Detection of stress/anxiety state from EEG features during video watching

Giannakakis, Giorgos; Grigoriadis, Dimitris; Tsiknakis, Manolis

doi:10.1109/embc.2015.7319767

Cited by 59 publications

(37 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For what concerns the EEG, we decided to process the signals provided from the F3 electrode. This choice was due to the fact that, in the literature, prefrontal electrodes are typically considered for the analysis of brain signals concerned with the detection of stress conditions [ 41 , 42 , 43 ]. In order to obtain time series representing the variations in time of the amplitude of the various EEG rhythms, the power spectral density (PSD) was computed on the recorded EEG signals using the periodogram method and quantifying the total power content of the

(

–3

(3–8

(8–12

) and

(12–25

) frequency bands.…”

Section: Methodsmentioning

confidence: 99%

Information Dynamics of the Brain, Cardiovascular and Respiratory Network during Different Levels of Mental Stress

Zanetti

Faes

Nollo

et al. 2019

Entropy

View full text Add to dashboard Cite

In this study, an analysis of brain, cardiovascular and respiratory dynamics was conducted combining information-theoretic measures with the Network Physiology paradigm during different levels of mental stress. Starting from low invasive recordings of electroencephalographic, electrocardiographic, respiratory, and blood volume pulse signals, the dynamical activity of seven physiological systems was probed with one-second time resolution measuring the time series of the δ , θ , α and β brain wave amplitudes, the cardiac period (RR interval), the respiratory amplitude, and the duration of blood pressure wave propagation (pulse arrival time, PAT). Synchronous 5-min windows of these time series, obtained from 18 subjects during resting wakefulness (REST), mental stress induced by mental arithmetic (MA) and sustained attention induced by serious game (SG), were taken to describe the dynamics of the nodes composing the observed physiological network. Network activity and connectivity were then assessed in the framework of information dynamics computing the new information generated by each node, the information dynamically stored in it, and the information transferred to it from the other network nodes. Moreover, the network topology was investigated using directed measures of conditional information transfer and assessing their statistical significance. We found that all network nodes dynamically produce and store significant amounts of information, with the new information being prevalent in the brain systems and the information storage being prevalent in the peripheral systems. The transition from REST to MA was associated with an increase of the new information produced by the respiratory signal time series (RESP), and that from MA to SG with a decrease of the new information produced by PAT. Each network node received a significant amount of information from the other nodes, with the highest amount transferred to RR and the lowest transferred to δ , θ , α and β . The topology of the physiological network underlying such information transfer was node- and state-dependent, with the peripheral subnetwork showing interactions from RR to PAT and between RESP and RR, PAT consistently across states, the brain subnetwork resulting more connected during MA, and the subnetwork of brain–peripheral interactions involving different brain rhythms in the three states and resulting primarily activated during MA. These results have both physiological relevance as regards the interpretation of central and autonomic effects on cardiovascular and respiratory variability, and practical relevance as regards the identification of features useful for the automatic distinction of different mental states.

show abstract

(

–3

(3–8

(8–12

) and

(12–25

) frequency bands.…”

Section: Methodsmentioning

confidence: 99%

Information Dynamics of the Brain, Cardiovascular and Respiratory Network during Different Levels of Mental Stress

Zanetti

Faes

Nollo

et al. 2019

Entropy

View full text Add to dashboard Cite

show abstract

“…The electrodes are connected accordingly to the EXG electrodes port based on the international standard positioning system (10-20 system). The electrode port at the EXG Brain Marker device with the universal size standard (DIN Standard) which allowing enabling the use of most available electrodes [11] This device was assembled for the purpose of dynamic data collection whereas the subjects or participant can move along together while completing the given task. In this study, combination and additional device were used.…”

Section: Methodsmentioning

confidence: 99%

“…Stress become very serious health crisis nowadays [4] which lead to affected to public work performance. Currently, most of study in stress were done in psychology area [7], [9], very less study on stress were done in neurophysiological in computational studies using EEG signal [11], [15]. EEG (electroencephalogram) is defined as a graphical representation of the brain's electrical activity and one of the neuroimaging tools.…”

