发表机构
Faculty of Information Technology, Van Lang School of Technology, Van Lang University; Department of Computer Engineering, Ho Chi Minh City University of Transport（信息技术学院，范朗理工学院，范朗大学; 胡志明市交通大学计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对运动想象脑电信号分类因个体差异和跨会话非平稳性导致的稳健解码难题，提出将CIAM集成到ATCNet框架的ATCNet - CIAM，实验表明该模型能提升分类稳定性和稳健性，在多数据集上取得较好准确率。
AI 中文摘要
基于运动想象（MI）的脑电图广泛应用于非侵入性脑机接口（BCI），但由于个体差异和跨会话非平稳性，稳健解码仍具挑战。本文提出ATCNet - CIAM，一种增强的注意力时间卷积网络，将轻量级通道集成注意力模块（CIAM）集成到ATCNet框架中以改善MI解码的通道 - 空间特征表示。在BCI竞赛IV - 2a、BCI竞赛IV - 2b和多日WBCIC - MI数据集上按标准、会话内和跨会话协议评估该模型。实验结果表明，在标准协议下，ATCNet - CIAM在BCI IV - 2a上准确率达86.32%，在BCI IV - 2b上达87.96%，在会话内WBCIC - MI的2C和3C上分别达89.46%和83.64%。该框架在会话变化条件下持续提高分类稳定性和稳健性，消融研究证实了所提架构组件的互补作用。
英文摘要
Motor imagery (MI)-based electroencephalography is widely used in non-invasive brain--computer interfaces (BCIs), but robust decoding remains challenging due to inter-subject variability and cross-session non-stationarity. This work proposes ATCNet-CIAM, an enhanced attention temporal convolutional network that integrates a lightweight channel-integrated attention module (CIAM) into the ATCNet framework to improve channel-spatial feature representation for MI decoding. The proposed model is evaluated on BCI Competition IV-2a, BCI Competition IV-2b, and the multi-day WBCIC-MI dataset under standard, within-session, and cross-session protocols. Experimental results show that ATCNet-CIAM achieves 86.32% accuracy on BCI IV-2a and 87.96% on BCI IV-2b under the standard protocol, while reaching 89.46% and 83.64% in the within-session WBCIC-MI on 2C and 3C, respectively. The proposed framework consistently improves classification stability and robustness under session-varying conditions, and ablation study confirms the complementary contribution of the proposed architectural components.
CommentsThis manuscript has been accepted for publication in the International Conference on Intelligence Systems and Robotics for Sustainable Development (ISRSD) 2026
