Offline LabVIEW-based EEG Signals Analysis for Human Stress Monitoring

• Main Authors: Norizam, Sulaiman, Beh, See Ying, Mahfuzah, Mustafa, Mohd Shawal, Jadin
• Format: Conference or Workshop Item
• Language: English
• Published: IEEE 2019
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/28264/7/Offline%20LabVIEW-based%20EEG%20Signals%20Analysis.pdf; http://umpir.ump.edu.my/id/eprint/28264/; https://doi.org/10.1109/ICSGRC.2018.8657606
• Institution: Universiti Malaysia Pahang

Stress is often known as a state of mental or emotional tension resulting from adverse circumstances. Consequently, people nowadays are facing stress where different people will have a different level of stress. Hence, EEG technology is invented to assist people to determine the level of stress by using brain signals. This paper describes the development of a LabVIEW-based system that can determine the level of stress based on the analysis of brain signals in LabVIEW. In this study, 1-channel EEG amplifier is employed to record EEG signals from five subjects at three different cognitive states which are closed eyes (do nothing), playing game and doing IQ test. The eegID application in mobile phone is used to capture recorded EEG signals from EEG amplifier and then the captured EEG signals are analysed in LabVIEW. The result shows that the average centroid which was applied on the EEG Power Spectrum of Alpha band is higher than Beta band when the subject is at relax cognitive state meanwhile the average centroid of EEG Power Spectrum of Beta band is higher than Alpha band when the subject is at stress cognitive state. Thus, it can be concluded that the subject are in the stress cognitive state when playing game and doing IQ test. At the end of this project, the LabVIEW Graphical User Interface (GUI) is created to display the level of stress for each subject after undergoing several mental exercises. Beside LabVIEW GUI, a device is constructed to display the level of stress in offline manner.