关键词: 运动想象, 共空间模式, 小波变换, 卷积神经网络, 卷积注意力模块

Abstract:

Aiming at the problem of low recognition accuracy of multi-class motion imaging EEG signals, this paper proposes a convolutional neural network model that incorporates attention modules. The model utilizes the attention module to fully mine the channels and spatial features of the EEG signals, and establishes the relationship between the importance and the recognition tasks, thereby improving the recognition accuracy of the motor imaging EEG signals. The signal undergoes a common space mode to improve the signal-to-noise ratio. The wavelet transform is used to convert the signal into a two-dimensional time-frequency map. The feature adjustment is carried out through the two dimensions of the channel and space in the attention module to strengthen the weakening of useful features. The useless features enable the convolutional network to fully extract higher-level abstract features and finally perform the recognition of the motion imaging task. The method in this paper has been evaluated on the BCI Competition IV Datasets 2a and BCI Competition III-IIIa datasets respectively, and compared with convolutional neural networks and other algorithms. The experimental results show that the method proposed in this paper can achieve good accuracy and can effectively improve the recognition accuracy of EEG signal motor imaging tasks.

Key words: motor imagination, common spatial pattern, wavelet transform, convolutional neural network, convolutional attention module