SSL-SE-EEG: A Framework for Robust Learning from Unlabeled EEG Data with Self-Supervised Learning and Squeeze-Excitation Networks

TL;DR

SSL-SE-EEG is a novel framework combining self-supervised learning and squeeze-excitation networks to improve EEG signal analysis, robustness, and reduce labeling needs for brain-computer interface applications.

Contribution

It introduces a new deep learning framework that transforms EEG signals into 2D images and integrates SSL with SE-Nets for enhanced feature extraction and noise robustness.

Findings

Achieved state-of-the-art accuracy on multiple EEG datasets

Demonstrated robustness to noise and artifacts in EEG signals

Reduced reliance on labeled data for EEG analysis

Abstract

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.