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Classification of movement-related single-trial MEG data using adaptive spatial filter

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4 Author(s)
Futoshi Asano ; Honda Research Institute Japan (HRI-JP), Japan ; Masahiro Kimura ; Tatsuhiko Sekiguchi ; Yukiyasu Kamitani

In this paper, a method for extracting and classifying movement-related brain signals is proposed. A single-trial MEG observation is first processed with a pre-whitening filter so that strong stationary interference is eliminated. Next, a brain signal effective for classification is extracted using an adaptive spatial filter. The extracted signal is then classified with a support vector machine. From the experimental results, it is shown that the classification rate of 62.6 % is obtained for the brain signals related to the three types of hand movements (ldquoscissors-paper-rockrdquo).

Published in:

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

Date of Conference:

19-24 April 2009