Frontiers in Human Neuroscience (Jul 2022)

Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces

  • Nicolina Sciaraffa,
  • Gianluca Di Flumeri,
  • Gianluca Di Flumeri,
  • Daniele Germano,
  • Andrea Giorgi,
  • Andrea Giorgi,
  • Antonio Di Florio,
  • Gianluca Borghini,
  • Gianluca Borghini,
  • Alessia Vozzi,
  • Alessia Vozzi,
  • Vincenzo Ronca,
  • Vincenzo Ronca,
  • Fabio Babiloni,
  • Fabio Babiloni,
  • Fabio Babiloni,
  • Pietro Aricò,
  • Pietro Aricò,
  • Pietro Aricò

DOI
https://doi.org/10.3389/fnhum.2022.901387
Journal volume & issue
Vol. 16

Abstract

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Technologies like passive brain-computer interfaces (BCI) can enhance human-machine interaction. Anyhow, there are still shortcomings in terms of easiness of use, reliability, and generalizability that prevent passive-BCI from entering real-life situations. The current work aimed to technologically and methodologically design a new gel-free passive-BCI system for out-of-the-lab employment. The choice of the water-based electrodes and the design of a new lightweight headset met the need for easy-to-wear, comfortable, and highly acceptable technology. The proposed system showed high reliability in both laboratory and realistic settings, performing not significantly different from the gold standard based on gel electrodes. In both cases, the proposed system allowed effective discrimination (AUC > 0.9) between low and high levels of workload, vigilance, and stress even for high temporal resolution (<10 s). Finally, the generalizability of the proposed system has been tested through a cross-task calibration. The system calibrated with the data recorded during the laboratory tasks was able to discriminate the targeted human factors during the realistic task reaching AUC values higher than 0.8 at 40 s of temporal resolution in case of vigilance and workload, and 20 s of temporal resolution for the stress monitoring. These results pave the way for ecologic use of the system, where calibration data of the realistic task are difficult to obtain.

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