The Research of the Feature Extraction and Classification Algorithm Based on EEG Signal of Motor Imagery

BCI based on machine learning could makes use of the egg signals to communicate to output under the condition of without the participation of peripheral nerves and muscles. Extracting the essential features of the EEG signals in the presence of artifacts, training the classification algorithms and optimalizing the performance of classifier is critical procedure for BCI system. To some extent the BCI system can be treated as a pattern recognition system whose performance depends on both the features extraction and the features classification algorithm employed. Independent component analysis (ICA) can remove the artifact in the electroencephalogram (EEG) signal spontaneous evoked by left and right hand motor imagery and the classifier based on the Support Vector Machine (SVM) algorithm and on the common spatial pattern (CSP) algorithm apply the feature extracted from purified EEG signal to recognize and discriminate distinctive motor imagery pattern.