Diagnosis of disorders of consciousness using nonlinear feature derived EEG topographic maps via deep learning

Abstract This study explored the value of nonlinear features extracted from EEG signals to facilitate the assessment of patients with disorders of consciousness (DOC) with limited communication capacity. We utilized a dataset comprising 104 participants, 56 with vegetative state (VS)/unresponsive wa...

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Detalles Bibliográficos

Principais autores:	Sheng Qu, Xinchun Wu, Laigang Huang, Yancai Zhou, Qiangsan Sun, Fanshuo Zeng
Formato:	Artigo
Idioma:	Inglês
Publicado:	Nature Portfolio 2026-02-01
Series:	Scientific Reports
Assuntos:	Convolutional neural networks Disorder of consciousness Support vector machine Generalized regression neural networks Electroencephalography Nonlinear dynamics analysis
Acceso en liña:	https://doi.org/10.1038/s41598-026-36733-6
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