One-Channel Wearable Mental Stress State Monitoring System.

Assessments of stress can be performed using physiological signals, such as electroencephalograms (EEGs) and galvanic skin response (GSR). Commercialized systems that are used to detect stress with EEGs require a controlled environment with many channels, which prohibits their daily use. Fortunately, there is a rise in the utilization of wearable devices for stress monitoring, offering more flexibility. In this paper, we developed a wearable monitoring system that integrates both EEGs and GSR. The novelty of our proposed device is that it only requires one channel to acquire both physiological signals. Through sensor fusion, we achieved an improved accuracy, lower cost, and improved ease of use. We tested the proposed system experimentally on twenty human subjects. We estimated the power spectrum of the EEG signals and utilized five machine learning classifiers to differentiate between two levels of mental stress. Furthermore, we investigated the optimum electrode location on the scalp when using only one channel. Our results demonstrate the system's capability to classify two levels of mental stress with a maximum accuracy of 70.3% when using EEGs alone and 84.6% when using fused EEG and GSR data. This paper shows that stress detection is reliable using only one channel on the prefrontal and ventrolateral prefrontal regions of the brain.

Mental Stress and Cognitive Deficits Management.

Mental stress is a prevalent aspect of contemporary life that affects individuals from diverse backgrounds [...].

Detection of Astronaut's Stress Levels During 240-Day Confinement using EEG Signals and Machine Learning.

Early detection of mental stress is particularly important in prolonged space missions. In this study, we propose utilizing electroencephalography (EEG) with multiple machine learning models to detect elevated stress levels during a 240-day confinement. We quantified the levels of stress using alpha amylase levels, reaction time (RT) to stimuli, accuracy of target detection, and functional connectivity of EEG estimated by Phase Locking Value (PLV). Our results show that, alpha amylase level increased every 60-days (with 0.76 correlation) In-mission resulting in four elevated levels of stress. The RT and accuracy of target detection did not show any significant difference with time In-mission. The functional connectivity network showed different patterns between the frontal/occipital with other regions, and parietal to central region. The machine learning classifiers differentiate between four levels of stress with classification accuracy of 91.8%, 91.4%, 90.2%, 87.8, and 81% using linear discriminate analysis (LDA), Support Vector Machine (SVM), k-nearest neighbor (KNN), Naïve bayes (NB) and decision trees (DT). Our results suggest that EEG and machine learning can be used to detect elevated levels of mental stress in isolation and confined environments.

Classification of Mental Stress Levels using EEG Connectivity and Convolutional Neural Networks.

Classifying mental stress is important as it helps in identifying the type and severity of stress, which can inform the most appropriate treatment or intervention. In this study, we propose utilizing electroencephalography (EEG) signals with convolutional neural networks (CNNs) to classify four mental states: rest, control-alert, stress and stress mitigation. The mental stress state was induced using Stroop color word test (SCWT) with time constrains and was then mitigated using 16 Hz Binaural beat stimulation (BBs). We quantified the four mental states using the reaction time (RT) to stimuli, accuracy of target detection, subjective score, and functional connectivity images of EEG estimated by Phase Locking Value (PLV). Our results show that, the SCWT reduced the accuracy of target detection by 70% with (F= 24.56, p = .00001), and the BBs improved the accuracy by 28% (F= 4.54, p = .00470). The functional connectivity network showed different patterns between the frontal/occipital and parietal regions, under the four mental states. The proposed CNNs with PLV images differentiated between the four mental states with highest classification performance at beta frequency band with 80.95% accuracy, 80.36% sensitivity, 94.75% specificity, 83.63% precision and 81.96% F-score. The overall results suggest that 16 Hz BBs can be used as an effective method to mitigate stress and the proposed CNNs with EEG-PLV images as a promising method for classifying different mental states.

Mental Stress Detection and Mitigation using Machine Learning and Binaural Beat Stimulation.

