Increasing accessibility to a large brain–computer interface dataset: Curation of physionet EEG motor movement/imagery dataset for decoding and classification-Reference-Cited by-同舟云学术

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Increasing accessibility to a large brain–computer interface dataset: Curation of physionet EEG motor movement/imagery dataset for decoding and classification

Published:2024-06 Issue: Volume:54 Page:110181
ISSN:2352-3409
Container-title:Data in Brief
language:en
Short-container-title:Data in Brief

Author:

Shuqfa Zaid^ORCID,Lakas Abderrahmane,Belkacem Abdelkader Nasreddine

Publisher

Elsevier BV

Reference18 articles.

1. Physionet EEGMMIDB in MATLAB structure to leverage accessibility and exploitation;Shuqfa;Mendeley Data,2023

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

3. Decoding multi-class motor imagery and motor execution tasks using Riemannian geometry algorithms on large EEG datasets;Shuqfa;Sensors,2023

4. Classification of multi-class motor imagery with a novel hierarchical SVM algorithm for brain–computer interfaces;Dong;Med. Biol. Eng. Comput.,2017

5. Brain-computer interfaces based on visual evoked potentials;Wang;IEEE Eng. Med. Biol. Mag.,2008

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