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Open AccessArticle

Characterizing Motif Dynamics of Electric Brain Activity Using Symbolic Analysis

Faculdade de Ciências e Tecnologia, Departamento de Engenharia Electrotécnica, Universidade Novade Lisboa, 2829-516 Caparica, Lisbon, Portugal
Innaxis Foundation & Research Institute, José Ortega y Gasset 20, 28006 Madrid, Spain
Center for Biomedical Technology, Technical University of Madrid, Campus Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain
Author to whom correspondence should be addressed.
Entropy 2014, 16(11), 5654-5667;
Received: 9 September 2014 / Revised: 15 October 2014 / Accepted: 23 October 2014 / Published: 27 October 2014
(This article belongs to the Special Issue Entropy and Electroencephalography)
Motifs are small recurring circuits of interactions which constitute the backbone of networked systems. Characterizing motif dynamics is therefore key to understanding the functioning of such systems. Here we propose a method to define and quantify the temporal variability and time scales of electroencephalogram (EEG) motifs of resting brain activity. Given a triplet of EEG sensors, links between them are calculated by means of linear correlation; each pattern of links (i.e., each motif) is then associated to a symbol, and its appearance frequency is analyzed by means of Shannon entropy. Our results show that each motif becomes observable with different coupling thresholds and evolves at its own time scale, with fronto-temporal sensors emerging at high thresholds and changing at fast time scales, and parietal ones at low thresholds and changing at slower rates. Finally, while motif dynamics differed across individuals, for each subject, it showed robustness across experimental conditions, indicating that it could represent an individual dynamical signature. View Full-Text
Keywords: motifs; entropy; forbidden patterns; electroencephalogram (EEG); time scales motifs; entropy; forbidden patterns; electroencephalogram (EEG); time scales
MDPI and ACS Style

Zanin, M.; Papo, D. Characterizing Motif Dynamics of Electric Brain Activity Using Symbolic Analysis. Entropy 2014, 16, 5654-5667.

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