Second-order Blind Identification Research Articles

Comparison of Blind Source Separation Methods in Fast Somatosensory-Evoked Potential Detection

Blind source separation (BSS) is a promising method for extracting somatosensory-evoked potential (SEP). Although various BSS algorithms are available for SEP extraction, few studies have addressed the performance differences between them. In this study, we compared the performance of a number of typical BSS algorithms on SEP extraction from both computer simulations and clinical experiment. The algorithms we compared included second-order blind identification, estimation of signal parameters via rotation invariance technique, algorithm for multiple unknown signals extraction, joint approximate diagonalization of eigenmatrices, extended infomax, and fast independent component analysis. The performances of these BSS algorithms were determined by the correlation coefficients between the true and the extracted SEP signals. There were significant differences in the performances of the various BSS algorithms in a simulation study. In summary, second-order blind identification using six covariance matrix denoting SOBI6 was recommended as the most appropriate BSS method for fast SEP extraction from noisy backgrounds.

Considerations for Application of SOBI to Order Tracking

Current methods of order tracking, such as synchronous resampling, Gabor filtering, and Vold-Kalman filtering have undesirable traits. Each method has two or more of the following deficiencies: requires measurement or estimate of rotational speed over time, failure to isolate the contribution of crossing orders in the vicinity of the crossing time, large computational expense, end effects. In this work a new approach to the order tracking problem is taken. The Second Order Blind Identification (SOBI) algorithm is applied to synthesized data. The technique is shown to be very successful at isolating crossing orders and circumvents all of the above deficiencies. The method has its own restric-tions: multiple sensors are required and sensors must be mounted on a structure that responds quasi-statically to exci-tation of the rotational system.

Covariance and Time-Scale Methods for Blind Separation of Delayed Sources

An extension of the blind source separation technique based on the second-order blind identification (SOBI) approach is presented to separate mixtures of delayed sources. When the delay is small such that the first-order Taylor approximation holds, the delayed mixture is transformed as the mixture of the original sources and their derivatives. Two algorithms are proposed for the rotation step that recovers the extended source vector (original sources and the corresponding derivatives). The first approach is based on the odd symmetry of the derivative of the autocorrelation function; and the second method identifies the locations of single auto terms in the optimized time-scale plane. A simulation analysis was conducted to evaluate the performance of the proposed algorithms. The results showed that the proposed methods substantially improved the performance of SOBI and its extension in the time-scale plane when the sources presented delays in the mixtures. In addition, the proposed algorithms were applied representatively to experimental multichannel surface electromyographic signals to identify motor unit action potential trains from the interference signal. The performance of the proposed methods was superior to previous methods also in this representative application. In conclusion, extensions of the SOBI approach of source separation have been proposed for the case of sources being delayed in the mixtures. These techniques were proven superior to previous approaches.

A New Blind Source Separation Algorithm in Application of Over-Determined Mode under Lower SNR

The paper proposes a new BSS algorithm based on the second-order statistics. By jointly diagonalizing the time delay correlation matrix of the observed signals and using the improved new non-orthogonal joint diagonalization (NOJD) method, a better solution is achieved. The proposed algorithm can successfully separate communication signals under SNR as low as 10dB and the over-determined mode regardless of the signals’ modulation methods. Signal to Interference Noise Ratio (SINR) is used to prove the superiority of the proposed algorithm over the classical Second-Order Blind Identification (SOBI).

