AUTHOR=Ratnadurai Giridharan Shivakeshavan , Gupta Disha , Pal Ajay , Mishra Asht M. , Hill N. Jeremy , Carmel Jason B. TITLE=Motometrics: A Toolbox for Annotation and Efficient Analysis of Motor Evoked Potentials JOURNAL=Frontiers in Neuroinformatics VOLUME=13 YEAR=2019 URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2019.00008 DOI=10.3389/fninf.2019.00008 ISSN=1662-5196 ABSTRACT=

Stimulating the nervous system and measuring muscle response offers a unique opportunity to interrogate motor system function. Often, this is performed by stimulating motor cortex and recording muscle activity with electromyography; the evoked response is called the motor evoked potential (MEP). To understand system dynamics, MEPs are typically recorded through a range of motor cortex stimulation intensities. The MEPs increase with increasing stimulation intensities, and these typically produce a sigmoidal response curve. Analysis of MEPs is often complex and analysis of response curves is time-consuming. We created an MEP analysis software, called Motometrics, to facilitate analysis of MEPs and response curves. The goal is to combine robust signal processing algorithms with a simple user interface. Motometrics first enables the user to annotate data files acquired from the recording system so that the responses can be extracted and labeled with the correct subject and experimental condition. The software enables quick visual representations of entire datasets, to ensure uniform quality of the signal. It then enables the user to choose a variety of response curve analyses and to perform near real time quantification of the MEPs for quick feedback during experimental procedures. This is a modular open source tool that is compatible with several popular electrophysiological systems. Initial use indicates that Motometrics enables rapid, robust, and intuitive analysis of MEP response curves by neuroscientists without programming or signal processing expertise.