Publication: An eigen based feature on time-frequency representation of EMG
2
0
Issued Date
2009
Resource Type
File Type
application/pdf
Other identifier(s)
2-s2.0-71049141198
Rights Holder(s)
Scopus
Bibliographic Citation
2009 IEEE-RIVF International Conference on Computing and Communication Technologies: Research, Innovation and Vision for the Future, RIVF 2009. (2009)
Suggested Citation
Sueaseenak D., Praliwanon C., Sangworasil M., Chanwimalueang T., Pintavirooj C. An eigen based feature on time-frequency representation of EMG. 2009 IEEE-RIVF International Conference on Computing and Communication Technologies: Research, Innovation and Vision for the Future, RIVF 2009. (2009). doi:10.1109/RIVF.2009.5174621 Retrieved from: https://hdl.handle.net/20.500.14740/3782
Abstract
In this research we used a multi-channel electromyogram acquisition system using programmable system on chip (PSOC) microcontroller from previous work to acquire surface EMG signals. The two channel surface electrodes were used to measure and record EMG signals on forearm muscles. These two channels of EMG signals were performed a blind signal separation by using an independent component analysis (ICA) technique. The well known ICA algorithm called FASTICA is a useful method to separate two or more linear combination of source signals into statistically independent components. We purposed A novel features for the EMG contraction classification. Our feature is derived from the application of time-frequency analysis of the EMG signal followed by the computation of Eigen vector of the timefrequency magnitude spectrum. Our feature is the ratio between the two Eigen values. We have shown the robustness of our features for a variety of muscular contraction. The result is very promising. © 2009 IEEE.
Subject(s)
Acquisition systems
Blind Signal Separation
Eigen based feature extraction
Eigen-value
Eigenvectors
Electromyogram
Electromyogram(EMG)
EMG signal
FastICA
ICA algorithms
Independent components
Linear combinations
Magnitude spectrum
Multi-channel
Muscular contraction
ON time
Programmable system on chips
Source signals
Surface EMG
Time frequency
Time-frequency analysis
Two channel
Computer science
Feature extraction
Hemodynamics
Multivariant analysis
Muscle
Research
Shrinkage
Independent component analysis
Blind Signal Separation
Eigen based feature extraction
Eigen-value
Eigenvectors
Electromyogram
Electromyogram(EMG)
EMG signal
FastICA
ICA algorithms
Independent components
Linear combinations
Magnitude spectrum
Multi-channel
Muscular contraction
ON time
Programmable system on chips
Source signals
Surface EMG
Time frequency
Time-frequency analysis
Two channel
Computer science
Feature extraction
Hemodynamics
Multivariant analysis
Muscle
Research
Shrinkage
Independent component analysis