Sparse Bayesian Classification of EEG for Brain-Computer Interface

• Published in: IEEE transaction on neural networks and learning systems Vol. 27; no. 11; pp. 2256 - 2267
• Main Authors: Yu Zhang, Guoxu Zhou, Jing Jin, Qibin Zhao, Xingyu Wang, Cichocki, Andrzej
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2016
• Subjects: Algorithms; Bayes methods; Bayes Theorem; Brain modeling; Brain-Computer Interfaces; Brain–computer interface (BCI); Classification algorithms; electroencephalogram (EEG); Electroencephalography; event-related potential (ERP); Humans; Laplace prior; Neural Networks (Computer); sparse Bayesian classification; Support vector machines; Training; Training data
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2015.2476656

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.