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Background and Objective: Exposure to mental stress in everyday life leads to several diseases that deteriorate the quality of people’s lives, and hinders the development of society. The development of mobile technology and wearable devices has made it possible to monitor the mental stress in real time. This study aims to develop a single-channel EEG-based framework to identify the multiple types of mental stress. Methods: Four different tasks (i.e., arithmetic operations, successive subtraction, Stroop color-word test, and number memory) combining time pressure with negative feedback were designed to induce mental stress. The EEG data of 21 participants was recorded and a total of 338 multi-domain features, i.e., time domain, frequency domain, nonlinear and time–frequency domain, were extracted from five sub-frequency bands and full frequency band. After a one-way analysis of variance, recursive feature elimination and support vector machine were combined to classify the stress level of the participants. Results: For all four tasks, 67, 72, 20, and 74 features were statistically different, and they covered all the feature domains. Similarly, the highest classification accuracy of 97.78%, 97.50%, 95.00%, and 100% were achieved while combining features from all domains. Furthermore, the delta, theta, alpha, and full frequency band were more effective in quantifying the stress levels. Conclusion: Our proposed framework validates the effectiveness of a single-channel EEG in detecting mental stress and offers great promise for its application in clinical and portable devices in everyday life.
Jiao et al. (Thu,) studied this question.