Investigation of Electroencephalographic (EEG) Brainwave Signal on Mental Stress through Psychomotor Activities

Abstract

Mental well-being plays a crucial aspect in human life alongside physical health. A positive mental health allows individuals to engage in healthy relationship, navigate challenges in a positive manner and enjoys a sense of fulfilment upon achieving goals. Conversely, when mental health is not well taken care of, it can impair one’s ability to function well in daily life, which subsequently leads to depression and anxiety disorder. Hence, early detection and intervention is necessary for individuals experiencing mental health challenges. The aim of this work is to study the recorded Electroencephalogram (EEG) signals of human subjects for stress detection while engaging in psychomotor activity. EEG signals captured from electrodes were sent to OpenBCI on a laptop via a ganglion board for analysis. The data was then exported to Matlab for further analysis using Fast Fourier Transform (FFT) and was monitored in real time using Python. Volunteers between the ages of 18 and 50 were carefully chosen, and the experimental conditions were closely monitored to reduce any external factors that could affect stress levels and EEG readings. The findings reveal notable changes in brainwave frequencies observed during periods of stress: a decrease of 18.95% in alpha waves, a 9.89% increase in beta waves, and a 5% rise in delta waves. Comparison was also carried out by placing the electrodes at different location on the subject’s head Significantly, there was a 7.5% increase in alpha readings at the top electrode positions, with beta (4.76%) and theta (1.93%) readings also showing growth at the side positions on the head. An accuracy of 65% was obtained using the developed monitoring system. This research effectively illuminates the relationship between mental stress, physical activity, and EEG brainwave patterns. It highlights the important role that brainwave frequencies play in influencing stress and cognitive responses during physical activity. In addition, the advancement of real-time monitoring systems offers dynamic tools for observing stress, improving opportunities for personalized therapies and strategies for well-being.