Performance Evaluation of EEG Based Mental Stress Assessment Approaches for Wearable Devices - Al-Saggaf, Ubaid M.; Naqvi, Syed Faraz; Moinuddin, Muhammad; Alfakeh, Sulhi Ali; Ali, Syed Saad Azhar (Frontiers in Neurorobotics, 2022)
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 for rehabilitation, wearable or portable devices stress assessment have not been developed yet. Moreover, an efficient and accurate algorithm is required to develop a wearable EEG based mental stress rehabilitation device. This study investigates the performance of computer-aided EEG-based approaches for mental stress assessment for Rehabilitation. Machine learning 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 machine learning approaches are time-consuming due to the computations required for feature extraction, whereas a deep learning approach results in a time-efficient classification due to automated unsupervised feature extraction. This study emphasizes that deep learning approaches can be used in wearable devices for real-time EEG-based mental stress assessment for rehabilitation.