Support vector EEG classification in the Fourier and time-frequency correlation domains

摘要

In this paper we use support vector machines (SVM) for classifying EEG signals corresponding to imagined motor movements. The parameters of an SVM Kernel are optimized for minimizing a theoretical error bound. Fourier features and correlative time-frequency based features are extracted from EEG signals and compared with respect to their discriminatory power.