BIG FIVE PERSONALITY TRAIT ANALYSIS FROM RANDOM EEG SIGNAL USING CONVOLUTIONAL NEURAL NETWORK

Oindrila Sanyal, Subhankar Das

Abstract


Personality can be characterized as a remarkably steady form of theorizing, feeling and acting. These forms can be clarified by methods for the possibility of character attributes – hidden components that cause variation in perceptible personality traits. As indicated by a prevailing Five-Factor model (FFM), perceptible personality is generally decided by means of five fundamental properties – Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness. Automated recognition of an individual's personality traits has numerous applications. In the proposed method the brain activity has been analyzed to detect big five personality traits by gathering publicly available random EEG signal datasets taken from different subjects using a convolutional neural network (CNN). Five different networks with the same architecture have been used to train the system for the five personality traits. The outcomes surpass the current state of the art for each of the five patterns.

Keywords


Five-Factor model (FFM); convolutional neural network (CNN); EEG signal; Neuroticism; Extraversion, Openness, Agreeableness, Conscientiousness

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References


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DOI: https://doi.org/10.26483/ijarcs.v11i2.6517

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