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An analytic spatial filter and a hidden Markov model for enhanced information transfer rate in EEG-based brain computer interfaces

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3 Author(s)
Martin McCormick ; University of Illinois, Urbana, USA ; Rui Ma ; Todd P. Coleman

We propose a new classification method, termed the Common Spatial Analytic Pattern, for brain-computer interfaces based on a simple EEG signal source and channel model. This blind source separation procedure recovers underlying source signals near the motor cortex which are indicative of motor imagery. A hidden Markov source model is applied to the evolution of the source signals and is used to estimate the type (left or right) of motor imagery performed by a subject. As a whole, the resulting asynchronous classifier offers significant improvement upon the current prevailing techniques in classification. Experiments show information transfer rates between subject and computer as high as 60.9 bits/minute.

Published in:

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

Date of Conference:

14-19 March 2010