Passive Brain–Computer Interface Using Textile-Based Electroencephalography

TL;DR

This study shows that textile-based EEG can be used for passive brain-computer interfaces, accurately detecting changes in brain activity between eyes-open and eyes-closed states.

Contribution

The first application of fully textile-based EEG for passive brain-computer interface (pBCI) systems.

Findings

Textile EEG captured the expected increase in alpha power when moving from eyes-open to eyes-closed (p < 0.01).

Standalone classification accuracies were 96% for dry EEG and 92% for textile EEG.

Cross-sensor generalizability achieved 91% classification accuracy.

Abstract

Background: Passive brain–computer interface (pBCI) systems use a combination of electroencephalography (EEG) and machine learning (ML) to evaluate a user’s cognitive and physiological state, with increasing applications in both clinical and non-clinical scenarios. pBCI systems have been limited by their traditional reliance on sensor technologies that cannot easily be integrated into non-laboratory settings where pBCIs are most needed. Advances in textile-electrode-based EEG show promise in overcoming the operational limitations; however, no study has demonstrated their use in pBCIs. This study presents the first application of fully textile-based EEG for pBCIs in differentiating cognitive states. Methods: Cognitive state comparisons between eyes-open (EO) and eyes-closed (EC) conditions were conducted using publicly available data for both novel textile and traditional dry-electrode EEG. EO vs. EC differences across both EEG sensor technologies were assessed in delta, theta, alpha, and beta EEG power bands, followed by the application of a Support Vector Machine (SVM) classifier. The SVM was applied to each EEG system separately and in a combined setting, where the classifier was trained on dry EEG data and tested on textile EEG data. Results: The textile EEG system accurately captured the characteristic increase in alpha power from EO to EC (p < 0.01), but power values were lower than those of dry EEG across all frequency bands. Classification accuracies for the standalone dry and textile systems were 96% and 92%, respectively. The cross-sensor generalizability assessment resulted in a 91% classification accuracy. Conclusions: This study presents the first use of textile-based EEG for pBCI applications. Our results indicate that textile-based EEG can reliably capture changes in EEG power bands between EO and EC, and that a pBCI system utilizing non-traditional textile electrodes is both accurate and generalizable.

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