Journal of Engineering and Applied Sciences

Year: 2018
Volume: 13
Issue: 8
Page No. 2137 - 2144

Empirical Analysis of Intra vs. Inter-Subject Variability in VR EEG-Based Emotion Modelling

Authors : N.S. Suhaimi, J. Teo and J. Mountstephens

References

AlZoubi, O., R.A. Calvo and R.H. Stevens, 2009. Classification of EEG for Affect Recognition: An Adaptive Approach. In: AI 2009: Advances in Artificial Intelligence, Nicholson A. and X. Li (Eds.). Springer, Berlin, Germany, ISBN:978-3-642-10438-1, pp: 52-61.

Cassani, R., H. Banville and T.H. Falk, 2015. MuLES: An open source EEG acquisition and streaming server for quick and simple prototyping and recording. Proceedings of the 20th International Conference on Intelligent User Interfaces Companion, March 29-April 01, 2015, ACM, Atlanta, Georgia, ISBN:978-1-4503-3308-5, pp: 9-12.

Chanel, G., C. Rebetez, M. Betrancourt and T. Pun, 2011. Emotion assessment from physiological signals for adaptation of game difficulty. IEEE. Trans. Syst. Man Cybern. Part A. Syst. Hum., 41: 1052-1063.
CrossRef  |  Direct Link  |  

Dong-Mei, Y.U. and J.A. Fang, 2007. Research on a methodology to model speech emotion. J. Eng. Appl. Sci., 2: 1262-1267.
Direct Link  |  

Galway, L., P. McCullagh, G. Lightbody, C. Brennan and D. Trainor, 2015. The potential of the brain-computer interface for learning: A technology review. Proceedings of the 2015 IEEE International Conference on Computer and Information Technology Ubiquitous Computing and Communications: Dependable, Autonomic and Secure Computing Pervasive Intelligence and Computing (CIT-IUCC-DASC-PICOM’15), October 26-28, 2015, IEEE, Liverpool, UK., ISBN:978-1-5090-0154-5, pp: 1554-1559.

Hosseinifard, B., M.H. Moradi and R. Rostami, 2013. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Comput. Methods Programs Biomed., 109: 339-345.
Direct Link  |  

Kim, M.K., M. Kim, E. Oh and S.P. Kim, 2013. A review on the computational methods for emotional state estimation from the human EEG. Comput. Math. Methods Med., 2013: 1-13.
Direct Link  |  

Kober, S.E. and C. Neuper, 2012. Using auditory event-related EEG potentials to assess presence in virtual reality. Intl. J. Hum. Comput. Stud., 70: 577-587.
Direct Link  |  

Koelstra, S. and I. Patras, 2013. Fusion of facial expressions and EEG for implicit affective tagging. Image Vision Comput., 31: 164-174.
Direct Link  |  

Koelstra, S., A. Yazdani, M. Soleymani, C. Muhl and J.S. Lee et al., 2010. Single Trial Classification of EEG and Peripheral Physiological Signals for Recognition of Emotions Induced by Music Videos. In: Brain Informatics, Yao, Y., R. Sun, T. Poggio, J. Liu and N. Zhong, et al., (Eds.). Springer, Berlin, Germany, ISBN:978-3-642-15313-6, pp: 89-100.

Li, Z., J. Xu and T. Zhu, 2015. Prediction of brain states of concentration and relaxation in real time with portable electroencephalographs. Hum. Comput. Interact., 1: 1-18.
Direct Link  |  

Lin, Y.P., C.H. Wang, T.P. Jung, T.L. Wu and S.K. Jeng et al., 2010. EEG-based emotion recognition in music listening. IEEE. Trans. Biomed. Eng., 57: 1798-1806.
CrossRef  |  Direct Link  |  

Mann, S., R., Janzen, H. Wu, M.H. Lu and N. Guleria, 2015. Bright ideas: A wearable interactive Inventometer (brainwave-based idea display). Proceedings of the 2015 IEEE Conference on Games Entertainment Media (GEM’15), October 14-16, 2015, IEEE, Toronto, Ontario, ISBN:978-1-4673-7453-8, pp: 1-8.

Myrden, A. and T. Chau, 2017. A passive EEG-BCI for single-trial detection of changes in mental state. IEEE. Trans. Neural Syst. Rehabil. Eng., 25: 345-356.
CrossRef  |  Direct Link  |  

Pugnetti, L., L. Mendozzi, E. Barberi, F.D. Rose and E.A. Attree, 1996. Nervous system correlates of virtual reality experience. Proceedings of the 1st European Conference on Disability, Virtual Reality and Associated Technology, July 8-10, 1996, University of Reading, Reading, England, ISBN:0-7049-1140-X, pp: 239-246.

Sepehri, S., S. Ghahari and R.H. Zadeh, 2016. Efficacy of Dialecticalical Behavior Therapy (DBT) techniques on improving cognitive emotion regulation strategies in women with MS. Soc. Sci., 11: 3395-3400.

Sreeja, P.S. and G.S. Mahalakshmi, 2016. Comparison of probabilistic corpus-based method and vector space model for emotion recognition from poems. Asian J. Inf. Technol., 15: 908-915.
Direct Link  |  

Subasi, A. and M.I. Gursoy, 2010. EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst. Appl., 37: 8659-8666.
CrossRef  |  Direct Link  |  

Tran, Y., R. Thuraisingham, N. Wijesuriya, A. Craig and H. Nguyen, 2014. Using S-transform in EEG analysis for measuring an alert versus mental fatigue state. Proceedings of the IEEE 36th Annual International Conference on Engineering in Medicine and Biology Society (EMBC’14), August 26-30, 2014, IEEE, Chicago, Illinois, ISBN:978-1-4244-7929-0, pp: 5880-5883.

Wang, X.W., D. Nie and B.L. Lu, 2014. Emotional state classification from EEG data using machine learning approach. Neurocomput., 129: 94-106.
Direct Link  |  

Wu, D., C.G. Courtney, B.J. Lance, S.S. Narayanan and M.E. Dawson et al., 2010. Optimal arousal identification and classification for affective computing using physiological signals: Virtual reality stroop task. IEEE. Trans. Affective Comput., 1: 109-118.
CrossRef  |  Direct Link  |  

Yazdani, A., T. Ebrahimi and U. Hoffmann, 2009. Classification of EEG signals using Dempster Shafer theory and a K-nearest neighbor classifier. Proceedings of the 4th International IEEE-EMBS Conference on Neural Engineering (NER'09), April 29-May 2, 2009, IEEE, Antalya, Turkey, ISBN:978-1-4244-2072-8, pp: 327-330.

Zulkifli, N.A.A., S.H. Sawal, S.A. Ahmad and M.S. Islam, 2015. Review on Support Vector Machine (SVM) classifier for human emotion pattern recognition from EEG signals. Asian J. Inf. Technol., 14: 135-146.
Direct Link  |  

Design and power by Medwell Web Development Team. © Medwell Publishing 2024 All Rights Reserved