@article {3571, title = {EEG based zero-phase phase-locking value (PLV) and effects of spatial filtering during actual movement.}, journal = {Brain research bulletin}, volume = {130}, year = {2017}, month = {Apr}, pages = {156{\textendash}164}, abstract = {Phase-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.}, issn = {1873-2747}, doi = {10.1016/j.brainresbull.2017.01.023}, url = {http://www.ncbi.nlm.nih.gov/pubmed/28161192}, author = {Jian, Wenjuan and Chen, Minyou and Dennis J. McFarland} } @article {3574, title = {EEG Error Prediction as a Solution for Combining the Advantages of Retrieval Practice and Errorless Learning.}, journal = {Frontiers in human neuroscience}, volume = {11}, year = {2017}, month = {Mar}, pages = {140}, abstract = {Given 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.}, doi = {10.3389/fnhum.2017.00140}, url = {http://www.ncbi.nlm.nih.gov/pubmed/28396630}, author = {Riley, Ellyn A. and Dennis J. McFarland} } @article {4093, title = {EEG-based brain-computer interfaces}, journal = {Current Opinion in Biomedical Engineering}, volume = {4}, year = {2017}, month = {Oct}, pages = {194-200}, abstract = {Brain{\textendash}Computer Interfaces (BCIs) are real-time computer-based systems that translate brain signals into useful commands. To date most applications have been demonstrations of proof-of-principle; widespread use by people who could benefit from this technology requires further development. Improvements in current EEG recording technology are needed. Better sensors would be easier to apply, more comfortable for the user, and produce higher quality and more stable signals. Although considerable effort has been devoted to evaluating classifiers using public datasets, more attention to real-time signal processing issues and to optimizing the mutually adaptive interaction between the brain and the BCI are essential for improving BCI performance. Further development of applications is also needed, particularly applications of BCI technology to rehabilitation. The design of rehabilitation applications hinges on the nature of BCI control and how it might be used to induce and guide beneficial plasticity in the brain.}, keywords = {brain-computer interface, neurotechnology, Rehabilitation}, issn = {2468-4511}, doi = {doi.org/10.1016/j.cobme.2017.11.004.}, url = {https://www.ncbi.nlm.nih.gov/pubmed/21438193}, author = {McFarland, D. J. and Jonathan Wolpaw} } @article {3575, title = {Evaluation of multidimensional models of WAIS-IV subtest performance.}, journal = {The Clinical neuropsychologist}, year = {2017}, month = {Apr}, pages = {1{\textendash}14}, abstract = {The 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.}, issn = {1744-4144}, doi = {10.1080/13854046.2017.1320426}, url = {http://www.ncbi.nlm.nih.gov/pubmed/28430031}, author = {Dennis J. McFarland} } @article {3572, title = {How neuroscience can inform the study of individual differences in cognitive abilities.}, journal = {Reviews in the neurosciences}, year = {2017}, month = {Feb}, abstract = {Theories 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.}, issn = {0334-1763}, doi = {10.1515/revneuro-2016-0073}, url = {http://www.ncbi.nlm.nih.gov/pubmed/28195556}, author = {Dennis J. McFarland} } @article {3570, title = {Prediction of subjective ratings of emotional pictures by EEG features.}, journal = {Journal of neural engineering}, volume = {14}, year = {2017}, month = {Feb}, pages = {016009}, abstract = {Emotion 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.}, issn = {1741-2552}, doi = {10.1088/1741-2552/14/1/016009}, url = {http://www.ncbi.nlm.nih.gov/pubmed/27934776}, author = {Dennis J. McFarland and Parvaz, Muhammad A. and Sarnacki, William A. and Goldstein, Rita Z. and Jonathan Wolpaw} } @article {3573, title = {Use of phase-locking value in sensorimotor rhythm-based brain-computer interface: zero-phase coupling and effects of spatial filters.}, journal = {Medical \& biological engineering \& computing}, year = {2017}, month = {Mar}, abstract = {Phase-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.}, issn = {1741-0444}, doi = {10.1007/s11517-017-1641-y}, url = {http://www.ncbi.nlm.nih.gov/pubmed/28343333}, author = {Jian, Wenjuan and Chen, Minyou and Dennis J. McFarland} } @article {3576, title = {Modeling General and Specific Abilities: Evaluation of Bifactor Models for the WJ-III.}, journal = {Assessment}, volume = {23}, year = {2016}, month = {Dec}, pages = {698{\textendash}706}, abstract = {The 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.}, issn = {1552-3489}, doi = {10.1177/1073191115595070}, url = {http://www.ncbi.nlm.nih.gov/pubmed/26187901}, author = {Dennis J. McFarland} } @article {3569, title = {Effects of training pre-movement sensorimotor rhythms on behavioral performance.}, journal = {Journal of neural engineering}, volume = {12}, year = {2015}, month = {Dec}, pages = {066021}, abstract = {Brain-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.}, keywords = {sensorimotor cortex}, issn = {1741-2552}, doi = {10.1088/1741-2560/12/6/066021}, url = {http://www.ncbi.nlm.nih.gov/pubmed/26529119}, author = {Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw} } @article {3381, title = {P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls.}, journal = {Clin Neurophysiol}, year = {2015}, month = {02/2015}, abstract = {

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 ({\textpm}9.5SD) (range 0-25)). Each subject attended to a target item as the items in a 6{\texttimes}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.

}, keywords = {alternative and augmentative communication (AAC), amyotrophic lateral sclerosis (ALS), Brain-computer interface (BCI), brain-machine interface (BMI), electroencephalography (EEG), event-related potentials (ERP)}, issn = {1872-8952}, doi = {10.1016/j.clinph.2015.01.013}, url = {http://www.ncbi.nlm.nih.gov/pubmed/25703940}, author = {McCane, Lynn M and Susan M Heckman and Dennis J. McFarland and Townsend, George and Mak, Joseph N and Sellers, Eric W and Zeitlin, Debra and Tenteromano, Laura M and Jonathan Wolpaw and Theresa M Vaughan} } @article {3391, title = {Word-level language modeling for P300 spellers based on discriminative graphical models.}, journal = {J Neural Eng}, volume = {12}, year = {2015}, month = {04/2015}, pages = {026007}, abstract = {

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.

