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

Abstract

Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results.

Complex Pearson Correlation Coefficient for EEG Connectivity Analysis

“…By averaging over the trial set, the proposed measures could also be used as a solution to improve the prediction results of the phases of the synchronization and desynchronization tasks [34]. The potential application of CPCC also lies in the assessment of mental stress levels using functional connectivity as a parameter [35,36] and in the diagnosis dyslexia [37]. In addition, the proposed measures could be used as parameters for the evaluation of simulated EEG data based on the theory of functional connectivity of the brain [38].…”

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

“…In our study, we utilize the most important features from multiple domains, seeking better informative features for stress detection. As a result, a fusion of multi-domain features showed a promising result in different fields as there could be multi-way interactions among features [14,60]. The drawback of multi-domain features is that they are vulnerable to redundant and unrelated features.…”

“…Current research studies employed EEG to acquire brain activities because it is reliable, affordable, portable, and provides high temporal resolution of the brain signals [7,8]. 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].…”

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

“…Our study utilizes the most important features from multiple domains, seeking better informative features for stress detection. As a result, a fusion of multi-domain features showed a promising result in different fields as there could be multi-way interactions among features [14,67]. The drawback of multi-domain features is that they are vulnerable to redundant and unrelated features.…”

“…Current research studies employed EEG to acquire brain activities because it is reliable, affordable, portable, and provides high temporal resolution of the brain signals' activities [8]. In multichannel EEG, several features extracted from the time domain, frequency domain, time-frequency domain, spatial domain, etc., contribute to form a 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]. Thus, many feature extraction methods (in the time, frequency, and time-frequency domains) have been employed to extract meaningful information from an EEG signal associated with a mental task.…”