Classification of Game Demand and the Presence of Experimental Pain Using Functional Near-Infrared Spectroscopy.

Autor:	Fairclough SH; School of Psychology, Liverpool John Moores University, Liverpool, United Kingdom., Dobbins C; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia., Stamp K; School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom.
Jazyk:	angličtina
Zdroj:	Frontiers in neuroergonomics [Front Neurogenom] 2021 Dec 21; Vol. 2, pp. 695309. Date of Electronic Publication: 2021 Dec 21 (Print Publication: 2021).
DOI:	10.3389/fnrgo.2021.695309
Abstrakt:	Pain tolerance can be increased by the introduction of an active distraction, such as a computer game. This effect has been found to be moderated by game demand, i.e., increased game demand = higher pain tolerance. A study was performed to classify the level of game demand and the presence of pain using implicit measures from functional Near-InfraRed Spectroscopy (fNIRS) and heart rate features from an electrocardiogram (ECG). Twenty participants played a racing game that was configured to induce low (Easy) or high (Hard) levels of demand. Both Easy and Hard levels of game demand were played with or without the presence of experimental pain using the cold pressor test protocol. Eight channels of fNIRS data were recorded from a montage of frontal and central-parietal sites located on the midline. Features were generated from these data, a subset of which were selected for classification using the RELIEFF method. Classifiers for game demand (Easy vs. Hard) and pain (pain vs. no-pain) were developed using five methods: Support Vector Machine (SVM), k-Nearest Neighbour (kNN), Naive Bayes (NB) and Random Forest (RF). These models were validated using a ten fold cross-validation procedure. The SVM approach using features derived from fNIRS was the only method that classified game demand at higher than chance levels (accuracy = 0.66, F1 = 0.68). It was not possible to classify pain vs. no-pain at higher than chance level. The results demonstrate the viability of utilising fNIRS data to classify levels of game demand and the difficulty of classifying pain when another task is present. Competing Interests: The authors declare that this study received support from Onteca Ltd who supplied the game used during the study, and an SDK that allowed the authors to manipulate the level of game demand. The company was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. (Copyright © 2021 Fairclough, Dobbins and Stamp.)
Databáze:	MEDLINE
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