关键词: 运动想象, 脑电信号, 轻量化, 多域特征融合, 挤压激励注意力

Abstract: To address the issues of slow decoding speed in large-scale networks and insufficient utilization of feature information in decoding motor imagery electroencephalogram (MI-EEG) signals, a lightweight MI-EEG decoding neural network based on multi-domain feature fusion was proposed. The proposed network introduced lightweight modules to extract multi-domain features, including the use of SincNet for frequency-domain feature extraction and temporal convolutional networks (TCN) for time-domain feature extraction. Additionally, after extracting time-frequency domain features, the squeeze-and-excitation (SE) attention was incorporated to adaptively recalibrate feature maps, emphasizing important features and suppressing redundant information. Finally, separable convolution was employed to effectively fuse time-frequency features, addressing the limitation of single-domain feature information. Furthermore, a joint loss function combining cross-entropy and center loss was adopted to optimize both intra-class and inter-class classification performance during network training. The proposed network has parameter counts of 6,870, 5,690, and 6,870 on the BCI 2a, SMR-BCI, and OpenBMI motor imagery public datasets, respectively, achieving average accuracies of 74.78%, 71.93%, and 65.40%, with average Kappa values of 0.70, 0.66, and 0.59. The experimental results show that on the BCI 2a dataset, compared with networks such as DeepConvNet, EEGNet and EEGTCNet, the improvement is 11.06, 8.85 and 6.36 percentage points, respectively; on the SMR-BCI dataset, compared with networks such as DeepConvNet, EEGNet and EEGTCNet, the improvement is 10.53, 4.17, and 3.57 percentage points; and on the OpenBMI dataset, 5.09, 4.99, and 2.33 percentage points compared to networks such as DeepConvNet, EEGNet, and EEGTCNet, respectively. The proposed network balances decoding performance while ensuring lightweight.

Key words: Motor Imagery &#40, MI&#41, Electroencephalogram &#40, EEG&#41