118 – EMG Frequency Signal Analysis
Analysis of the EMG signal in the frequency domain involves measurements and parameters which describe specific aspects of the frequency spectrum of the signal. Parameters of the power density spectrum may be easily used to provide useful measures of the EMG frequency spectrum.
This Application Note will explain how to extract several measures derived from the power spectrum of an EMG signal. The measures to be extracted are: Median Frequency, Mean Frequency, Peak Frequency, Mean Power, Total Power. If the EMG spectrum has a normal distribution, the Median and Mean frequencies will be identical; any deviation from normality will result in differing values for the Median and Mean frequencies.
AcqKnowledge is used to calculate these parameters after the EMG data has been collected. Methodology varies based on the version of AcqKnowledge that is used.
- Automated Scoring—Starting with AcqKnowledge 4.0 for Windows or 3.9.2 for Mac the software can extract the measurements in an automated fashion.
- Case A: Extracting measurements from a single contraction
- Case B: Extracting measurements from multiple contractions
- Manual Scoring is required when older versions of AcqKnowledge are used.
Thongpanja, S. & Phinyomark, Angkoon & Limsakul, Chusak & Phukpattaranont, P.. (2015). Application of Mean and Median Frequency Methods for Identification of Human Joint Angles Using EMG Signal. Lecture Notes in Electrical Engineering. 339. 10.1007/978-3-662-46578-3_81.
Abstract: The analysis of surface electromyography (EMG) signals is generally based on three major issues, i.e., the detection of muscle force, muscle geometry, and muscle fatigue. Recently, there are not any techniques that can analyse all the issues. Mean frequency (MNF) and median frequency (MDF) have been successfully applied to be used as muscle force and fatigue indices in previous studies. However, there is the lack of consensus upon the effect of muscle geometry on the basis of varying joint angles. In this paper, the modification of MNF and MDF using a min-max normalization technique was proposed to provide a consistent relationship between feature value and joint angle across subjects…
Keywords: Feature extraction, Frequency analysis, Muscle fatigue, Spectral analysis, Surface electromyography signal
Phinyomark, Angkoon & Thongpanja, S. & Hu, Huosheng & Phukpattaranont, P. & Limsakul, Chusak. (2012). The Usefulness of Mean and Median Frequencies in Electromyography Analysis. 10.5772/50639.
Abstract: Mean frequency (MNF) and median frequency (MDF) are two useful and popular frequency-domain features for electromyography analysis both in clinical and engineering applications. MNF and MDF are frequently used as the gold standard tool to detect fatigue in the target muscles using EMG signals. The effectiveness of MNF and MDF under many experimental conditions is presented and confirmed in this chapter…
Keywords: electromyography, muscle force, muscle geometry, muscle fatigue, human-computer interaction (HCI), ergonomics, occupational therapy, sport science