Emotion detection using noisy EEG data

Emotion is an important aspect in the interaction between humans. It is fundamental to human experience and rational decision-making. There is a great interest for detecting emotions automatically. A number of techniques have been employed for this purpose using channels such as voice and facial expressions. However, these channels are not very accurate because they can be affected by users' intentions. Other techniques use physiological signals along with electroencephalography (EEG) for emotion detection. However, these approaches are not very practical for real time applications because they either ask the participants to reduce any motion and facial muscle movement or reject EEG data contaminated with artifacts. In this paper, we propose an approach that analyzes highly contaminated EEG data produced from a new emotion elicitation technique. We also use a feature selection mechanism to extract features that are relevant to the emotion detection task based on neuroscience findings. We reached an average accuracy of 51% for joy emotion, 53% for anger, 58% for fear and 61% for sadness.

[1]  Norbert Schwarz,et al.  Salience of comparison standards and the activation of social norms: Consequences for judgements of happiness and their communication , 1990 .

[2]  M. Kostyunina,et al.  Frequency characteristics of EEG spectra in the emotions , 1996, Neuroscience and Behavioral Physiology.

[3]  Toshimitsu Musha,et al.  Feature extraction from EEGs associated with emotions , 1997, Artificial Life and Robotics.

[4]  John J. B. Allen,et al.  Varieties of Emotional Experience during Voluntary Emotional Facial Expressions , 2003, Annals of the New York Academy of Sciences.

[5]  K. H. Kim,et al.  Emotion recognition system using short-term monitoring of physiological signals , 2004, Medical and Biological Engineering and Computing.

[6]  Thierry Pun,et al.  A channel selection method for EEG classification in emotion assessment based on synchronization likelihood , 2007, 2007 15th European Signal Processing Conference.

[7]  F. Strack,et al.  Inhibiting and facilitating conditions of the human smile: a nonobtrusive test of the facial feedback hypothesis. , 1988, Journal of personality and social psychology.

[8]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

[9]  Rohan Shiloh Shah,et al.  Support Vector Machines for Classiflcation and Regression , 2007 .

[10]  Panayiotis G. Georgiou,et al.  Real-time Emotion Detection System using Speech: Multi-modal Fusion of Different Timescale Features , 2007, 2007 IEEE 9th Workshop on Multimedia Signal Processing.

[11]  John J. B. Allen,et al.  Voluntary facial expression and hemispheric asymmetry over the frontal cortex. , 2001, Psychophysiology.

[12]  Peter Robinson,et al.  Mind reading machines: automated inference of cognitive mental states from video , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[13]  P. Ekman,et al.  Autonomic nervous system activity distinguishes among emotions. , 1983, Science.

[14]  L. H. Viet,et al.  Emotion Detection in the Loop from Brain Signals and Facial Images , 2006 .

[15]  Guillaume Chanel,et al.  Emotion Assessment: Arousal Evaluation Using EEG's and Peripheral Physiological Signals , 2006, MRCS.

[16]  E. Vesterinen,et al.  Affective Computing , 2009, Encyclopedia of Biometrics.

[17]  J. Allen,et al.  Resting frontal electroencephalographic asymmetry in depression: inconsistencies suggest the need to identify mediating factors. , 1998, Psychophysiology.

[18]  David Sander,et al.  A systems approach to appraisal mechanisms in emotion , 2005, Neural Networks.

[19]  R. Plutchik.  A GENERAL PSYCHOEVOLUTIONARY THEORY OF EMOTION , 1980 .

[20]  Tanja Schultz,et al.  Towards an EEG-based emotion recognizer for humanoid robots , 2009, RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication.