Abstract

Mental state changes induced by stimuli under experimental settings or by daily events in real life affect task performance and are entwined with physical and mental health. In this study, we developed a physiological state indicator with five parameters that reflect the subject’s real-time physiological states based on online EEG signal processing. These five parameters are attention, fatigue, stress, and the brain activity shifts of the left and right hemispheres. We designed a target detection experiment modified by a cognitive attention network test for validating the effectiveness of the proposed indicator, as such conditions would better approximate a real chaotic environment. Results demonstrated that attention levels while performing the target detection task were significantly higher than during rest periods, but also exhibited a decay over time. In contrast, the fatigue level increased gradually and plateaued by the third rest period. Similar to attention levels, the stress level decreased as the experiment proceeded. These parameters are therefore shown to be highly correlated to different stages of the experiment, suggesting their usage as primary factors in passive brain-computer interfaces (BCI). In addition, the left and right brain activity indexes reveal the EEG neural modulations of the corresponding hemispheres, which set a feasible reference of activation for an active BCI control system, such as one executing motor imagery tasks. The proposed indicator is applicable to potential passive and active BCI applications for monitoring the subject’s physiological state change in real-time, along with providing a means of evaluating the associated signal quality to enhance the BCI performance.

Highlights

  • Brain-computer interfaces (BCI) are methods that offer direct communication pathways between the human brain and external devices which have attracted much attention in various fields

  • Mental fatigue produced by prolonged sequences of a cognitive task affects the accuracy of BCIs, as it decreases the separability of EEG signals (Talukdar et al, 2019)

  • We developed a physiological state indicator integrated with wireless EEG equipment, enabling the assessment of physiological states for potential passive and active BCI applications

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Summary

Introduction

Brain-computer interfaces (BCI) are methods that offer direct communication pathways between the human brain and external devices which have attracted much attention in various fields. In a recent study by Zhang et al (2020), it has been found that resting EEG modulation induced by stress is one of the key factors to signal-to-noise ratio and BCI performance. To address such impact of mental state changes on active BCIs, passive BCI has been proposed to assess the user’s cognitive state during ongoing BCI tasks, allowing the improvement in the interaction of human-machine system (Zander and Kothe, 2011)

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