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 modified transformers and TCNs to surmount these limitations. We innovatively propose a fusion architecture, namely Dual-Stream Temporal-Spatial Block (DSTS), to capture temporal and spatial features, improving the accuracy of Motor Imagery classification task. Additionally, we use multiple parallel structures to enhance the performance of the model. When tested on the BCI Competition IV-2a dataset, our model results outperform current state-of-the-art techniques.

翻译：脑机接口（BCI）利用脑电图信号实现对设备的直接神经控制，为运动功能受损的个体提供了重要帮助。基于脑电图的运动想象（MI）分类的传统机器学习方法面临着手工特征提取和对噪声敏感等挑战。本文提出了EEGEncoder，一种采用改进的Transformer和TCN来克服这些限制的深度学习框架。我们创新性地提出了一种融合架构，即双流时空模块（DSTS），以捕捉时间和空间特征，从而提高运动想象分类任务的准确性。此外，我们采用多个并行结构来增强模型的性能。在BCI Competition IV-2a数据集上进行测试时，我们的模型结果优于当前最先进的技术。