AI Article Synopsis
- Blind Source Separation (BSS) techniques are essential for processing biomedical signals, as these signals often contain multiple mixed sources that need to be separated for analysis.
- Many existing algorithms, like Independent Component Analysis (ICA), require a number of channels equal to or greater than the number of sources, leaving a gap in methods for cases with fewer channels.
- This work introduces a new technique that merges Empirical Mode Decomposition (EMD) and ICA, effectively separating independent sources even when the number of sources exceeds the available channels, demonstrating its effectiveness in single and two-channel biosignal processing.
Article Abstract
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 mixtures into its independent sources (e.g. different Independent Component Analysis (ICA) techniques). These techniques only work if the number of channels is larger than, or equal to the number of sources present in the signal. On the other hand, only a few algorithms have been reported for the analysis of single channel sources, or other mixtures where the number of sources is higher than the number of channels. In this work we show a new technique which combines Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA). We will show that this technique is capable in separating independent sources when the number of these sources is higher than the number of channels available. We show the performance in single channel and two-channel biosignal processing.
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| http://dx.doi.org/10.1109/IEMBS.2010.5626482 | DOI Listing |
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