Stress is an inevitable problem experienced by people worldwide. Continuous exposure to stress can greatly impact mental activity as well as physical health thereby leading to several diseases. In this study, we investigate the effectiveness of audio binaural beat stimulation (BBs) in mitigating mental stress. We developed an experimental protocol to induce four mental states: rest, control, stress, and stress mitigation. The stress was induced by utilizing Stroop Color Word Test (SCWT) with time constraints and mitigated, by listening to 16 Hz of BBs. The four mental states were assessed using behavioral responses (accuracy of target detection), a perceived stress state questionnaire (PSS-10), and electroencephalography (EEG). The mean spectral power of four frequency bands was estimated using Power Spectral Density (PSD), and five different machine learning classifiers were used to classify the four mental states. Our results show that SCWT reduced the detection accuracy by 59.58% while listening to 16-Hz BBs significantly increased the accuracy of detection by 27.08%, (p = .00392). Furthermore, the support vector machine (SVM) significantly outperformed other classifiers achieving the highest accuracy of 82.5 ± 2.0 % using the beta band information. Similarly, the PSD topographical maps showed different patterns between the four mental states, where the temporal region's PSD was mostly affected by stress. Nevertheless, under mitigation, there was a noticeable restoration in the temporal activity. Overall, our results demonstrate that BBs at 16 Hz can be used to mitigate stress levels.

Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters.

Electroencephalography (EEG) signals offer invaluable insights into diverse activities of the human brain, including the intricate physiological and psychological responses associated with mental stress. A major challenge, however, is accurately identifying mental stress while mitigating the limitations associated with a large number of EEG channels. Such limitations encompass computational complexity, potential overfitting, and the prolonged setup time for electrode placement, all of which can hinder practical applications. To address these challenges, this study presents the novel CCHP method, aimed at identifying and ranking commonly optimal EEG channels based on their sensitivity to the mental stress state. This method's uniqueness lies in its ability not only to find common channels, but also to prioritize them according to their responsiveness to stress, ensuring consistency across subjects and making it potentially transformative for real-world applications. From our rigorous examinations, eight channels emerged as universally optimal in detecting stress variances across participants. Leveraging features from the time, frequency, and time-frequency domains of these channels, and employing machine learning algorithms, notably RLDA, SVM, and KNN, our approach achieved a remarkable accuracy of 81.56% with the SVM algorithm outperforming existing methodologies. The implications of this research are profound, offering a stepping stone toward the development of real-time stress detection devices, and consequently, enabling clinicians to make more informed therapeutic decisions based on comprehensive brain activity monitoring.

Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures.

[This corrects the article DOI: 10.3389/fpsyt.2023.1155812.].

Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures.

Introduction: The early diagnosis and classification of social anxiety disorder (SAD) are crucial clinical support tasks for medical practitioners in designing patient treatment programs to better supervise the progression and development of SAD. This paper proposes an effective method to classify the severity of SAD into different grading (severe, moderate, mild, and control) by using the patterns of brain information flow with their corresponding graphical networks. Methods: We quantified the directed information flow using partial directed coherence (PDC) and the topological networks by graph theory measures at four frequency bands (delta, theta, alpha, and beta). The PDC assesses the causal interactions between neuronal units of the brain network. Besides, the graph theory of the complex network identifies the topological structure of the network. Resting-state electroencephalogram (EEG) data were recorded for 66 patients with different severities of SAD (22 severe, 22 moderate, and 22 mild) and 22 demographically matched healthy controls (HC). Results: PDC results have found significant differences between SAD groups and HCs in theta and alpha frequency bands (p < 0.05). Severe and moderate SAD groups have shown greater enhanced information flow than mild and HC groups in all frequency bands. Furthermore, the PDC and graph theory features have been used to discriminate three classes of SAD from HCs using several machine learning classifiers. In comparison to the features obtained by PDC, graph theory network features combined with PDC have achieved maximum classification performance with accuracy (92.78%), sensitivity (95.25%), and specificity (94.12%) using Support Vector Machine (SVM). Discussion: Based on the results, it can be concluded that the combination of graph theory features and PDC values may be considered an effective tool for SAD identification. Our outcomes may provide new insights into developing biomarkers for SAD diagnosis based on topological brain networks and machine learning algorithms.

