Abstract
We introduce a system called AMBER (Advanced Multi-modal Biometric Emotion Recognition), which combines Electroencephalography (EEG) with Electro Dermal Activity (EDA) and pulse sensors to provide low cost, portable real-time emotion recognition. A single-subject pilot experiment was carried out to evaluate the ability of the system to distinguish between positive and negative states of mind provoked by audio stimuli. Eight single classifiers and six ensemble classifiers were compared using Weka. All ensemble classifiers outperformed the single classifiers, with Bagging, Rotation Forest and Random Subspace showing the highest overall accuracy.
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References
Boss, D.O.: EEG-based emotion recognition (2006), http://emi.uwi.utwente.nl/-verslagen/capita-selecta/CS-oude=Bos-Danny
Chanel, G., Kronegg, J., Grandjean, D., Pun, T.: Emotion assessment: Arousal evaluation using eEG’s and peripheral physiological signals. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds.) MRCS 2006. LNCS, vol. 4105, pp. 530–537. Springer, Heidelberg (2006)
De Martino, F., Valente, G., Staeren, N., Ashburner, J., Goebela, R., Formisano, E.: Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. NeuroImage 43(1), 44–58 (2008)
Hardoon, D.R., Mourao-Miranda, J., Brammer, M., Shawe-Taylor, J.: Unsupervised analysis of fMRI data using kernel canonical correlation. NeuroImage 37(4), 1250–1259 (2007)
Ko, K., Yang, H., Sim, K.: Emotion recognition using EEG signals with relative power values and Bayesian network. International Journal of Control, Automation, and Systems 7, 865–870 (2009)
Kuncheva, L.I.: Combining Pattern Classifiers. Methods and Algorithms. John Wiley and Sons, N.Y (2004)
Kuncheva, L.I., Rodríguez, J.J.: Classifier ensembles for fMRI data analysis: An experiment. Magnetic Resonance Imaging 28(4), 583–593 (2010)
Kuncheva, L.I., Rodriguez, J.J., Plumpton, C.O., Linden, D.E.J., Johnston, S.J.: Random subspace ensembles for fMRI classification. IEEE Transactions on Medical Imaging 29(2), 531–542 (2010)
Liao, W., Zhang, W., Zhu, Z., Ji, Q.: A decision theoretic model for stress recognition and user assistance. In: Proceedings of the National Conference on Artificial Intellegence, vol. 20, PART 2, pp. 529–534 (2005)
Liao, W., Zhang, W., Zhu, Z., Ji, Q., Gray, W.D.: Towards a decision-theoretic framework for affect recognition and user assistance. International Journal of Man-Machine Studies 64(9), 847–873 (2006)
Mourao-Miranda, J., Bokde, A.L.W., Born, C., Hampel, H., Stetter, M.: Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional mri data. NeuroImage 28(4), 980–995 (2005)
Nakasone, A., Prendinger, H., Ishizuka, M.: Emotion recognition from electromyography and skin conductance. In: Proc. of the 5th International Workshop on Biosignal Interpretation, pp. 219–222 (2005)
Petrantonakis, P., Hadjileontiadis, L.: Emotion recognition from EEG using higher-order crossings. IEEE Transactions on Information Technology in Biomedicine 14(2), 186–197 (2010)
Picard, R.W.: Affective computing. Technical Report 321, M.I.T Media Laboratory Perceptual Computing Section (1995)
Picard, R.W.: Emotion research by the people, for the people. Emotion Review (2010) (to appear)
Rebolledo-Mendez, G., Dunwell, I., Martínez-Mirón, E.A., Vargas-Cerdán, M.D., de Freitas, S., Liarokapis, F., García-Gaona, A.R.: Assessing neuroSky’s usability to detect attention levels in an assessment exercise. In: Jacko, J.A. (ed.) HCI International 2009. LNCS, vol. 5610, pp. 149–158. Springer, Heidelberg (2009)
Rodríguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10), 1619–1630 (2006)
Sato, J.R., Fujita, A., Thomaz, C.E., Martin, M.G.M., Mourao-Miranda, J., Brammer, M.J., Amaro Junior, E.: Evaluating SVM and MLDA in the extraction of discriminant regions for mental state prediction. NeuroImage 46, 105–114 (2009)
Sherwood, J., Derakhshani, R.: On classifiability of wavelet features for eeg-based brain-computer interfaces. In: Proceedings of the 2009 International Joint Conference on Neural Networks, pp. 2508–2515 (2009)
Takahashi, K.: Remarks on emotion recognition from biopotential signals. In: 2nd International Conference on Autonomous Robots and Agents, pp. 186–191 (2004)
van Gerven, M., Farquhar, J., Schaefer, R., Vlek, R., Geuze, J., Nijholt, A., Ramsey, N., Haselager, P., Vuurpijl, L., Gielen, S., Desain, P.: The brain-computer interface cycle. Journal of Neural Engineering 6(4), 41001 (2009)
Wang, Z., Childress, A.R., Wang, J., Detre, J.A.: Support vector machine learning-based fMRI data group analysis. NeuroImage 36(4), 1139–1151 (2007)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Yasui, Y.: A brainwave signal measurement and data processing technique for daily life applications. Journal Of Physiological Anthropology 38(3), 145–150 (2009)
Yazicioglu, R.F., Torfs, T., Merken, P., Penders, J., Leonov, V., Puers, R., Gyselinckx, B., Hoof, C.V.: Ultra-low-power biopotential interfaces and their applications in wearable and implantable systems. Microelectronics Journal 40(9), 1313–1321 (2009)
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Kuncheva, L.I., Christy, T., Pierce, I., Mansoor, S.P. (2011). Multi-modal Biometric Emotion Recognition Using Classifier Ensembles. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21822-4_32
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DOI: https://doi.org/10.1007/978-3-642-21822-4_32
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