{"title":"A Psychophysiological Evaluation of an Effective Recognition Technique Using Interactive Dynamic Virtual Environments","authors":"Mohammadhossein Moghimi, Robert Stone, Pia Rotshtein","volume":125,"journal":"International Journal of Computer and Information Engineering","pagesStart":571,"pagesEnd":580,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10007098","abstract":"<p>Recording psychological and physiological correlates of human performance within virtual environments and interpreting their impacts on human engagement, &lsquo;immersion&rsquo; and related emotional or &lsquo;effective&rsquo; states is both academically and technologically challenging. By exposing participants to an effective, real-time (game-like) virtual environment, designed and evaluated in an earlier study, a psychophysiological database containing the EEG, GSR and Heart Rate of 30 male and female gamers, exposed to 10 games, was constructed. Some 174 features were subsequently identified and extracted from a number of windows, with 28 different timing lengths (e.g. 2, 3, 5, etc. seconds). After reducing the number of features to 30, using a feature selection technique, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) methods were subsequently employed for the classification process. The classifiers categorised the psychophysiological database into four effective clusters (defined based on a 3-dimensional space &ndash; valence, arousal and dominance) and eight emotion labels (relaxed, content, happy, excited, angry, afraid, sad, and bored). The KNN and SVM classifiers achieved average cross-validation accuracies of 97.01% (&plusmn;1.3%) and 92.84% (&plusmn;3.67%), respectively. 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