Peculiarities of information transfer within functional cortical network during emotional face perception

M. Chernykh, I. Zyma
Taras Shevchenko National University of Kyiv, Kyiv; Taras Shevchenko National University of Kyiv, Kyiv


Aim: The research aimed to study and model the emotion-related activity of functional networks within the human brain cortex using power spectrum density and detrended phase transfer entropy methods. Attention was focused on revealing alterations in cognitive mechanisms, caused by presentation of neutral human faces as rare stimuli among faces with either negative or positive expression. Methods: EEG-data was recorded during the perception and processing of neutral human facial expressions, presented among positive and negative faces in two series of images, alongside with resting state with open and closed eyes, which was further analyzed using power spectrum density and detrended phase transfer entropy methods. Results: Specific EEG-bands (θ and β) were chosen for the analysis based on their prominent role in memory- and emotion-related mechanisms. The topography of the spectral power density corresponded to the generally accepted ideas describing perception and visual stimuli processing mechanisms. The phase transfer entropy method was not sufficient to analyze resting state data. The results of the analysis performed using the phase transfer entropy method revealed the problems of neutral faces differentiation when presented in a positive emotional context. Simultaneously, enhanced processes of motivational coding and self-reflection were observed during the presentation of neutral faces in a negative emotional context. These results corresponded with the data obtained in our previous
ERP-based study. Conclusions: Phase transfer entropy and spectral power density have demonstrated their effectiveness in analyzing the mechanisms of emotional visual stimuli processing mediated in different cortical areas.


EEG; Emotion; Facial expression; Functional network

Full Text:



Başar E, Başar-Eroglu C, Karakaş S, Schürmann M. Gamma, alpha, delta, and theta oscillations govern cognitive processes. Int Journ of Psychophys. 2001; 39(2-3):241-248. DOI:10.1016/s0167-8760(00)00145-8.

Kukleta M, Brázdil M, Roman R, Jurák P. Identical event-related potentials to target and frequent stimuli of visual oddball task recorded by intracerebral electrodes. Clin Neurophys. 2003;114(7):1292-1297. DOI:10.1016/s1388-2457(03)00108-1.

Vuilleumier P, Pourtois G. Distributed and interactive brain mechanisms during emotion face perception: Evidence from functional neuroimaging. Neuropsychologia. 2006;14:153–167. DOI: 10.1016/j.neuropsychologia.2006.06.003.

Черних М, Зима І. Вплив поточного контексту, створеного сприйняттям емоційно забарвлених виразів обличь, на таргетне пред'явлення нейтральних зорових образів. Проблеми регуляції фізіологічних функцій. 2018;1(24):46.

Pastötter B, Bäuml KT. Distinct slow and fast cortical theta dynamics in episodic memory retrieval. NeuroIm. 2014; 94:155-161. DOI:10.1016/j.neuroimage.2014.03.002.

Lindner M, Vicente R, Priesemann V, Wibral M. TRENTOOL: a Matlab open-source toolbox to analyse information flow in time series data with transfer entropy. BMC Neurosci. 2011;12: 119. DOI: 10.1186/1471-2202-12-119.

Friston KJ. Functional and Effective Connectivity: A Review. Brain Conn. 2011; 1:13–36. DOI:

Schreiber T. Measuring information transfer. Phys Rev Lett. 2000;85:461–464. DOI:

Kraskov A, Stögbauer H, Grassberger P. Estimating mutual information. Phys Rev E. 2004;69:1–16. DOI:

Jensen O, and Colgin LL. Cross-frequency coupling between neuronal oscillations. Trends Cogn Sci. 2007; 11(7):267-269. DOI:10.1016/j.tics.2007.05.003.

Osipova D, Takashima A, Oostenveld R, Fernández G, Maris E, Jensen O. Theta and gamma oscillations predict encoding and retrieval of declarative memory. Journ of Neurosci. 2006; 26(28):7523-7531.

Özgören M, Oniz A. Beta in simple and complex cognitive processes. Intern Journ Psychophys. 2008;69(3):192.

Stam CJ, Van Woerkom TCAM, Pritchard WS. Use of non-linear EEG measures to characterize EEG changes during mental activity. Electroenceph Clin Neurophys. 1996; 99(3):214-224.

Lang P. International Affective Picture System (IAPS): Technical Manual and Affective Ratings.The Center for Research in Psychophysiology, University of Florida, Gainesville, Fl; 1999.

Welch J. Cortical coordination dynamics and cognition. Trends Cogn Sci. 2001;5(1): 26-36.

Balconi, M, Ferrari C. Subliminal and supraliminal processing of facial expression of emotions: brain oscillation in the left/right frontal area. Brain Sci. 2012;2:85–100. DOI: 10.3390/brainsci2020085.

Kret ME, Pichon S, Grèzes J, de Gelder B. Similarities and differences in perceiving threat from dynamic faces and bodies. An fMRI study. Neuroim. 2011; 54:1755–1762. DOI: 10.1016/j.neuroimage.2010.08.012.

Müsch K, Hamamé C M, Perrone-Bertolotti M, Minotti L, Kahane P, Engel AK. Selective attention modulates high-frequency activity in the faceprocessing network. Cortex. 2014; 60: 34–51.

Paulmann S, Bleichner M, Kotz SA. Valence, arousal and task effects in emotional prosody processing. Front Psychol. 2013;4:345. DOI: 10.3389/fpsyg.2013.00345.

Received: 06.01.2021

Revised: 03.02.2021

Signed for the press: 03.02.2021



  • There are currently no refbacks.

Лицензия Creative Commons
This journal is available according to the Creative Commons License «Attribution» («Атрибуція») 4.0 Global (CC-BY).