Table of Contents Author Guidelines Submit a Manuscript
Advances in Artificial Intelligence
Volume 2011 (2011), Article ID 384169, 13 pages
Research Article

Towards a Brain-Sensitive Intelligent Tutoring System: Detecting Emotions from Brainwaves

HERON Lab, Computer Science Department, University of Montreal, P.O. Box 6128, Centre Ville Montréal, QC, H3T-1J4, Canada

Received 14 May 2010; Accepted 21 February 2011

Academic Editor: Jun Hong

Copyright © 2011 Alicia Heraz and Claude Frasson. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes and evaluates a multiagents system called NORA that predicts emotional attributes from learners' brainwaves within an intelligent tutoring system. The measurements from the electrical brain activity of the learner are combined with information about the learner's emotional attributes. Electroencephalogram was used to measure brainwaves and self-reports to measure the three emotional dimensions: pleasure, arousal, and dominance, the eight emotions occurring during learning: anger, boredom, confusion, contempt curious, disgust, eureka, and frustration, and the emotional valence positive for learning and negative for learning. The system is evaluated on natural data, and it achieves an accuracy of over 63%, significantly outperforming classification using the individual modalities and several other combination schemes.