End-to-End Multi-Granular Motor Imagery EEG Signal Analysis Method Based on C-LSTM Model

Electroencephalography (EEG) is a bioelectric signal generated by the human brain in different states. Motor Imagery EEG signals are one of the most typical types of EEG signals and are widely used in brain-computer interface technology. The research on motor imagery EEG signal analysis has a long history. And the methods such as Common Spatial Pattern are mainly used to extract features in the signal. It is still a problem to be solved how to extract more effective EEG signal features and how to model the timing information. Therefore, an end-to-end multi-granular EEG analysis method based on C-LSTM (Convolutional-Long Short Term Memory) model is designed in this paper. Then spatial information and wavelet brain network are used to improve the model. Compared with the traditional method, the experiment result on BCI2008 data set has improved by nearly 10%, reaching a recognition rate of 93.6%.