@article {3415, title = {Toward independent home use of brain-computer interfaces: a decision algorithm for selection of potential end-users.}, journal = {Arch Phys Med Rehabil}, volume = {96}, year = {2015}, month = {03/2015}, pages = {S27-32}, abstract = {

Noninvasive brain-computer interfaces (BCIs) use scalp-recorded electrical activity from the brain to control an application. Over the past 20 years, research demonstrating that BCIs can provide communication and control to individuals with severe motor impairment has increased almost exponentially. Although considerable effort has been dedicated to offline analysis for improving signal detection and translation, far less effort has been made to conduct online studies with target populations. Thus, there remains a great need for both long-term and translational BCI studies that include individuals with disabilities in their own homes. Completing these studies is the only sure means to answer questions about BCI utility and reliability. Here we suggest an algorithm for candidate selection for electroencephalographic (EEG)-based BCI home studies. This algorithm takes into account BCI end-users and their environment and should assist in study design and substantially improve subject retention rates, thereby improving the overall efficacy of BCI home studies. It is the result of a workshop at the Fifth International BCI Meeting that allowed us to leverage the expertise of multiple research laboratories and people from multiple backgrounds in BCI research.

}, keywords = {Algorithms, brain-computer interfaces, Cognition, Disabled Persons, Electroencephalography, Environment, Humans, Patient Selection, Physical Therapy Modalities}, issn = {1532-821X}, doi = {10.1016/j.apmr.2014.03.036}, url = {http://www.ncbi.nlm.nih.gov/pubmed/25721544}, author = {K{\"u}bler, Andrea and Holz, Elisa Mira and Sellers, Eric W and Theresa M Vaughan} } @article {3370, title = {Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis.}, journal = {Amyotroph Lateral Scler Frontotemporal Degener}, volume = {15}, year = {2014}, month = {06/2014}, pages = {207-15}, abstract = {Brain-computer interfaces (BCIs) might restore communication to people severely disabled by amyotrophic lateral sclerosis (ALS) or other disorders. We sought to: 1) define a protocol for determining whether a person with ALS can use a visual P300-based BCI; 2) determine what proportion of this population can use the BCI; and 3) identify factors affecting BCI performance. Twenty-five individuals with ALS completed an evaluation protocol using a standard 6 {\texttimes} 6 matrix and parameters selected by stepwise linear discrimination. With an 8-channel EEG montage, the subjects fell into two groups in BCI accuracy (chance accuracy 3\%). Seventeen averaged 92 ({\textpm} 3)\% (range 71-100\%), which is adequate for communication (G70 group). Eight averaged 12 ({\textpm} 6)\% (range 0-36\%), inadequate for communication (L40 subject group). Performance did not correlate with disability: 11/17 (65\%) of G70 subjects were severely disabled (i.e. ALSFRS-R < 5). All L40 subjects had visual impairments (e.g. nystagmus, diplopia, ptosis). P300 was larger and more anterior in G70 subjects. A 16-channel montage did not significantly improve accuracy. In conclusion, most people severely disabled by ALS could use a visual P300-based BCI for communication. In those who could not, visual impairment was the principal obstacle. For these individuals, auditory P300-based BCIs might be effective.}, keywords = {Adult, Aged, Amyotrophic Lateral Sclerosis, Biofeedback, Psychology, brain-computer interfaces, Communication Disorders, Electroencephalography, Event-Related Potentials, P300, Female, Humans, Male, Middle Aged, Online Systems, Photic Stimulation, Psychomotor Performance, Reaction Time}, issn = {2167-9223}, doi = {10.3109/21678421.2013.865750}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24555843}, author = {McCane, Lynn M and Sellers, Eric W and Dennis J. McFarland and Mak, Joseph N and Carmack, C Steve and Zeitlin, Debra and Jonathan Wolpaw and Theresa M Vaughan} } @article {3386, title = {A practical, intuitive brain-computer interface for communicating {\textquoteright}yes{\textquoteright} or {\textquoteright}no{\textquoteright} by listening.}, journal = {J Neural Eng}, volume = {11}, year = {2014}, month = {06/2014}, pages = {035003}, abstract = {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 {\textquoteright}yes{\textquoteright} and {\textquoteright}no{\textquoteright}) 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{\textquoteright} performance with word stimuli (mean 77\% {\textpm} 3.3 s.e.) was slightly but not significantly better than their performance with beep stimuli (mean 73\% {\textpm} 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.}, keywords = {Adult, Aged, Algorithms, Auditory Perception, brain-computer interfaces, Communication Aids for Disabled, Electroencephalography, Equipment Design, Equipment Failure Analysis, Female, Humans, Male, Man-Machine Systems, Middle Aged, Quadriplegia, Treatment Outcome, User-Computer Interface}, issn = {1741-2552}, doi = {10.1088/1741-2560/11/3/035003}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24838278}, author = {Jeremy Jeremy Hill and Ricci, Erin and Haider, Sameah and McCane, Lynn M and Susan M Heckman and Jonathan Wolpaw and Theresa M Vaughan} } @article {2177, title = {The Wadsworth BCI Research and Development Program: At Home with BCI.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {229-33}, abstract = {

