Sparse Bayesian Classification of EEG for Brain-Computer Interface

Yu Zhang, Guoxu Zhou, Jing Jin, Qibin Zhao, Xingyu Wang, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

201 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2256-2267
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number11
Publication statusPublished - Nov 2016
Externally publishedYes


  • Brain-computer interface (BCI)
  • electroencephalogram (EEG)
  • event-related potential (ERP)
  • Laplace prior
  • sparse Bayesian classification


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