A Lightweight End-to-End Three-domain Feature Fusion Network for Motor Imagery Decoding

To decode Motor Imagery EEG signals (MI-EEG), most studies have increasingly complicated network models and parameters without fully considering EEG characteristics, thereby limiting advancements in Brain-Computer Interface (BCI) systems and classification performance. To address these issues, we propose a lightweight end-to-end tri-domain feature fusion network named LTDFNet. Firstly, we introduce an Attention-based Spatio-temporal Convolution module (ABST) to extract low-dimensional spatio-temporal features from EEG. This module employs a lightweight Squeeze-and-Excitation (SE) attention mechanism to enhance the model's perception of crucial information. Secondly, Temporal Domain Convolutional (TDC) and Frequency Domain Convolution (SDC) modules utilize Temporal Convolutional Networks (TCN) and Fast Fourier Transform (FFT) to respectively learn high-dimensional temporal and frequency domain information. Finally, the Feature Fusion (FF) module integrates low-dimensional spatio-temporal features and high-dimensional temporal-frequency features effectively through learnable parameters. LTDFNet is trained with joint constraints of Softmax loss and Center loss functions to achieve optimal inter-class separation and intra-class compactness, thereby enhancing overall model performance. This study conducts extensive experimental validation on BCI Competition IV datasets 2a (BCI 2a) and 2b (BCI 2b). LTDFNet achieves classification accuracies of 76.89% (kappa score: 0.692) and 85.22% (kappa score: 0.704) on the BCI 2a and BCI 2b datasets, respectively. Compared to other high-performance decoding methods, LTDFNet utilizes only 20,576 parameters, balancing network scale and decoding performance requirements.