ABSTRACT
Our project focused on recognizing emotion from human brain activity, measured by EEG signals. We have proposed a system to analyze EEG signals and classify them into 5 classes on two emotional dimensions, valence and arousal. This system was designed using prior knowledge from other research, and is meant to assess the quality of emotion recognition using EEG signals in practice. In order to perform this assessment, we have gathered a dataset with EEG signals. This was done by measuring EEG signals from people that were emotionally stimulated by pictures. This method enabled us to teach our system the relationship between the characteristics of the brain activity and the emotion. We found that the EEG signals contained enough information to separate five different classes on both the valence and arousal dimension. However, using a 3-fold cross validation method for training and testing, we reached classification rates of 32% for recognizing the valence dimension from EEG signals and 37% for the arousal dimension. Much better classification rates were achieved when using only the extreme values on both dimensions, the rates were 71% and 81%.
- Aftanas, L., N. Reva, A. Varlamov, S. Pavlov, V. Makhnev. Analysis of evoked eeg synchronization and desynchronization in conditions of emotional activation in humans: Temporal and topographic characteristics, Neuroscience and Behavioral Physiology, vol. 34, no. 8, pp. 859--867, 2004.Google ScholarCross Ref
- Bradley, M. M., P. J. Lang. Measuring emotion: The self-assessment manikin (sam) and the semantic differential, Journal of Experimental Psychiatry & Behavior Therapy, vol. 25, no. 1, pp. 49--59, 1994.Google Scholar
- Bush, G., P. Luu, M. Posner. Cognitive and emotional influences in anterior cingulate cortex, Trends in cognitive sciences, vol. 4, pp. 215--222, 2000.Google Scholar
- Busso, C., Z. Deng, S. Yildirim, M. Bulut, C. Lee, A. Kazemzadeh, S. Lee, U. Neumann, S. Narayanan. Analysis of emotion recognition using facial expressions, speech and multimodal information, in ICMI '04: Proceedings of the 6th international conference on Multimodal interfaces. New York, NY, USA: ACM Press, 2004. Google ScholarDigital Library
- Chanel, G., J. Kronegg, D. Grandjean, T. Pun. Emotion assessment: Arousal evaluation using eeg's and peripheral physiological signals, Computer Vision Group, Computing Science Center, University of Geneva, Tech. Rep., 2005.Google Scholar
- Choppin, A. Eeg-based human interface for disabled individuals: Emotion expression with neural networks, Master's thesis, Tokyo Institure of Technology, 2000.Google Scholar
- Dellaert, F., T. Polzin, A. Waibel. Recognizing emotion in speech, in ICSLP 96. Proceedings, vol. 3, October 1996, pp. 1970--1973.Google Scholar
- Ekman, P. Basic emotions, in Handbook of cognition and emotion, 1999.Google Scholar
- Hagemann, D., E. Naumann, J. Thayer. The quest for the eeg reference revisited: A glance from brain asymmetry research, Psychophysiology, 2001.Google Scholar
- Hjorth, B. Eeg analysis based on time domain properties, Electroencephalography and Clinical Neurophysiology, vol. 29, no. 3, pp. 306--310, 1970.Google ScholarCross Ref
- Lang, P., M. Bradley, B. Cuthbert. International affective picture system (iaps): Affective ratings of pictures and instruction manual, University of Florida, Gainesville, FL, Tech. Rep. Technical Report A-6, 2005.Google Scholar
- Pantic, M., L. Rothkrantz. Automatic analysis of facial expressions: The state of the art," in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000. Google ScholarDigital Library
- Peng, H., F. Long, C. Ding. Feature selection based on mutual information: Criteria of max-dependency, max-relevance and minredundancy, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, 2005. Google ScholarDigital Library
- Reeves, B., C. Nass. The media equation; how people treat computers, television, and new media like real people and places. Stanford: CSLI, 1996. Google ScholarDigital Library
- Savran, A., K. Ciftci, G. Chanel, J. Mota, L. Viet, B. Sankur, L. Akarun, A. Caplier, M. Rombaut. Emotion detection in the loop from brain signals and facial images, http://www.enterface.net/results/, 2006, visited on September, 26, 2007.Google Scholar
- Schiffer, F., M. Teicher, C. Anderson, A. Tomoda, A. Polcari, C. Navalta, S. Andersen. Determination of hemispheric emotional valence in individual subjects: A new approach with research and therapeutic implications, Behavioral and Brain Functions, 2007.Google ScholarCross Ref
- Shemyakina, N., S. Danko. Influence of the emotional perception of a signal on the electroencephalographic correlates of creative activity, Human Physiology, 2004.Google Scholar
- Emotion recognition using brain activity
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