02550nas 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/22144944