Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms.
The Wadsworth brain-computer interface (BCI), based on mu and beta sensorimotor rhythms, uses one- and two-dimensional cursor movement tasks and relies on user training. This is a real-time closed-loop system. Signal processing consists of channel selection, spatial filtering, and spectral analysis. Feature translation uses a regression approach and normalization. Adaptation occurs at several points in this process on the basis of different criteria and methods. It can use either feedforward (e.g., estimating the signal mean for normalization) or feedback control (e.g., estimating feature weights for the prediction equation). We view this process as the interaction between a dynamic user and a dynamic system that coadapt over time. Understanding the dynamics of this interaction and optimizing its performance represent a major challenge for BCI research.
McFarland
Dennis J
DJ
Laboratory of Nervous System Disorders, Wadsworth Center, New York State Department of Health and State University of New York, Albany, NY 12201, USA. mcfarland@wadsworth.org
Krusienski
Dean J
DJ
Wolpaw
Jonathan R
JR
D000222:Adaptation, Physiological / Q000502:physiology; D001611:Beta Rhythm; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface