Static Versus Dynamic Decoding Algorithms in a Non-Invasive Body-Machine Interface

Ismael Seáñez-González, Camilla Pierella, Ali Farshchiansadegh, Elias B. Thorp, Farnaz Abdollahi, Jessica P. Pedersen, Ferdinando A. Mussa-Ivaldi

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In this study, we consider a non-invasive body-machine interface that captures body motions still available to people with spinal cord injury (SCI) and maps them into a set of signals for controlling a computer user interface while engaging in a sustained level of mobility and exercise. We compare the effectiveness of two decoding algorithms that transform a high-dimensional body-signal vector into a lower dimensional control vector on six subjects with high-level SCI and eight controls. One algorithm is based on a static map from current body signals to the current value of the control vector set through principal component analysis (PCA), the other on dynamic mapping a segment of body signals to the value and the temporal derivatives of the control vector set through a Kalman filter. SCI and control participants performed straighter and smoother cursor movements with the Kalman algorithm during center-out reaching, but their movements were faster and more precise when using PCA. All participants were able to use the BMI's continuous, two-dimensional control to type on a virtual keyboard and play pong, and performance with both algorithms was comparable. However, seven of eight control participants preferred PCA as their method of virtual wheelchair control. The unsupervised PCA algorithm was easier to train and seemed sufficient to achieve a higher degree of learnability and perceived ease of use.

Original languageEnglish (US)
Article number7784844
Pages (from-to)893-905
Number of pages13
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume25
Issue number7
DOIs
StatePublished - Jul 2017

Keywords

  • Body-machine interface (BMI)
  • cervical spinal cord injury
  • handicapped aids
  • tetraplegia
  • wheelchair control

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

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