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Author: search.filters.author.Chanwimalueang, Theerasak

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    An accurate forearm EMG signal classification method using two-channel electrode
    (2013-01-01)
    Sueaseenak, Direk
    ;
    Chanwimalueang, Theerasak
    ;
    Pintavirooj, Chuchart
    ;
    Sangworasil, Manas
    An accurate electromyography (EMG) classification algorithm to control a virtual hand prosthesis with 12 degrees of freedom using two surface EMG electrodes is presented in this paper. We propose the application of independent component analysis (ICA) for blind-source separation of the EMG signals obtained from two electrodes. One of the problems affecting the EMG classification accuracy is the location dependence of the EMG signal due to the superposition of signals from multiple sources. ICA is used to separate the two signals obtained from two surface electrodes into two independent EMG signals prior to the feature extraction and classification processes. We demonstrate that the EMG classification accuracy can be improved using the ICA algorithm. We also propose a novel eigen-based feature that is extracted from the short-time Fourier transform (STFT) magnitude spectrum. Our new feature not only decreases feature dimensions but also performs better than other well-known features. We also implement the EMG classification scheme on the virtual robot arm. The performance shows promising result as indicated by a decrease in the Davies-Bolden (DB) index after applying the ICA. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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    An eigen based feature on time-frequency representation of EMG
    (2009-11-16)
    Sueaseenak, Direk
    ;
    Praliwanon, Chaleeya
    ;
    Sangworasil, Manas
    ;
    Chanwimalueang, Theerasak
    ;
    Pintavirooj, Chuchart
    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.
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    Comparison study of muscular-contraction classification between independent component analysis and artificial neural network
    (2008-12-01)
    Sueaseenak, Direk
    ;
    Wibirama, Sunu
    ;
    Chanwimalueang, Theerasak
    ;
    Pintavirooj, Chuchart
    ;
    Sangworasil, Manus
    We developed a multi-channel electromyogram acquisition system using PSOC microcontroller to acquire multichannel EMG signals. An array of 4 x 4 surface electrodes was used to record the EMG signal. The obtained signals were classified by a back-propagation-type artificial neural network. B-spline interpolation technique has been utilized to map the EMG signal on the muscle surface. The topological mapping of the EMG is then analyzed to classify the pattern of muscle contraction using independent component analysis. The proposed system was successfully demonstrated to record EMG data and its surface mapping. The comparison study of muscular contraction classification using independent component analysis and artificial neural network demonstrates shows that performance of ANN classification is as comparable as that of the ICA. The computational time of ANN is also less than that of the ICA. © 2008 IEEE.