Mental Stress Management Using fNIRS Directed Connectivity and Audio Stimulation.

In this study, we propose a method to enhance cognitive vigilance and mitigate mental stress in the workplace. We designed an experiment to induce stress by putting participants through Stroop Color-Word Task (SCWT) under time constraint and negative feedback. Then, we used 16 Hz binaural beats auditory stimulation (BBs) for 10 minutes to enhance cognitive vigilance and mitigate stress. Functional Near-Infrared Spectroscopy (fNIRS), salivary alpha-amylase, and behavioral reactions were used to determine the stress level. The level of stress was assessed using reaction time to stimuli (RT), accuracy of target detection, directed functional connectivity based on partial directed coherence, graph theory measures, and the laterality index (LI). We discovered that 16 Hz BBs mitigated mental stress by substantially increasing the target detection accuracy by 21.83% ( p <0.001) and decreasing salivary alpha amylase levels by 30.28% (p<0.01). The partial directed coherence, graph theory analysis measures, and LI results indicated that mental stress decreased information flow from the left to the right prefrontal cortex under stress, whereas the 16 Hz BBs had a major impact on enhancing vigilance and mitigating mental stress via boosting connectivity network on the dorsolateral and left ventrolateral prefrontal cortex.

Stress management using fNIRS and binaural beats stimulation.

In this study, we investigate the effectiveness of binaural beats stimulation (BBs) in enhancing cognitive vigilance and mitigating mental stress level at the workplace. We developed an experimental protocol under four cognitive conditions: high vigilance (HV), vigilance enhancement (VE), mental stress (MS) and stress mitigation (SM). The VE and SM conditions were achieved by listening to 16 Hz of BBs. We assessed the four cognitive conditions using salivary alpha-amylase, behavioral responses, and Functional Near-Infrared Spectroscopy (fNIRS). We quantified the vigilance and stress levels using the reaction time (RT) to stimuli, accuracy of detection, and the functional connectivity metrics of the fNIRS estimated by Phase Locking Values (PLV). We propose using the orthogonal minimum spanning tree (OMST) to determine the true connectivity network patterns of the PLV. Our results show that listening to 16-Hz BBs has significantly reduced the level of alpha amylase by 44%, reduced the RT to stimuli by 20% and increased the accuracy of target detection by 25%, (p < 0.001). The analysis of the connectivity network across the four different cognitive conditions revealed several statistically significant trends. Specifically, a significant increase in connectivity between the right and left dorsolateral prefrontal cortex (DLPFC) areas and left orbitofrontal cortex was found during the vigilance enhancement condition compared to the high vigilance. Likewise, similar patterns were found between the right and left DLPFC, orbitofrontal cortex, right ventrolateral prefrontal cortex (VLPFC) and right frontopolar PFC (prefrontal cortex) area during stress mitigation compared to mental stress. Furthermore, the connectivity network under stress condition alone showed significant connectivity increase between the VLPFC and DLPFC compared to other areas. The laterality index demonstrated left frontal laterality under high vigilance and VE conditions, and right DLPFC and left frontopolar PFC while under mental stress. Overall, our results showed that BBs can be used for vigilance enhancement and stress mitigation.

Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis.

