Confusion State Induction and EEG-based Detection in Learning.

Confusion, as an affective state, has been proved beneficial for learning, although this emotion is always mentioned as negative affect. Confusion causes the learner to solve the problem and overcome difficulties in order to restore the cognitive equilibrium. Once the confusion is successfully resolved, a deeper learning is generated. Therefore, quantifying and visualizing the confusion that occurs in learning as well as intervening has gained great interest by researchers. Among these researches, triggering confusion precisely and detecting it is the critical step and underlies other studies. In this paper, we explored the induction of confusion states and the feasibility of detecting confusion using EEG as a first step towards an EEG-based Brain Computer Interface for monitoring the confusion and intervening in the learning. 16 participants EEG data were recorded and used. Our experiment design to induce confusion was based on tests of Raven's Standard Progressive Matrices. Each confusing and not-confusing test item was presented during 15 seconds and the raw EEG data was collected via Emotiv headset. To detect the confusion emotion in learning, we propose an end-to-end EEG analysis method. End-to-end classification of Deep Learning in Machine Learning has revolutionized computer vision, which has gained interest to adopt this method to EEG analysis. The result of this preliminary study was promising, which showed a 71.36% accuracy in classifying users' confused and unconfused states when they are inferring the rules in the tests.