No Access

Evaluation of Brain Functional Connectivity from Electroencephalographic Signals Under Different Emotional States

Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingenieros Industriales, Universidad de Castilla-La Mancha, 02071 Albacete, Spain

Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, 02071 Albacete, Spain

E-mail Address: [email protected]

Corresponding author.

Search for more papers by this author

Antonio Fernández-Caballero

Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingenieros Industriales, Universidad de Castilla-La Mancha, 02071 Albacete, Spain

Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, 02071 Albacete, Spain

CIBERSAM (Biomedical Research Networking Centre in Mental Health), Madrid, Spain

Search for more papers by this author

Arturo Martínez-Rodrigo

Research Group in Electronic, Biomedical and Telecommunication Engineering, Facultad de Comunicación, Universidad de, Castilla-La Mancha, 16071 Cuenca, Spain

Instituto de Tecnologías Audiovisuales de, Castilla-La Mancha, Universidad de Castilla-La, Mancha, 16071 Cuenca, Spain

Search for more papers by this author

Raúl Alcaraz

Research Group in Electronic, Biomedical and Telecommunication Engineering, Escuela Politécnica de Cuenca, Universidad, de Castilla-La Mancha, 16071 Cuenca, Spain

Search for more papers by this author

, and

Paulo Novais

Algoritmi Center, Department of Informatics, Universidade do Minho, 4800-058 Guimaräes, Portugal

Search for more papers by this author

https://doi.org/10.1142/S0129065722500265Cited by:16 (Source: Crossref)

This article is part of the issue:

Special Issue: Neural Cognition and Affective Computing
Guest Editors: José M. Ferrández, Eduardo Fernandez, Diego Andina and Kazuyuki Murase

Abstract

The identification of the emotional states corresponding to the four quadrants of the valence/arousal space has been widely analyzed in the scientific literature by means of multiple techniques. Nevertheless, most of these methods were based on the assessment of each brain region separately, without considering the possible interactions among different areas. In order to study these interconnections, this study computes for the first time the functional connectivity metric called cross-sample entropy for the analysis of the brain synchronization in four groups of emotions from electroencephalographic signals. Outcomes reported a strong synchronization in the interconnections among central, parietal and occipital areas, while the interactions between left frontal and temporal structures with the rest of brain regions presented the lowest coordination. These differences were statistically significant for the four groups of emotions. All emotions were simultaneously classified with a 95.43% of accuracy, overcoming the results reported in previous studies. Moreover, the differences between high and low levels of valence and arousal, taking into account the state of the counterpart dimension, also provided notable findings about the degree of synchronization in the brain within different emotional conditions and the possible implications of these outcomes from a psychophysiological point of view.

Keywords:

