Unsupervised representation learning of spontaneous MEG data with nonlinear ICA

Unsupervised representation learning of spontaneous MEG data with nonlinear ICA

Resting-state magnetoencephalography (MEG) data show complex but structured spatiotemporal patterns. However, the neurophysiological basis of these signal patterns is not fully known and the underlying signal sources are mixed in MEG measurements. Here, we developed a method based on the nonlinear i...

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Bibliographic Details
Main Authors: Yongjie Zhu, Tiina Parviainen, Erkka Heinilä, Lauri Parkkonen, Aapo Hyvärinen
Format: Article
Language:English
Published: Elsevier 2023-07-01
Series:NeuroImage
Subjects:
Nonlinear independent component analysis (ICA)
Unsupervised learning
Deep generative model
Resting-state network
Non-stationarity
Neurofeedback
Online Access:http://www.sciencedirect.com/science/article/pii/S1053811923002938

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http://www.sciencedirect.com/science/article/pii/S1053811923002938

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