Quantification of Human Stress Using Commercially Available Single Channel EEG Headset

Saeed, Sanay Muhammad Umar; Anwar, Syed Muhammad; Majid, Muhammad

doi:10.1587/transinf.2016edl8248

Cited by 30 publications

(12 citation statements)

References 13 publications

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“…Electrophysiological measurements have been used to examine the players' response to games [14,15]. EEG has also been used to measure the cognitive state of the brain such as stress [16]. In [17], field experts discussed the methodological advancements within player experience and playability research considering EEG as a good measure for cognitive processing.…”

Section: Introductionmentioning

confidence: 99%

A Game Player Expertise Level Classification System Using Electroencephalography (EEG)

et al. 2017

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Abstract:The success and wider adaptability of smart phones has given a new dimension to the gaming industry. Due to the wide spectrum of video games, the success of a particular game depends on how efficiently it is able to capture the end users' attention. This leads to the need to analyse the cognitive aspects of the end user, that is the game player, during game play. A direct window to see how an end user responds to a stimuli is to look at their brain activity. In this study, electroencephalography (EEG) is used to record human brain activity during game play. A commercially available EEG headset is used for this purpose giving fourteen channels of recorded EEG brain activity. The aim is to classify a player as expert or novice using the brain activity as the player indulges in the game play. Three different machine learning classifiers have been used to train and test the system. Among the classifiers, naive Bayes has outperformed others with an accuracy of 88%, when data from all fourteen EEG channels are used. Furthermore, the activity observed on electrodes is statistically analysed and mapped for brain visualizations. The analysis has shown that out of the available fourteen channels, only four channels in the frontal and occipital brain regions show significant activity. Features of these four channels are then used, and the performance parameters of the four-channel classification are compared to the results of the fourteen-channel classification. It has been observed that support vector machine and the naive Bayes give good classification accuracy and processing time, well suited for real-time applications.

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Section: Introductionmentioning

confidence: 99%

A Game Player Expertise Level Classification System Using Electroencephalography (EEG)

et al. 2017

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“…Similarly, classification accuracy is improved by 7.1% as compared to the study [24], in which low beta is used as a sole feature for classification. F-measure of proposed features is equal to that of low beta-based classification.…”

Section: Discussionmentioning

confidence: 79%

“…Table 2 compares the performance of the algorithms used in single channel-based studies for classification of human stress in terms of various performance measures. The execution times for [23, 24] and the proposed methodology are computed on an Intel i5 system with RAM of 4 GB. It is observed that the SVM algorithm, when used with the proposed features selected by CFS, gives 7.1% higher accuracy as compared to the case, where all neural oscillations are used as features.…”

Section: Discussionmentioning

confidence: 99%

Selection of Neural Oscillatory Features for Human Stress Classification with Single Channel EEG Headset

Saeed

Anwar

Majid

et al. 2018

BioMed Research International

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A study on classification of psychological stress in humans using electroencephalography (EEG) is presented. The stress is classified using a correlation-based feature subset selection method that efficiently reduces the feature vector length. In this study, twenty-eight participants are involved by filling in the perceived stress scale-10 (PSS-10) questionnaire and their EEG is also recorded in closed eye condition to measure the baseline stress. The recorded data is labelled on the basis of the stress level that is indicated by the participant's PSS score. The feature selection method has shown that, among the EEG oscillations, low beta, high beta, and low gamma are the most significant neural oscillations for classifying human stress. The proposed method not only reduces the time to build a classification model but also improves the classification accuracy up to 78.57% using a single channel wearable EEG device.

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“…The frontal theta activity was shown to decrease due to a stressful mental arithmetic task [32]. In [33], low beta waves in closed eye condition were found to be a strong predictor of perceived stress, where PSS score was predicted by using multiple linear regression. The pre-frontal relative gamma power i.e., the ratio of gamma band and slow brain rhythms, was proposed as a bio marker for identification of stress [34,35].…”

Section: Related Workmentioning

confidence: 94%

“…This also helped in minimizing the muscular motion and reduce these artifacts, since we recorded data at the frontal electrodes. A closed-eye condition was used, since correlates of long-term stress have been found in this condition in previous studies [10,33]. Another advantage of using the closed eye condition is the minimization of eye blink artifact.…”

Section: Data Acquisitionmentioning

confidence: 99%

EEG Based Classification of Long-Term Stress Using Psychological Labeling

Saeed

Anwar

Khalid

et al. 2020

Sensors

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Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavailability of mental health facilities. In this study, long-term stress was classified with machine learning algorithms using resting state EEG signal recordings. The labeling for the stress and control groups was performed using two currently accepted clinical practices: (i) the perceived stress scale score and (ii) expert evaluation. The frequency domain features were extracted from five-channel EEG recordings in addition to the frontal and temporal alpha and beta asymmetries. The alpha asymmetry was computed from four channels and used as a feature. Feature selection was also performed to identify statistically significant features for both stress and control groups (via t-test). We found that support vector machine was best suited to classify long-term human stress when used with alpha asymmetry as a feature. It was observed that the expert evaluation-based labeling method had improved the classification accuracy by up to 85.20%. Based on these results, it is concluded that alpha asymmetry may be used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation.

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Quantification of Human Stress Using Commercially Available Single Channel EEG Headset

Cited by 30 publications

References 13 publications

A Game Player Expertise Level Classification System Using Electroencephalography (EEG)

A Game Player Expertise Level Classification System Using Electroencephalography (EEG)

Selection of Neural Oscillatory Features for Human Stress Classification with Single Channel EEG Headset

EEG Based Classification of Long-Term Stress Using Psychological Labeling

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