}, keywords = {Brain Computer Interfaces, inference algorithms, language models, P300 speller, probabilistic graphical models}, issn = {1741-2552}, doi = {10.1088/1741-2560/12/2/026007}, url = {http://www.ncbi.nlm.nih.gov/pubmed/25686293}, author = {Saa, Jaime F Delgado and Pesters, Adriana de and Dennis J. McFarland and {\c C}etin, M{\"u}jdat} } @article {3395, title = {Adaptive spatio-temporal filtering for movement related potentials in EEG-based brain-computer interfaces.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {22}, year = {2014}, month = {07/2014}, pages = {847-57}, abstract = {Movement 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.}, keywords = {Algorithms, Artificial Intelligence, brain-computer interfaces, Data Interpretation, Statistical, Electroencephalography, Evoked Potentials, Motor, Humans, Imagination, Motor Cortex, Movement, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Spatio-Temporal Analysis}, issn = {1558-0210}, doi = {10.1109/TNSRE.2014.2315717}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24723632}, author = {Lu, Jun and Xie, Kan and Dennis J. McFarland} } @article {3394, title = {The advantages of the surface Laplacian in brain-computer interface research.}, journal = {Int J Psychophysiol}, year = {2014}, month = {08/2014}, abstract = {

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{\textquoteright}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.

}, keywords = {brain-computer interface, sensorimotor rhythms, surface laplacian}, issn = {1872-7697}, doi = {10.1016/j.ijpsycho.2014.07.009}, url = {http://www.ncbi.nlm.nih.gov/pubmed/25091286}, author = {Dennis J. McFarland} } @article {3370, title = {Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis.}, journal = {Amyotroph Lateral Scler Frontotemporal Degener}, volume = {15}, year = {2014}, month = {06/2014}, pages = {207-15}, abstract = {Brain-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 {\texttimes} 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 ({\textpm} 3)\% (range 71-100\%), which is adequate for communication (G70 group). Eight averaged 12 ({\textpm} 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.}, keywords = {Adult, Aged, Amyotrophic Lateral Sclerosis, Biofeedback, Psychology, brain-computer interfaces, Communication Disorders, Electroencephalography, Event-Related Potentials, P300, Female, Humans, Male, Middle Aged, Online Systems, Photic Stimulation, Psychomotor Performance, Reaction Time}, issn = {2167-9223}, doi = {10.3109/21678421.2013.865750}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24555843}, author = {McCane, Lynn M and Sellers, Eric W and Dennis J. McFarland and Mak, Joseph N and Carmack, C Steve and Zeitlin, Debra and Jonathan Wolpaw and Theresa M Vaughan} } @article {3393, title = {Modality specificity is the preferred method for diagnosing the auditory processing disorder (APD): response to Moore and Ferguson.}, journal = {J Am Acad Audiol}, volume = {25}, year = {2014}, month = {08/2014}, pages = {698-9}, keywords = {Auditory Perception, Auditory Perceptual Disorders, Evoked Potentials, Auditory, Brain Stem, Humans, Neuropsychological Tests, Psychoacoustics}, issn = {2157-3107}, url = {http://www.ncbi.nlm.nih.gov/pubmed/25365373}, author = {Dennis J. McFarland and Cacace, Anthony T} } @article {3392, title = {Modality Specificity trumps other methods for diagnosing the auditory processing disorder (APD): response to Dillon et al.}, journal = {J Am Acad Audiol}, volume = {25}, year = {2014}, month = {08/2014}, pages = {703-5}, keywords = {Auditory Perception, Auditory Perceptual Disorders, Evoked Potentials, Auditory, Brain Stem, Humans, Neuropsychological Tests, Psychoacoustics}, issn = {2157-3107}, url = {http://www.ncbi.nlm.nih.gov/pubmed/25365375}, author = {Cacace, Anthony T and Dennis J. McFarland} } @article {3396, title = {Simulating the effects of common and specific abilities on test performance: an evaluation of factor analysis.}, journal = {J Speech Lang Hear Res}, volume = {57}, year = {2014}, month = {10/2014}, pages = {1919-28}, abstract = {

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.

