PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Graphical Model Framework for Decoding in the Visual ERP-Based BCI Speller
Suzanne Martens, Joris Mooij, Jeremy Hill, Jason Farquhar and Bernhard Schölkopf
Neural Computation Volume 23, Number 1, pp. 160-182, 2011. ISSN 0899-7667

Abstract

We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating brain signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between brain signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the brain signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
Theory & Algorithms
ID Code:7865
Deposited By:Joris Mooij
Deposited On:17 March 2011