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

}, keywords = {Animals, Brain, Electroencephalography, Evoked Potentials, Humans, Neuromuscular Diseases, New York, Research, Switzerland, Therapy, Computer-Assisted, Universities, User-Computer Interface}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875577}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792301}, author = {Theresa M Vaughan and Dennis J. McFarland and Gerwin Schalk and Sarnacki, William A and Krusienski, Dean J and Sellers, Eric W and Jonathan Wolpaw} } @article {2169, title = {Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface.}, journal = {Neurology}, volume = {64}, year = {2005}, month = {05/2005}, pages = {1775-7}, abstract = {

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

}, keywords = {Aged, Amyotrophic Lateral Sclerosis, Electroencephalography, Evoked Potentials, Motor, Evoked Potentials, Somatosensory, Female, Humans, Imagination, Male, Middle Aged, Motor Cortex, Movement, Paralysis, Photic Stimulation, Prostheses and Implants, Somatosensory Cortex, Treatment Outcome, User-Computer Interface}, issn = {1526-632X}, doi = {10.1212/01.WNL.0000158616.43002.6D}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15911809}, author = {K{\"u}bler, A. and Nijboer, F and Mellinger, J{\"u}rgen and Theresa M Vaughan and Pawelzik, H and Gerwin Schalk and Dennis J. McFarland and Niels Birbaumer and Jonathan Wolpaw} } @article {2167, title = {The BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials.}, journal = {IEEE Trans Biomed Eng}, volume = {51}, year = {2004}, month = {06/2004}, pages = {1044-51}, abstract = {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.}, keywords = {Adult, Algorithms, Amyotrophic Lateral Sclerosis, Artificial Intelligence, Brain, Cognition, Databases, Factual, Electroencephalography, Evoked Potentials, Humans, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface}, issn = {0018-9294}, doi = {10.1109/TBME.2004.826692}, author = {Benjamin Blankertz and M{\"u}ller, Klaus-Robert and Curio, Gabriel and Theresa M Vaughan and Gerwin Schalk and Jonathan Wolpaw and Schl{\"o}gl, Alois and Neuper, Christa and Pfurtscheller, Gert and Hinterberger, T. and Schr{\"o}der, Michael and Niels Birbaumer} } @article {2165, title = {The Wadsworth Center brain-computer interface (BCI) research and development program.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {11}, year = {2003}, month = {06/2003}, pages = {204-7}, abstract = {

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

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

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

}, keywords = {Brain Diseases, Communication Aids for Disabled, Computer Systems, Electroencephalography, Humans, User-Computer Interface}, issn = {1388-2457}, doi = {10.1016/S1388-2457(02)00057-3}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12048038}, author = {Jonathan Wolpaw and Niels Birbaumer and Dennis J. McFarland and Pfurtscheller, Gert and Theresa M Vaughan} } @article {2163, title = {Brain-computer interface technology: a review of the first international meeting.}, journal = {IEEE Trans Rehabil Eng}, volume = {8}, year = {2000}, month = {06/2000}, pages = {164-73}, abstract = {

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

}, keywords = {Algorithms, Cerebral Cortex, Communication Aids for Disabled, Disabled Persons, Electroencephalography, Evoked Potentials, Humans, Neuromuscular Diseases, Signal Processing, Computer-Assisted, User-Computer Interface}, issn = {1063-6528}, doi = {10.1109/TRE.2000.847807}, url = {http://www.ncbi.nlm.nih.gov/pubmed/10896178}, author = {Jonathan Wolpaw and Niels Birbaumer and Heetderks, W J and Dennis J. McFarland and Peckham, P H and Gerwin Schalk and Emanuel Donchin and Quatrano, L A and Robinson, C J and Theresa M Vaughan} }