References

1. J. Liu, M. Li, Y. Pan, W. Lan, R. Zheng, F.-X. Wu and J. Wang , Complex brain network analysis and its applications to brain disorders: A survey, Complexity 2017 (2017) 8362741. Crossref, Web of Science, Google Scholar
2. S. Zola-Morgan , Localization of brain function: The legacy of Franz Joseph Gall (1758–1828), Annu. Rev. Neurosci. 18(1) (1995) 359–383. Crossref, Medline, Web of Science, Google Scholar
3. S. Anzellotti and M. N. Coutanche , Beyond functional connectivity: Investigating networks of multivariate representations, Trends Cogn. Sci. 22 (2018) 258–269. Crossref, Medline, Web of Science, Google Scholar
4. C. OReilly, J. D. Lewis and M. Elsabbagh , Is functional brain connectivity atypical in autism? A systematic review of EEG and MEG studies, PLoS ONE 12(5) (2017) e0175870. Medline, Web of Science, Google Scholar
5. V. Sakkalis , Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG, Comput. Biol. Med. 41(12) (2011) 1110–1117. Crossref, Medline, Web of Science, Google Scholar
6. C. Liu, B. Abu-Jamous, E. Brattico and A. K. Nandi , Towards tunable consensus clustering for studying functional brain connectivity during affective processing, Int. J. Neural Syst. 27 (2017) 1650042. Link, Web of Science, Google Scholar
7. J. del’Etoile and H. Adeli , Graph theory and brain connectivity in Alzheimers disease, Neuroscientist 23 (2017) 616–626. Crossref, Medline, Web of Science, Google Scholar
8. N. Mammone, L. Bonanno, S. D. Salvo, S. Marino, P. Bramanti, A. Bramanti and F. C. Morabito , Permutation disalignment index as an indirect, EEG-based, measure of brain connectivity in MCI and AD patients, Int. J. Neural Syst. 27(05) (2017) 1750020. Link, Web of Science, Google Scholar
9. M. Ahmadlou, H. Adeli and A. Adeli , Spatiotemporal analysis of relative convergence of EEGs reveals differences between brain dynamics of depressive women and men, Clin. EEG Neurosci. 44 (2013) 175–181. Crossref, Medline, Web of Science, Google Scholar
10. R. Yuvaraj, M. Murugappan, U. R. Acharya, H. Adeli, N. M. Ibrahim and E. Mesquita , Brain functional connectivity patterns for emotional state classification in Parkinson’s disease patients without dementia, Behav. Brain Res. 298(B) (2016) 248–260. Crossref, Medline, Web of Science, Google Scholar
11. M. Ahmadlou, A. Adeli, R. Bajo and H. Adeli , Complexity of functional connectivity networks in mild cognitive impairment subjects during a working memory task, Clin. Neurophysiol. 125(4) (2014) 694–702. Crossref, Medline, Web of Science, Google Scholar
12. M. Ahmadlou, H. Adeli and A. Adeli , Fuzzy synchronization likelihood-wavelet methodology for diagnosis of autism spectrum disorder, J. Neurosci. Methods 211(2) (2012) 203–209. Crossref, Medline, Web of Science, Google Scholar
13. M. Ahmadlou and H. Adeli , Complexity of weighted graph: A new technique to investigate structural complexity of brain activities with applications to aging and autism, Neurosci. Lett. 650 (2017) 103–108. Crossref, Medline, Web of Science, Google Scholar
14. F. Al-Shargie, U. Tariq, M. Alex, H. Mir and H. Al-Nashash , Emotion recognition based on fusion of local cortical activations and dynamic functional networks connectivity: An EEG study, IEEE Access 7 (2019) 143550–143562. Crossref, Web of Science, Google Scholar
15. Y. Gao, X. Wang, T. Potter, J. Zhang and Y. Zhang , Single-trial EEG emotion recognition using Granger causality/transfer entropy analysis, J. Neurosci. Methods 346 (2020) 108904. Crossref, Medline, Web of Science, Google Scholar
16. S. M. Pincus , Assessing serial irregularity and its implications for health, Ann. N.Y. Acad. Sci. 954 (2001) 245–267. Crossref, Medline, Web of Science, Google Scholar
17. B. García-Martínez, A. Fernández-Caballero, R. Alcaraz and A. Martínez-Rodrigo , Cross-sample entropy for the study of coordinated brain activity in calm and distress conditions with electroencephalographic recordings, Neural. Comput. Appl. 33 (2021) 9343–9352. Crossref, Web of Science, Google Scholar
18. J. A. Russell , A circumplex model of affect, J. Pers. Soc. Psychol. 39(6) (1980) 1161–1178. Crossref, Web of Science, Google Scholar
