AUTHOR=Guo Yi , Gok Sinan , Sahin Mesut TITLE=Convolutional Networks Outperform Linear Decoders in Predicting EMG From Spinal Cord Signals JOURNAL=Frontiers in Neuroscience VOLUME=12 YEAR=2018 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.00689 DOI=10.3389/fnins.2018.00689 ISSN=1662-453X ABSTRACT=
Advanced algorithms are required to reveal the complex relations between neural and behavioral data. In this study, forelimb electromyography (EMG) signals were reconstructed from multi-unit neural signals recorded with multiple electrode arrays (MEAs) from the corticospinal tract (CST) in rats. A six-layer convolutional neural network (CNN) was compared with linear decoders for predicting the EMG signal. The network contained three session-dependent Rectified Linear Unit (ReLU) feature layers and three Gamma function layers were shared between sessions. Coefficient of determination (