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Detection of Emotions from Electroencephalographic Recordings by Means of a Nonlinear Functional Connectivity Measure

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Advances in Computational Intelligence (IWANN 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12861))

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Abstract

The brain has been typically assessed as a group of independent structures focused on the realization of determined processes separately. Nevertheless, recent findings have confirmed the existence of interconnections between all brain regions, thus demonstrating that the brain works as a network. These areas can be interconnected either physically, by anatomical links, or functionally, through functional associations created for a coordinated development of mental tasks. In this sense, the assessment of functional connectivity is crucial for discovering new information about the brain’s behavior in different scenarios. In the present study, the nonlinear functional connectivity metric cross-sample entropy (CSE) is applied in the research field of emotions recognition from EEG recordings. Concretely, CSE is computed to discern between four different emotional states. The results obtained indicated that the strongest coordination appears in intra- and inter-hemispheric interactions of central, parietal and occipital brain regions, whereas associations between left frontal and temporal lobes with the rest of areas show the most dissimilar dynamics, thus a higher uncoordinated activity. In addition, coordination is globally higher under emotional conditions of high arousal/low valence (like fear or distress) and low arousal/high valence (such as relaxation or calmness).

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References

  1. Al-Shargie, F., Tariq, U., Alex, M., Mir, H., Al-Nashash, H.: Emotion recognition based on fusion of local cortical activations and dynamic functional networks connectivity: an EEG study. IEEE Access 7, 143550–143562 (2019)

    Article  Google Scholar 

  2. Alia-Klein, N., et al.: Trait anger modulates neural activity in the fronto-parietal attention network. PloS one 13(4), e0194444 (2018)

    Google Scholar 

  3. Anzellotti, S., Coutanche, M.N.: Beyond functional connectivity: investigating networks of multivariate representations. Trends Cogn. Sci. 22, 258–269 (2018)

    Article  Google Scholar 

  4. Aydın, S., Demirtaş, S., Tunga, M.A., Ateş, K.: Comparison of hemispheric asymmetry measurements for emotional recordings from controls. Neural Comput. Appl. 30(4), 1341–1351 (2017)

    Google Scholar 

  5. Barrós-Loscertales, A., Hernández, S.E., Xiao, Y., González-Mora, J.L., Rubia, K.: Resting state functional connectivity associated with Sahaja Yoga Meditation. Front. Hum. Neurosci. 15, 65 (2021)

    Article  Google Scholar 

  6. Breakspear, M.: Nonlinear phase desynchronization in human electroencephalographic data. Hum. Brain Mapp. 15(3), 175–198 (2002)

    Article  Google Scholar 

  7. Buckner, R.L., Andrews-Hanna, J.R., Schacter, D.L.: The brain’s default network: anatomy, function, and relevance to disease. Annals New York Acad. Sci. 1124, 1–38 (2008)

    Google Scholar 

  8. Cai, L., Wei, X., Wang, J., Yu, H., Deng, B., Wang, R.: Reconstruction of functional brain network in Alzheimer’s disease via cross-frequency phase synchronization. Neurocomputing 314, 490–500 (2018)

    Article  Google Scholar 

  9. Cohen, M.X.: Analyzing Neural Time Series Data: Theory and Practice. MIT Press, Cambridge (2014)

    Book  Google Scholar 

  10. Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9–21 (2004)

    Article  Google Scholar 

  11. Deshpande, G., Santhanam, P., Hu, X.: Instantaneous and causal connectivity in resting state brain networks derived from functional MRI data. Neuroimage 54(2), 1043–1052 (2011)

    Article  Google Scholar 

  12. Dolcos, F., Cabeza, R.: Event-related potentials of emotional memory: encoding pleasant, unpleasant, and neutral pictures. Cogn. Affect. Behav. Neurosci. 2(3), 252–263 (2002)

    Article  Google Scholar 

  13. Fan, M., Chou, C.A.: Detecting abnormal pattern of epileptic seizures via temporal synchronization of EEG signals. IEEE Trans. Biomed. Eng. 66(3), 601–608 (2018)

    Article  Google Scholar 

  14. Farokhzadi, M., Hossein-Zadeh, G.A., Soltanian-Zadeh, H.: Nonlinear effective connectivity measure based on adaptive neuro fuzzy inference system and Granger causality. Neuroimage 181, 382–394 (2018)

    Article  Google Scholar 

  15. Friston, K.J.: Book review: brain function, nonlinear coupling, and neuronal transients. Neuroscientist 7, 406–418 (2001)

    Article  CAS  Google Scholar 

  16. García-Martínez, B., Fernández-Caballero, A., Alcaraz, R., Martínez-Rodrigo, A.: Cross-sample entropy for the study of coordinated brain activity in calm and distress conditions with electroencephalographic recordings. Neural Comput. Appl. 33, 9343–9352 (2021)