19. P. Kuppens, F. Tuerlinckx, J. A. Russell and L. F. Barrett , The relation between valence and arousal in subjective experience, Psychol. Bull. 139(4) (2013) 917–940. Crossref, Medline, Web of Science, Google Scholar
20. S. Koelstra, C. Muhl, M. Soleymani, J.-S. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt and I. Patras , DEAP: A database for emotion analysis using physiological signals, IEEE Trans. Affect. Comput. 3(1) (2012) 18–31. Crossref, Web of Science, Google Scholar
21. G. H. Klem et al., The ten-twenty electrode system of the International Federation, Electroencephalogr. Clin. Neurophysiol. 52 (1999) 3–6. Google Scholar
22. A. Delorme and S. Makeig , EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis, J. Neurosci. Methods 134(1) (2004) 9–21. Crossref, Medline, Web of Science, Google Scholar
23. S. Tsuchimoto, S. Shibusawa, S. Iwama, M. Hayashi, K. Okuyama, N. Mizuguchi, K. Kato and J. Ushiba , Use of common average reference and large-Laplacian spatial-filters enhances EEG signal-to-noise ratios in intrinsic sensorimotor activity, J. Neurosci. Methods 353 (2021) p. 109089. Crossref, Medline, Web of Science, Google Scholar
24. W. W. Ismail, M. Hanif, S. Mohamed, N. Hamzah and Z. I. Rizman , Human emotion detection via brain waves study by using electroencephalogram (EEG), Int. J. Adv. Sci. Eng. Inf. Technol. 6(6) (2016) 1005–1011. Crossref, Google Scholar
25. A. Pedroni, A. Bahreini and N. Langer , Automagic: Standardized preprocessing of big EEG data, NeuroImage 200 (2019) 460–473. Crossref, Medline, Web of Science, Google Scholar
26. J. S. Richman and J. R. Moorman , Physiological time-series analysis using approximate entropy and sample entropy, Am. J. Physiol. - Heart Circ. Physiol. 278(6) (2000) H2039–H2049. Crossref, Medline, Web of Science, Google Scholar
27. S. M. Pincus , Irregularity and asynchrony in biologic network signals, Methods Enzymol. 321 (2000) 149–182. Crossref, Medline, Web of Science, Google Scholar
28. J. D. Veldhuis, S. M. Pincus, M. C. Garcia-Rudaz, M. G. Ropelato, M. E. Escobar and M. Barontini , Disruption of the joint synchrony of luteinizing hormone, testosterone, and androstenedione secretion in adolescents with polycystic ovarian syndrome, J. Clin. Endocrinol. Metab. 86(1) (2001) 72–79. Medline, Web of Science, Google Scholar
29. S. J. Reeves and Z. Zhe , Sequential algorithms for observation selection, IEEE Trans. Signal Process. 47(1) (1999) 123–132. Crossref, Web of Science, Google Scholar
30. N. Alia-Klein, R. N. Preston-Campbell, S. J. Moeller, M. A. Parvaz, K. Bachi, G. Gan, A. Zilverstand, A. B. Konova and R. Z. Goldstein , Trait anger modulates neural activity in the fronto-parietal attention network, PLoS ONE 13(4) (2018) e0194444. Crossref, Medline, Web of Science, Google Scholar
31. H. Saarimäki, A. Gotsopoulos, I. P. Jääskeläinen, J. Lampinen, P. Vuilleumier, R. Hari, M. Sams and L. Nummenmaa , Discrete neural signatures of basic emotions, Cereb. Cortex 26(6) (2016) 2563–2573. Crossref, Medline, Web of Science, Google Scholar
32. R. J. Davidson , Affect, cognition, and hemispheric specialization, in Emotion, Cognition, and Behavior (Cambridge University Press, New York, 1988), pp. 320–365. Google Scholar
33. A. Martínez-Rodrigo, B. García-Martínez, R. Alcaraz, P. González and A. Fernández-Caballero , Multiscale entropy analysis for recognition of visually elicited negative stress from EEG recordings, Int. J. Neural Syst. 29(02) (2019) 1850038. Link, Web of Science, Google Scholar
34. J. Cai, W. Chen and Z. Yin , Multiple transferable recursive feature elimination technique for emotion recognition based on EEG signals, Symmetry 11(5) (2019) 683. Crossref, Web of Science, Google Scholar
35. Z. Gao, X. Cui, W. Wan and Z. Gu , Recognition of emotional states using multiscale information analysis of high frequency EEG oscillations, Entropy 21 (2019) 609. Crossref, Medline, Web of Science, Google Scholar
36. M. Z. Soroush, K. Maghooli, S. K. Setarehdan and A. M. Nasrabadi , Emotion recognition through EEG phase space dynamics and Dempster–Shafer theory, Med. Hypotheses 127 (2019) 34–45. Crossref, Medline, Web of Science, Google Scholar