Vigilance level assessment is of prime importance to avoid life-threatening human error. Critical working environments such as air traffic control, driving, or military surveillance require the operator to be alert the whole time. The electroencephalogram (EEG) is a very common modality that can be used in assessing vigilance. Unfortunately, EEG signals are prone to artifacts due to eye movement, muscle contraction, and electrical noise. Mitigating these artifacts is important for an accurate vigilance level assessment. Independent Component Analysis (ICA) is an effective method and has been extensively used in the suppression of EEG artifacts. However, in vigilance assessment applications, it was found to suffer from leakage of the cerebral activity into artifacts. In this work, we show that the wavelet ICA (wICA) method provides an alternative for artifact reduction, leading to improved vigilance level assessment results. We conducted an experiment in nine human subjects to induce two vigilance states, alert and vigilance decrement, while performing a Stroop Color-Word Test for approximately 45 min. We then compared the performance of the ICA and wICA preprocessing methods using five classifiers. Our classification results showed that in terms of features extraction, the wICA method outperformed the existing ICA method. In the delta, theta, and alpha bands, we obtained a mean classification accuracy of 84.66% using the ICA method, whereas the mean accuracy using the wICA methodwas 96.9%. However, no significant improvement was observed in the beta band. In addition, we compared the topographical map to show the changes in power spectral density across the brain regions for the two vigilance states. The proposed method showed that the frontal and central regions were most sensitive to vigilance decrement. However, in this application, the proposed wICA shows a marginal improvement compared to the Fast-ICA.

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

In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance. We extracted multi-domain features within the time domain, frequency domain, time-frequency domain, and network connectivity features to form a prominent feature vector space for stress. We then proposed a hybrid feature selection (FS) method using minimum redundancy maximum relevance with particle swarm optimization and support vector machines (mRMR-PSO-SVM) to select the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art metaheuristic methods. The proposed model significantly reduced the features vector space by an average of 70% compared with the state-of-the-art methods while significantly increasing overall detection performance.

Asymmetry of Regional Phase Synchrony Cortical Networks Under Cognitive Alertness and Vigilance Decrement States.

This study investigates intra-regional connectivity and regional hemispheric asymmetry under two vigilance states: alertness and vigilance decrement. The vigilance states were induced on nine healthy subjects while performing 30 min in-congruent Stroop color-word task (I-SCWT). We measured brain activity using Electroencephalography (EEG) signals with 64-channels. We quantified the regional network connectivity using the phase-locking value (PLV) with graph theory analysis (GTA) and Support Vector Machines (SVM). Results showed that the vigilance decrement state was associated with impaired information processing within the frontal and central regions in delta and theta frequency bands. Meanwhile, the hemispheric asymmetry results showed that the laterality shifted to the right-temporal in delta, right-central, parietal, and left frontal in theta, right-frontal and left-central, temporal and parietal in alpha, and right-parietal and left temporal in beta frequency bands. These findings represent the first demonstration of intra-regional connectivity and hemispheric asymmetry changes as a function of cognitive vigilance states. The overall results showed that vigilance decrement is region and frequency band-specific. Our SVM model achieved the highest classification accuracy of 99.73% in differentiating between the two vigilance states based on the frontal and central connectivity networks measures.

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

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.

A Review on Mental Stress Assessment Methods Using EEG Signals.

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.

Brain Connectivity Analysis Under Semantic Vigilance and Enhanced Mental States.

In this paper, we present a method to quantify the coupling between brain regions under vigilance and enhanced mental states by utilizing partial directed coherence (PDC) and graph theory analysis (GTA). The vigilance state is induced using a modified version of stroop color-word task (SCWT) while the enhancement state is based on audio stimulation with a pure tone of 250 Hz. The audio stimulation was presented to the right and left ears simultaneously for one-hour while participants perform the SCWT. The quantification of mental states was performed by means of statistical analysis of indexes based on GTA, behavioral responses of time-on-task (TOT), and Brunel Mood Scale (BRMUS). The results show that PDC is very sensitive to vigilance decrement and shows that the brain connectivity network is significantly reduced with increasing TOT, p < 0.05. Meanwhile, during the enhanced state, the connectivity network maintains high connectivity as time passes and shows significant improvements compared to vigilance state. The audio stimulation enhances the connectivity network over the frontal and parietal regions and the right hemisphere. The increase in the connectivity network correlates with individual differences in the magnitude of the vigilance enhancement assessed by response time to stimuli. Our results provide evidence for enhancement of cognitive processing efficiency with audio stimulation. The BRMUS was used to evaluate the emotional states of vigilance task before and after using the audio stimulation. BRMUS factors, such as fatigue, depression, and anger, significantly decrease in the enhancement group compared to vigilance group. On the other hand, happy and calmness factors increased with audio stimulation, p < 0.05.

