Hybrid EEG-fNIRS brain-computer interface based on the non-linear features extraction and stacking ensemble learning-Reference-Cited by-同舟云学术

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Hybrid EEG-fNIRS brain-computer interface based on the non-linear features extraction and stacking ensemble learning

Published:2023-04 Issue:2 Volume:43 Page:463-475
ISSN:0208-5216
Container-title:Biocybernetics and Biomedical Engineering
language:en
Short-container-title:Biocybernetics and Biomedical Engineering

Author:

Maher Asmaa,Mian Qaisar Saeed,Salankar N.,Jiang Feng,Tadeusiewicz Ryszard^ORCID,Pławiak Paweł^ORCID,Abd El-Latif Ahmed A.,Hammad Mohamed^ORCID

Funder

Prince Sultan University

Politechnika Krakowska

Effat University

Akademia Górniczo-Hutnicza im. Stanislawa Staszica

Harbin Institute of Technology

Polska Akademia Nauk

Menofia University

Publisher

Elsevier BV

Subject

Biomedical Engineering

Reference40 articles.

1. Brain computer interface advancement in neurosciences: Applications and issues;Mudgal;Interdiscipl Neurosurg,2020

2. Current status, challenges, and possible solutions of EEG-based brain-computer interface: A comprehensive review;Rashid;Front Neurorob,2020

3. A systematic review on hybrid EEG/fNIRS in brain-computer interface;Liu;Biomed Signal Process Control,2021

4. Multi-ganglion ANN based feature learning with application to P300-BCI signal classification;Gao;Biomed Signal Process Control,2015

5. How many people are able to control a P300-based brain–computer interface (BCI)?;Guger;Neurosci Lett,2009

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5. Learning based motion artifacts processing in fNIRS: a mini review;Frontiers in Neuroscience;2023-11-08

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