Key result
EEG-based machine learning models show high potential for objectively classifying acute and chronic mental stress.
Why the study?
EEG is a widely chosen non-invasive method to record real-time brain signals for detecting mental stress, but methods, protocols, and classification models across studies required review.
This review summarizes current EEG-based methodologies, protocols, and classification models for detecting acute and chronic mental stress, highlighting the need for standardized protocols.
Answers are grounded in this paper and cite their sources.
May enable objective stress assessment in practice; leaves open protocol standardization and prospective validation.
Over the past several decades, numerous methods have been applied by research and professionals to detect and measure mental stress, varying from subjective methods such questionnaires and face-to-face interviews up to the objective methods using physiological signals and neuroimaging equipment such as salivary cortisol and functional magnetic resonance imaging (fMRI), respectively. Among those methods, an Electroencephalograph (EEG) is one of the utmost chosen non-invasive methods by professionals and researchers in recording real time brain signals. This paper highlights the state of art for each of the studies, by comparing and analyzing the method and protocol of EEG data collection, including the selection of electrodes and brain regions involving two major categories of mental stress: acute and chronic. Selection of EEG features, with the necessary signal pre-processing and processing techniques, and the classification models used in these studies have been summarized and discussed.
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Bahrain et al. (2024) conducted a review in Mental Stress. Electroencephalograph (EEG) was evaluated on Classification accuracy of mental stress. EEG-based classification models utilizing various feature extraction techniques and machine learning algorithms demonstrate high potential for objectively recognizing and classifying acute and chronic mental stress.
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