%0 Journal Article %J Epilepsy & Behavior %D 2013 %T Proceedings of the Fourth International Workshop on Advances in Electrocorticography. %A A L Ritaccio %A Peter Brunner %A Nathan E. Crone %A Gunduz, Aysegul %A Hirsch, Lawrence J. %A Kanwisher, Nancy %A Litt, Brian %A Kai J. Miller %A Morani, Daniel %A Parvizi, Josef %A Ramsey, Nick F %A Richner, Thomas J. %A Tandon, Niton %A Williams, Justin %A Gerwin Schalk %K Brain Mapping %K Brain–computer interface %K Electrocorticography %K Gamma-frequency electroencephalography %K High-frequency oscillations %K Neuroprosthetics %K Seizure detection %K Subdural grid %X The Fourth International Workshop on Advances in Electrocorticography (ECoG) convened in New Orleans, LA, on October 11–12, 2012. The proceedings of the workshop serves as an accurate record of the most contemporary clinical and experimental work on brain surface recording and represents the insights of a unique multidisciplinary ensemble of expert clinicians and scientists. Presentations covered a broad range of topics, including innovations in passive functional mapping, increased understanding of pathologic high-frequency oscillations, evolving sensor technologies, a human trial of ECoG-driven brain–machine interface, as well as fresh insights into brain electrical stimulation. %B Epilepsy & Behavior %V 29 %P 259–68 %8 11/2013 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/24034899 %N 2 %R 10.1016/j.yebeh.2013.08.012 %0 Conference Proceedings %B International Conference on Pattern Recognition - ICPR %D 2010 %T Decoding finger flexion from electrocorticographic signals using sparse Gaussian process. %A Zuoguan Wang %A Ji, Q %A Kai J. Miller %A Gerwin Schalk %X A brain-computer interface (BCI) creates a direct communication pathway between the brain and an external device, and can thereby restore function in people with severe motor disabilities. A core component in a BCI system is the decoding algorithm that translates brain signals into action commands of an output device. Most of current decoding algorithms are based on linear models (e.g., derived using linear regression) that may have important shortcomings. The use of nonlinear models (e.g., neural networks) could overcome some of these shortcomings, but has difficulties with high dimensional feature spaces. Here we propose another decoding algorithm that is based on the sparse gaussian process with pseudo-inputs (SPGP). As a nonparametric method, it can model more complex relationships compared to linear methods. As a kernel method, it can readily deal with high dimensional feature space. The evaluations shown in this paper demonstrate that SPGP can decode the flexion of finger movements from electrocorticographic (ECoG) signals more accurately than a previously described algorithm that used a linear model. In addition, by formulating problems in the bayesian probabilistic framework, SPGP can provide estimation of the prediction uncertainty. Furthermore, the trained SPGP offers a very effective way for identifying important features. %B International Conference on Pattern Recognition - ICPR %G eng %R 10.1109/ICPR.2010.915