Classification of Imaginary Movements in ECoG

The electrocorticogram (ECoG) is a kind of signal source that can be classified for making use of a human brain computer interface (BCI) field. The feature extraction is crucial for increasing classification accuracy rate. In this paper, Power Spectral Density is used for the selection of the optimal electrodes. Common spatial pattern (CSP) algorithm is used for feature extraction, and the nonlinear classification of motor imagery with support vector machines (SVM).The classification accuracy rate of 83% is achieved on Data set I of BCI Competition III.