Literature DB >> 26341935

Remapping residual coordination for controlling assistive devices and recovering motor functions.

Camilla Pierella1, Farnaz Abdollahi2, Ali Farshchiansadegh3, Jessica Pedersen4, Elias B Thorp3, Ferdinando A Mussa-Ivaldi5, Maura Casadio6.   

Abstract

The concept of human motor redundancy attracted much attention since the early studies of motor control, as it highlights the ability of the motor system to generate a great variety of movements to achieve any well-defined goal. The abundance of degrees of freedom in the human body may be a fundamental resource in the learning and remapping problems that are encountered in human-machine interfaces (HMIs) developments. The HMI can act at different levels decoding brain signals or body signals to control an external device. The transformation from neural signals to device commands is the core of research on brain-machine interfaces (BMIs). However, while BMIs bypass completely the final path of the motor system, body-machine interfaces (BoMIs) take advantage of motor skills that are still available to the user and have the potential to enhance these skills through their consistent use. BoMIs empower people with severe motor disabilities with the possibility to control external devices, and they concurrently offer the opportunity to focus on achieving rehabilitative goals. In this study we describe a theoretical paradigm for the use of a BoMI in rehabilitation. The proposed BoMI remaps the user's residual upper body mobility to the two coordinates of a cursor on a computer screen. This mapping is obtained by principal component analysis (PCA). We hypothesize that the BoMI can be specifically programmed to engage the users in functional exercises aimed at partial recovery of motor skills, while simultaneously controlling the cursor and carrying out functional tasks, e.g. playing games. Specifically, PCA allows us to select not only the subspace that is most comfortable for the user to act upon, but also the degrees of freedom and coordination patterns that the user has more difficulty engaging. In this article, we describe a family of map modifications that can be made to change the motor behavior of the user. Depending on the characteristics of the impairment of each high-level spinal cord injury (SCI) survivor, we can make modifications to restore a higher level of symmetric mobility (left versus right), or to increase the strength and range of motion of the upper body that was spared by the injury. Results showed that this approach restored symmetry between left and right side of the body, with an increase of mobility and strength of all the degrees of freedom in the participants involved in the control of the interface. This is a proof of concept that our BoMI may be used concurrently to control assistive devices and reach specific rehabilitative goals. Engaging the users in functional and entertaining tasks while practicing the interface and changing the map in the proposed ways is a novel approach to rehabilitation treatments facilitated by portable and low-cost technologies.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Dimensionality reduction; Human–machine interface; Motor learning; Movement reorganization; Rehabilitation; Spinal cord injury

Mesh:

Year:  2015        PMID: 26341935      PMCID: PMC4679682          DOI: 10.1016/j.neuropsychologia.2015.08.024

Source DB:  PubMed          Journal:  Neuropsychologia        ISSN: 0028-3932            Impact factor:   3.139


  51 in total

1.  A blueprint for movement: functional and anatomical representations in the human motor system.

Authors:  M Rijntjes; C Dettmers; C Büchel; S Kiebel; R S Frackowiak; C Weiller
Journal:  J Neurosci       Date:  1999-09-15       Impact factor: 6.167

2.  Brain-computer interface technology: a review of the first international meeting.

Authors:  J R Wolpaw; N Birbaumer; W J Heetderks; D J McFarland; P H Peckham; G Schalk; E Donchin; L A Quatrano; C J Robinson; T M Vaughan
Journal:  IEEE Trans Rehabil Eng       Date:  2000-06

Review 3.  Brain-computer interfaces for communication and control.

Authors:  Jonathan R Wolpaw; Niels Birbaumer; Dennis J McFarland; Gert Pfurtscheller; Theresa M Vaughan
Journal:  Clin Neurophysiol       Date:  2002-06       Impact factor: 3.708

4.  Body machine interface: remapping motor skills after spinal cord injury.

Authors:  M Casadio; A Pressman; S Acosta; Z Danzinger; A Fishbach; F A Mussa-Ivaldi; K Muir; H Tseng; D Chen
Journal:  IEEE Int Conf Rehabil Robot       Date:  2011

5.  Behavioral and neural correlates of visuomotor adaptation observed through a brain-computer interface in primary motor cortex.

