The recognition of EEG with CSSD and SVM

With time-varying volatility and individual differences, EEG signals are difficult to analyse. The recognition performance of the traditional feature extraction is lowered due of the difficulty in tracking the dynamic changes of EEG. In this paper the Common Spatial Subspace Decomposition (CSSD) algorithm was improved (named Improved-CSSD), putting forward a kind feature extraction method which has the performance of adaptive ability. This method introduced control parameters, which added the training samples of the assistants to that of the target subject in some way. Finally, based on the data of the international BCI competition database, some simulation experiments were conducted by recognizing EEG signals by Improved-CSSD and SVM. Compared with the traditional CSSD, classification accuracy was increased about 8.26% by Improved-CSSD. The result showed that the approach, proposed in this paper, had a good adaptability and a low time loss.