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== Overview ==
== Overview ==
Electromyography (EMG) signals are electrical signals generated by the activity of skeletal muscles that are responding to inputs from the central nervous system. When a muscle contracts, it generates electrical activity that can be measured using electrodes. The electrodes can be placed on the surface of the skin or they can be intramuscular (inserted under the skin with a narrow needle). EMG data is collected as a voltage difference between a recording site and a reference site. Typically the recording site will be the fleshy part of a muscle and the reference site would be near a bony part of the body. EMG signals are used as diagnostics tools for identifying neuromuscular disorders or in research for studying muscle activation, motor control and muscle fatigue. They can also be used as control signals for prosthetic devices.
Electromyography (EMG) signals are electrical signals generated by the activity of skeletal muscles when they respond to inputs from the central nervous system. When a muscle contracts, it generates electrical activity that can be measured using electrodes. The electrodes can be placed on the surface of the skin or they can be intramuscular (inserted under the skin with a narrow needle). EMG data is collected as a voltage difference between a recording site and a reference site. Typically the recording site will be the fleshy part of a muscle and the reference site would be near a bony part of the body. EMG signals are used as diagnostics tools for identifying neuromuscular disorders or in research for studying muscle activation, motor control and muscle fatigue. They can also be used as control signals for prosthetic devices.


== Sources of Noise in EMG Data ==
== Sources of Noise in EMG Data ==
One of the challenges with collecting EMG data is that because the signals are very small (typically around 0-10 mV), and the electrodes used to measure them are prone to picking up other signals that can obscure the data we interested in collecting.
[[File:ArmElectrodePlacement.jpg|right|300 px]]
One of the challenges with collecting EMG data is that because the signals are very small (typically in the range of 0 - 10 mV), and the electrodes used to measure them are prone to picking up other signals that can obscure the data researchers are interested in collecting.


Sources of noise in EMG signals include:
Sources of noise in EMG signals include:
Line 24: Line 25:
== Sampling Rate and Synchronization ==
== Sampling Rate and Synchronization ==
'''Sampling Rate'''
'''Sampling Rate'''
Visual3D expects the EMG signals to be stored as ANALOG data, not unlike force platform data. One point of importance is that EMG typically has very high frequency content, which means that it must be sampled at high data sampling rates.
Visual3D expects the EMG signals to be stored as ANALOG data, not unlike force platform data. One point of importance is that EMG typically has very high frequency content, which means that it must be sampled at high data sampling rates.


''Synchronization'''
'''Synchronization'''
 
If you plan to analyze your EMG and motion capture data together, the data must be synchronized. Data can be out of sync due to issues such as:
If you plan to analyze your EMG and motion capture data together, the data must be synchronized. Data can be out of sync due to issues such as:
:*A physiological issue known as electromechanical delay (EMD)
:*A physiological issue known as electromechanical delay (EMD)
:*Technical issues with data collection (e.g. the saved EMG signal might be "delayed" because the EMG system is wireless)
:*Technical issues with data collection (e.g. the saved EMG signal might be "delayed" because the EMG system is wireless)


You must know the sync between the analog and motion capture data, but you can use the [[Shift_Frames]] command to synchronize the analog data to the motion capture data.
You must know the sync between the analog and motion capture data, and you can use the [[Shift_Frames]] command to synchronize the analog data to the motion capture data.
 
== References ==


----
== Further Information ==
Old Tutorial


'''EMG''' stands for '''electromyography''', which is the study of electrical signals from active muscles that are receiving input from the central nervous system. More information on EMG can be found in most good biomechanics and motor control textbooks, and on [http://en.wikipedia.org/wiki/Electromyography Wikipedia].  
'''[[EMG Processing Tools |Processing Tools]]:''' High level descriptions of Visual3D features for general processing of EMG data, with links to detailed examples.


For additional information on EMG processing requirements for the [http://www.isek.org/publications/emg-standards/ International Society of Electrophysiology and Kinesiology].
'''Feature Extraction:''' Visual3D pipeline commands for extracting statistical features in EMG applications (in development).


You will find other useful information on EMG signal processing posted by [https://www.noraxon.com/support-learn/technical-support/download-authorization-form/?fid=10014 Noraxon]
'''[[EMG Detecting Muscle Onset | Detecting Muscle Onset]]:''' Examples of using Visual3D pipeline commands to detect the onset of muscle activation, with examples.


=Step 1: Data Collection - Set Up=
'''[[Tutorial EMG | Tutorial: EMG Processing Workflow]]:''' Basic start to finish tutorial.


Before you begin collecting your data you should know and review this information. When reporting your EMG data, you should also report this information:<ref name="ISEK"/><ref name="SENIAM"/>
== References ==
# Electrode shape and size
# Electrode type
# Skin preparation
# Position and Orientation on Muscle ([http://www.seniam.org/ seniam.org, Recommendations -> Sensor Location])<ref name="SENIAM"/>
 
<b>Sampling Rate:</b>
 
Visual3D expects the EMG signals to be stored as ANALOG data, not unlike force platform data. One point of importance is that EMG typically has very high frequency content, which means that it must be sampled at high data sampling rates.
 
