Evaluation of human stress using EEG Power Spectrum

Hamid, Noor Hayatee Abdul; Sulaiman, Norizam; Aris, Siti Armiza Mohd; Murat, Zunairah Hj; Taib, Mohd Nasir

doi:10.1109/cspa.2010.5545282

Cited by 46 publications

(26 citation statements)

References 11 publications

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“…Several studies have previously explored the potential of EEG as a biomarker for acute stress (Hamid et al, 2010; Sulaiman et al, 2011; Putman et al, 2014). Hamid et al (2010) found that high subjective stress scores correlated with lower right-to-left hemispheric ratios in the alpha and beta band power. Putman et al (2014) found that the theta/beta power ratio negatively correlated with self-reported trait attentional control.…”

Section: Introductionmentioning

confidence: 99%

Independent Component Analysis and Source Localization on Mobile EEG Data Can Identify Increased Levels of Acute Stress

Schlink

Peterson

Hairston

et al. 2017

Front. Hum. Neurosci.

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Mobile electroencephalography (EEG) is a very useful tool to investigate the physiological basis of cognition under real-world conditions. However, as we move experimentation into less-constrained environments, the influence of state changes increases. The influence of stress on cortical activity and cognition is an important example. Monitoring of modulation of cortical activity by EEG measurements is a promising tool for assessing acute stress. In this study, we test this hypothesis and combine EEG with independent component analysis and source localization to identify cortical differences between a control condition and a stressful condition. Subjects performed a stationary shooting task using an airsoft rifle with and without the threat of an experimenter firing a different airsoft rifle in their direction. We observed significantly higher skin conductance responses and salivary cortisol levels (p < 0.05 for both) during the stressful conditions, indicating that we had successfully induced an adequate level of acute stress. We located independent components in five regions throughout the cortex, most notably in the dorsolateral prefrontal cortex, a region previously shown to be affected by increased levels of stress. This area showed a significant decrease in spectral power in the theta and alpha bands less than a second after the subjects pulled the trigger. Overall, our results suggest that EEG with independent component analysis and source localization has the potential of monitoring acute stress in real-world environments.

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Section: Introductionmentioning

confidence: 99%

Independent Component Analysis and Source Localization on Mobile EEG Data Can Identify Increased Levels of Acute Stress

Schlink

Peterson

Hairston

et al. 2017

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Feature extraction also done with the help of power spectral entropy in case to recognize left and right hand movement [9]. To evaluate the human stress power spectrum analysis is done in some studies which results in related frequencies identification, IQ indexing with the help of EEG signals in terms of correlation between different frequency band is also calculate by power spectrum analysis [10][11].In this paper, the EEG signals of visual attention task have been analyzed. For this EEG data set of 32 channels [12] has been used.…”

Section: Introductionmentioning

confidence: 99%

Power Spectrum Analysis of EEG Signals for Estimating Visual Attention

KumarAhirwal¹,

Londhe²

2012

IJCA

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The task oriented brain activity analysis and classification is a prime issue in EEG signal processing these days. The similar attempt has been done here to estimate the brain activity on the basis of power spectrum analysis. For this, the modified approach involving both Independent Component Analysis (ICA) and Principal Component Analysis (PCA) methodologies has been used in this paper to investigate the behavior of brain's electrical activity for a simple case of visual attention. The proposed method of EEG classification can be very useful in predicting the action or the intention of action performed on the basis of EEG which leads to more development in brain computer interface. The EEG data has been referred from a website and the mathematical tool for EEG analysis called EEGLAB has been used to perform work in this paper.

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“…EEG signals are arranged in several frequency bands that represent different physical and emotional states. EEG analysis has been used to investigate the stress level of computer players [22] and to classify the execution of different stressful tasks [42].…”

Section: Related Workmentioning

confidence: 99%