TY - JOUR T1 - A practical, intuitive brain-computer interface for communicating 'yes' or 'no' by listening. JF - J Neural Eng Y1 - 2014 A1 - Jeremy Jeremy Hill A1 - Ricci, Erin A1 - Haider, Sameah A1 - McCane, Lynn M A1 - Susan M Heckman A1 - Jonathan Wolpaw A1 - Theresa M Vaughan KW - Adult KW - Aged KW - Algorithms KW - Auditory Perception KW - brain-computer interfaces KW - Communication Aids for Disabled KW - Electroencephalography KW - Equipment Design KW - Equipment Failure Analysis KW - Female KW - Humans KW - Male KW - Man-Machine Systems KW - Middle Aged KW - Quadriplegia KW - Treatment Outcome KW - User-Computer Interface AB - OBJECTIVE: Previous work has shown that it is possible to build an EEG-based binary brain-computer interface system (BCI) driven purely by shifts of attention to auditory stimuli. However, previous studies used abrupt, abstract stimuli that are often perceived as harsh and unpleasant, and whose lack of inherent meaning may make the interface unintuitive and difficult for beginners. We aimed to establish whether we could transition to a system based on more natural, intuitive stimuli (spoken words 'yes' and 'no') without loss of performance, and whether the system could be used by people in the locked-in state. APPROACH: We performed a counterbalanced, interleaved within-subject comparison between an auditory streaming BCI that used beep stimuli, and one that used word stimuli. Fourteen healthy volunteers performed two sessions each, on separate days. We also collected preliminary data from two subjects with advanced amyotrophic lateral sclerosis (ALS), who used the word-based system to answer a set of simple yes-no questions. MAIN RESULTS: The N1, N2 and P3 event-related potentials elicited by words varied more between subjects than those elicited by beeps. However, the difference between responses to attended and unattended stimuli was more consistent with words than beeps. Healthy subjects' performance with word stimuli (mean 77% ± 3.3 s.e.) was slightly but not significantly better than their performance with beep stimuli (mean 73% ± 2.8 s.e.). The two subjects with ALS used the word-based BCI to answer questions with a level of accuracy similar to that of the healthy subjects. SIGNIFICANCE: Since performance using word stimuli was at least as good as performance using beeps, we recommend that auditory streaming BCI systems be built with word stimuli to make the system more pleasant and intuitive. Our preliminary data show that word-based streaming BCI is a promising tool for communication by people who are locked in. VL - 11 UR - http://www.ncbi.nlm.nih.gov/pubmed/24838278 IS - 3 ER - TY - JOUR T1 - Brain-computer interfaces in medicine. JF - Mayo Clinic proceedings. Mayo Clinic Y1 - 2012 A1 - Shih, Jerry J. A1 - Krusienski, Dean J. A1 - Jonathan Wolpaw KW - User-Computer Interface AB - Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function. VL - 87 UR - http://www.ncbi.nlm.nih.gov/pubmed/22325364 ER - TY - JOUR T1 - EEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis. JF - Journal of neural engineering Y1 - 2012 A1 - Mak, Joseph N. A1 - Dennis J. McFarland A1 - Theresa M Vaughan A1 - McCane, Lynn M. A1 - Tsui, Phillippa Z. A1 - Zeitlin, Debra J. A1 - Sellers, Eric W. A1 - Jonathan Wolpaw KW - User-Computer Interface AB - 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. VL - 9 UR - http://www.ncbi.nlm.nih.gov/pubmed/22350501 ER - TY - JOUR T1 - Silent Communication: toward using brain signals. JF - IEEE Pulse Y1 - 2012 A1 - Pei, Xiao-Mei A1 - Jeremy Jeremy Hill A1 - Gerwin Schalk KW - Animals KW - Brain KW - Brain Waves KW - Humans KW - Movement KW - User-Computer Interface AB -

From the 1980s movie Firefox to the more recent Avatar, popular science fiction has speculated about the possibility of a persons thoughts being read directly from his or her brain. Such braincomputer interfaces (BCIs) might allow people who are paralyzed to communicate with and control their environment, and there might also be applications in military situations wherever silent user-to-user communication is desirable. Previous studies have shown that BCI systems can use brain signals related to movements and movement imagery or attention-based character selection. Although these systems have successfully demonstrated the possibility to control devices using brain function, directly inferring which word a person intends to communicate has been elusive. A BCI using imagined speech might provide such a practical, intuitive device. Toward this goal, our studies to date addressed two scientific questions: (1) Can brain signals accurately characterize different aspects of speech? (2) Is it possible to predict spoken or imagined words or their components using brain signals?

VL - 3 UR - http://www.ncbi.nlm.nih.gov/pubmed/22344951 IS - 1 ER - TY - JOUR T1 - Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface. JF - Brain Res Bull Y1 - 2012 A1 - Krusienski, Dean J A1 - Dennis J. McFarland A1 - Jonathan Wolpaw KW - Algorithms KW - Brain KW - Electroencephalography KW - Humans KW - Motor Cortex KW - User-Computer Interface AB - 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. VL - 87 UR - http://www.ncbi.nlm.nih.gov/pubmed/21985984 IS - 1 ER - TY - JOUR T1 - Causal influence of gamma oscillations on the sensorimotor rhythm. JF - Neuroimage Y1 - 2011 A1 - Grosse-Wentrup, Moritz A1 - Schölkopf, B A1 - Jeremy Jeremy Hill KW - Adult KW - Cerebral Cortex KW - Electroencephalography KW - Female KW - Humans KW - Imagination KW - Male KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

Gamma oscillations of the electromagnetic field of the brain are known to be involved in a variety of cognitive processes, and are believed to be fundamental for information processing within the brain. While gamma oscillations have been shown to be correlated with brain rhythms at different frequencies, to date no empirical evidence has been presented that supports a causal influence of gamma oscillations on other brain rhythms. In this work, we study the relation of gamma oscillations and the sensorimotor rhythm (SMR) in healthy human subjects using electroencephalography. We first demonstrate that modulation of the SMR, induced by motor imagery of either the left or right hand, is positively correlated with the power of frontal and occipital gamma oscillations, and negatively correlated with the power of centro-parietal gamma oscillations. We then demonstrate that the most simple causal structure, capable of explaining the observed correlation of gamma oscillations and the SMR, entails a causal influence of gamma oscillations on the SMR. This finding supports the fundamental role attributed to gamma oscillations for information processing within the brain, and is of particular importance for brain-computer interfaces (BCIs). As modulation of the SMR is typically used in BCIs to infer a subject's intention, our findings entail that gamma oscillations have a causal influence on a subject's capability to utilize a BCI for means of communication.

VL - 56 UR - http://www.ncbi.nlm.nih.gov/pubmed/20451626 IS - 2 ER - TY - JOUR T1 - Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery. JF - J Neural Eng Y1 - 2011 A1 - Gomez-Rodriguez, M A1 - Peters, J A1 - Jeremy Jeremy Hill A1 - Schölkopf, B A1 - Gharabaghi, A A1 - Grosse-Wentrup, Moritz KW - Brain KW - Evoked Potentials, Motor KW - Evoked Potentials, Somatosensory KW - Feedback, Physiological KW - Female KW - Humans KW - Imagination KW - Male KW - Movement KW - Robotics KW - Touch KW - User-Computer Interface AB -

The combination of brain-computer interfaces (BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular brain damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a BCI. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.

VL - 8 UR - http://www.ncbi.nlm.nih.gov/pubmed/21474878 IS - 3 ER - TY - JOUR T1 - Current Trends in Hardware and Software for Brain-Computer Interfaces (BCIs). JF - J Neural Eng Y1 - 2011 A1 - Peter Brunner A1 - Bianchi, L A1 - Guger, C A1 - Cincotti, F A1 - Gerwin Schalk KW - Biofeedback, Psychology KW - Brain KW - Brain Mapping KW - Electroencephalography KW - Equipment Design KW - Equipment Failure Analysis KW - Humans KW - Man-Machine Systems KW - Software KW - User-Computer Interface AB -

brain-computer interface (BCI) provides a non-muscular communication channel to people with and without disabilities. BCI devices consist of hardware and software. BCI hardware records signals from the brain, either invasively or non-invasively, using a series of device components. BCI software then translates these signals into device output commands and provides feedback. One may categorize different types of BCI applications into the following four categories: basic research, clinical/translational research, consumer products, and emerging applications. These four categories use BCI hardware and software, but have different sets of requirements. For example, while basic research needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of BCI applications. The results indicate that BCI technology is in transition from isolated demonstrations to systematic research and commercial development. This process requires several multidisciplinary efforts, including the development of better integrated and more robust BCI hardware and software, the definition of standardized interfaces, and the developmentof certification, dissemination and reimbursement procedures.

VL - 8 UR - http://www.ncbi.nlm.nih.gov/pubmed/21436536 IS - 2 ER - TY - JOUR T1 - Decoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2011 A1 - Pei, Xiao-Mei A1 - Barbour, Dennis L A1 - Leuthardt, E C A1 - Gerwin Schalk KW - Adolescent KW - Adult KW - Brain KW - Brain Mapping KW - Cerebral Cortex KW - Communication Aids for Disabled KW - Data Interpretation, Statistical KW - Discrimination (Psychology) KW - Electrodes, Implanted KW - Electroencephalography KW - Epilepsy KW - Female KW - Functional Laterality KW - Humans KW - Male KW - Middle Aged KW - Movement KW - Speech Perception KW - User-Computer Interface AB -

Several stories in the popular media have speculated that it may be possible to infer from the brain which word a person is speaking or even thinking. While recent studies have demonstrated that brain signals can give detailed information about actual and imagined actions, such as different types of limb movements or spoken words, concrete experimental evidence for the possibility to 'read the mind', i.e. to interpret internally-generated speech, has been scarce. In this study, we found that it is possible to use signals recorded from the surface of the brain (electrocorticography) to discriminate the vowels and consonants embedded in spoken and in imagined words, and we defined the cortical areas that held the most information about discrimination of vowels and consonants. The results shed light on the distinct mechanisms associated with production of vowels and consonants, and could provide the basis for brain-based communication using imagined speech.

