Neural network for step anomaly detection in head motion during fMRI using meta-learning adaptation

Neural network for step anomaly detection in head motion during fMRI using meta-learning adaptation

Quality assessment and artifact detection in functional magnetic resonance imaging (fMRI) data is essential for clinical applications and brain research. Subject head motion remains the main source of artifacts - even the tiniest head movement can perturb the structural and functional data derived f...

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Bibliographic Details
Main Authors: N.S. Davydov, V.V. Evdokimova, P.G. Serafimovich, V.I. Protsenko, A.G. Khramov, A.V. Nikonorov
Format: Article
Language:English
Published: Samara National Research University 2023-12-01
Series:Компьютерная оптика
Subjects:
recurrent neural networks
anomaly detection
signal analysis
functional magnetic resonance imaging
meta-learning
Online Access:https://www.computeroptics.ru/eng/KO/Annot/KO47-6/470617e.html
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