Authors:  Steven M Chase; Robert E Kass; Andrew B Schwartz
Journal:  J Neurophysiol       Date:  2012-04-11       Impact factor: 2.714

6.  Spinal cord injury and its treatment: current management and experimental perspectives.

Authors:  F Scholtes; G Brook; D Martin
Journal:  Adv Tech Stand Neurosurg       Date:  2012

7.  Behavioural report of single neuron stimulation in somatosensory cortex.

Authors:  Arthur R Houweling; Michael Brecht
Journal:  Nature       Date:  2007-12-19       Impact factor: 49.962

8.  A body machine interface based on inertial sensors.

Authors:  Ali Farshchiansadegh; Farnaz Abdollahi; David Chen; Jessica Pedersen; Camilla Pierella; Elliot J Roth; Ismael Seanez Gonzalez; Elias B Thorp; Ferdinando A Mussa-Ivaldi
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2014

9.  Brain-machine interface in chronic stroke rehabilitation: a controlled study.

Authors:  Ander Ramos-Murguialday; Doris Broetz; Massimiliano Rea; Leonhard Läer; Ozge Yilmaz; Fabricio L Brasil; Giulia Liberati; Marco R Curado; Eliana Garcia-Cossio; Alexandros Vyziotis; Woosang Cho; Manuel Agostini; Ernesto Soares; Surjo Soekadar; Andrea Caria; Leonardo G Cohen; Niels Birbaumer
Journal:  Ann Neurol       Date:  2013-08-07       Impact factor: 10.422

10.  Sensitivity of smoothness measures to movement duration, amplitude, and arrests.

Authors:  Neville Hogan; Dagmar Sternad
Journal:  J Mot Behav       Date:  2009-11       Impact factor: 1.328

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  10 in total

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

Authors:  Ismael Seanez-Gonzalez; Camilla Pierella; Ali Farshchiansadegh; Elias B Thorp; Farnaz Abdollahi; Jessica P Pedersen; Ferdinando A Sandro Mussa-Ivaldi
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2016-12-15       Impact factor: 3.802

2.  Using noise to shape motor learning.

Authors:  Elias B Thorp; Konrad P Kording; Ferdinando A Mussa-Ivaldi
Journal:  J Neurophysiol       Date:  2016-11-23       Impact factor: 2.714

3.  Body-Machine Interface Enables People With Cervical Spinal Cord Injury to Control Devices With Available Body Movements: Proof of Concept.

Authors:  Farnaz Abdollahi; Ali Farshchiansadegh; Camilla Pierella; Ismael Seáñez-González; Elias Thorp; Mei-Hua Lee; Rajiv Ranganathan; Jessica Pedersen; David Chen; Elliot Roth; Maura Casadio; Ferdinando Mussa-Ivaldi
Journal:  Neurorehabil Neural Repair       Date:  2017-02-01       Impact factor: 3.919

4.  Body-Machine Interfaces after Spinal Cord Injury: Rehabilitation and Brain Plasticity.

Authors:  Ismael Seáñez-González; Camilla Pierella; Ali Farshchiansadegh; Elias B Thorp; Xue Wang; Todd Parrish; Ferdinando A Mussa-Ivaldi
Journal:  Brain Sci       Date:  2016-12-19

5.  Learning new movements after paralysis: Results from a home-based study.

Authors:  Camilla Pierella; Farnaz Abdollahi; Elias Thorp; Ali Farshchiansadegh; Jessica Pedersen; Ismael Seáñez-González; Ferdinando A Mussa-Ivaldi; Maura Casadio
Journal:  Sci Rep       Date:  2017-07-06       Impact factor: 4.379

6.  Age-dependent differences in learning to control a robot arm using a body-machine interface.

Authors:  Rajiv Ranganathan; Mei-Hua Lee; Malavika R Padmanabhan; Sanders Aspelund; Florian A Kagerer; Ranjan Mukherjee
Journal:  Sci Rep       Date:  2019-02-13       Impact factor: 4.379

7.  Reorganization of finger coordination patterns through motor exploration in individuals after stroke.

Authors:  Rajiv Ranganathan
Journal:  J Neuroeng Rehabil       Date:  2017-09-11       Impact factor: 4.262

8.  Guiding functional reorganization of motor redundancy using a body-machine interface.

Authors:  Dalia De Santis; Ferdinando A Mussa-Ivaldi
Journal:  J Neuroeng Rehabil       Date:  2020-05-11       Impact factor: 4.262

9.  Data-driven body-machine interface for the accurate control of drones.

Authors:  Jenifer Miehlbradt; Alexandre Cherpillod; Stefano Mintchev; Martina Coscia; Fiorenzo Artoni; Dario Floreano; Silvestro Micera
Journal:  Proc Natl Acad Sci U S A       Date:  2018-07-16       Impact factor: 11.205

10.  Recovery of Distal Arm Movements in Spinal Cord Injured Patients with a Body-Machine Interface: A Proof-of-Concept Study.

Authors:  Camilla Pierella; Elisa Galofaro; Alice De Luca; Luca Losio; Simona Gamba; Antonino Massone; Ferdinando A Mussa-Ivaldi; Maura Casadio
Journal:  Sensors (Basel)       Date:  2021-03-23       Impact factor: 3.576

  10 in total

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