VL - 8 UR - http://www.ncbi.nlm.nih.gov/pubmed/21750369 IS - 4 ER - TY - JOUR T1 - A graphical model framework for decoding in the visual ERP-based BCI speller. JF - Neural Comput Y1 - 2011 A1 - Martens, S M M A1 - Mooij, J M A1 - Jeremy Jeremy Hill A1 - Farquhar, Jason A1 - Schölkopf, B KW - Artificial Intelligence KW - Computer User Training KW - Discrimination Learning KW - Electroencephalography KW - Evoked Potentials KW - Evoked Potentials, Visual KW - Humans KW - Language KW - Models, Neurological KW - Models, Theoretical KW - Reading KW - Signal Processing, Computer-Assisted KW - User-Computer Interface KW - Visual Cortex KW - Visual Perception AB -

We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating brain signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between brain signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the brain signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.

VL - 23 UR - http://www.ncbi.nlm.nih.gov/pubmed/20964540 IS - 1 ER - TY - JOUR T1 - Proceedings of the Second International Workshop on Advances in Electrocorticography. JF - Epilepsy Behav Y1 - 2011 A1 - A L Ritaccio A1 - Boatman-Reich, Dana A1 - Peter Brunner A1 - Cervenka, Mackenzie C A1 - Cole, Andrew J A1 - Nathan E. Crone A1 - Duckrow, Robert A1 - Korzeniewska, Anna A1 - Litt, Brian A1 - Miller, John W A1 - Moran, D A1 - Parvizi, Josef A1 - Viventi, Jonathan A1 - Williams, Justin C A1 - Gerwin Schalk KW - Brain KW - Brain Mapping KW - Brain Waves KW - Diagnosis, Computer-Assisted KW - Electroencephalography KW - Epilepsy KW - Humans KW - United States KW - User-Computer Interface AB -

The Second International Workshop on Advances in Electrocorticography (ECoG) was convened in San Diego, CA, USA, on November 11-12, 2010. Between this meeting and the inaugural 2009 event, a much clearer picture has been emerging of cortical ECoG physiology and its relationship to local field potentials and single-cell recordings. Innovations in material engineering are advancing the goal of a stable long-term recording interface. Continued evolution of ECoG-driven brain-computer interface technology is determining innovation in neuroprosthetics. Improvements in instrumentation and statistical methodologies continue to elucidate ECoG correlates of normal human function as well as the ictal state. This proceedings document summarizes the current status of this rapidly evolving field.

VL - 22 UR - http://www.ncbi.nlm.nih.gov/pubmed/22036287 IS - 4 ER - TY - JOUR T1 - Special issue containing contributions from the Fourth International Brain-Computer Interface Meeting. JF - Journal of neural engineering Y1 - 2011 A1 - Theresa M Vaughan A1 - Jonathan Wolpaw KW - User-Computer Interface VL - 8 UR - http://www.ncbi.nlm.nih.gov/pubmed/21436522 ER - TY - JOUR T1 - Toward a gaze-independent matrix speller brain-computer interface. JF - Clin Neurophysiol Y1 - 2011 A1 - Peter Brunner A1 - Gerwin Schalk KW - Attention KW - Brain KW - Fixation, Ocular KW - Humans KW - User-Computer Interface VL - 122 UR - http://www.ncbi.nlm.nih.gov/pubmed/21183404 IS - 6 ER - TY - JOUR T1 - Transition from the locked in to the completely locked-in state: a physiological analysis. JF - Clin Neurophysiol Y1 - 2011 A1 - Murguialday, A Ramos A1 - Jeremy Jeremy Hill A1 - Bensch, M A1 - Martens, S M M A1 - S Halder A1 - Nijboer, F A1 - Schoelkopf, Bernhard A1 - Niels Birbaumer A1 - Gharabaghi, A KW - Adult KW - Amyotrophic Lateral Sclerosis KW - Area Under Curve KW - Brain KW - Communication Aids for Disabled KW - Disease Progression KW - Electroencephalography KW - Electromyography KW - Humans KW - Male KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

OBJECTIVE: 

To clarify the physiological and behavioral boundaries between locked-in (LIS) and the completely locked-in state (CLIS) (no voluntary eye movements, no communication possible) through electrophysiological data and to secure brain-computer-interface (BCI) communication.

METHODS: 

Electromyography from facial muscles, external anal sphincter (EAS), electrooculography and electrocorticographic data during different psychophysiological tests were acquired to define electrophysiological differences in an amyotrophic lateral sclerosis (ALS) patient with an intracranially implanted grid of 112 electrodes for nine months while the patient passed from the LIS to the CLIS.

RESULTS: 

At the very end of the LIS there was no facial muscle activity, nor external anal sphincter but eye control. Eye movements were slow and lasted for short periods only. During CLIS event related brainpotentials (ERP) to passive limb movements and auditory stimuli were recorded, vibrotactile stimulation of different body parts resulted in no ERP response.

CONCLUSIONS: 

The results presented contradict the commonly accepted assumption that the EAS is the last remaining muscle under voluntary control and demonstrate complete loss of eye movements in CLIS. The eye muscle was shown to be the last muscle group under voluntary control. The findings suggest ALS as a multisystem disorder, even affecting afferent sensory pathways.

SIGNIFICANCE: 

Auditory and proprioceptive brain-computer-interface (BCI) systems are the only remaining communication channels in CLIS.

VL - 122 UR - http://www.ncbi.nlm.nih.gov/pubmed/20888292 IS - 5 ER - TY - JOUR T1 - Using the electrocorticographic speech network to control a brain-computer interface in humans. JF - J Neural Eng Y1 - 2011 A1 - Leuthardt, E C A1 - Charles M Gaona A1 - Sharma, Mohit A1 - Szrama, Nicholas A1 - Roland, Jarod A1 - Zachary V. Freudenberg A1 - Solisb, Jamie A1 - Breshears, Jonathan A1 - Gerwin Schalk KW - Adult KW - Brain KW - Brain Mapping KW - Computer Peripherals KW - Electroencephalography KW - Evoked Potentials KW - Feedback, Physiological KW - Female KW - Humans KW - Imagination KW - Male KW - Middle Aged KW - Nerve Net KW - Speech Production Measurement KW - User-Computer Interface AB -

Electrocorticography (ECoG) has emerged as a new signal platform for brain-computer interface (BCI) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been brain signals from the sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We demonstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional computer cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency oscillations and enabled users to achieve final target accuracies between 68% and 91% within 15 min. Additionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could provide an additional cognitive and physiologic substrate for BCI operation and that these signals can be acquired from a cortical array that is small and minimally invasive.

VL - 8 UR - http://www.ncbi.nlm.nih.gov/pubmed/21471638 IS - 3 ER - TY - JOUR T1 - A brain-computer interface for long-term independent home use. JF - Amyotrophic lateral sclerosis : official publication of the World Federation of Neurology Research Group on Motor Neuron Diseases Y1 - 2010 A1 - Sellers, Eric W. A1 - Theresa M Vaughan A1 - Jonathan Wolpaw KW - User-Computer Interface AB - Our objective was to develop and validate a new brain-computer interface (BCI) system suitable for long-term independent home use by people with severe motor disabilities. The BCI was used by a 51-year-old male with ALS who could no longer use conventional assistive devices. Caregivers learned to place the electrode cap, add electrode gel, and turn on the BCI. After calibration, the system allowed the user to communicate via EEG. Re-calibration was performed remotely (via the internet), and BCI accuracy assessed in periodic tests. Reports of BCI usefulness by the user and the family were also recorded. Results showed that BCI accuracy remained at 83% (r = -.07, n.s.) for over 2.5 years (1.4% expected by chance). The BCI user and his family state that the BCI had restored his independence in social interactions and at work. He uses the BCI to run his NIH-funded research laboratory and to communicate via e-mail with family, friends, and colleagues. In addition to this first user, several other similarly disabled people are now using the BCI in their daily lives. In conclusion, long-term independent home use of this BCI system is practical for severely disabled people, and can contribute significantly to quality of life and productivity. VL - 11 UR - http://www.ncbi.nlm.nih.gov/pubmed/20583947 ER - TY - JOUR T1 - Brain-computer interfacing based on cognitive control. JF - Ann Neurol Y1 - 2010 A1 - Vansteensel, Mariska J A1 - Hermes, Dora A1 - Aarnoutse, Erik J A1 - Bleichner, Martin G A1 - Gerwin Schalk A1 - van Rijen, Peter C A1 - Leijten, Frans S S A1 - Ramsey, Nick F KW - Cognition KW - Computers KW - Electrodes KW - Electroencephalography KW - Epilepsy KW - Humans KW - Image Processing, Computer-Assisted KW - Magnetic Resonance Imaging KW - Neuropsychological Tests KW - Oxygen KW - Prefrontal Cortex KW - Psychomotor Performance KW - Spectrum Analysis KW - Time Factors KW - User-Computer Interface AB -

OBJECTIVE: 

Brain-computer interfaces (BCIs) translate deliberate intentions and associated changes in brain activity into action, thereby offering patients with severe paralysis an alternative means of communication with and control over their environment. Such systems are not available yet, partly due to the high performance standard that is required. A major challenge in the development of implantable BCIs is to identify cortical regions and related functions that an individual can reliably and consciously manipulate. Research predominantly focuses on the sensorimotor cortex, which can be activated by imagining motor actions. However, because this region may not provide an optimal solution to all patients, other neuronal networks need to be examined. Therefore, we investigated whether the cognitive control network can be used for BCI purposes. We also determined the feasibility of using functional magnetic resonance imaging (fMRI) for noninvasive localization of the cognitive control network.