<b>Synchronize Data:</b>
*If you plan to analyze your EMG and motion capture data together, the data must be synchronized
*Data can be out of sync due to:
**a physiological issue known as electromechanical delay (EMD)
**technical issues with data collection (e.g. the saved EMG signal might be "delayed" because the EMG system is wireless)
You must know the sync between the analog and motion capture data, but you can use the [[Shift_Frames]] command to synchronize the analog data to the motion capture data.
 
=Step 2: Data Collection - Review=
 
While collecting your data (especially during patient setup), it's necessary to look at your EMG signals to ensure you're collecting good data.
 
<b>There are plenty of examples of what an EMG signal should look like. </b>
*<b>Biomechanics and Motor Control of Human Movement</b>
**page 270, Figure 10.15<ref name="WintersText"/>
**page 265, Figure 10.11<ref name="WintersText"/>
*<b>Research Methods in Biomechanics 2nd Edition</b>
**<b>page 183, Figure 8.4</b><ref name="ResearchMethods"/>
**page 189, Figure 8.7<ref name="ResearchMethods"/>
**page 196, Figure 8.13<ref name="ResearchMethods"/>
 
Figure 8.4<ref name="ResearchMethods"/> shows what the EMG signal should look like, along with common distortions that you should check your EMG signal for PRIOR TO DATA COLLECTION. Check for clipping, excessive noise, movement artifact (something hitting the electrode). Many of these issues (such as clipping your signal) cannot be corrected for in post processing, so it is necessary to look at your EMG signals prior to data collection.
 
A quick example of what an EMG signal should look like is below:<br>
[[File:EMG_Burst.png|350px]]<br>
*There should be a clear "burst" when the muscle contracts (red)
*There should be periods of "rest" (blue)
 
=Step 3: Post Processing=
 
<b>There are many different ways, some common methods are:</b>
#Half or full wave rectified
#Linear Envelope (page 269<ref name="WintersText"/>)
##Half or [[Rectify|Full Wave]] Rectified
##[[Lowpass_Filter|Lowpass Filter]]
#Integration of full wave rectified signal over various time periods (page 269<ref name="WintersText"/>)
#Root Mean Square (RMS) Amplitude
 
=Step 3a: Remove DC Offset=
 
 
{| style= width="100%"
| style="width: 50%" align="left" style="vertical-align:top"|
 
 
| style= "width: 50%" align="right" style="vertical-align:top"|
[[File:EMG_DC_Shift.png|350px]]
|}
 
<b>Examples:</b>
#[[EMG:_Removing_DC_Bias|Highpass Filter]]
#[[EMG:_Removing_Background_Noise|Background or Resting C3D File]]
 
<b>NOTE:</b> Either method is acceptable to remove the offset.
 
=Step 3b: Process EMG Signal=
 
You should choose one variation of the processing methods below.
 
==[[EMG_Linear_Envelope|Linear Envelope]]==
 
A linear envelope is simply a half or [[Rectify|full wave rectified signal]] (absolute value) followed by a [[Lowpass_Filter|Lowpass Filter]].<ref name="WintersText"/>
 
A half wave rectified signal signal would only contain the positive component of the EMG signal, a full wave rectified signal is the absolute value of the signal. The examples here will use a full wave rectified signal.
 
<b>Examples:</b>
#[[EMG_Linear_Envelope|Linear Envelope Example 1]]
 
==[[EMG_Envelope|Root Mean Square (RMS) Amplitude]]==
 
*Remove DC Offset prior
*Does not require full-wave rectification<ref name="ResearchMethods"/>
 
Calculate the [[Moving_RMS|RMS]] of a signal.
 
<b>Examples:</b>
#[[EMG_Envelope|RMS Processing Example 1]]
#[[EMG_Example_1|RMS Processing Example 2]]
 
==[[EMG_Integrate|Integrate]]==
 
<b>Examples:</b>
#[[EMG_Integrate|Integrate Example 1]]
 
==Matlab Filter==
 
It's also possible to design a filter in Matlab and implement the filter in Visual3D. [[Meta_Commands_Example_2|This is described here]].
 
=Step 3c: Determine when the Muscle is On/Off=
 
==Normalize==
 
<b>Examples:</b>
#[[EMG_Example_2|Normalize EMG to MVC Maximum]]
#[[EMG_Scale_by_Global_Value|Normalize EMG to Generic Global Variable]]
#[[EMG_Compute_Normalization_Scale_Factors|Normalize to Maximum 30 second interval]]
#[[EMG_Example_3|Normalize to the Maximum within each Gait Cycle]]
 
==Onset==
 
<b>Examples:</b>
#[[EMG_Onset|Onset Example]]
#[[EMG_Onset_Based_on_TKO|Onset using Teager-Kaiser Energy]]
 
=Other EMG Examples=
 
*[[Tutorial_EMG|Example Processing EMG Signals]]
*[[Pipeline_Processing_Example_4|Example Assigning Tags to Files based on Filenames]]
 
=References=
<!--
katie <ref name="WintersText"/><ref name="ResearchMethods"/>
<ref name="SENIAM"/> <ref name="ISEK"/>
-->
<references>
 
