01958nas a2200253   4500008004100000245010000041210006900141260001200210490000600222520117500228653002901403653000901432653003401441653003001475653002401505653002901529100001201558700002001570700001901590700001801609700001001627700001901637856004801656       2012                        eng d00aDecoding Onset and Direction of Movements using Electrocorticographic (ECoG) Signals in Humans.0 aDecoding Onset and Direction of Movements using Electrocorticogr  c08/20120 v53 aCommunication of intent usually requires motor function. This requirement can be limiting when a person is engaged in a task, or prohibitive for some people suffering from neuromuscular disorders. Determining a person's intent, e.g., where and when to move, from brain signals rather than from muscles would have important applications in clinical or other domains. For example, detection of the onset and direction of intended movements may provide the basis for restoration of simple grasping function in people with chronic stroke, or could be used to optimize a user's interaction with the surrounding environment. Detecting the onset and direction of actual movements are a first step in this direction. In this study, we demonstrate that we can detect the onset of intended movements and their direction using electrocorticographic (ECoG) signals recorded from the surface of the cortex in humans. We also demonstrate in a simulation that the information encoded in ECoG about these movements may improve performance in a targeting task. In summary, the results in this paper suggest that detection of intended movement is possible, and may serve useful functions.10abrain computer interface10aECoG10amovement direction prediction10amovement onset prediction10aneurorehabilitation10aperformance augmentation1 aWang, Z1 aGunduz, Aysegul1 aBrunner, Peter1 aRitaccio, A L1 aJi, Q1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2289105800377nas a2200133   4500008004100000245003100041210003100072260001200103100001200115700001100127700001900138700001000157856007600167       2012                        eng d00aLearning with Target Prior0 aLearning with Target Prior  c11/20121 aWang, Z1 aLyu, S1 aSchalk, Gerwin1 aJi, Q  uhttps://www.neurotechcenter.org/publications/2012/learning-target-prior00483nas a2200109   4500008004100000245009100041210006900132100001800201700001900219700001000238856012500248       2011                        eng d00aAnatomically Constrained Decoding of Finger Flexion from Electrocorticographic Signals0 aAnatomically Constrained Decoding of Finger Flexion from Electro1 aWang, Zuoguan1 aSchalk, Gerwin1 aJi, Q  uhttps://www.neurotechcenter.org/publications/2011/anatomically-constrained-decoding-finger-flexion-electrocorticographic02550nas a2200253   4500008004100000022001400041245009200055210006900147260001200216300000800228490000600236520180100242653002902043653002302072653002602095653001902121653002102140653002002161100001802181700001002199700002002209700001902229856004802248       2011                        eng d  a1662-453X00aPrior knowledge improves decoding of finger flexion from electrocorticographic signals.0 aPrior knowledge improves decoding of finger flexion from electro  c11/2011  a1270 v53 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interfaces (BCIs) use brain signals to convey a user's intent. Some BCI approaches begin by decoding kinematic parameters of movements from brain signals, and then proceed to using these signals, in absence of movements, to allow a user to control an output. Recent results have shown that electrocorticographic (ECoG) recordings from the surface of the brain in humans can give information about kinematic parameters (e.g., hand velocity or finger flexion). The decoding approaches in these studies usually employed classical classification/regression algorithms that derive a linear mapping between brain signals and outputs. However, they typically only incorporate little prior information about the target movement parameter. In this paper, we incorporate prior knowledge using a Bayesian decoding method, and use it to decode finger flexion from ECoG signals. Specifically, we exploit the constraints that govern finger flexion and incorporate these constraints in the construction, structure, and the probabilistic functions of the prior model of a switched non-parametric dynamic system (SNDS). Given a measurement model resulting from a traditional linear regression method, we decoded finger flexion using posterior estimation that combined the prior and measurement models. Our results show that the application of the Bayesian decoding model, which incorporates prior knowledge, improves decoding performance compared to the application of a linear regression model, which does not incorporate prior knowledge. Thus, the results presented in this paper may ultimately lead to neurally controlled hand prostheses with full fine-grained finger articulation.</span></p>10abrain-computer interface10adecoding algorithm10aelectrocorticographic10afinger flexion10amachine learning10aprior knowledge1 aWang, Zuoguan1 aJi, Q1 aMiller, John, W1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2214494401885nas 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