Authors - Jutika Borah, Debarun Chakraborty, Bhabesh Deka, Rosy Sarmah, Siddeswara Bargur Linganna, Diptadhi Mukherjee, Ram Bilas Pachori, Mohit Khamele Abstract - Electroencephalogram (EEG) signal modeling for downstream tasks, such as classifying neurological states and identifying biomarkers, is essential for designing effective brain-computer interfaces. Conventional methods often treat EEG channels independently, overlooking inter-channel dependencies, while existing graph-based approaches address this limitation either through fixed electrode geometry or entirely data-driven connectivity. In this paper, we propose a graph representation framework that combines coherence-based spectral connectivity with domain-informed priors, such as anatomical structure and regional proximity, based on graph signal processing (GSP). The resulting representation embeds multichannel EEG signals as attributed graphs through graph convolutional networks (GCNN) to learn discriminative embeddings. Experimental results demonstrate that the hybrid framework enhances classification performance, with the proposed GCNN-deep model achieving the highest area under the receiver operating characteristic curve (AUC) across all datasets and reaching 93% on Dataset 1. These EEG datasets correspond to three independent populations and include recordings from both healthy individuals and patients with neurological disorders such as major depressive disorder (MDD) and epilepsy.
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