The brain-computer interface (BCI) is a cutting-edge technology that has the potential to change the world. Electroencephalogram (EEG) motor imagery (MI) signal has been used extensively in many BCI applications to assist disabled people, control devices or environments, and even augment human capabilities. However, the limited performance of brain signal decoding is restricting the broad growth of the BCI industry. In this article, we propose an attention-based temporal convolutional network (ATCNet) for EEG-based motor imagery classification. The ATCNet model utilizes multiple techniques to boost the performance of MI classification with a relatively small number of parameters. ATCNet employs scientific machine learning to design a domain-specific deep learning model with interpretable and explainable features, multihead self-attention to highlight the most valuable features in MI-EEG data, temporal convolutional network to extract high-level temporal features, and convolutional-based sliding window to augment the MI-EEG data efficiently. The proposed model outperforms the current state-of-the-art techniques in the BCI Competition IV-2a dataset with an accuracy of 85.38% and 70.97% for the subject-dependent and subject-independent modes, respectively.

中文翻译：

---

用于基于 EEG 的运动意象分类的物理知情注意时间卷积网络

脑机接口（BCI）是一项具有改变世界潜力的尖端技术。脑电图 (EEG) 运动图像 (MI) 信号已广泛用于许多 BCI 应用中，以帮助残疾人、控制设备或环境，甚至增强人类能力。然而，脑信号解码的有限性能制约着脑机接口产业的广泛发展。在本文中，我们提出了一种基于注意力的时间卷积网络 (ATCNet)，用于基于 EEG 的运动图像分类。ATCNet 模型利用多种技术以相对较少的参数提高 MI 分类的性能。ATCNet 采用科学的机器学习来设计具有可解释和可解释特征的特定领域的深度学习模型，多头自注意力突出 MI-EEG 数据中最有价值的特征，时间卷积网络提取高级时间特征，基于卷积的滑动窗口有效地增强 MI-EEG 数据。所提出的模型优于 BCI 竞赛 IV-2a 数据集中当前最先进的技术，对于主题相关和主题独立模式的准确度分别为 85.38% 和 70.97%。