Abstract
Emotion classification based on electroencephalogram (EEG) signals is a relatively new area of research in the development of brain computer interface (BCI) system with challenging issues like induction of the emotional states and the extraction of the features in order to obtain optimum classification of human emotions. The emotion classification system based on BCI can be useful in many areas like as entertainment, education, and health care. This chapter presents a new method for human emotion classification using multiwavelet transform of EEG signals. The EEG signal contains useful information related to the different emotional states, which helps us to understand the psychology and neurology of the human brain. The features namely, ratio of the norms based measure, Shannon entropy measure, and normalized Renyi entropy measure are computed from the sub-signals generated by multiwavelet decomposition of EEG signals. These features have been used as an input to multiclass least squares support vector machine (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for classification of human emotions from EEG signals. The classification performance of the proposed method for classification of emotions using EEG signals determined by computing the classification accuracy, ten-fold cross-validation, and confusion matrix. The proposed method has provided classification accuracy of 84.79 % for classification of human emotions namely happy, neutral, sadness, and fear from EEG signals with Morlet wavelet kernel function of MC-LS-SVM. The audio–video stimulus has been used for inducing the emotions in EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of human emotions from EEG signals.
Keywords
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Azar, A.T., Balas, V.E., Olariu, T.: Classification of EEG-based brain-computer interfaces. Adv. Intell. Comput. Technol. Decis. Support Syst. Stud. Comput. Intell. 486(2014), 97–106 (2014). doi:10.1007/978-3-319-00467-9-9
Murugappan, M., Rizon, M., Nagarajan, R., Yaacob, S.: Inferring of human emotional states using multichannel EEG. Eur. J. Sci. Res. 48(2), 281–299 (2010)
Smith, M.J.L., Montagne, B., Perrett, D.I., Gill, M., Gallaghser, L.: Detecting subtle facial emotion recognition deficits in high functioning autism using dynamic stimuli of varying intensities. Neuropsychologia 48(9), 2777–2781 (2010)
Vera-Munoz, C., Pastor-Sanz, L., Fico, G., Arredondo, M.T., Benuzzi, F., Blanco, A.: A wearable EMG monitoring system for emotions assessment. Probing Experience Philips Res. 8, 139–148 (2008)
Essa, I.A., Pentland, A.P.: Coding analysis interpretation and recognition of facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 757–763 (1997)
Nwe, T.L., Foo, S.W., Silva, L.D.: Speech emotion recognition using hidden Markov models. Speech Commun. 41(4), 603–623 (2003)
Ekman, P., Levenson, R.W., Freison, W.V.: Autonomic nervous system activity distinguishes among emotions. Science 221(4616), 1208–1210 (1983)
Winton, W.M., Putnam, L., Krauss, R.: Facial and autonomic manifestations of the dimensional structure of emotion. J. Exp. Soc. Psychol. 20(3), 195–216 (1984)
Sander, D., Grandjean, D., Scherer, K.R.: A systems approach to appraisal mechanisms in emotion. Neural Netw. 18(4), 317–352 (2005)
Baumgartner, T., Esslen, M., Jancke, L.: From emotion perception to emotion experience: emotions evoked by pictures and classical music. Int. J. Psychophysiol. 60(1), 34–43 (2006)
Davidson, R.J.: Anterior cerebral asymmetry and the nature of emotion. Brain Cogn. 20(1), 125–151 (1992)
Petrides, M., Milner, B.: Deficits on subject-ordered tasks after frontal- and temporal-lobe lesions in man. Neuropsychologia 20(3), 249–262 (1982)
Sobotka, S.S., Davidson, R.J., Senulis, J.A.: Anterior brain electrical asymmetries in response to reward and punishment. Electroencephalogr. Clin. Neurophysiol. 83(4), 236–247 (1997)
