提升脑机接口中运动想象信号跨会话识别准确率
ATCNet-CIAM for Multi-Session Motor Imagery EEG Signal Classification
- 引入轻量级通道融合注意力模块增强空间特征表示
- 跨会话测试下最高达89.46%准确率，优于基准模型
- 适合需要稳定解码的长期脑机接口应用
基于运动想象的脑电图广泛应用于非侵入式脑机接口，但个体差异和跨会话非平稳性导致解码困难。本文提出ATCNet-CIAM，一种在ATCNet框架中集成轻量级通道融合注意力模块（CIAM）的增强型注意力时序卷积网络，以改善运动想象解码中的通道-空间特征表示。该模型在BCI Competition IV-2a、IV-2b及多日WBCIC-MI数据集上，分别采用标准、会话内与跨会话协议进行评估。实验结果表明，在标准协议下，对BCI IV-2a和IV-2b的分类准确率分别达到86.32%和87.96%；在会话内测试中，多日数据集2类和3类任务的准确率分别为89.46%和83.64%。所提框架显著提升了不同会话条件下的分类稳定性与鲁棒性，消融实验证明了各组件的互补作用。
原文摘要 · Abstract (English)
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.
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