Task Classifcation of Right-hand and Foot Motion Imagery Based on Wavelet Packet Transform

Brain-computer interface (BCI) provides a new choice for people who lose communication ability, so the recognition of EEG has been paid attention. In this paper, wavelet packet transform (WPT) and transfer learning (TL) were used to classify right-hand and foot motion imagery tasks. Firstly, based on analyzing the channels and frequency bands closely related to event-related desynchronization (ERD), the EEG signals are decomposed by WPT. Then the relevant nodes were selected to calculate wavelet packet energy. Finally, TL was used to classify the BCI competition Ⅲ data IVa. The ideal classification result was obtained. The results show that the method is simple and effective, and it is valuable for online application of BCI.