Genetic Algorithm based Emotional State Evaluation from Filtered EEG Data
Emotion plays an important role in the daily life of man and is an important feature of human interaction. Because of its role of adaptation, it motivates people to respond quickly to stimuli in their environment to improve communication, learning and decision making. With the increasing role of the brain-computer interface (BCI) in user-computer interaction, automatic recognition of emotions has become an area of interest in the last decade. The recognition of emotions could be facial expression, gesture, speech and text and could be recorded in different ways, such as electroencephalogram (EEG), positron emission tomography (PET), magnetic resonance imaging (MRI), etc. In this research work, feature extraction feature reduction and classification of emotions have been evaluated on different methods to recognize and classify different emotional states such as fear, sad, frustrated, happy, pleasant and satisfied from inner emotion EEG signals.
 Y.P. Lin, C.H. Wang, T.P. Jung, T.L. Wu, S.K. Jeng, J.R. Duann, and J.H. Chen, “EEG-Based Emotion Recognition in Music Listening,” IEEE Transactions on Biomedical Engineering IEEE Trans. Biomed. Eng., Vol. 57, No. 7, pp. 1798–1806, 2010.
 A.Choppin, “EEG-based human interface for disabled individuals: Emotion expression with neural networks,” Master's thesis, Tokyo Institute of Technology, 2000.
 E. Niedermeyer, and F.H. Lopes da Silva, “Electroencephalography: Basic Principles, Clinical Applications, and Related Fields,” Lippincot Williams & Wilkins, 2005.
 M. Othman, A. Wahab, and R. Khosrowabadi, “MFCC for robust emotion detection using EEG,” in In proceedings of the 2009 IEEE 9th Malaysia International Conference on Communications, December 2009.
 R.J. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “ Brain- computer interfaces for communication and control,” Clinical Neurophysiology, Vol. 113, No. 6, pp. 767-791, March 2002.
 J. Wang, , N. Yan, H. Liu, M. Liu, and C. Tai, “ Brain-computer interfaces based on attention and complex mental tasks,” In Proceedings of International Conference on Digital Human Modeling, pp. 467-473, Springer Berlin Heidelberg, 2007.
 R.Plutchik, “The Emotions: Facts, Theories and a New Model,” Random House, New York, 1962.
 P. Ekman, W. V. Friesen, M. O'Sullivan, A. Chan, I. D.-Tarlatzis, K. Heider, and R. Krause , “Universals and Cultural Differences in the Judgments of Facial Expressions of Emotion,” Journal of Personality and Social Psychology, Vol. 53, No. 4, pp. 712-717, October 1987.
 J.A.Russell, “A Circumflex Model of Affect,” Journal of Personality and Social Psychology, Vol. 39, pp. 1161-1178, 1980.
 M. M. Bradley, and P. J. Lang, “Measuring emotions: The self assement manikin and the semantic differential,” Journal of behavior therapy and experimental psychiarty, Vol. 25, No.1, pp. 49-59, March 1994.
 Van Erp J, Lotte F, Tangermann M. “Brain-computer interfaces: beyond medical applications” IEEE Computer, 2012, Vol. 45, issue 4, pp 26–34.
 Bi L, Fan X-A, Liu Y., “EEG-based brain-controlled mobile robots: a survey”, Human-Machine Syst, IEEE Trans, 2013, Vol. 43, Issue 2, pp. 161–76.
 L. F. Nicolas-Alonso and J. Gomez-Gil, “Brain computer interfaces, a review,” Sensors, 2012, Vol. 12, Issue. 2, pp. 1211–1279.
 D. Göhring, D. Latotzky, M. Wang, and R. Rojas, “Semi-autonomous car control using brain computer interfaces,” Intelligent Autonomous Systems, Springer, 2013, Vol. 194, pp. 393–408.
 D. J. McFarland, C. W. Anderson, K. Muller, A. Schlogl, and D. J. Krusienski, “Bci meeting 2005-workshop on bci signal processing: feature extraction and translation,” IEEE transactions on neural systems and rehabilitation engineering, 2006, Vol. 14, Issue. 2, pp. 135.
 M. M. Moore, “Real-World Applications For Brain-Computer Interface Technology,” Neural Systems and Rehabilitation Engineering, IEEE Transactions, 2003, Vol.11, Issue. 2, pp. 162–165.
 P. W. Ferrez& J. del R Millan, “Error-related eeg potentials generated during simulated Brain computer interaction,” Biomedical Engineering, IEEE Transactions, 2008, Vol 55, Issue. 3, pp. 923–929.
 C.A. Frantzidis, C. Bratsas, C.L. Papadelis, E. Konstantinidis, C. Pappas, P.D. Bamidis,, “Toward emotion aware computing: an integrated approach using multichannel neuro-physiological recordings and affective visual stimuli”, IEEE Transaction on Information Technology and Biomedical, Vol. 14 pp. 589–597, 2010.
 E.I. Konstantinidis , C.A. Frantzidis , C. Pappas , P.D. Bamidis , “Real time emotion aware applications: a case study employing emotion evocative pictures and neuro-physiological sensing enhanced by graphic processor units”, Computer Methods and Programs in Biomedicine. Vol 107, pp. 16–27, 2012.
 D. Iacoviello , A. Petracca , M. Spezialetti , G. Placidi , “A real-time classification al- gorithm for EEG-based BCI driven by self-induced emotions”, Comput. Methods Prog. Biomed. Vol. 122, pp. 293–303, 2015.
 M. Khezri , M. Firoozabadi , A.R. Sharafat , “Reliable emotion recognition system based on dynamic adaptive fusion of forehead biopotentials and physiological signals”, Comput. Methods Prog. Biomed. Vol. 122, pp. 149–164, 2015.
 G.K. Verma , U.S. Tiwary, “Multimodal fusion framework: a multiresolution approach for emotion classification and recognition from physiological signal”, NeuroImage, Vol. 102, pp. 162–172, 2014.
 X. Li , P. Zhang , D. Song , G. Yu , Y. Hou , B. Hu , “EEG based emotion identification using unsupervised deep feature learning”, SIGIR2015 Workshop on Neuro-Physiological Methods in IR Research, Vol. 13, 2015.
 X. Jia , K. Li , X. Li , A. Zhang , “A Novel semi-supervised deep learning framework for affective state recognition on EEG signals”, IEEE 14th International Conference on Bioinformatics and Bioengineering, 2014.
 Noufal Sulthan, “Emotion Recognition Using Brain Signals”, IEEE, 2018.
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