Diversity-based combination of non-parametric classifiers for EMG signal decomposition
Pattern Analysis & Applications - Special Issue: Non-parametric distance-based classification techniques and their applications
The ANN-based computing of drowsy level
Expert Systems with Applications: An International Journal
An integrated intelligent computing model for the interpretation of EMG based neuromuscular diseases
Expert Systems with Applications: An International Journal
Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients
Expert Systems with Applications: An International Journal
Wavelet basis functions in biomedical signal processing
Expert Systems with Applications: An International Journal
Feature reduction and selection for EMG signal classification
Expert Systems with Applications: An International Journal
Fractal analysis features for weak and single-channel upper-limb EMG signals
Expert Systems with Applications: An International Journal
Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals
Expert Systems with Applications: An International Journal
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This paper presents a new technique for feature extraction of forearm electromyographic (EMG) signals using a proposed mother wavelet matrix (MWM). A MWM including 45 potential mother wavelets is suggested to help the classification of surface and intramuscular EMG signals recorded from multiple locations on the upper forearm for ten hand motions. Also, a surface electrode matrix (SEM) and a needle electrode matrix (NEM) are suggested to select the proper sensors for each pair of motions. For this purpose, EMG signals were recorded from sixteen locations on the forearms of six subjects in ten hand motion classes. The main goal in classification is to define a proper feature vector able to generate acceptable differences among the classes. The MWM includes the mother wavelets which make the highest difference between two particular classes. Six statistical feature vectors were compared using the continuous form of wavelet packet transform. The mother wavelet functions are selected with the aim of optimum classification between two classes using one of the feature vectors. The locations where the satisfactory signals are captured are selected from several mounted electrodes. Finally, three ten-by-ten symmetric MWM, SEM, and NEM represent the proper mother wavelet function and the surface and intramuscular selection for recording the ten hand motions.