Performance Evaluation of EEG Based Mental Stress Assessment Approaches for Wearable Devices

Mental stress has been identified as the root cause of various physical and psychological disorders. Therefore, it is crucial to conduct timely diagnosis and assessment considering the severe effects of mental stress. In contrast to other health-related wearable devices, wearable or portable devices for stress assessment have not been developed yet. A major requirement for the development of such a device is a time-efficient algorithm. This study investigates the performance of computer-aided approaches for mental stress assessment. Machine learning (ML) approaches are compared in terms of the time required for feature extraction and classification. After conducting tests on data for real-time experiments, it was observed that conventional ML approaches are time-consuming due to the computations required for feature extraction, whereas a deep learning (DL) approach results in a time-efficient classification due to automated unsupervised feature extraction. This study emphasizes that DL approaches can be used in wearable devices for real-time mental stress assessment.

Automated summary

Mental stress has severe effects on physical and psychological health, making timely diagnosis and assessment crucial. Currently, there is no wearable or portable device developed specifically for stress assessment, which requires a time-efficient algorithm. This study compared machine learning and deep learning approaches in terms of time required for feature extraction and classification, finding that deep learning provides automated unsupervised feature extraction and efficient classification, making it suitable for real-time mental stress assessment in wearable devices.