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Efficient MEG signal decoding of direction in wrist movement using curve fitting (EMDC)

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3 Author(s)
Krishna, S. ; Dept. of Electron. & Commun. Eng., Univ. Visvesvaraya, Bangalore, India ; Vinay, K.C. ; Raja, K.B.

Magnetoencephalography (MEG) can be used as an effective non-invasive interface with the brain to provide movement-related information similar to invasive signal recordings. This paper proposes a reliable and efficient algorithm for classification of wrist movement in four directions from MEG signals of two subjects. Our approach involves signal smoothing, design of a class-specific Unique Identifier Signal (UIS) and curve fitting to identify the direction in a given test signal. Our algorithm is evaluated with the data set provided in BCI competition 2008. Our simulations show the best average prediction accuracy of 88.84% for this four-class classification problem. The results of the proposed model are found to be superior to most other techniques in vogue.

Published in:

Image Information Processing (ICIIP), 2011 International Conference on

Date of Conference:

3-5 Nov. 2011