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  Temporal structure of EEG microstates assessed via long-shortterm- memory network analysis

Jamalabadi, H. (2019). Temporal structure of EEG microstates assessed via long-shortterm- memory network analysis. Neuropsychobiology, 77(3): S3-3, 124.

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Genre: Meeting Abstract

Externe Referenzen

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externe Referenz:
https://www.karger.com/Article/Pdf/496817 (Verlagsversion)
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Urheber

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 Urheber:
Jamalabadi, H1, Autor           
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1External Organizations, ou_persistent22              

Inhalt

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Schlagwörter: -
 Zusammenfassung: To study the temporal structure of EEG microstates, we trained recurrent neural networks (RNNs) consisting of long-short-term-memories (LSTMs) with microstate sequences of different lengths to 1) reconstruct the input microstate sequence and 2) predict the future trajectory of microstates. We tested the reconstruction and prediction accuracies on nonoverlapping subsets of resting state data preceding and following social stress within and between subjects and investigated the activation patterns of the neurons in the hidden layer. The results show that the microstates’ trajectory 1) can be learned successfully across sessions by RNNs, 2) is largely subject-invariant at shorter time scales, 3) is affected by stress. These findings suggest that the sequence of microstates is governed by different processes at different time scales.

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 Datum: 2018-112019-03
 Publikationsstatus: Erschienen
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Veranstaltung

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Titel: 20th Biennial Conference of the International Pharmaco-EEG Society (IPEG 2018): Pharmaco-EEG, Pharmaco-Sleep and EEG-Based Personalized Medicine
Veranstaltungsort: Zürich, Switzerland
Start-/Enddatum: 2018-11-21 - 2018-11-25

Entscheidung

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Projektinformation

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Quelle 1

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Titel: Neuropsychobiology
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Basel : Karger.
Seiten: - Band / Heft: 77 (3) Artikelnummer: S3-3 Start- / Endseite: 124 Identifikator: ISSN: 0302-282X
CoNE: https://pure.mpg.de/cone/journals/resource/954925510411