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Video Game Assessment

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While my research usually focusses on doing things better than before, the term "better" is highly subjective and differs from one research community to another. Some research communities are data-driven and expect things like performance graphs showing better run-time or memory usage. In the graphics community, on the other hand, it's usually enough to show pretty pictures. Increasingly I find that referees in game development conferences and journals expect tests on human subjects. After all, the final measure of what's good in a game is what sells well, and that depends on impressing a suitably large number of capricious human beings whose tastes are difficult to predict. This component of research had been missing from my laboratory until 2013, when I started what I hope will be a long and fruitful collaboration with UNT's Dr. Thomas D. Parsons, who is a Fellow of the National Academy of Neuropsychology, Director of the Clinical Neuropsychology and Simulation (CNS) Laboratory, and teaches in the Clinical Psychology and Behavioral Science programs in the Department of Psychology.

Neurogaming (2015)

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Thomas D. Parsons, Timothy McMahan, and Ian Parberry, "Neurogaming-based Classification of Player Experience Using Consumer-Grade Electroencephalography", Technical Report LARC-2015-04, Laboratory for Recreational Computing, Dept. of Computer Science & Engineering, University of North Texas, April 2015. [pdf, BibTeX]

Abstract

A growing body of literature has emerged that demonstrates the potential of neurogaming platforms for interfacing with well-known videogames. 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, timely, and ecologically valid manner. Whilst establishing the optimal relation among frequency bands, task engagement, and arousal states is one of the main goals 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, the commercial-grade Emotiv EPOC headset was used to collect electroencephalographic (EEG) signals from users as participants experienced various stimulus modalities aimed at assessing cognitive and affective processing. The EEG data were then filtered to get separate frequency bands to train cognitive-affective classifiers with three classification techniques: Support Vector Machines (SVM), Naïve Bayes (NB), and k-Nearest Neighbors (kNN). Results revealed that the NB classifier was the most robust classifier for identifying game-based Death Events. However, the identification of General Gameplay Events is best identified using kNN and the Beta band. From this study’s findings, it is suggested that using a combination of classifiers is preferable over selecting just one classifier.

Engagement and Arousal (2015)

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Timothy McMahan, Ian Parberry, and Thomas D. Parsons, "Evaluating Player Task Engagement and Arousal using Electroencephalography", Proceedings of the 3rd International Conference on Affective and Pleasurable Design, pp. 1616-1623, 2015. [pdf, BibTeX]

Abstract

Advances in affective computing technologies have made it possible for researchers to investigate brain function while users interact in virtual environments. Progress in sensors and algorithms for off-the-shelf EEG systems has made it possible for gaming researchers to perform real-time estimation of human cognitive and affective states using EEG. In this study our aim was to coordinate "Task Engagement" data with "Arousal-Valence" data. The resulting coordinate was designed for application to expressive transformations to video game play in real time by tuning different performance parameters in an Engagement-Arousal rule system. Results revealed that the engagement index (Beta / (Alpha + Theta)) was capable of differentiating high intensity game events (Player Death) from general game play. Given that higher levels of engagement during death events may reflect increase in autonomic response, we also measured arousal by using (BetaF3 + BetaF4) / (AlphaF3 + AlphaF4) and valence using (AlphaF4 / BetaF4) - (AlphaF3 / BetaF3). Results revealed that arousal increases and valence decreases during high intensity game events (Player Death) when compared to lower intensity game events (General Game Play). Given our desire to establish "Task Engagement" data with "Arousal-Valence" coordinates for a flow model, we divided the data into quartiles, which allowed us to establish upper and lower thresholds to indicate when the player has left a state of flow. Our aim was to use an off-the-shelf EEG system to establish "Task Engagement" and "Arousal-Valence" coordinates during video game play that can be used for a flow model. It is believed that this model will allow for future use of the Emotiv for assessing the cognitive and emotional processing of the player.

Engagement Indices (2015)

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Timothy McMahan, Ian Parberry, and Thomas D. Parsons, "Evaluating Electroencephalography Engagement Indices During Video Game Play", Proceedings of the 10th International Conference on the Foundations of Digital Games (FDG 2015), June 22-25, 2015, Pacific Grove, CA, USA, 2015.

Abstract

Given the growing popularity of video gaming and off-the shelf electroencephalographic (EEG) devices like the Emotiv, there is a growing need for methods of measuring gamer experience in real-time. Engagement indices developed to monitor human engagement have yet to be implemented with the Emotiv. In this study, we compared three different engagement indices during various video game modalities using the Emotiv device. EEG data was collected from 30 participants during video game play of Super Meat Boy. Two EEG indices (frontal theta and the ratio of beta to alpha+theta) showed a significant difference in engagement level among the different gaming modalities with the increasingly difficult cognitive demand. Our findings suggest that the Emotive EEG can be to measure a player’s varying levels of engagement as they play a video game.

Author's Comment

A preliminary version of this paper was published as Technical Report LARC-2015-03, Laboratory for Recreational Computing, Dept. of Computer Science & Engineering, University of North Texas, February 2015 [pdf, BibTeX].

SokoStroop (2015)

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Joshua Taylor, Thomas D. Parsons, and Ian Parberry, "Comparing Player Attention on Procedurally Generated vs. Hand Crafted Sokoban Levels with an Auditory Stroop Test", Proceedings of the 10th International Conference on the Foundations of Digital Games (FDG 2015), June 22-25, 2015, Pacific Grove, CA, USA, 2015.

Abstract

Evidence is provided that players pay at least as much attention to a set of procedurally generated Sokoban levels as they do to levels hand crafted by expert designers. Data were collected from 40 participants who played Sokoban under laboratory conditions while simultaneously performing an auditory Stroop test. Three performance measures from the Stroop test were analyzed and compared after accounting for differences in individual players.

Author's Comments

A preliminary version of this paper was published as Technical Report LARC-2015-02, Laboratory for Recreational Computing, Dept. of Computer Science & Engineering, University of North Texas, February 2015 [pdf, BibTeX] For more information on the procedural level generator that was used in this evaluation, see Joshua Taylor and Ian Parberry, "Procedural Generation of Sokoban Levels", Proceedings of the 6th International North American Conference on Intelligent Games and Simulation, pp. 5-12, 2011.

Cognitive Assessment (2015)

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Timothy McMahan, Ian Parberry, and Thomas D. Parsons, "Modality Specific Assessment of Video Game Player's Experience Using the Emotiv", Entertainment Computing, Vol. 7, pp. 1-6, March 2015. [Article and BibTex at Science Direct]

Abstract

A growing body of literature has emerged that focuses upon cognitive assessment of video game player experience. Given the growing popularity of video gaming and the increasing literature on cognitive aspects of video gamers, there is a growing need for novel approaches to assessment of the cognitive processes that occur while persons are immersed in video games. In this study, we assessed various stimulus modalities and gaming events using an off-the-shelf EEG devise. A significant difference was found among different stimulus modalities with increasingly difficult cognitive demands. Specifically, beta and gamma power were significantly increased during high intensity events when compared to low intensity gaming events. Our findings suggest that the Emotiv EEG can be used to differentiate between varying stimulus modalities and accompanying cognitive processes.

Author's Comments

A preliminary version of this paper was published under the title "Modality Specific Assessment of Video Game Player's Cognitive Workload Using Off-the-Shelf Electroencephalographic Technologies", as Technical Report LARC-2014-03, Laboratory for Recreational Computing, Dept. of Computer Science & Engineering, University of North Texas, April 2014. [pdf, BibTeX]

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