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MAEEG: Masked Auto-encoder for EEG Representation Learning

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arxiv 2211.02625 v1 pith:4EVMLTBV submitted 2022-10-27 eess.SP cs.LG

Hsiang-Yun Sherry Chien , Hanlin Goh , Christopher M. Sandino , Joseph Y. Cheng This is my paper · ORCID
classification eess.SPcs.LG
keywords learningmaskedmaeegreconstruction-basedauto-encoderclassificationfoundlabels
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Decoding information from bio-signals such as EEG, using machine learning has been a challenge due to the small data-sets and difficulty to obtain labels. We propose a reconstruction-based self-supervised learning model, the masked auto-encoder for EEG (MAEEG), for learning EEG representations by learning to reconstruct the masked EEG features using a transformer architecture. We found that MAEEG can learn representations that significantly improve sleep stage classification (~5% accuracy increase) when only a small number of labels are given. We also found that input sample lengths and different ways of masking during reconstruction-based SSL pretraining have a huge effect on downstream model performance. Specifically, learning to reconstruct a larger proportion and more concentrated masked signal results in better performance on sleep classification. Our findings provide insight into how reconstruction-based SSL could help representation learning for EEG.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Masked Autoencoders for Ultrasound Signals: Robust Representation Learning for Downstream Applications

    cs.LG 2025-08 conditional novelty 6.0 of 10

    MAE pre-training on synthetic ultrasound signals transfers to real measured signals and beats from-scratch and CNN baselines on time-of-flight classification, with the biggest gains in low-label regimes.

  2. DIVER-0 : A Fully Channel Equivariant EEG Foundation Model

    eess.SP 2025-06 conditional novelty 6.0 of 10

    A channel-permutation-equivariant EEG transformer with full spatio-temporal attention achieves competitive BCI performance with only 10% of pretraining data.

  3. LSM-2: Learning from Incomplete Wearable Sensor Data

    cs.LG 2025-06 conditional novelty 6.0 of 10

    LSM-2 with Adaptive and Inherited Masking learns usable representations directly from incomplete day-long wearable data, outperforming imputation-based baselines on most tasks.

  4. MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification

    eess.SP 2025-02 conditional novelty 6.0 of 10

    MC2SleepNet fuses raw EEG and spectrogram views with contrastive learning and cross-masking to reach state-of-the-art sleep staging accuracy on two public datasets.

  5. Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS-ANS Dynamics

    cs.LG 2026-07 conditional novelty 5.5 of 10

    Topology-aware hierarchical contrastive pretraining on CNS/ANS PSG partitions yields stronger out-of-domain sleep staging and multi-disease AUROC than SleepFM and SleepGPT under modality ablation.

  6. Transformer-based EEG Decoding: A Survey

    cs.LG 2025-07 conditional novelty 5.0 of 10

    A survey that classifies Transformer-based EEG decoding models into backbone, hybrid, and customized categories and reviews their applications and limitations.

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