1. Artifact & Interference
The EMG Signal
Artifact & Interference
Sampling Rate
Signal References
Signal Processing.1

2. EMG Noise A form of artifact Obscures a “clean” signal
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 Hz slow twitch motor units

3. Sources of Noise (Interference)
Driver amplifier
Poor quality
High CMRR > 100,000
Broken
Ground fault
Amps not “tied together”
Ground prong on cable
Absent
Loose cable connection
Loose controls

4. 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

5. 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

6. Sources of Noise (Interference)
Driver amplifier
Electrodes
Cable fatigue
Along length
At connector
Stripped insulation
Poor reference (ground) contact

7. 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) Hz

8. 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 Corp
701 Brooks Ave South
Thief River Falls, MN

9. 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

10. Sources of Noise (Interference)
Driver amplifier
Electrodes
Cable movement artifact
Electro-static/-magnetic radiation
Radio waves AM FM

11. Cross-Talk
Electrodes over an adjacent muscle pick-up a signal via skin conduction M1 M2

12. Cross-Talk
Visually inspect a tracing (monitor or printout) of a signal
If they have the same shape there is probably cross-talk
Muscle 1
Muscle 2

13. 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

14. 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

15. Sampling Rate Lost Data Points Signal Adequately Sampled
Baseline
Signal Adequately
Sampled
Signal Under-sampled

16. Consequences - Sampling
Under-sampling
Lost data points
Signal not truly representative
Can’t be trusted

17. 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

18. Selecting the Sampling Rate The “Two Times Rule”
Analyze the signal (or movement) and determine the highest possible operating frequency
Example: motor unit frequency range = (10) Hz
Double the top rate
Sampling rate: 250 Hz x 2 = 500 Hz ~ 1000Hz

19. Sampling at 1000 Hz
For data plotted on a graph sampled at 1000 Hz, each tic on the X-axis is 1msec
1000 msec
1 second

20. 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

21. Voltage Spike from Event Marker
Raw
Rectified
Voltage
Spike
Event

22. Correlate EMG Signal with Torque Channel
Rectified
EMG

23. Signal Processing.1 Timing - Phase transition Duration Onset - Offset

24. Phase Transition
Visual assessment of phasic activity
1st
2nd
3rd

25. Question: At what (data) point do I start counting?

26. Baseline Noise vs. Signal Differentiation
Manual visual identification using a cursor

27. 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

28. Baseline Noise vs. Signal Differentiation
Baseline Raw
Signal
Baseline
Rectified Signal
500 pts

29. 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,
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,

30. Reference Sources
DeLuca, C.J. (1997). The use of surface electromyography in biomechanics. Journal of Applied Biomechnics, 13,
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,
Winter, D.A., Fugerlan, A.J. & Archer, S.E. (1994). Crosstalk in surface electromyography: theoretical and practical estimates. Journal of Electromyography and Kinesiology, 4,

31. 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),
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,
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,