Motion Artifact Removal in Functional Near‐Infrared Spectroscopy Based on Long Short‐Term Memory‐Autoencoder Model

ABSTRACT Motion artifact removal is a critical issue in functional near‐infrared spectroscopy (fNIRS) analysis tasks, with traditional methods relying heavily on expert‐based knowledge and optimal selection of model parameters within brain regions. In this paper, we propose a deep learning denoising...

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Veröffentlicht in:	The European journal of neuroscience Jg. 61; H. 2
Hauptverfasser:	Yang, Pan, Wang, Junhong, Wang, Ting, Li, Lihua, Xu, Dongjuan, Xi, Xugang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Chichester Wiley Subscription Services, Inc 01.01.2025
Schlagworte:	Algorithms Deep learning functional near‐infrared spectroscopy Hemodynamics Infrared spectroscopy LSTM‐AE Morphology motion Neural networks reconstruct hemodynamic response Spectrum analysis Statistical analysis
ISSN:	0953-816X, 1460-9568
Online-Zugang:	Volltext
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