METHODS: 

Three patients with intractable epilepsy, who were temporarily implanted with subdural grid electrodes for diagnostic purposes, attempted to gain BCI control using the electrocorticographic (ECoG) signal of the left dorsolateral prefrontal cortex (DLPFC).

RESULTS: 

All subjects quickly gained accurate BCI control by modulation of gamma-power of the left DLPFC. Prelocalization of the relevant region was performed with fMRI and was confirmed using the ECoG signals obtained during mental calculation localizer tasks.

INTERPRETATION: 

The results indicate that the cognitive control network is a suitable source of signals for BCI applications. They also demonstrate the feasibility of translating understanding about cognitive networks derived from functional neuroimaging into clinical applications.

VL - 67 UR - http://www.ncbi.nlm.nih.gov/pubmed/20517943 IS - 6 ER - TY - JOUR T1 - Does the 'P300' speller depend on eye gaze?. JF - J Neural Eng Y1 - 2010 A1 - Peter Brunner A1 - Joshi, S A1 - S Briskin A1 - Jonathan Wolpaw A1 - H Bischof A1 - Gerwin Schalk KW - Adult KW - Event-Related Potentials, P300 KW - Eye Movements KW - Female KW - Humans KW - Male KW - Middle Aged KW - Models, Neurological KW - Photic Stimulation KW - User-Computer Interface KW - Young Adult AB -

Many people affected by debilitating neuromuscular disorders such as amyotrophic lateral sclerosis, brainstem stroke or spinal cord injury are impaired in their ability to, or are even unable to, communicate. A brain-computer interface (BCI) uses brain signals, rather than muscles, to re-establish communication with the outside world. One particular BCI approach is the so-called 'P300 matrix speller' that was first described by Farwell and Donchin (1988 Electroencephalogr. Clin. Neurophysiol. 70 510-23). It has been widely assumed that this method does not depend on the ability to focus on the desired character, because it was thought that it relies primarily on the P300-evoked potential and minimally, if at all, on other EEG features such as the visual-evoked potential (VEP). This issue is highly relevant for the clinical application of this BCI method, because eye movements may be impaired or lost in the relevant user population. This study investigated the extent to which the performance in a 'P300' speller BCI depends on eye gaze. We evaluated the performance of 17 healthy subjects using a 'P300' matrix speller under two conditions. Under one condition ('letter'), the subjects focused their eye gaze on the intended letter, while under the second condition ('center'), the subjects focused their eye gaze on a fixation cross that was located in the center of the matrix. The results show that the performance of the 'P300' matrix speller in normal subjects depends in considerable measure on gaze direction. They thereby disprove a widespread assumption in BCI research, and suggest that this BCI might function more effectively for people who retain some eye-movement control. The applicability of these findings to people with severe neuromuscular disabilities (particularly in eye-movements) remains to be determined.

VL - 7 UR - http://www.ncbi.nlm.nih.gov/pubmed/20858924 IS - 5 ER - TY - JOUR T1 - Electroencephalographic (EEG) control of three-dimensional movement. JF - Journal of neural engineering Y1 - 2010 A1 - Dennis J. McFarland A1 - Sarnacki, William A. A1 - Jonathan Wolpaw KW - User-Computer Interface AB - 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' use requires activity-dependent adaptations in the brain that maintain stable relationships between the person's intent and the signals that convey it. This study shows that humans can learn over a series of training sessions to use EEG for three-dimensional control. The responsible EEG features are focused topographically on the scalp and spectrally in specific frequency bands. People acquire simultaneous control of three independent signals (one for each dimension) and reach targets in a virtual three-dimensional space. Such BCI control in humans has not been reported previously. The results suggest that with further development noninvasive EEG-based BCIs might control the complex movements of robotic arms or neuroprostheses. VL - 7 UR - http://www.ncbi.nlm.nih.gov/pubmed/20460690 ER - TY - JOUR T1 - A procedure for measuring latencies in brain-computer interfaces. JF - IEEE Trans Biomed Eng Y1 - 2010 A1 - Adam J Wilson A1 - Mellinger, Jürgen A1 - Gerwin Schalk A1 - Williams, Justin C KW - Brain KW - Computer Systems KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Models, Neurological KW - Reproducibility of Results KW - Signal Processing, Computer-Assisted KW - Time Factors KW - User-Computer Interface AB -

Brain-computer interface (BCI) systems must process neural signals with consistent timing in order to support adequate system performance. Thus, it is important to have the capability to determine whether a particular BCI configuration (i.e., hardware and software) provides adequate timing performance for a particular experiment. This report presents a method of measuring and quantifying different aspects of system timing in several typical BCI experiments across a range of settings, and presents comprehensive measures of expected overall system latency for each experimental configuration.

VL - 57 UR - http://www.ncbi.nlm.nih.gov/pubmed/20403781 IS - 7 ER - TY - JOUR T1 - Brain-computer interface research at the wadsworth center developments in noninvasive communication and control. JF - International review of neurobiology Y1 - 2009 A1 - Krusienski, Dean J. A1 - Jonathan Wolpaw KW - User-Computer Interface AB - Brain-computer interface (BCI) research at the Wadsworth Center focuses on noninvasive, electroencephalography (EEG)-based BCI methods for helping severely disabled individuals communicate and interact with their environment. We have demonstrated that these individuals, as well as able-bodied individuals, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one and two dimensions. We have also developed a practical P300-based BCI that enables users to access and control the full functionality of their personal computer. We are currently translating this laboratory-proved BCI technology into a system that can be used by severely disabled individuals in their homes with minimal ongoing technical oversight. Our comprehensive approach to BCI design has led to several innovations that are applicable in other BCI contexts, such as space missions. VL - 86 UR - http://www.ncbi.nlm.nih.gov/pubmed/19607997 ER - TY - Generic T1 - Detection of spontaneous class-specific visual stimuli with high temporal accuracy in human electrocorticography. T2 - Conf Proc IEEE Eng Med Biol Soc Y1 - 2009 A1 - Miller, John W A1 - Hermes, Dora A1 - Gerwin Schalk A1 - Ramsey, Nick F A1 - Jagadeesh, Bharathi A1 - den Nijs, Marcel A1 - Ojemann, J G A1 - Rao, Rajesh P N KW - Algorithms KW - Electrocardiography KW - Evoked Potentials, Visual KW - Humans KW - Male KW - Pattern Recognition, Automated KW - Pattern Recognition, Visual KW - Photic Stimulation KW - Reproducibility of Results KW - Sensitivity and Specificity KW - User-Computer Interface KW - Visual Cortex AB - Most brain-computer interface classification experiments from electrical potential recordings have been focused on the identification of classes of stimuli or behavior where the timing of experimental parameters is known or pre-designated. Real world experience, however, is spontaneous, and to this end we describe an experiment predicting the occurrence, timing, and types of visual stimuli perceived by a human subject from electrocorticographic recordings. All 300 of 300 presented stimuli were correctly detected, with a temporal precision of order 20 ms. The type of stimulus (face/house) was correctly identified in 95% of these cases. There were approximately 20 false alarm events, corresponding to a late 2nd neuronal response to a previously identified event. JF - Conf Proc IEEE Eng Med Biol Soc VL - 2009 ER - TY - Generic T1 - Effective brain-computer interfacing using BCI2000. T2 - Conf Proc IEEE Eng Med Biol Soc Y1 - 2009 A1 - Gerwin Schalk KW - Algorithms KW - Brain KW - Electrocardiography KW - Equipment Design KW - Equipment Failure Analysis KW - Rehabilitation KW - Reproducibility of Results KW - Sensitivity and Specificity KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB - To facilitate research and development in Brain-Computer Interface (BCI) research, we have been developing a general-purpose BCI system, called BCI2000, over the past nine years. This system has enjoyed a growing adoption in BCI and related areas and has been the basis for some of the most impressive studies reported to date. This paper gives an update on the status of this project by describing the principles of the BCI2000 system, its benefits, and impact on the field to date. JF - Conf Proc IEEE Eng Med Biol Soc VL - 2009 ER - TY - JOUR T1 - Evolution of brain-computer interfaces: going beyond classic motor physiology. JF - Neurosurg Focus Y1 - 2009 A1 - Leuthardt, E C A1 - Gerwin Schalk A1 - Roland, Jarod A1 - Rouse, Adam A1 - Moran, D KW - Brain KW - Cerebral Cortex KW - Humans KW - Man-Machine Systems KW - Motor Cortex KW - Movement KW - Movement Disorders KW - Neuronal Plasticity KW - Prostheses and Implants KW - Research KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future.

