Feature Extraction and Classification of EEG for Imaging Left-right Hands Movement

brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. This paper presents a new method for classifying the off-line experimental electroencephalogram (EEG) signals from the BCI Competition 2003, which achieved higher accuracy. The method has three main steps. First, wavelet coefficient was reconstructed by using wavelet transform in order to extract feature of EEG for mental tasks. At the same time, in frequency extraction, we use the AR model power spectral density as the frequency feature. Second, we combine the power spectral density feature and the wavelet coefficient feature as the final feature vector. Finally, linear algorithm is introduced to classify the feature vector based on iteration to obtain weight of the vector's components. The classified result shows that the effect using feature vector is better than just using one feature. This research provides a new idea for the identification of motor imagery tasks and establishes a substantial theory and experimental support for BCI application..