}, issn = {1558-9102}, doi = {10.1044/2014_JSLHR-H-13-0166}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24686438}, author = {Dennis J. McFarland} } @article {3083, title = {Adaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {10}, year = {2013}, month = {02/2013}, pages = {016002}, abstract = {OBJECTIVE: 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 {\textquoteright}adaptive Laplacian (ALAP) filter{\textquoteright}, 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.}, keywords = {assistive communication, brain computer interface (BCI), brain-machine interface (BMI), electroencephalogram (EEG), leave-one-out (LOO) cross-validation, spatial filter}, issn = {1741-2552}, doi = {10.1088/1741-2560/10/1/016002}, url = {http://www.ncbi.nlm.nih.gov/pubmed/23220879}, author = {Lu, Jun and Dennis J. McFarland and Jonathan Wolpaw} } @article {3120, title = {Characterizing multivariate decoding models based on correlated EEG spectral features.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {124}, year = {2013}, month = {07/2013}, pages = {1297{\textendash}1302}, abstract = {OBJECTIVE: 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.}, keywords = {brain-computer interface, multicollinearity, multivariate decoding, sensorimotor rhythm}, issn = {1872-8952}, doi = {10.1016/j.clinph.2013.01.015}, url = {http://www.ncbi.nlm.nih.gov/pubmed/23466267}, author = {Dennis J. McFarland} } @article {3079, title = {Factors Influencing Tests of Auditory Processing: A Perspective on Current Issues and Relevant Concerns.}, journal = {Journal of the American Academy of Audiology}, volume = {24}, year = {2013}, month = {07/2013}, pages = {572{\textendash}589}, abstract = {Background: 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.}, keywords = {adaptive forced-choice psychophysical methods, auditory processing disorder, dissociation, double dissociation, forced-choice psychophysical methods, modality specificity, response selection}, issn = {2157-3107}, doi = {10.3766/jaaa.24.7.6}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24047945}, author = {Anthony T. Cacace and Dennis J. McFarland} } @article {3118, title = {Modeling Individual Subtests of the WAIS IV with Multiple Latent Factors.}, journal = {PloS one}, volume = {8}, year = {2013}, month = {09/2013}, pages = {e74980}, abstract = {Performance 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.}, issn = {1932-6203}, doi = {10.1371/journal.pone.0074980}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24058643}, author = {Dennis J. McFarland} } @article {3119, title = {Reliability of broadband middle-ear power-reflectance in younger and older adults: Application of Generalizability Theory.}, journal = {American journal of audiology}, year = {2013}, month = {07/2013}, abstract = {PURPOSE: 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{\textquoteright}s product-moment correlation coefficients (Pearson{\textquoteright}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{\textquoteright}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{\textquoteright}s r values than chirps for all frequencies tested; Pearson{\textquoteright}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.}, issn = {1558-9137}, doi = {10.1044/1059-0889(2013/12-0063)}, url = {http://www.ncbi.nlm.nih.gov/pubmed/23824437}, author = {Mahoney, Marty J. and Dennis J. McFarland and Carpenter, Michelle S. and Rizvi, Sabahet and Anthony T. Cacace} } @article {3086, title = {EEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis.}, journal = {Journal of neural engineering}, volume = {9}, year = {2012}, month = {04/2012}, pages = {026014}, abstract = {The 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.}, keywords = {User-Computer Interface}, issn = {1741-2552}, doi = {10.1088/1741-2560/9/2/026014}, url = {http://www.ncbi.nlm.nih.gov/pubmed/22350501}, author = {Mak, Joseph N. and Dennis J. McFarland and Theresa M Vaughan and McCane, Lynn M. and Tsui, Phillippa Z. and Zeitlin, Debra J. and Sellers, Eric W. and Jonathan Wolpaw} } @article {3397, title = {Questionable reliability of the speech-evoked auditory brainstem response (sABR) in typically-developing children.}, journal = {Hear Res}, volume = {287}, year = {2012}, month = {05/2012}, pages = {1-2; author reply 3-5}, keywords = {Evoked Potentials, Auditory, Brain Stem, Female, Hearing Tests, Humans, Male, Speech Perception}, issn = {1878-5891}, doi = {10.1016/j.heares.2012.02.014}, url = {http://www.ncbi.nlm.nih.gov/pubmed/22446178}, author = {Dennis J. McFarland and Cacace, Anthony T} } @article {3398, title = {A single g factor is not necessary to simulate positive correlations between cognitive tests.}, journal = {J Clin Exp Neuropsychol}, volume = {34}, year = {2012}, month = {01/2012}, pages = {378-84}, abstract = {In 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.}, keywords = {Aptitude, Cognition, Humans, Models, Theoretical, Neuropsychological Tests}, issn = {1744-411X}, doi = {10.1080/13803395.2011.645018}, url = {http://www.ncbi.nlm.nih.gov/pubmed/22260190}, author = {Dennis J. McFarland} } @article {3399, title = {Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface.}, journal = {Brain Res Bull}, volume = {87}, year = {2012}, month = {01/2012}, pages = {130-4}, abstract = {Measures 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.}, keywords = {Algorithms, Brain, Electroencephalography, Humans, Motor Cortex, User-Computer Interface}, issn = {1873-2747}, doi = {10.1016/j.brainresbull.2011.09.019}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21985984}, author = {Krusienski, Dean J and Dennis J. McFarland and Jonathan Wolpaw} } @article {3123, title = {Brain-Computer Interfaces for Communication and Control.}, journal = {Communications of the ACM}, volume = {54}, year = {2011}, month = {05/2011}, pages = {60{\textendash}66}, issn = {0001-0782}, doi = {10.1145/1941487.1941506}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21984822}, author = {Dennis J. McFarland and Jonathan Wolpaw} } @article {3126, title = {Covariance is the proper measure of test-retest reliability.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {122}, year = {2011}, month = {09/2011}, pages = {1893; author reply 1893{\textendash}1893; author reply 1895}, keywords = {Speech Perception}, issn = {1872-8952}, doi = {10.1016/j.clinph.2011.01.052}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21414840}, author = {Dennis J. McFarland and Anthony T. Cacace} } @article {3400, title = {Dichotic and dichoptic digit perception in normal adults.}, journal = {J Am Acad Audiol}, volume = {22}, year = {2011}, month = {06/2011}, pages = {332-41}, abstract = {BACKGROUND: 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.}, keywords = {Adolescent, Adult, Auditory Perception, Dichotic Listening Tests, Female, Functional Laterality, Humans, Male, Recognition (Psychology), Reference Values, Reproducibility of Results, Task Performance and Analysis, Visual Perception, Young Adult}, issn = {1050-0545}, doi = {10.3766/jaaa.22.6.3}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21864471}, author = {Lawfield, Angela and Dennis J. McFarland and Cacace, Anthony T} } @article {3401, title = {Neurological principles and rehabilitation of action disorders: rehabilitation interventions.}, journal = {Neurorehabil Neural Repair}, volume = {25}, year = {2011}, month = {06/2011}, pages = {33S-43S}, abstract = {This 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.}, keywords = {Humans, Models, Neurological, Movement Disorders, Recovery of Function, Upper Extremity}, issn = {1552-6844}, doi = {10.1177/1545968311410942}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21613536}, author = {Pomeroy, Valerie and Aglioti, Salvatore M and Mark, Victor W and Dennis J. McFarland and Stinear, Cathy and Wolf, Steven L and Corbetta, Maurizio and Fitzpatrick, Susan M} } @article {3125, title = {Neurological principles and rehabilitation of action disorders: rehabilitation interventions.}, journal = {Neurorehabilitation and neural repair}, volume = {25}, year = {2011}, month = {06/2011}, pages = {33S{\textendash}43S}, abstract = {This 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.