Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders

• URL: http://arxiv.org/abs/2501.16626v1
• Date: Mon, 13 Jan 2025 17:29:31 GMT
• Title: Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders
• Authors: Aditya Mishra, Ahnaf Mozib Samin, Ali Etemad, Javad Hashemi,
• Abstract summary: GC-VASE is a graph convolutional-based variational autoencoder that leverages contrastive learning for subject representation learning from EEG data. Our method successfully learns robust subject-specific latent representations using the split-latent space architecture tailored for subject identification.
• Score: 20.364067310176054
• License:
• Abstract: We propose GC-VASE, a graph convolutional-based variational autoencoder that leverages contrastive learning for subject representation learning from EEG data. Our method successfully learns robust subject-specific latent representations using the split-latent space architecture tailored for subject identification. To enhance the model's adaptability to unseen subjects without extensive retraining, we introduce an attention-based adapter network for fine-tuning, which reduces the computational cost of adapting the model to new subjects. Our method significantly outperforms other deep learning approaches, achieving state-of-the-art results with a subject balanced accuracy of 89.81% on the ERP-Core dataset and 70.85% on the SleepEDFx-20 dataset. After subject adaptive fine-tuning using adapters and attention layers, GC-VASE further improves the subject balanced accuracy to 90.31% on ERP-Core. Additionally, we perform a detailed ablation study to highlight the impact of the key components of our method.

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