The brain under pressure: Exploring neurophysiological responses to cognitive stress DOI Creative Commons
Selina C. Wriessnegger, Martin Leitner, Kyriaki Kostoglou

и другие.

Brain and Cognition, Год журнала: 2024, Номер 182, С. 106239 - 106239

Опубликована: Ноя. 17, 2024

Stress is an increasingly dominating part of our daily lives and higher performance requirements at work or to ourselves influence the physiological reaction body. Elevated stress levels can be reliably identified through electroencephalogram (EEG) heart rate (HR) measurements. In this study, we examined how arithmetic stress-inducing task impacted EEG HR, establishing meaningful correlations between behavioral data recordings. Thirty-one healthy participants (15 females, 16 males, aged 20 37) willingly participated. Under time pressure, completed calculations filled out questionnaires before after task. Linear mixed effects (LME) allowed us generate topographical association maps showing significant relations features (delta, theta, alpha, beta, gamma power) factors such as difficulty, error rate, response time, scores, HR. With observed left centroparietal parieto-occipital theta power decreases, alpha increases. Furthermore, frontal delta activity increased with relative while parieto-temporo-occipital decreased. Practice on included increases in temporal, parietal, activity. HR was positively associated delta, whereas decreases. Significant laterality scores were for all except difficulty overall parietal regions. asymmetries emerged sex, run number, occipital also found number Additionally explored practice noted sex-related differences features, questionnaire scores. Overall, study enhances understanding EEG/ECG-based mental detection, crucial early interventions, personalized treatment objective assessment towards development a neuroadaptive system.

Язык: Английский

Correlation between brain activity and comfort at different illuminances based on electroencephalogram signals during reading DOI
Chao Liu, Nan Zhang,

Zihe Wang

и другие.

Building and Environment, Год журнала: 2024, Номер 261, С. 111694 - 111694

Опубликована: Июнь 7, 2024

Язык: Английский

Процитировано

10
Neurophysiological response to social feedback in stressful situations DOI
Michela Balconi, Laura Angioletti, Katia Rovelli

и другие.

European Journal of Neuroscience, Год журнала: 2024, Номер unknown

Опубликована: Сен. 18, 2024

Abstract The relationship between external feedback and cognitive neurophysiological performance has been extensively investigated in social neuroscience. However, few studies have considered the role of positive negative on electroencephalographic (EEG) moderate stress response. Twenty‐six healthy adults underwent a moderately stressful job interview consisting modified version Trier Social Stress Test. After each preparation, was provided by an committee, ranging from to with increasing impact subjects. response measured analysing times (RTs) during speech phase, while assessed using Stroop‐like task before after test. Results indicate that RTs used deliver final speeches were significantly lower compared those for initial feedback. Moreover, generalized improvement observed post‐SST pre‐SST. Consistent behavioural results, EEG data indicated greater delta, theta, alpha band responses right prefrontal left central areas, delta theta bands, also parietal areas aversive‐neutral feedback, highlighting effort required former. Conversely, increase these bands temporal occipital following aversive indicative adaptive emotion‐regulatory processes. These findings suggest noncritical conditions could contribute improving individual performance.

Язык: Английский

Процитировано

4
Automated Detection of Neurological and Mental Health Disorders Using EEG Signals and Artificial Intelligence: A Systematic Review DOI Creative Commons
Hakan Uyanık, Abdulkadir Sengur, Massimo Salvi

и другие.

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Год журнала: 2025, Номер 15(1)

Опубликована: Март 1, 2025

ABSTRACT Mental and neurological disorders significantly impact global health. This systematic review examines the use of artificial intelligence (AI) techniques to automatically detect these conditions using electroencephalography (EEG) signals. Guided by Preferred Reporting Items for Systematic Reviews Meta‐Analysis (PRISMA), we reviewed 74 carefully selected studies published between 2013 August 2024 that used machine learning (ML), deep (DL), or both two methods mental health EEG The most common prevalent disorder types were sourced from major databases, including Scopus, Web Science, Science Direct, PubMed, IEEE Xplore. Epilepsy, depression, Alzheimer's disease are studied meet our evaluation criteria, 32, 12, 10 identified on topics, respectively. Conversely, number meeting criteria regarding stress, schizophrenia, Parkinson's disease, autism spectrum was relatively more average: 6, 4, 3, diseases least met one study each seizure, stroke, anxiety diseases, examining epilepsy together. Support Vector Machines (SVM) widely in ML methods, while Convolutional Neural Networks (CNNs) dominated DL approaches. generally outperformed traditional ML, as they yielded higher performance huge data. We observed complex decision process during feature extraction signals ML‐based models impacted results, DL‐based handled this efficiently. AI‐based analysis shows promise automated detection conditions. Future research should focus multi‐disease studies, standardizing datasets, improving model interpretability, developing clinical support systems assist diagnosis treatment disorders.

Язык: Английский

Процитировано

0
Information-Theoretical Analysis of the Cycle of Creation of Knowledge and Meaning in Brains under Multiple Cognitive Modalities DOI Creative Commons
J. J. Joshua Davis,

Florian Schübeler,

Róbert Kozma

и другие.

