Exploring Motor Imagery Eeg Patterns for Stroke Patients with Deep Neural Networks

Dawei Cheng, Ye Liu, Liqing Zhang

Abstract: Studies show that motor imagery based Brain-Computer Interface (BCI) systems can be utilized therapeutically in stroke rehabilitation. Efficient decoding of subjects' motor intentions is essential in BCI-based rehabilitation systems to manipulate a neural prosthesis or other devices for motor relearning. However, due to cortical reorganization, the desynchro-nization potential evoked by the motor imagery of patients with brain lesions is quite different from that evoked by the motor imagery of normal subjects. These differences can be attributed to active cortex regions, frequency bands and amplitude. In this paper, we use a deep learning method to explore the EEG patterns of key channels and the frequency band for stroke patients. The EEG data is bandpass filtered into multiple sub-bands split by a sliding window strategy. Under these sub-bands, diverse spatial-spectral features are extracted and fed into a deep neural network for classification, uncovering the spectral patterns (bandpass filters) and spatial patterns (spatial weights). Experimental results from five stroke patients show that our method has higher classification accuracy than several state-of-the-art approaches. By tracking gradual changes in EEG patterns during rehabilitation, we try to uncover the neurophysiological plasticity mechanism in the impaired cortexes of stroke patients.