Frequency spectrum recurrence analysis
- 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 ofIn 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.…
Verfasserangaben: | Guenia LadeiraORCiD, Norbert MarwanORCiDGND, Joao-Batista Destro-FilhoORCiD, Camila Davi Ramos, Gabriela Lima |
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DOI: | https://doi.org/10.1038/s41598-020-77903-4 |
ISSN: | 2045-2322 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/33277526 |
Titel des übergeordneten Werks (Englisch): | Scientific reports |
Verlag: | Nature portfolio |
Verlagsort: | Berlin |
Publikationstyp: | Wissenschaftlicher Artikel |
Sprache: | Englisch |
Datum der Erstveröffentlichung: | 04.12.2020 |
Erscheinungsjahr: | 2020 |
Datum der Freischaltung: | 17.10.2022 |
Freies Schlagwort / Tag: | Biomedical engineering; Brain injuries; Computational models; Computational neuroscience; Data acquisition; Data processing; Electrical and electronic engineering; Neural circuits; Visual system |
Band: | 10 |
Ausgabe: | 1 |
Aufsatznummer: | 21241 |
Seitenanzahl: | 9 |
Fördernde Institution: | Coordination for the Improvement of Higher Education Personnel of; Brazil, Ministry of Education of Brazil; National Council for Scientific; and Technological Development of BrazilConselho Nacional de; Desenvolvimento Cientifico e Tecnologico (CNPQ) |
Organisationseinheiten: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften |
Extern / Potsdam Institute for Climate Impact Research (PIK) e. V. | |
Peer Review: | Referiert |
Publikationsweg: | Open Access / Gold Open-Access |
Lizenz (Deutsch): | CC-BY - Namensnennung 4.0 International |