002802437100002102465700002102486700001902507700002602526700001702552700001902569700002102588700001802609700001802627700002402645856004802669 2000 eng d a1063-652800aBrain-computer interface technology: a review of the first international meeting.0 aBraincomputer interface technology a review of the first interna c06/2000 a164-730 v83 a

Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.

10aAlgorithms10aCerebral Cortex10aCommunication Aids for Disabled10aDisabled Persons10aElectroencephalography10aEvoked Potentials10aHumans10aNeuromuscular Diseases10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aHeetderks, W J1 aMcFarland, Dennis, J.1 aPeckham, P H1 aSchalk, Gerwin1 aDonchin, Emanuel1 aQuatrano, L A1 aRobinson, C J1 aVaughan, Theresa, M uhttp://www.ncbi.nlm.nih.gov/pubmed/1089617802866nas a2200289 4500008004100000022001400041245008600055210006900141260001200210300001400222490000600236520197100242653003102213653003502244653003302279100002102312700002102333700002202354700002602376700002002402700001902422700002102441700002102462700002102483700002402504856004802528 2000 eng d a1063-652800aBrain-computer interface technology: a review of the first international meeting.0 aBraincomputer interface technology a review of the first interna c06/2000 a164–1730 v83 aOver the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.10aaugmentative communication10aBrain-computer interface (BCI)10aelectroencephalography (EEG)1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aHeetderks, W., J.1 aMcFarland, Dennis, J.1 aPeckham, P., H.1 aSchalk, Gerwin1 aDonchin, Emanuel1 aQuatrano, L., A.1 aRobinson, C., J.1 aVaughan, Theresa, M uhttp://www.ncbi.nlm.nih.gov/pubmed/1089617801315nas a2200289 4500008004100000022001400041245006100055210005800116260001200174300001600186490000800202520045900210653003100669653002900700653002700729653002000756653002900776653002800805653001400833653001900847653002400866100001900890700002100909700002600930700002100956856004800977 2000 eng d a1388-245700aEEG-based communication: presence of an error potential.0 aEEGbased communication presence of an error potential c12/2000 a2138–21440 v1113 aEEG-based communication could be a valuable new augmentative communication technology for those with severe motor disabilities. Like all communication methods, it faces the problem of errors in transmission. In the Wadsworth EEG-based brain-computer interface (BCI) system, subjects learn to use mu or beta rhythm amplitude to move a cursor to targets on a computer screen. While cursor movement is highly accurate in trained subjects, it is not perfect.10aaugmentative communication10abrain-computer interface10aElectroencephalography10aerror potential10aerror related negativity10aevent related potential10amu rhythm10aRehabilitation10asensorimotor cortex1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.1 aPfurtscheller, G uhttp://www.ncbi.nlm.nih.gov/pubmed/1109076302265nas a2200241 4500008004100000022001400041245007900055210006900134260001200203300001400215490000700229520157600236653001601812653000801828653001201836653001401848653002401862100002601886700001801912700002401930700002101954856004801975 2000 eng d a0896-026700aMu and beta rhythm topographies during motor imagery and actual movements.0 aMu and beta rhythm topographies during motor imagery and actual  c03/2000 a177–1860 v123 aPeople can learn to control the 8-12 Hz mu rhythm and/or the 18-25 Hz beta rhythm in the EEG recorded over sensorimotor cortex and use it to control a cursor on a video screen. Subjects often report using motor imagery to control cursor movement, particularly early in training. We compared in untrained subjects the EEG topographies associated with actual hand movement to those associated with imagined hand movement. Sixty-four EEG channels were recorded while each of 33 adults moved left- or right-hand or imagined doing so. Frequency-specific differences between movement or imagery and rest, and between right- and left-hand movement or imagery, were evaluated by scalp topographies of voltage and r spectra, and principal component analysis. Both movement and imagery were associated with mu and beta rhythm desynchronization. The mu topographies showed bilateral foci of desynchronization over sensorimotor cortices, while the beta topographies showed peak desynchronization over the vertex. Both mu and beta rhythm left/right differences showed bilateral central foci that were stronger on the right side. The independence of mu and beta rhythms was demonstrated by differences for movement and imagery for the subjects as a group and by principal components analysis. The