EEG classification of imagined syllable rhythm using Hilbert spectrum methods

We conducted an experiment to determine whether the rhythm with which imagined syllables are produced may be decoded from EEG recordings. High density EEG data were recorded for seven subjects while they produced in imagination one of two syllables in one of three different rhythms. We used a modified second-order blind identification (SOBI) algorithm to remove artefact signals and reduce data dimensionality. The algorithm uses the consistent temporal structure along multi-trial EEG data to blindly decompose the original recordings. For the four primary SOBI components, joint temporal and spectral features were extracted from the Hilbert spectra (HS) obtained by a Hilbert–Huang transformation (HHT). The HS provide more accurate time-spectral representations of non-stationary data than do conventional techniques like short-time Fourier spectrograms and wavelet scalograms. Classification of the three rhythms yields promising results for inter-trial transfer, with performance for all subjects significantly greater than chance. For comparison, we tested classification performance of three averaging-based methods, using features in the temporal, spectral and time–frequency domains, respectively, and the results are inferior to those of the SOBI-HHT-based method. The results suggest that the rhythmic structure of imagined syllable production can be detected in non-invasive brain recordings and provide a step towards the development of an EEG-based system for communicating imagined speech.

Analysis of traffic-induced vibrations by blind source separation with application in building monitoring

The paper presents an approach that may enable the separation of the vibrations induced by underground traffic from the vibrations induced by other sources, based on Second Order Blind Identification (SOBI) algorithm. The signals recorded in different locations of an instrumented building are mixed signals from different internal and external vibration sources. The blind source separation algorithm will estimate the independent vibration sources together with their mixing model. This model can be used to determine the contribution of each source in different measurement points, to evaluate the effect of the vibration sources and their potential for building damage. The above approach has been tested in simulation and on a building subject to different traffic forms.

Gastroparesis disrupts normal gastric slow wave magnetic field patterns

Event Abstract Back to Event Gastroparesis disrupts normal gastric slow wave magnetic field patterns Alan Bradshaw1*, Chibuke Obioha1, Eric Chung1, Jon Erickson2 and Bill Richards3 1 Vanderbilt University, United States 2 Washington & Lee University, United States 3 University of South Alabama, United States The gastric slow wave (SW) originates along the greater curvature of the antrum with a frequency of about 3 cycles per minute (cpm) and propagates toward the pylorus. The electrogastrogram (EGG) is capable of measuring SW frequency, but recent studies have demonstrated that the magnetogastrogram (MGG) also reflects spatiotemporal properties of the SW. Gastroparesis causes slow gastric emptying, dyspepsia, nausea, early satiety, discomfort and intermittent vomiting. We compared MGG recordings from patients with diabetic gastroparesis (N=7) and normal controls (N=7) using the Tristan 637i SQUID gradiometer. We processed MGG signals with Second Order Blind Identification (SOBI) to identify the SW. We then reconstructed the MGG signal and compiled false-color spatiotemporal (ST) maps. By tracking the location of the maximum in these maps, we tracked the propagation of the SW and compared the patterns in maps from controls and patients. SW frequencies were similar for both patients (2.8±0.5 cpm) and controls (2.7±0.1 cpm), but standard deviations of the dominant frequency were substantially larger in patients. The percentage of power distributed (PPD) in normogastric (2-4 cpm) and tachygastric (4-9 cpm) ranges were significantly different between controls and patients, and a borderline significant difference was observed for bradygastric frequencies (1-2 cpm). Propagation patterns in ST maps showed dramatic differences between gastroparesis patients and normal controls, and several types of abnormal patterns were observed in patients, including rotational, static and retrograde propagation patterns in contrast to the anterograde patterns noted in controls. Propagation velocity was significantly different (8.3±0.6 mm/s anterograde in controls; -2.8±2.8 mm/s retrograde in patients) with substantially more variation noted in patients. We conclude that abnormal SW patterns in the MGGs of gastroparetic patients may indicate pathology better than frequency dynamics. Conference: Biomag 2010 - 17th International Conference on Biomagnetism , Dubrovnik, Croatia, 28 Mar - 1 Apr, 2010. Presentation Type: Poster Presentation Topic: Nanoparticles, Biosusceptometry and Effects of Magnetic Fields Citation: Bradshaw A, Obioha C, Chung E, Erickson J and Richards B (2010). Gastroparesis disrupts normal gastric slow wave magnetic field patterns. Front. Neurosci. Conference Abstract: Biomag 2010 - 17th International Conference on Biomagnetism . doi: 10.3389/conf.fnins.2010.06.00399 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 08 Apr 2010; Published Online: 08 Apr 2010. * Correspondence: Alan Bradshaw, Vanderbilt University, Nashville, United States, alan.bradshaw@vanderbilt.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Alan Bradshaw Chibuke Obioha Eric Chung Jon Erickson Bill Richards Google Alan Bradshaw Chibuke Obioha Eric Chung Jon Erickson Bill Richards Google Scholar Alan Bradshaw Chibuke Obioha Eric Chung Jon Erickson Bill Richards PubMed Alan Bradshaw Chibuke Obioha Eric Chung Jon Erickson Bill Richards Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Modified Cross-Correlation Method for the Blind Identification of Structures

