Parietal EEG Theta/Beta Ratio as an Electrophysiological Marker for Extraversion-Related Differences

Ivaskevych, Daryna

doi:10.19080/pbsij.2019.11.555814

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Along with this idea, electroencephalography (EEG) is a powerful non‐invasive tool for investigating the brain bases of human psychological processes (Li et al., 2020 ). The method has been applied in various decision‐making research domains (Ivaskevych, 2019 ; Lee et al., 2017 ; Pornpattananangkul et al., 2019 ; Ramsøy et al., 2018 ; Si et al., 2020 ; Wilson & Vassileva, 2018 ; Zheng et al., 2020 ). The use of intelligent systems based on learning from EEG signals has also been considered by most researchers (Al‐Nafjan et al., 2017 ; Anjum et al., 2020 ; Ieracitano et al., 2020 ; Maitín et al., 2020 ; Noor & Ibrahim, 2020 ; Rasheed et al., 2020 ; Roy et al., 2019 ; Tzimourta et al., 2021 ).…”

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confidence: 99%

Machine learning‐based classifying of risk‐takers and risk‐aversive individuals using resting‐state EEG data: A pilot feasibility study

et al. 2023

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BackgroundDecision‐making is vital in interpersonal interactions and a country's economic and political conditions. People, especially managers, have to make decisions in different risky situations. There has been a growing interest in identifying managers’ personality traits (i.e., risk‐taking or risk‐averse) in recent years. Although there are findings of signal decision‐making and brain activity, the implementation of an intelligent brain‐based technique to predict risk‐averse and risk‐taking managers is still in doubt.MethodsThis study proposes an electroencephalogram (EEG)‐based intelligent system to distinguish risk‐taking managers from risk‐averse ones by recording the EEG signals from 30 managers. In particular, wavelet transform, a time‐frequency domain analysis method, was used on resting‐state EEG data to extract statistical features. Then, a two‐step statistical wrapper algorithm was used to select the appropriate features. The support vector machine classifier, a supervised learning method, was used to classify two groups of managers using chosen features.ResultsIntersubject predictive performance could classify two groups of managers with 74.42% accuracy, 76.16% sensitivity, 72.32% specificity, and 75% F1‐measure, indicating that machine learning (ML) models can distinguish between risk‐taking and risk‐averse managers using the features extracted from the alpha frequency band in 10 s analysis window size.ConclusionsThe findings of this study demonstrate the potential of using intelligent (ML‐based) systems in distinguish between risk‐taking and risk‐averse managers using biological signals.

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