… It is widely used in the EMG recognition system. In this study, we have investigated usefulness
of extraction of the EMG features from multiple-level wavelet decomposition of the EMG …

… (FEBANN) and wavelet neural networks (WNN) … in classification of EMG signals. In these
methods, we used an autoregressive (AR) model of EMG signals as an input to classification …

… electromyogram signals for different subjects. During the study we adopted 16 different wavelet
functions … The raw surface electromyogram signals were used to calculate the roots mean …

… classification error estimated from a training set of signals and an efficient method for exploring
a large family of wavelet functions is wavelet … by our group for simulated EMG signals [7], …

… in the wavelet analysis, a suitable wavelet function … wavelet family is the most suitable
wavelet for sEMG signal analysis [6, 22, 23], and in this study, the Daubechies orthogonal wavelets…

… prostheses we intend to exploit electromyographic (EMG) signals more than in the usual …
EMG signals coming from two different subjects using a novel integration of ANN and wavelet…

… to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform
(… of DWT factors on the performance of EMG signals classification. This problem is still an …

… We focus on discrete orthogonal wavelet bases driven by the … wavelet. As an example of
application of the proposed method, we present results on the classification of surface EMG …

… -Q factor wavelet transform (TQWT) based algorithm is proposed for the classification of physical
actions … In this paper, two non-linear kernel functions morlet wavelet and RBF is used for …

… This paper reports on the effectiveness of the wavelet transform applied to the SEMG signal
… EMG signal from right rectus femoris muscle was performed using various wavelet functions (…