Section: Introductionmentioning

confidence: 99%

“…These potentials are generated by neural activity within the brain. Common categorizations of frequencies in EEG include alpha (8)(9)(10)(11)(12)(13), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), gamma (30-600 Hz), delta (<4 Hz), theta (4)(5)(6)(7)(8), and mu (8)(9)(10)(11)(12)(13) [1]. Theta and alpha frequency bands are generally used for studying affects (emotions) [16], [17] From the signal of EEG been captured from the device, it goes through on some process lead to the respond to the affective state.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Perceiving Stress on Affective State Based on Features from EEG

Adnan

Yaacob²

2017

IJPCC

View full text Add to dashboard Cite

The EEG (Electroencephalogram) from brain signal has been broadly used to reveal human affects based on Valence, Arousal and dominance through computational modelling. Recently, less study on EEG been done in detail, directly to reveal and quantify the stress from affects dynamically based on EEG signal. In addition. There is no study currently perform to identify stress from the features based on EEG signals simultaneously. As for the objective of this paper, this study will only try to do features analysis of stress by comparing the statistical features of affects from the data obtained from three subjects. The data was collected during the trial session for new assembled EEG mobile system at Simulation Centre Akademi Laut Malaysia (ALAM). Subjects consist of three marine pilot who were given tasks to navigate ships in the simulated scenes. Findings and observations from the analysis are reported in this paper.

show abstract

“…We found correlations of EEG biomarkers with psychological states that match reports about previous efforts into this direction. It was shown that EEG spectral properties correspond to stress states [32][33][34][35]. However, the exact EEG-frequency range, where this stress response might be found, varies over the full range of frequency bands from delta to gamma across studies.…”

Section: Correlation Of Eeg Biomarkers With Psychological Statesmentioning

confidence: 99%

Correlation of EEG spectra, connectivity, and information theoretical biomarkers with psychological states in the epilepsy monitoring unit — A pilot study

Höller

Trinka

Kalss

et al. 2019

Epilepsy & Behavior

View full text Add to dashboard Cite

A B S T R A C TAt the level of individual experience, the relation between electroencephalographic (EEG) phenomena and subjective ratings of psychological states is poorly examined. This study investigated the correlation of quantitative EEG markers with systematic high-frequency monitoring of psychological states in patients admitted to the epilepsy monitoring unit (EMU). We used a digital questionnaire, including eight standardized items about stress, energy level, mood, ward atmosphere, seizure likelihood, hopefulness/frustration, boredom, and self-efficacy. Self-assessments were collected four times per day, in total 15 times during the stay in the EMU. We extracted brainrate, Hjorth parameters, Hurst exponent, Wackermann parameters, and power spectral density from the EEG. We performed correlation between these quantitative EEG measures and responses to the 8 items and evaluated their significance on single subject and on group level. Twenty-one consecutive patients (12 women/9 men, median age: 29 years, range: 18-74 years) were recruited. On group level, no significant correlations were found whereas on single-subject level, we found significant correlations for 6 out of 21 patients. Most significant correlations were found between Hjorth parameters and items that reflect changes in mood or stress. This study supports the feasibility of correlating quantitative EEG measures with psychological states in routine EMU settings and emphasizes the need for single-subject statistics when assessing aspects with high interindividual variance. Future studies should select samples with high within-subject variability of psychological states and examine a subsample with patients encountering a critical number of seizures needed in order to relate the psychological states to the ultimate question: Are psychological states potential indicators for seizure likelihood?

show abstract

Detection of stress/anxiety state from EEG features during video watching

Cited by 59 publications

References 17 publications

Information Dynamics of the Brain, Cardiovascular and Respiratory Network during Different Levels of Mental Stress

Information Dynamics of the Brain, Cardiovascular and Respiratory Network during Different Levels of Mental Stress

Perceiving Stress on Affective State Based on Features from EEG

Correlation of EEG spectra, connectivity, and information theoretical biomarkers with psychological states in the epilepsy monitoring unit — A pilot study

Contact Info

Product

Resources

About