Title: Using minimal number of electrodes for emotion detection using brain signals produced from a new elicitation technique

Authors: Mina Mikhail; Khaled El-Ayat; James A. Coan; John J.B. Allen

Addresses: Department of Computer Science and Engineering, The American University in Cairo, AUC Avenue, P.O. Box 74, New Cairo 11835, Egypt ' Department of Computer Science and Engineering, The American University in Cairo, AUC Avenue, P.O. Box 74, New Cairo 11835, Egypt ' Department of Psychology, University of Virginia, 102 Gilmer Hall, Charlottesville, VA 22904-4400, USA ' Department of Psychology, University of Arizona, 1503 E University Blvd., Tucson, AZ 85721-0068, USA

Abstract: Emotion is an important aspect in the interaction between humans. There is a great interest for detecting emotions automatically. Current approaches for emotion detection using EEG are not practical for real-life situations because researchers ask participants to reduce any motion and facial muscle movement, reject noisy EEG data and rely on large number of electrodes. In this paper, we propose an approach that analyses highly contaminated brain signals. We then extract relevant features for the emotion detection task based on neuroscience findings. We reached an average accuracy of 51%, 53%, 58% and 61% for joy, anger, fear and sadness, respectively. We are also applying our approach on fewer number of electrodes that ranges from 4 to 25 electrodes and we reached an average classification accuracy of 33% for joy emotion, 38% for anger, 33% for fear and 37.5% for sadness using 4 or 6 electrodes only.

Keywords: emotion detection; brain signals; emotion elicitation; brain-computer interface; affective BCI; human-computer interaction; HCI; adaptation; emotions; emotional response; neuroscience; electrodes; joy; anger; fear; sadness; classification accuracy.

DOI: 10.1504/IJAACS.2013.050696

International Journal of Autonomous and Adaptive Communications Systems, 2013 Vol.6 No.1, pp.80 - 97

Published online: 13 Sep 2014 *

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