Motometrics: A Toolbox for Annotation and Efficient Analysis of Motor Evoked Potentials

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.

A Novel Latency Estimation Algorithm of Motor Evoked Potential Signals

“…Some of the previous studies related to the MEP latency estimation were based on measuring latency from the averaged rectified MEP records [29,30]. Currently, the available algorithms for latency estimation can be divided into the following categories: algorithms based on absolute hard threshold estimator (AHTE), which are commonly used in devices of manufacturer Motometrics [23], and algorithms based on statistical measures (SM), which are widely used in devices of manufacturers Signal Hunter and CortexTools [24][25][26]. According to our knowledge, this work, for the first time, proposes a novel approach for estimating MEP latencies based on MEP signal squaring.…”

“…The presented AHTE algorithm is incorporated in the software of manufacturer Motometrics and has the option to change the threshold value thr V [23]. In general, the AHTE method yields good results in determining MEP latency for the MEP signals with higher amplitudes.…”

“…It is a typical MEP from the abductor pollicis-brevis (APB) muscle, with a latency of 25.5 ms and a PTP amplitude of 1.8 mV. The index value (n0) is determined at the intersection of the MEP and -Vthreshold dashed line, and the latency is estimated as a time instant where the index value is subtracted from the magic number [23]. Fig.…”

“…The amplitude and the latency of the MEP signal are the most used features for quantifying MEP signals [17] - [19]. Although the MEP amplitude varies significantly in patients with pathologies, as well as in healthy subjects [18], [20] - [22], the peak-to-peak (PTP) amplitude defined as the difference between the maximum and minimum value of the MEP response, represents an accurate indicator for estimating the MEP amplitude oscillation [23]- [26]. On the other hand, the MEP latencies are considered more robust; however, MEP latency variability is well known in neurosurgical patients [18], [27].…”

“…This can challenge MEP amplitude detection and raise the problem of determining MEPs latency due to a higher signal-to-noise ratio. Most of the MEP latency estimation algorithms suffer from this problem, which significantly degrades detection precision, especially at the low PTP MEP signal amplitudes (<100 µV) [23][24][25][26]. Additionally, suppose a large number of signals have been collected in a clinic or research.…”