VL - 27 UR - http://www.ncbi.nlm.nih.gov/pubmed/19569892 IS - 1 ER - TY - JOUR T1 - A note on ethical aspects of BCI. JF - Neural Netw Y1 - 2009 A1 - Haselager, Pim A1 - Vlek, Rutger A1 - Jeremy Jeremy Hill A1 - Nijboer, F KW - Bioethics KW - Brain KW - Communication KW - Communications Media KW - Cooperative Behavior KW - Humans KW - Informed Consent KW - Professional-Patient Relations KW - Quadriplegia KW - User-Computer Interface AB -

This paper focuses on ethical aspects of BCI, as a research and a clinical tool, that are challenging for practitioners currently working in the field. Specifically, the difficulties involved in acquiring informed consent from locked-in patients are investigated, in combination with an analysis of the shared moral responsibility in BCI teams, and the complications encountered in establishing effective communication with media.

VL - 22 UR - http://www.ncbi.nlm.nih.gov/pubmed/19616405 IS - 9 ER - TY - JOUR T1 - Overlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential. JF - J Neural Eng Y1 - 2009 A1 - Martens, S M M A1 - Jeremy Jeremy Hill A1 - Farquhar, Jason A1 - Schölkopf, B KW - Algorithms KW - Brain KW - Cognition KW - Computer Simulation KW - Electroencephalography KW - Event-Related Potentials, P300 KW - Humans KW - Models, Neurological KW - Pattern Recognition, Automated KW - Photic Stimulation KW - Semantics KW - Signal Processing, Computer-Assisted KW - Task Performance and Analysis KW - User-Computer Interface KW - Writing AB -

We reveal the presence of refractory and overlap effects in the event-related potentials in visual P300 speller datasets, and we show their negative impact on the performance of the system. This finding has important implications for how to encode the letters that can be selected for communication. However, we show that such effects are dependent on stimulus parameters: an alternative stimulus type based on apparent motion suffers less from the refractory effects and leads to an improved letter prediction performance.

VL - 6 UR - http://www.ncbi.nlm.nih.gov/pubmed/19255462 IS - 2 ER - TY - JOUR T1 - Using an EEG-based brain-computer interface for virtual cursor movement with BCI2000. JF - J Vis Exp Y1 - 2009 A1 - Adam J Wilson A1 - Gerwin Schalk A1 - Walton, Léo M A1 - Williams, Justin C KW - Brain KW - Calibration KW - Electrodes KW - Electroencephalography KW - Humans KW - User-Computer Interface AB -

A brain-computer interface (BCI) functions by translating a neural signal, such as the electroencephalogram (EEG), into a signal that can be used to control a computer or other device. The amplitude of the EEG signals in selected frequency bins are measured and translated into a device command, in this case the horizontal and vertical velocity of a computer cursor. First, the EEG electrodes are applied to the user s scalp using a cap to record brain activity. Next, a calibration procedure is used to find the EEG electrodes and features that the user will learn to voluntarily modulate to use the BCI. In humans, the power in the mu (8-12 Hz) and beta (18-28 Hz) frequency bands decrease in amplitude during a real or imagined movement. These changes can be detected in the EEG in real-time, and used to control a BCI ([1],[2]). Therefore, during a screening test, the user is asked to make several different imagined movements with their hands and feet to determine the unique EEG features that change with the imagined movements. The results from this calibration will show the best channels to use, which are configured so that amplitude changes in the mu and beta frequency bands move the cursor either horizontally or vertically. In this experiment, the general purpose BCI system BCI2000 is used to control signal acquisition, signal processing, and feedback to the user [3].

UR - http://www.ncbi.nlm.nih.gov/pubmed/19641479 IS - 29 ER - TY - JOUR T1 - Advanced neurotechnologies for chronic neural interfaces: new horizons and clinical opportunities. JF - J Neurosci Y1 - 2008 A1 - Kipke, Daryl R A1 - Shain, William A1 - Buzsáki, György A1 - Fetz, Eberhard E A1 - Henderson, Jaimie M A1 - Hetke, Jamille F A1 - Gerwin Schalk KW - Cerebral Cortex KW - Electrodes, Implanted KW - Electroencephalography KW - Electronics, Medical KW - Electrophysiology KW - Evoked Potentials KW - Movement Disorders KW - Neurons KW - Prostheses and Implants KW - User-Computer Interface VL - 28 UR - http://www.ncbi.nlm.nih.gov/pubmed/19005048?report=abstract IS - 46 ER - TY - JOUR T1 - Brain-computer interfaces (BCIs): Detection Instead of Classification. JF - J Neurosci Methods Y1 - 2008 A1 - Gerwin Schalk A1 - Peter Brunner A1 - Lester A Gerhardt A1 - H Bischof A1 - Jonathan Wolpaw KW - Adult KW - Algorithms KW - Brain KW - Brain Mapping KW - Electrocardiography KW - Electroencephalography KW - Humans KW - Male KW - Man-Machine Systems KW - Normal Distribution KW - Online Systems KW - Signal Detection, Psychological KW - Signal Processing, Computer-Assisted KW - Software Validation KW - User-Computer Interface AB -

Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer through brain-computer interfaces (BCIs). These devices operate by recording signals from the brain and translating these signals into device commands. They can be used by people who are severely paralyzed to communicate without any use of muscle activity. One of the major impediments in translating this novel technology into clinical applications is the current requirement for preliminary analyses to identify the brain signal features best suited for communication. This paper introduces and validates signal detection, which does not require such analysis procedures, as a new concept in BCI signal processing. This detection concept is realized with Gaussian mixture models (GMMs) that are used to model resting brain activity so that any change in relevant brain signals can be detected. It is implemented in a package called SIGFRIED (SIGnal modeling For Real-time Identification and Event Detection). The results indicate that SIGFRIED produces results that are within the range of those achieved using a common analysis strategy that requires preliminary identification of signal features. They indicate that such laborious analysis procedures could be replaced by merely recording brain signals during rest. In summary, this paper demonstrates how SIGFRIED could be used to overcome one of the present impediments to translation of laboratory BCI demonstrations into clinically practical applications.

VL - 167 UR - http://www.ncbi.nlm.nih.gov/pubmed/17920134 IS - 1 ER - TY - JOUR T1 - Brain-computer interfaces in neurological rehabilitation. JF - Lancet neurology Y1 - 2008 A1 - Janis J. Daly A1 - Jonathan Wolpaw KW - User-Computer Interface AB - Recent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the efficacy of a rehabilitation protocol and thus improve muscle control for the patient. VL - 7 UR - http://www.ncbi.nlm.nih.gov/pubmed/18835541 ER - TY - JOUR T1 - Brain-computer symbiosis. JF - J Neural Eng Y1 - 2008 A1 - Gerwin Schalk KW - Brain KW - Computers KW - Humans KW - User-Computer Interface AB -

The theoretical groundwork of the 1930s and 1940s and the technical advance of computers in the following decades provided the basis for dramatic increases in human efficiency. While computers continue to evolve, and we can still expect increasing benefits from their use, the interface between humans and computers has begun to present a serious impediment to full realization of the potential payoff. This paper is about the theoretical and practical possibility that direct communication between the brain and the computer can be used to overcome this impediment by improving or augmenting conventional forms of human communication. It is about the opportunity that the limitations of our body's input and output capacities can be overcome using direct interaction with the brain, and it discusses the assumptions, possible limitations and implications of a technology that I anticipate will be a major source of pervasive changes in the coming decades.

VL - 5 UR - http://www.ncbi.nlm.nih.gov/pubmed/18310804 IS - 1 ER - TY - JOUR T1 - Electrocorticographic interictal spike removal via denoising source separation for improved neuroprosthesis control. JF - Conf Proc IEEE Eng Med Biol Soc Y1 - 2008 A1 - Gunduz, Aysegul A1 - Sanchez, Justin C A1 - Principe, Jose KW - Algorithms KW - Artifacts KW - Diagnosis, Computer-Assisted KW - Electroencephalography KW - Epilepsy KW - Evoked Potentials, Motor KW - Motor Cortex KW - Reproducibility of Results KW - Sensitivity and Specificity KW - User-Computer Interface AB -

Electrocorticographic (ECoG) neuroprosthesis is a promising area of research that could provide channels of communication and control for patients who have lost their motor functions due to damage to the nervous system. However, implantation of subdural electrodes are clinically restricted to diagnostics of pre-surgical epileptic patients. Hence, interictal activity is present in the recordings across various areas of the sensorimotor cortex and suppresses the amplitude modulated features extracted to model hand trajectories. Denoising source separation is a recently introduced framework which extracts hidden structures of interest within the data through denoising the source estimates with filters designed around prior knowledge on the observations. Herein, we exploit the high amplitude quasiperiodic nature of the observed interictal spikes and show that removal of the interictal activity improves linear prediction of hand trajectories.

