Comprehensive Review of EEG- Based Algorithms for Mental Stress Analysis

Electroencephalography (EEG) as a tool for analyzing mental stress has recently gained significant attention. The field of neuroscience employs electroencephalography (EEG) to investigate brain activity by measuring electrical signals on the scalp. The effective utilization of electroencephalography (EEG) for evaluating psychological stress has resulted in the emergence of innovative approaches for detecting levels of stress. This paper provides a comprehensive review of algorithms along with techniques used for mental stress analysis using EEG signals. The traditional algorithms discussed include power spectral density (PSD) analysis, Wavelet transform, and independent component analysis (ICA). This paper also explores the examination of more recent machine learning algorithms, including support vector machines, artificial neural networks, and fuzzy logic, for mental stress analysis. This paper examines the constraints of electroencephalogram (EEG) derived algorithms, prospects for further investigation, and a summary of the outcomes and their importance. Early investigations show that stress affects EEG signal power throughout frequency ranges. EEG signals may accurately distinguish stressed from non-stressed individuals using machine learning methods.