Improved common spatial pattern for brain-computer interfacing

Signal processing of electroencephalography (EEG) plays an important role in brain-computer-interface (BCI) system. It is crucial to select suitable features from EEG signals. This paper proposed a feature selection method which combing independent component analysis (ICA) and common spatial patterns (CSP) to improve the performances of classification. Firstly, EEG signals were filtered with 8-30HZ bandpass filter. Secondly, relative frequency band signals were decomposed into independent components to obtain the solution matrix by ICA, and then the EEG signals were reconstructed from the main components to improve the signal-to-noise ratios. CSP was used to extract the features of EEG signals. Finally, linear discriminate analysis classifier (LDA) and support vector machines (SVM) were used to classify the EEG feature signals. The experiment results showed that the average accuracy achieved by the proposed method were higher, compared common CSP method.