<ref name="WintersText">[https://www.wiley.com/en-us/Biomechanics+and+Motor+Control+of+Human+Movement%2C+4th+Edition-p-9780470398180 Winter D. Biomechanics and Motor Control of Human Movement-4th Edition. Wiley. 2009]</ref>
 
<ref name="ResearchMethods">[http://www.humankinetics.com/products/all-products/research-methods-in-biomechanics-2nd-edition Robertson G, Caldwell G, Hamill J, Kamen G, Whittlesey S. Research Methods in Biomechanics-2nd Edition. Human Kinetics. 2014]</ref>
 
<ref name="SENIAM">[http://www.seniam.org/ Surface ElectroMyoGraphy for the Non-Invasive Assessment of Muscles (SENIAM)]</ref>


1. [https://en.wikipedia.org/wiki/Electromyography Electromyography]


<ref name="ISEK">[https://www.c-motion.com/download/examples/EMG/ISEK_Standards.pdf International Society of Electrophysiology and Kinesiology Standards for Reporting EMG Data]</ref>
2. [https://isek.org/emg-standards/ ISEK EMG Reporting Standards ]


3. [http://www.seniam.org/ Surface Electromyography For Non-Invasive Assessment of Muscles]


</references>
4. [http://www.humankinetics.com/products/all-products/research-methods-in-biomechanics-2nd-edition Robertson G, Caldwell G, Hamill J, Kamen G, Whittlesey S. Research Methods in Biomechanics 2nd Edition]


[[Category:EMG]]
5. [https://www.mdpi.com/1424-8220/13/9/12431 Surface Electromyography Signal Processing and Classification Techniques]


6. [https://www.noraxon.com/higher-education/ Noraxon Higher Education]


[[Category:EMG]]
[[Category:EMG]]

Latest revision as of 17:31, 12 May 2023

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Overview

Electromyography (EMG) signals are electrical signals generated by the activity of skeletal muscles when they respond to inputs from the central nervous system. When a muscle contracts, it generates electrical activity that can be measured using electrodes. The electrodes can be placed on the surface of the skin or they can be intramuscular (inserted under the skin with a narrow needle). EMG data is collected as a voltage difference between a recording site and a reference site. Typically the recording site will be the fleshy part of a muscle and the reference site would be near a bony part of the body. EMG signals are used as diagnostics tools for identifying neuromuscular disorders or in research for studying muscle activation, motor control and muscle fatigue. They can also be used as control signals for prosthetic devices.

Sources of Noise in EMG Data

One of the challenges with collecting EMG data is that because the signals are very small (typically in the range of 0 - 10 mV), and the electrodes used to measure them are prone to picking up other signals that can obscure the data researchers are interested in collecting.

Sources of noise in EMG signals include:

  • Physiological features: fat, blood flow velocity, skin temperature and different measuring sites can introduce noise into EMG signals.
  • Inherent noise in electrodes: electrical equipment generates electrical noise, which can be picked up in EMG measurements.
  • Motion artifacts: the motion of cables during EMG measurements can produce noise in EMG data.
  • Cross talk: depending on the placement of electrodes relative to the location of the muscle that is being studied, sometimes an EMG can pick up signals from a muscle that is not the intended target of study.
  • Signal instability: EMG signals are quasi random in nature. The number of active motor units, motor firing rate and interaction between muscle fibers can influence an EMG signal.
  • Electromagnetic noise: ambient noise from the human body and background radiation can obscure the desired signal to be measured.

What to Look For

The figure below shows what EMG data should look like. There should be a clear burst (red) when the muscle contracts and periods of rest (blue) when the muscle relaxes. Be sure to check your data for clipping, excessive noise, and movement artifacts, as these cannot be corrected after collection.

Sampling Rate and Synchronization

Sampling Rate

Visual3D expects the EMG signals to be stored as ANALOG data, not unlike force platform data. One point of importance is that EMG typically has very high frequency content, which means that it must be sampled at high data sampling rates.

Synchronization

If you plan to analyze your EMG and motion capture data together, the data must be synchronized. Data can be out of sync due to issues such as:

  • A physiological issue known as electromechanical delay (EMD)
  • Technical issues with data collection (e.g. the saved EMG signal might be "delayed" because the EMG system is wireless)

You must know the sync between the analog and motion capture data, and you can use the Shift_Frames command to synchronize the analog data to the motion capture data.

Further Information

Processing Tools: High level descriptions of Visual3D features for general processing of EMG data, with links to detailed examples.

Feature Extraction: Visual3D pipeline commands for extracting statistical features in EMG applications (in development).

Detecting Muscle Onset: Examples of using Visual3D pipeline commands to detect the onset of muscle activation, with examples.

Tutorial: EMG Processing Workflow: Basic start to finish tutorial.

References

1. Electromyography

2. ISEK EMG Reporting Standards

3. Surface Electromyography For Non-Invasive Assessment of Muscles

4. Robertson G, Caldwell G, Hamill J, Kamen G, Whittlesey S. Research Methods in Biomechanics 2nd Edition

5. Surface Electromyography Signal Processing and Classification Techniques

6. Noraxon Higher Education

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