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.