Abstract – There are generally two nonparametric approaches in feature extraction from temporal signals: temporal and spectral approach. Both approaches were used in classifica-tion of prehensile electromyographic (EMG) signals. The goal of this paper is to define and evaluate some successful meth-ods in both approaches and to determine experimentally which method and approach is the most appropriate. The evaluation is based on classification of real EMGs with an ART-based classifier. The efficiency analysis is also provided. The results have shown that a less expensive temporal ap-proach has strong advantages over the spectral methods
In recent years, there has been major interest in the exposure to physical therapy during rehabilita...
In this work, a classification method for electromygraphic (EMG) signals is presented. Dynamic progr...
International audienceEEG signals are highly correlated, both in space (electrodes) and time (sample...
Electromyography (EMG) is widely used in various fields to investigate the muscular activities. Sinc...
Electromyography (EMG) is widely used in various fields to investigate the muscular activities. Sinc...
Features extraction is important for achievement in Electromyography (EMG) signals analysis. Hence, ...
In this paper, we introduce a new time-evolved spectral analysis-SLEX for analyzing the EMG signal. ...
Recent studies on the myoelectric control of powered prosthetics revealed several factors that affec...
Abstract: A new feature extraction method based on five moments applied to three wavelet transform s...
Electromyography (EMG) signals are becoming increasingly important in many applications, including c...
Electromyography (EMG) is a technique to acquire and study the signal of skeletal muscles. Skeletal ...
© 2001-2011 IEEE. The extraction of the accurate and efficient descriptors of muscular activity play...
This paper presents a novel method for feature extraction from electroencephalogram (EEG) signals us...
Features extraction is important for electromyography (EMG) signal analysis. The paper’s objective i...
In recent years, there has been major interest in the exposure to physical therapy during rehabilita...
In recent years, there has been major interest in the exposure to physical therapy during rehabilita...
In this work, a classification method for electromygraphic (EMG) signals is presented. Dynamic progr...
International audienceEEG signals are highly correlated, both in space (electrodes) and time (sample...
Electromyography (EMG) is widely used in various fields to investigate the muscular activities. Sinc...
Electromyography (EMG) is widely used in various fields to investigate the muscular activities. Sinc...
Features extraction is important for achievement in Electromyography (EMG) signals analysis. Hence, ...
In this paper, we introduce a new time-evolved spectral analysis-SLEX for analyzing the EMG signal. ...
Recent studies on the myoelectric control of powered prosthetics revealed several factors that affec...
Abstract: A new feature extraction method based on five moments applied to three wavelet transform s...
Electromyography (EMG) signals are becoming increasingly important in many applications, including c...
Electromyography (EMG) is a technique to acquire and study the signal of skeletal muscles. Skeletal ...
© 2001-2011 IEEE. The extraction of the accurate and efficient descriptors of muscular activity play...
This paper presents a novel method for feature extraction from electroencephalogram (EEG) signals us...
Features extraction is important for electromyography (EMG) signal analysis. The paper’s objective i...
In recent years, there has been major interest in the exposure to physical therapy during rehabilita...
In recent years, there has been major interest in the exposure to physical therapy during rehabilita...
In this work, a classification method for electromygraphic (EMG) signals is presented. Dynamic progr...
International audienceEEG signals are highly correlated, both in space (electrodes) and time (sample...