Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering a significant benefit for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI) classification encounter challenges such as manual feature extraction and susceptibility to noise. This paper introduces EEGEncoder, a deep learning framework that employs transformer models to surmount these limitations. Our innovative multi-scale fusion architecture captures both immediate and extended temporal features, thereby enhancing MI task classification precision. EEGEncoder's key innovations include the inaugural application of transformers in MI-EEG signal classification, a mixup data augmentation strategy for bolstered generalization, and a multi-task learning approach for refined predictive accuracy. When tested on the BCI Competition IV dataset 2a, our model established a new benchmark with its state-of-the-art performance. EEGEncoder signifies a substantial advancement in BCI technology, offering a robust, efficient, and effective tool for transforming thought into action, with the potential to significantly enhance the quality of life for those dependent on BCIs.

翻译：脑机接口利用脑电信号实现对外部设备的直接神经控制，为运动障碍患者带来重要益处。针对基于EEG的运动想象分类任务，传统机器学习方法面临手动特征提取、对噪声敏感等挑战。本文提出EEGEncoder这一深度学习框架，采用Transformer模型突破上述局限。创新性多尺度融合架构能够同时捕获即时与长期时间特征，从而提升MI任务分类精度。EEGEncoder的核心创新包括：首次将Transformer应用于MI-EEG信号分类、采用混合数据增强策略提升泛化能力、以及通过多任务学习优化预测准确性。在BCI Competition IV数据集2a上的测试中，该模型以尖端性能树立了新基准。EEGEncoder标志着脑机接口技术的重大进步，为将思维转化为行动提供了稳健、高效且有效的工具，有望显著改善依赖BCI技术人群的生活质量。