Characterizing Motif Dynamics of Electric Brain Activity Using Symbolic Analysis

Entropy

David Papo¹  Massimiliano Zanin² 

[1] Center for Biomedical Technology, Technical University of Madrid, Campus Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain;Faculdade de Ciências e Tecnologia, Departamento de Engenharia Electrotécnica, Universidade Novade Lisboa, 2829-516 Caparica, Lisbon, Portugal;

关键词: motifs;    entropy;    forbidden patterns;    electroencephalogram (EEG);    time scales;   

DOI  :  10.3390/e16115654

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown