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

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. 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 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 Corp 701 Brooks Ave South Thief River Falls, MN 56701-0677 1-800-344-4539 www.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-talk 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 Signal Adequately Sampled Baseline Signal Adequately Sampled 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 Rate The “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 1000 msec 1 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 Raw Rectified Voltage Spike Event

Correlate EMG Signal with Torque Channel Rectified EMG

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

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

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 Baseline Raw Signal Baseline Rectified 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.