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.

翻译：脑机接口（BCI）实现了大脑与外部设备的直接通信，为运动障碍患者提供了关键支持。然而，由于噪声和跨会话变异性，从脑电图（EEG）中准确解码运动想象（MI）信号仍具挑战性。本研究提出EEG-MFTNet，一种基于EEGNet架构的新型深度学习模型，通过多尺度时间卷积和Transformer编码器流进行增强。这些组件旨在捕捉EEG信号中的短程和长程时间依赖性。模型在SHU数据集上采用受试者依赖的跨会话设置进行评测，性能优于包括EEGNet及其近期衍生模型在内的基线模型。EEG-MFTNet在保持低计算复杂度和推理延迟的同时，实现了58.9%的平均分类准确率。结果突显了该模型在实时BCI应用中的潜力，并强调了架构创新对提升运动想象解码性能的重要性。本研究为开发更鲁棒且自适应的BCI系统提供了技术支撑，对辅助技术和神经康复领域具有重要应用价值。