Garcia O., Favela J., Machorro R.: Emotional awareness in collaborative systems. In: IEEE Proceedings on String Processing and Information Retrieval Symposium, pp. 296–303. Cancun, 22–24 Sep 1999. doi: 10.1109/SPIRE.1999.796607
Picard, R.W.: Toward machines with emotional intelligence. In: Matthews, G., Zeidner, M., Roberts, R.D. (eds.) The Science of Emotional Intelligence: Knowns and Unknowns. Oxford University Press, Oxford (2007)
Plutchik, R., Kellerman, H.: Emotion theory research and experience. New York Academic Press, New York (1980)
Olofsson, J.K., Nordin, S., Sequeira, H., Polich, J.: Affective picture processing: an integrative review of ERP findings. Biol. Psychol. 77(3), 247–265 (2008)
Codispoti, M., Ferrari, V., Bradley, M.M.: Repetition and event-related potentials: distinguishing early and late processes in affective picture perception. J. Cogn. Neurosci. 19(4), 577–586 (2007)
Olofsson, J.K., Polich, J.: Affective visual event-related potentials: arousal, repetition, and time-on-task. Biol. Psychol. 75(1), 101–108 (2007)
Bernat, E., Bunce, S., Shevrin, H.: Event-related brain potentials differentiate positive and negative mood adjectives during both supraliminal and subliminal visual processing. Int. J. Psychophysiol. 42(1), 11–34 (2001)
Cuthbert, B.N., Schu, H.T., Bradley, M.M., Birbaumer, N., Lang, P.J.: Brain potentials in affective picture processing: covariation with automic arousal and affective report. Biol. Psychol. 52(2), 95–111 (2000)
Blankertz, B., Lemm, S., Treder, M., Haufe, S., Muller, K.R.: Single-trial analysis and classification of ERP components-a tutorial. NeuroImage 56(2), 814–825 (2011)
Jarchi, D., Sanei, S., Principe, J.C., Makkiabadi, B.: A new spatiotemporal filtering method for single-trial estimation ofcorrelated ERP subcomponents. IEEE Trans. Biomed. Eng. 58(1), 132–143 (2011)
Vanderperren, K., Mijovic, B., Novitskiy, N., Vanrumste, B., Stiers, P., Van den Bergh, B.R., Lagae, L., Sunaert, S., Wagemans, J., Van Huffel, S., De Vos, M.: Single trial ERP reading based on parallel factor analysis. Psychophysiology 50(1), 97–110 (2013)
Balconi, M., Lucchiari, C.: EEG correlates (event-related desynchronization) of emotional face elaboration: a temporal analysis. Neurosci. Lett. 392(1–2), 118–123 (2006)
Balconi, M., Mazza, G.: Brain oscillations and BIS/BAS (behavioral inhibition/activation system) effects on processing masked emotional cues ERS/ERD and coherence measures of alpha band. Int. J. Psychophysiol. 74(2), 158–165 (2009)
Gotlib, I.H., Ranganath, C., Rosenfeld, J.P.: Frontal EEG alpha asymmetry, depression, and cognitive functioning. Cogn. Emot. 12(3), 449–478 (1998)
Keil, A., Muller, M.M., Gruber, T., Wienbruch, C., Stolarova, M., Elbert, T.: Effects of emotional arousal in the cerebral hemispheres: a study of oscillatory brain activity and event related potentials. Clin. Neurophysiol. 112(11), 2057–2068 (2001)
Muller, M.M., Keil, A., Gruber, T., Elbert, T.: Processing of affective pictures modulates right-hemispheric gamma band EEG activity. Clin. Neurophysiol. 110(11), 1913–1920 (1999)
Aftanas, L.I., Golocheikine, S.A.: Non-linear dynamic complexity of the human EEG during meditation. Nerurosci. Lett. 330(2), 143–146 (2002)
Aftanas, L.I., Reva, N.V., Varlamov, A.A., Pavlov, S.V., Makhnev, V.P.: Analysis of evoked EEG synchronization and desynchronization in conditions of emotional activation in humans: temporal and topographic characteristics. Neurosci. Behav. Physiol. 34(8), 859–867 (2004)
Aftanas, L.I., Varlamov, A.A., Pavlov, S.V., Makhnev, V.P., Reva, N.V.: Affective picture processing: event-related synchronization within individually defined human theta band is modulated by valence dimension. Neurosci. Lett. 303(2), 115–118 (2001)
Bos D.O.: EEG-based emotion recognition. The Influence of Visual and Auditory Stimuli, 1–17 (2006)
