%0 Journal Article %J IEEE Pulse %D 2012 %T Silent Communication: toward using brain signals. %A Pei, Xiao-Mei %A Jeremy Jeremy Hill %A Gerwin Schalk %K Animals %K Brain %K Brain Waves %K Humans %K Movement %K User-Computer Interface %X

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?

%B IEEE Pulse %V 3 %P 43-6 %8 01/2012 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/22344951 %N 1 %R 10.1109/MPUL.2011.2175637 %0 Journal Article %J J Neural Eng %D 2011 %T Current Trends in Hardware and Software for Brain-Computer Interfaces (BCIs). %A Peter Brunner %A Bianchi, L %A Guger, C %A Cincotti, F %A Gerwin Schalk %K Biofeedback, Psychology %K Brain %K Brain Mapping %K Electroencephalography %K Equipment Design %K Equipment Failure Analysis %K Humans %K Man-Machine Systems %K Software %K User-Computer Interface %X

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.

%B J Neural Eng %V 8 %P 025001 %8 04/2011 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/21436536 %N 2 %R 10.1088/1741-2560/8/2/025001 %0 Journal Article %J J Neural Eng %D 2011 %T Decoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans. %A Pei, Xiao-Mei %A Barbour, Dennis L %A Leuthardt, E C %A Gerwin Schalk %K Adolescent %K Adult %K Brain %K Brain Mapping %K Cerebral Cortex %K Communication Aids for Disabled %K Data Interpretation, Statistical %K Discrimination (Psychology) %K Electrodes, Implanted %K Electroencephalography %K Epilepsy %K Female %K Functional Laterality %K Humans %K Male %K Middle Aged %K Movement %K Speech Perception %K User-Computer Interface %X

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.

%B J Neural Eng %V 8 %P 046028 %8 08/2011 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/21750369 %N 4 %R 10.1088/1741-2560/8/4/046028 %0 Journal Article %J Epilepsy Behav %D 2011 %T Proceedings of the Second International Workshop on Advances in Electrocorticography. %A A L Ritaccio %A Boatman-Reich, Dana %A Peter Brunner %A Cervenka, Mackenzie C %A Cole, Andrew J %A Nathan E. Crone %A Duckrow, Robert %A Korzeniewska, Anna %A Litt, Brian %A Miller, John W %A Moran, D %A Parvizi, Josef %A Viventi, Jonathan %A Williams, Justin C %A Gerwin Schalk %K Brain %K Brain Mapping %K Brain Waves %K Diagnosis, Computer-Assisted %K Electroencephalography %K Epilepsy %K Humans %K United States %K User-Computer Interface %X

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.

%B Epilepsy Behav %V 22 %P 641-50 %8 12/2011 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/22036287 %N 4 %R 10.1016/j.yebeh.2011.09.028 %0 Journal Article %J Clin Neurophysiol %D 2011 %T Toward a gaze-independent matrix speller brain-computer interface. %A Peter Brunner %A Gerwin Schalk %K Attention %K Brain %K Fixation, Ocular %K Humans %K User-Computer Interface %B Clin Neurophysiol %V 122 %P 1063-4 %8 06/2011 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/21183404 %N 6 %R 10.1016/j.clinph.2010.11.014 %0 Journal Article %J J Neural Eng %D 2011 %T Using the electrocorticographic speech network to control a brain-computer interface in humans. %A Leuthardt, E C %A Charles M Gaona %A Sharma, Mohit %A Szrama, Nicholas %A Roland, Jarod %A Zachary V. Freudenberg %A Solisb, Jamie %A Breshears, Jonathan %A Gerwin Schalk %K Adult %K Brain %K Brain Mapping %K Computer Peripherals %K Electroencephalography %K Evoked Potentials %K Feedback, Physiological %K Female %K Humans %K Imagination %K Male %K Middle Aged %K Nerve Net %K Speech Production Measurement %K User-Computer Interface %X

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.