}, keywords = {Upper Extremity}, issn = {1552-6844}, doi = {10.1177/1545968311410942}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21613536}, author = {Pomeroy, Valerie and Aglioti, Salvatore M. and Mark, Victor W. and Dennis J. McFarland and Stinear, Cathy and Wolf, Steven L. and Corbetta, Maurizio and Fitzpatrick, Susan M.} } @article {3096, title = {The P300-based brain-computer interface (BCI): effects of stimulus rate.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {122}, year = {2011}, month = {04/2011}, pages = {731{\textendash}737}, abstract = {OBJECTIVE: 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{\texttimes}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.}, keywords = {brain-computer interface, neuroprosthesis, P300}, issn = {1872-8952}, doi = {10.1016/j.clinph.2010.10.029}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21067970}, author = {Dennis J. McFarland and Sarnacki, William A. and Townsend, George and Theresa M Vaughan and Jonathan Wolpaw} } @article {3091, title = {Should the parameters of a BCI translation algorithm be continually adapted?.}, journal = {Journal of neuroscience methods}, volume = {199}, year = {2011}, month = {07/2011}, pages = {103{\textendash}107}, abstract = {People 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.}, keywords = {adaptation, brain-computer interface, EEG}, issn = {1872-678X}, doi = {10.1016/j.jneumeth.2011.04.037}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21571004}, author = {Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw} } @article {3093, title = {Trained modulation of sensorimotor rhythms can affect reaction time.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {122}, year = {2011}, month = {09/2011}, pages = {1820{\textendash}1826}, abstract = {OBJECTIVE: 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.}, keywords = {brain-computer interface, EEG, Reaction Time}, issn = {1872-8952}, doi = {10.1016/j.clinph.2011.02.016}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21411366}, author = {Chadwick B. Boulay and Sarnacki, W. A. and Jonathan Wolpaw and Dennis J. McFarland} } @article {3105, title = {A comparison of regression techniques for a two-dimensional sensorimotor rhythm-based brain-computer interface.}, journal = {Journal of neural engineering}, volume = {7}, year = {2010}, month = {02/2010}, pages = {16003}, abstract = {People 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.}, keywords = {Young Adult}, issn = {1741-2552}, doi = {10.1088/1741-2560/7/1/016003}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20075503}, author = {Fruitet, Joan and Dennis J. McFarland and Jonathan Wolpaw} } @article {3103, title = {Electroencephalographic (EEG) control of three-dimensional movement.}, journal = {Journal of neural engineering}, volume = {7}, year = {2010}, month = {06/2010}, pages = {036007}, abstract = {Brain-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{\textquoteright} use requires activity-dependent adaptations in the brain that maintain stable relationships between the person{\textquoteright}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.}, keywords = {User-Computer Interface}, issn = {1741-2552}, doi = {10.1088/1741-2560/7/3/036007}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20460690}, author = {Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw} } @article {3403, title = {Symptoms as latent variables.}, journal = {Behav Brain Sci}, volume = {33}, year = {2010}, month = {06/2010}, pages = {165-6}, abstract = {In 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.}, keywords = {Diagnostic and Statistical Manual of Mental Disorders, Humans, Interview, Psychological, Mental Disorders, Models, Psychological, Sleep Disorders, Stress Disorders, Post-Traumatic}, issn = {1469-1825}, doi = {10.1017/S0140525X1000066X}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20584384}, author = {Dennis J. McFarland and Malta, Loretta S} } @article {3113, title = {A scanning protocol for a sensorimotor rhythm-based brain-computer interface.}, journal = {Biological psychology}, volume = {80}, year = {2009}, month = {02/2009}, pages = {169{\textendash}175}, abstract = {The 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.}, keywords = {BCI, brain-computer interface, scanning protocol, sensorimotor rhythm}, issn = {1873-6246}, doi = {10.1016/j.biopsycho.2008.08.004}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18786603}, author = {Friedrich, Elisabeth V. C. and Dennis J. McFarland and Neuper, Christa and Theresa M Vaughan and Peter Brunner and Jonathan Wolpaw} } @article {3132, title = {An auditory brain-computer interface (BCI).}, journal = {Journal of neuroscience methods}, volume = {167}, year = {2008}, month = {01/2008}, pages = {43{\textendash}50}, abstract = {Brain-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.}, keywords = {auditory feedback, brain-computer interface, EEG, locked-in state, motivation, sensorimotor rhythm}, issn = {0165-0270}, doi = {10.1016/j.jneumeth.2007.02.009}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17399797}, author = {Nijboer, Femke and Adrian Furdea and Gunst, Ingo and Mellinger, J{\"u}rgen and Dennis J. McFarland and Niels Birbaumer and K{\"u}bler, Andrea} } @article {3128, title = {Electrophysiological markers of skill-related neuroplasticity.}, journal = {Biological psychology}, volume = {78}, year = {2008}, month = {07/2008}, pages = {221{\textendash}230}, abstract = {Neuroplasticity 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{\textquoteright}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.}, keywords = {EEG, ERP, neuroplasticity, skill learning}, issn = {0301-0511}, doi = {10.1016/j.biopsycho.2008.03.014}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18455861}, author = {Romero, Stephen G. and Dennis J. McFarland and Faust, Robert and Farrell, Lori and Anthony T. Cacace} } @article {3131, title = {Emulation of computer mouse control with a noninvasive brain-computer interface.}, journal = {Journal of neural engineering}, volume = {5}, year = {2008}, month = {06/2008}, pages = {101{\textendash}110}, abstract = {Brain-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{\textquoteright}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{\textquoteright}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.}, keywords = {User-Computer Interface}, issn = {1741-2560}, doi = {10.1088/1741-2560/5/2/001}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18367779}, author = {Dennis J. McFarland and Krusienski, Dean J. and Sarnacki, William A. and Jonathan Wolpaw} } @article {3130, title = {Phase-alignment of delayed sensory signals by adaptive filters.}, journal = {The Behavioral and brain sciences}, volume = {31}, year = {2008}, month = {04/2008}, pages = {212}, abstract = {Correction 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.}, issn = {1469-1825}, doi = {10.1017/S0140525X08003956}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20706556}, author = {Dennis J. McFarland} } @article {3129, title = {Sensorimotor rhythm-based brain-computer interface (BCI): model order selection for autoregressive spectral analysis.}, journal = {Journal of neural engineering}, volume = {5}, year = {2008}, month = {06/2008}, pages = {155{\textendash}162}, abstract = {People 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.}, keywords = {User-Computer Interface}, issn = {1741-2560}, doi = {10.1088/1741-2560/5/2/006}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18430974}, author = {Dennis J. McFarland and Jonathan Wolpaw} } @article {3205, title = {Toward enhanced P300 speller performance.}, journal = {Journal of neuroscience methods}, volume = {167}, year = {2008}, month = {01/2008}, pages = {15{\textendash}21}, abstract = {This 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.}, keywords = {brain-computer interface, event related potentials, P300 speller, stepwise linear discriminant analysis}, issn = {0165-0270}, doi = {10.1016/j.jneumeth.2007.07.017}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17822777}, author = {Krusienski, D. J. and Sellers, E. W. and Dennis J. McFarland and Theresa M Vaughan and Jonathan Wolpaw} } @article {2184, title = {Towards an independent brain-computer interface using steady state visual evoked potentials.}, journal = {Clin Neurophysiol}, volume = {119}, year = {2008}, month = {02/2008}, pages = {399-408}, abstract = {

