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

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
Tianyou YuYuanqing Li

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.

References

Dec 1, 1988·Electroencephalography and Clinical Neurophysiology·L A Farwell, E Donchin
Jun 6, 2002·Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology·Jonathan R WolpawTheresa M Vaughan
Jun 11, 2004·IEEE Transactions on Bio-medical Engineering·Thomas Navin LalBernhard Schölkopf
Jun 11, 2004·IEEE Transactions on Bio-medical Engineering·Matthias KaperHelge Ritter
Mar 3, 2006·International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology·John Polich, José R Criado
Jan 20, 2007·The Journal of Physiology·Niels Birbaumer, Leonardo G Cohen
Apr 21, 2007·Journal of Neuroscience Methods·Ulrich HoffmannKarin Diserens
Sep 8, 2007·Journal of Neuroscience Methods·D J KrusienskiJ R Wolpaw
Mar 13, 2008·IEEE Transactions on Bio-medical Engineering·Alain Rakotomamonjy, Vincent Guigue
Jun 23, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Hubert Cecotti, Axel Gräser
Jan 20, 2011·Journal of Neural Engineering·H CecottiJ Mattout
Dec 12, 2012·Journal of Neural Engineering·Jun LuJonathan R Wolpaw

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Citations

Mar 10, 2018·IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society·Jingcong LiLianwen Jin
Sep 15, 2018·Journal of Neural Engineering·Zied TayebJörg Conradt
Nov 16, 2019·Medical & Biological Engineering & Computing·Yanina AtumJosé Biurrun Manresa
May 14, 2016·Journal of Neural Engineering·Andrea CancelliMarom Bikson
May 2, 2018·Frontiers in Neuroscience·Jiuqi HanChangyong Wang

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