文献类型：期刊文献

英文题名：Sparse Bayesian Classification of EEG for Brain-Computer Interface

作者：Zhang, Yu[1];Zhou, Guoxu[2];Jin, Jing[1];Zhao, Qibin[2];Wang, Xingyu[1];Cichocki, Andrzej[2,3]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]RIKEN Brain Sci Inst, Lab Adv Brain Signal Proc, Saitama 3510198, Japan;[3]Polish Acad Sci, Syst Res Inst, PL-00901 Warsaw, Poland

年份：2016

卷号：27

期号：11

起止页码：2256

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20173404063894);WOS:【SCI-EXPANDED(收录号:WOS:000386940300009)】；

基金：This work was supported in part by the Grants-in-Aid for Scientific Research through the Japan Society for the Promotion of Science under Grant 15K15955 and Grant 26730125, in part by the National Natural Science Foundation of China under Grant 61201124, Grant 61202155, Grant 61203127, Grant 61305028, Grant 61573142, and Grant 91420302, in part by the Fundamental Research Funds for the Central Universities under Grant WG1414005, Grant WH1314023, and Grant WH1414022, and in part by the Guangdong Natural Science Foundation under Grant 2014A030308009. (Corresponding author: Yu Zhang).

语种：英文

外文关键词：Brain-computer interface (BCI); electroencephalogram (EEG); event-related potential (ERP); Laplace prior; sparse Bayesian classification

摘要：Regularization has been one of the most popular approaches to prevent overfitting in electroencephalogram (EEG) classification of brain-computer interfaces (BCIs). The effectiveness of regularization is often highly dependent on the selection of regularization parameters that are typically determined by cross-validation (CV). However, the CV imposes two main limitations on BCIs: 1) a large amount of training data is required from the user and 2) it takes a relatively long time to calibrate the classifier. These limitations substantially deteriorate the system's practicability and may cause a user to be reluctant to use BCIs. In this paper, we introduce a sparse Bayesian method by exploiting Laplace priors, namely, SBLaplace, for EEG classification. A sparse discriminant vector is learned with a Laplace prior in a hierarchical fashion under a Bayesian evidence framework. All required model parameters are automatically estimated from training data without the need of CV. Extensive comparisons are carried out between the SBLaplace algorithm and several other competing methods based on two EEG data sets. The experimental results demonstrate that the SBLaplace algorithm achieves better overall performance than the competing algorithms for EEG classification.