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  Frequency spectrum recurrence analysis

Ladeira, G., Marwan, N., Destro-Filho, J.-B., Davi Ramos, C., Lima, G. (2020): Frequency spectrum recurrence analysis. - Scientific Reports, 10, 21241.
https://doi.org/10.1038/s41598-020-77903-4

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Ladeira, Guênia1, Author
Marwan, Norbert2, Author              
Destro-Filho, João-Batista1, Author
Davi Ramos, Camila1, Author
Lima, Gabriela1, Author
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, Potsdam, ou_persistent13              

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 Abstract: In this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for time series analysis with noise and disturbances. EES were collected, and alpha waves of the occipital region were analysed by comparing the signals from participants in two states, eyes open and eyes closed. Firstly, EES were characterized and analysed by means of techniques already known to compare with the results of the innovative technique that we present here. We verified that, standard recurrence quantification analysis by means of EES time series cannot statistically distinguish the two states. However, the new frequency spectrum recurrence quantification exhibit quantitatively whether the participants have their eyes open or closed. In sequence, new quantifiers are created for analysing the recurrence concentration on frequency bands. These analyses show that EES with similar frequency spectrum have different recurrence levels revealing different behaviours of the nervous system. The technique can be used to deepen the study on depression, stress, concentration level and other neurological issues and also can be used in any complex system.

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 Dates: 2020-12-042020-12-04
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41598-020-77903-4
PIKDOMAIN: RD4 - Complexity Science
MDB-ID: No data to archive
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
Organisational keyword: RD4 - Complexity Science
Working Group: Development of advanced time series analysis techniques
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Title: Scientific Reports
Source Genre: Journal, SCI, Scopus, p3, OA
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Pages: - Volume / Issue: 10 Sequence Number: 21241 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals2_395
Publisher: Nature