[1] A. Yaghoubiy and K. Maghooli, “Emotions extraction methods and applications, ” presented at the 4th Int. Conf. Applied Research, Computer Engineering and Signal Processing, Tehran, Iran, 2016.
[2] G. Chanel, C. Rebetez, M. Betrancourt and T. Pun, “Emotion assessment from physio signals for adaptation of game difficulty, ” IEEE Trans. Systems Man Cybernetics A: Systems and Humans, vol. 41, pp. 1052-1063, 2011.
[3] P. Rani, L. Changchon, S. Nilanjan and V. Eric, “An empirical study of machine learning techniques for affect recognition in human–robot interaction, ” Journal Pattern Anal. Appl., vol. 9, pp. 58-69, 2006.
[4] M. Soleymani, S. Asghari-Esfeden, Y. Fu and M. Pantic, “Analysis of EEG signals and facial expressions for continuous emotion detection, ” IEEE Trans. Affective Comp., vol. 7, pp. 17-28, 2016.
[5] M. Murugappan, “Human emotion classification using Wavelet transform and KNN, ” presented at the Int. IEEE Conf. Pattern Analysis Intelligent Robotics, Putrajaya, Malaysia, 2011.
[6] M. Soleymani, J. Lichtenauer, M. Pantic and T. Pun, “A multimodal database for affect recognition and implicit tagging, ” IEEE Trans. Affective Comp., vol. 3, 42-55, 2012.
[7] T. Balli, S. M. Deniz, B. Cebeci, M. Erbey, A. D. Duru and T. Demiralp, “Emotion recognition based on spatially smooth spectral features of the EEG, ” presented at the 6th Int. IEEE/EMBS Conf. Neural Engineering, San Diego, California, USA, 2013.
[8] M. Murugappan and S. Murugappan, “Human emotion recognition through short time electroencephalogram (EEG) signals using fast fourier transform (FFT), ” presented at the 9th Int. IEEE Colloquium Signal Processing Applications, Kuala Lumpur, Malaysia, 2013.
[9] C. A. Torres, A. A. Orozco and M. A. Alvarez, “Feature selection for multimodal emotion recognition in the Arousal-Valence space, ” presented at the 35th Annual Int. Conf. IEEE Engineering Medicine Biology Society, Osaka, Japan, 2013.
[10] R. N. Duan, J. Y. Zhu and B. L. Lu, “Differential entropy feature for EEG-based emotion classification, ” presented at the 6th Int. IEEE/EMBS Conf. Neural Engineering, San Diego, California, USA, 2013.
[11] J. W. Matiko, S. P. Beeby and J. Tudor, “Fuzzy logic based emotion classification, ” presented at the IEEE Int. Conf. Acoustics Speech Signal Processing, Florence, Italy, 2014.
[12] S. Hatamikia and A. M. Nasrabadi, “Recognition of emotional states induced by music videos based on nonlinear feature extraction and SOM classification, ” presented at the 21th Iranian Conf. Biomedical Engineering, Tehran, Iran, 2014.
[13] H. Xu and K. N. Plataniotis, “Subject independent affective states classification using EEG signals, ” presented at the IEEE Global Conf. Signal Information Processing, Orlando, Florida, USA, 2015.
[14] M. Kołodziej, A. Majkowski, P. Tarnowski and J. R. Remigiusz, “Recognition of visually induced emotions based on electroencephalography, ” presented at the 8th Int. Conf. Intelligent Data Acquisition Advanced Computing Systems Technology Applications, Warsaw, Poland, 2015.
[15] A. Bhardwaj, A. Gupta, P. Jain, A. Rani and J. Yadav, “Classification of human emotions from EEG signals using SVM and LDA Classifiers, ” presented at the 2nd Int. Conf. Signal Processing Integrated Networks, Noida, India, 2015.
[16] R. M. Mehmood and H. J. Lee, “Emotion classification of EEG brain signal using SVM and KNN, ” presented at the IEEE Int. Conf. Multimedia Expo Workshops, Turin, Italy, 2015.
[17] H. Candra, M. Yuwono, A. Handojoseno, R. Chai, S. Su and H. T. Nguyen, “Recognizing emotions from EEG subbands using wavelet analysis, ” presented at the 37th Annual Int. Conf. IEEE Engineering Medicine Biology Society, Milan, Italy, 2015.
[18] C. Aracena, S. Basterrech, V. Snael and J. Velasquez, “Neural networks for emotion recognition based on eye tracking data, ” presented at the IEEE Int. Conf. Systems, Man Cybernetics, Kowloon, China, 2015.
[19] M. J. Maguire, G. Magnon and A. E. Fitzhugh, “Improving data retention in EEG research with children using child-centered eye tracking, ” Journal Neurosci. Methods, vol. 238, pp. 78-81, 2014.
[20] K. H. Kim, S. W. Bang and S. R. Kim, “Emotion recognition system using short-term monitoring of physiological signals, ” Medical and Biological Eng. Comp., vol. 42, pp. 419-427, 2004.