    Google Scholar 

  17. García-Martínez, B., Fernández-Caballero, A., Zunino, L., Martínez-Rodrigo, A.: Recognition of emotional states from EEG signals with nonlinear regularity-and predictability-based entropy metrics. Cogn. Comput. 13(2), 403–417 (2021)

    Article  Google Scholar 

  18. García-Martínez, B., Martínez-Rodrigo, A., Alcaraz, R., Fernández-Caballero, A.: A review on nonlinear methods using electroencephalographic recordings for emotion recognition. IEEE Trans. Affect. Comput. (2019)

    Google Scholar 

  19. García-Martínez, B., Martínez-Rodrigo, A., Zangróniz, R., Pastor, J.M., Alcaraz, R.: Application of entropy-based metrics to identify emotional distress from electroencephalographic recordings. Entropy 18(6), 221 (2016)

    Article  Google Scholar 

  20. Hasanzadeh, F., Mohebbi, M., Rostami, R.: Graph theory analysis of directed functional brain networks in major depressive disorder based on EEG signal. J. Neural Eng. 17(02), 026010 (2020)

    Google Scholar 

  21. Ismail, W.W., Hanif, M., Mohamed, S., Hamzah, N., Rizman, Z.I.: Human emotion detection via brain waves study by using electroencephalogram (EEG). Int. J. Adv. Sci. Eng. Inf. Technol. 6(6), 1005–1011 (2016)

    Article  Google Scholar 

  22. Klem, G.H., Lüders, H.O., Jasper, H., Elger, C., et al.: The ten-twenty electrode system of the International Federation. Electroencephal. Clin. Neurophysiol. 52(3), 3–6 (1999)

    CAS  Google Scholar 

  23. Knyazev, G.G.: Cross-frequency coupling of brain oscillations: an impact of state anxiety. Int. J. Psychophysiol. 80(3), 236–245 (2011)

    Article  Google Scholar 

  24. Knyazev, G.G., Savostyanov, A.N., Levin, E.A.: Alpha synchronization and anxiety: Implications for inhibition vs. alertness hypotheses. Int. J. Psychophysiol. 59(2), 151–158 (2006)

    Google Scholar 

  25. Koelstra, S., et al.: DEAP: a database for emotion analysis using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)

    Article  Google Scholar 

  26. Lee, A., Litt, B., Pathmanathan, J.: Normalized transfer entropy used as an informational transfer measure of ictal pathophysiology in patients undergoing stereo-EEG for epilepsy surgery (P4.5-023). Neurol. 92(15 Supplement) (2019)

    Google Scholar 

  27. Li, P., Liu, H., Si, Y., Li, C., Li, F., Zhu, X., Huang, X., Zeng, Y., Yao, D., Zhang, Y., et al.: EEG based emotion recognition by combining functional connectivity network and local activations. IEEE Trans. Biomed. Eng. 66(10), 2869–2881 (2019)

    Article  Google Scholar 

  28. Liu, X., Li, T., Tang, C., Xu, T., Chen, P., Bezerianos, A., Wang, H.: Emotion recognition and dynamic functional connectivity analysis based on EEG. IEEE Access 7, 143293–143302 (2019)

    Article  Google Scholar 

  29. Mammone, N., et al.: Permutation disalignment index as an indirect, EEG-based, measure of brain connectivity in MCI and AD patients. Int. J. Neural Syst. 27(05), 1750020 (2017)

    Article  Google Scholar 

  30. Min, B., et al.: Prediction of individual responses to electroconvulsive therapy in patients with schizophrenia: machine learning analysis of resting-state electroencephalography. Schizophr. Res. 216, 147–153 (2019)

    Article  Google Scholar 

  31. Morris, J.D.: Observations SAM: the Self-Assessment Manikin - an efficient cross-cultural measurement of emotional response. J. Advert. Res. 35(6), 63–68 (1995)

    Google Scholar 

  32. O’Reilly, C., Lewis, J.D., Elsabbagh, M.: Is functional brain connectivity atypical in autism? a systematic review of EEG and MEG studies. PLoS ONE 12(5), e0175870 (2017)

    Google Scholar 

  33. Park, H.J., Friston, K.: Structural and functional brain networks: from connections to cognition. Science 342(6158), 1238411 (2013)

    Article  Google Scholar 

  34. Pedroni, A., Bahreini, A., Langer, N.: Automagic: standardized preprocessing of big EEG data. Neuroimage 200, 460–473 (2019)

