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Perceiving Systems Conference Paper 2013

Mixing Decoded Cursor Velocity and Position from an Offline Kalman Filter Improves Cursor Control in People with Tetraplegia

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Perceiving Systems
Michael Black
Director
Embs2013

Kalman filtering is a common method to decode neural signals from the motor cortex. In clinical research investigating the use of intracortical brain computer interfaces (iBCIs), the technique enabled people with tetraplegia to control assistive devices such as a computer or robotic arm directly from their neural activity. For reaching movements, the Kalman filter typically estimates the instantaneous endpoint velocity of the control device. Here, we analyzed attempted arm/hand movements by people with tetraplegia to control a cursor on a computer screen to reach several circular targets. A standard velocity Kalman filter is enhanced to additionally decode for the cursor’s position. We then mix decoded velocity and position to generate cursor movement commands. We analyzed data, offline, from two participants across six sessions. Root mean squared error between the actual and estimated cursor trajectory improved by 12.2 ±10.5% (pairwise t-test, p<0.05) as compared to a standard velocity Kalman filter. The findings suggest that simultaneously decoding for intended velocity and position and using them both to generate movement commands can improve the performance of iBCIs.

Author(s): Mark Homer and Matthew Harrison and Michael J. Black and János Perge and Sydney Cash and Gerhard Friehs and Leigh Hochberg
Book Title: 6th International IEEE EMBS Conference on Neural Engineering
Pages: 715-718
Year: 2013
Month: November
Project(s):
Bibtex Type: Conference Paper (inproceedings)
Address: San Diego
Electronic Archiving: grant_archive
Links:

BibTex 

@inproceedings{Homer:EMBS:2013,
  title = {Mixing Decoded Cursor Velocity and Position from an Offline Kalman Filter Improves Cursor Control in People with Tetraplegia},
  booktitle = { 6th International IEEE EMBS Conference on Neural Engineering},
  abstract = {Kalman filtering is a common method to decode neural signals from the motor cortex. In clinical research investigating the use of intracortical brain computer interfaces (iBCIs), the technique enabled people with tetraplegia to control assistive devices such as a computer or robotic arm directly from their neural activity. For reaching movements, the Kalman filter typically estimates the instantaneous endpoint velocity of the control device. Here, we analyzed attempted arm/hand movements by people with tetraplegia to control a cursor on a computer screen to reach several circular targets. A standard velocity Kalman filter is enhanced to additionally decode for the cursor’s position. We then mix decoded velocity and position to generate cursor movement commands. We analyzed data, offline, from two participants across six sessions. Root mean squared error between the actual and estimated
  cursor trajectory improved by 12.2 ±10.5% (pairwise t-test, p<0.05) as compared to a standard velocity Kalman filter. The findings suggest that simultaneously decoding for intended velocity and position and using them both to generate movement commands can improve the performance of iBCIs.},
  pages = {715-718},
  address = {San Diego},
  month = nov,
  year = {2013},
  slug = {homer-embs-2013},
  author = {Homer, Mark and Harrison, Matthew and Black, Michael J. and Perge, János and Cash, Sydney and Friehs, Gerhard and Hochberg, Leigh},
  month_numeric = {11}
}