Paper Info
Title
Pattern recognition with surface EMG signal based wavelet transformation.
Abstract
EMG based input device is a natural means of human computer interface (HCI) because the electrical activity induced by the human's arm muscle movements can be interpreted and transformed into computer's control commands. In this paper, we describe an approach for classifying electromyography (EMG) signals using a multilayer perceptron neural network (MLP) and Bayesian classifier (BC) with the wavelet transformation technique for feature selection to discriminate 6 classes of motions to control a mouse. Wavelet Transformation (WT) was applied to raw EMG data and in order to decrease the dimension of the feature sets, principle component analysis (PCA) and sequential forward selection (SFS) were utilized.
Year
DOI
Venue
2012
10.1109/ICSMC.2012.6377717
SMC
Keywords
Field
DocType
Bayes methods,electromyography,feature extraction,human computer interaction,medical signal processing,mouse controllers (computers),multilayer perceptrons,principal component analysis,signal classification,wavelet transforms,BC,Bayesian classifier,EMG based input device,HCI,MLP,PCA,SFS,WT,computer control commands,electrical activity,electromyography signal classification,feature selection,feature set dimension,human arm muscle movements,human computer interface,mouse control,multilayer perceptron neural network,pattern recognition,principal component analysis,sequential forward selection,surface EMG signal,wavelet transformation,Bayesian Classification,EMG,Human Computer Interface,Mouse Control,Neural Network,PCA,Wavelet transformation
Naive Bayes classifier,Feature selection,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Artificial neural network,Machine learning,Principal component analysis,Wavelet,Wavelet transform,Input device
Conference
ISSN
Citations 
PageRank 
1062-922X
4
0.48
References 
Authors
13
2
Name
Order
Citations
PageRank
Ugur Sahin140.48
Ferat Sahin270645.49