Classification of electroencephalogram (EEG) and electrocorticogram (ECoG) signals obtained during motor imagery (MI) has substantial application potential, including for communication assistance and rehabilitation support for patients with motor impairments. These signals remain inherently susceptible to physiological artifacts (e.g., eye blinking, swallowing), which pose persistent challenges. Although Transformer-based approaches for classifying EEG and ECoG signals have been widely adopted, they often struggle to capture fine-grained dependencies within them. To overcome these limitations, we propose Cortical-SSM, a novel architecture that extends deep state space models to capture integrated dependencies of EEG and ECoG signals across temporal, spatial, and frequency domains. We validated our method across three benchmarks: 1) two large-scale public MI EEG datasets containing more than 50 subjects, and 2) a clinical MI ECoG dataset recorded from a patient with amyotrophic lateral sclerosis. Our method outperformed baseline methods on the three benchmarks. Furthermore, visual explanations derived from our model indicate that it effectively captures neurophysiologically relevant regions of both EEG and ECoG signals.

翻译：基于运动想象（MI）的脑电图（EEG）与皮层脑电图（ECoG）信号分类具有重要的应用潜力，包括为运动功能障碍患者提供通讯辅助与康复支持。这些信号本质上仍易受生理伪影（如眨眼、吞咽）干扰，这构成了持续性的挑战。尽管基于Transformer的EEG与ECoG信号分类方法已被广泛采用，但其往往难以捕捉信号内部细粒度的依赖关系。为克服这些局限，我们提出Cortical-SSM——一种将深度状态空间模型扩展至EEG与ECoG信号处理的新型架构，旨在捕捉信号跨时间、空间与频域的综合依赖关系。我们在三个基准数据集上验证了所提方法：1）两个包含超过50名受试者的大规模公开MI-EEG数据集；2）从一名肌萎缩侧索硬化症患者处采集的临床MI-ECoG数据集。实验表明，我们的方法在三个基准测试中均优于基线方法。此外，从模型获得的视觉解释表明，该模型能有效捕捉EEG与ECoG信号中与神经生理学相关的关键区域。