A New Approach to Emotion Assessment Based on Biometric Data

Several knowledge areas such as psychology, medicine and computer science have been devoting serious efforts regarding emotional state definition, identification and assessment. This project consists in an automatic emotion assessment tool based on biometric data acquisition supported by low-budget biometric devices as a electroencephalograph and a galvanic skin response. The classification is grounded on data analysis and processing of standard emotional induction methods. The numerous conducted experimental sessions,alongside with the developed support tools, allowed the extraction of conclusions such as the capability of effectively performing automatic classification of the subjectpsilas predominant emotional state. The developed tool's success rate, validated against self assessment interviews, was approximately 75%. It was also experimentally concluded that female subjects are emotionally more active and easily induced than males.

[1]  A. Damasio.  Descarte's error : emotion, reason, and the human brain , 1994 .

[2]  L. Aftanas,et al.  Time-dependent cortical asymmetries induced by emotional arousal: EEG analysis of event-related synchronization and desynchronization in individually defined frequency bands. , 2002, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[3]  G. Cascino,et al.  Subcortical Dementia , 1991, Neurology.

[4]  Gerwin Schalk,et al.  A brain–computer interface using electrocorticographic signals in humans , 2004, Journal of neural engineering.

[5]  N. V. Lotova,et al.  Nonlinear Forecasting Measurements of the Human EEG During Evoked Emotions , 2004, Brain Topography.

[6]  P. Lang,et al.  International Affective Picture System (IAPS): Instruction Manual and Affective Ratings (Tech. Rep. No. A-4) , 1999 .

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

[8]  P. Lang.  International affective picture system (IAPS) : affective ratings of pictures and instruction manual , 2005 .

[9]  Masafumi Hagiwara,et al.  A feeling estimation system using a simple electroencephalograph , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[10]  T. Pedley.  Current Practice of Clinical Electroenceph‐alography , 1980, Neurology.

[11]  Kazuhiko Takahashi.  Remarks on Emotion Recognition from Bio-Potential Signals , 2004 .

[12]  L. Aftanas,et al.  Analysis of Evoked EEG Synchronization and Desynchronization in Conditions of Emotional Activation in Humans: Temporal and Topographic Characteristics , 2004, Neuroscience and Behavioral Physiology.

[13]  M. A. Kulikov,et al.  Spatial Distribution of Coefficients of Asymmetry of Brain Bioelectrical Activity during the Experiencing of Negative Emotions , 2003, Neuroscience and Behavioral Physiology.

[14]  T. Pedley,et al.  Current practice of clinical electroencephalography, 3rd edn , 2003 .

[15]  Luís Paulo Reis,et al.  Multichannel Emotion Assessment Framework - Positive and Negative Emotional Dichotomy , 2008, ICINCO-SPSMC.

[16]  A. Damasio.  Descartes’ Error. Emotion, Reason and the Human Brain. New York (Grosset/Putnam) 1994. , 1994 .

[17]  L. Aftanas,et al.  Neurophysiological Correlates of Induced Discrete Emotions in Humans: An Individually Oriented Analysis , 2006, Neuroscience and Behavioral Physiology.

[18]  Luís Paulo Reis,et al.  Multichannel Emotion Assessment Framework - Gender and High-Frequency Electroencephalography as Key-Factors , 2008, ICEIS.

[19]  Touradj Ebrahimi,et al.  Brain-computer interface in multimedia communication , 2003, IEEE Signal Process. Mag..