01886nas a2200397   4500008004100000245009000041210006900131260001200200300001300212490000700225520069800232653001800930653003100948653002500979653004301004653003201047653002101079653002201100653001801122100001801140700001901158700002201177700002001199700002501219700002101244700001601265700001901281700001901300700001901319700002001338700002401358700001801382700002101400700001901421856004801440       2013                        eng d00aProceedings of the Fourth International Workshop on Advances in Electrocorticography.0 aProceedings of the Fourth International Workshop on Advances in   c11/2013  a259–680 v293 aThe 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.10aBrain Mapping10aBrain–computer interface10aElectrocorticography10aGamma-frequency electroencephalography10aHigh-frequency oscillations10aNeuroprosthetics10aSeizure detection10aSubdural grid1 aRitaccio, A L1 aBrunner, Peter1 aCrone, Nathan, E.1 aGunduz, Aysegul1 aHirsch, Lawrence, J.1 aKanwisher, Nancy1 aLitt, Brian1 aMiller, Kai, J1 aMorani, Daniel1 aParvizi, Josef1 aRamsey, Nick, F1 aRichner, Thomas, J.1 aTandon, Niton1 aWilliams, Justin1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2403489901885nas a2200133   4500008004100000245009400041210006900135520136000204100001801564700001001582700001901592700001901611856012101630       2010                        eng d00aDecoding finger flexion from electrocorticographic signals using sparse Gaussian process.0 aDecoding finger flexion from electrocorticographic signals using3 aA 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.1 aWang, Zuoguan1 aJi, Q1 aMiller, Kai, J1 aSchalk, Gerwin  uhttps://www.neurotechcenter.org/publications/2010/decoding-finger-flexion-electrocorticographic-signals-using-sparse