    Article  Google Scholar 

  35. Pincus, S.M.: Irregularity and asynchrony in biologic network signals. Methods Enzymol. 321, 149–82 (2000)

    Article  CAS  Google Scholar 

  36. Pincus, S.M.: Assessing serial irregularity and its implications for health. Ann. N. Y. Acad. Sci. 954, 245–67 (2001)

    Article  CAS  Google Scholar 

  37. Popper, K.R., Eccles, J.C.: The Self and its Brain. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-61891-8

  38. Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278(6), H2039–H2049 (2000)

    Article  CAS  Google Scholar 

  39. Rodríguez-Bermúdez, G., Garcia-Laencina, P.J.: Analysis of EEG signals using nonlinear dynamics and chaos: a review. Appl. Math. Inf. Sci. 9(5), 2309 (2015)

    Google Scholar 

  40. Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)

    Article  Google Scholar 

  41. Sala-Llonch, R., Bartrés-Faz, D., Junqué, C.: Reorganization of brain networks in aging: a review of functional connectivity studies. Front. Psychol. 6, 663 (2015)

    Article  Google Scholar 

  42. Sanei, S.: Adaptive Processing of Brain Signals. Wiley, Hoboken (2013)

    Book  Google Scholar 

  43. Trujillo, L.T., Peterson, M.A., Kaszniak, A.W., Allen, J.J.: EEG phase synchrony differences across visual perception conditions may depend on recording and analysis methods. Clin. Neurophysiol. 116(1), 172–189 (2005)

    Article  Google Scholar 

  44. Tu, P.C., et al.: Reduced synchronized brain activity in schizophrenia during viewing of comedy movies. Sci. Rep. 9(1), 1–11 (2019)

    Google Scholar 

  45. Perez Velazquez, J.L., Erra, R.G., Wennberg, R., Dominguez, L.G.: Correlations of cellular activities in the nervous system: physiological and methodological considerations. In: Velazquez, J., Wennberg, R. (eds.) Coordinated Activity in the Brain. Springer Series in Computational Neuroscience, vol. 2. Springer, New York (2009). https://doi.org/10.1007/978-0-387-93797-7_1

  46. Veldhuis, J.D., Pincus, S.M., Garcia-Rudaz, M.C., Ropelato, M.G., Escobar, M.E., Barontini, M.: Disruption of the joint synchrony of luteinizing hormone, testosterone, and androstenedione secretion in adolescents with polycystic ovarian syndrome. J. Clin. Endocrinol. Metab. 86(1), 72–9 (2001)

    CAS  PubMed  Google Scholar 

  47. Zola-Morgan, S.: Localization of brain function: the legacy of Franz Joseph Gall (1758–1828). Annu. Rev. Neurosci. 18(1), 359–383 (1995)

    Article  CAS  Google Scholar 

  48. Zuchowicz, U., Wozniak-Kwasniewska, A., Szekely, D., Olejarczyk, E., David, O.: EEG phase synchronization in persons with depression subjected to transcranial magnetic stimulation. Front. Neurosci. 12, 1037 (2019)

    Article  Google Scholar 

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Acknowledgments

This work was partially supported by Spanish Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación (AEI)/European Regional Development Fund (FEDER, UE) under EQC2019-006063-P, PID2020-115220RB-C21, and 2018/11744 grants, and by Biomedical Research Networking Centre in Mental Health (CIBERSAM) of the Instituto de Salud Carlos III. Beatriz García-Martínez holds FPU16/03740 scholarship from Spanish Ministerio de Educación y Formación Professional.

Author information

Authors and Affiliations

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

    Beatriz García-Martínez & Antonio Fernández-Caballero

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

    Beatriz García-Martínez & Antonio Fernández-Caballero

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

    Antonio Fernández-Caballero

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

    Raúl Alcaraz

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

    Arturo Martínez-Rodrigo

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

    Arturo Martínez-Rodrigo

Authors
  1. Beatriz García-Martínez
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  2. Antonio Fernández-Caballero
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  3. Raúl Alcaraz
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  4. Arturo Martínez-Rodrigo
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Corresponding author

Correspondence to Beatriz García-Martínez .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Málaga, Málaga, Spain

    Gonzalo Joya

  3. Technical University of Catalonia, Barcelona, Spain

    Andreu Català

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García-Martínez, B., Fernández-Caballero, A., Alcaraz, R., Martínez-Rodrigo, A. (2021). Detection of Emotions from Electroencephalographic Recordings by Means of a Nonlinear Functional Connectivity Measure. In: Rojas, I., Joya, G., Català, A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12861. Springer, Cham. https://doi.org/10.1007/978-3-030-85030-2_20

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  • DOI: https://doi.org/10.1007/978-3-030-85030-2_20

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