Novel channel selection model based on graph convolutional network for motor imagery-Reference-Cited by-同舟云学术

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Novel channel selection model based on graph convolutional network for motor imagery

Published:2022-10-10 Issue: Volume: Page:
ISSN:1871-4080
Container-title:Cognitive Neurodynamics
language:en
Short-container-title:Cogn Neurodyn

Author:

Liang Wei,Jin Jing^ORCID,Daly Ian,Sun Hao,Wang Xingyu,Cichocki Andrzej

Funder

Grant National Natural Science Foundation of China

Shanghai Municipal Science and Technology Major Project

Program of Introducing Talents of Discipline to Universities through the 111 Project

ShuGuang Project supported by the Shanghai Municipal Education Commission and the Shanghai Education Development Foundation

Ministry of Education and Science of the Russian Federation

Polish National Science Center

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience

Link

https://link.springer.com/content/pdf/10.1007/s11571-022-09892-1.pdf

Reference50 articles.

1. Arvaneh M, Guan C, Ang KK et al (2011) Optimizing the channel selection and classification accuracy in EEG-based BCI. IEEE Trans Biomed Eng 58(6):1865–1873. https://doi.org/10.1109/TBME.2011.2131142

2. Baig MZ, Aslam N, Shum HPH (2020) Filtering techniques for channel selection in motor imagery EEG applications: a survey. Artif Intell Rev 53(2):1207–1232. https://doi.org/10.1007/s10462-019-09694-8

3. Blankertz B, Muller KR, Krusienski D et al (2006) The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabil Eng 14(2):153–159. https://doi.org/10.1109/TNSRE.2006.875642

4. Carrasco DG, Cantalapiedra JA (2016) Effectiveness of motor imagery or mental practice in functional recovery after stroke: a systematic review. Neurología (English Edition) 31(1):43–52. https://doi.org/10.1016/j.nrleng.2013.02.008

5. Chang W, Huang W, Yan G, et al (2021) EEG based graph network analysis for motor imagery task. In: 2021 6th international conference on computational intelligence and applications (ICCIA), pp 185–189. https://doi.org/10.1109/ICCIA52886.2021.00043

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