Comparing Multi-Dimensional fNIRS Features Using Bayesian Optimization-Based Neural Networks for Mild Cognitive Impairment (MCI) Detection-Reference-Cited by-同舟云学术

Comparing Multi-Dimensional fNIRS Features Using Bayesian Optimization-Based Neural Networks for Mild Cognitive Impairment (MCI) Detection

Published:2023 Issue: Volume:31 Page:1019-1029
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Zhang Chutian¹^ORCID,Yang Hongjun²^ORCID,Fan Chen-Chen²^ORCID,Chen Sheng²^ORCID,Fan Chenyu³,Hou Zeng-Guang²^ORCID,Chen Jingyao¹,Peng Liang²^ORCID,Xiang Kexin²^ORCID,Wu Yi³,Xie Hongyu³

Affiliation:

1. Macau Institute of Systems Engineering, Macau University of Science and Technology, Macau, China

2. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

3. Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Beijing Science and Technology Program

Alliance of International Science Organizations (ANSO) Collaborative Research Project

Strategic Priority Research Program of Chinese Academy of Science

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

Link

http://xplorestaging.ieee.org/ielx7/7333/10031624/10015102.pdf?arnumber=10015102

Reference48 articles.

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3. Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis System Based on EEG

4. FGANet: fNIRS-Guided Attention Network for Hybrid EEG-fNIRS Brain-Computer Interfaces

5. Neurophysiological investigation of the basis of the fMRI signal

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