Wei Zhang ¹ Xianlun Tang ^1* Mengzhou Wang ^2*

• ¹ Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
• ² Chongqing College of Traditional Chinese Medicine, Chongqing University of Posts and Telecommunications, Chongqing, China

Applying convolutional neural networks to a large number of EEG signal samples is computationally expensive because the computational complexity is linearly proportional to the number of dimensions of the EEG signal. We propose a new Gated Recurrent Unit (GRU) network model based on reinforcement learning, which considers the implementation of attention mechanisms in Electroencephalogram (EEG) signal processing scenarios as a reinforcement learning problem. The model can adaptively select target regions or position sequences from inputs and effectively extract information from EEG signals of different resolutions at multiple scales. Just as convolutional neural networks benefit from translation invariance, our proposed network also has a certain degree of translat ion invariance, making its computational complexity independent of the EEG signal dimension, thus maintaining a lower learning cost. Although the introduction of reinforcement learning makes the model non differentiable, we use policy gradient methods to achieve end-to-end learning of the model. We evaluated our proposed model on publicly available EEG dataset (BCI Competition IV-2a). The proposed model outperforms the current state-of-theart techniques in the BCI Competition IV-2a dataset with an accuracy of 86.78% and 71.54% for the subjectdependent and subject-independent modes, respectively.