Electromyography Signal Analysis and Characterization

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.

Autorentext

Sazzad Hussain was born in Bangladesh, in November 1983. He is a Ph.D. candidate with the Learning and Affect Technologies Engineering (LATTE) Research Group at the University of Sydney, Australia. His research focus is on Multimodal Affect & Cognitive Load Detection, Machine Learning, Information Fusion and Physiological Information Processing.