TY - JOUR
T1 - Prediction of Forelimb EMGs and Movement Phases from Corticospinal Signals in the Rat During the Reach-to-Pull Task
AU - Gok, Sinan
AU - Sahin, Mesut
N1 - Publisher Copyright:
© 2019 World Scientific Publishing Company.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - 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.
AB - 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.
KW - Brain-computer interfaces
KW - EMG
KW - corticospinal tract
KW - microelectrode arrays
KW - supervised machine learning
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U2 - 10.1142/S0129065719500096
DO - 10.1142/S0129065719500096
M3 - Article
C2 - 31111753
AN - SCOPUS:85065959221
SN - 0129-0657
VL - 29
JO - International journal of neural systems
JF - International journal of neural systems
IS - 7
M1 - 1950009
ER -