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HL-20533 Biomechanics and Motor Control > Electromyography 2.1 - Recording, Analysing, and Processing EMG > Flashcards

Electromyography 2.1 - Recording, Analysing, and Processing EMG Flashcards

1
Q

give three pros for the use of surface electrodes

A

1) less obtrusive
2) capture from a wider surface (whole muscle activity?)
3) easier to use / less training needed / cheaper

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2
Q

give two cons for the use of surface electrodes

A

1) higher risk of cross talk

2) bias towards slow fibres?

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3
Q

state three pros for the use of indwelling electrodes

A

1) finer detection / capture from a few motor neurones
2) can reach deeper muscles
3) less risk of cross talk and noise

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4
Q

state 4 cons for the use of indwelling electrodes

A

1) more obtrusive/painful
2) does not allow for very dynamic movements
3) lacks information about whole muscle activity
4) requires more skill/training/cost

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5
Q

explain 2 things to be weary about when placing EMG electrodes

A

1) place on middle of the muscle between origin and insertion
- away from tendinous area and motor plate

2) place on a line of that parallel to the underlying muscle fibres
- care taken with pennate fibres (EMG 1.2)

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6
Q

what 3 things does proper placement of EMG electrodes allow for?

A

1) increased signal
2) improves signal-to-noise ratio
3) reduces ‘cross-talk’

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7
Q

state what it is meant by the key term - ‘cross-talk-

A

‘cross-talk’: signals from muscles other than those that the electrodes are intended to measure

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8
Q

what 3 things reduce cross-talk?

A
  • careful preparation and knowledge of anatomy (use large, superficial muscles)
  • less adipose tissue
  • the use of smaller electrodes
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9
Q

why are EMG signals different? state 3 influences of EMG signal

A

1) skin-electrode interface
2) sub-cutaneous fat
3) distance from electrodes
4) changes in fibre type
5) blood flow fatigue, changes in Na+, dehydration
6) changes in muscle length

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10
Q

explain how the ‘skin-electrode interface’ effects the ENG signal

A

dead skin layer, grease, etc… provide a resistance (impedance) to the current from the underlying muscle

by shaving hair, abrasing the skin, and rubbing with a medical swab, you can improve the skin-electrode interface (higher = better)

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11
Q

how does sub-cutaneous fat influence EMG ?

A

the lower the levels of sub-cutaneous fat, the higher the skin-electrode interface

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12
Q

how does distance of the signal from the electrode influence EMG ?

A

attenuation (high frequency components in particular)

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13
Q

how does changes in fibre type influence EMG?

A

slower fibre types are usually deeper in the muscle

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14
Q

how does changes in muscle length influence EMG?

A

changes conduction velocity (larger fibres = faster velocity)

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15
Q

what pieces of information do we get from Raw EMG Data ?

A
  • Timings of activation (relative and absolute)

- Can look at the coordination/synergy between different muscle groups

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16
Q

what is it difficult to do with Raw EMG Data ?

A
  • difficult to quantify the amount of activation from a raw signal
  • the mean of the signal is zero (or very close to zero)
  • therefore, it is very difficult to get intensity by looking at the raw data
  • we therefore need to do some form of processing to be able to analyse intensity
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17
Q

what is ‘Full-Wave Rectification’ of EMG

A

the reversal of all negative phases of EMG

√EMG

e.g. - √-2 mV² = 2 mV

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18
Q

what is ‘EMG Envelope and Integration’ also known as?

A

a ‘low-pass filter’

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19
Q

how is ‘EMG Envelope and Integration’ carried out?

A
  • select a window of time and calculate the average over that window of time
  • move that window over time and continue to calculate the average
  • by doing this, you smoothen the signal and develop a linear envelope - a line which represents the overall trend of your signal
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20
Q

what can you also do with ‘EMG Envelope and Integration’ ?

A
  • you can also calculate the area below the curve as a measure of the amount of muscle activity (integrated EMG)
  • the sum of all of the area which is under the EMG curve once it has been created into a linear envelope
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21
Q

what is ‘Integrated EMG’ (iEMG) ?

