Mental stress detection and performance enhancement using fNIRS and wrist vibrator biofeedback

Abstract

Daily life activities frequently expose individuals to varying levels of mental stress, which can adversely affect their performance. Therefore, it is crucial to develop effective strategies for stress management and performance improvement. This paper presents a comprehensive, portable, and real-time biofeedback system aimed at improving individuals’ stress management capabilities, ultimately leading to enhanced mental task performance. The system consists of a real-time brain signal acquisition device, a wireless vibration biofeedback unit, and a software-based program for stress level classification. Notably, the system is designed to minimize the time delay by efficiently integrating all components. Various signal processing and feature extraction techniques combined with machine learning have been employed for online stress detection. The experimental results demonstrate an accuracy of 83% and a recall of 92% in detecting true levels of mental stress in the stress classification module. In addition, the complete biofeedback system is tested on 20 participants in a controlled experimental setup, revealing a 55% reduction in stress levels and a 24.5% improvement in task accuracy. These findings support the effectiveness of the proposed system in stress management and performance improvement, validating the core premises of stress reduction and performance improvement through reward-based learning.