Schaaff, K., Schultz, T.: Towards an EEG-based emotion recognizer for humanoid robots. In: The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 792–796. Toyama, 27 Sept.-2 Oct. 2009. doi: 10.1109/ROMAN.2009.5326306
Murugappan, M., Rizon, M., Nagarajan, R., Yaacob, S., Hazry, D., Zunaidi, I.: Time-frequency analysis of EEG signals for human emotion detection. In: 4th Kuala Lumpur International Conference on Biomedical Engineering, pp. 262–265. Kuala Lumpur, Malaysia, 25–28 June 2008. doi: 10.1007/978-3-540-69139-6-68
Chanel G., Ansari-Asl K., Pun T.: Valence-arousal evaluation using physiological signals in an emotion recall paradigm. In: IEEE International Conference on Systems, Man and Cybernetics Montreal Que, pp. 2662–2667, 7–10 Oct. 2007. doi: 10.1109/ICSMC.2007.4413638
Hosseini, S.A., Khalilzadeh, M.A., Changiz, S.: Emotional stress recognition system for affective computing based on bio-signals. J. Biol. Syst. 18(1), 101–114 (2010)
Kim, K.H., Bang, S.W., Kim, S.R.: Emotion recognition system using short-term monitoring of physiological signals. Med. Biol. Eng. Comput. 42(3), 419–427 (2004)
Takahashi, K.: Remarks on emotion recognition from bio-potential signals. In: 2nd International Conference on Automous Robots and Agents, pp. 186–191. Palmerston North, New Zealand, 13–15 Dec. 2004
Murugaan, M., Nagarajan, R., Yaacob, S.: Appraising human emotions using time frequency analysis based EEG alpha band features. In: Invative Techlogies in Intelligent Systems and Industrial Applications, pp. 70–75. Monash, 25–26 July 2009. doi: 10.1109/CITISIA.2009.5224237
Petrantonakis, P.C., Hadjileontiadis, L.J.: Adaptive emotional information retrieval from EEG signals in the time-frequency domain. IEEE Trans. Signal Process. 60(5), 2604–2616 (2012)
Knyazev, G.G.: Motivation, emotion, and their inhibitory control mirrored in brain oscillations. Neurosci. Biobehav. Rev. 31(3), 377–395 (2007)
Choppin A.: EEG-based human interface for disabled individuals: emotion expression with neural networks. Master thesis, Information processing, Tokyo Institute of Technology, Yokohama, Japan (2000)
Chanel G., Kronegg J., Grandjean D., Pun T.: Emotion assessment arousal evaluation using EEG’s and peripheral physiological signals. In: Gunsel, B., Tekalp, AM., Jain, AK., Sankur, B. (eds.) Multimedia Content Representation Classification and Security Springer Lectures Notes in Computer Sciences 4105, pp. 530–537 (2006). doi: 10.1007/11848035-70
Khosrowabadi, R., Rahman, A.W.A.: Classification of EEG correlates on emotion using features from Gaussian mixtures of EEG spectrogram. In: International Conference on Information and Communication Technology for the Muslim World, pp. E102–E107. Jakarta, 13–14 Dec. 2010. doi: 10.1109/ICT4M.2010.5971942
Lin, Y.P., Jung, T.P., Chen, J.H.: EEG dynamics during music appreciation. In: 31st Annual International Conference of the IEEE EMBS, pp. 5316-5319. Minneapolis, MN, USA, 3–6 Sept. 2009. doi: 10.1109/IEMBS.2009.5333524
Ishino, K., Hagiwara, M.: A feeling estimation system using a simple electroencephalograph. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 4204–4209, 5–8 Oct. 2003. doi: 10.1109/ICSMC.2003.1245645
Rani, P., Liu, C., Sarkar, N., Vanman, E.: An empirical study of machine learning techniques for affect recognition in human-robot interaction. Pattern Anal. Appl. 9(1), 58–69 (2006)
Liu, C., Agrawal, P., Sarkar, N., Chen, S.: Dynamic difficulty adjustment in computer games through real-time anxiety-based affective feedback. Int. J. Hum. Compu. Interac. 25(6), 506–529 (2009)
Mandryk, R.L., Atkins, M.S.: A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. Int. J. Hum Comput Stud. 65(4), 329–347 (2007)
Chanel G., Rebetez C., Bétrancourt M., Pun T.: Boredom, engagement and anxiety as indicators for adaptation to difficulty in games. In: Proceedings of the 12th International Conference on Entertainment Media Ubiquitous Era (MindTrek ‘08), pp. 13–17 (2008). doi: 10.1145/1457199.1457203
