mlsanityData to 7 Oct 2026

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

arXiv 2510.19829 ↗First posted 7 Oct 2025eess.SP cs.AI cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 47 references that are in the map. First posted on arXiv 7 Oct 2025.

Builds on

14 of 47 references.

References · 14

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  3. 2017Squeeze-and-Excitation Networkscited by 4,763
  4. 2020A Survey on Contrastive Self-supervised Learningcited by 553
  5. 2023A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trendscited by 205
  6. 2020Uncovering the structure of clinical EEG signals with self-supervised learningcited by 109
  7. 2022Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Languagecited by 108
  8. 2022Speech Emotion Recognition using Self-Supervised Featurescited by 52
  9. 2022Dissecting Self-Supervised Learning Methods for Surgical Computer Visioncited by 48
  10. 2022Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlookcited by 24
  11. 2023Systematic comparison of semi-supervised and self-supervised learning for medical image classificationcited by 8
  12. 2022SB-SSL: Slice-Based Self-Supervised Transformers for Knee Abnormality Classification from MRIcited by 6
2 more
  1. 2016A 0.3-2.6 TOPS/W Precision-Scalable Processor for Real-Time Large-Scale ConvNetscited by 5
  2. 2024Invited: Human-Inspired Distributed Wearable AIcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026BRIDGE-EEG: Bridging Self-Supervised Pretraining and Efficient Deployment for Cross-Dataset EEG Classificationcited by 0