37. B. García-Martínez, A. Fernández-Caballero, R. Alcaraz and A. Martínez-Rodrigo , Application of dispersion entropy for the detection of emotions with electroencephalographic signals, IEEE Trans. Cogn. Develop. Syst. (2021). Medline, Web of Science, Google Scholar
38. Y. Zhang, X. Ji and S. Zhang , An approach to eeg-based emotion recognition using combined feature extraction method, Neurosci. Lett. 633 (2016) 152–157. Crossref, Medline, Web of Science, Google Scholar
39. S. Bagherzadeh, K. Maghooli, J. Farhadi and M. Z. Soroush , Emotion recognition from physiological signals using parallel stacked autoencoders, Neurophysiology 50(6) (2018) 428–435. Crossref, Web of Science, Google Scholar
40. B. García-Martínez, A. Fernández-Caballero, L. Zunino and A. Martínez-Rodrigo , Recognition of emotional states from EEG signals with nonlinear regularity-and predictability-based entropy metrics, Cogn. Comput. 13(2) (2021) 403–417. Crossref, Web of Science, Google Scholar
41. G. G. Knyazev , Cross-frequency coupling of brain oscillations: An impact of state anxiety, Int. J. Psychophysiol. 80(3) (2011) 236–245. Crossref, Medline, Web of Science, Google Scholar
42. A. Barrós-Loscertales, S. E. Hernández, Y. Xiao, J. L. González-Mora and K. Rubia , Resting state functional connectivity associated with Sahaja Yoga Meditation, Front. Hum. Neurosci. 15 (2021) 65. Crossref, Web of Science, Google Scholar
43. R. Cao, Y. Hao, X. Wang, Y. Gao, H. Shi, S. Huo, B. Wang, H. Guo and J. Xiang , EEG functional connectivity underlying emotional valance and arousal using minimum spanning trees, Front. Neurosci. 14 (2020) 355. Crossref, Medline, Web of Science, Google Scholar
44. M. A. Ferdek, C. M. van Rijn and M. Wyczesany , Depressive rumination and the emotional control circuit: An EEG localization and effective connectivity study, Cogn. Affect. Behav. Neurosci. 16(6) (2016) 1099–1113. Crossref, Medline, Web of Science, Google Scholar
45. N. Martini, D. Menicucci, L. Sebastiani, R. Bedini, A. Pingitore, N. Vanello, M. Milanesi, L. Landini and A. Gemignani , The dynamics of EEG gamma responses to unpleasant visual stimuli: From local activity to functional connectivity, NeuroImage 60(2) (2012) 922–932. Crossref, Medline, Web of Science, Google Scholar
46. H.-J. Park and K. Friston , Structural and functional brain networks: From connections to cognition, Science 342(6158) (2013) 1238411. Crossref, Medline, Web of Science, Google Scholar
47. C. Schmidt, D. Piper, B. Pester, A. Mierau and H. Witte , Tracking the reorganization of module structure in time-varying weighted brain functional connectivity networks, Int. J. Neural Syst. 28 (2018) 1750051. Link, Web of Science, Google Scholar
48. J. L. P. Velazquez, R. G. Erra, R. Wennberg and L. G. Dominguez , Correlations of cellular activities in the nervous system: Physiological and methodological considerations, in Coordinated Activity in the Brain (Springer, New York, USA, 2009), pp. 1–24. Crossref, Google Scholar
49. L. Cheng, Y. Zhu, J. Sun, L. Deng, N. He, Y. Yang, H. Ling, H. Ayaz, Y. Fu and S. Tong , Principal states of dynamic functional connectivity reveal the link between resting-state and task-state brain: An fMRI study, Int. J. Neural Syst. 28 (2018) 1850002. Link, Web of Science, Google Scholar
50. R. L. Buckner, J. R. Andrews-Hanna and D. L. Schacter , The brain’s default network: Anatomy, function, and relevance to disease, Ann. N.Y. Acad. Sci. 1124(1) (2008) 1–38. Crossref, Medline, Web of Science, Google Scholar
51. M. D. Fox, A. Z. Snyder, J. L. Vincent, M. Corbetta, D. C. Van Essen and M. E. Raichle , The human brain is intrinsically organized into dynamic, anticorrelated functional networks, Proc. Natl. Acad. Sci. 102(27) (2005) 9673–9678. Crossref, Medline, Web of Science, Google Scholar
52. U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, H. Adeli and D. P. Subha , Automated EEG-based screening of depression using deep convolutional neural network, Comput. Methods Programs Biomed. 161 (2018) 103–113. Crossref, Medline, Web of Science, Google Scholar
53. U. Raghavendra, U. R. Acharya and H. Adeli , Artificial intelligence techniques for automated diagnosis of neurological disorders, Eur. Neurol. 82 (2019) 41–64. Crossref, Medline, Web of Science, Google Scholar