# MAEEG: Masked Auto-encoder for EEG Representation Learning

Source: [MAEEG: Masked Auto-encoder for EEG Representation Learning](<https://www.sota2.com/research/paper/68fa6ddf1e209131cea3c9db>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:45:11.347Z

Authors: Hsiang-Yun Sherry Chien, Hanlin Goh, Christopher M. Sandino, Joseph Y. Cheng

Published: 2022-10-27T21:43:35.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [EEG Classification on Emotiv Attention](<https://www.sota2.com/research/sota/eeg-classification-on-emotiv-attention.md>): EEG Classification; Emotiv Attention; Accuracy: 82.61.

- [EEG Classification on Emotiv STEW](<https://www.sota2.com/research/sota/eeg-classification-on-emotiv-stew.md>): EEG Classification; Emotiv STEW; Accuracy: 72.46.

- [EEG Classification on Emotiv Crowdsourced](<https://www.sota2.com/research/sota/eeg-classification-on-emotiv-crowdsourced.md>): EEG Classification; Emotiv Crowdsourced; Accuracy: 86.75.

- [EEG Classification on Emotiv DriverDistraction](<https://www.sota2.com/research/sota/eeg-classification-on-emotiv-driverdistraction.md>): EEG Classification; Emotiv DriverDistraction; Accuracy (ACC): 74.58.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2211.02625>)

[PDF](<https://arxiv.org/pdf/2211.02625.pdf>)