EEG-based motor imagery classification using convolutional neural networks with local reparameterization trick

Deep learning (DL) method has emerged as a powerful tool in studying the behavior of Electroencephalogram (EEG)-based motor imagery (MI). Although prospective studies have demonstrated promising performance, most of these studies have been affected by the lack of research between groups and individual subjects, and the accuracy of MI classification still has room for improvement. Due to the inter-individual variability in the EEG classification, enhancing the adaptability and robustness between different individuals is especially critical. We developed a novel DL model based on the EEG signals to improve MI classification performance by introducing the local reparameterization trick into convolutional neural networks (LRT-CNN). 109 subjects from PhysioNet Dataset were used to test the proposed model. Firstly, a global classifier was evaluated by four groups. Secondly, individual variability was examined by testing individual subjects. The classification accuracy of global classifier in 20 subjects, 50 subjects, 80 subjects, and 109 subjects are 93.86%, 98.94%, 93.04%, and 92.41%, respectively. The maximum classification accuracy of one individual subject is 99.79%, which is better than the state-of-the-art method and proves the proposed method can handle the challenge of individual variability. We conclude that introducing the local reparameterization trick into convolutional neural networks can significantly improve the accuracy of the MI tasks based on the EEG signals without any complicated and tedious feature engineering works. Besides, encouraging results were obtained both between groups (multiple subjects) and on a single subject. The experimental results add to the rapidly expanding field of brain science and contribute to our understanding of applying the DL method to address EEG-based classification problems (not limited to MI classification issues).