%0 Journal Article %J Neural Comput %D 2011 %T A graphical model framework for decoding in the visual ERP-based BCI speller. %A Martens, S M M %A Mooij, J M %A Jeremy Jeremy Hill %A Farquhar, Jason %A Schölkopf, B %K Artificial Intelligence %K Computer User Training %K Discrimination Learning %K Electroencephalography %K Evoked Potentials %K Evoked Potentials, Visual %K Humans %K Language %K Models, Neurological %K Models, Theoretical %K Reading %K Signal Processing, Computer-Assisted %K User-Computer Interface %K Visual Cortex %K Visual Perception %X

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.

%B Neural Comput %V 23 %P 160-82 %8 01/2011 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/20964540 %N 1 %R 10.1162/NECO_a_00066 %0 Journal Article %J IEEE Trans Neural Syst Rehabil Eng %D 2006 %T Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects. %A Jeremy Jeremy Hill %A Lal, T.N %A Schröder, Michael %A Hinterberger, T. %A Wilhelm, Barbara %A Nijboer, F %A Mochty, Ursula %A Widman, Guido %A Elger, Christian %A Schölkopf, B %A Kübler, A. %A Niels Birbaumer %K Algorithms %K Artificial Intelligence %K Cluster Analysis %K Computer User Training %K Electroencephalography %K Evoked Potentials %K Female %K Humans %K Imagination %K Male %K Middle Aged %K Paralysis %K Pattern Recognition, Automated %K User-Computer Interface %X

We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.

%B IEEE Trans Neural Syst Rehabil Eng %V 14 %P 183-6 %8 06/2006 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/16792289 %N 2 %R 10.1109/TNSRE.2006.875548