%0 Journal Article %J J Neural Eng %D 2014 %T A practical, intuitive brain-computer interface for communicating 'yes' or 'no' by listening. %A Jeremy Jeremy Hill %A Ricci, Erin %A Haider, Sameah %A McCane, Lynn M %A Susan M Heckman %A Jonathan Wolpaw %A Theresa M Vaughan %K Adult %K Aged %K Algorithms %K Auditory Perception %K brain-computer interfaces %K Communication Aids for Disabled %K Electroencephalography %K Equipment Design %K Equipment Failure Analysis %K Female %K Humans %K Male %K Man-Machine Systems %K Middle Aged %K Quadriplegia %K Treatment Outcome %K User-Computer Interface %X 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. %B J Neural Eng %V 11 %P 035003 %8 06/2014 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/24838278 %N 3 %R 10.1088/1741-2560/11/3/035003 %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 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 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