Recently, blind source separation (BSS) methods have gained significant attention in the area of signal processing. Independent component analysis (ICA) and second-order blind identification (SOBI) are two popular BSS methods that have been applied to modal identification of mechanical and structural systems. Published results by several researchers have shown that ICA performs satisfactorily for systems with very low levels of structural damping, for example, for damping ratios of the order of 1% critical. For practical structural applications with higher levels of damping, methods based on SOBI have shown significant improvement over ICA methods. However, traditional SOBI methods suffer when nonstationary sources are present, such as those that occur during earthquakes and other transient excitations. In this paper, a new technique based on SOBI, called the modified cross-correlation method, is proposed to address these shortcomings. The conditions in which the problem of structural system identification can be posed as a BSS problem is also discussed. The results of simulation described in terms of identified natural frequencies, mode shapes, and damping ratios are presented for the cases of synthetic wind and recorded earthquake excitations. The results of identification show that the proposed method achieves better performance over traditional ICA and SOBI methods. Both experimental and large-scale structural simulation results are included to demonstrate the applicability of the newly proposed method to structural identification problems.

Wavelet-based blind identification of the UCLA Factor building using ambient andearthquake responses

Blind source separation using second-order blind identification (SOBI) has beensuccessfully applied to the problem of output-only identification, popularly known asambient system identification. In this paper, the basic principles of SOBI for the staticmixtures case is extended using the stationary wavelet transform (SWT) in order toimprove the separability of sources, thereby improving the quality of identification.Whereas SOBI operates on the covariance matrices constructed directly frommeasurements, the method presented in this paper, known as the wavelet-based modifiedcross-correlation method, operates on multiple covariance matrices constructed from thecorrelation of the responses. The SWT is selected because of its time-invariance property,which means that the transform of a time-shifted signal can be obtained as a shiftedversion of the transform of the original signal. This important property is exploited in theconstruction of several time-lagged covariance matrices. The issue of non-stationarysources is addressed through the formation of several time-shifted, windowedcovariance matrices. Modal identification results are presented for the UCLAFactor building using ambient vibration data and for recorded responses fromthe Parkfield earthquake, and compared with published results for this building.Additionally, the effect of sensor density on the identification results is also investigated.

Blind Separation of Noisy Multivariate Data Using Second-Order Statistics: Remote-Sensing Applications

In this paper a second-order method for blind source separation of noisy instantaneous linear mixtures is presented for the case where the signal order <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> is unknown. Its performance advantages are illustrated by simulations and by application to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) multichannel visible/infrared data. The model assumes that <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> mixtures <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</b> of dimension <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> are observed, where <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</b> = <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ap</b> + <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gw</b> , and the underlying signal vector <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</b> has k < n/3 independent unit-variance elements. <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> is the mixing matrix, <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> is diagonal, and <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</b> is a normalized white-noise vector. The algorithm estimates the Second-Order separation matrix <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> , signal Order <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> , and Noise and is therefore designated as SOON. SOON first iteratively estimates <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> and <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> using a scree metric, singular-value decomposition, and the expectation-maximization algorithm, and then determines the values of <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AP</b> and <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</b> . The final step estimates <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> and the set of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> signal vectors <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</b> using a variant of the joint-diagonalization method used in the Second-Order Blind Identification (SOBI) and Second-Order NonStationary (SONS) source-separation algorithms. The SOON extension of SOBI and SONS significantly improves their separation of simulated sources hidden in noise. SOON also reveals interesting thermal dynamics within AVIRIS multichannel visible/infrared imaging data not found by noise-adjusted principal-component analysis.

