Feature Extraction and Classification of Mental EEG for Motor Imagery

Electroencephalography (EEG) recognition was one of the key technology in brain-computer interface (BCI). For motor imagery EEG, a new EEG recognition algorithm (DWT-BP algorithm) which combined discrete wavelet transform (DWT) with BP neural network was presented. In DWT-BP, a rational time window was set through calculating the average power of motor imagery EEG on electrode C3 and C4, and then the average power during the time window was taken into DWT. The combinational signal of approximate coefficient A6 on the sixth level was selected as a signal feature and BP neural network was used as classifier to analyze the observed EEG data. The experiment results on ¿BCI Competition 2003¿ competition database showed that the recognition rate was better than the other several traditional algorithms. So, it proved that the algorithm was effective for EEG recognition of motor imagery, and provided a new idea for motor imagery recognition in brain computer interface.