Deep source transfer learning for the estimation of internal brain dynamics using scalp EEG

Electroencephalography (EEG) provides high temporal resolution neural data for brain-computer interfacing via noninvasive electrophysiological recording. Estimating the internal brain activity by means of source imaging techniques can further improve the spatial resolution of EEG and enhance the rel...

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Vydáno v:	Cognitive neurodynamics Ročník 18; číslo 6; s. 3507 - 3520
Hlavní autoři:	Yu, Haitao, Hu, Zhiwen, Zhao, Quanfa, Liu, Jing
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.12.2024 Springer Nature B.V
Témata:	Accuracy Acupuncture Algorithms Artificial Intelligence Biochemistry Biochips Biomedical and Life Sciences Biomedicine Brain Cognitive Psychology Computer applications Computer Science Decoding Deep learning Dynamics EEG Electroencephalography Electrophysiological recording Epilepsy Estimates Estimation Human-computer interface Imaging techniques Implants Information processing Localization Machine learning Medical imaging Neural coding Neural networks Neuroimaging Neurosciences Recurrent neural networks Research Article Seizures Sensory integration Spatial discrimination learning Spatial resolution Spatiotemporal data Stimulation Temporal resolution Thalamic nuclei Thalamus Transfer learning Deep source imaging Transfer learning Thalamocortical dynamics EEG Brain model
ISSN:	1871-4080, 1871-4099
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