%B J Neural Eng %V 8 %P 036004 %8 06/2011 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/21471638 %N 3 %R 10.1088/1741-2560/8/3/036004 %0 Journal Article %J Ann Neurol %D 2010 %T Brain-computer interfacing based on cognitive control. %A Vansteensel, Mariska J %A Hermes, Dora %A Aarnoutse, Erik J %A Bleichner, Martin G %A Gerwin Schalk %A van Rijen, Peter C %A Leijten, Frans S S %A Ramsey, Nick F %K Cognition %K Computers %K Electrodes %K Electroencephalography %K Epilepsy %K Humans %K Image Processing, Computer-Assisted %K Magnetic Resonance Imaging %K Neuropsychological Tests %K Oxygen %K Prefrontal Cortex %K Psychomotor Performance %K Spectrum Analysis %K Time Factors %K User-Computer Interface %X

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.

%B Ann Neurol %V 67 %P 809-16 %8 06/2010 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/20517943 %N 6 %R 10.1002/ana.21985 %0 Journal Article %J J Neural Eng %D 2010 %T Does the 'P300' speller depend on eye gaze?. %A Peter Brunner %A Joshi, S %A S Briskin %A Jonathan Wolpaw %A H Bischof %A Gerwin Schalk %K Adult %K Event-Related Potentials, P300 %K Eye Movements %K Female %K Humans %K Male %K Middle Aged %K Models, Neurological %K Photic Stimulation %K User-Computer Interface %K Young Adult %X

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.

%B J Neural Eng %V 7 %P 056013 %8 10/2010 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/20858924 %N 5 %R 10.1088/1741-2560/7/5/056013 %0 Journal Article %J IEEE Trans Biomed Eng %D 2010 %T A procedure for measuring latencies in brain-computer interfaces. %A Adam J Wilson %A Mellinger, Jürgen %A Gerwin Schalk %A Williams, Justin C %K Brain %K Computer Systems %K Electroencephalography %K Evoked Potentials %K Humans %K Models, Neurological %K Reproducibility of Results %K Signal Processing, Computer-Assisted %K Time Factors %K User-Computer Interface %X

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.

%B IEEE Trans Biomed Eng %V 57 %P 1785-97 %8 06/2010 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/20403781 %N 7 %R 10.1109/TBME.2010.2047259 %0 Conference Proceedings %B Conf Proc IEEE Eng Med Biol Soc %D 2009 %T Detection of spontaneous class-specific visual stimuli with high temporal accuracy in human electrocorticography. %A Miller, John W %A Hermes, Dora %A Gerwin Schalk %A Ramsey, Nick F %A Jagadeesh, Bharathi %A den Nijs, Marcel %A Ojemann, J G %A Rao, Rajesh P N %K Algorithms %K Electrocardiography %K Evoked Potentials, Visual %K Humans %K Male %K Pattern Recognition, Automated %K Pattern Recognition, Visual %K Photic Stimulation %K Reproducibility of Results %K Sensitivity and Specificity %K User-Computer Interface %K Visual Cortex %X 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. %B Conf Proc IEEE Eng Med Biol Soc %V 2009 %P 6465-8 %8 2009 %G eng %R 10.1109/IEMBS.2009.5333546 %0 Conference Proceedings %B Conf Proc IEEE Eng Med Biol Soc %D 2009 %T Effective brain-computer interfacing using BCI2000. %A Gerwin Schalk %K Algorithms %K Brain %K Electrocardiography %K Equipment Design %K Equipment Failure Analysis %K Rehabilitation %K Reproducibility of Results %K Sensitivity and Specificity %K Signal Processing, Computer-Assisted %K User-Computer Interface %X 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. %B Conf Proc IEEE Eng Med Biol Soc %V 2009 %P 5498-501 %8 2009 %G eng %R 10.1109/IEMBS.2009.5334558 %0 Journal Article %J Neurosurg Focus %D 2009 %T Evolution of brain-computer interfaces: going beyond classic motor physiology. %A Leuthardt, E C %A Gerwin Schalk %A Roland, Jarod %A Rouse, Adam %A Moran, D %K Brain %K Cerebral Cortex %K Humans %K Man-Machine Systems %K Motor Cortex %K Movement %K Movement Disorders %K Neuronal Plasticity %K Prostheses and Implants %K Research %K Signal Processing, Computer-Assisted %K User-Computer Interface %X

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.

