English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

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

Jamalabadi, H. (2018). Temporal structure of EEG microstates assessed via long-shortterm- memory network analysis. In 20th Biennial IPEG Meeting (pp. 56).

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0002-B447-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-B448-2
Genre: Meeting Abstract

Files

show Files

Creators

show
hide
 Creators:
Jamalabadi, H1, Author              
Affiliations:
1University of Tübingen, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: 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.

Details

show
hide
Language(s):
 Dates: 2018-11
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: -
 Degree: -

Event

show
hide
Title: 20th Biennial IPEG Meeting
Place of Event: Zürich, Switzerland
Start-/End Date: 2018-11-21 - 2018-11-25

Legal Case

show

Project information

show

Source 1

show
hide
Title: 20th Biennial IPEG Meeting
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 56 Identifier: -