Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification - researchr publication related

researchr

You are not signed in
Sign in
Sign up

Yu Zhang, Yu Wang, Jing Jin, Xingyu Wang. Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification. Int. J. Neural Syst., 27(2):1-13, 2017. [doi]

The following publications are possibly variants of this publication:

A spatial-frequency-temporal optimized feature sparse representation-based classification method for motor imagery EEG pattern recognitionMinmin Miao, Aimin Wang, Feixiang Liu. mbec, 55(9):1589-1603, 2017. [doi]

Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG ClassificationQingshan She, Kang Chen, Yuliang Ma, Thinh Nguyen, Yingchun Zhang. cin, 2018, 2018. [doi]

Feature Extraction and Classification of Mental EEG for Motor ImageryMing-Ai Li, Rui Wang, Dong-Mei Hao, Jin-fu Yang. icnc 2009: 139-143 [doi]

An Improved Support Vector Machine Classifier for EEG-Based Motor Imagery ClassificationHui Zhou, Qi Xu, Yongji Wang, Jian Huang, Jun Wu. isnn 2009: 267-275 [doi]

EEG Motor Imagery Classification With Sparse Spectrotemporal Decomposition and Deep LearningBiao Sun, Xing Zhao, Han Zhang, Ruifeng Bai, Ting Li. tase, 18(2):541-551, 2021. [doi]

The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG ClassificationShaorong Zhang, Zhibin Zhu, Benxin Zhang, Bao-Feng, Tianyou Yu, Zhi Li. sensors, 20(17):4749, 2020. [doi]

runs on WebDSL