Prediction of forelimb muscle EMGs from the corticospinal signals in rats
Document Type
Conference Proceeding
Publication Date
10-13-2016
Abstract
To generate voluntary forearm movements, the information that is encoded in the activity of the cortical neurons has to travel through the spinal cord and activate the skeletal muscles. The axons carrying these signals are tightly bundled together in the descending tracts that control the spinal circuitry innervating the forearm muscles. In this paper, we show that corticospinal tract (CST) signals can be used to predict forearm electromyographic (EMG) activities that are recorded during an isometric-pull task. Rats were trained to pull on a metal bar through a window. A flexible-substrate multi-electrode array was chronically implanted into the dorsal column of the cervical spinal cord. Field potentials and multi-unit activities were recorded from the descending axons of the CST while the rat performed the task. Forelimb forces and EMG signals from a wrist extensor and a flexor, and the biceps and triceps were reconstructed using the neural signals in multiple sessions over three weeks. The regression coefficients found from the trial set were cross-validated on the other trials recorded on the same day. The maximum correlation coefficient between the actual and predicted signal was for the biceps (R=0.88). These results suggest the feasibility of an EMG-based spinal-cord-computer-interface (SCCI) for subjects with spinal cord injury.
Identifier
85009063759 (Scopus)
ISBN
[9781457702204]
Publication Title
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS
External Full Text Location
https://doi.org/10.1109/EMBC.2016.7591307
ISSN
1557170X
PubMed ID
28268895
First Page
2780
Last Page
2783
Volume
2016-October
Grant
R01NS072385
Fund Ref
National Institutes of Health
Recommended Citation
Gok, Sinan and Sahin, Mesut, "Prediction of forelimb muscle EMGs from the corticospinal signals in rats" (2016). Faculty Publications. 10212.
https://digitalcommons.njit.edu/fac_pubs/10212