%B Neurosurg Focus %V 27 %P E4 %8 07/2009 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/19569892 %N 1 %R 10.3171/2009.4.FOCUS0979 %0 Journal Article %J J Vis Exp %D 2009 %T Using an EEG-based brain-computer interface for virtual cursor movement with BCI2000. %A Adam J Wilson %A Gerwin Schalk %A Walton, Léo M %A Williams, Justin C %K Brain %K Calibration %K Electrodes %K Electroencephalography %K Humans %K User-Computer Interface %X

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].

%B J Vis Exp %8 07/2009 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/19641479 %N 29 %R 10.3791/1319 %0 Journal Article %J J Neurosci %D 2008 %T Advanced neurotechnologies for chronic neural interfaces: new horizons and clinical opportunities. %A Kipke, Daryl R %A Shain, William %A Buzsáki, György %A Fetz, Eberhard E %A Henderson, Jaimie M %A Hetke, Jamille F %A Gerwin Schalk %K Cerebral Cortex %K Electrodes, Implanted %K Electroencephalography %K Electronics, Medical %K Electrophysiology %K Evoked Potentials %K Movement Disorders %K Neurons %K Prostheses and Implants %K User-Computer Interface %B J Neurosci %V 28 %P 11830-8 %8 11/2008 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/19005048?report=abstract %N 46 %R 10.1523/JNEUROSCI.3879-08.2008 %0 Journal Article %J J Neurosci Methods %D 2008 %T Brain-computer interfaces (BCIs): Detection Instead of Classification. %A Gerwin Schalk %A Peter Brunner %A Lester A Gerhardt %A H Bischof %A Jonathan Wolpaw %K Adult %K Algorithms %K Brain %K Brain Mapping %K Electrocardiography %K Electroencephalography %K Humans %K Male %K Man-Machine Systems %K Normal Distribution %K Online Systems %K Signal Detection, Psychological %K Signal Processing, Computer-Assisted %K Software Validation %K User-Computer Interface %X

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.

%B J Neurosci Methods %V 167 %P 51-62 %8 01/2008 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/17920134 %N 1 %R 10.1016/j.jneumeth.2007.08.010 %0 Journal Article %J J Neural Eng %D 2008 %T Brain-computer symbiosis. %A Gerwin Schalk %K Brain %K Computers %K Humans %K User-Computer Interface %X

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.

%B J Neural Eng %V 5 %P P1-P15 %8 03/2008 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/18310804 %N 1 %R 10.1088/1741-2560/5/1/P01 %0 Journal Article %J Brain Res Bull %D 2008 %T Non-invasive brain-computer interface system: towards its application as assistive technology. %A Cincotti, F %A Mattia, Donatella %A Aloise, Fabio %A Bufalari, Simona %A Gerwin Schalk %A Oriolo, Giuseppe %A Cherubini, Andrea %A Marciani, Maria Grazia %A Babiloni, Fabio %K Activities of Daily Living %K Adolescent %K Adult %K Brain %K Child %K Electroencephalography %K Evoked Potentials, Motor %K Female %K Humans %K Learning %K Male %K Middle Aged %K Motor Skills %K Muscular Dystrophy, Duchenne %K Pilot Projects %K Prostheses and Implants %K Robotics %K Self-Help Devices %K Software %K Spinal Muscular Atrophies of Childhood %K User-Computer Interface %K Volition %X

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.

