Intro to EMG signal processing

Manuel Hernandez, University of Illinois at Urbana-Champaign, Kinesiology and Community Health

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Summary

In this activity, students will be learning about how electromyography (EMG) signals are acquired and analyzed to assess motor function and diagnose certain neurological disorders, such as muscular dystrophy, sciatica, and others. This lab will also serve as a lower level introduction to machine learning and how it can be used clinically, as well as some of the challenges of training and implementing a model. Building upon concepts of biopotential data acquisition, we will examine how Teager-kaiser energy operators (TKEO) can be used for differentiating EMG signals.

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Learning Goals

Students should be learning about identifying potential sources of noise in electromyographic signals, comparing and contrasting techniques for analyzing biological signals, and discussing the utility of different approaches.

Context for Use

For this lab, aimed at professional students, or first year graduate students, with at least some cursory programming experience, at least 2 hours are needed for the activity, with additional time prior to class for installing and downloading the appropriate software. Students will have been exposed to prior clinical and engineering labs aimed at collecting biopotential signals and an understanding of the neurophysiology underlying biopotential generation. At least cursory MATLAB experience would be useful but not necessary for this team-based activity.

Description and Teaching Materials

Students are broken up into small teams of 3-5 students to go over an example MATLAB script used for loading electromyogram data and signal processing. Students are provided with sample data from different neurological populations, and are provided with visualizations before and after different signal processing methods are applied, such as TKEO or continuous wavelet transforms. Students are free to explore different parameters and examine the impact on signal quality and differences in EMG properties between different neurological populations. Prior to class students are asked to pre-read material focused on measuring biopotential, signal processing, and clinical applications of EMG data.

Teaching Notes and Tips

For helping students use MATLAB, please be sure to set up installation session, at least one week prior if students will be working on personal machines, or provide brief orientation on using MATLAB for novice users.

Assessment

Students will be asked to provide a brief report discussing potential sources of noise in electromyographic signals, comparing and contrasting techniques for analyzing biological signals, and discussing the clinical utility of different approaches. In addition, students should provide images and data analysis outputs for supporting their conclusions.