Literature DB >> 28268895

Prediction of forelimb muscle EMGs from the corticospinal signals in rats.

Sinan Gok, Mesut Sahin.   

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.

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Year:  2016        PMID: 28268895     DOI: 10.1109/EMBC.2016.7591307

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  1 in total

1.  Convolutional Networks Outperform Linear Decoders in Predicting EMG From Spinal Cord Signals.

Authors:  Yi Guo; Sinan Gok; Mesut Sahin
Journal:  Front Neurosci       Date:  2018-10-17       Impact factor: 4.677

  1 in total

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