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{\textquoteright}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.

}, keywords = {Adolescent, Adult, Attention, Brain, Brain Mapping, Dose-Response Relationship, Radiation, Electroencephalography, Evoked Potentials, Visual, Female, Humans, Male, Pattern Recognition, Visual, Photic Stimulation, Spectrum Analysis, User-Computer Interface}, issn = {1388-2457}, doi = {10.1016/j.clinph.2007.09.121}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18077208}, author = {Brendan Z. Allison and Dennis J. McFarland and Gerwin Schalk and Zheng, Shi Dong and Moore-Jackson, Melody and Jonathan Wolpaw} } @article {2174, title = {A {\textmu}-rhythm Matched Filter for Continuous Control of a Brain-Computer Interface.}, journal = {IEEE Trans Biomed Eng}, volume = {54}, year = {2007}, month = {02/2007}, pages = {273-80}, abstract = {

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.

}, keywords = {Algorithms, Cerebral Cortex, Cortical Synchronization, Electroencephalography, Evoked Potentials, Humans, Imagination, Pattern Recognition, Automated, User-Computer Interface}, issn = {0018-9294}, doi = {10.1109/TBME.2006.886661}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17278584}, author = {Krusienski, Dean J and Gerwin Schalk and Dennis J. McFarland and Jonathan Wolpaw} } @article {3135, title = {BCI Meeting 2005{\textendash}workshop on BCI signal processing: feature extraction and translation.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {14}, year = {2006}, month = {06/2006}, pages = {135{\textendash}138}, abstract = {This 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.}, keywords = {Brain-computer interface (BCI), prediction, Signal Processing}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875637}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792278}, author = {Dennis J. McFarland and Anderson, Charles W. and M{\"u}ller, Klaus-Robert and Schl{\"o}gl, Alois and Krusienski, Dean J.} } @article {3133, title = {Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms.}, journal = {Progress in brain research}, volume = {159}, year = {2006}, month = {02/2006}, pages = {411{\textendash}419}, abstract = {The 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.}, keywords = {adaptation, BCI, Signal Processing}, issn = {0079-6123}, doi = {10.1016/S0079-6123(06)59026-0}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17071245}, author = {Dennis J. McFarland and Krusienski, Dean J. and Jonathan Wolpaw} } @article {3212, title = {A comparison of classification techniques for the P300 Speller.}, journal = {Journal of neural engineering}, volume = {3}, year = {2006}, month = {12/2006}, pages = {299{\textendash}305}, abstract = {This 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{\textquoteright}s correlation method (PCM), Fisher{\textquoteright}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.}, keywords = {Normal Distribution}, issn = {1741-2560}, doi = {10.1088/1741-2560/3/4/007}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17124334}, author = {Krusienski, Dean J. and Sellers, Eric W. and Cabestaing, Fran{\c c}ois and Bayoudh, Sabri and Dennis J. McFarland and Theresa M Vaughan and Jonathan Wolpaw} } @article {3202, title = {An evaluation of autoregressive spectral estimation model order for brain-computer interface applications.}, journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference}, volume = {1}, year = {2006}, month = {09/2006}, pages = {1323{\textendash}1326}, abstract = {Autoregressive (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.}, keywords = {User-Computer Interface}, issn = {1557-170X}, doi = {10.1109/IEMBS.2006.259822}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17946038}, author = {Krusienski, D. J. and Dennis J. McFarland and Jonathan Wolpaw} } @article {3134, title = {A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance.}, journal = {Biological psychology}, volume = {73}, year = {2006}, month = {10/2006}, pages = {242{\textendash}252}, abstract = {We 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.}, keywords = {Amyotrophic Lateral Sclerosis, brain-computer interface, electroencephalogram, event-related potentials, P300, Rehabilitation}, issn = {0301-0511}, doi = {10.1016/j.biopsycho.2006.04.007}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16860920}, author = {Sellers, Eric W. and Krusienski, Dean J. and Dennis J. McFarland and Theresa M Vaughan and Jonathan Wolpaw} } @article {2177, title = {The Wadsworth BCI Research and Development Program: At Home with BCI.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {229-33}, abstract = {

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{\textquoteright}s homes.

}, keywords = {Animals, Brain, Electroencephalography, Evoked Potentials, Humans, Neuromuscular Diseases, New York, Research, Switzerland, Therapy, Computer-Assisted, Universities, User-Computer Interface}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875577}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792301}, author = {Theresa M Vaughan and Dennis J. McFarland and Gerwin Schalk and Sarnacki, William A and Krusienski, Dean J and Sellers, Eric W and Jonathan Wolpaw} } @article {3138, title = {Brain-computer interface (BCI) operation: signal and noise during early training sessions.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {116}, year = {2005}, month = {01/2005}, pages = {56{\textendash}62}, abstract = {OBJECTIVE: 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{\textquoteright} 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{\textquoteright}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.}, keywords = {brain-computer interface, EEG, Electroencephalography, Learning, mu rhythm, sensorimotor cortex}, issn = {1388-2457}, doi = {10.1016/j.clinph.2004.07.004}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15589184}, author = {Dennis J. McFarland and Sarnacki, William A. and Theresa M Vaughan and Jonathan Wolpaw} } @article {3136, title = {The importance of modality specificity in diagnosing central auditory processing disorder.}, journal = {American journal of audiology}, volume = {14}, year = {2005}, month = {12/2005}, pages = {112{\textendash}123}, abstract = {PURPOSE: 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.}, keywords = {central auditory processing disorder, modality specificity}, issn = {1059-0889}, doi = {10.1044/1059-0889(2005/012)}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16489868}, author = {Anthony T. Cacace and Dennis J. McFarland} } @article {2169, title = {Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface.}, journal = {Neurology}, volume = {64}, year = {2005}, month = {05/2005}, pages = {1775-7}, abstract = {

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.

