Andreu-Perez, Javier and Kiani, Mehrin (2021) Single-Trial Recognition of Video Gamer’s Expertise from Brain Haemodynamic and Facial Emotion Responses. Brain Sciences, 11 (1). p. 106. DOI https://doi.org/10.3390/brainsci11010106
Andreu-Perez, Javier and Kiani, Mehrin (2021) Single-Trial Recognition of Video Gamer’s Expertise from Brain Haemodynamic and Facial Emotion Responses. Brain Sciences, 11 (1). p. 106. DOI https://doi.org/10.3390/brainsci11010106
Andreu-Perez, Javier and Kiani, Mehrin (2021) Single-Trial Recognition of Video Gamer’s Expertise from Brain Haemodynamic and Facial Emotion Responses. Brain Sciences, 11 (1). p. 106. DOI https://doi.org/10.3390/brainsci11010106
Abstract
With an increase in consumer demand of video gaming entertainment, the game industry is exploring novel ways of game interaction such as providing direct interfaces between the game and the gamers’ cognitive or affective responses. In this work, gamer’s brain activity has been imaged using functional near infrared spectroscopy (fNIRS) whilst they watch video of a video game (League of Legends) they play. A video of the face of the participants is also recorded for each of a total of 15 trials where a trial is defined as watching a gameplay video. From the data collected, i.e., gamer’s fNIRS data in combination with emotional state estimation from gamer’s facial expressions, the expertise level of the gamers has been decoded per trial in a multi-modal framework comprising of unsupervised deep feature learning and classification by state-of-the-art models. The best tri-class classification accuracy is obtained using a cascade of random convolutional kernel transform (ROCKET) feature extraction method and deep classifier at 91.44%. This is the first work that aims at decoding expertise level of gamers using non-restrictive and portable technologies for brain imaging, and emotional state recognition derived from gamers’ facial expressions. This work has profound implications for novel designs of future human interactions with video games and brain-controlled games.
Item Type: | Article |
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Uncontrolled Keywords: | mind-controlled games; brain signals; fNIRS; facial expressions |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 28 Jan 2021 10:08 |
Last Modified: | 07 Aug 2024 19:26 |
URI: | http://repository.essex.ac.uk/id/eprint/29452 |
Available files
Filename: brainsci-11-00106.pdf
Licence: Creative Commons: Attribution 3.0