VL - 2008 UR - http://www.ncbi.nlm.nih.gov/pubmed/19163895 ER - TY - JOUR T1 - Emulation of computer mouse control with a noninvasive brain-computer interface. JF - Journal of neural engineering Y1 - 2008 A1 - Dennis J. McFarland A1 - Krusienski, Dean J. A1 - Sarnacki, William A. A1 - Jonathan Wolpaw KW - User-Computer Interface AB - 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's commands. Although noninvasive BCIs are used for simple applications, it has frequently been assumed that only invasive BCIs, which use electrodes implanted in the brain, will be able to provide multidimensional sequential control of a robotic arm or a neuroprosthesis. The present study shows that a noninvasive BCI using scalp-recorded electroencephalographic (EEG) activity and an adaptive algorithm can provide people, including people with spinal cord injuries, with two-dimensional cursor movement and target selection. Multiple targets were presented around the periphery of a computer screen, with one designated as the correct target. The user's task was to use EEG to move a cursor from the center of the screen to the correct target and then to use an additional EEG feature to select the target. If the cursor reached an incorrect target, the user was instructed not to select it. Thus, this task emulated the key features of mouse operation. The results indicate that people with severe motor disabilities could use brain signals for sequential multidimensional movement and selection. VL - 5 UR - http://www.ncbi.nlm.nih.gov/pubmed/18367779 ER - TY - JOUR T1 - Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics. JF - J Neurosci Methods Y1 - 2008 A1 - Sanchez, Justin C A1 - Gunduz, Aysegul A1 - Carney, Paul R A1 - Principe, Jose KW - Adolescent KW - Biofeedback, Psychology KW - Brain Mapping KW - Cerebral Cortex KW - Electroencephalography KW - Epilepsies, Partial KW - Female KW - Hand KW - Humans KW - Magnetic Resonance Imaging KW - Physical Therapy Modalities KW - Psychomotor Performance KW - Signal Processing, Computer-Assisted KW - Spectrum Analysis KW - User-Computer Interface AB -

Electrocorticogram (ECoG) recordings for neuroprosthetics provide a mesoscopic level of abstraction of brain function between microwire single neuron recordings and the electroencephalogram (EEG). Single-trial ECoG neural interfaces require appropriate feature extraction and signal processing methods to identify and model in real-time signatures of motor events in spontaneous brain activity. Here, we develop the clinical experimental paradigm and analysis tools to record broadband (1Hz to 6kHz) ECoG from patients participating in a reaching and pointing task. Motivated by the significant role of amplitude modulated rate coding in extracellular spike based brain-machine interfaces (BMIs), we develop methods to quantify spatio-temporal intermittent increased ECoG voltages to determine if they provide viable control inputs for ECoG neural interfaces. This study seeks to explore preprocessing modalities that emphasize amplitude modulation across frequencies and channels in the ECoG above the level of noisy background fluctuations in order to derive the commands for complex, continuous control tasks. Preliminary experiments show that it is possible to derive online predictive models and spatially localize the generation of commands in the cortex for motor tasks using amplitude modulated ECoG.

VL - 167 UR - http://www.ncbi.nlm.nih.gov/pubmed/17582507 IS - 1 ER - TY - JOUR T1 - Non-invasive brain-computer interface system: towards its application as assistive technology. JF - Brain Res Bull Y1 - 2008 A1 - Cincotti, F A1 - Mattia, Donatella A1 - Aloise, Fabio A1 - Bufalari, Simona A1 - Gerwin Schalk A1 - Oriolo, Giuseppe A1 - Cherubini, Andrea A1 - Marciani, Maria Grazia A1 - Babiloni, Fabio KW - Activities of Daily Living KW - Adolescent KW - Adult KW - Brain KW - Child KW - Electroencephalography KW - Evoked Potentials, Motor KW - Female KW - Humans KW - Learning KW - Male KW - Middle Aged KW - Motor Skills KW - Muscular Dystrophy, Duchenne KW - Pilot Projects KW - Prostheses and Implants KW - Robotics KW - Self-Help Devices KW - Software KW - Spinal Muscular Atrophies of Childhood KW - User-Computer Interface KW - Volition AB -

The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user's residual motor abilities. Brain-computer interfaces (BCIs) are systems that can translate brain activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options. Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Patients (n=14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-clinical demonstrations to a clinical useful BCI.

VL - 75 UR - http://www.ncbi.nlm.nih.gov/pubmed/18394526 IS - 6 ER - TY - JOUR T1 - Sensorimotor rhythm-based brain-computer interface (BCI): model order selection for autoregressive spectral analysis. JF - Journal of neural engineering Y1 - 2008 A1 - Dennis J. McFarland A1 - Jonathan Wolpaw KW - User-Computer Interface AB - 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. VL - 5 UR - http://www.ncbi.nlm.nih.gov/pubmed/18430974 ER - TY - JOUR T1 - SPIDER image processing for single-particle reconstruction of biological macromolecules from electron micrographs. JF - Nat Protoc Y1 - 2008 A1 - Shaikh, Tanvir R A1 - Gao, Haixiao A1 - Baxter, Bill A1 - Asturias, Francisco J A1 - Boisset, Nicolas A1 - Leith, ArDean A1 - Frank, Joachim KW - Image Processing, Computer-Assisted KW - Microscopy, Electron KW - Models, Molecular KW - Molecular Structure KW - Software KW - User-Computer Interface AB -

This protocol describes the reconstruction of biological molecules from the electron micrographs of single particles. Computation here is performed using the image-processing software SPIDER and can be managed using a graphical user interface, termed the SPIDER Reconstruction Engine. Two approaches are described to obtain an initial reconstruction: random-conical tilt and common lines. Once an existing model is available, reference-based alignment can be used, a procedure that can be iterated. Also described is supervised classification, a method to look for homogeneous subsets when multiple known conformations of the molecule may coexist.

VL - 3 UR - http://www.ncbi.nlm.nih.gov/pubmed/19180078 IS - 12 ER - TY - CONF T1 - Three cases of feature correlation in an electrocorticographic BCI. T2 - Engineering in Medicine and Biology Society, 2008. Y1 - 2008 A1 - Miller, Kai J A1 - Blakely, Timothy A1 - Gerwin Schalk A1 - den Nijs, Marcel A1 - Rao, Rajesh PN A1 - Ojemann, Jeffrey G KW - Adolescent KW - Adult KW - Algorithms KW - automated pattern recognition KW - control systems KW - decorrelation KW - Electrocardiography KW - Electrodes KW - Electroencephalography KW - evoked motor potentials KW - Feedback KW - Female KW - frequency KW - hospitals KW - Humans KW - Male KW - Middle Aged KW - Motor Cortex KW - Signal Processing KW - Statistics as Topic KW - Task Performance and Analysis KW - Tongue KW - User-Computer Interface AB - Three human subjects participated in a closed-loop brain computer interface cursor control experiment mediated by implanted subdural electrocorticographic arrays. The paradigm consisted of several stages: baseline recording, hand and tongue motor tasks as the basis for feature selection, two closed-loop one-dimensional feedback experiments with each of these features, and a two-dimensional feedback experiment using both of the features simultaneously. The two selected features were simple channel and frequency band combinations associated with change during hand and tongue movement. Inter-feature correlation and cross-correlation between features during different epochs of each task were quantified for each stage of the experiment. Our anecdotal, three subject, result suggests that while high correlation between horizontal and vertical control signal can initially preclude successful two-dimensional cursor control, a feedback-based learning strategy can be successfully employed by the subject to overcome this limitation and progressively decorrelate these control signals. JF - Engineering in Medicine and Biology Society, 2008. PB - IEEE CY - Vancouver, BC UR - http://www.ncbi.nlm.nih.gov/pubmed/19163918 ER - TY - Generic T1 - Three cases of feature correlation in an electrocorticographic BCI. T2 - Conf Proc IEEE Eng Med Biol Soc Y1 - 2008 A1 - Miller, John W A1 - Blakely, Timothy A1 - Gerwin Schalk A1 - den Nijs, Marcel A1 - Rao, Rajesh P N A1 - Ojemann, J G KW - Adolescent KW - Adult KW - Algorithms KW - Electrocardiography KW - Evoked Potentials, Motor KW - Female KW - Humans KW - Male KW - Middle Aged KW - Motor Cortex KW - Pattern Recognition, Automated KW - Statistics as Topic KW - Task Performance and Analysis KW - User-Computer Interface AB - Three human subjects participated in a closed-loop brain computer interface cursor control experiment mediated by implanted subdural electrocorticographic arrays. The paradigm consisted of several stages: baseline recording, hand and tongue motor tasks as the basis for feature selection, two closed-loop one-dimensional feedback experiments with each of these features, and a two-dimensional feedback experiment using both of the features simultaneously. The two selected features were simple channel and frequency band combinations associated with change during hand and tongue movement. Inter-feature correlation and cross-correlation between features during different epochs of each task were quantified for each stage of the experiment. Our anecdotal, three subject, result suggests that while high correlation between horizontal and vertical control signal can initially preclude successful two-dimensional cursor control, a feedback-based learning strategy can be successfully employed by the subject to overcome this limitation and progressively decorrelate these control signals. JF - Conf Proc IEEE Eng Med Biol Soc ER - TY - JOUR T1 - Towards an independent brain-computer interface using steady state visual evoked potentials. JF - Clin Neurophysiol Y1 - 2008 A1 - Brendan Z. Allison A1 - Dennis J. McFarland A1 - Gerwin Schalk A1 - Zheng, Shi Dong A1 - Moore-Jackson, Melody A1 - Jonathan Wolpaw KW - Adolescent KW - Adult KW - Attention KW - Brain KW - Brain Mapping KW - Dose-Response Relationship, Radiation KW - Electroencephalography KW - Evoked Potentials, Visual KW - Female KW - Humans KW - Male KW - Pattern Recognition, Visual KW - Photic Stimulation KW - Spectrum Analysis KW - User-Computer Interface AB -

OBJECTIVE: 

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

METHODS: 

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

RESULTS: 

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

CONCLUSIONS: 

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

SIGNIFICANCE: 

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

VL - 119 UR - http://www.ncbi.nlm.nih.gov/pubmed/18077208 IS - 2 ER - TY - JOUR T1 - Two-dimensional movement control using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2008 A1 - Gerwin Schalk A1 - Miller, K.J. A1 - Nicholas R Anderson A1 - Adam J Wilson A1 - Smyth, Matt A1 - Ojemann, J G A1 - Moran, D A1 - Jonathan Wolpaw A1 - Leuthardt, E C KW - Adolescent KW - Adult KW - Brain Mapping KW - Data Interpretation, Statistical KW - Drug Resistance KW - Electrocardiography KW - Electrodes, Implanted KW - Electroencephalography KW - Epilepsy KW - Female KW - Humans KW - Male KW - Movement KW - User-Computer Interface AB -

We show here that a brain-computer interface (BCI) using electrocorticographic activity (ECoG) and imagined or overt motor tasks enables humans to control a computer cursor in two dimensions. Over a brief training period of 12-36 min, each of five human subjects acquired substantial control of particular ECoG features recorded from several locations over the same hemisphere, and achieved average success rates of 53-73% in a two-dimensional four-target center-out task in which chance accuracy was 25%. Our results support the expectation that ECoG-based BCIs can combine high performance with technical and clinical practicality, and also indicate promising directions for further research.

