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

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.