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Open AccessArticle

Causal Shannon–Fisher Characterization of Motor/Imagery Movements in EEG

IFLYSIB, CONICET & Universidad Nacional de La Plata, La Plata 1900, Argentina
Departamento de Física, Facultad de Ciencias Exactas, UNLP Calle 49 y 115. C.C. 67, La Plata 1900, Argentina
Departamento de Informática en Salud, Hospital Italiano de Buenos Aires & CONICET, Ciudad Autónoma de Buenos Aires C1199ABB, Argentina
Instituto de Física, Universidade Federal de Alagoas (UFAL), Maceió 57072-900, Brazil
Complex Systems Group, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago 12455, Chile
Author to whom correspondence should be addressed.
Entropy 2018, 20(9), 660;
Received: 24 July 2018 / Revised: 30 August 2018 / Accepted: 30 August 2018 / Published: 2 September 2018
(This article belongs to the Special Issue Permutation Entropy & Its Interdisciplinary Applications)
The electroencephalogram (EEG) is an electrophysiological monitoring method that allows us to glimpse the electrical activity of the brain. Neural oscillations patterns are perhaps the best salient feature of EEG as they are rhythmic activities of the brain that can be generated by interactions across neurons. Large-scale oscillations can be measured by EEG as the different oscillation patterns reflected within the different frequency bands, and can provide us with new insights into brain functions. In order to understand how information about the rhythmic activity of the brain during visuomotor/imagined cognitive tasks is encoded in the brain we precisely quantify the different features of the oscillatory patterns considering the Shannon–Fisher plane H × F . This allows us to distinguish the dynamics of rhythmic activities of the brain showing that the Beta band facilitate information transmission during visuomotor/imagined tasks. View Full-Text
Keywords: EEG signals; brain oscillation patterns; bandt and pompe methodology; Fisher information and Shannon entropy EEG signals; brain oscillation patterns; bandt and pompe methodology; Fisher information and Shannon entropy
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Baravalle, R.; Rosso, O.A.; Montani, F. Causal Shannon–Fisher Characterization of Motor/Imagery Movements in EEG. Entropy 2018, 20, 660.

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