Brain functional connectivity changes in long-term mental stress

Darzi, Ali; Azami, Hamed; Khosrowabadi, Reza

doi:10.29252/jncog.1.1.16

Cited by 7 publications

(19 citation statements)

References 0 publications

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“…Phase-Slope Index (PSI) is another method of FC estimator to detect the directional connectivity between two EEG channels in the frequency domain. Studies in [45], [61] found several pattern of brain locations connectivity during the perception of external stimuli that chronic stress can change them, whereas the synchronization between left parietal and right temporal showed a decrease of 55% in the stressful subjects. Darzi et al [45] has shown a high performance when using PSI with either SVM, KNN, or Naïve Bayes (NB) classifiers whereas the results of Khosrowabadi et al [61] achieved low accuracy (when using SVM and KNN) for PSI comparing to DTF and PDC features.…”

Section: Connectivity Methodsmentioning

confidence: 96%

“…Studies in [45], [61] found several pattern of brain locations connectivity during the perception of external stimuli that chronic stress can change them, whereas the synchronization between left parietal and right temporal showed a decrease of 55% in the stressful subjects. Darzi et al [45] has shown a high performance when using PSI with either SVM, KNN, or Naïve Bayes (NB) classifiers whereas the results of Khosrowabadi et al [61] achieved low accuracy (when using SVM and KNN) for PSI comparing to DTF and PDC features. The main advantages of using PSI are overcoming the independent background activity generated between two electrodes and the ability to give meaningful information even though the nonlinear phase spectrum [61].…”

Section: Connectivity Methodsmentioning

confidence: 96%

“…Magnitude Square Coherence (MSC) [45,46] is another measure of functional connectivity in stress studies. Study in [47] found significant reduction in the functional connectivity (as reflected by a decrease in MSC value) from control to the stress situation in intra-hemispheric and inter-hemispheric prefrontal cortex (PFC).…”

Section: Connectivity Methodsmentioning

confidence: 99%

“…However, this behaviour was not reproduced when dealing with stroop color word task where there was only elevated beta coherence for sagittal middle regions. Darzi et al [45] has proved that extracting MSC features with length of 56 seconds achieved the highest accuracy by applying support vector machines (SVM) as a classifier compared to the Directed Transfer Function (DTF), Phase-Slope Index (PSI), Canonical Correlation (CC), and Power Spectral Density (PSD) techniques. Likewise, Khosrowabadi et al [46], reported that MSC accuracy, sensitivity and specificity were greater than those obtained by Gaussian Mixture Models (GMM) and Fractal Dimension (FD) features using K-Nearest Neighbors (KNN) or SVM classifiers.…”

Section: Connectivity Methodsmentioning

confidence: 99%

“…Mutual Information (MI) is used to detect dynamic concatenate and similarity of joint portability distribution function between two EEG signals [45,56]. Therefore, MI aims to find the statistical dependency between signals and analyse EEG with different spectral bands [57].…”

Section: Connectivity Methodsmentioning

confidence: 99%

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A Review on Mental Stress Assessment Methods Using EEG Signals

Katmah

Al-Shargie

Tariq

et al. 2021

Preprint

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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 contain 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. Over this, 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.

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Section: Connectivity Methodsmentioning

confidence: 96%

Section: Connectivity Methodsmentioning

confidence: 96%

Section: Connectivity Methodsmentioning

confidence: 99%

Section: Connectivity Methodsmentioning

confidence: 99%

Section: Connectivity Methodsmentioning

confidence: 99%

See 3 more Smart Citations

A Review on Mental Stress Assessment Methods Using EEG Signals

Katmah

Al-Shargie

Tariq

et al. 2021

Preprint

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A review on evaluating mental stress by deep learning using EEG signals

Badr,

Tariq,

Al-Shargie

et al. 2024

Neural Comput & Applic

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Mental stress is a common problem that affects individuals all over the world. Stress reduces human functionality during routine work and may lead to severe health defects. Early detection of stress is important for preventing diseases and other negative health-related consequences of stress. Several neuroimaging techniques have been utilized to assess mental stress, however, due to its ease of use, robustness, and non-invasiveness, electroencephalography (EEG) is commonly used. This paper aims to fill a knowledge gap by reviewing the different EEG-related deep learning algorithms with a focus on Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for the evaluation of mental stress. The review focuses on data representation, individual deep neural network model architectures, hybrid models, and results amongst others. The contributions of the paper address important issues such as data representation and model architectures. Out of all reviewed papers, 67% used CNN, 9% LSTM, and 24% hybrid models. Based on the reviewed literature, we found that dataset size and different representations contributed to the performance of the proposed networks. Raw EEG data produced classification accuracy around 62% while using spectral and topographical representation produced up to 88%. Nevertheless, the roles of generalizability across different deep learning models and individual differences remain key areas of inquiry. The review encourages the exploration of innovative avenues, such as EEG data image representations concurrently with graph convolutional neural networks (GCN), to mitigate the impact of inter-subject variability. This novel approach not only allows us to harmonize structural nuances within the data but also facilitates the integration of temporal dynamics, thereby enabling a more comprehensive assessment of mental stress levels.

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