文献类型：期刊文献

英文题名：EEG classification using sparse Bayesian extreme learning machine for brain-computer interface

作者：Jin, Zhichao[1];Zhou, Guoxu[2];Gao, Daqi[1];Zhang, Yu[3]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Dept Comp Sci & Engn, Shanghai, Peoples R China;[2]Guangdong Univ Technol, Sch Automat, Guangzhou, Peoples R China;[3]Stanford Univ, Dept Psychiat & Behav Sci, Stanford, CA 94305 USA

年份：2020

卷号：32

期号：11

起止页码：6601

外文期刊名：NEURAL COMPUTING & APPLICATIONS

收录：;EI(收录号：20184105934104);WOS:【SCI-EXPANDED(收录号:WOS:000536371900018)】；

基金：This study was supported in part by National Natural Science Foundation of China under Grant 61673124.

语种：英文

外文关键词：Brain-computer interface; Electroencephalogram; Motor imagery; Extreme learning machine; Sparse Bayesian learning

摘要：Mu rhythm is a spontaneous neural response occurring during a motor imagery (MI) task and has been increasingly applied to the design of brain-computer interface (BCI). Accurate classification of MI is usually rather difficult to be achieved since mu rhythm is very weak and likely to be contaminated by other background noises. As an extension of the single layer feedforward network, extreme learning machine (ELM) has recently proven to be more efficient than support vector machine that is a benchmark for MI-related EEG classification. With probabilistic inference, this study introduces a sparse Bayesian ELM (SBELM)-based algorithm to improve the classification performance of MI. SBELM is able to automatically control the model complexity and exclude redundant hidden neurons by combining advantageous of both ELM and sparse Bayesian learning. The effectiveness of SBELM for MI-related EEG classification is validated on a public dataset from BCI Competition IV IIb in comparison with several other competing algorithms. Superior classification accuracy confirms that the proposed SBELM-based algorithm is a promising candidate for performance improvement of an MI BCI.