[21] K. Jonghwa and E. Ande, “Emotion recognition based on physiological changes in music listening, ” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, pp. 2067-2083, 2008.
[22] W. L. Zheng, B. N. Dong and B. L. Lu, “Multimodal emotion recognition using EEG and eye tracking data, ” presented at the 36th Annual Int. Conf. of the IEEE Engineering Medicine Biology Society, Chicago, IL, USA, 2014.
[23] S. Alghowinem, M. AlShehri, R. Goecke and M. Wagner, “Exploring eye activity as an indication of emotional states using an eye-tracking sensor, ” in Intelligent Systems for Science and Information, L. Chen, S. Kapoor and R. Bhatia, Ed. Switzerland: Springer, 2014, pp. 261-276.
[24] Y. Lu, W. L. Zheng, B. Li and B. L. Lu, “Combining eye movements and EEG to enhance emotion recognition, ” presented at the 24th Int. Joint Conf. Artificial Intelligence, Buenos Aires, Argentina, 2015.
[25] C. Aracena, S. Basterrech, V. Snasel and J. Velasquez, “Neural networks for emotion recognition based on eye tracking data, ” presented at the IEEE Int. Conf. Systems Man Cybernetics, Kowloon, China, 2015.
[26] K. Pasupa, P. Chatkamjuncharoen, C. Wuttilertdeshar and M. Sugimoto, “Using image features and eye tracking device to predict human emotions, ” presented at the 7th Pacific-Rim Symposium Image Video Technology, Auckland, New Zealand, 2015.
[27] P. Ekman and W. Friesen, “Universals and cultural differences in the judgments of facial expressions of emotion, ” Journal Personal Soc. Psychol., vol. 53, pp. 712-714, 1998.
[28] P. J. Lang, “The emotion probe: Studies of motivation and attention, ” American Psychol., vol. 50, pp. 372-385, 1995.
[29] M. Soleymani, Implicit and automated emotional tagging of videos, ” Ph.D. dissertation, Dept. Comp. Science, Univ. Geneva, Switzerland, 2011.
[30] P. Christian and H. Antje, “Emotion representation and physiology assignments in digital systems, ” Journal Interact. Comp., vol. 18, pp. 139-170, 2006.
[31] R. A. Adams, E. Aponte, L. Marshall and K. J. Friston, “Active inference and oculomotor pursuit: The dynamic causal modelling of eye movements, ” Journal Neurosci. Methods, vol. 242, pp. 1-14, 2015.
[32] D. W. Hansen and Q. Ji, “In the eye of the beholder: A survey of models for eyes and gaze, ” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, pp. 478-500, 2010.
[33] P. De Luna, B. M. Faiz, M. Mustafar and G. Rainer, “A MATLAB-based eye tracking control system using non-invasive helmet head restraint in the macaque, ” Journal Neurosci. Methods, vol. 235, pp. 41-50, 2014.
[34] L. E. Chul, W. J. Cheol, K. J. Hwa, M. Whang and P. K. Ryoung, “A brain-computer interface method combined with eye tracking for 3D interaction, ” Journal Neurosci. Methods, vol. 190, pp. 289-298, 2010.
[35] J. K. Ong and T. Haslwanter, “Measuring torsional eye movements by tracking stable iris features, ” Journal Neurosci. Methods, vol. 192, pp. 261-267, 2010.
[36] J. Zimmermann, Y. Vazquez, P. W. Glimcher, B. Pesaran and K. Louie, “Oculomatic: High speed, reliable, and accurate open-source eye tracking for humans and non-human primates, ” Journal Neurosci. Methods, vol. 270, pp. 138-146, 2016.
[37] HCI Tagging Database, [Online]. Available: http://mahnob-db.eu/hci-tagging/
[38] Tobii Eye Tracking Device, [Online]. Available: http://www.tobii.com/
[39] A. T. Ozdemir and K. Danisman, “A comparative study of two different FPGA-based arrhythmia classier architectures, ” Turkish Journal Elect. Eng. Comp. Science, vol. 23, pp. 2089-2106, 2015.
[40] A. G. Khandizod, R. R. Deshmukh, S. N. Borade, “Spectral biometric verification system for person identification, ” in Information and communication technology for sustainable development, D. K. Mishra, M. K. Nayak and A. Joshi, Ed. Singapore: Springer, 2018. pp. 103-111.
[41] D. T. Laros, “Data mining methods and models, ” New Jersey, USA: John Wiley and Sons Press, 2006.
[42] S. H. Nabavi Karizi and E. Kabir, “Combining classifiers: Diversifying and rules of composition, ” CSI Journal Comp. Science Eng., vol. 3, pp. 95-108, 2005.
[43] J. Zhu, H. Zou, S. Rosset and T. Hastie, “Multi-class AdaBoost, ” Journal Stat. Interface, vol. 2, pp. 349-360, 2009.
)