VL - 5 UR - http://www.ncbi.nlm.nih.gov/pubmed/18310813 IS - 1 ER - TY - JOUR T1 - Unique cortical physiology associated with ipsilateral hand movements and neuroprosthetic implications. JF - Stroke Y1 - 2008 A1 - Wisneski, Kimberly A1 - Nicholas R Anderson A1 - Gerwin Schalk A1 - Smyth, Matt A1 - Moran, D A1 - Leuthardt, E C KW - Adolescent KW - Adult KW - Artificial Limbs KW - Bionics KW - Brain Mapping KW - Child KW - Dominance, Cerebral KW - Electroencephalography KW - Female KW - Hand KW - Humans KW - Male KW - Middle Aged KW - Motor Cortex KW - Movement KW - Paresis KW - Prosthesis Design KW - Psychomotor Performance KW - Stroke KW - User-Computer Interface KW - Volition AB -

BACKGROUND AND PURPOSE: 

Brain computer interfaces (BCIs) offer little direct benefit to patients with hemispheric stroke because current platforms rely on signals derived from the contralateral motor cortex (the same region injured by the stroke). For BCIs to assist hemiparetic patients, the implant must use unaffected cortex ipsilateral to the affected limb. This requires the identification of distinct electrophysiological features from the motor cortex associated with ipsilateral hand movements.

METHODS: 

In this study we studied 6 patients undergoing temporary placement of intracranial electrode arrays. Electrocorticographic (ECoG) signals were recorded while the subjects engaged in specific ipsilateral or contralateral hand motor tasks. Spectral changes were identified with regards to frequency, location, and timing.

RESULTS: 

Ipsilateral hand movements were associated with electrophysiological changes that occur in lower frequency spectra, at distinct anatomic locations, and earlier than changes associated with contralateral hand movements. In a subset of 3 patients, features specific to ipsilateral and contralateral hand movements were used to control a cursor on a screen in real time. In ipsilateral derived control this was optimal with lower frequency spectra.

CONCLUSIONS: 

There are distinctive cortical electrophysiological features associated with ipsilateral movements which can be used for device control. These findings have implications for patients with hemispheric stroke because they offer a potential methodology for which a single hemisphere can be used to enhance the function of a stroke induced hemiparesis.

VL - 39 UR - http://www.ncbi.nlm.nih.gov/pubmed/18927456 IS - 12 ER - TY - JOUR T1 - Voluntary brain regulation and communication with electrocorticogram signals. JF - Epilepsy Behav Y1 - 2008 A1 - Hinterberger, T. A1 - Widman, Guido A1 - Lal, T.N A1 - Jeremy Jeremy Hill A1 - Tangermann, Michael A1 - Rosenstiel, W. A1 - Schölkopf, B A1 - Elger, Christian A1 - Niels Birbaumer KW - Adult KW - Biofeedback, Psychology KW - Cerebral Cortex KW - Communication Aids for Disabled KW - Dominance, Cerebral KW - Electroencephalography KW - Epilepsies, Partial KW - Female KW - Humans KW - Imagination KW - Male KW - Middle Aged KW - Motor Activity KW - Motor Cortex KW - Signal Processing, Computer-Assisted KW - Software KW - Somatosensory Cortex KW - Theta Rhythm KW - User-Computer Interface KW - Writing AB -

Brain-computer interfaces (BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of brain signals such as the electroencephalogram (EEG). Three of five patients with epilepsy were able to spell their names with electrocorticogram (ECoG) signals derived from motor-related areas within only one or two training sessions. Imagery of finger or tongue movements was classified with support-vector classification of autoregressive coefficients derived from the ECoG signals. After training of the classifier, binary classification responses were used to select letters from a computer-generated menu. Offline analysis showed increased theta activity in the unsuccessful patients, whereas the successful patients exhibited dominant sensorimotor rhythms that they could control. The high spatial resolution and increased signal-to-noise ratio in ECoG signals, combined with short training periods, may offer an alternative for communication in complete paralysis, locked-in syndrome, and motor restoration.

VL - 13 UR - http://www.ncbi.nlm.nih.gov/pubmed/18495541 IS - 2 ER - TY - JOUR T1 - Brain-computer interfaces as new brain output pathways. JF - The Journal of physiology Y1 - 2007 A1 - Jonathan Wolpaw KW - User-Computer Interface AB - Brain-computer interfaces (BCIs) can provide non-muscular communication and control for people with severe motor disabilities. Current BCIs use a variety of invasive and non-invasive methods to record brain signals and a variety of signal processing methods. Whatever the recording and processing methods used, BCI performance (e.g. the ability of a BCI to control movement of a computer cursor) is highly variable and, by the standards applied to neuromuscular control, could be described as ataxic. In an effort to understand this imperfection, this paper discusses the relevance of two principles that underlie the brain's normal motor outputs. The first principle is that motor outputs are normally produced by the combined activity of many CNS areas, from the cortex to the spinal cord. Together, these areas produce appropriate control of the spinal motoneurons that activate muscles. The second principle is that the acquisition and life-long preservation of motor skills depends on continual adaptive plasticity throughout the CNS. This plasticity optimizes the control of spinal motoneurons. In the light of these two principles, a BCI may be viewed as a system that changes the outcome of CNS activity from control of spinal motoneurons to, instead, control of the cortical (or other) area whose signals are used by the BCI to determine the user's intent. In essence, a BCI attempts to assign to cortical neurons the role normally performed by spinal motoneurons. Thus, a BCI requires that the many CNS areas involved in producing normal motor actions change their roles so as to optimize the control of cortical neurons rather than spinal motoneurons. The disconcerting variability of BCI performance may stem in large part from the challenge presented by the need for this unnatural adaptation. This difficulty might be reduced, and BCI development might thereby benefit, by adopting a 'goal-selection' rather than a 'process- control' strategy. In 'process control', a BCI manages all the intricate high-speed interactions involved in movement. In 'goal selection', by contrast, the BCI simply communicates the user's goal to software that handles the high-speed interactions needed to achieve the goal. Not only is 'goal selection' less demanding, but also, by delegating lower-level aspects of motor control to another structure (rather than requiring that the cortex do everything), it more closely resembles the distributed operation characteristic of normal motor control. VL - 579 UR - http://www.ncbi.nlm.nih.gov/pubmed/17255164 ER - TY - JOUR T1 - An MEG-based brain-computer interface (BCI). JF - Neuroimage Y1 - 2007 A1 - Mellinger, Jürgen A1 - Gerwin Schalk A1 - Christoph Braun A1 - Preissl, Hubert A1 - Rosenstiel, W. A1 - Niels Birbaumer A1 - Kübler, A. KW - Adult KW - Algorithms KW - Artifacts KW - Brain KW - Electroencephalography KW - Electromagnetic Fields KW - Electromyography KW - Feedback KW - Female KW - Foot KW - Hand KW - Head Movements KW - Humans KW - Magnetic Resonance Imaging KW - Magnetoencephalography KW - Male KW - Movement KW - Principal Component Analysis KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography(EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

VL - 36 UR - http://www.ncbi.nlm.nih.gov/pubmed/17475511 IS - 3 ER - TY - Generic T1 - Non-invasive brain-computer interface system to operate assistive devices. T2 - Conf Proc IEEE Eng Med Biol Soc Y1 - 2007 A1 - Cincotti, F A1 - Aloise, Fabio A1 - Bufalari, Simona A1 - Gerwin Schalk A1 - Oriolo, Giuseppe A1 - Cherubini, Andrea A1 - Davide, Fabrizio A1 - Babiloni, Fabio A1 - Marciani, Maria Grazia A1 - Mattia, Donatella KW - Brain KW - Communication Aids for Disabled KW - Computer Systems KW - Humans KW - Neurodegenerative Diseases KW - Quality of Life KW - Self-Help Devices KW - Software KW - User-Computer Interface AB - In this pilot study, a system that allows disabled persons to improve or recover their mobility and communication within the surrounding environment was implemented and validated. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Fourteen patients with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program. All users utilized regular assistive control options (e.g., microswitches or head trackers) while four patients learned to operate the system by means of a non-invasive EEG-based Brain-Computer Interface, based on the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp. JF - Conf Proc IEEE Eng Med Biol Soc ER - TY - JOUR T1 - A µ-rhythm Matched Filter for Continuous Control of a Brain-Computer Interface. JF - IEEE Trans Biomed Eng Y1 - 2007 A1 - Krusienski, Dean J A1 - Gerwin Schalk A1 - Dennis J. McFarland A1 - Jonathan Wolpaw KW - Algorithms KW - Cerebral Cortex KW - Cortical Synchronization KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Imagination KW - Pattern Recognition, Automated KW - User-Computer Interface AB -

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.