%B Brain Res Bull %V 75 %P 796-803 %8 04/2008 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/18394526 %N 6 %R 10.1016/j.brainresbull.2008.01.007 %0 Conference Proceedings %B Conf Proc IEEE Eng Med Biol Soc %D 2008 %T Three cases of feature correlation in an electrocorticographic BCI. %A Miller, John W %A Blakely, Timothy %A Gerwin Schalk %A den Nijs, Marcel %A Rao, Rajesh P N %A Ojemann, J G %K Adolescent %K Adult %K Algorithms %K Electrocardiography %K Evoked Potentials, Motor %K Female %K Humans %K Male %K Middle Aged %K Motor Cortex %K Pattern Recognition, Automated %K Statistics as Topic %K Task Performance and Analysis %K User-Computer Interface %X 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. %B Conf Proc IEEE Eng Med Biol Soc %P 5318-21 %8 2008 %G eng %R 10.1109/IEMBS.2008.4650415 %0 Conference Paper %B Engineering in Medicine and Biology Society, 2008. %D 2008 %T Three cases of feature correlation in an electrocorticographic BCI. %A Miller, Kai J %A Blakely, Timothy %A Gerwin Schalk %A den Nijs, Marcel %A Rao, Rajesh PN %A Ojemann, Jeffrey G %K Adolescent %K Adult %K Algorithms %K automated pattern recognition %K control systems %K decorrelation %K Electrocardiography %K Electrodes %K Electroencephalography %K evoked motor potentials %K Feedback %K Female %K frequency %K hospitals %K Humans %K Male %K Middle Aged %K Motor Cortex %K Signal Processing %K Statistics as Topic %K Task Performance and Analysis %K Tongue %K User-Computer Interface %X 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. %B Engineering in Medicine and Biology Society, 2008. %I IEEE %C Vancouver, BC %8 08/2008 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/19163918 %R 10.1109/IEMBS.2008.4650415 %0 Journal Article %J Clin Neurophysiol %D 2008 %T Towards an independent brain-computer interface using steady state visual evoked potentials. %A Brendan Z. Allison %A Dennis J. McFarland %A Gerwin Schalk %A Zheng, Shi Dong %A Moore-Jackson, Melody %A Jonathan Wolpaw %K Adolescent %K Adult %K Attention %K Brain %K Brain Mapping %K Dose-Response Relationship, Radiation %K Electroencephalography %K Evoked Potentials, Visual %K Female %K Humans %K Male %K Pattern Recognition, Visual %K Photic Stimulation %K Spectrum Analysis %K User-Computer Interface %X

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.

%B Clin Neurophysiol %V 119 %P 399-408 %8 02/2008 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/18077208 %N 2 %R 10.1016/j.clinph.2007.09.121 %0 Journal Article %J J Neural Eng %D 2008 %T Two-dimensional movement control using electrocorticographic signals in humans. %A Gerwin Schalk %A Miller, K.J. %A Nicholas R Anderson %A Adam J Wilson %A Smyth, Matt %A Ojemann, J G %A Moran, D %A Jonathan Wolpaw %A Leuthardt, E C %K Adolescent %K Adult %K Brain Mapping %K Data Interpretation, Statistical %K Drug Resistance %K Electrocardiography %K Electrodes, Implanted %K Electroencephalography %K Epilepsy %K Female %K Humans %K Male %K Movement %K User-Computer Interface %X

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.

%B J Neural Eng %V 5 %P 75-84 %8 03/2008 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/18310813 %N 1 %R 10.1088/1741-2560/5/1/008 %0 Journal Article %J Stroke %D 2008 %T Unique cortical physiology associated with ipsilateral hand movements and neuroprosthetic implications. %A Wisneski, Kimberly %A Nicholas R Anderson %A Gerwin Schalk %A Smyth, Matt %A Moran, D %A Leuthardt, E C %K Adolescent %K Adult %K Artificial Limbs %K Bionics %K Brain Mapping %K Child %K Dominance, Cerebral %K Electroencephalography %K Female %K Hand %K Humans %K Male %K Middle Aged %K Motor Cortex %K Movement %K Paresis %K Prosthesis Design %K Psychomotor Performance %K Stroke %K User-Computer Interface %K Volition %X

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.

%B Stroke %V 39 %P 3351-9 %8 12/2008 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/18927456 %N 12 %R 10.1161/STROKEAHA.108.518175 %0 Journal Article %J Neuroimage %D 2007 %T An MEG-based brain-computer interface (BCI). %A Mellinger, Jürgen %A Gerwin Schalk %A Christoph Braun %A Preissl, Hubert %A Rosenstiel, W. %A Niels Birbaumer %A Kübler, A. %K Adult %K Algorithms %K Artifacts %K Brain %K Electroencephalography %K Electromagnetic Fields %K Electromyography %K Feedback %K Female %K Foot %K Hand %K Head Movements %K Humans %K Magnetic Resonance Imaging %K Magnetoencephalography %K Male %K Movement %K Principal Component Analysis %K Signal Processing, Computer-Assisted %K User-Computer Interface %X

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.

