Prior knowledge improves decoding of finger flexion from electrocorticographic signals

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 s...

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Bibliographic Details
Published in:Frontiers in neuroscience 2011-01, Vol.5, p.127-127
Main Authors: Wang, Z, Ji, Q, Miller, K J, Schalk, Gerwin
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Brain Computer Interface
Brain mapping
Data collection
Datasets
Decoding Algorithm
Electrocorticographic
Finger
Finger Flexion
Interfaces
Knowledge
machine learning
Mathematical models
Methods
Neuroscience
Pattern recognition
Prior Knowledge
Prosthetics
Signal processing
Studies
Citations: Items that cite this one
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Summary: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.
ISSN:1662-4548
1662-453X
1662-4548
DOI:10.3389/fnins.2011.00127