A
  • a measure of quantity of the EMG signal
  • calculation of the area underneath the rectified EMG time curve
  • over what period is the integration performed (e.g. - 1 stride; 1 second)
  • units are uV/s or mV/s
  • iEMG is often calculated over successive 50 - 150 ms time windows
  • the new iEMG curve is plotted to show trends in muscle activity
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22
Q

what is ‘Average Rectified EMG) (arEMG) also known as ?

A

average absolute value (MAV)

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23
Q

how is Average Rectified EMG (arEMG) calculated ?

A

calculated by dividing the integrated EMG by the time window over which it was integrated

arEMG/MAV = (iEMG / t)

24
Q

what two things can you do with Average Rectified EMG (aEMG) ?

A
  • can compare events of different duration (average over time)
  • can also be calculated as for the overall movement duration (e.g. - stride period)
25
Q

how does Average Rectified EMG (arEMG) differ from iEMG ?

A

differs from iEMG as you are calculating the average intensity for your EMG signal over that time period

as you divide by time, you can compare the activity for the durations that may be different

26
Q

what is the advantage of using Average Rectified EMG (arEMG) over iEMG?

A

could potentially compare the intensity of a contraction over time periods of different lengths

i.e. - it is no longer time dependent

27
Q

talk about the effect of using different window durations for your arEMG

A
  • the longer the time window, the smoother the processed EMG
  • you use smaller windows when you want to follow peaks in the signal closer
  • however, you then have the disadvantage of a less sooth pattern
  • longer windows –> less able to follow the peaks, but you have a smoother signal to analyse
28
Q

what is ‘Normalisation of the EMG Signal’ ?

A

normalising the EMG signal involves expressing the signal as a percentage of the processed EMGas an isometric maximal voluntary contraction

29
Q

what is the Normalised EMG Signal formula ?

A

EMG% = (EMG Task / EMG mvc) * 100

30
Q

what is good about Normalised EMG ?

A
  • provides a measure of muscle activation level during a task
  • allows for comparison of processed EMG signals between different muscles and different individuals
  • can normalise to other activities such as the level of walking or running which may be more repeatable
31
Q

however, MVC’s are not particularly reliable. what are the implications of this to Normalised EMG ?

A
  • what activity is used to evoke EMG

- the fatigue level on that specific occasion

32
Q

what 3 main pieces of information give about EMG?

A
  • evidence of muscle activity (on/off)
  • information about the level of activation (amplitude)
  • indication of muscle fatigue (frequency analysis)
33
Q

is there a direct relationship between EMG and force ?

A
  • no, there is not
  • under dynamic conditions, force depends of fascicle length, contraction velocity, type of fibres, fatigue, etc…
  • two electrode placements are never identical
  • generally, you cannot compare EMG traces from two different muscles, participants, or sessions, unless you normalise to a known reference (e.g. - MVC)
34
Q

define ‘Temporal Processing’

A

Temporal Processing: related to the amplitude of the signal content, or the ‘amount of activity’

35
Q

so, explain what Temporal Processing is/does (2 points)

A
  • often referred to as ‘amplitude estimation’

- usually followed by a full wave rectification as raw EM value has a mean value of approx. zero

36
Q

what is the time known as at which EMG amplitude is usually expressed in Full Wave Rectification ?

A

‘Epoch Duration’ (such as the duration of the contraction or running stride)

37
Q

what are the SENIUAM recommendations for epoch durations ?

A

0.25 - 2 seconds for isometric contractions

1 - 2 seconds for contractions < 50 MVC

0.25 - 0.5 seconds for contractions > 50% MVC

38
Q

how is arEMG calculated?

A

easily computed from a digital signal by adding individual EMG values for each sample and dividing them by the sample time

39
Q

what factors is arEMG influenced by?

A

the number of active motor units, the firing rates of motor units, the amount of signal cancellation by superposition, and the waveform of the MUAP

40
Q

define ‘Root Mean Square EMG’ (RMS EMG)

A

RMS EMG is the square root of the average power (voltage squared) of the signal in a given time

41
Q

how is Root Mean Square EMG (RMS EMG) calculated?

