TY - JOUR T1 - A graphical model framework for decoding in the visual ERP-based BCI speller. JF - Neural Comput Y1 - 2011 A1 - Martens, S M M A1 - Mooij, J M A1 - Jeremy Jeremy Hill A1 - Farquhar, Jason A1 - Schölkopf, B KW - Artificial Intelligence KW - Computer User Training KW - Discrimination Learning KW - Electroencephalography KW - Evoked Potentials KW - Evoked Potentials, Visual KW - Humans KW - Language KW - Models, Neurological KW - Models, Theoretical KW - Reading KW - Signal Processing, Computer-Assisted KW - User-Computer Interface KW - Visual Cortex KW - Visual Perception AB -

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.

VL - 23 UR - http://www.ncbi.nlm.nih.gov/pubmed/20964540 IS - 1 ER -