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Evaluating the Determinism of Brain Signals Using Recurrence Chaotic Features in Positive, Negative and Neutral Emotional States in the Sources Achieved From ICA Algorithm

Mehdi Abdossalehi, Ali Motie Nasrabadi
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Abstract

Background: This study investigates electroencephalogram (EEG) signals in positive, negative and neutral emotion states.

Method: It is assumed that the brain draws on several independent sources in any activity that are observable by independent component algorithm (ICA). To overcome the problem of ill-posedness of extracted components from ICA algorithm, first these sources are sorted out by Shannon entropy and then based on these sources, the features of trapping time and determinism of Recurrence Quantification Analysis (RQA) are extracted as representative of determination.

Result: The results show that the degree of determinism of sorted sources related by emotions is significantly different over time and in three positive, negative and neutral states. The degree of determinism increases in neutral, positive and negative emotional states respectively.

Keywords

Emotion; Electroencephalogram (EEG); Independent Component Analysis (ICA); Recurrence Quantification Analysis (RQA); Determinism; trapping time

References

Kim MK, Kim M, Oh E, Kim SP. A review on the computational methods for emotional state estimation from the human EEG. Comput Math Methods Med. 2013;2013:573734.

Adolphs, R. The social brain: neural basis of social knowledge. Annu Rev Psychol; 2009:60: 693–716.

Agrawal D, Thorne JD, Viola FC, Timm L, Debener S, Büchner A, et al. Electrophysiological responses to emotional prosody perception in cochlear implant users. Neuroimage Clin. 2013 Jan 14;2:229-38.

Zhang Q, Lee M. A hierarchical positive and negative emotion understanding system based on integrated analysis of visual and brain signals. Neurocomputing. 2010:73:3264–3272.

Chanel G. Emotion assessment for affective computing based on brain and peripheral signals (Doctoral dissertation, University of Geneva). Colombo C, Del Bimbo A, Pala P. Semantics in visual information retrieval. IEEE MultiMedia. 1999 Jul;6(3):38-53.

Assfalg J, Bertini M, Colombo C, Bimbo AD. Semantic annotation of sports videos. IEEE MultiMedia. 2002 Apr;9(2):52-60.

Yu C, Xu L. An emotion-based approach to decision making and self learning in autonomous robot control. InIntelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on 2004;15(3):2386-2390.

Almedia L. MISEP – Linear and Nonlinear ICA Based on Mutual Information, Journal of Machine Learning Research;2003:4:1297-1318.

Hyvärinen A, Oja E. Independent Component Analysis: Algorithms and Applications, Neural Networks. 2000:13(4-5):411-430.

Knyazev GG, Bocharov AV, Pylkova LV. Extraversion and fronto-posterior EEG spectral power gradient: an independent component analysis. Biol Psychol. 2012 Feb;89(2):515-24.

Jansen B, Brandt M. Nonlinear dynamical analysis of the EEG, World Scientific; 1993.

Takens F. Detecting strange attractors in fluid turbulence. In D. Rand and L.-S. Young, editors, Dynamical Systems and Turbulence; 1981:366.

Cornelius RR. "Theoretical approaches to emotion," Proc. Int. Speech Communication Association (ISCA) Workshop on Speech and Emotion, Belfast, Ireland, 2000.

Marwan N, Romano MC, Thiel M, Kurths J. Recurrence plots for the analysis of complex systems. Physics reports. 2007 Jan 31;438(5):237-329.

Abdossalehia1 M, Motie nasrabadib A, Firoozabadi M. Combining Independent Component Analysis with chaotic quantifiers for the recognition of positive, negative and neutral emotions using EEG signals. International journal of science and technology; 2014:5(1):432

Goshvarpour A, Abbasi A, Goshvarpour A. Dynamical analysis of emotional states from electroencephalogram signals. Biomed Eng Appl Basis Commun. 2016;28:1650015.




DOI: https://doi.org/10.22037/icnj.v4i2.17165

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