A comparison of forearm EMG and psychophysical EEG signals using statistical signal processing

This paper presents Daubechies 44 (db44) as a mother wavelet function for complex signals of human beings. During the last two decades, wavelet transform has been developing as one of the most powerful processors in various areas of science and technology. Many papers have been documented with different types of mother wavelet functions in various fields using different criteria (e.g. similarity between signals and mother wavelet). To name a few, machine condition monitoring including vibration, acoustic and ultrasonic signals, image processing, Electromyographic (EMG) and Electroencephalographic (EEG) signals have been taken into consideration using wavelet transform. The selection wavelet function represents an ongoing challenge in biosignal processing. This paper focuses on the selection of mother wavelet function for human biological signals. In this research, three measures were analyzed. These include surface and intramuscular EMG of the upper limb and psychophysical EEG in response to visual stimuli. 324 mother wavelet functions from wavelet families; including Haar, Daubechies (db), Symlet, Coiflet, Gaussian, Morlet, complex Morlet, Mexican hat, bio-orthogonal, reverse bio-orthogonal, Meyer, discrete approximation of Meyer, complex Gaussian, Shannon, and frequency B-spline were studied.