Khosrowabadi, R., Heijnen, M., Wahab, A., Quek, H.C.: The dynamic emotion recognition system based on functional connectivity of brain regions. In: IEEE Intelligent Vehicles Symposium, pp. 377–381. San Diego, 21–24 June 2010. doi: 10.1109/IVS.2010.5548102
Petersen, M., Stahlhut, C., Stopczynski, A., Larsen, J., Hansen, L.: Smartphones get emotional: mind reading images and reconstructing the neural sources. Affective Computing and Intelligent Interaction, volume 6975 of Lecture Notes in Computer Science, pp. 578–587. Springer, Berlin (2011). doi: 10.1007/978-3-642-24571-8-72
Horlings, R., Datcu, D., Rothkrantz, L.J.M.: Emotion recognition using brain activity. In: Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing, pp. 1–6 (2008). doi: 10.1145/1500879.1500888
Hjorth, B.: EEG analysis based on time domain properties. Electroencephalogr. Clin. Neurophysiol. 29(3), 306–310 (1970)
Aftanas, L.I., Lotova, N.V., Koshkarov, V.I., Pokrovskaja, V.L., Popov, S.A., Makhnev, V.P.: Non-linear analysis of emotion EEG: calculation of Kolmogorov entropy and the principal Lyapunov exponent. Neurosci. Lett. 226(1), 13–16 (1997)
Boostani, R., Moradi, M.H.: A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier. J. Neural Eng. 1(4), 212–217 (2004)
Hoseingholizade, S., Golpaygani, M.R.H., Monfared, A.S.: Studying emotion through nonlinear processing of EEG. In: Procedia-Social and Behavioral Sciences, The 4th International Conference of Cognitive Science, vol 32, pp. 163–169 (2012)
Murugappan, M., Ramachandran, N., Sazali, Y.: Classification of human emotion from EEG using discrete wavelet transform. J. Biomed. Sci. Eng. 3(4), 390–396 (2010)
Brown L., Grundlehner B., Penders J.: Towards wireless emotional valence detection from EEG. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society pp. 2188–2191. Boston, MA, 30 Aug.-3 Sept. 2011. doi: 10.1109/IEMBS.2011.6090412
Hosseini, S.A., Khalilzadeh, M.A., Naghibi-Sistani, M.B., Niazmand, V.: Higher order spectra analysis of EEG signals in emotional stress states. In: 2010 Proceedings of the 2nd International Conference on Information Technology and Computer Science, pp. 60–63. Kiev (2010b), 24–25 July 2010. doi: 10.1109/ITCS.2010.21
Frantzidis, C.A., Bratsas, C., Papadelis, C.L., Konstantinidis, E., Pappas, C., Bamidis, P.D.: Toward emotion aware computing an integrated approach using multichannel neurophysiological recordings and affective visual stimuli. IEEE Trans. Inf. Technol. Biomed. 14(3), 589–597 (2010)
Chanel, G., Kierkels, J.J.M., Soleymani, M., Pun, T.: Short-term emotion assessment in a recall paradigm. Int. J. Hum Comput Stud. 67(8), 607–627 (2009)
Murugappan, M., Nagarajan, R., Yaacob, S.: Combining spatial filtering and wavelet transform for classifying human emotions using EEG signals. J. Med. Biol. Eng. 31(1), 45–51 (2010)
Lin, Y.P., Wang, C.H., Jung, T.P., Wu, T.L., Jeng, S.K., Duann, J.R., Chen, J.H.: EEG-based emotion recognition in music listening. IEEE Trans. Biomed. Eng. 57(7), 1798–1806 (2010)
Lin, Y.P., Wang, C.H., Wu, T.L., Jeng, S.K., Chen, J.H.: Multilayer perceptron for EEG signal classification during listening to emotional music. In: IEEE Region 10 Conference on TENCON 2007, pp. 1–3. Taipei, 30 Oct.-2 Nov. 2007. doi: 10.1109/TENCON.2007.4428831
Petrantonakis, P.C., Hadjileontiadis, L.J.: Emotion recognition from EEG using higher order crossings. IEEE Trans. Inf Technol. Biomed. 14(2), 186–197 (2010)
Wang, X.W., Nie, D., Lu, B.L.: EEG-based emotion recognition using frequency domain features and support vector machines. In: Neural Information Processing of Lecture Notes in Computer Science, vol. 7062, pp. 734–743. China, 13–17 Nov. 2011
