Int. J. of Bioinformatics Research and Applications   »   2014 Vol.10, No.2

 

 

Title: Understanding the importance of natural neuromotor strategy in upper extremity neuroprosthetic control

 

Authors: Dominic E. Nathan; Robert W. Prost; Stephen J. Guastello; Dean C. Jeutter

 

Addresses:
Department of Biomedical Engineering, Marquette University, 1515W Wisconsin Ave, Milwaukee, WI 53233, USA
Department of Radiology and Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA
Department of Psychology, Marquette University, 604 N. 16th St. Milwaukee, WI 53201, USA
Department of Biomedical Engineering, Marquette University, 1515W Wisconsin Ave, Milwaukee, WI 53233, USA

 

Abstract: A key challenge in upper extremity neuroprosthetics is variable levels of skill and inconsistent functional recovery. We examine the feasibility and benefits of using natural neuromotor strategies through the design and development of a proof-of-concept model for a feed-forward upper extremity neuroprosthetic controller. Developed using Artificial Neural Networks, the model is able to extract and classify neural correlates of movement intention from multiple brain regions that correspond to functional movements. This is unique compared to contemporary controllers that record from limited physiological sources or require learning of new strategies. Functional MRI (fMRI) data from healthy subjects (N = 13) were used to develop the model, and a separate group (N = 4) of subjects were used for validation. Results indicate that the model is able to accurately (81%) predict hand movement strictly from the neural correlates of movement intention. Information from this study is applicable to the development of upper extremity technology aided interventions.

 

Keywords: upper extremity neuroprosthetics; artificial neural networks; ANNs; feedforward neuroprosthetic controller; physical disability; natural neuromotor control; whole brain fMRI; functional MRI; neuromotor strategy; movement intention; magnetic resonance imaging; hand movement prediction; technology aided interventions; controller design.

 

DOI: 10.1504/IJBRA.2014.059521

 

Int. J. of Bioinformatics Research and Applications, 2014 Vol.10, No.2, pp.217 - 234

 

Available online: 25 Feb 2014

 

 

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