A

easily computed from a digital signal by adding the squares of the individual EMG values for each sample, then taking the square root of the sum before dividing by the sample size

42
Q

why is Root Mean Square EMG (RMS EMG) used?

A

considered to provide a measure of recruited motor units during voluntary contractions where there is little correlation among motor units

43
Q

what does SENIAM recommend for amplitude estimation during RMS EMG

A

SENIAM recommended RMS EMG for amplitude estimation of the EMG in non-dynamic contractions

44
Q

what method of EMG is not recommended at all by SENIAM?

A

integrated EMG (iEMG)

this is simply the area underneath the rectified EMG

45
Q

what is the only EMG technique recommended by SENIAM for dynamic contractions ?

A

Smooth, Rectified EMG

46
Q

what is the main issue with Rectified EMG?

A
  • the choice of epoch duration which, for the estimator, is related to the filter cut-off frequency
47
Q

talk about a low cut-off frequency in Rectified EMG (2 points)

A
  • low cut-off frequency gives a reliable estimate of signal intensity for ‘stationary’ activation of the muscle
  • however, are inaccurate when the activation is non-stationary, when the statistical properties of the signal vary with time
48
Q

talk about high cut-off frequencies in Rectified EMG (3 points)

A
  • high cut-off frequency results in a noisy estimator for stationary activities and a linear envelope that follows closely the changes in EMG
  • typical cut-off points for slow movements (e.g. - walking0 are 2 Hz and 6 Hz for faster movements (e.g. - running)
  • cut-off points must be reported with type and order of filter (SENIAM)
49
Q

what is the purpose of the use of an ‘Ensemble Average’ ?

A
  • this approach theoretically reduces the error in the amplitude estimation (of identical movements) by a factor equal to the square root of the number of cycles
  • often used in clinical gait analysis but ignores the variability of movement patterns
  • appears inappropriate for fast sports movements
  • SENIAM recommendations are that, if used, the number of cycles over which the ensemble average is calculated should be reported along with the SE of the mean, to show up any movement variability
50
Q

what situations can’t ‘Frequency Domain Analysis’ (Spectral Estimation) be used

A

can be used unless data is explicitly time-limited, as in a single action potential

51
Q

what is Frequency Domain Analysis (Spectral Estimation)?

3 points

A
  • EMG data is usually presented as a power spectrum (power = amplitude^2) at a series of discrete frequencies –> often represented as a continuous curve
  • the EPOCH duration, over which the transformation of the time domain signal into the frequency domain occurs, which is recommended by SENIAM t be 0.05 - 0.5 s
  • the spread of power spectrum is best expressed by statistical parameters, which depend on the distribution of the signal power over the constitute frequencies
52
Q

Two statistical parameters are used to express central tendency of a spectrum, which are comparable to the mean and median in statistics. What are they?

A
  • for a spectrum of discrete frequencies, the mean frequency is obtained by dividing the sum of the products of the power at each frequency, and the frequency by the sum of all the powers
  • the median frequency is the frequency that divides the spectrum into two parts of equal power - the areas under the power spectrum to the two sides of the median frequency are equal
53
Q

is the mean or median less sensitive to noise ?

A

the median is less sensitive to noise than the mean

54
Q

how can the spread of power be calculated

A

the spread of power can be expressed by the statistical bandwidth, which is calculated in the same way as the standard deviation

55
Q

what is a use of the EMG power spectrum?

A

the EMG power spectrum can be used, for example, to indicate the onset of muscle fatigue. This is accompanied by a noticeable shift in the power spectrum towards lower frequencies, and a reduction in the median and mean frequencies. This frequency shift is caused by an increase in the duration of the MUAP

this results from either lowering the conduction velocity of all the AP’s or through faster, higher frequency motor units switching on and off while slower, lower frequency motor units remain active

HL-20533 Biomechanics and Motor Control (12 decks)

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