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

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

Large-scale auralised sound localisation experiment

status: publishe

A Spectral Analysis of Rotator Cuff Musculature Electromyographic Activity: Surface and Indwelling

Electromyography (EMG) of the shoulder girdle is commonly performed; however, EMG spectral properties of shoulder muscles have not been clearly defined. The purpose of this study was to determine the maximum power frequency, Nyquist rate, and minimum sampling rate for indwelling and surface EMG of the normal shoulder girdle musculature. EMG signals were recorded using indwelling electrodes for the rotator cuff muscles and surface electrodes for ten additional shoulder muscles in ten healthy volunteers. A fast Fourier transform was performed on the raw EMG signal collected during maximal isometric contractions to derive the power spectral density. The 95% power frequency was calculated during the ramp and plateau subphase of each contraction. Data were analyzed with analysis of variance (ANOVA) and paired t tests. Indwelling EMG signals had more than twice the frequency content of surface EMG signals (p < .001). Mean 95% power frequencies ranged from 495 to 560 Hz for indwelling electrodes and from 152 to 260 Hz for surface electrodes. Significant differences in the mean 95% power frequencies existed among muscles monitored with surface electrodes (p = .002), but not among muscles monitored with indwelling electrodes (p = .961). No significant differences in the 95% power frequencies existed among contraction subphases for any of the muscle–electrode combinations. Maximum Nyquist rate was 893 Hz for surface electrodes and 1,764 Hz for indwelling electrodes. Our results suggest that when recording EMG of shoulder muscles, the minimum sampling frequency is 1,340 Hz for surface electrodes and 2,650 Hz for indwelling electrodes. The minimum sampling recommendations are higher than the 1,000 Hz reported in many studies involving EMG of the shoulder

Author Correction: CHD3 helicase domain mutations cause a neurodevelopmental syndrome with macrocephaly and impaired speech and language

The original version of this Article contained an error in the spelling of the author Laurence Faivre, which was incorrectly given as Laurence Faive. This has now been corrected in both the PDF and HTML versions of the Article