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An Approach of Human Emotional States Classification and Modeling from EEG
Monira Islam, Md. Salah Uddin Yusuf, Mohiuddin Ahmad
Pages - 73 - 89     |    Revised - 30-04-2019     |    Published - 01-06-2019
Volume - 13   Issue - 3    |    Publication Date - June 2019  Table of Contents
EEG, Emotional State, Emotion Modeling, DWT, FFT, Trust-region Algorithm.
In this paper, a new approach is proposed to model the emotional states from EEG signals with mathematical expressions based on wavelet analysis and trust region algorithm. EEG signals are collected in different emotional states and some salient features are extracted through temporal and spectral analysis to indicate the dispersion which will unify different states. The maximum classification accuracy of emotion is obtained for DWT analysis rather than FFT and statistical analysis. So DWT analysis is considered as the best suited for mathematical modeling of human emotions. The emotional states are modeled with different mathematical expressions using the obtained coefficients from trust region algorithm that can be compared with the sub-band wavelet coefficients of different states. The proposed approach is verified with the adjusted R-square percentage and the sum of square errors. The adjusted R- square percentage of the mathematical modeled states are 78.4% for relax, 77.18% for motor action; however for memory, pleasant, enjoying music and fear they are 93%, 95.6%, 97.7% and 91.5% respectively. The proposed system is reliable that can be applied for practical real time implementation of human emotion based systems.
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1 Akin, M. (2002). Comparison of wavelet transform and FFT methods in the analysis of EEG signals. Journal of medical systems, 26(3), 241-247.
2 Polikar, R. (1996). The wavelet tutorial.
3 Prisnyakov, V. F., & Prisnyakova, L. M. (1994). Mathematical modeling of emotions. Cybernetics and Systems Analysis, 30(1), 142-149.
4 Hartmann, K., Siegert, I., Glüge, S., Wendemuth, A., Kotzyba, M., & Deml, B. (2012). Describing human emotions through mathematical modelling. IFAC Proceedings Volumes, 45(2), 463-468.
5 Murugappan, M., Rizon, M., Nagarajan, R., Yaacob, S., Hazry, D., & Zunaidi, I. (2008). Time-frequency analysis of EEG signals for human emotion detection. In 4th Kuala Lumpur international conference on biomedical engineering 2008 (pp. 262-265). Springer, Berlin, Heidelberg.
6 Srinivasan, N. (2007). Cognitive neuroscience of creativity: EEG based approaches. Methods, 42(1), 109-116.
7 Deore, R. S., Chaudhari, R. D., & Mehrotra, S. C. (2014). Development of EEG based Emotion Recognition System using Song Induced Activity. International Journal of Computer Applications, 86(1).
8 Murugappan, M., Rizon, M., Nagarajan, R., & Yaacob, S. (2010). Inferring of human emotional states using multichannel EEG. European Journal of Scientific Research, 48(2), 281-299.
9 Nasehi, S., Pourghassem, H., & Isfahan, I. R. A. N. (2012). An optimal EEG-based emotion recognition algorithm using gabor. WSEAS transactions on signal processing, 3(8), 87-99.
10 Yuen, C. T., San San, W., Seong, T. C., & Rizon, M. (2009). Classification of human emotions from EEG signals using statistical features and neural network. International Journal of Integrated Engineering, 1(3).
11 AlMejrad, A. S. (2010). Human emotions detection using brain wave signals: A challenging. European Journal of Scientific Research, 44(4), 640-659.
12 Swangnetr, M., & Kaber, D. B. (2012). Emotional state classification in patient-robot interaction using wavelet analysis and statistics-based feature selection. IEEE Transactions on Human-Machine Systems, 43(1), 63-75.
13 Gratch, J., & Marsella, S. (2004). A domain-independent framework for modeling emotion. Cognitive Systems Research, 5(4), 269-306.
14 Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis & Machine Intelligence, (7), 674-693.
15 Yuan, Y. X. (2000). A review of trust region algorithms for optimization. In Iciam (Vol. 99, No. 1, pp. 271-282).
16 Islam, M., Ahmed, T., Mostafa, S. S., Yusuf, M. S. U., & Ahmad, M. (2013, May). Human emotion recognition using frequency & statistical measures of EEG signal. In 2013 International Conference on Informatics, Electronics and Vision (ICIEV) (pp. 1-6). IEEE.
17 Ahmed, T., Islam, M., Yusuf, M. S. U., & Ahmad, M. (2013, May). Wavelet based analysis of EEG signal for evaluating mental behavior. In 2013 International Conference on Informatics, Electronics and Vision (ICIEV) (pp. 1-6). IEEE.
18 Ahmed, T., Islam, M., & Ahmad, M. (2013, December). Human emotion modeling based on salient global features of EEG signal. In 2013 2nd International Conference on Advances in Electrical Engineering (ICAEE) (pp. 246-251). IEEE.
19 Islam, M., Ahmed, T., Yusuf, M. S. U., & Ahmad, M. (2015). Cognitive state estimation by effective feature extraction and proper channel selection of EEG signal. Journal of Circuits, Systems and Computers, 24(02), 1540005.
20 Faghihi, U., Poirier, P., & Larue, O. (2011, October). Emotional cognitive architectures. In International Conference on Affective Computing and Intelligent Interaction (pp. 487-496). Springer, Berlin, Heidelberg.
21 Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2008). A survey of affect recognition methods: Audio, visual, and spontaneous expressions. IEEE transactions on pattern analysis and machine intelligence, 31(1), 39-58.
22 Sanei, S., & Chambers, J. A. (2007). EEG signal processing.
23 Guler, I., & Ubeyli, E. D. (2007). Multiclass support vector machines for EEG-signals classification. IEEE Transactions on Information Technology in Biomedicine, 11(2), 117-126.
24 Thakor, N. V., Gramatikov, B., Sherman, D., & Bronzino, J. (2000). Wavelet (time-scale) analysis in biomedical signal processing. The Biomedical Engineering Handbook, 56, 1-56.
25 Chandra, A. (1997, December). A computational architecture to model human emotions. In Proceedings Intelligent Information Systems. IIS'97 (pp. 86-89). IEEE.
26 Kristina, G., & Ruslan, G. (2017). Mathematical modelling of human fear and disgust emotional reactions based on skin surface electric potential changes.
27 Kowalczuk, Z., & Czubenko, M. (2016). Computational approaches to modeling artificial emotion-an overview of the proposed solutions. Frontiers in Robotics and AI, 3, 21.
28 Lu, Y., Bi, L., Lian, J., & Li, H. (2018). Mathematical modeling of EEG signals-based brain-control behavior. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(8), 1535-1543.
29 Khasraghi BJ, Setayeshi S (2017) Applying Fuzzy Mathematical Model of Emotional Learning for EEG Signal Classification Between Schizophrenics and Control Participant. Int J Comput Neural Eng. 4(1), 49-54.
30 Takahashi, M., Kitamura, M., & Yoshikawa, H. (1995). Development of a real-time cognitive state estimator. Control Engineering Practice, 3(2), 275-280.
Miss Monira Islam
Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology (KUET) - Bangladesh
Mr. Md. Salah Uddin Yusuf
Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology (KUET) - Bangladesh
Mr. Mohiuddin Ahmad
Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology (KUET) - Bangladesh