Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis |
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Authors: | Sergiy Vorobyov Andrzej Cichocki |
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Institution: | (1) Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan, JP |
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Abstract: | In many applications of signal processing, especially in communications and biomedicine, preprocessing is necessary to remove
noise from data recorded by multiple sensors. Typically, each sensor or electrode measures the noisy mixture of original source
signals. In this paper a noise reduction technique using independent component analysis (ICA) and subspace filtering is presented.
In this approach we apply subspace filtering not to the observed raw data but to a demixed version of these data obtained
by ICA. Finite impulse response filters are employed whose vectors are parameters estimated based on signal subspace extraction.
ICA allows us to filter independent components. After the noise is removed we reconstruct the enhanced independent components
to obtain clean original signals; i.e., we project the data to sensor level. Simulations as well as real application results
for EEG-signal noise elimination are included to show the validity and effectiveness of the proposed approach.
Received: 6 November 2000 / Accepted in revised form: 12 November 2001 |
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