results indicated that the effects of imagery were not simply an attenuated version of the effects of movement. They supply evidence that motor imagery could play an important role in EEG-based communication, and suggest that mu and beta rhythms might provide independent control signals.10abeta rhythm10aEEG10aimagery10amu rhythm10asensorimotor cortex1 aMcFarland, Dennis, J.1 aMiner, L., A.1 aVaughan, Theresa, M1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1079168102346nas a2200181 4500008004100000022001400041245008500055210006900140260001200209300001600221490000700237520178000244653002802024100001802052700002602070700002102096856004702117 1998 eng d a0003-999300aAnswering questions with an electroencephalogram-based brain-computer interface.0 aAnswering questions with an electroencephalogrambased braincompu c09/1998 a1029–10330 v793 aOBJECTIVE: To demonstrate that humans can learn to control selected electroencephalographic components and use that control to answer simple questions. METHODS: Four adults (one with amyotrophic lateral sclerosis) learned to use electroencephalogram (EEG) mu rhythm (8 to 12Hz) or beta rhythm (18 to 25Hz) activity over sensorimotor cortex to control vertical cursor movement to targets at the top or bottom edge of a video screen. In subsequent sessions, the targets were replaced with the words YES and NO, and individuals used the cursor to answer spoken YES/NO questions from single- or multiple-topic question sets. They confirmed their answers through the response verification (RV) procedure, in which the word positions were switched and the question was answered again. RESULTS: For 5 consecutive sessions after initial question training, individuals were asked an average of 4.0 to 4.6 questions per minute; 64% to 87% of their answers were confirmed by the RV procedure and 93% to 99% of these answers were correct. Performances for single- and multiple-topic question sets did not differ significantly. CONCLUSIONS: The results indicate that (1) EEG-based cursor control can be used to answer simple questions with a high degree of accuracy, (2) attention to auditory queries and formulation of answers does not interfere with EEG-based cursor control, (3) question complexity (at least as represented by single versus multiple-topic question sets) does not noticeably affect performance, and (4) the RV procedure improves accuracy as expected. Several options for increasing the speed of communication appear promising. An EEG-based brain-computer interface could provide a new communication and control modality for people with severe motor disabilities.10aUser-Computer Interface1 aMiner, L., A.1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/974967802088nas a2200265 4500008004100000022001400041245006600055210006300121260001200184300001400196490000800210520131500218653003101533653001701564653002701581653002101608653001401629653001901643653002401662100002401686700001801710700002601728700002101754856004701775 1998 eng d a0013-469400aEEG-based communication: analysis of concurrent EMG activity.0 aEEGbased communication analysis of concurrent EMG activity c12/1998 a428–4330 v1073 aOBJECTIVE: Recent studies indicate that people can learn to control the amplitude of mu or beta rhythms in the EEG recorded from the scalp over sensorimotor cortex and can use that control to move a cursor to targets on the computer screen. While subjects do not move during performance, it is possible that inapparent or unconscious muscle contractions contribute to the changes in the mu and beta rhythm activity responsible for cursor movement. We evaluated this possibility. METHODS: EMG was recorded from 10 distal limb muscle groups while five trained subjects used mu or beta rhythms to move a cursor to targets at the bottom or top edge of a computer screen. RESULTS: EMG activity was very low during performance, averaging 4.0+/-4.4% (SD) of maximum voluntary contraction. Most important, the correlation, measured as r2, between target position and EMG activity averaged only 0.01+/-0.02, much lower than the correlation between target position and the EEG activity that controlled cursor movement, which averaged 0.39+/-0.18. CONCLUSIONS: These results strongly support the conclusion that EEG-based cursor control does no depend on concurrent muscle activity. EEG-based communication and control might provide a new augmentative communication option for those with severe motor disabilities.10aaugmentative communication10aconditioning10aElectroencephalography10aElectromyography10amu rhythm10aRehabilitation10asensorimotor cortex1 aVaughan, Theresa, M1 