Cortical-SSM: A Deep State Space Model for EEG and ECoG Motor Imagery Decoding
This work addresses motor imagery decoding for communication and rehabilitation in patients with motor impairments, representing an incremental improvement over existing methods.
The authors tackled the problem of classifying EEG and ECoG signals for motor imagery, which is challenging due to artifacts and fine-grained dependencies, by proposing Cortical-SSM, a deep state space model that outperformed baseline methods on three benchmarks including datasets with over 50 subjects and a clinical patient.
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.