Abstract: Abstract： The brain-computer interface technology based on motor imagination is helpful to the rehabilitation of patients with hand movement disorders， so it is widely used in the field of rehabilitation medicine. Aiming at the problem of poor classification of motor imagination-electroencephalogram （MI-EEG） due to its low signal-to-noise ratio in current motor imagination-electroencephalogram， in view of the ability of the attention module to focus on important features related to motor imagination classification tasks and ignore unimportant features， we propose a convolutional neural network based on the efficient channel attention （ECA） module for feature extraction and classification of left and right-handed MI-EEG. In order to facilitate the recognition of EEG signals by convolutional neural network （CNN）， this paper uses wavelet transform to convert the timing signals of C3 and C4 channels into two-dimensional time-frequency graphs， then designs a CNN structure and parameters based on ECA. Finally， the proposed method is tested on EEG data set. The experimental results show that compared with CNN and the CNN method based on fusion convolution attention， the CNN method based on ECA can effectively improve the recognition accuracy of MI-EEG， indicating that the proposed method is effective in motor imagination EEG recognition.

Key words: Key words： motor imagination, EEG recognition, wavelet transform, efficiency channel attention module, convolutional neural network, brain-computer interface