Adaptive Blind Signal and Image Processing: Learning by Cichocki A., Amari Sh.-H.

By Cichocki A., Amari Sh.-H.

With strong theoretical foundations and various power purposes, Blind sign Processing (BSP) is likely one of the most well liked rising parts in sign Processing. This quantity unifies and extends the theories of adaptive blind sign and photograph processing and offers sensible and effective algorithms for blind resource separation, self sufficient, crucial, Minor part research, and Multichannel Blind Deconvolution (MBD) and Equalization. Containing over 1400 references and mathematical expressions Adaptive Blind sign and photograph Processing grants an exceptional choice of helpful options for adaptive blind signal/image separation, extraction, decomposition and filtering of multi-variable indications and knowledge.

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Cancellation of artifacts and noise from electroencephalographic and magnetoencephalographic recordings. • Enhancement evoked potentials (EP) and categorize detected brain signals. (The brain potentials evoked by sensory stimulations such as visual, acoustic or somatosensory are generally called evoked potentials). • Detection and estimation of sleep-spindles. 5-15 Hz). • Decomposition of brain sources as independent components and then localizing them in time and space. Let us consider in more detail, some exemplary promising biomedical applications.

Moreover, the FECG will occasionally overlap the MECG and make it normally impossible to detect. Along with the MECG, extensive electromyographic (EMG) noise also interferes with the FECG and it can completely mask the FECG. 16). The recordings pick up a mixture of FECG, MECG contributions, and other interferences, such as maternal electromyogram (MEMG), power supply interference, thermal noise from the electrodes and other electronic equipment. In fact, BSP techniques can be successfully applied to efficiently solve this problem and the first results are very promising [230, 232, 883].

Furthermore, we assume that training sources are available only for short-time slots. During the time windows in which the training signals are not available, we can apply an unsupervised learning algorithm which performs a fine adjustment of the output matrices C and D (by keeping the nonlinear model fixed). In this way, we will be able to estimate continuously in time the source signals. 15 (see Chapter 12 for detail). 8 Why State Space Demixing Models? , n) may have different mathematical or physical models, depending on specific applications.

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Adaptive Blind Signal and Image Processing: Learning by Cichocki A., Amari Sh.-H.
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