the emg signal artifact & interference sampling rate signal references signal processing.1
TRANSCRIPT
The EMG Signal
Artifact & Interference
Sampling Rate
Signal References
Signal Processing.1
EMG Noise
A form of artifact– Interference with signal recording
Obscures a “clean” signal– Electromagnetic sources from the environment
may overlay or cancel the signal being recorded from a muscle
» Especially problematic when the interfering frequency is the same as being recorded from muscle
Example: 60 Hz from power lines vs. 20 - 125 Hz slow twitch motor units
Sources of Noise (Interference)
Driver amplifier Poor quality– High CMRR > 100,000
Broken Ground fault
– Amps not “tied together”
– Ground prong on cable» Broken
» Absent
Loose cable connection Loose controls
Sources of Noise (Interference)
Driver amplifier Electrodes
Pre-amp faulty Broken/cracked Poor skin prep
– Increases resistance
– Attenuates signal
Poor electrode-to-skin contact– Electrode “tipped’
– No/too little conducting gel/paste
Sources of Noise (Interference)
Driver amplifier Electrodes
Small electrodes may cause poor contact
Different electrode disk impedance
Fixation failure over time– Tape loosens 20 to
» Movement
» Perspiration
Sources of Noise (Interference)
Driver amplifier Electrodes
Cable fatigue– Along length
– At connector
– Stripped insulation
Poor reference (ground) contact
Sources of Noise (Interference)
Driver amplifier Electrodes Cable movement
artifact
Swinging cables– Especially if un- or
poorly-shielded
“Swing frequency” will probably be under 10 Hz– Slow twitch mu’s:
(20) 70 - 120 Hz
Sources of Noise (Interference)
Driver amplifier Electrodes Cable movement
artifact
Shorter cable minimizes swing
Use shielded cable1
Apply shield cables - tie to ground
1Digi-Key Corp701 Brooks Ave SouthThief River Falls, MN 56701-06771-800-344-4539www.digikey.com
Sources of Noise (Interference)
Driver amplifier Electrodes Cable movement
artifact Electro-static/-
magnetic radiation
Light bulbs– Especially florescent
Motors– AC
– Fans
– Experiment component
Power lines - 60 Hz Phone lines Ethernet cables Cable dishes
Sources of Noise (Interference)
Driver amplifier Electrodes Cable movement
artifact Electro-static/-
magnetic radiation Radio waves
AM FM
Cross-Talk
Electrodes over an adjacent muscle pick-up a signal via skin conduction
M1 M2
Cross-Talk
Visually inspect a tracing (monitor or printout) of a signal– If they have the same shape there is probably
cross-talk4.0
-2.0
0.0
2.0
20000 500 1000 1500
4.0
-2.0
0.0
2.0
20000 500 1000 1500
Muscle 1
Muscle 2
Cross-Talk Fixes
Check skin prep Check skin resistance Reposition electrodes Check reference (ground) electrode
– Move between electrode sets Use a narrower OC distance between
electrodes, if available
Sampling Rate
Number of data points (cycles) collected per unit of time - usually seconds– Example: 1000 cps = 1000 Hertz (Hz)
An adequate sampling rate ensures that what’s being recorded is truly representative of the signal
Sampling Rate
Lost Data Points
Sampling rate
Baseline
Signal AdequatelySampled
Signal Under-sampled
Consequences - Sampling
Under-sampling Lost data points Signal not truly
representative– Can’t be trusted
Consequences - Sampling
Under-sampling At or over-sampling
rate
Signal adequately sampled
With over-sampling more data points are recorded than necessary– Could tax storage
capacity
Selecting the Sampling RateThe “Two Times Rule”
Analyze the signal (or movement) and determine the highest possible operating frequency– Example: motor unit frequency range = (10) 70
- 250 Hz Double the top rate
– Sampling rate: 250 Hz x 2 = 500 Hz ~ 1000Hz
Sampling at 1000 Hz
For data plotted on a graph sampled at 1000 Hz, each tic on the X-axis is 1msec
4.0
-2.0
0.0
2.0
20000 500 1000 1500
1000 msec1 second
Signal Reference (Events)
Event marker “stamps” the point-in-time (point-in-the range, etc.) from which to start counting– Voltage spike– Concurrent video
» Ariel synch method - drop a ball
– Electrogoniometer– Torque signal
Voltage Spike from Event Marker
0.40
-0.20
0.00
0.20
1.0
-1.0
-0.5
0.0
0.5
20000 250 500 750 1000 1250 1500 1750
1.0
0.0
0.5
20000 250 500 750 1000 1250 1500 1750
Event
Raw
Rectified
VoltageSpike
Correlate EMG Signal with Torque Channel
300
-100
0
100
200
80000 2000 4000 6000
12000
0
2000
4000
6000
8000
10000
80000 2000 4000 6000
Torque
RectifiedEMG
Signal Processing.1
Timing - Phase transition– Onset - Offset
Duration
OffsetOnset Duration
Phase Transition
Visual assessment of phasic activity
1st 2nd 3rd
Question: At what (data) point do I start counting?
?
Baseline Noise vs. Signal Differentiation
Manual visual identification using a cursor
10000
-10000
-5000
0
5000
80000 1000 2000 3000 4000 5000 6000 7000
Baseline Noise vs. Signal Differentiation
2 SD Method– Select a filtered segment of the pre-signal
baseline to analyze» Example: 500 points
» “Zoom-in” on baseline
– Calculate descriptive statistics for the segment using full-wave rectification
» Mean & SD
– Double the SD and add to mean value = point where the true signal rises from the baseline
Baseline Noise vs. Signal Differentiation
1.0
-1.5
-1.0
-0.5
0.0
0.5
20000 250 500 750 1000 1250 1500 1750
1.5
0.0
0.5
1.0
20000 250 500 750 1000 1250 1500 1750
Baseline RawSignal
BaselineRectified Signal
500 pts
Reference Sources
Soderberg, G.L., Cook, T.M., Rider, S.C., & Stephenitch, B.L. (1991). Electromyographic activity of selected leg musculature in subjects with normal and chronically sprained ankles performing on a BAPS board. Physical Therapy, 71, 514-522.
Winter, D.A. (1991). Electromyogram recording, processing and normalization: procedures and consideration. Journal of Human Muscle Performance, 1, 5-15.
Soderberg, G.L., & Cook, T.M. (1984). Electromyography in biomechanics. Physical Therapy, 64, 1813-1820
Reference Sources
DeLuca, C.J. (1997). The use of surface electromyography in biomechanics. Journal of Applied Biomechnics, 13, 135-163.
Powers, C.M., Landel, R., & Perry, J. (1996). Timing and intensity of vastus medialis muscle activity during functional activites in subjects with and without patellofemoral pain. Physical Therapy 76, 946-967.
Winter, D.A., Fugerlan, A.J. & Archer, S.E. (1994). Crosstalk in surface electromyography: theoretical and practical estimates. Journal of Electromyography and Kinesiology, 4, 15-26.
Reference Sources
Koh, T.J., Grabiner, M.D. (1993). Evaluation and methods to minimize cross talk in surface electromyography. Journal of Biomechnics, 26(supplement 1), 151-157.
Karst, G.M., & Willett, G.M. (1995). Onset timing of electromyographic activity in vastus medialis oblique and vastus lateralis muscles in subjects with and without patellofemoral pain syndrome. Physical Therapy, 75, 813-823
Hodges, P.W., & Bui, B.H. (1996). A comparison of computer-based methods for the determination of onset of muscle contractions using electromyography. Electroencephalography and Clinical Neurophysiology, 101,511-519.