Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network

Electromagnetic source imaging (ESI) requires solving a highly ill-posed inverse problem. To seek a unique solution, traditional ESI methods impose various forms of priors that may not accurately reflect the actual source properties, which may hinder their broad applications. To overcome this limita...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 35; H. 5; S. 6423 - 6437
Hauptverfasser:	Huang, Gexin, Liu, Ke, Liang, Jiawen, Cai, Chang, Gu, Zheng Hui, Qi, Feifei, Li, Yuanqing, Yu, Zhu Liang, Wu, Wei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.05.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Brain Coders Convolutional encoder–decoder network (Ced-Net) Convolutional neural networks data synthesis Deep learning EEG Electroencephalography electromagnetic source imaging (ESI) Electromagnetics Epilepsy Inverse problems Machine learning Magnetic resonance imaging Magnetoencephalography Neuroimaging Numerical experiments Spatiotemporal phenomena Synthesis Training
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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