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Analysis and classification of EEG signals of extroverts and introverts
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1
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| Subject:
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Cognitive-Social Applications of Brain Mapping, such as talent identification, neuroleadership, and neurocognitive recruitment
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Presenter Author:
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| Name:
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Nasrin Shoouri
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Author:
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| Name 1:
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Nasrin Shoouri - Corresponding - Presenter
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| Affiliation 1:
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Department of Biomedical Engineering, CT.C., Islamic Azad University, Tehran, Iran.
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Abstract
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Original Research
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introduction :
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Personality is defined as distinctive patterns of behaving and thinking that form each person’s specific style in interacting with the social environment. Extroversion/introversion is considered one of the most important personality traits, which affects the electrical activity of the brain.
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Methods:
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The present study aimed to record the EEGs of 15 healthy subjects with different degrees of extroversion/introversion in four conditions of eyes-open, eyes-closed, while watching pleasant images, and while watching unpleasant images. Then, the relative power of different frequency bands was calculated along with approximate entropy, Katz fractal dimension (FD), Petrosian’s FD, Higuchi’s FD and scaling exponent of recorded signals. Standardized beta coefficients were computed to estimate the effect of each feature on the degrees of extroversion/introversion. A genetic algorithm was used to find the best set of features for the classification of EEGs of extroverts and introverts. In addition, the groups were also classified using SVM classifier.
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Results:
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The analysis reveals that in all task conditions, the relative alpha power has a significant negative effect on degree of extraversion (unlike Eysenck’s hypothesis). The relative power values of SMR and low beta provide significant evidence of a positive effect on degree of extraversion in the eyes-closed condition and a significant negative effect on degree of extraversion in the other three conditions. Finally, EEGs of the two groups were classified using only one feature, with an average recognition accuracy of 100%.
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Conclusion:
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The findings of this study can help to designing strategies to moderate extroversion/introversion level.
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| Attachment:
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| Keywords:
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Personality; Extroversion/introversion; EEG; Genetic algorithm; SVM.
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| Key message:
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