@article {2542,
	title = {Decoding Onset and Direction of Movements using Electrocorticographic (ECoG) Signals in Humans.},
	journal = {Frontiers in Neuroengineering},
	volume = {5},
	year = {2012},
	month = {08/2012},
	abstract = {Communication 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{\textquoteright}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{\textquoteright}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.},
	keywords = {brain computer interface, ECoG, movement direction prediction, movement onset prediction, neurorehabilitation, performance augmentation},
	doi = {10.3389/fneng.2012.00015},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/22891058},
	author = {Wang, Z. and Gunduz, Aysegul and Peter Brunner and A L Ritaccio and Ji, Q and Gerwin Schalk}
}
@proceedings {2866,
	title = {Learning with Target Prior},
	year = {2012},
	month = {11/2012},
	author = {Wang, Z. and Lyu, S. and Gerwin Schalk and Ji, Q}
}
@proceedings {2246,
	title = {Anatomically Constrained Decoding of Finger Flexion from Electrocorticographic Signals},
	year = {2011},
	author = {Zuoguan Wang and Gerwin Schalk and Ji, Q}
}
@article {2207,
	title = {Prior knowledge improves decoding of finger flexion from electrocorticographic signals.},
	journal = {Front Neurosci},
	volume = {5},
	year = {2011},
	month = {11/2011},
	pages = {127},
	abstract = {<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{\textquoteright}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>},
	keywords = {brain-computer interface, decoding algorithm, electrocorticographic, finger flexion, machine learning, prior knowledge},
	issn = {1662-453X},
	doi = {10.3389/fnins.2011.00127},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/22144944},
	author = {Zuoguan Wang and Ji, Q and Miller, John W and Gerwin Schalk}
}
@proceedings {2258,
	title = {Decoding finger flexion from electrocorticographic signals using sparse Gaussian process.},
	year = {2010},
	abstract = {A 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.},
	doi = {10.1109/ICPR.2010.915},
	author = {Zuoguan Wang and Ji, Q and Kai J. Miller and Gerwin Schalk}
}
