A Novel Feature Fusion Approach for Classification of Motor Imagery EEG Based on Hierarchical Extreme Learning Machine-Reference-Cited by-同舟云学术

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A Novel Feature Fusion Approach for Classification of Motor Imagery EEG Based on Hierarchical Extreme Learning Machine

Published:2023-11-10 Issue:2 Volume:16 Page:566-580
ISSN:1866-9956
Container-title:Cognitive Computation
language:en
Short-container-title:Cogn Comput

Author:

Duan Lijuan,Lian Zhaoyang,Qiao Yuanhua^ORCID,Chen Juncheng,Miao Jun,Li Mingai

Funder

National Natural Science Foundation of China

Beijing Municipal Education Commission and the Municipal Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience,Computer Science Applications,Computer Vision and Pattern Recognition

Link

https://link.springer.com/content/pdf/10.1007/s12559-023-10217-5.pdf

Reference39 articles.

1. Lopes DSF. EEG and MEG: relevance to neuroscience. Neuron. 2013;80(5):1112.

2. Ruffino C, Papaxanthis C, Lebon F. Neural plasticity during motor learning with motor imagery practice: review and perspectives. Neuroscience. 2017;341:61.

3. Wong CK, Luo Q, Zotev V, et al. Automatic cardiac cycle determination directly from EEG-fMRI data by multi-scale peak detection method. J Neurosci Methods. 2018.

4. Jain A, Abbas B, Farooq O, et al. Fatigue detection and estimation using auto-regression analysis in EEG. International Conference on Advances in Computing, Communications and Informatics. IEEE. 2016:1092-1095.

5. Ahirwal MK, Kumar A, Singh GK. Adaptive filtering of EEG/ERP through bounded range artificial bee colony (BR-ABC) algorithm. Digital Signal Process. 2014;25(1):164–72.

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