SSL-SE-EEG: A Framework for Robust Learning from Unlabeled EEG Data with Self-Supervised Learning and Squeeze-Excitation Networks
This work addresses the problem of robust EEG processing for real-time brain-computer interfaces and biomedical applications, representing an incremental improvement by combining existing techniques in a novel way.
The paper tackles the challenges of noise artifacts, missing data, and high annotation costs in EEG data for brain-computer interfaces and diagnostics by introducing SSL-SE-EEG, a framework that integrates self-supervised learning with squeeze-and-excitation networks, achieving state-of-the-art accuracies of 91% on MindBigData and 85% on TUH-AB datasets.
Electroencephalography (EEG) plays a crucial role in brain-computer interfaces (BCIs) and neurological diagnostics, but its real-world deployment faces challenges due to noise artifacts, missing data, and high annotation costs. We introduce SSL-SE-EEG, a framework that integrates Self-Supervised Learning (SSL) with Squeeze-and-Excitation Networks (SE-Nets) to enhance feature extraction, improve noise robustness, and reduce reliance on labeled data. Unlike conventional EEG processing techniques, SSL-SE-EEG} transforms EEG signals into structured 2D image representations, suitable for deep learning. Experimental validation on MindBigData, TUH-AB, SEED-IV and BCI-IV datasets demonstrates state-of-the-art accuracy (91% in MindBigData, 85% in TUH-AB), making it well-suited for real-time BCI applications. By enabling low-power, scalable EEG processing, SSL-SE-EEG presents a promising solution for biomedical signal analysis, neural engineering, and next-generation BCIs.