Sensors, Год журнала: 2024, Номер 24(5), С. 1605 - 1605

Опубликована: Фев. 29, 2024

It is of great interest to develop advanced sensory technologies allowing non-invasive monitoring neural correlates cognitive processing in people performing everyday tasks. A lot progress has been reported recent years this research area using scalp EEG arrays, but the high level noise electrode signals poses a challenges. This study presents results detailed statistical analysis experimental data on cycle creation knowledge and meaning human brains under multiple modalities. We measure brain dynamics HydroCel Geodesic Sensor Net, 128-electrode dense-array electroencephalography (EEG). compute pragmatic information (PI) index derived from analytic amplitude phase, by Hilbert transforming 20 participants six modalities, which combine various audiovisual stimuli, leading different mental states, including relaxed cognitively engaged conditions. derive several relevant measures classify states based PI indices. demonstrate significant differences between that require create for intentional action, relaxed-meditative with less demand psychophysiological resources. also point out kinds meanings may lead behavioral responses.

Язык: Английский

Процитировано

2
Measurement and Quantification of Stress in the Decision Process: A Model-Based Systematic Review DOI Creative Commons
Chang Su,

Morteza Zangeneh Soroush,

Nakisa Torkamanrahmani

и другие.

Intelligent Computing, Год журнала: 2024, Номер 3

Опубликована: Янв. 1, 2024

This systematic literature review comprehensively assesses the measurement and quantification of decisional stress using a model-based, theory-driven approach. It adopts dual-mechanism model capturing both System 1 2 thinking. Mental stress, influenced by factors such as workload, affect, skills, knowledge, correlates with mental effort. aims to address 3 research questions: (a) What constitutes an effective experiment protocol for measuring physiological responses related stresses? (b) How can signals triggered be measured? (c) stresses quantified features? We developed search syntax inclusion/exclusion criteria based on model. The we conducted in databases (Web Science, Scopus, PubMed) resulted 83 papers published between 1990 September 2023. synthesis focuses design, measurement, quantification, addressing questions. emphasizes historical context, recent advancements, identified knowledge gaps, potential future trends. Insights into markers, techniques, proposed analyses, machine-learning approaches are provided. Methodological aspects, including participant selection, stressor configuration, choosing devices, critically examined. comprehensive describes practical implications decision-making practitioners offers insights research.

Язык: Английский

Процитировано

1
Spatial-Frequency Characteristics of EEG Associated With the Mental Stress in Human-Machine Systems DOI
Qunli Yao, Heng Gu, Shaodi Wang

и другие.

IEEE Journal of Biomedical and Health Informatics, Год журнала: 2024, Номер 28(10), С. 5904 - 5916

Опубликована: Июль 3, 2024

Accurate assessment of user mental stress in human-machine system plays a crucial role ensuring task performance and safety. However, the underlying neural mechanisms tasks methods based on physiological indicators remain fundamental challenges. In this paper, we employ virtual unmanned aerial vehicle (UAV) control experiment to explore reorganization functional brain network patterns under conditions. The results indicate enhanced connectivity frontal theta band central beta band, as well reduced left parieto-occipital alpha which is associated with increased stress. Evaluation metrics reveals that decreased global efficiency bands linked elevated levels. Subsequently, inspired by frequency-specific network, cross-band graph convolutional (CBGCN) model constructed for state recognition. proposed method captures spatial-frequency topological relationships networks through multiple branches, aim integrating complex dynamic hidden learning discriminative cognitive features. Experimental demonstrate neuro-inspired CBGCN improves classification enhances interpretability. study suggests approach provides potentially viable solution recognizing states using EEG signals.

Язык: Английский

Процитировано

0
Wavelength selection for real-time detection of human stress based on StO2 DOI
Xinyu Liu, Xiao Xiao, Ju Zhou

и другие.

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 99, С. 106874 - 106874

Опубликована: Сен. 11, 2024

Язык: Английский

Процитировано

0
The brain under pressure: Exploring neurophysiological responses to cognitive stress DOI Creative Commons
Selina C. Wriessnegger, Martin Leitner, Kyriaki Kostoglou

и другие.

Brain and Cognition, Год журнала: 2024, Номер 182, С. 106239 - 106239

Опубликована: Ноя. 17, 2024

Stress is an increasingly dominating part of our daily lives and higher performance requirements at work or to ourselves influence the physiological reaction body. Elevated stress levels can be reliably identified through electroencephalogram (EEG) heart rate (HR) measurements. In this study, we examined how arithmetic stress-inducing task impacted EEG HR, establishing meaningful correlations between behavioral data recordings. Thirty-one healthy participants (15 females, 16 males, aged 20 37) willingly participated. Under time pressure, completed calculations filled out questionnaires before after task. Linear mixed effects (LME) allowed us generate topographical association maps showing significant relations features (delta, theta, alpha, beta, gamma power) factors such as difficulty, error rate, response time, scores, HR. With observed left centroparietal parieto-occipital theta power decreases, alpha increases. Furthermore, frontal delta activity increased with relative while parieto-temporo-occipital decreased. Practice on included increases in temporal, parietal, activity. HR was positively associated delta, whereas decreases. Significant laterality scores were for all except difficulty overall parietal regions. asymmetries emerged sex, run number, occipital also found number Additionally explored practice noted sex-related differences features, questionnaire scores. Overall, study enhances understanding EEG/ECG-based mental detection, crucial early interventions, personalized treatment objective assessment towards development a neuroadaptive system.

Язык: Английский

Процитировано

0