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

Abstract

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.

A Novel EEG-Based Assessment of Distraction in Simulated Driving under Different Road and Traffic Conditions

“…Results demonstrated the reliability of the proposed EEG-based distraction index in identifying the drivers’ distraction while driving along different conditions, representing a synthetic, objective, and more ecological solution with respect to the largely validated subjective and behavioral measurements. More importantly, the technical implementation of the proposed index allows for the driving distraction online evaluation while driving, even in real context since the proposed index was derived from EEG signal collected through a wearable EEG system, which was demonstrated to be reliable and compatible with out-of-the-lab applications [ 35 ]. In summary, this study, alongside its findings, lays the groundwork for integrating neurophysiological evaluation of driving distraction into real-world settings to enhance road traffic safety, as well as a powerful tool for further investigation of the driver’s psychology and behavior.…”

Reproducible machine learning research in mental workload classification using EEG

“…Among the relevant thirteen studies, reproducibility was demonstrated for different electrode configurations and preprocessing pipelines (Mastropietro et al, 2023 ), for different settings of 2D and 3D environments (Kakkos et al, 2019 ), for a larger number of participants (Radüntz et al, 2020 ), for different tasks (Parekh et al, 2018 ; Boring et al, 2020 ; Sciaraffa et al, 2022 ), and over time (Gevins et al, 1998 ; Putze et al, 2013 ; Aricò et al, 2015 , 2016b ; Ortiz et al, 2020 ; Fox et al, 2022 ; Roy et al, 2022 ). Gevins et al ( 1998 ) also tested their findings on separate tasks to check cross-task performance and finally on data from a new participant to observe cross-subject performance.…”

EEG-based finger movement classification with intrinsic time-scale decomposition

“…EEG-based BCI systems that manipulated motor imagery signals generated through the movements of large body parts such as hands, feet, and tongue have been proposed to control assistive devices throughout the past several decades (Pfurtscheller and Neuper, 2001 ; Alazrai et al, 2019 ; Degirmenci et al, 2023 ). However, such systems propose only limited control dimensions for prosthetic devices, thereby, the potential of utilizing these systems to control further complex assistive devices is restricted (Sciaraffa et al, 2022 ). In the last decade, numerous research studies have examined the decoding of movements of fine body parts to improve such systems (Alazrai et al, 2019 ).…”