改进EEGNet架构，提升跨会话运动想象解码准确率
EEG-MFTNet: An Enhanced EEGNet Architecture with Multi-Scale Temporal Convolutions and Transformer Fusion for Cross-Session Motor Imagery Decoding
- 融合多尺度时序卷积与Transformer，捕捉脑电长短时依赖
- 在SHU数据集上达58.9%平均准确率，优于现有模型
- 适合实时脑机接口应用，尤其关注神经康复场景
脑机接口（BCI）为运动障碍患者提供与外部设备直接通信的途径，但脑电信号中的噪声和跨会话差异仍使运动想象（MI）解码困难。本文提出EEG-MFTNet，一种基于EEGNet的新型深度学习模型，引入多尺度时序卷积和Transformer编码器流，以捕捉脑电信号中短程与长程时间依赖。在SHU数据集上采用受试者依赖的跨会话设置进行评估，结果表明该模型优于基线模型（包括EEGNet及其近期变体），平均分类准确率达58.9%，同时保持低计算复杂度和推理延迟。实验验证了架构创新对提升MI解码性能的有效性，凸显其在实时脑机接口中的潜力，并推动更鲁棒、自适应的辅助技术与神经康复系统发展。
原文摘要 · Abstract (English)
Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices, providing critical support for individuals with motor impairments. However, accurate motor imagery (MI) decoding from electroencephalography (EEG) remains challenging due to noise and cross-session variability. This study introduces EEG-MFTNet, a novel deep learning model based on the EEGNet architecture, enhanced with multi-scale temporal convolutions and a Transformer encoder stream. These components are designed to capture both short and long-range temporal dependencies in EEG signals. The model is evaluated on the SHU dataset using a subject-dependent cross-session setup, outperforming baseline models, including EEGNet and its recent derivatives. EEG-MFTNet achieves an average classification accuracy of 58.9% while maintaining low computational complexity and inference latency. The results highlight the model's potential for real-time BCI applications and underscore the importance of architectural innovations in improving MI decoding. This work contributes to the development of more robust and adaptive BCI systems, with implications for assistive technologies and neurorehabilitation.
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