}, keywords = {Aged, Amyotrophic Lateral Sclerosis, Electroencephalography, Evoked Potentials, Motor, Evoked Potentials, Somatosensory, Female, Humans, Imagination, Male, Middle Aged, Motor Cortex, Movement, Paralysis, Photic Stimulation, Prostheses and Implants, Somatosensory Cortex, Treatment Outcome, User-Computer Interface}, issn = {1526-632X}, doi = {10.1212/01.WNL.0000158616.43002.6D}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15911809}, author = {K{\"u}bler, A. and Nijboer, F and Mellinger, J{\"u}rgen and Theresa M Vaughan and Pawelzik, H and Gerwin Schalk and Dennis J. McFarland and Niels Birbaumer and Jonathan Wolpaw} } @article {3220, title = {Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface.}, journal = {Neurology}, volume = {64}, year = {2005}, month = {05/2005}, pages = {1775{\textendash}1777}, abstract = {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.}, keywords = {User-Computer Interface}, issn = {1526-632X}, doi = {10.1212/01.WNL.0000158616.43002.6D}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15911809}, author = {K{\"u}bler, A. and Nijboer, F. and Mellinger, J. and Theresa M Vaughan and Pawelzik, H. and Gerwin Schalk and Dennis J. McFarland and Niels Birbaumer and Jonathan Wolpaw} } @article {3137, title = {Sensorimotor rhythm-based brain-computer interface (BCI): feature selection by regression improves performance.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {13}, year = {2005}, month = {09/2005}, pages = {372{\textendash}379}, abstract = {People 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{\textquoteright}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{\textquoteright}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{\textquoteright}s control of these features, and can accommodate additional targets without major modifications.}, issn = {1534-4320}, doi = {10.1109/TNSRE.2005.848627}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16200760}, author = {Dennis J. McFarland and Jonathan Wolpaw} } @proceedings {2693, title = {Tracking of the mu rhythm using an empirically derived matched filter}, year = {2005}, month = {03/2005}, author = {Krusienski, Dean J and Gerwin Schalk and Dennis J. McFarland and Jonathan Wolpaw} } @conference {3436, title = {Tracking of the mu rhythm using an empirically derived matched filter.}, booktitle = {Proc. IEEE International Conference of Neural Engineering}, year = {2005}, month = {03/2005}, publisher = {IEEE}, organization = {IEEE}, address = {Arlington, VA}, keywords = {bioelectric potentials, Brain Computer Interfaces, brain modeling, brain-computer interface, communication device, communication system control, cortical mu rhythm modulation, EEG, Electroencephalography, empirically derived matched filter, handicapped aids, laboratories, matched filters, medical signal detection, medical signal processing, monitoring, motor imagery, mu rhythm tracking, noninvasive treatment, rhythm, synchronous motors, two-dimensional cursor control data}, isbn = {0-7803-8710-4}, doi = {10.1109/CNE.2005.1419559}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1419559}, author = {Krusienski, Dean J and Gerwin Schalk and Dennis J. McFarland and Jonathan Wolpaw} } @article {3223, title = {BCI2000: a general-purpose brain-computer interface (BCI) system.}, journal = {IEEE transactions on bio-medical engineering}, volume = {51}, year = {2004}, month = {06/2004}, pages = {1034{\textendash}1043}, abstract = {Many 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.}, keywords = {User-Computer Interface}, issn = {0018-9294}, doi = {10.1109/TBME.2004.827072}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15188875}, author = {Gerwin Schalk and Dennis J. McFarland and Hinterberger, Thilo and Niels Birbaumer and Jonathan Wolpaw} } @article {2166, title = {BCI2000: a general-purpose brain-computer interface (BCI) system.}, journal = {IEEE Trans Biomed Eng}, volume = {51}, year = {2004}, month = {06/2004}, pages = {1034-43}, abstract = {Many 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.}, keywords = {Algorithms, Brain, Cognition, Communication Aids for Disabled, Computer Peripherals, Electroencephalography, Equipment Design, Equipment Failure Analysis, Evoked Potentials, Humans, Systems Integration, User-Computer Interface}, issn = {0018-9294}, doi = {10.1109/TBME.2004.827072}, author = {Gerwin Schalk and Dennis J. McFarland and Hinterberger, T. and Niels Birbaumer and Jonathan Wolpaw} } @article {3139, title = {Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {101}, year = {2004}, month = {12/2004}, pages = {17849{\textendash}17854}, abstract = {Brain-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{\textquoteright}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.}, keywords = {brain-machine interface, Electroencephalography}, issn = {0027-8424}, doi = {10.1073/pnas.0403504101}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15585584}, author = {Jonathan Wolpaw and Dennis J. McFarland} } @article {3140, title = {Conversion of EEG activity into cursor movement by a brain-computer interface (BCI).}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {12}, year = {2004}, month = {09/2004}, pages = {331{\textendash}338}, abstract = {The 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.}, keywords = {augmentative communication, Brain-computer interface (BCI), Electroencephalography, Feedback}, issn = {1534-4320}, doi = {10.1109/TNSRE.2004.834627}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15473195}, author = {Fabiani, Georg E. and Dennis J. McFarland and Jonathan Wolpaw and Pfurtscheller, Gert} } @article {2269, title = {P300 for communication: Evidence from patients with amyotrophic lateral sclerosis (ALS).}, journal = {Biomedizinische Technik}, year = {2004}, author = {Mellinger, J{\"u}rgen and Nijboer, F and Pawelzik, H and Gerwin Schalk and Dennis J. McFarland and Theresa M Vaughan and Jonathan Wolpaw and Niels Birbaumer and Kuebler, A.} } @article {3141, title = {Separating stimulus-locked and unlocked components of the auditory event-related potential.}, journal = {Hearing research}, volume = {193}, year = {2004}, month = {07/2004}, pages = {111{\textendash}120}, abstract = {A 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.}, keywords = {event-related desynchronization, event-related potentials, event-related synchronization, phase, spectral dynamics}, issn = {0378-5955}, doi = {10.1016/j.heares.2004.03.014}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15219326}, author = {Dennis J. McFarland and Anthony T. Cacace} } @article {3144, title = {Brain-computer interface (BCI) operation: optimizing information transfer rates.}, journal = {Biological psychology}, volume = {63}, year = {2003}, month = {07/2003}, pages = {237{\textendash}251}, abstract = {People 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{\textquoteright}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.}, keywords = {augmentative communication, Electroencephalography, information, Learning, mu rhythm, operant conditioning, prosthesis, Rehabilitation, sensorimotor cortex}, issn = {0301-0511}, doi = {10.1016/S0301-0511(03)00073-5}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12853169}, author = {Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw} } @article {3142, title = {EEG-based communication and control: speed-accuracy relationships.}, journal = {Applied psychophysiology and biofeedback}, volume = {28}, year = {2003}, month = {09/2003}, pages = {217{\textendash}231}, abstract = {People 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{\textquoteright}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.}, keywords = {Video Recording}, issn = {1090-0586}, doi = {10.1023/A:1024685214655}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12964453}, author = {Dennis J. McFarland and Jonathan Wolpaw} } @article {3145, title = {Electroencephalographic(EEG)-based communication: EEG control versus system performance in humans.}, journal = {Neuroscience letters}, volume = {345}, year = {2003}, month = {07/2002}, pages = {89{\textendash}92}, abstract = {People 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.}, keywords = {augmentative communication, brain-computer interface, brain-machine interface, Electroencephalography, mu and beta rhythms, neuroprosthesis, Rehabilitation}, issn = {0304-3940}, doi = {10.1016/S0304-3940(03)00470-1}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12821178}, author = {Sheikh, Hesham and Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw} } @article {3224, title = {EMG contamination of EEG: spectral and topographical characteristics.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {114}, year = {2003}, month = {09/2003}, pages = {1580{\textendash}1593}, abstract = {OBJECTIVE: 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.}, keywords = {artifact, brain-computer interface, electroencephalogram, electromyogram, Rehabilitation}, issn = {1388-2457}, doi = {10.1016/S1388-2457(03)00093-2}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12948787}, author = {Goncharova, I. I. and Dennis J. McFarland and Theresa M Vaughan and Jonathan Wolpaw} } @article {3143, title = {Potential problems in the differential diagnosis of (central) auditory processing disorder (CAPD or APD) and attention-deficit hyperactivity disorder (ADHD).}, journal = {Journal of the American Academy of Audiology}, volume = {14}, year = {2003}, month = {07/2003}, pages = {278{\textendash}80; author reply 280}, keywords = {Humans}, issn = {1050-0545}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12956312}, author = {Dennis J. McFarland and Anthony T. Cacace} } @article {3146, title = {Quantifying signal-to-noise ratio of mismatch negativity in humans.}, journal = {Neuroscience letters}, volume = {341}, year = {2003}, month = {05/2003}, pages = {251{\textendash}255}, abstract = {Mismatch 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.}, keywords = {auditory brainstem responses, event related potential, mismatch negativity, otoacoustic emissions, signal-to-noise ratio}, issn = {0304-3940}, doi = {10.1016/S0304-3940(03)00196-4}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12697295}, author = {Anthony T. Cacace and Dennis J. McFarland} } @article {3147, title = {Spectral dynamics of electroencephalographic activity during auditory information processing.}, journal = {Hearing research}, volume = {176}, year = {2003}, month = {02/2003}, pages = {25{\textendash}41}, abstract = {Dynamics 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{\textquoteright}s response to stimulation while at the same time provide powerful information not contained in typical time domain analysis.}, keywords = {Electroencephalography, event-related brain dynamics, event-related desynchronization, event-related synchronization, psychophysics, spectral analysis, time domain analysis}, issn = {0378-5955}, doi = {10.1016/S0378-5955(02)00715-3}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12583879}, author = {Anthony T. Cacace and Dennis J. McFarland} } @article {2165, title = {The Wadsworth Center brain-computer interface (BCI) research and development program.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {11}, year = {2003}, month = {06/2003}, pages = {204-7}, abstract = {

