A $\mu $-Rhythm Matched Filter for Continuous Control of a Brain-Computer Interface

Dean J. Krusienski, Gerwin Schalk, Dennis J. McFarland, Jonathan R. Wolpaw
2007 IEEE Transactions on Biomedical Engineering  
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 rhythm and 18-26 Hz rhythm) over the sensorimotor cortex and use them to control a cursor on a computer screen. Conventional spectral techniques for monitoring the continuous amplitude
more » ... ctuations fail to capture essential amplitude/phase relationships of the and rhythms in a compact fashion and, therefore, are suboptimal. By extracting the characteristic rhythm for a user, the exact morphology can be characterized and exploited as a matched filter. A simple, parameterized model for the characteristic 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 and bands during event-related desynchronization, and that an appropriate matched filter can provide improved performance. Index Terms-Brain-computer interface, electroencephalogram, matched filter, sensorimotor rhythms, spectral analysis. Dean J. Krusienski (M'01) received the B.he is researching the application of adaptive signal processing and pattern recognition techniques to a non-invasive brain-computer interface that allows individuals with severe neuromuscular disabilities to operate a communication device via the EEG. His research interests include brain-computer interfaces, adaptive filtering, evolutionary algorithms, biomedical signal processing, computational intelligence, neural networks, and blind source separation. University of New York, Albany. His research interests include use of operant conditioning of spinal reflexes as a new model for defining the plasticity underlying a simple form of learning in vertebrates and development of EEG-based communication and control technology for people with severe motor disabilities.
doi:10.1109/tbme.2006.886661 pmid:17278584 fatcat:qhjzcc3tbzhgvfy3qxczrlfzwa