Hadjidimitriou, S.K., Hadjileontiadis, L.J.: Toward an EEG-based recognition of music liking using time-frequency analysis. IEEE Trans. Biomed. Eng. 59(12), 3498–3510 (2012)
Ball G., Breese J.: Modeling the emotional state of computer users. In: Workshop on Attitude, Personality and Emotions in User-Adapted Interaction, Banff, Canada (1999)
Hudlicka, E.: Increasing SIA architecture realism by modeling and adapting to affect and personality. In: Socially Intelligent Agents Multiagent Systems, Artificial Societies, and Simulated Organizations, vol 3, pp. 53–60 (2002). doi: 10.1007/0-306-47373-96
Dubois, D., Prade, H.: Possibility theory, probability theory and multiple-valued logics: a clarification. Ann. Math. Artif. Intell. 32(1–4), 35–66 (2001)
Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Aroach to Learning and Machine Intelligence. Prentice Hall Inc, Saddle River (1997)
Zhang, Q., Lee, M.: Fuzzy-gist for emotion recognition in natural scene images. In: IEEE 8th International Conference on Development and Learning, pp. 1–7. Shanghai, 5–7 June 2009. doi: 10.1109/DEVLRN.2009.5175518
Dubois, D., Prade, H.: An introduction to fuzzy systems. Clin. Chim. Acta 270(1), 3–29 (1998)
Kuncheva, L.I., Steimann, F.: Fuzzy diagnosis. Artif. Intell. Med. 16, 121–128 (1999)
Nauck, D., Kruse, R.: Obtaining interpretable fuzzy classification rules from medical data. Artif. Intell. Med. 16(2), 149–169 (1999)
Jang, J.S.R.: Self-learning fuzzy controllers based on temporal backpropagation. IEEE Trans. Neural Networks 3(5), 714–723 (1992)
Jang, J.S.R.: ANFIS adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)
Belal, S.Y., Taktak, A.F.G., Nevill, A.J., Spencer, S.A., Roden, D., Bevan, S.: Automatic detection of distorted plethysmogram pulses in neonates and paediatric patients using an adaptive-network-based fuzzy inference system. Artif. Intell. Med. 24(2), 149–165 (2002)
Usher, J., Campbell, D., Vohra, J., Cameron, J.: A fuzzy logic-controlled classifier for use in implantable cardioverter defibrillators. Pacing Clin. Electrophysiol. 22(1), 183–186 (1999)
Übeyli, E.D., Güler, I.: Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems. Comput. Biol. Med. 35(5), 421–433 (2005)
Übeyli, E.D., Güler, I.: Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals. Comput. Biol. Med. 35(8), 608–702 (2005)
Virant-Klun, I., Virant, J.: Fuzzy logic alternative for analysis in the biomedical sciences. Int. J. Comput. Biomed. Res. 32(4), 305–321 (1999)
Cristianini N., Taylor J.S.: An introduction to support vector machines and other kernel-based learning methods. Cambridge UK Cambridge University Press, Cambridge (2000)
Yang, Y.H., Liu, C.C., Chen, H.H.: Music emotion classification: a fuzzy approach. In: Proceedings of ACM Multimedia, pp. 81–84. Santa Barbara, CA, 23–27 Oct. 2006. doi: 10.1145/1180639.1180665
Srinivasa, K.G., Venugopal, K.R., Patnaik, L.M.: Feature extraction using fuzzy C-means clustering for data mining systems. Int. J. Comput. Sci. Netw. Secur. 6(3A), 230–236 (2006)
Murugappan, M., Rizon, M., Nagarajan, R., Yaacob, S., Zunaidi, I., Hazry, D.: EEG feature extraction for classifying emotions using FCM and FKM. Int. J. Comput. Commun. 1(2), 21–25 (2007)
Murugappan, M., Rizon, M., Nagarajan, R., Yaacob, S., Zunaidi, I., Hazry, D.: Lifting scheme for human emotion recognition using EEG. In: International Symposium on Information Technology, pp. 1–7. Kuala Lumpur, Malaysia, 26–28 Aug. 2008. doi: 10.1109/ITSIM.2008.4631646
Berka, C., Levendowski, D.J., Cvetinovic, M.M., Petrovic, M.M., Davis, G., Lumicao, M.N., Zivkovic, V.T., Popovic, M.V., Olmstead, R.: Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset. Int. J. Hum. Comput. Interact. 17(2), 151–170 (2004)
Besserve, M., Philippe, M., Florence, G., Laurent, F., Garnero, L., Martinerie, J.: Prediction of performance level during a cognitive task from ongoing EEG oscillatory activities. Clin. Neurophysiol. 119(4), 897–908 (2008)
