Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.

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Techniques of EMG signal analysis: detection, processing, classification and applications

This paper was originally published in Biological Procedures Online (BPO) on March 23, 2006. It was brought to the attention of the journal and authors that reference 74 was incorrect. The original citation for reference 74, “Stanford V. Biosignals offer potential for direct interfaces and health monitoring. Pervasive Computing, IEEE 2004; 3(1):99–103.” should read “Costanza E, Inverso SA, Allen R. ‘Toward Subtle Intimate Interfaces for Mobile Devices Using an EMG Controller’ in Proc CHI2005, April 2005, Portland, OR, USA.”

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Techniques of EMG signal analysis: detection, processing, classification and applications (Correction).

The aim of this work is to present an original double-threshold detector of muscle activation, specifically developed for gait analysis. This detector operates on the raw myoelectric signal and, hence, it does not require any envelope detection. Its performances are fixed by the values of 3 parameters, namely, false-alarm probability (P/sub fa/), detection probability, and time resolution. Double-threshold detectors are preferable to single-threshold ones because, for a fixed value of the P/sub fa/, they yield higher detection probability; furthermore, they allow the user to select the couple false alarm-detection probability with a higher degree of freedom, thus, adapting the performances of the detector to the characteristics of the myoelectric signal of interest and of the experimental situation. Here, first the authors derive the detection algorithm and describe different strategies for selecting its parameters, then they present the performances of the proposed procedure evaluated by means of computer simulations, and finally they report an example of application to myoelectric signals recorded during gait. The characterization of the proposed double-threshold detector demonstrates that, in most practical situations, the bias of the estimates of the on-off transitions is smaller than 10 ms, the standard deviation may be kept lower than 15 ms, and the percentage of erroneous patterns is below 5%. These results show that this detection approach is satisfactory in research applications as well as in the clinical practice.

A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait

Use of cluster analysis for gait pattern classification of patients in the early and late recovery phases following stroke.

A study of various normalization procedures for within day electromyographic data.

A technique for automatically clustering linear envelopes of EMGs (electromyograms) during gait has been developed. It uses a temporal feature representation and a maximum peak matching scheme. This technique provides a viable way to define compact and meaningful EMG waveform features. The envelope matching is performed by dynamic programming, providing qualitatively the largest number of matched peaks and quantitatively a minimum distance measurement. The resulting averaged EMG profiles have low statistical variation and can serve as templates for EMG comparison and further classification. >

Temporal feature extraction and clustering analysis of electromyographic linear envelopes in gait studies

The muscular synergy patterns of human locomotion can be described by the phasic activity of electromyographic linear envelopes (LE) and the interphasic spatio-temporal relations. To represent the phasic activity, the LE is modeled as the summation of Gaussian pulses of various lengths. The parameters of interest are the temporal features: time, duration, and amplitude of the phases of activity. A maximum likelihood approach to the parameter estimation for a mixture of normal distributions is adopted for extracting the temporal features. Based on the derived temporal features, a set of relational descriptors can be defined to describe the spatio-temporal relations between the multichannel phasic activities. The strength of this approach is not only that the plastic activity of the LE can be quantitatively represented accurately, but also that the resulting synergy patterns can be easily interpreted by observers. >

A quantitative and qualitative description of electromyographic linear envelopes for synergy analysis

A technique has been developed for performing pattern analysis of electromyographic (EMG) activities generated during locomotion. It was found that the shapes of the EMG linear envelopes (LE) are mainly determined by their phase spectra; their magnitude spectra are much less important. Autoregressive (AR) parametric models and discrete Fourier transform (DFT) approaches were tested and compared. The latter proved to be a better way to describe the EMG LEs. Feature extraction and clustering were performed by performing a DFT of EMG LEs, extracting part of the phase and magnitude spectra as features, and using the percent powers to weigh the corresponding harmonics. The approach was applied to the clustering analysis of EMG LEs of normal and anterior cruciate ligaments (ACL) injured subjects during walking. >

J.-J.J. Chen

Papers

Temporal feature extraction and clustering analysis of electromyographic linear envelopes in gait studies

A quantitative and qualitative description of electromyographic linear envelopes for synergy analysis

Clustering analysis and pattern discrimination of EMG linear envelopes