Inverse Modeling on Decomposed Electroencephalographic Data: A Way Forward?

Inverse modeling is typically applied to instantaneous electroencephalogram signals. However, this approach has several shortcomings including its instability to model multiple and deep located dipole sources and the interference of background noise may hamper the sensitivity, stability, and precision of the estimated dipoles. This article validates different dipole estimation techniques to find the most optimal combination of different analysis principles using both simulations and recordings. Electroencephalogram data were simulated with six known source locations. First, a dataset was simulated with sources chosen to mimic somatosensory-evoked potentials to electrical stimuli. Additionally, 20 further datasets were simulated each containing six randomly located and oriented sources. The simulated sources included superficial, deep, and simultaneously active sources. Furthermore, somatosensory-evoked potentials to median nerve stimuli were recorded from one subject. On both simulated and recorded evoked potential data, three different methods of signal decomposition were compared: independent component analysis (ICA), second-order blind identification (SOBI), and multichannel matching pursuit (MMP). For inverse modeling of the brain sources, the DIPFIT function of the EEGLAB software was used on raw and decomposed data. MMP was able to separate all simulated components that corresponded to superficial, deep, and simultaneously active sources. ICA and SOBI were only able to find components that corresponded to superficial sources. For the 20 randomized simulations, the results from the evoked potential simulation were reproduced. Inverse modeling on MMP components (atoms) was better than on ICA or SOBI components (P < 0.001). DIPFIT on MMP atoms localized 99.2% of the simulated dipoles in correct areas with their correct time/frequency distribution. DIPFIT on ICA and SOBI components localized 35% and 39.6%, respectively of the simulated dipoles in correct areas. As for the real-evoked potentials recorded in one subject, DIPFIT on MMP atoms allowed us to build a dipole model closer to the current physiological knowledge than dipole modeling of ICA and SOBI components. The results show that using MMP before inverse modeling is a reliable way to noninvasively estimate cortical activation.

Detection of Small Bowel Slow-Wave Frequencies From Noninvasive Biomagnetic Measurements

We report a novel method for identifying the small intestine electrical activity slow-wave frequencies (SWFs) from noninvasive biomagnetic measurements. Superconducting quantum interference device magnetometer measurements are preprocessed to remove baseline drift and high-frequency noise. Subsequently, the underlying source signals are separated using the well-known second-order blind identification (SOBI) algorithm. A simple classification scheme identifies and assigns some of the SOBI components to a section of small bowel. SWFs were clearly identified in 10 out of 12 test subjects to within 0.09-0.25 cycles per minute. The method is sensitive at the 40.3 %-55.9 % level, while false positive rates were 0 %-8.6 %. This technique could potentially be used to help diagnose gastrointestinal ailments and obviate some exploratory surgeries.

ENSO signals and their spatial-temporal variation characteristics recorded by the sea-level changes in the northwest Pacific margin during 1965–2005

