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Cortical Source Analysis of High-Density EEG Recordings in Children
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Contrastive fine-grained domain adaptation network for EEG-based vigilance estimation.

Kangning Wang1, Wei Wei2, Weibo Yi3

  • 1Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China; Laboratory of Brain Atlas and Brain-Inspired Intelligence, Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

Neural Networks : the Official Journal of the International Neural Network Society
|August 24, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for estimating vigilance in brain-computer interface (BCI) users with minimal data. The contrastive fine-grained domain adaptation network (CFGDAN) reduces calibration needs for practical BCI applications.

Keywords:
Brain-computer interface (BCI)Domain adaptationElectroencephalogram (EEG)Vigilance estimation

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Vigilance state is critical for effective brain-computer interface (BCI) performance.
  • Current vigilance estimation methods require extensive labeled data, hindering practical use.
  • Reducing calibration data is essential for widespread BCI adoption.

Purpose of the Study:

  • To develop a reliable vigilance estimation method using minimal unlabeled calibration data.
  • To address the limitations of data-intensive training in BCI vigilance monitoring.
  • To enhance the practical applicability of BCI systems through efficient vigilance estimation.

Main Methods:

  • A vigilance experiment using a BCI-based cursor-control task with 18 participants.
  • Recording electroencephalogram (EEG) signals across two sessions and two days.
  • Proposing a contrastive fine-grained domain adaptation network (CFGDAN) incorporating an adaptive graph convolution network (GCN) for feature alignment and information preservation.

Main Results:

  • The proposed CFGDAN demonstrated superior performance compared to existing methods on BCI and SEED-VIG datasets.
  • Visualization confirmed the effectiveness of the fine-grained feature alignment mechanisms.
  • The method successfully estimated vigilance using limited unlabeled calibration data.

Conclusions:

  • The CFGDAN offers an effective solution for vigilance estimation in BCI systems.
  • The study significantly reduces calibration requirements, promoting practical BCI applications.
  • This approach enhances the usability and accessibility of BCI technology.