Enhancing Safety in Industry 4.0: The Use of Passive Brain Computer Interfaces for Vigilance Monitoring

In the context of Industry 4.0 and human-robot interaction, ensuring the safety of operators by avoiding col- lisions and human errors is crucial. One important aspect of this is monitoring vigilance decrement in order to limit safety risks and improve productivity. While existing solutions for this problem rely on passive brain computer interface (BCI) based on electroencephalography (EEG) recordings, there has been limited exploration of their use in industrial environments. The presented study proposes a new experimental protocol for predicting vigilance degradation using EEG data. The dataset was acquired from five healthy volunteers observing a robotic arm performing three different movements for 23 minutes. The EEG power spectrum over time was computed using the continuous wavelet transform (CWT) and its increment was examined using a linear regression model. The results of the study show that there is an increasing α power band over the parietal brain area, while the vigilance of the operator is decreasing. The first order polynomial regressed to the time variant power spectrum had positive angular coefficients (values between 0 and 0.4) for all subjects. The findings suggest that monitoring vigilance degradation in Industry 4.0 is crucial to prevent accidents and reduced performance in repetitive human tasks.