Vigilance Decrement and Enhancement Techniques: A Review.

This paper presents the first comprehensive review on vigilance enhancement using both conventional and unconventional means, and further discusses the resulting contradictory findings. It highlights the key differences observed between the research findings and argues that variations of the experimental protocol could be a significant contributing factor towards such contradictory results. Furthermore, the paper reveals the effectiveness of unconventional means of enhancement in significant reduction of vigilance decrement compared to conventional means. Meanwhile, a discussion on the challenges of enhancement techniques is presented, with several suggested recommendations and alternative strategies to maintain an adequate level of vigilance for the task at hand. Additionally, this review provides evidence in support of the use of unconventional means of enhancement on vigilance studies, regardless of their practical challenges.

Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach.

Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression, and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals to discriminate between different stress levels. We propose a new assessment protocol whereby the stress level is represented by the complexity of mental arithmetic (MA) task for example, at three levels of difficulty, and the stressors are time pressure and negative feedback. Using 18-male subjects, the experimental results showed that there were significant differences in EEG response between the control and stress conditions at different levels of MA task with p values < 0.001. Furthermore, we found a significant reduction in alpha rhythm power from one stress level to another level, p values < 0.05. In comparison, results from self-reporting questionnaire NASA-TLX approach showed no significant differences between stress levels. In addition, we developed a discriminant analysis method based on multiclass support vector machine (SVM) with error-correcting output code (ECOC). Different stress levels were detected with an average classification accuracy of 94.79%. The lateral index (LI) results further showed dominant right prefrontal cortex (PFC) to mental stress (reduced alpha rhythm). The study demonstrated the feasibility of using EEG in classifying multilevel mental stress and reported alpha rhythm power at right prefrontal cortex as a suitable index.

Assessment of mental stress effects on prefrontal cortical activities using canonical correlation analysis: an fNIRS-EEG study.

This paper presents an investigation about the effects of mental stress on prefrontal cortex (PFC) subregions using simultaneous measurement of functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) signals. The aim is to explore canonical correlation analysis (CCA) technique to study the relationship among the bi-modality signals in mental stress assessment, and how we could fuse the signals for better accuracy in stress detection. Twenty-five male healthy subjects participated in the study while performing mental arithmetic task under control and stress (under time pressure with negative feedback) conditions. The fusion of brain signals acquired by fNIRS-EEG was performed at feature-level using CCA by maximizing the inter-subject covariance across modalities. The CCA result discovered the associations across the modalities and estimated the components responsible for these associations. The experiment results showed that mental stress experienced by this cohort of subjects is subregion specific and localized to the right ventrolateral PFC subregion. These suggest the right ventrolateral PFC as a suitable candidate region to extract biomarkers as performance indicators of neurofeedback training in stress coping.

Mental stress assessment using simultaneous measurement of EEG and fNIRS.

Previous studies reported mental stress as one of the major contributing factors leading to various diseases such as heart attack, depression and stroke. An accurate stress assessment method may thus be of importance to clinical intervention and disease prevention. We propose a joint independent component analysis (jICA) based approach to fuse simultaneous measurement of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) on the prefrontal cortex (PFC) as a means of stress assessment. For the purpose of this study, stress was induced by using an established mental arithmetic task under time pressure with negative feedback. The induction of mental stress was confirmed by salivary alpha amylase test. Experiment results showed that the proposed fusion of EEG and fNIRS measurements improves the classification accuracy of mental stress by +3.4% compared to EEG alone and +11% compared to fNIRS alone. Similar improvements were also observed in sensitivity and specificity of proposed approach over unimodal EEG/fNIRS. Our study suggests that combination of EEG (frontal alpha rhythm) and fNIRS (concentration change of oxygenated hemoglobin) could be a potential means to assess mental stress objectively.