Freeman, F.G., Mikulka, P.J., Scerbo, M.W., Scott, L.: An evaluation of an adaptive automation system using a cognitive vigilance task. Biol. Psychol. 67(3), 283–297 (2004)
Pope, A.T., Bogart, E.H., Bartolome, D.S.: Biocybernetic system evaluates indexes of operator engagement in automated task. Biol. Psychol. 40(1–2), 187–195 (1995)
Wilson, G.F., Russell, C.A.: Real-time assessment of mental workload using psychophysiological measures and artificial neural networks. Hum. Factors 45(4), 635–643 (2003)
Davidson, R.J., Jackson, D.C., Kalin, N.H.: Emotion plasticity context and regulation perspectives from affective neuroscience. Psychol. Bull. 126(6), 890–909 (2000)
Niedermeyer, E., Silva, F.L.: Electroencephalography basic principles clinical applications and related fields. Baltimore MD Williams and Wilkins, New York (1993)
Ekman P.: Basic emotions. In: Dalgleish, T., Power, M. (eds.) Handbook of Cognition and Emotion Sussex. UK John Wiley & Sons Ltd, New York (1999)
Lang, P.J.: The emotion probe studies of motivation and attention. Am. Psychol. 50(5), 372–385 (1995)
Kim, J., Andre, E.: Emotion recognition based on physiological changes in music listening. IEEE Trans. Pattern Anal. Mach. Intell. 30(12), 2067–2083 (2008)
Koelstra, S., Muhl, C., Soleymani, M., Lee, J.S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I.: DEAP: a database for emotion analysis using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)
Murugappan, M., Juhari, M.R.B.M., Nagarajan, R., Yaacob, S.: An investigation on visual and audiovisual stimulus based emotion recognition using EEG. Int. J. Med. Eng. Inform. 1(3), 342–356 (2009)
Cowie R., Douglas-Cowie E., Savvidou S., McMahon E., Sawey M., Schroder M.: ‘Feeltrace’ an instrument for recording perceived emotion in real time. In: Proceedings of ISCA Workshop Speech and Emotion, pp. 19–24. Newcastle, UK (2000)
Strela, V., Heller, P.N., Strang, G., Topiwala, P., Heil, C.: The application of multiwavelet filter banks to image processing. IEEE Trans. Image Process. 8(4), 548–563 (1999)
Cotronei, M., Lazzaro, D., Montefusco, L.B., Puccio, L.: Image compression through embedded multiwavelet transform coding. IEEE Trans. Image Process. 9(2), 184–189 (2000)
Cotronei, M., Montefusco, L.B., Puccio, L.: Multiwavelet analysis and signal processing. IEEE Trans. Circ. Syst. II 45(8), 970–987 (1998)
Hsung, T.S., Lun, D.P.K., Ho, K.C.: Optimizing the multiwavelet shrinkage denoising. IEEE Trans. Signal Process. 53(1), 240–251 (2005)
Khouzani, K.J., Zadeh, H.S.: Multiwavelet grading of pathological images of prostate. IEEE Trans. Biomed. Eng. 50(6), 697–704 (2003)
Wang, J.W.: Multiwavelet packet transforms with application to texture segmentation. Electron. Lett. 38, 1021–1023 (2002)
Jahankhani, P., Kodogiannis, V., Revett, K.: EEG signal classification using wavelet feature extraction and neural networks. In: IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing, pp. 52–57. Sofia, 3–6 Oct. 2006. doi: 10.1109/JVA.2006.17
Kalayci, T., Ozdamar, O.: Wavelet preprocessing for automated neural network detection of EEG spikes. IEEE Eng. Med. Biol. Mag. 14(2), 160–166 (1995)
Guo, L., Rivero, D., Pazos, A.: Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J. Neurosci. Methods 193(1), 156–163 (2010)
Plonka, G., Strela, V.: From wavelets to multiwavelet. In: Dahlen, M., Lyche, T., Scchumaker, LL. (eds.) Mathematical Methods for Curves and Surfaces II. Vanderbilt University Press, Nashville, pp. 375–399 (1998)
Geronimo, J.S., Hardin, D.P., Massopust, P.R.: Fractal functions and wavelet expansions based on several scaling functions. J. Approximation Theor. 78(3), 373–401 (1994)
Qumar, J., Pachori, R.B.: A novel technique for merging of multisensor and defocussed images using multiwavelets. In: IEEE Region 10 (TENCON 2005), pp. 1733–1738. Melbourne, 21–24 Nov. 2005. doi: 10.1109/TENCON.2005.300836
