Electromyography (EMG) signal gives an electrical representation of
neuromuscular activation associated with contracting muscle
provides information about the performance of muscles and nerves.
EMG signal acquires noise while traveling through different
tissues. With the appropriate choice of the Wavelet Function (WF),
it is possible to remove interference noise. Higher Order
Statistics (HOS) can suppress white Gaussian noise in detection,
parameter estimation and solve classification problems. Based on
the RMS error, it is noticed that WF db2 can perform denoising most
effectively among the other WFs (db6, db8, dmey). Power spectrum
analysis is performed to the denoised EMG where mean power
frequency is calculated to indicate changes in muscle contraction.
Gaussianity and linearity tests are conducted to understand changes
in muscle contraction. According to the results, increase in muscle
contraction provides significant increase in EMG mean power
frequency. The study also verifies that the power spectrum of EMG
shows a shift to lower frequencies during fatigue. The bispectrum
analysis shows that the signal becomes less Gaussian and more
linear with increasing muscle force.
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