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EEGMamba: An EEG foundation model with Mamba.

Jiquan Wang1, Sha Zhao1, Zhiling Luo2

  • 1State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, 311121, China; College of Computer Science and Technology, Zhejiang University, Hangzhou, 310013, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 27, 2025
PubMed
Summary
This summary is machine-generated.

Researchers developed EEGMamba, a novel foundation model using Mamba, to decode electroencephalography (EEG) signals. This approach enhances brain-computer interface (BCI) performance by learning generic EEG representations from extensive data.

Keywords:
EEG foundation modelGeneric EEG representationMambaPre-training

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Electroencephalography (EEG) is crucial for clinical applications and brain-computer interfaces (BCIs).
  • Traditional supervised learning methods for EEG decoding face limitations in performance and generalizability.
  • Foundation models show promise for advancing EEG signal processing.

Purpose of the Study:

  • To explore the potential of state space models (SSMs), specifically Mamba, for EEG representation learning.
  • To introduce EEGMamba, a novel EEG foundation model designed for effective spatiotemporal dependency modeling.
  • To pre-train EEGMamba on a large, diverse EEG corpus for robust representation learning.

Main Methods:

  • Utilized the Mamba encoder as the core architecture for EEGMamba.
  • Employed patch-based masked EEG reconstruction for unsupervised representation learning.
  • Pre-trained the model on 16,724 hours of EEG data from five distinct datasets.

Main Results:

  • EEGMamba demonstrated state-of-the-art performance across six different brain-computer interface (BCI) tasks.
  • The model achieved superior results on six public datasets, validating its effectiveness.
  • The findings highlight EEGMamba's strong capability and generalizability in EEG decoding.

Conclusions:

  • EEGMamba represents a significant advancement in EEG foundation models.
  • The Mamba architecture is well-suited for capturing complex spatiotemporal dynamics in EEG signals.
  • EEGMamba offers a powerful and generalizable tool for various BCI applications.