Xiaodong, W., Yanyang, Z., Zhengjia, H.: Multiwavelet denoising with improved neighboring coefficients for application on rolling bearing fault diagnosis. Mech. Syst. Signal Process. 25(1), 285–304 (2011)
Shaw, R.: Strange attractors chaotic behavior and information flow. Naturforsch 36A, 80–112 (1981)
Grassberger, P., Procaccia, I.: Estimation of the kolmogorov entropy from a chaotic signal. Phys. Rev. A 28(4), 2591–2593 (1983)
Eckmann, J.P., Ruelle, D.: Ergodic theory of chaos and strange attractors. Rev. Mod. Phys. 57(3), 617–656 (1985)
Takens, F.: Invariants related to dimension and entropy. In: Proceedings of 13th Coloquio Brasileiro de Matematica, Rio de Janeiro, Brazil (1983)
Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. 88(6), 2297–2301 (1991)
Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278(6), H2039–H2049 (2000)
Chen X., Solomon I.C., Chon K.H.: Comparison of the use of approximate entropy and sample entropy applications to neural respiratory signal. In: Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 4212–4215. Shanghai China (2005), 17–18 Jan. 2006. doi: 10.1109/IEMBS.2005.1615393
Song, Y., Lio, P.: A new approach for epileptic seizure detection sample entropy based extraction and extreme learning machine. J. Biomed. Sci. Eng. 3(6), 556–567 (2010)
Jones, D., Parks, T.W.: A high resolution data-adaptive time-frequency representation. IEEE Trans. Acoust. Speech Sign. Process. 38(12), 2127–2135 (1990)
Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)
Renyi, A.: On measures of entropy and information. Proc. Fourth Berkeley Symp. Math. Stat. Probab. 1, 547–561 (1961)
Sengur, A.: Multiclass least-squares support vector machines for analog modulation classification. Expert Syst. Appl. 36(3), 6681–6685 (2009)
Bajaj, V., Pachori, R.B.: Automatic classification of sleep stages based on the time-frequency image of EEG signals. Comput. Methods Programs Biomed. 112(3), 320–328 (2013)
Bajaj, V., Pachori, R.B.: Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans. Inf Technol. Biomed. 16(6), 1135–1142 (2012)
Bajaj V., Pachori R.B.: EEG signal classification using empirical mode decomposition and support vector machine. In: International Conference on Soft Computing for Problem Solving, AISC 131, pp. 623–635. Roorkee, India, (2012b), 20–22 December 2011. doi: 10.1007/978-81-322-0491-6-57
Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)
Vapnik, V.: The nature of statistical learning theory. Springer, New York (1995)
Suykens, J.A.K., Vandewalle, J.: Multiclass least squares support vector machines. In: International Joint Conference on Neural Networks, pp. 900–903. Washington, DC, Jul 1999. doi: 10.1109/IJCNN.1999.831072
Xing, Y., Wu, X., Xu, Z.: Multiclass least square wavelet support vector machines. In: IEEE International Conference on Networking Sensing and Control, pp. 498–502. Sanya, 6–8 April 2008. doi: 10.1109/ICNSC.2008.4525268
Zavar, M., Rahati, S., Akbarzabeh, M.R., Ghasemifard, H.: Evolutionary model selection in a wavelet-based support vector machine for automated seizure detection. Expert Syst. Appl. 38(9), 10751–10758 (2011)
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Financial support obtained from the Department of Science and Technology (DST) India, Fast track project titled “Analysis and Classification of EEG Signals based on Nonlinear and Non-stationary Signal Models”, project number SR/FTP/ETA-90/2010 is greatly acknowledged.
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Bajaj, V., Pachori, R.B. (2015). Detection of Human Emotions Using Features Based on the Multiwavelet Transform of EEG Signals. In: Hassanien, A., Azar, A. (eds) Brain-Computer Interfaces. Intelligent Systems Reference Library, vol 74. Springer, Cham. https://doi.org/10.1007/978-3-319-10978-7_8
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