00479nas a2200109 4500008004100000245009100041210006900132100001800201700001900219700001000238856012100248 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://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

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.

10abrain-computer interface10adecoding algorithm10aelectrocorticographic10afinger flexion10amachine learning10aprior knowledge1 aWang, Zuoguan1 aJi, Q1 aMiller, John, W1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2214494401881nas a2200133 4500008004100000245009400041210006900135520136000204100001801564700001001582700001901592700001901611856011701630 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://neurotechcenter.org/publications/2010/decoding-finger-flexion-electrocorticographic-signals-using-sparse04762nas a2200325 4500008004100000022001400041245009800055210006900153260001200222300000900234490000600243520379900249653001504048653002704063653002904090653001104119653001604130653001704146653001304163653003504176653003104211653003204242653002804274100001804302700002004320700001204340700001804352700001804370856004804388 2006 eng d a1741-256000aMulti-channel linear descriptors for event-related EEG collected in brain computer interface.0 aMultichannel linear descriptors for eventrelated EEG collected i c03/2006 a52-80 v33 a

By three multi-channel linear descriptors, i.e. spatial complexity (omega), field power (sigma) and frequency of field changes (phi), event-related EEG data within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the event-related EEG data indicate that a two-channel version of omega, sigma and phi could reflect the antagonistic ERD/ERS patterns over contralateral and ipsilateral areas and also characterize different phases of the changing brain states in the event-related paradigm. Based on the selective two-channel linear descriptors, the left and right hand motor imagery tasks are classified to obtain satisfactory results, which testify the validity of the three linear descriptors omega, sigma and phi for characterizing event-related EEG. The preliminary results show that omega, sigma together with phi have good separability for left and right hand motor imagery tasks, which could be considered for classification of two classes of EEG patterns in the application of brain computer interfaces.

10aAlgorithms10aElectroencephalography10aEvoked Potentials, Motor10aHumans10aImagination10aMotor Cortex10aMovement10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity10aUser-Computer Interface1 aPei, Xiao-Mei1 aZheng, Shi Dong1 aXu, Jin1 aBin, Guang-yu1 aWang, Zuoguan uhttp://www.ncbi.nlm.nih.gov/pubmed/16510942