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Assessment and Communication for People with Disorders of Consciousness
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Passive Brain-Computer Interface Using Textile-Based Electroencephalography.

Alec Anzalone1, Emily Acampora1, Careesa Liu1

  • 1Department of Biomedical Engineering and Science, Florida Institute of Technology, Melbourne, FL 32901, USA.

Sensors (Basel, Switzerland)
|October 16, 2025
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Summary
This summary is machine-generated.

This study introduces textile-based electroencephalography (EEG) for passive brain-computer interfaces (pBCI). Textile EEG reliably detects cognitive state changes and achieves high accuracy, demonstrating its potential for real-world applications.

Keywords:
cognitive state classificationelectroencephalography (EEG)passive brain-computer interface (pBCI)support vector machine (SVM)textile electrode

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Signal Processing

Background:

  • Passive brain-computer interface (pBCI) systems leverage electroencephalography (EEG) and machine learning (ML) for cognitive state assessment.
  • Traditional EEG sensors hinder pBCI integration into non-laboratory settings.
  • Textile-electrode-based EEG offers a promising solution to overcome operational limitations.

Purpose of the Study:

  • To present the first application of fully textile-based EEG for pBCIs.
  • To differentiate cognitive states using textile EEG.
  • To assess the generalizability of textile EEG in pBCI systems.

Main Methods:

  • Compared eyes-open (EO) and eyes-closed (EC) conditions using textile and dry-electrode EEG data.
  • Analyzed EEG power bands (delta, theta, alpha, beta).
  • Applied Support Vector Machine (SVM) classification, including cross-sensor training and testing.

Main Results:

  • Textile EEG captured the characteristic alpha power increase from EO to EC (p < 0.01).
  • Standalone textile and dry EEG systems achieved 92% and 96% classification accuracy, respectively.
  • Cross-sensor generalizability yielded 91% classification accuracy.

Conclusions:

  • Textile-based EEG is suitable for pBCI applications.
  • Textile EEG reliably detects changes in EEG power bands between cognitive states.
  • pBCI systems using textile electrodes are accurate and generalizable.