Faculty Collaboration Database

Understanding the importance of natural neuromotor strategy in upper extremity neuroprosthetic control. Int J Bioinform Res Appl 2014;10(2):217-34

Date

03/05/2014

Pubmed ID

24589839

DOI

10.1504/IJBRA.2014.059521

Scopus ID

2-s2.0-85047686772 (requires institutional sign-in at Scopus site)   2 Citations

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.

Author List

Nathan DE, Prost RW, Guastello SJ, Jeutter DC

Author

Stephen Guastello BA,MA,PhD Professor in the Psychology department at Marquette University

MESH terms used to index this publication - Major topics in bold

Adult
Brain
Brain Mapping
Electric Stimulation Therapy
Humans
Magnetic Resonance Imaging
Male
Man-Machine Systems
Movement
Paralysis
Prostheses and Implants
Prosthesis Design
Young Adult