Processing EMG

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Processing EMG

• David DeLion
• UNLV Biomechanics Lab

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Why do we process EMG?

• Raw EMG offers us valuable information in a
  practically useless form
• Raw EMG signals cannot be quantitatively compared
  between subjects
• If electrodes are moved raw EMG signals cannot be
  quantitatively compared for the same subject

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Types of Signal Processing

• Raw
• Half-wave rectified
• Full-wave rectified
• Filtering
• Averaging
• Smoothing
• Integration

• Root-mean Square
• Frequency spectrum
• Fatigue analysis
• Number of Zero-crossings
• Amplitude Probability Distribution Function
• Wavelet

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Removing Bias

• Low amplitude voltage offset present in hardware
• Can be AC or DC
• Calculate the mean of all the data
• Subtract mean from each data point

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Raw EMG

• Unprocessed signal -Amplitude of 0-6 mV
  -Frequency of 10-500 Hz
• Peak-to-Peak -Measured in mV
  -Represents the amount of muscle
  energy measured
• Onset times can be determined
• Analysis is mostly qualitative

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Rectification

• Only positive values are analyzed -Mean would
  be zero
• Half-wave rectification - all negative data is
  discarded, positive data is kept.
• Full-wave rectification- the absolute value of
  each data point is used
• Full-wave is preferred

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Filtering

• Notch filter -Band reject filter usually
  very narrow -For EMG normally set from
  59-61 Hz -Used to remove 60 Hz
  electrical noise -Also removes real
  data! -Too much noise will overwhelm the
  filter

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Filtering

• Band Pass filter -allows specified
  frequencies to pass -low end cutoff
  removes electrical noise associated with wire
  sway and biological artifacts -high end
  cutoff eliminates tissue noise at the electrode
  site -often set between 20-300 Hz

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Filtering

• There are no perfect filters!
• Face muscles can emit frequencies up to 500 Hz
• Heart rate artifact can be eliminated with low
  end cutoffs of 100 Hz
• Filters which include 60 Hz include the noise
  from equipment

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Averaging

• Average EMG can be used to quantify muscle
  activity over time
• Measured in mV
• Values are averaged over a specified time window
• Window can be moved or static
• Moving windows are a digital smoothing technique
• For moving windows the smaller the time window
  the less smooth the data will be

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Averaging

• For EMG window is typically between 100-200 ms
• Window is moved over the length of the sample
• Moving averages introduce a phase shift
• Moving averages create biased values -values are
  calculated from data which are common to the
  data used to calculate the previous value
• Very commonly used technique

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Integration

• Calculation of area under the rectified signal
• Measured in Vs
• Values are summed over the specified time then
  divided by the total number of values
• Values will increase continuously over time
• The integrated average will represent 0.637 of
  one-half of the peak to peak value
• Quantifies muscle activity
• Can be reset over a specified time or voltage

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Root Mean Square

• Recommended quantification method by Basmajian
  and DeLuca
• Calculated by squaring each data point, summing
  the squares, dividing the sum by the number of
  observations, and taking the square root
• Represents 0.707 of one half of the peak-to-peak
  value

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Number of Zero Crossings

• Counting the number of times the amplitude of the
  signal crosses the zero line
• Based on the idea that a more active muscle will
  generate more action potentials, which will cause
  more zero crossings in the signal
• Primarily used before the FFT algorithm was
  widely available

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Frequency Analysis

• Fast Fourrie Transformation is used to break the
  EMG signal into its frequency components.
• Frequency components are graphed as function of
  the probability of their occurrence
• Useful in determining cutoff frequencies and
  muscle fatigue

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Fatigue Analysis

• Isometric contraction
• The two most important parameters for fatigue
  analysis are the median and mean frequency.
• Median frequency decreases with the onset of
  fatigue
• If fatigue is being measured it is important to
  have a large band pass filter

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Amplitude Probability Distribution Function

• Illustrate variance in the signal
• X-axis shows range of amplitudes
• Y-axis shows the percentage of time spent at any
  given amplitude
• Distribution during work should be bimodal -peak
  associated with effort -peak associated with
  rest

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Wavelet analysis

• Used for the processing of signals that are
  non-stationary and time varying
• Wavelets are parts of functions or any function
  consists of an infinite number of wavelets
• The goal is to express the signal as a linear
  combination of a set of functions
• Obtained by running a wavelet of a given
  frequency through the original signal

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Wavelet Analysis

• This process creates wavelet coefficients
• When an adequate number of coefficients have been
  calculated the signal can be accurately
  reconstructed
• The signal is reconstructed as a linear
  combination of the basis functions which are
  weighted by the wavelet coefficients

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Wavelet Analysis

• Time-frequency localization
• Most of the energy of the wavelet is restricted
  to a finite time interval
• Fourier transform is band limited
• Produces good frequency localization at low
  frequencies, and good time localization at high
  frequencies
• Segments, or tiles the time-frequency plane

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Wavelet Analysis

• Wavelets remove noise from the signal
• Signal energy becomes concentrated into fewer
  coefficients while noise energy does not

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Normalizing

• There is no absolute scale so direct comparisons
  between subjects or conditions cannot be made
• Maximum voluntary contraction levels are often
  used to compare EMG readings between subjects
  (i.e..50 MVC)
• Relies on subject to give max effort

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Normalizing

• Record contractions over a dynamic movement cycle
• At least 4 repetitions are required
• Peak values are averaged which creates an anchor
  point
• Subsequent values are represented as a percentage
  of the anchor point

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Conclusion

• EMG offers a great deal of useful information
• The information is only useful if it can be
  quantified
• Quantifying EMG data can be a qualitative process

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• Thank You
• Any Questions?

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Bibliography

• Kleissen, R.F.M, Buurke, J.H., Harlaar, J.,
  Zilvold, G. (1998) Electromyography in the
  Biomechanical analysis of human movement and its
  clinical application. Gait and Posture. Vol.
  8,143-158
• Aminoff, M.J. (1978) Electromyography in Clinical
  Practice. Addison-Wesley Publishing Company,
  Menlo Park, CA
• Dainty, D.A., Norman, R.W. (1987) Standardized
  Biomechanical Testing in Sport. Human Kinetics
  Publishers, Champaign, IL
• Cram, J.R., Kasman, G.S. (1998) Introduction to
  Surface Electromyography. Aspen Publishers,
  Gaithersburg, MD
• Medved, V. (2001) Measurement of Human
  Locomotion. CRC Press, New York, NY

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Bibliography

• Loeb, G.E., Gans, C. (1986) Electromyography for
  Experimentalists. The University of Chicago
  Press, Chicago, IL
• Basmajian, J.V., DeLuca, C.J. (1985) Muscles
  Alive. Williams Wilkins, Baltimore, MD
• Moshou, D., Hostens, I., Ramon, H. (2000)
  Wavelets and Self-Organizing Maps in
  Electromyogram Analysis. Katholieke Universiteit
  Leuven
• DeLuca, C.J. (1993) The Use of Surface
  Electromyography in Biomechanics. NeuroMuscular
  Research Center, Boston University
• DeLuca, C.J. (2002) Surface Electromyography
  Detection and Recording. Delsys Incorporated.