%B Neuroimage %V 36 %P 581-93 %8 07/2007 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/17475511 %N 3 %R 10.1016/j.neuroimage.2007.03.019 %0 Conference Proceedings %B Conf Proc IEEE Eng Med Biol Soc %D 2007 %T Non-invasive brain-computer interface system to operate assistive devices. %A Cincotti, F %A Aloise, Fabio %A Bufalari, Simona %A Gerwin Schalk %A Oriolo, Giuseppe %A Cherubini, Andrea %A Davide, Fabrizio %A Babiloni, Fabio %A Marciani, Maria Grazia %A Mattia, Donatella %K Brain %K Communication Aids for Disabled %K Computer Systems %K Humans %K Neurodegenerative Diseases %K Quality of Life %K Self-Help Devices %K Software %K User-Computer Interface %X 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. %B Conf Proc IEEE Eng Med Biol Soc %P 2532-5 %8 04/2007 %G eng %R 10.1109/IEMBS.2007.4352844 %0 Journal Article %J IEEE Trans Biomed Eng %D 2007 %T A µ-rhythm Matched Filter for Continuous Control of a Brain-Computer Interface. %A Krusienski, Dean J %A Gerwin Schalk %A Dennis J. McFarland %A Jonathan Wolpaw %K Algorithms %K Cerebral Cortex %K Cortical Synchronization %K Electroencephalography %K Evoked Potentials %K Humans %K Imagination %K Pattern Recognition, Automated %K User-Computer Interface %X

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.

%B IEEE Trans Biomed Eng %V 54 %P 273-80 %8 02/2007 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/17278584 %N 2 %R 10.1109/TBME.2006.886661 %0 Journal Article %J IEEE Trans Neural Syst Rehabil Eng %D 2006 %T The BCI competition III: Validating alternative approaches to actual BCI problems. %A Benjamin Blankertz %A Müller, Klaus-Robert %A Krusienski, Dean J %A Gerwin Schalk %A Jonathan Wolpaw %A Schlögl, Alois %A Pfurtscheller, Gert %A Millán, José del R %A Schröder, Michael %A Niels Birbaumer %K Algorithms %K Brain %K Communication Aids for Disabled %K Databases, Factual %K Electroencephalography %K Evoked Potentials %K Humans %K Neuromuscular Diseases %K Software Validation %K Technology Assessment, Biomedical %K User-Computer Interface %X

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.

%B IEEE Trans Neural Syst Rehabil Eng %V 14 %P 153-9 %8 06/2006 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/16792282 %N 2 %R 10.1109/TNSRE.2006.875642 %0 Journal Article %J IEEE Trans Neural Syst Rehabil Eng %D 2006 %T BCI meeting 2005 - Workshop on Technology: Hardware and Software. %A Cincotti, F %A Bianchi, L %A Birch, Gary %A Guger, C %A Mellinger, Jürgen %A Scherer, Reinhold %A Schmidt, Robert N %A Yáñez Suárez, Oscar %A Gerwin Schalk %K Algorithms %K Biotechnology %K Brain %K Communication Aids for Disabled %K Computers %K Electroencephalography %K Equipment Design %K Humans %K Internationality %K Man-Machine Systems %K Neuromuscular Diseases %K Software %K User-Computer Interface %X

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.

%B IEEE Trans Neural Syst Rehabil Eng %V 14 %P 128-31 %8 06/2006 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/16792276 %N 2 %R 10.1109/TNSRE.2006.875584 %0 Journal Article %J IEEE Trans Neural Syst Rehabil Eng %D 2006 %T ECoG factors underlying multimodal control of a brain-computer interface. %A Adam J Wilson %A Felton, Elizabeth A %A Garell, P Charles %A Gerwin Schalk %A Williams, Justin C %K Adult %K Brain Mapping %K Cerebral Cortex %K Communication Aids for Disabled %K Computer Peripherals %K Evoked Potentials %K Female %K Humans %K Imagination %K Male %K Man-Machine Systems %K Neuromuscular Diseases %K Systems Integration %K User-Computer Interface %K Volition %X

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.

