A Review on Mental Stress Assessment Methods Using EEG Signals

Abstract

Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contains rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Accordingly, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.

EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features

“…For the aim of finding the relative EEG markers that explain mental stress and increase its detection rate, several studies employed different types of features from the time domain, frequency domain, and time-frequency domain [ 8 , 32 , 33 , 34 , 35 , 36 ], and several machine learning algorithms have been used to predict the mental stress state, such as SVM [ 37 ], K-Nearest Neighbors(KNN) [ 29 , 38 ], LR [ 1 ], Feed-Forward Neural Network (FF-NN) [ 30 ], Naive Bayes(NB) [ 9 , 38 ], and Random Forest(RF) [ 39 ]. In the literature, non-invasive EEG-based stress studies suggested that bio-markers (i.e., alpha, beta, and gamma) in specific brain areas could reveal the mental stress state [ 18 , 40 , 41 ].…”

“…In the literature, non-invasive EEG-based stress studies suggested that bio-markers (i.e., alpha, beta, and gamma) in specific brain areas could reveal the mental stress state [ 18 , 40 , 41 ]. However, no consensus has been reached about the particular established EEG patterns/features that differentiate stress levels, see review [ 36 ]. In studies [ 8 , 29 , 42 ], different frequency band features have been demonstrated to classify stress tasks.…”

“…The low beta was considered as one approach to recognize mental stress [ 9 ]. Similarly, alpha rhythm power at the right PFC was shown to be more discriminative to stress and rest states [ 28 , 36 ]. Another study in [ 11 ] showed that the PFC relative gamma power (RG) was more discriminative between stress levels than alpha asymmetry.…”

“…We present a novel methodology to identify mental stress by investigating the statistical difference between stress and rest conditions. The proposed method is analyzed and evaluated using seven classifiers [ 36 ], namely: KNN, RF, Logistic Regression (LR), SVM, classification and regression Decision Tree (CART), Linear Discrimination Analysis (LDA), and NB. The accuracy, precision, recall, and F-score matrices were used to evaluate the performance of the classifiers.…”

Enhancing EEG-Based Mental Stress State Recognition using an Improved Hybrid Feature Selection Algorithm

“…In multi-channel EEG, several features from the time domain, frequency domain, time-frequency domain, spatial domain, etc., contribute to the high dimensional feature space in which one aims to recognize or assess several brain states such as seizure detection (epilepsy) [9], motor imaginary [10], depression [11], emotion detection [12,13], and mental stress recognition [14]. Recently, EEG signals have been used extensively in the field of emotion recognition, particularly in the recognition of distress due to its harmful influence on physical and mental health [15,16]. However, one of the major challenges in building a successful model for stress detection is finding the most appropriate features.…”