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.

}, keywords = {Academic Medical Centers, Adult, Algorithms, Artifacts, Brain, Brain Mapping, Electroencephalography, Evoked Potentials, Visual, Feedback, Humans, Middle Aged, Nervous System Diseases, Research, Research Design, User-Computer Interface, Visual Perception}, issn = {1534-4320}, doi = {10.1109/TNSRE.2003.814442}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12899275}, author = {Jonathan Wolpaw and Dennis J. McFarland and Theresa M Vaughan and Gerwin Schalk} } @article {2268, title = {Brain-computer interfaces for communication and control.}, journal = {Clin Neurophysiol}, volume = {113}, year = {2002}, month = {06/2002}, pages = {767-91}, abstract = {

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 {\textquoteright}locked in{\textquoteright}, 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.

}, keywords = {Brain Diseases, Communication Aids for Disabled, Computer Systems, Electroencephalography, Humans, User-Computer Interface}, issn = {1388-2457}, doi = {10.1016/S1388-2457(02)00057-3}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12048038}, author = {Jonathan Wolpaw and Niels Birbaumer and Dennis J. McFarland and Pfurtscheller, Gert and Theresa M Vaughan} } @article {3148, title = {Factor analysis in CAPD and the "unimodal" test battery: do we have a model that will satisfy?.}, journal = {American journal of audiology}, volume = {11}, year = {2002}, month = {06/2002}, pages = {7{\textendash}9; author reply 9-12}, keywords = {Reproducibility of Results}, issn = {1059-0889}, doi = {10.1044/1059-0889(2002/ltr01)}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12227358}, author = {Dennis J. McFarland and Anthony T. Cacace} } @article {3235, title = {Brain-computer interface research at the Wadsworth Center.}, journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {8}, year = {2000}, month = {06/2000}, pages = {222{\textendash}226}, abstract = {Studies 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.}, keywords = {User-Computer Interface}, issn = {1063-6528}, doi = {10.1109/86.847823}, url = {http://www.ncbi.nlm.nih.gov/pubmed/10896194}, author = {Jonathan Wolpaw and Dennis J. McFarland and Theresa M Vaughan} } @article {3236, title = {Brain-computer interface technology: a review of the first international meeting.}, journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {8}, year = {2000}, month = {06/2000}, pages = {164{\textendash}173}, abstract = {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{\textquoteright}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{\textquoteright}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{\textquoteright}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.}, keywords = {augmentative communication, Brain-computer interface (BCI), electroencephalography (EEG)}, issn = {1063-6528}, doi = {10.1109/TRE.2000.847807}, url = {http://www.ncbi.nlm.nih.gov/pubmed/10896178}, author = {Jonathan Wolpaw and Niels Birbaumer and Heetderks, W. J. and Dennis J. McFarland and Peckham, P. H. and Gerwin Schalk and Emanuel Donchin and Quatrano, L. A. and Robinson, C. J. and Theresa M Vaughan} } @article {2163, title = {Brain-computer interface technology: a review of the first international meeting.}, journal = {IEEE Trans Rehabil Eng}, volume = {8}, year = {2000}, month = {06/2000}, pages = {164-73}, abstract = {

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{\textquoteright}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{\textquoteright}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{\textquoteright}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.

