Organizations vie to develop insights into the psychological aspects of consumer decision-making to enhance their products accordingly. Understanding how emotions and personality traits influence the choices we make is an integral part of product design. In this paper, we have employed machine learning algorithms to profile discrete emotions, in response to video games stimuli, based on features extracted from recorded electroencephalography (EEG) and to understand certain characteristics of personality. Four video games from different genres have been used for emotion elicitation and players' EEG signals are recorded. EEG being a non-stationary, non-linear and extremely noisy signal has been cleaned using a Savitzky-Golay filter which is found to be suitable for single-channel EEG devices. Seven out of sixteen features from time, frequency and time-frequency domains have been selected using Random Forest and used to classify emotions. Support Vector Machine, k-Nearest Neighbour and Gradient Boosted Trees classifiers have been used where the highest classification accuracy 82.26% is achieved with Boosted Trees classifier. Our findings propagate that for a single-channel EEG device, only four discrete emotions (happy, bored, relaxed, stressed) can be classified where two emotions happy and bored achieved the highest individual accuracy of 88.89% and 85.29% respectively with the Gradient Boosted Trees Classifier. In this study, we have also identified personality traits, extroversion and neuroticism influence players' perception of video games. The results indicate that players with low extroversion prefer relatively slow and strategy games as compared to highly extroverted. It has also been identified that puzzle and racing games are well-liked irrespective of the levels of the two personality traits.
Keywords: Brain computer interface; Emotion classification; Machine learning; Personality traits; Video games.
Copyright © 2022 Elsevier Ltd. All rights reserved.