Format

Send to

Choose Destination
J Neuroeng Rehabil. 2015 Sep 22;12:85. doi: 10.1186/s12984-015-0076-7.

MEG-based neurofeedback for hand rehabilitation.

Foldes ST1,2,3, Weber DJ1,2,3,4, Collinger JL5,6,7,8.

Author information

1
VA Pittsburgh Healthcare System, Human Engineering Research Laboratories, Pittsburgh, PA, 15206, USA.
2
Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
3
Center for the Neural Basis of Cognition, Carnegie Mellon University, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
4
Department of Bioengineering, University of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
5
VA Pittsburgh Healthcare System, Human Engineering Research Laboratories, Pittsburgh, PA, 15206, USA. collingr@pitt.edu.
6
Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, 15213, USA. collingr@pitt.edu.
7
Center for the Neural Basis of Cognition, Carnegie Mellon University, University of Pittsburgh, Pittsburgh, PA, 15213, USA. collingr@pitt.edu.
8
Department of Bioengineering, University of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, 15213, USA. collingr@pitt.edu.

Abstract

BACKGROUND:

Providing neurofeedback (NF) of motor-related brain activity in a biologically-relevant and intuitive way could maximize the utility of a brain-computer interface (BCI) for promoting therapeutic plasticity. We present a BCI capable of providing intuitive and direct control of a video-based grasp.

METHODS:

Utilizing magnetoencephalography's (MEG) high temporal and spatial resolution, we recorded sensorimotor rhythms (SMR) that were modulated by grasp or rest intentions. SMR modulation controlled the grasp aperture of a stop motion video of a human hand. The displayed hand grasp position was driven incrementally towards a closed or opened state and subjects were required to hold the targeted position for a time that was adjusted to change the task difficulty.

RESULTS:

We demonstrated that three individuals with complete hand paralysis due to spinal cord injury (SCI) were able to maintain brain-control of closing and opening a virtual hand with an average of 63 % success which was significantly above the average chance rate of 19 %. This level of performance was achieved without pre-training and less than 4 min of calibration. In addition, successful grasp targets were reached in 1.96 ± 0.15 s. Subjects performed 200 brain-controlled trials in approximately 30 min excluding breaks. Two of the three participants showed a significant improvement in SMR indicating that they had learned to change their brain activity within a single session of NF.

CONCLUSIONS:

This study demonstrated the utility of a MEG-based BCI system to provide realistic, efficient, and focused NF to individuals with paralysis with the goal of using NF to induce neuroplasticity.

PMID:
26392353
PMCID:
PMC4578759
DOI:
10.1186/s12984-015-0076-7
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center