Gamers’ involvement detection from EEG data with cGAAM – A method for feature selection for clustering.

This paper reports the results of an experiment to identify EEG patterns specific to different levels of player involvement when playing a video game. To obtain unbiased results, we based our patterns on both raw EEG data and expert knowledge. We used a three-step procedure to identify patterns. First, we looked for clusters in the reduced feature space extracted from EEG data. Next, we assigned experts’ interpretations to the clusters. Finally, we analysed relations between features used to form the clusters and the class labels provided by experts. The most challenging part of the procedure was feature selection simultaneous with unsupervised classification. To accomplish this task, we developed a new approach for simultaneous feature selection and clustering based on modified GAAM (genetic algorithm with aggressive mutation). When the cGAAM algorithm was applied to EEG data, it returned the feature subsets that (a) were highly consistent across subjects and (b) provided 50% more compact clusters than clusters built over the feature subsets returned by a forward selection search strategy. The main cognitive outcome of EEG signal analysis was a set of patterns differentiating players’ involvement in a game. Conclusions included: 1. For a majority of subjects the most discriminative features were activity in the theta band in the left and right frontal areas, and activity in the delta band in the left frontal area; 2. All three features significantly differentiated between low and high, or medium and high engagement; 3. All subjects showed positive correlations between selected feature values and levels of engagement. [ABSTRACT FROM AUTHOR]