Forecasting brain activity based on models of spatiotemporal brain dynamics: A comparison of graph neural network architectures

URN to cite this document:: urn:nbn:de:bvb:355-epub-529304
DOI to cite this document:: 10.5283/epub.52930

Wein, Simon

; Schüller, A. ; Tomé, A. M. ; Malloni, W. M.

; Greenlee, M. W. ; Lang, E. W.

License: Creative Commons Attribution 4.0
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Date of publication of this fulltext: 28 Sep 2022 06:21

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Abstract

Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph-structured signals like those observed in complex brain networks. In our study we compare different spatiotemporal GNN architectures and study their ...