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Bio-Affective Computer Interface for Game Interaction

Bio-Affective Computer Interface for Game Interaction

Jorge Arroyo-Palacios, Daniela M. Romano
Copyright: © 2010 |Volume: 2 |Issue: 4 |Pages: 17
ISSN: 1942-3888|EISSN: 1942-3896|EISBN13: 9781613502532|DOI: 10.4018/jgcms.2010100102
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MLA

Arroyo-Palacios, Jorge, and Daniela M. Romano. "Bio-Affective Computer Interface for Game Interaction." IJGCMS vol.2, no.4 2010: pp.16-32. http://doi.org/10.4018/jgcms.2010100102

APA

Arroyo-Palacios, J. & Romano, D. M. (2010). Bio-Affective Computer Interface for Game Interaction. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 2(4), 16-32. http://doi.org/10.4018/jgcms.2010100102

Chicago

Arroyo-Palacios, Jorge, and Daniela M. Romano. "Bio-Affective Computer Interface for Game Interaction," International Journal of Gaming and Computer-Mediated Simulations (IJGCMS) 2, no.4: 16-32. http://doi.org/10.4018/jgcms.2010100102

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Abstract

Affective bio-feedback can be an important instrument to enhance the game experience. Several studies have provided evidence of the usefulness of physiological signals for affective gaming; however, due to the limited knowledge about the distinctive autonomic signatures for every emotion, the pattern matching models employed are limited in the number of emotions they are able to classify. This paper presents a bio-affective gaming interface (BAGI) that can be used to customize a game experience according to the player’s emotional response. Its architecture offers important characteristics for gaming that are important because they make possible the reusability of previous findings and the inclusion of new models to the system. In order to prove the effectiveness of BAGI, two different types of neural networks have been trained to recognize emotions. They were incorporated into the system to customize, in real-time, the computer wallpaper according to the emotion experienced by the user. Best results were obtained with a probabilistic neural network with accuracy results of 84.46% on the training data and 78.38% on the validation for new independent data sets.

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