aMiner, L., A.1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/992208901613nas a2200181 4500008004100000022001400041245007000055210006600125260001200191300001100203490000600214520106600220653003201286100002601318700001901344700002101363856004701384 1998 eng d a1063-652800aEEG-based communication and control: short-term role of feedback.0 aEEGbased communication and control shortterm role of feedback c03/1998 a7–110 v63 aWhen people learn to control the amplitudes of certain electroencephalogram (EEG) components (e.g., the 8-12 Hz mu-rhythm over sensorimotor cortex) and use them to move a cursor to a target on a video screen, feedback about performance is normally provided by cursor movement and by trial outcome (i.e., success or failure). We assessed the short-term effects of this feedback on EEG control. After subjects received initial training with feedback present, feedback was removed intermittently for periods of several minutes. Subjects still displayed EEG control when feedback was removed. Removal of cursor movement alone appeared to have effects comparable to removal of both cursor movement and trial outcome. These results show that, in the short-term at least, mu-rhythm control is not dependent on the sensory input provided by cursor movement. They also suggest that feedback can have inhibitory as well as facilitory effects on EEG control, and that these effects vary across subjects. This finding has implications for the design of training procedures.10aSensitivity and Specificity1 aMcFarland, Dennis, J.1 aMcCane, L., M.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/953551801936nas a2200205 4500008004100000022001400041245007300055210006900128260001200197300001400209490000600223520132600229653002201555653002201577100002101599700001601620700002601636700002101662856004701683 1998 eng d a1063-652800aEEG-based communication: improved accuracy by response verification.0 aEEGbased communication improved accuracy by response verificatio c09/1998 a326–3330 v63 aHumans can learn to control the amplitude of electroencephalographic (EEG) activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. EEG-based communication could provide a new augmentative communication channel for individuals with motor disabilities. In the present system, each dimension of cursor movement is controlled by a linear equation. While the intercept in the equation is continually updated, it does not perfectly eliminate the impact of spontaneous variations in EEG amplitude. This imperfection reduces the accuracy of cursor movement. We evaluated a response verification (RV) procedure in which each outcome is determined by two opposite trials (e.g., one top-target trial and one bottom-target trial). Success, or failure, on both is required for a definitive outcome. The RV procedure reduces errors due to imperfection in intercept selection. Accuracy for opposite-trial pairs exceeds that predicted from the accuracies of individual trials, and greatly exceeds that for same-trial pairs. The RV procedure should be particularly valuable when the first trial has >2 possible targets, because the second trial need only confirm or deny the outcome of the first, and it should be applicable to nonlinear as well as to linear algorithms.10aComputer-Assisted10aSignal Processing1 aWolpaw, Jonathan1 aRamoser, H.1 aMcFarland, Dennis, J.1 aPfurtscheller, G uhttp://www.ncbi.nlm.nih.gov/pubmed/974991002193nas a2200265 4500008004100000022001400041245005800055210005600113260001200169300001400181490000800195520144100203653002801644653002701672653001401699653002501713653001501738653001901753653002401772100002601796700001901822700001801841700002101859856004701880 1997 eng d a0013-469400aSpatial filter selection for EEG-based communication.0 aSpatial filter selection for EEGbased communication c09/1997 a386–3940 v1033 aIndividuals can learn to control the amplitude of mu-rhythm activity in the EEG recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. The speed and accuracy of cursor movement depend on the consistency of the control signal and on the signal-to-noise ratio achieved by the spatial and temporal filtering methods that extract the activity prior to its translation into cursor movement. The present study compared alternative spatial filtering methods. Sixty-four channel EEG data collected while well-trained subjects were moving the cursor to targets at the top or bottom edge of a video screen were analyzed offline by four different spatial filters, namely a standard ear-reference, a common average reference (CAR), a small Laplacian (3 cm to set of surrounding electrodes) and a