Graph Neural Network-Based Regional Analysis of EEG During Imagined Swallowing: Identifying Cortical Contributions

Abstract

Imagined swallowing (MI-SW) is a promising paradigm for brain-computer interface (BCI)-assisted dysphagia rehabilitation; however, reliable EEG-based decoding remains challenging because of the low signal-to-noise ratio and high inter-subject variability. In addition, prior EEG-based MI-SW studies have not explicitly modeled interregional brain interactions and have rarely employed subject-independent validation frameworks, limiting their generalizability. To address these limitations, this study proposes a regional graph-based EEG decoding framework that integrates (i) region-wise graph modeling of EEG channels, (ii) leave-one-subject-out (LOSO) subject-independent validation, and (iii) occlusion-based interpretability analysis. EEG data were collected from 30 healthy participants under two paradigms: volitional imagined swallowing and sensory-induced imagined swallowing, which was achieved by holding water in the mouth before imagery. Spectral, event-related desynchronization, entropy-based, and nonlinear features were extracted and classified using Graph Convolutional Networks (GCN). The classification task was formulated as a binary problem distinguishing Rest versus Imagined Swallowing conditions. All experiments were evaluated using leave-one-subject-out (LOSO) cross-validation to assess subject-independent decoding performance. The proposed framework achieved robust subject-independent performance across regions, with the sensory-induced paradigm consistently outperforming volitional conditions. Peak classification accuracy of >87% was obtained in the premotor region under sensory-induced conditions, whereas the volitional paradigm showed optimal performance using whole-head electrode integration. No direct comparison with conventional CNN or CSP-based baselines was performed in this study; therefore, performance gains are reported within the proposed framework under a consistent evaluation setting. Occlusion-based interpretability analysis further confirmed physiologically meaningful patterns, with low-frequency (δ -) desynchronization and entropy/complexity features emerging as the dominant discriminative markers of imagined swallowing. These findings demonstrate that integrating spatial graph representations with the peripheral somatosensory context enhances the EEG-based decoding of swallowing motor imagery and supports the development of reliable and interpretable BCI systems for dysphagia rehabilitation.