01885nas 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