Blind signal separation: statistical principles

Cardoso, J.-F.

doi:10.1109/5.720250

Cited by 1,532 publications

(940 citation statements)

References 50 publications

Supporting

Mentioning

869

Contrasting

Unclassified

Order By: Relevance

“…For this reason, the ICA principles as well as the ICA algorithms will not be developed in this paper. For more details, we recommend the reader to refer to [1,[11][12][13].…”

Section: Hyperspectral Data Analysis By Icamentioning

confidence: 99%

On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation

Moussaoui¹,

et al. 2008

View full text Add to dashboard Cite

The surface of Mars is currently being imaged with an unprecedented combination of spectral and spatial resolution. This high resolution, and its spectral range, give the ability to pinpoint chemical species on the surface and the atmosphere of Mars moreaccurately than before. The subject of this paper is to present a method to extract informations on these chemicals from hyperspectral images. A first approach, based on Independent Component Analysis (ICA) [1], is able to extract artifacts and locations of CO2 and H2O ices. However, the main independence assumption and some basic properties (like the positivity of images and spectra) being unverified, the reliability of all the independent components (ICs) is weak. For improving the component extraction and consequently the endmember classification, a combination of spatial ICA with spectral Bayesian Positive Source Separation (BPSS) [2] is proposed. To reduce the computational burden, the basic idea is to use spatial ICA yielding a rough classification of pixels, which allows selection of small, but relevant, number of pixels and then BPSS is applied for the estimation of the source spectra using the spectral mixtures provided by this reduced set of pixels. Finally, the abundances of the components is assessed on the whole pixels of the images. Results of this approach are shown and evaluated by comparison with available reference spectra.

show abstract

“…For this reason, the ICA principles as well as the ICA algorithms will not be developed in this paper. For more details, we recommend the reader to refer to [1,[11][12][13].…”

Section: Hyperspectral Data Analysis By Icamentioning

confidence: 99%

On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation

Moussaoui¹,

et al. 2008

View full text Add to dashboard Cite

show abstract

“…These epochs are then eliminated from the datasets. A second approach is to use decomposing methods, also called blind-source separation methods, such as independent component analysis (ICA) (Comon, 1994;Hyvrinenand Oja, 2000) or second order blind identification (SOBI) (Belouchrani et al, 1997;Cardoso, 1998). These methods are less commonly employed.…”

Section: Introductionmentioning

confidence: 99%

Signal-to-noise ratio of the MEG signal after preprocessing

Gonzalez-Moreno

Aurtenetxe

Lopez-Garcia

et al. 2014

Journal of Neuroscience Methods

View full text Add to dashboard Cite

Signal-to-noise ratio of the MEG signal after preprocessing HIGHLIGHTSThe signal-to-noise ratio of event-related fields is used to evaluate the effectiveness of various preprocessing algorithms for magnetoencephalography data. Signal Space Separation algorithms provide approximately a 100% increase in signal to noise ratio. Epoch-based artifact rejection and decomposition methods such as independent component analysis yielded a signal to noise ratio increase of 5-10% and 35% respectively. The use of decomposition methods seems advisable. The evaluation of the signal-to-noise ratio increase can help to guide the choice of preprocessing methods. ABSTRACTBackground: Magnetoencephalography (MEG) provides a direct measure of brain activity with high combined spatiotemporal resolution. Preprocessing is necessary to reduce contributions from environmental interference and biological noise. New method: The effect on the signal-to-noise ratio of different preprocessing techniques is evaluated. The signal-to-noise ratio (SNR) was defined as the ratio between the mean signal amplitude (evoked field) and the standard error of the mean over trials. Results: Recordings from 26 subjects obtained during and event-related visual paradigm with an Elekta MEG scanner were employed. Two methods were considered as first-step noise reduction: Signal Space Separation and temporal Signal Space Separation, which decompose the signal into components with origin inside and outside the head. Both algorithm increased the SNR by approximately 100%. Epoch-based methods, aimed at identifying and rejecting epochs containing eye blinks, muscular artifacts and sensor jumps provided an SNR improvement of 5-10%. Decomposition methods evaluated were independent component analysis (ICA) and second-order blind identification (SOBI). The increase in SNR was of about 36% with ICA and 33% with SOBI. Comparison with existing methods:No previous systematic evaluation of the effect of the typical preprocessing steps in the SNR of the MEG signal has been performed. Conclusions: The application of either SSS or tSSS is mandatory in Elekta systems. No significant differences were found between the two. While epoch-based methods have been routinely applied the less often considered decomposition methods were clearly superior and therefore their use seems advisable.

show abstract

“…To extract multiple components, we parallelise ANNICA by Algorithm 2.2 in [6] (refferred here as PANNICA). PANLICA is compared with four existing linear ICA algorithms, FastICA [3], JADE [11], PCFICA [5] and RADICAL [12]. The separation performance/quality is measured by the Amari error [13], i.e., …”

Section: Numerical Experimentsmentioning

confidence: 99%

Newton-Like Methods for Nonparametric Independent Component Analysis

Shen¹,

Hüper²,

Smola

2006

Neural Information Processing

View full text Add to dashboard Cite

Abstract. The performance of ICA algorithms significantly depends on the choice of the contrast function and the optimisation algorithm used in obtaining the demixing matrix. In this paper we focus on the standard linear nonparametric ICA problem from an optimisation point of view. It is well known that after a pre-whitening process, the problem can be solved via an optimisation approach on a suitable manifold. We propose an approximate Newton's method on the unit sphere to solve the one-unit linear nonparametric ICA problem. The local convergence properties are discussed. The performance of the proposed algorithms is investigated by numerical experiments.

show abstract

Blind signal separation: statistical principles

Cited by 1,532 publications

References 50 publications

On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation

On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation

Signal-to-noise ratio of the MEG signal after preprocessing

Newton-Like Methods for Nonparametric Independent Component Analysis

Contact Info

Product

Resources

About