Emotion Recognition based on EEG Signals

Rustem Popa

Abstract


In this paper we propose some methods for analyzing EEG signals in order to recognize emotions, using the representation of signals on Poincaré plots and the calculation of the fractal dimension. EEG signals were acquired on a single channel, using a laboratory equipment produced by BIOPAC, and the subject was relaxed with his eyes open or in one of the states of joy, anger, and music listening for about 60 seconds. Separate analyzes were also performed for the 4 frequency bands of the EEG signals: alpha, beta, theta and delta waves.

Keywords


electroencephalography, chaos, fractal dimension, nonlinear dynamics, emotion recognition

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References


G. Rodriguez-Bermudez and P. J. Garcia-Laencina, Analysis of EEG Signals using Nonlinear Dynamics and Chaos: A review, Appl. Math. Inf. Sci.9, No. 5, pp. 2309-2321, 2015.

J. E. Jacob, A. Cherian, K. Gopakumar, T. Iype, D. G. Yohannan, and K. P Divya, Can Chaotic Analysis of Electroencephalogram Aid the Diagnosis of Encephalopathy?, Hindawi Neurology Research Int., 2018, Article ID 8192820, 8 pages, https://doi.org/10.1155/2018/8192820

X. Wang, J. Meng, G. Tan, and L. Zou, Research on the relation of EEG signal chaos characteristics with high-level intelligence activity of human brain, in Nonlinear Biomedical Physics 2010, 4:2, http://www.nonlinearbiomedphys.com/content/4/1/2

J. Kriz, Chaos in the Brain, in ACTA PHYSICA POLONICA A, vol. 120, pp. A127 – A131, 2011

M. S. Ozerdem and H. Polat, Emotion recognition based on EEG features in movie clips with channel selection, Brain Informatics, vol. 4, pp. 241–252, 2017, DOI 10.1007/s40708-017-0069-3

N. Thammasan, K. Moriyama, K. Fukui, and M. Numao, Familiarity effects in EEG-based emotion recognition, Brain Informatics, vol. 4, pp. 39–50, 2017, DOI 10.1007/s40708-016-0051-5

A. L. Tandle, M. S. Joshi, A. S. Dharmadhikari, and S. V. Jaiswal, Mental state and emotion detection from musically stimulated EEG, Brain Informatics, vol. 5, pp. 1-13, https://doi.org/10.1186/s40708-018-0092-z

L. Frangu and R. Popa, "Change Detection in EEG Signals," in 6th International Symposium on Electrical and Electronics Engineering (ISEEE), Galati, Romania, 2019, pp. 1-6, doi: 10.1109/ISEEE48094.2019.9136145.

A. Garfinkel, J. Shevtsov, and Y. Guo, Modeling Life. The Mathematics of Biological Systems, Cham, Switzerland: Springer International Publishing AG, 2017

P. A. Watters, Fractal Structure in the Electroencephalogram, in Complexity International, ISSN 1320-0682, vol. 5, 1998

H. O. Peitgen, H. Jurgens, and D. Saupe, Chaos and Fractals. New Frontiers of Science, 2nd ed., New-York, USA: Springer-Verlag, 2004

S. Kotresh, G. B. Mukartihal, B. N. Gangadhar, and G. Siddalingesh, EEG Seizure Analysis Using Fractal Dimensions During Electroconvulsive Therapy, in V. Sridhar et al. (eds.), Emerging Research in Electronics, Computer Science and Technology, Lecture Notes in Electrical Engineering 248, pp. 227-234, DOI: 10.1007/978-81-322-1157-0_24, Springer India, 2014




DOI: http://dx.doi.org/10.52155/ijpsat.v22.2.2089

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