Machine learning approach for stress detection based on alpha-beta and theta-beta ratios of EEG signals

• Main Authors: Altaf, Hunain, Ibrahim, Siti Noorjannah, Mohd Azmin, Nor Fadhillah, Asnawi, Ani Liza, Walid, Balqis Hanisah, Harun, Noor Hasmiza
• Format: Conference or Workshop Item
• Language: English; English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Subjects: T10.5 Communication of technical information; TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices; TK7885 Computer engineering
• Online Access: http://irep.iium.edu.my/98772/7/98772_Machine%20learning%20approach%20for%20stress%20detection.pdf; http://irep.iium.edu.my/98772/8/98772_Machine%20learning%20approach%20for%20stress%20detection.pdf; http://irep.iium.edu.my/98772/; https://ieeexplore.ieee.org/document/9608810

The contribution to stress detection and classification is far beyond demand as the statistics show that the health and mental illness of society have kept on deteriorating. Electroencephalogram (EEG) signals have the potential to detect stress levels reliably due to their high accuracy. Majority of...