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Appl. Sci. 2018, 8(1), 18; https://doi.org/10.3390/app8010018

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

1
Department of Software Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
2
Department of Computer Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
3
Department of Electrical Engineering, COMSATS Institute of Information Technology, Sahiwal 54700, Pakistan
4
Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbotabad 22060, Pakistan
5
Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 4DE, UK
*
Author to whom correspondence should be addressed.
Received: 30 October 2017 / Revised: 11 December 2017 / Accepted: 19 December 2017 / Published: 23 December 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. View Full-Text
Keywords: electroencephalography (EEG); machine learning; consumer gaming; feature extraction; classification electroencephalography (EEG); machine learning; consumer gaming; feature extraction; classification
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Anwar, S.M.; Saeed, S.M.U.; Majid, M.; Usman, S.; Mehmood, C.A.; Liu, W. A Game Player Expertise Level Classification System Using Electroencephalography (EEG). Appl. Sci. 2018, 8, 18.

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