}, keywords = {Algorithms, Cerebral Cortex, Communication Aids for Disabled, Disabled Persons, Electroencephalography, Evoked Potentials, Humans, Neuromuscular Diseases, Signal Processing, Computer-Assisted, User-Computer Interface}, issn = {1063-6528}, doi = {10.1109/TRE.2000.847807}, url = {http://www.ncbi.nlm.nih.gov/pubmed/10896178}, author = {Jonathan Wolpaw and Niels Birbaumer and Heetderks, W J and Dennis J. McFarland and Peckham, P H and Gerwin Schalk and Emanuel Donchin and Quatrano, L A and Robinson, C J and Theresa M Vaughan} } @article {3234, title = {EEG-based communication: presence of an error potential.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {111}, year = {2000}, month = {12/2000}, pages = {2138{\textendash}2144}, abstract = {EEG-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.}, keywords = {augmentative communication, brain-computer interface, Electroencephalography, error potential, error related negativity, event related potential, mu rhythm, Rehabilitation, sensorimotor cortex}, issn = {1388-2457}, doi = {10.1016/S1388-2457(00)00457-0}, url = {http://www.ncbi.nlm.nih.gov/pubmed/11090763}, author = {Gerwin Schalk and Jonathan Wolpaw and Dennis J. McFarland and Pfurtscheller, G.} } @article {3238, title = {Mu and beta rhythm topographies during motor imagery and actual movements.}, journal = {Brain topography}, volume = {12}, year = {2000}, month = {03/2000}, pages = {177{\textendash}186}, abstract = {People 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.}, keywords = {beta rhythm, EEG, imagery, mu rhythm, sensorimotor cortex}, issn = {0896-0267}, doi = {10.1023/A:1023437823106}, url = {http://www.ncbi.nlm.nih.gov/pubmed/10791681}, author = {Dennis J. McFarland and Miner, L. A. and Theresa M Vaughan and Jonathan Wolpaw} } @article {3243, title = {Answering questions with an electroencephalogram-based brain-computer interface.}, journal = {Archives of physical medicine and rehabilitation}, volume = {79}, year = {1998}, month = {09/1998}, pages = {1029{\textendash}1033}, abstract = {OBJECTIVE: 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.}, keywords = {User-Computer Interface}, issn = {0003-9993}, doi = {10.1016/S0003-9993(98)90165-4}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9749678}, author = {Miner, L. A. and Dennis J. McFarland and Jonathan Wolpaw} } @article {3241, title = {EEG-based communication: analysis of concurrent EMG activity.}, journal = {Electroencephalography and clinical neurophysiology}, volume = {107}, year = {1998}, month = {12/1998}, pages = {428{\textendash}433}, abstract = {OBJECTIVE: 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.}, keywords = {augmentative communication, conditioning, Electroencephalography, Electromyography, mu rhythm, Rehabilitation, sensorimotor cortex}, issn = {0013-4694}, doi = {10.1016/S0013-4694(98)00107-2}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9922089}, author = {Theresa M Vaughan and Miner, L. A. and Dennis J. McFarland and Jonathan Wolpaw} } @article {3244, title = {EEG-based communication and control: short-term role of feedback.}, journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {6}, year = {1998}, month = {03/1998}, pages = {7{\textendash}11}, abstract = {When 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.}, keywords = {Sensitivity and Specificity}, issn = {1063-6528}, doi = {10.1109/86.662615}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9535518}, author = {Dennis J. McFarland and McCane, L. M. and Jonathan Wolpaw} } @article {3242, title = {EEG-based communication: improved accuracy by response verification.}, journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {6}, year = {1998}, month = {09/1998}, pages = {326{\textendash}333}, abstract = {Humans 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.}, keywords = {Computer-Assisted, Signal Processing}, issn = {1063-6528}, doi = {10.1109/86.712231}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9749910}, author = {Jonathan Wolpaw and Ramoser, H. and Dennis J. McFarland and Pfurtscheller, G.} } @article {3249, title = {Spatial filter selection for EEG-based communication.}, journal = {Electroencephalography and clinical neurophysiology}, volume = {103}, year = {1997}, month = {09/1997}, pages = {386{\textendash}394}, abstract = {Individuals 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.}, keywords = {assistive communication, Electroencephalography, mu rhythm, operant conditioning, prosthesis, Rehabilitation, sensorimotor cortex}, issn = {0013-4694}, doi = {10.1016/S0013-4694(97)00022-2}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9305287}, author = {Dennis J. McFarland and McCane, L. M. and David, S. V. and Jonathan Wolpaw} } @article {3245, title = {Timing of EEG-based cursor control.}, journal = {Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society}, volume = {14}, year = {1997}, month = {11/1997}, pages = {529{\textendash}538}, abstract = {Recent 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{\textquoteright}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.}, keywords = {assistive communication, Electroencephalography, mu rhythm, operant conditioning, prosthesis, Rehabilitation, sensorimotor cortex}, issn = {0736-0258}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9458060}, author = {Jonathan Wolpaw and Flotzinger, D. and Pfurtscheller, G. and Dennis J. McFarland} } @article {3256, title = {EEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components.}, journal = {Medical progress through technology}, volume = {21}, year = {1995}, month = {1996}, pages = {111{\textendash}121}, abstract = {Several 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.}, keywords = {User-Computer Interface}, issn = {0047-6552}, url = {http://www.ncbi.nlm.nih.gov/pubmed/8776708}, author = {Pfurtscheller, G. and Flotzinger, D. and Pregenzer, M. and Jonathan Wolpaw and Dennis J. McFarland} } @article {3262, title = {Multichannel EEG-based brain-computer communication.}, journal = {Electroencephalography and clinical neurophysiology}, volume = {90}, year = {1994}, month = {06/1994}, pages = {444{\textendash}449}, abstract = {Individuals 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.}, keywords = {assistive communication, Electroencephalography, mu rhythm, operant conditioning, prosthesis, Rehabilitation, sensorimotor cortex}, issn = {0013-4694}, doi = {10.1016/0013-4694(94)90135-X}, url = {http://www.ncbi.nlm.nih.gov/pubmed/7515787}, author = {Jonathan Wolpaw and Dennis J. McFarland} } @article {3267, title = {An EEG-based brain-computer interface for cursor control.}, journal = {Electroencephalography and clinical neurophysiology}, volume = {78}, year = {1991}, month = {03/1991}, pages = {252{\textendash}259}, abstract = {This 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.}, keywords = {Communication, computer control, EEG, mu rhythm, operant conditioning, prosthesis, sensorimotor rhythm}, issn = {0013-4694}, doi = {10.1016/0013-4694(91)90040-B}, url = {http://www.ncbi.nlm.nih.gov/pubmed/1707798}, author = {Jonathan Wolpaw and Dennis J. McFarland and Neat, G. W. and Forneris, C. A.} } @article {3281, title = {Retrograde transport of the lectin Phaseolus vulgaris leucoagglutinin (PHA-L) by rat spinal motoneurons.}, journal = {Neuroscience letters}, volume = {86}, year = {1988}, month = {03/1988}, pages = {133{\textendash}138}, abstract = {The 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.}, keywords = {lectin, phaseolus vulgaris leucoagglutinin (PHA-L), rat, retrograde transport, spinal motoneuron}, issn = {0304-3940}, doi = {10.1016/0304-3940(88)90559-9}, url = {http://www.ncbi.nlm.nih.gov/pubmed/2453002}, author = {Lee, C. L. and Dennis J. McFarland and Jonathan Wolpaw} }