EEG in game user analysis: A framework for expertise classification during gameplay.

Video games have become a ubiquitous part of demographically diverse cultures. Numerous studies have focused on analyzing the cognitive aspects involved in game playing that could help in providing an optimal gaming experience by improving video game design. To this end, we present a framework for c...

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Autores principales:	Tehmina Hafeez, Sanay Muhammad Umar Saeed, Aamir Arsalan, Syed Muhammad Anwar, Muhammad Usman Ashraf, Khalid Alsubhi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/303f7f6b3dd2481a856da3dd5cf6a32f
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spelling	oai:doaj.org-article:303f7f6b3dd2481a856da3dd5cf6a32f2021-12-02T20:05:20ZEEG in game user analysis: A framework for expertise classification during gameplay.1932-620310.1371/journal.pone.0246913https://doaj.org/article/303f7f6b3dd2481a856da3dd5cf6a32f2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0246913https://doaj.org/toc/1932-6203Video games have become a ubiquitous part of demographically diverse cultures. Numerous studies have focused on analyzing the cognitive aspects involved in game playing that could help in providing an optimal gaming experience by improving video game design. To this end, we present a framework for classifying the game player's expertise level using wearable electroencephalography (EEG) headset. We hypothesize that expert and novice players' brain activity is different, which can be classified using frequency domain features extracted from EEG signals of the game player. A systematic channel reduction approach is presented using a correlation-based attribute evaluation method. This approach lead us in identifying two significant EEG channels, i.e., AF3 and P7, among fourteen channels available in Emotiv EPOC headset. In particular, features extracted from these two EEG channels contributed the most to the video game player's expertise level classification. This finding is validated by performing statistical analysis (t-test) over the extracted features. Moreover, among multiple classifiers used, K-nearest neighbor is the best classifier in classifying game player's expertise level with a classification accuracy of up to 98.04% (without data balancing) and 98.33% (with data balancing).Tehmina HafeezSanay Muhammad Umar SaeedAamir ArsalanSyed Muhammad AnwarMuhammad Usman AshrafKhalid AlsubhiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0246913 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tehmina Hafeez Sanay Muhammad Umar Saeed Aamir Arsalan Syed Muhammad Anwar Muhammad Usman Ashraf Khalid Alsubhi EEG in game user analysis: A framework for expertise classification during gameplay.
description	Video games have become a ubiquitous part of demographically diverse cultures. Numerous studies have focused on analyzing the cognitive aspects involved in game playing that could help in providing an optimal gaming experience by improving video game design. To this end, we present a framework for classifying the game player's expertise level using wearable electroencephalography (EEG) headset. We hypothesize that expert and novice players' brain activity is different, which can be classified using frequency domain features extracted from EEG signals of the game player. A systematic channel reduction approach is presented using a correlation-based attribute evaluation method. This approach lead us in identifying two significant EEG channels, i.e., AF3 and P7, among fourteen channels available in Emotiv EPOC headset. In particular, features extracted from these two EEG channels contributed the most to the video game player's expertise level classification. This finding is validated by performing statistical analysis (t-test) over the extracted features. Moreover, among multiple classifiers used, K-nearest neighbor is the best classifier in classifying game player's expertise level with a classification accuracy of up to 98.04% (without data balancing) and 98.33% (with data balancing).
format	article
author	Tehmina Hafeez Sanay Muhammad Umar Saeed Aamir Arsalan Syed Muhammad Anwar Muhammad Usman Ashraf Khalid Alsubhi
author_facet	Tehmina Hafeez Sanay Muhammad Umar Saeed Aamir Arsalan Syed Muhammad Anwar Muhammad Usman Ashraf Khalid Alsubhi
author_sort	Tehmina Hafeez
title	EEG in game user analysis: A framework for expertise classification during gameplay.
title_short	EEG in game user analysis: A framework for expertise classification during gameplay.
title_full	EEG in game user analysis: A framework for expertise classification during gameplay.
title_fullStr	EEG in game user analysis: A framework for expertise classification during gameplay.
title_full_unstemmed	EEG in game user analysis: A framework for expertise classification during gameplay.
title_sort	eeg in game user analysis: a framework for expertise classification during gameplay.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/303f7f6b3dd2481a856da3dd5cf6a32f
work_keys_str_mv	AT tehminahafeez eegingameuseranalysisaframeworkforexpertiseclassificationduringgameplay AT sanaymuhammadumarsaeed eegingameuseranalysisaframeworkforexpertiseclassificationduringgameplay AT aamirarsalan eegingameuseranalysisaframeworkforexpertiseclassificationduringgameplay AT syedmuhammadanwar eegingameuseranalysisaframeworkforexpertiseclassificationduringgameplay AT muhammadusmanashraf eegingameuseranalysisaframeworkforexpertiseclassificationduringgameplay AT khalidalsubhi eegingameuseranalysisaframeworkforexpertiseclassificationduringgameplay
_version_	1718375471573368832

EEG in game user analysis: A framework for expertise classification during gameplay.

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