Machine learning

Psychology

Stress (linguistics)

Fast Fourier transform

Philosophy

Artificial intelligence

Feature (linguistics)

Naive Bayes classifier

Pattern recognition (psychology)

Neuroscience

Mental stress

Frontal lobe

Computer science

Algorithm

Support vector machine

Linguistics

Feature extraction

Internal medicine

Brain–computer interface

Electroencephalography

Medicine

Journal of Integrative Neuroscience

Stress has become a dangerous health problem in our life, especially in student education journey. Accordingly, previous methods have been conducted to detect mental stress based on biological and biochemical effects. Moreover, hormones, physiological effects, and skin temperature have been extensively used for stress detection. However, based on the recent literature, biological, biochemical, and physiological-based methods have shown inconsistent findings, which are initiated due to hormones' instability. Therefore, it is crucial to study stress using different mechanisms such as Electroencephalogram (EEG) signals. In this research study, the frontal lobes EEG spectrum analysis is applied to detect mental stress. Initially, we apply a Fast Fourier Transform (FFT) as a feature extraction stage to measure all bands' power density for the frontal lobe. After that, we used two type of classifications such as subject wise and mix (mental stress vs. control) using Support Vector Machine (SVM) and Naive Bayes (NB) machine learning classifiers. Our obtained results of the average subject wise classification showed that the proposed technique has better accuracy (98.21%). Moreover, this technique has low complexity, high accuracy, simple and easy to use, no over fitting, and it could be used as a real-time and continuous monitoring technique for medical applications.

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection