Biomedical Data Acquisition and Processing to Recognize Emotions for Affective Learning

Emotion recognition is a increasingly popular topic because of its potential applications in the field of affective learning. It allows the development of systems able to adapt themselves to the users' emotional state to improve the learner's experience and learning. In this paper, we introduce a new biomedical multi-sensor platform for realtime acquisition of physiological data comprising Temperature, Electroencephalography (EEG), Electroocculography (EOG), Galvanic Skin Response (GSR), Heart Rate and Blood Oxygen Saturation. We describe experimental scenarios for the induction of emotions relevant in a context of affective learning (happiness, frustration, boredom) to build a set of emotion-related data. We carry out a basic classification study by computing hand-crafted features on the time and frequency domains of signals, and training a Support-Vector-Machine (SVM) classifier to demonstrate the feasibility of our approach.