ABSTRACT In this paper, we summarize the human emotion recognition using different set of electroencephalogram (EEG) channels using discrete wavelet transform. An audio-visual induction based protocol has been designed with more dynamic emotional content for inducing discrete emotions (disgust, happy, surprise, fear and neutral). EEG signals are collected using 64 electrodes from 20 subjects and are placed over the entire scalp using International 10-10 system. The raw EEG signals are preprocessed using Surface Laplacian (SL) filtering method and decomposed into three different frequency bands (alpha, beta and gamma) using Discrete Wavelet Transform (DWT). We have used “db4” wavelet function for deriving a set of conventional and modified energy based features from the EEG signals for classifying emotions. Two simple pattern classification methods, K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA) methods are used and their performances are compared for emotional states classification. The experimental results indicate that, one of the proposed features (ALREE) gives the maximum average classification rate of 83.26% using KNN and 75.21% using LDA compared to those of conventional features. Finally, we present the average classification rate and subsets of emotions classification rate of these two different classifiers for justifying the performance of our emotion recognition system.
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nullMurugappan, M. , Ramachandran, N. and Sazali, Y. (2010) Classification of human emotion from EEG using discrete wavelet transform. Journal of Biomedical Science and Engineering, 3, 390-396. doi: 10.4236/jbise.2010.34054.
 Bung, H. and Furui, S. (2000) Automatic recognition and understanding of spoken languages-a first step toward natural human machine communication. Proceedings of IEEE, 88, 1142-1165.
Cowie, R., Douglas, E., Tsapatsoulis, N., Votsis, G., Kollias, G., Fellenz, W. and Taylor, J.G. (2001) Emotion Recognition in human-computer interaction. IEEE Signal Processing, 1, 3773-3776.
Takahashi, K. (2004) Remarks on emotion recognition from bio-potential signals. The Second International Conference on Autonomous Robots and Agents, 186-191.
Picard, R.W. (2000) Toward computers that recognize and respond to user emotion. IBM Systems Journal, 39(3), 705-719.
Picard, R.W. and Healey, J. (1997) Affective wearable’s. Personal Technologies, 1(4), 231-240.
Kim, K.H., Band, S.W. and Kim, S.B. (2004) Emotion recognition system using short-term monitoring of physiological signals. Proceedings on Medical & Biological Engineering & Computing, 42, 419-427.
Chanel, G., Karim, A.A. and Pun, T. (2007) Valence-arousal evaluation using physiological signals in an emotion recall paradigm. Lecturer Notes in Computer Science, 1, 530-537.
Chanel, G., Kronegg, J., Grandjean, D. and Pun, T. (2005) Emotion assessment: Arousal evaluation using EEG’s and peripheral physiological signals. Technical Report, 4105, 530-537.
Murugappan, M., Rizon, M., Nagarajan, R. and Yaacob, S. (2009a) Inferring of human emotion states using multichannel EEG. International Journal of Soft Computing and Applications (IJSCA), EURO Journals, United Kingdom. (Accepted).
Russell, J.A. (1980) A Circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161- 1178.
Lang. P.J. (1995) The emotion probe: Studies of motivation and attention. American Psychologist, 50(5), 372- 385.
Danny, O.B. (2008) Automated artifact detection in brain stream. Technical Report, 1-8.
Wang, Y. and Guan, L. (2005) Recognizing human emotion from audiovisual information. Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, 2, 1125-1128.
Murugappan, M., Rizon, M., Nagarajan, R. and Yaacob, S. (2008) Asymmetric ratio and FCM based salient channel selection for human emotion recognition using EEG. WSEAS Transactions on Signal Processing, 10(4), 596-603.
Murugappan, M., Rizon, M., Nagarajan, R., Yaacob, S., Hazry, D. and Zunaidi, I. (2008) Time-frequency analysis of EEG signals for human emotion detection. IFMBE Proceedings, 21, 262-265.
Takahashi, K. and Tsukaguchi, A. (2004) Remarks on emotion recognition from multi-modal bio-potential signals. Proceedings of IEEE International Workshop on Robot and Human Interactive Communication, 95-100.
Jonghwa, K. and Elisabeth, A. (2008) Emotion recognition based on physiological changes in music listening. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(12), 1-17.
Murugappan, M., Rizon, M., Nagarajan, R. and Yaacob, S. (2009) An Investigation on visual and audiovisual stimulus based emotion recognition using EEG. Transactions on Medical Engineering and Informatics, 1(3), 342-356.
Jung, T. (2000) Removing electroencephalographic artifacts by blind source separation. Journal of Psychophysiology, 37(2), 163-178.
Gott, P.S., Hughes, E.C. and Whipple. (1984) Voluntary control of two lateralized conscious states: Validation by electrical and behavioral studies. Journal of Neuropsychological, 22, 65-72.
Mallat, S.G. (1989) A theory for multi-resolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674-693.
Merzagora, A.C., Bunce, S., Izzetoglu, M. and Onaral, B. (2006) Wavelet analysis for EEG feature extraction in deceptive detection. IEEE Proceedings on EBMS, 6, 2434- 2437.
Chethan, P. and Cox, M. (2002) Frequency characteristics of wavelets. IEEE Transactions on Power Delivery, 17(3), 800-804.
Charles, W.A. and Zlatko, S. (1997) Classification of EEG signals from four subjects during five mental tasks. IEEE Proceedings on Engineering Application in Neural Networks, 407-414.
Wanpracha, A.C., Ya-ju, F. and Rajesh, C.S. (2007) On the time series k-nearest neighbor classification of abnormal brain activity. IEEE Transactions on Systems, Man and Cybernetics–Part A: Systems and Humans, 37(6), 1005- 1016.