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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.<\/p>","DOI":"10.4018\/jgcms.2010100102","type":"journal-article","created":{"date-parts":[[2011,2,3]],"date-time":"2011-02-03T11:59:19Z","timestamp":1296734359000},"page":"16-32","source":"Crossref","is-referenced-by-count":4,"title":["Bio-Affective Computer Interface for Game Interaction"],"prefix":"10.4018","volume":"2","author":[{"given":"Jorge","family":"Arroyo-Palacios","sequence":"first","affiliation":[{"name":"University of Sheffield, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniela M.","family":"Romano","sequence":"additional","affiliation":[{"name":"University of Sheffield, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"jgcms.2010100102-0","unstructured":"Arroyo-Palacios, J., & Romano, D. (2008). 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