CosELU: An improved activation deep neural network for EEG signal recognition in stroke patients

Motor imagery electroencephalograme (MI-EEG) signal recognition algorithms are essential for rehabilitation training of clinical stroke patients. However, EEG signals of stroke patients using traditional activation functions are prone to exploding gradient and vanishing gradient problems in deep learning deep neural network training, leading to a decrease in the recognition rate of EEG signals. In this paper, an adaptive activation function is developed and investigated to address this problem. The method monitors the gradient of the activation function by adjustable parameter a and adaptive parameter λ_i⁺, which alleviates the exploding gradient and vanishing gradient problems. In addition, a suitable attention mechanism convolutional neural network (AMCNN) is designed for the classification of EEG signals, and CosELU is utilized to the AMCNN network for training of EEG signals, and finally the effectiveness of the CosELU activation function is verified on a stroke patient dataset. The source code is publicly available at https://github.com/ccut3329lab/jwk_code.git.