EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs

• URL: http://arxiv.org/abs/2402.17772v2
• Date: Tue, 18 Jun 2024 06:31:49 GMT
• Title: EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs
• Authors: Navid Mohammadi Foumani, Geoffrey Mackellar, Soheila Ghane, Saad Irtza, Nam Nguyen, Mahsa Salehi,
• Abstract summary: We introduce textitEEG2Rep, a self-prediction approach for self-supervised representation learning from EEG. Instead of learning to predict the masked input from raw EEG, EEG2Rep learns to predict masked input in latent representation space. EEG2Rep is robust to noise addressing a significant challenge that exists in EEG data.
• Score: 4.028059312496666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised approaches for electroencephalography (EEG) representation learning face three specific challenges inherent to EEG data: (1) The low signal-to-noise ratio which challenges the quality of the representation learned, (2) The wide range of amplitudes from very small to relatively large due to factors such as the inter-subject variability, risks the models to be dominated by higher amplitude ranges, and (3) The absence of explicit segmentation in the continuous-valued sequences which can result in less informative representations. To address these challenges, we introduce \textit{EEG2Rep}, a self-prediction approach for self-supervised representation learning from EEG. Two core novel components of EEG2Rep are as follows: 1) Instead of learning to predict the masked input from raw EEG, EEG2Rep learns to predict masked input in latent representation space, and 2) Instead of conventional masking methods, EEG2Rep uses a new semantic subsequence preserving (SSP) method which provides informative masked inputs to guide EEG2Rep to generate rich semantic representations. In experiments on 6 diverse EEG tasks with subject variability, EEG2Rep significantly outperforms state-of-the-art methods. We show that our semantic subsequence preserving improves the existing masking methods in self-prediction literature and find that preserving 50\% of EEG recordings will result in the most accurate results on all 6 tasks on average. Finally, we show that EEG2Rep is robust to noise addressing a significant challenge that exists in EEG data. Models and code are available at:\url{https://github.com/Navidfoumani/EEG2Rep}

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