Prediction of Forelimb EMGs and Movement Phases from Corticospinal Signals in the Rat During the Reach-to-Pull Task

Sinan Gok, Mesut Sahin

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Brain-computer interfaces access the volitional command signals from various brain areas in order to substitute for the motor functions lost due to spinal cord injury or disease. As the final common pathway of the central nervous system (CNS) outputs, the descending tracts of the spinal cord offer an alternative site to extract movement-related command signals. Using flexible 2D microelectrode arrays, we have recorded the corticospinal tract (CST) signals in rats during a reach-to-pull task. The CST activity was then classified by the forelimb movement phases into two or three classes in a training dataset and cross validated in a test set. The average classification accuracies were 80 ± 10% (min: 62% to max: 97%) and 55 ± 8% (min: 43% to max: 71%) for two-class and three-class cases, respectively. The forelimb flexor and extensor EMG envelopes were also predicted from the CST signals using linear regression. The average correlation coefficient between the actual and predicted EMG signals was 0.5 ± 0.13 (n = 124), whereas the highest correlation was 0.81 for the biceps EMG. Although the forelimb motor function cannot be explained completely by the CST activity alone, the success rates obtained in reconstructing the EMG signals support the feasibility of a spinal-cord-computer interface as a concept.

Original languageEnglish (US)
Article number1950009
JournalInternational journal of neural systems
Volume29
Issue number7
DOIs
StatePublished - Sep 1 2019

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Keywords

  • Brain-computer interfaces
  • EMG
  • corticospinal tract
  • microelectrode arrays
  • supervised machine learning

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