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We applied Agglomerative Hierarchical Clustering (AHC) technique on independent components of low back surface electromyography (sEMG) signals, in order to differ sitting and standing postures. Preliminary results from small group of healthy subjects, suggested that presented method might be used to distinguish between two postures in different conditions. Clustering accuracy varied from 60% to 70%...
The paper developed a block-wise approach for ICA algorithms which can improve the computational efficiency of ICA without the degradation of performance for the separation of biomedical signals. Source signals including electrocardiogram (ECG), electromyogram (EMG) and 60-Hz sinusoid are linearly mixed for experimental tests. The mean-square errors (MSE) between the original sources and the separated...
Blind Source Separation (BSS) techniques are frequently needed in the processing of biomedical signals. This need comes from the fact that these signals are often composed of many different sources, which are mixed in the measured signal. However, we are usually only interested in examining one or a limited set of sources of interest separately. A variety of algorithms exist for separating multichannel...
The performance of speech recognition systems is commonly degraded by either speech-related disabilities or by real-world factors such as the environmentpsilas noise level and reverberation. In this work, we propose a subvocal speech recognition system based on EMG signal for subvocal acquisition, Independent Component Analysis (ICA) for feature extraction and Neural Networks for classification. We...
In this paper we show how independent component analysis (ICA) algorithms can be used to perform spatio-temporal filtration of electromyographic (EMG) and electrocardiographic (ECG) signals. The technique was used to decompose the EMG signals into motor unit action potential (MUAP) trains. From the 88 outputs of the adaptive spatio-temporal filtration, three groups of different MUAP train patterns...
This paper reports analysis of the limitations of using independent component analysis (ICA) for biosignal analysis especially artefact removal. The possible difficulty is that there are limited number of electrodes (recordings) making it an overcomplete problem (non-square ICA). The other difficulty is the distribution of biosignal being close to Gaussian. These two properties of the signals may...
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