Abstract
It is common for data to be contaminated with artifacts, interference, and noise. Several methods including independent components analysis (ICA) and principal components analysis (PCA) have been used to suppress these undesired signals and/or to extract the underlying (desired) source waveforms. For some data it is known, or can be extracted post hoc, how to partition the data into periods of source activity and source inactivity. Two examples include cardiac data and data collected using the stimulus-evoked paradigm. However, neither ICA nor PCA are able to take full advantage of the knowledge of the partition. Here we introduce an interference suppression method, partitioned factor analysis (PFA), that takes into account the data partition.
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Keywords
- Independent Component Analysis
- Denoising Method
- Interference Suppression
- Source Extraction
- Undesired Signal
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© 2006 Springer-Verlag Berlin Heidelberg
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Nagarajan, S.S., Attias, H.T., Sekihara, K., Hild, K.E. (2006). Partitioned Factor Analysis for Interference Suppression and Source Extraction. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_24
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DOI: https://doi.org/10.1007/11679363_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32630-4
Online ISBN: 978-3-540-32631-1
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