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Robert Newton
09-09-1994, 09:33 PM
Thankyou to those who responded to my request for information on
frequency spectrum analysis of EMG signals. Following is a list of
references and a summary of the replies.

Regards
Robert Newton
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De Luca, C.J. Physiology and mahmatics of myoelectric signals. IEEE
Transactions on Biomedical Engineering, Vol. 26(6):313-325, 1979.

Desmedt, J.E. New concepts of the motor unit, Neuromuscular Disorders
Electromyographical kinesiology. New Developments in
Electromyography and Clinical Neurophysiology. Vol. 1.
S. Karger. 1973

Kwatny, E., Thomas, D.H. and H.G. Kwatney. An application of signal
processing techniques to the study of myoelectric signals.
IEEE Transactions on Biomedical Engineering,
Vol. 17(4):303-313, 1970.

Stulen, F.B. and De Luca, C.J. Frequency parameters of the myoelectric
signal as a measure of muscle conduction velocity. IEEE
Transactions on Biomedical Engineering, Vol. 28(7):515-523, 1981.
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From: Oyvind Stavdahl
Message-Id:
To: run1@psu.edu
Subject: Re: Frequency Analysis of EMG using FFT

Dear Robert,

The questions asked in your BIOMCH-L posting are interesting to me,
because I am just plowing into the world of EMG myself. I am studying
EMG as a means of controlling powered hand prostheses. I have just
realized that in order to obtain really good (smooth) estmates of
contraction force I have to utilize the information "hidden" in the
frequency spectrum of the signals (in contrast to only using amplitude).

If your work is somehow related to mine, or if you have any good
litterature references on the relationship between contraction force
and the correspopnding EMG spectrum I would greatly appreciate hearing
fron you. (I already have _some_ references.)

I will try to answer your questions:

1. The Fourier transform decomposes the time series into a number of
sine components that are equidistant with respect to frequency
(linearly distributed along the frequency axis). I therefore believe that
division by frequency is unnecessary. (I don't KNOW this - it just seems
to me that this is the case. By the way you will probably receive other
replies that will give you facts on this issue.) I think the division -by-
frequency issue might have something to do with conversion between linear
and logarithmic frequencies.

2. If you use your raw time series as input data to your FFT sofware, it
will produce the magnitude - in the _amplitude_ sense - of the 512 sine
components. To obtain the power spectrum you have to square each of these
values. The power spectrum (power spectral density) can also be obtained
by Fourier transforming the autocorrelation function of your time series.

3. I think you should always use a Window before FFT processing, as this
reduces the expected error in the FFT output - regardless of the signal's
frequency content. (This error is a consequence
of your time series always being a finite part of an ideally "everlasting"
signal - you sample this signal only in a limited time "window", and the
FFT acts as if all samples outside the window were equal to zero. The Rectangular
window does _nothing_ to your time series, while the Hamming, Hanning etc.
force the sampled values close to the ends of the series to approach zero,
reducing the "step" from nonzero samples to the (nonexisting) zero samples
outside the window.)
I won't go into more details about this, but I think most textbooks on Numerical
Signal Processing would give you the answers you request. The none-rectangular
windows have very similar effects, and I find it diffycult to recommend one
more than the others.


I hope you can use this.
Good luck in your further work!


Best regards,
Oyvind Stavdahl


From: "Claudia Ranniger"
Subject: Re: Frequency Analysis of EM
To: run1@psu.edu

Reply to: RE>Frequency Analysis of EMG u
Robert-
A discussion of mean vs median frequency, and how to calculate them, is
found in Stulen and DeLuca, "Frequency parameters of the Myoelectric Signal as
a Measure of Muscle Conduction Velocity," IEE Transaction of Biomed
Engineering, Vol 28, #7, July 1981.

good luck
-Claudia
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Robert Newton Email: run1@psu.edu
Center for Sports Medicine Telephone: Int+ 1 814 865 7107
The Pennsylvania State University Facsimile: Int+ 1 814 865 7077
117 Ann Building, University Park, PA 16802
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