%B IEEE Trans Neural Syst Rehabil Eng %V 14 %P 246-50 %8 06/2006 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/16792305 %N 2 %R 10.1109/TNSRE.2006.875570 %0 Journal Article %J IEEE Trans Neural Syst Rehabil Eng %D 2006 %T Electrocorticography-based brain computer interface--the Seattle experience. %A Leuthardt, E C %A Miller, John W %A Gerwin Schalk %A Rao, Rajesh P N %A Ojemann, J G %K Cerebral Cortex %K Electroencephalography %K Epilepsy %K Evoked Potentials %K Humans %K Therapy, Computer-Assisted %K User-Computer Interface %K Washington %X

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.

%B IEEE Trans Neural Syst Rehabil Eng %V 14 %P 194-8 %8 06/2006 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/16792292 %N 2 %R 10.1109/TNSRE.2006.875536 %0 Journal Article %J Neurosurgery %D 2006 %T The emerging world of motor neuroprosthetics: a neurosurgical perspective. %A Leuthardt, E C %A Gerwin Schalk %A Moran, D %A Ojemann, J G %K Brain %K Humans %K Man-Machine Systems %K Movement %K Neurosurgery %K Prostheses and Implants %K User-Computer Interface %X

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.

%B Neurosurgery %V 59 %P 1-14; discussion 1-14 %8 07/2006 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/16823294 %N 1 %R 10.1227/01.NEU.0000221506.06947.AC %0 Journal Article %J IEEE Trans Neural Syst Rehabil Eng %D 2006 %T The Wadsworth BCI Research and Development Program: At Home with BCI. %A Theresa M Vaughan %A Dennis J. McFarland %A Gerwin Schalk %A Sarnacki, William A %A Krusienski, Dean J %A Sellers, Eric W %A Jonathan Wolpaw %K Animals %K Brain %K Electroencephalography %K Evoked Potentials %K Humans %K Neuromuscular Diseases %K New York %K Research %K Switzerland %K Therapy, Computer-Assisted %K Universities %K User-Computer Interface %X

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.

%B IEEE Trans Neural Syst Rehabil Eng %V 14 %P 229-33 %8 06/2006 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/16792301 %N 2 %R 10.1109/TNSRE.2006.875577 %0 Journal Article %J Neurology %D 2005 %T Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. %A Kübler, A. %A Nijboer, F. %A Mellinger, J. %A Theresa M Vaughan %A Pawelzik, H. %A Gerwin Schalk %A Dennis J. McFarland %A Niels Birbaumer %A Jonathan Wolpaw %K User-Computer Interface %X 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. %B Neurology %V 64 %P 1775–1777 %8 05/2005 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/15911809 %R 10.1212/01.WNL.0000158616.43002.6D %0 Journal Article %J Neurology %D 2005 %T Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. %A Kübler, A. %A Nijboer, F %A Mellinger, Jürgen %A Theresa M Vaughan %A Pawelzik, H %A Gerwin Schalk %A Dennis J. McFarland %A Niels Birbaumer %A Jonathan Wolpaw %K Aged %K Amyotrophic Lateral Sclerosis %K Electroencephalography %K Evoked Potentials, Motor %K Evoked Potentials, Somatosensory %K Female %K Humans %K Imagination %K Male %K Middle Aged %K Motor Cortex %K Movement %K Paralysis %K Photic Stimulation %K Prostheses and Implants %K Somatosensory Cortex %K Treatment Outcome %K User-Computer Interface %X

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.