VL - 54 UR - http://www.ncbi.nlm.nih.gov/pubmed/17278584 IS - 2 ER - TY - JOUR T1 - The BCI competition III: Validating alternative approaches to actual BCI problems. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Benjamin Blankertz A1 - Müller, Klaus-Robert A1 - Krusienski, Dean J A1 - Gerwin Schalk A1 - Jonathan Wolpaw A1 - Schlögl, Alois A1 - Pfurtscheller, Gert A1 - Millán, José del R A1 - Schröder, Michael A1 - Niels Birbaumer KW - Algorithms KW - Brain KW - Communication Aids for Disabled KW - Databases, Factual KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Neuromuscular Diseases KW - Software Validation KW - Technology Assessment, Biomedical KW - User-Computer Interface AB -

brain-computer interface (BCI) is a system that allows its users to control external devices with brainactivity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792282 IS - 2 ER - TY - JOUR T1 - BCI meeting 2005 - Workshop on Technology: Hardware and Software. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Cincotti, F A1 - Bianchi, L A1 - Birch, Gary A1 - Guger, C A1 - Mellinger, Jürgen A1 - Scherer, Reinhold A1 - Schmidt, Robert N A1 - Yáñez Suárez, Oscar A1 - Gerwin Schalk KW - Algorithms KW - Biotechnology KW - Brain KW - Communication Aids for Disabled KW - Computers KW - Electroencephalography KW - Equipment Design KW - Humans KW - Internationality KW - Man-Machine Systems KW - Neuromuscular Diseases KW - Software KW - User-Computer Interface AB -

This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop to review and evaluate the current state of BCI-related hardware and software. Technical requirements and current technologies, standardization procedures and future trends are covered. The main conclusion was recognition of the need to focus technical requirements on the users' needs and the need for consistent standards in BCI research.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792276 IS - 2 ER - TY - JOUR T1 - Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Jeremy Jeremy Hill A1 - Lal, T.N A1 - Schröder, Michael A1 - Hinterberger, T. A1 - Wilhelm, Barbara A1 - Nijboer, F A1 - Mochty, Ursula A1 - Widman, Guido A1 - Elger, Christian A1 - Schölkopf, B A1 - Kübler, A. A1 - Niels Birbaumer KW - Algorithms KW - Artificial Intelligence KW - Cluster Analysis KW - Computer User Training KW - Electroencephalography KW - Evoked Potentials KW - Female KW - Humans KW - Imagination KW - Male KW - Middle Aged KW - Paralysis KW - Pattern Recognition, Automated KW - User-Computer Interface AB -

We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792289 IS - 2 ER - TY - JOUR T1 - ECoG factors underlying multimodal control of a brain-computer interface. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Adam J Wilson A1 - Felton, Elizabeth A A1 - Garell, P Charles A1 - Gerwin Schalk A1 - Williams, Justin C KW - Adult KW - Brain Mapping KW - Cerebral Cortex KW - Communication Aids for Disabled KW - Computer Peripherals KW - Evoked Potentials KW - Female KW - Humans KW - Imagination KW - Male KW - Man-Machine Systems KW - Neuromuscular Diseases KW - Systems Integration KW - User-Computer Interface KW - Volition AB -

Most current brain-computer interface (BCI) systems for humans use electroencephalographic activity recorded from the scalp, and may be limited in many ways. Electrocorticography (ECoG) is believed to be a minimally-invasive alternative to electroencephalogram (EEG) for BCI systems, yielding superior signal characteristics that could allow rapid user training and faster communication rates. In addition, our preliminary results suggest that brain regions other than the sensorimotor cortex, such as auditory cortex, may be trained to control a BCI system using similar methods as those used to train motor regions of the brain. This could prove to be vital for users who have neurological disease, head trauma, or other conditions precluding the use of sensorimotor cortex for BCI control.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792305 IS - 2 ER - TY - JOUR T1 - Electrocorticography-based brain computer interface--the Seattle experience. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Leuthardt, E C A1 - Miller, John W A1 - Gerwin Schalk A1 - Rao, Rajesh P N A1 - Ojemann, J G KW - Cerebral Cortex KW - Electroencephalography KW - Epilepsy KW - Evoked Potentials KW - Humans KW - Therapy, Computer-Assisted KW - User-Computer Interface KW - Washington AB -

Electrocorticography (ECoG) has been demonstrated to be an effective modality as a platform for brain-computer interfaces (BCIs). Through our experience with ten subjects, we further demonstrate evidence to support the power and flexibility of this signal for BCI usage. In a subset of four patients, closed-loop BCI experiments were attempted with the patient receiving online feedback that consisted of one-dimensional cursor movement controlled by ECoG features that had shown correlation with various real and imagined motor and speech tasks. All four achieved control, with final target accuracies between 73%-100%. We assess the methods for achieving control and the manner in which enhancing online control can be accomplished by rescreening during online tasks. Additionally, we assess the relevant issues of the current experimental paradigm in light of their clinical constraints.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792292 IS - 2 ER - TY - JOUR T1 - The emerging world of motor neuroprosthetics: a neurosurgical perspective. JF - Neurosurgery Y1 - 2006 A1 - Leuthardt, E C A1 - Gerwin Schalk A1 - Moran, D A1 - Ojemann, J G KW - Brain KW - Humans KW - Man-Machine Systems KW - Movement KW - Neurosurgery KW - Prostheses and Implants KW - User-Computer Interface AB -

A MOTOR NEUROPROSTHETIC device, or brain computer interface, is a machine that can take some type of signal from the brain and convert that information into overt device control such that it reflects the intentions of the user's brain. In essence, these constructs can decode the electrophysiological signals representing motor intent. With the parallel evolution of neuroscience, engineering, and rapid computing, the era of clinical neuroprosthetics is approaching as a practical reality for people with severe motor impairment. Patients with such diseases as spinal cord injury, stroke, limb loss, and neuromuscular disorders may benefit through the implantation of these brain computer interfaces that serve to augment their ability to communicate and interact with their environment. In the upcoming years, it will be important for the neurosurgeon to understand what a brain computer interface is, its fundamental principle of operation, and what the salient surgical issues are when considering implantation. We review the current state of the field of motor neuroprosthetics research, the early clinical applications, and the essential considerations from a neurosurgical perspective for the future.

VL - 59 UR - http://www.ncbi.nlm.nih.gov/pubmed/16823294 IS - 1 ER - TY - JOUR T1 - An evaluation of autoregressive spectral estimation model order for brain-computer interface applications. JF - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Y1 - 2006 A1 - Krusienski, D. J. A1 - Dennis J. McFarland A1 - Jonathan Wolpaw KW - User-Computer Interface AB - 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. VL - 1 UR - http://www.ncbi.nlm.nih.gov/pubmed/17946038 ER - TY - JOUR T1 - Multi-channel linear descriptors for event-related EEG collected in brain computer interface. JF - J Neural Eng Y1 - 2006 A1 - Pei, Xiao-Mei A1 - Zheng, Shi Dong A1 - Xu, Jin A1 - Bin, Guang-yu A1 - Zuoguan Wang KW - Algorithms KW - Electroencephalography KW - Evoked Potentials, Motor KW - Humans KW - Imagination KW - Motor Cortex KW - Movement KW - Pattern Recognition, Automated KW - Reproducibility of Results KW - Sensitivity and Specificity KW - User-Computer Interface AB -

By three multi-channel linear descriptors, i.e. spatial complexity (omega), field power (sigma) and frequency of field changes (phi), event-related EEG data within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the event-related EEG data indicate that a two-channel version of omega, sigma and phi could reflect the antagonistic ERD/ERS patterns over contralateral and ipsilateral areas and also characterize different phases of the changing brain states in the event-related paradigm. Based on the selective two-channel linear descriptors, the left and right hand motor imagery tasks are classified to obtain satisfactory results, which testify the validity of the three linear descriptors omega, sigma and phi for characterizing event-related EEG. The preliminary results show that omega, sigma together with phi have good separability for left and right hand motor imagery tasks, which could be considered for classification of two classes of EEG patterns in the application of brain computer interfaces.

