Blind signal extraction of signals with specified frequency band

Author

Cichocki, Andrzej ; Rutkowski, Tomasz ; Siwek, Krzysztof

Author_Institution

RIKEN, Brain Science Institute, Saitama, Japan

fYear

2002

fDate

2002

Firstpage

515

Lastpage

524

Abstract

Blind sources separation, independent component analysis (ICA) and related methods are promising approaches for analysis of biomedical signals, especially for EEG/MEG and fMRI data. However, most of the methods extract all sources simultaneously, so it is time consuming and not reliable especially, when the number of sensors is large (more than 100 sensors) and signals are contaminated by huge noise. The main objective of this paper is to present a new method for extraction of specific source signals using bandpass filters approach. Such a method allows us to extract source signals with specific stochastic properties, e.g., extraction of narrow band sources with specific frequency bandwidth.

Keywords

FIR filters; band-pass filters; biomedical MRI; blind source separation; electroencephalography; feature extraction; filtering theory; independent component analysis; magnetoencephalography; medical signal processing; EEG/MEG data; ICA; bandpass filters; biomedical signals; blind signal extraction; blind source separation; fMRI data; frequency band; frequency bandwidth; independent component analysis; linear FIR filter; narrowband sources; noise; sensors; sequential speech reconstruction; source signals; stochastic properties; Band pass filters; Bandwidth; Biosensors; Data mining; Electroencephalography; Frequency; Independent component analysis; Narrowband; Signal analysis; Stochastic resonance;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on

Print_ISBN

0-7803-7616-1

Type

conf

DOI

10.1109/NNSP.2002.1030063

Filename

1030063