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Classification of Video Game Player Experience Using Consumer-Grade Electroencephalography | IEEE Journals & Magazine | IEEE Xplore

Classification of Video Game Player Experience Using Consumer-Grade Electroencephalography


Abstract:

A growing body of literature has emerged that demonstrates the potential of neurogaming platforms for inter- facing with well-known video games. With the recent convergen...Show More

Abstract:

A growing body of literature has emerged that demonstrates the potential of neurogaming platforms for inter- facing with well-known video games. With the recent convergence of advances in consumer electronics, ubiquitous computing, and wearable sensor technologies real-time monitoring of neurocognitive and affective states can be studied in an objective manner. Whilst establishing the optimal relation among frequency bands, task engagement, and arousal states is a goal of neurogaming, a standardized method has yet to be established. Herein we aimed to test classifiers within the same context, group of participants, feature extraction methods, and protocol. Given the emphasis upon neurogaming, a commercial-grade electroencephalographic (EEG; Emotiv EPOC) headset was used to collect signals from 30 participants. The EEG data was then filtered to get separate frequency bands to train cognitive-affective classifiers with three classification techniques: Support Vector Machines (SVM), Naive Bayes (NB), and k-Nearest Neighbors (kNN). Results revealed that the NB classifier was the most robust classifier for identifying negative (e.g., character death) game-based events. The identification of general gameplay events is best identified using kNN and the Beta band. Results from this study suggest that a combination of classifiers is preferable over selection of a single classifier.
Published in: IEEE Transactions on Affective Computing ( Volume: 13, Issue: 1, 01 Jan.-March 2022)
Page(s): 3 - 15
Date of Publication: 07 May 2020

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