%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 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