large Laplacian (6 cm to set of surrounding electrodes). The CAR and large Laplacian methods proved best able to distinguish between top and bottom targets. They were significantly superior to the ear-reference method. The difference in performance between the large Laplacian and small Laplacian methods presumably indicated that the former was better matched to the topographical extent of the EEG control signal. The results as a whole demonstrate the importance of proper spatial filter selection for maximizing the signal-to-noise ratio and thereby improving the speed and accuracy of EEG-based communication.10aassistive communication10aElectroencephalography10amu rhythm10aoperant conditioning10aprosthesis10aRehabilitation10asensorimotor cortex1 aMcFarland, Dennis, J.1 aMcCane, L., M.1 aDavid, S., V.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/930528702490nas a2200265 4500008004100000022001400041245004000055210003800095260001200133300001400145490000700159520177200166653002801938653002701966653001401993653002502007653001502032653001902047653002402066100002102090700001902111700002102130700002602151856004702177 1997 eng d a0736-025800aTiming of EEG-based cursor control.0 aTiming of EEGbased cursor control c11/1997 a529–5380 v143 aRecent studies show that humans can learn to control the amplitude of electroencephalography (EEG) activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. EEG-based communication could be a valuable new communication and control option for those with severe motor disabilities. Realization of this potential requires detailed knowledge of the characteristic features of EEG control. This study examined the course of EEG control after presentation of a target. At the beginning of each trial, a target appeared at the top or bottom edge of the subject's video screen and 1 sec later a cursor began to move vertically as a function of EEG amplitude in a specific frequency band. In well-trained subjects, this amplitude was high at the time the target appeared and then either remained high (i.e., for a top target) or fell rapidly (i.e., for a bottom target). Target-specific EEG amplitude control began 0.5 sec after the target appeared and appeared to wax and wane with a period of approximately 1 sec until the cursor reached the target (i.e., a hit) or the opposite edge of the screen (i.e., a miss). Accuracy was 90% or greater for each subject. Top-target errors usually occurred later in the trial because of failure to reach and/or maintain sufficiently high amplitude, whereas bottom-target errors usually occurred immediately because of failure to reduce an initially high amplitude quickly enough. The results suggest modifications that could improve performance. These include lengthening the intertrial period, shortening the delay between target appearance and cursor movement, and including time within the trial as a variable in the equation that translates EEG into cursor movement.10aassistive communication10aElectroencephalography10amu rhythm10aoperant conditioning10aprosthesis10aRehabilitation10asensorimotor cortex1 aWolpaw, Jonathan1 aFlotzinger, D.1 aPfurtscheller, G1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/945806001530nas a2200205 4500008004100000022001400041245011100055210006900166260000900235300001400244490000700258520087900265653002801144100002101172700001901193700001801212700002101230700002601251856004701277 1995 eng d a0047-655200aEEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components.0 aEEGbased brain computer interface BCI Search for optimal electro c1996 a111–1210 v213 aSeveral laboratories around the world have recently started to investigate EEG-based brain computer interface (BCI) systems in order to create a new communication channel for subjects with severe motor impairments. The present paper describes an initial evaluation of 64-channel EEG data recorded while subjects used one EEG channel over the left sensorimotor area to control on-line vertical cursor movement. Targets were given at the top or bottom of a computer screen. Data from 3 subjects in the early stages of training were analyzed by calculating band power time courses and maps for top and bottom targets separately. In addition, the Distinction Sensitive Learning Vector Quantizer (DSLVQ) was applied to single-trial EEG data. It was found that for each subject there exist optimal electrode positions and frequency components for on-line EEG-based cursor control.10aUser-Computer Interface1 aPfurtscheller, G1 aFlotzinger, D.1 aPregenzer, M.1 aWolpaw, Jonathan1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/877670801539nas