VL - 3 UR - http://www.ncbi.nlm.nih.gov/pubmed/16510942 IS - 1 ER - TY - JOUR T1 - The Third International Meeting on Brain-Computer Interface Technology: making a difference. JF - IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Y1 - 2006 A1 - Theresa M Vaughan A1 - Jonathan Wolpaw KW - User-Computer Interface AB - This special issue of the IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING provides a representative and comprehensive bird's-eye view of the most recent developments in brain-computer interface (BCI) technology from laboratories around the world. The 30 research communications and papers are the direct outcome of the Third International Meeting on Brain-Computer Interface Technology held at the Rensselaerville Institute, Rensselaerville, NY, in June 2005. Fifty-three research groups from North and South America, Europe, and Asia, representing the majority of all the existing BCI laboratories around the world, participated in this highly focused meeting sponsored by the National Institutes of Health and organized by the BCI Laboratory of the Wadsworth Center of the New York State Department of Health. As demonstrated by the papers in this special issue, the rapid advances in BCI research and development make this technology capable of providing communication and control to people severely disabled by amyotrophic lateral sclerosis (ALS), brainstem stroke, cerebral palsy, and other neuromuscular disorders. Future work is expected to improve the performance and utility of BCIs, and to focus increasingly on making them a viable, practical, and affordable communication alternative for many thousands of severely disabled people worldwide. VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792275 ER - TY - JOUR T1 - The Wadsworth BCI Research and Development Program: At Home with BCI. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Theresa M Vaughan A1 - Dennis J. McFarland A1 - Gerwin Schalk A1 - Sarnacki, William A A1 - Krusienski, Dean J A1 - Sellers, Eric W A1 - Jonathan Wolpaw KW - Animals KW - Brain KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Neuromuscular Diseases KW - New York KW - Research KW - Switzerland KW - Therapy, Computer-Assisted KW - Universities KW - User-Computer Interface AB -

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

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792301 IS - 2 ER - TY - JOUR T1 - Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. JF - Neurology Y1 - 2005 A1 - Kübler, A. A1 - Nijboer, F A1 - Mellinger, Jürgen A1 - Theresa M Vaughan A1 - Pawelzik, H A1 - Gerwin Schalk A1 - Dennis J. McFarland A1 - Niels Birbaumer A1 - Jonathan Wolpaw KW - Aged KW - Amyotrophic Lateral Sclerosis KW - Electroencephalography KW - Evoked Potentials, Motor KW - Evoked Potentials, Somatosensory KW - Female KW - Humans KW - Imagination KW - Male KW - Middle Aged KW - Motor Cortex KW - Movement KW - Paralysis KW - Photic Stimulation KW - Prostheses and Implants KW - Somatosensory Cortex KW - Treatment Outcome KW - User-Computer Interface AB -

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.

VL - 64 UR - http://www.ncbi.nlm.nih.gov/pubmed/15911809 IS - 10 ER - TY - JOUR T1 - Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. JF - Neurology Y1 - 2005 A1 - Kübler, A. A1 - Nijboer, F. A1 - Mellinger, J. A1 - Theresa M Vaughan A1 - Pawelzik, H. A1 - Gerwin Schalk A1 - Dennis J. McFarland A1 - Niels Birbaumer A1 - Jonathan Wolpaw KW - User-Computer Interface AB - 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. VL - 64 UR - http://www.ncbi.nlm.nih.gov/pubmed/15911809 ER - TY - JOUR T1 - The BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials. JF - IEEE Trans Biomed Eng Y1 - 2004 A1 - Benjamin Blankertz A1 - Müller, Klaus-Robert A1 - Curio, Gabriel A1 - Theresa M Vaughan A1 - Gerwin Schalk A1 - Jonathan Wolpaw A1 - Schlögl, Alois A1 - Neuper, Christa A1 - Pfurtscheller, Gert A1 - Hinterberger, T. A1 - Schröder, Michael A1 - Niels Birbaumer KW - Adult KW - Algorithms KW - Amyotrophic Lateral Sclerosis KW - Artificial Intelligence KW - Brain KW - Cognition KW - Databases, Factual KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Reproducibility of Results KW - Sensitivity and Specificity KW - User-Computer Interface AB - Interest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms. VL - 51 IS - 6 ER - TY - JOUR T1 - BCI2000: a general-purpose brain-computer interface (BCI) system. JF - IEEE transactions on bio-medical engineering Y1 - 2004 A1 - Gerwin Schalk A1 - Dennis J. McFarland A1 - Hinterberger, Thilo A1 - Niels Birbaumer A1 - Jonathan Wolpaw KW - User-Computer Interface AB - 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. VL - 51 UR - http://www.ncbi.nlm.nih.gov/pubmed/15188875 ER - TY - JOUR T1 - BCI2000: a general-purpose brain-computer interface (BCI) system. JF - IEEE Trans Biomed Eng Y1 - 2004 A1 - Gerwin Schalk A1 - Dennis J. McFarland A1 - Hinterberger, T. A1 - Niels Birbaumer A1 - Jonathan Wolpaw KW - Algorithms KW - Brain KW - Cognition KW - Communication Aids for Disabled KW - Computer Peripherals KW - Electroencephalography KW - Equipment Design KW - Equipment Failure Analysis KW - Evoked Potentials KW - Humans KW - Systems Integration KW - User-Computer Interface AB - 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. VL - 51 IS - 6 ER - TY - JOUR T1 - A brain-computer interface using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2004 A1 - Leuthardt, E C A1 - Gerwin Schalk A1 - Jonathan Wolpaw A1 - Ojemann, J G A1 - Moran, D KW - Adult KW - Brain KW - Communication Aids for Disabled KW - Computer Peripherals KW - Diagnosis, Computer-Assisted KW - Electrodes, Implanted KW - Electroencephalography KW - Evoked Potentials KW - Female KW - Humans KW - Imagination KW - Male KW - Movement Disorders KW - User-Computer Interface AB -

Brain-computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain.

VL - 1 UR - http://www.ncbi.nlm.nih.gov/pubmed/15876624 IS - 2 ER - TY - JOUR T1 - The Wadsworth Center brain-computer interface (BCI) research and development program. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2003 A1 - Jonathan Wolpaw A1 - Dennis J. McFarland A1 - Theresa M Vaughan A1 - Gerwin Schalk KW - Academic Medical Centers KW - Adult KW - Algorithms KW - Artifacts KW - Brain KW - Brain Mapping KW - Electroencephalography KW - Evoked Potentials, Visual KW - Feedback KW - Humans KW - Middle Aged KW - Nervous System Diseases KW - Research KW - Research Design KW - User-Computer Interface KW - Visual Perception AB -

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.

VL - 11 UR - http://www.ncbi.nlm.nih.gov/pubmed/12899275 IS - 2 ER - TY - JOUR T1 - Brain-computer interfaces for communication and control. JF - Clin Neurophysiol Y1 - 2002 A1 - Jonathan Wolpaw A1 - Niels Birbaumer A1 - Dennis J. McFarland A1 - Pfurtscheller, Gert A1 - Theresa M Vaughan KW - Brain Diseases KW - Communication Aids for Disabled KW - Computer Systems KW - Electroencephalography KW - Humans KW - User-Computer Interface AB -

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

VL - 113 UR - http://www.ncbi.nlm.nih.gov/pubmed/12048038 IS - 6 ER - TY - JOUR T1 - Brain-computer communication: unlocking the locked in. JF - Psychological bulletin Y1 - 2001 A1 - Kübler, A. A1 - Kotchoubey, B. A1 - Kaiser, J. A1 - Jonathan Wolpaw A1 - Niels Birbaumer KW - User-Computer Interface AB - With the increasing efficiency of life-support systems and better intensive care, more patients survive severe injuries of the brain and spinal cord. Many of these patients experience locked-in syndrome: The active mind is locked in a paralyzed body. Consequently, communication is extremely restricted or impossible. A muscle-independent communication channel overcomes this problem and is realized through a brain-computer interface, a direct connection between brain and computer. The number of technically elaborated brain-computer interfaces is in contrast with the number of systems used in the daily life of locked-in patients. It is hypothesized that a profound knowledge and consideration of psychological principles are necessary to make brain-computer interfaces feasible for locked-in patients. VL - 127 UR - http://www.ncbi.nlm.nih.gov/pubmed/11393301 ER - TY - JOUR T1 - Brain-computer interface research at the Wadsworth Center. JF - IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Y1 - 2000 A1 - Jonathan Wolpaw A1 - Dennis J. McFarland A1 - Theresa M Vaughan KW - User-Computer Interface AB - 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. VL - 8 UR - http://www.ncbi.nlm.nih.gov/pubmed/10896194 ER - TY - JOUR T1 - Brain-computer interface technology: a review of the first international meeting. JF - IEEE Trans Rehabil Eng Y1 - 2000 A1 - Jonathan Wolpaw A1 - Niels Birbaumer A1 - Heetderks, W J A1 - Dennis J. McFarland A1 - Peckham, P H A1 - Gerwin Schalk A1 - Emanuel Donchin A1 - Quatrano, L A A1 - Robinson, C J A1 - Theresa M Vaughan KW - Algorithms KW - Cerebral Cortex KW - Communication Aids for Disabled KW - Disabled Persons KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Neuromuscular Diseases KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

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

VL - 8 UR - http://www.ncbi.nlm.nih.gov/pubmed/10896178 IS - 2 ER - TY - JOUR T1 - Answering questions with an electroencephalogram-based brain-computer interface. JF - Archives of physical medicine and rehabilitation Y1 - 1998 A1 - Miner, L. A. A1 - Dennis J. McFarland A1 - Jonathan Wolpaw KW - User-Computer Interface AB - 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. VL - 79 UR - http://www.ncbi.nlm.nih.gov/pubmed/9749678 ER - TY - JOUR T1 - EEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components. JF - Medical progress through technology Y1 - 1995 A1 - Pfurtscheller, G. A1 - Flotzinger, D. A1 - Pregenzer, M. A1 - Jonathan Wolpaw A1 - Dennis J. McFarland KW - User-Computer Interface AB - 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. VL - 21 UR - http://www.ncbi.nlm.nih.gov/pubmed/8776708 ER -