NDLI: Grouped Automatic Relevance Determination and Its Application in Channel Selection for P300 BCIs

Content Provider	IEEE Xplore Digital Library
Author	Tianyou Yu Zhuliang Yu Zhenghui Gu Yuanqing Li
Copyright Year	2001
Abstract	During the development of a brain-computer interface, it is beneficial to exploit information in multiple electrode signals. However, a small channel subset is favored for not only machine learning feasibility, but also practicality in commercial and clinical BCI applications. An embedded channel selection approach based on grouped automatic relevance determination is proposed. The proposed Gaussian conjugate group-sparse prior and the embedded nature of the concerned Bayesian linear model enable simultaneous channel selection and feature classification. Moreover, with the marginal likelihood (evidence) maximization technique, hyper-parameters that determine the sparsity of the model are directly estimated from the training set, avoiding time-consuming cross-validation. Experiments have been conducted on P300 speller BCIs. The results for both public and in-house datasets show that the channels selected by our techniques yield competitive classification performance with the state-of-the-art and are biologically relevant to P300.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	10
File Size	1963565
Starting Page	1068
Ending Page	1077
File Format	PDF
ISSN	15344320
Volume Number	23
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Bayes methods Brain modeling Vectors Training Feature extraction Accuracy P300 Automatic relevance determination (ARD) Bayesian group sparsity brain-computer interface (BCI) channel selection electroencephalogram (EEG)
Content Type	Text
Resource Type	Article
Subject	Neuroscience Rehabilitation Internal Medicine Biomedical Engineering Computer Science Applications

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