%B Neurology %V 64 %P 1775-7 %8 05/2005 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/15911809 %N 10 %R 10.1212/01.WNL.0000158616.43002.6D %0 Journal Article %J IEEE Trans Biomed Eng %D 2004 %T The BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials. %A Benjamin Blankertz %A Müller, Klaus-Robert %A Curio, Gabriel %A Theresa M Vaughan %A Gerwin Schalk %A Jonathan Wolpaw %A Schlögl, Alois %A Neuper, Christa %A Pfurtscheller, Gert %A Hinterberger, T. %A Schröder, Michael %A Niels Birbaumer %K Adult %K Algorithms %K Amyotrophic Lateral Sclerosis %K Artificial Intelligence %K Brain %K Cognition %K Databases, Factual %K Electroencephalography %K Evoked Potentials %K Humans %K Reproducibility of Results %K Sensitivity and Specificity %K User-Computer Interface %X 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. %B IEEE Trans Biomed Eng %V 51 %P 1044-51 %8 06/2004 %G eng %N 6 %R 10.1109/TBME.2004.826692 %0 Journal Article %J IEEE transactions on bio-medical engineering %D 2004 %T BCI2000: a general-purpose brain-computer interface (BCI) system. %A Gerwin Schalk %A Dennis J. McFarland %A Hinterberger, Thilo %A Niels Birbaumer %A Jonathan Wolpaw %K User-Computer Interface %X 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. %B IEEE transactions on bio-medical engineering %V 51 %P 1034–1043 %8 06/2004 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/15188875 %R 10.1109/TBME.2004.827072 %0 Journal Article %J IEEE Trans Biomed Eng %D 2004 %T BCI2000: a general-purpose brain-computer interface (BCI) system. %A Gerwin Schalk %A Dennis J. McFarland %A Hinterberger, T. %A Niels Birbaumer %A Jonathan Wolpaw %K Algorithms %K Brain %K Cognition %K Communication Aids for Disabled %K Computer Peripherals %K Electroencephalography %K Equipment Design %K Equipment Failure Analysis %K Evoked Potentials %K Humans %K Systems Integration %K User-Computer Interface %X 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. %B IEEE Trans Biomed Eng %V 51 %P 1034-43 %8 06/2004 %G eng %N 6 %R 10.1109/TBME.2004.827072 %0 Journal Article %J J Neural Eng %D 2004 %T A brain-computer interface using electrocorticographic signals in humans. %A Leuthardt, E C %A Gerwin Schalk %A Jonathan Wolpaw %A Ojemann, J G %A Moran, D %K Adult %K Brain %K Communication Aids for Disabled %K Computer Peripherals %K Diagnosis, Computer-Assisted %K Electrodes, Implanted %K Electroencephalography %K Evoked Potentials %K Female %K Humans %K Imagination %K Male %K Movement Disorders %K User-Computer Interface %X

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.

%B J Neural Eng %V 1 %P 63-71 %8 06/2004 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/15876624 %N 2 %R 10.1088/1741-2560/1/2/001 %0 Journal Article %J IEEE Trans Neural Syst Rehabil Eng %D 2003 %T The Wadsworth Center brain-computer interface (BCI) research and development program. %A Jonathan Wolpaw %A Dennis J. McFarland %A Theresa M Vaughan %A Gerwin Schalk %K Academic Medical Centers %K Adult %K Algorithms %K Artifacts %K Brain %K Brain Mapping %K Electroencephalography %K Evoked Potentials, Visual %K Feedback %K Humans %K Middle Aged %K Nervous System Diseases %K Research %K Research Design %K User-Computer Interface %K Visual Perception %X

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.

%B IEEE Trans Neural Syst Rehabil Eng %V 11 %P 204-7 %8 06/2003 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/12899275 %N 2 %R 10.1109/TNSRE.2003.814442 %0 Journal Article %J IEEE Trans Rehabil Eng %D 2000 %T Brain-computer interface technology: a review of the first international meeting. %A Jonathan Wolpaw %A Niels Birbaumer %A Heetderks, W J %A Dennis J. McFarland %A Peckham, P H %A Gerwin Schalk %A Emanuel Donchin %A Quatrano, L A %A Robinson, C J %A Theresa M Vaughan %K Algorithms %K Cerebral Cortex %K Communication Aids for Disabled %K Disabled Persons %K Electroencephalography %K Evoked Potentials %K Humans %K Neuromuscular Diseases %K Signal Processing, Computer-Assisted %K User-Computer Interface %X

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.

%B IEEE Trans Rehabil Eng %V 8 %P 164-73 %8 06/2000 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/10896178 %N 2 %R 10.1109/TRE.2000.847807