a2200241 4500008004100000022001400041245005700055210005400112260001200166300001400178490000700192520085200199653002801051653002701079653001401106653002501120653001501145653001901160653002401179100002101203700002601224856004701250 1994 eng d a0013-469400aMultichannel EEG-based brain-computer communication.0 aMultichannel EEGbased braincomputer communication c06/1994 a444–4490 v903 aIndividuals who are paralyzed or have other severe movement disorders often need alternative means for communicating with and controlling their environments. In this study, human subjects learned to use two channels of bipolar EEG activity to control 2-dimensional movement of a cursor on a computer screen. Amplitudes of 8-12 Hz activity in the EEG recorded from the scalp across right and left central sulci were determined by fast Fourier transform and combined to control vertical and horizontal cursor movements simultaneously. This independent control of two separate EEG channels cannot be attributed to a non-specific change in brain activity and appeared to be specific to the mu rhythm frequency range. With further development, multichannel EEG-based communication may prove of significant value to those with severe motor disabilities.10aassistive communication10aElectroencephalography10amu rhythm10aoperant conditioning10aprosthesis10aRehabilitation10asensorimotor cortex1 aWolpaw, Jonathan1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/751578702018nas a2200265 4500008004100000022001400041245006200055210005600117260001200173300001400185490000700199520128900206653001801495653002101513653000801534653001401542653002501556653001501581653002401596100002101620700002601641700001701667700002101684856004701705 1991 eng d a0013-469400aAn EEG-based brain-computer interface for cursor control.0 aEEGbased braincomputer interface for cursor control c03/1991 a252–2590 v783 aThis study began development of a new communication and control modality for individuals with severe motor deficits. We trained normal subjects to use the 8-12 Hz mu rhythm recorded from the scalp over the central sulcus of one hemisphere to move a cursor from the center of a video screen to a target located at the top or bottom edge. Mu rhythm amplitude was assessed by on-line frequency analysis and translated into cursor movement: larger amplitudes moved the cursor up and smaller amplitudes moved it down. Over several weeks, subjects learned to change mu rhythm amplitude quickly and accurately, so that the cursor typically reached the target in 3 sec. The parameters that translated mu rhythm amplitudes into cursor movements were derived from evaluation of the distributions of amplitudes in response to top and bottom targets. The use of these distributions was a distinctive feature of this study and the key factor in its success. Refinements in training procedures and in the distribution-based method used to translate mu rhythm amplitudes into cursor movements should further improve this 1-dimensional control. Achievement of 2-dimensional control is under study. The mu rhythm may provide a significant new communication and control option for disabled individuals.10aCommunication10acomputer control10aEEG10amu rhythm10aoperant conditioning10aprosthesis10asensorimotor rhythm1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.1 aNeat, G., W.1 aForneris, C., A. uhttp://www.ncbi.nlm.nih.gov/pubmed/170779801324nas a2200229 4500008004100000022001400041245010900055210006900164260001200233300001400245490000700259520060500266653001100871653004700882653000800929653002500937653002200962100001600984700002601000700002101026856004701047 1988 eng d a0304-394000aRetrograde transport of the lectin Phaseolus vulgaris leucoagglutinin (PHA-L) by rat spinal motoneurons.0 aRetrograde transport of the lectin Phaseolus vulgaris leucoagglu c03/1988 a133–1380 v863 aThe lectin Phaseolus vulgaris leucoagglutinin (PHA-L) has been used primarily as an anterograde transport tracer in the CNS. We present evidence of PHA-L retrograde transport by rat spinal motoneurons after injection into the triceps brachii. Labelled motoneurons were localized in specific and well-defined neuron pools in the ventral horn. Primary afferent labelling was not seen in the spinal gray matter. Dorsal rhizotomy did not eliminate or decrease motoneuron labelling. The retrograde transport rate was about 8 mm/day. PHA-L can clearly undergo retrograde, as well as anterograde, transport.10alectin10aphaseolus vulgaris leucoagglutinin (PHA-L)10arat10aretrograde transport10aspinal motoneuron1 aLee, C., L.1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/2453002