Sea-level is closely linked to the Earth’s climate and its change is important as a metric for global and regional climate change. Identifying, extracting, and revealing such information through detailed analysis is the prerequisite for understanding the mechanisms of sea-level change. The monthly- average tide-change records reported and examined in this paper are from 10 tide gauge stations distributed in the northwest margin of the Pacific Ocean, registered during the period 1965-2005. In particular, we have utilized the Second Order Blind Identification (SOBI) method to identify and the Empirical Mode Decomposition (EMD) method to extract the El Nino-Southern Oscillation (ENSO) signals imprinted in those tide gauge records; and subsequently, we have investigated the spatial-temporal characteristics of the extracted ENSO signals using wavelet analysis methods. The following results are obtained: (1) the ENSO events recorded by each tide gauge series are of different types and intensity, which show considerable temporal-spatial variation characteristics, with sea-level responses to ENSO signals remarkably stronger in low latitude areas than in medium-high latitude areas; (2) due to the influences of ocean currents, topographical conditions, and other factors, there exist variations in the type of relationship between the sea-level changes and the recorded ENSO events at different latitudes; (3) sea-level changes can also denote scale-variation characteristics of ENSO events, and particularly, since 1980s, all the tide gauges located south of Kanmen show intense responses to ENSO and the timescale of ENSO events extended gradually from around 4 years to 2–8 years, reflecting variations in the intensity and frequency of ENSO events; and (4) reverse changes of certain scale were noted before and after strong ENSO events recorded by sea-level changes, supporting the research findings about the interaction (mutual coupling and superimposition) between the probability of occurrence of strong ENSO events and their time scale.

Enhanced ${\mu }$ Rhythm Extraction Using Blind Source Separation and Wavelet Transform

The mu rhythm is an electroencephalogram (EEG) signal located at the central region of the brain that is frequently used for studies concerning motor activity. Quite often, the EEG data are contaminated with artifacts and the application of blind source separation (BSS) alone is insufficient to extract the mu rhythm component. We present a new two-stage approach to extract the mu rhythm component. The first stage uses second-order blind identification (SOBI) with stationary wavelet transform (SWT) to automatically remove the artifacts. In the second stage, SOBI is applied again to find the mu rhythm component. Our method is first compared with independent component analysis with discrete wavelet transform (ICA-DWT) as well as SOBI-DWT, ICA-SWT, and regression method for artifact removal using simulated EEG data. The results showed that the regression method is more effective in removing electrooculogram (EOG) artifacts, while SOBI-SWT is more effective in removing electromyogram (EMG) artifacts as compared to the other artifact removal methods. Then, all the methods are compared with the direct application of SOBI in extracting mu rhythm components on simulated and actual EEG data from ten subjects. The results showed that the proposed method of SOBI-SWT artifact removal enhances the extraction of the mu rhythm component.

Seizure source localization using a hybrid second order blind identification and extended rival penalized competitive learning algorithm

Localization of seizure sources prior to neurosurgery is crucial. In this paper, a new method is proposed to localize the seizure sources from multi-channel electroencephalogram (EEG) signals. Blind source separation based on second order blind identification (SOBI) is primarily applied to estimate the brain source signals in each window of the EEG signals. A new clustering method based on rival penalized competitive learning (RPCL) is then developed to cluster the rows of the estimated unmixing matrices in all the windows. The algorithm also includes pre and post-processing stages. By multiplying each cluster center to the EEG signals, the brain signal sources are approximated. According to a complexity value measure, the main seizure source signal is separated from the others. This signal is projected back to the electrodes’ space and is subjected to the dipole source localization using a single dipole model. The simulation results verify the accuracy of the system. In addition, correct localization of the seizure source is consistent with the clinical tests derived using the simultaneous intracranial recordings.

A Hybrid Technique for Blind Separation of Non-Gaussian and Time-Correlated Sources Using a Multicomponent Approach

Blind inversion of a linear and instantaneous mixture of source signals is a problem often encountered in many signal processing applications. Efficient fastICA (EFICA) offers an asymptotically optimal solution to this problem when all of the sources obey a generalized Gaussian distribution, at most one of them is Gaussian, and each is independent and identically distributed (i.i.d.) in time. Likewise, weights-adjusted second-order blind identification (WASOBI) is asymptotically optimal when all the sources are Gaussian and can be modeled as autoregressive (AR) processes with distinct spectra. Nevertheless, real-life mixtures are likely to contain both Gaussian AR and non-Gaussian i.i.d. sources, rendering WASOBI and EFICA severely suboptimal. In this paper, we propose a novel scheme for combining the strengths of EFICA and WASOBI in order to deal with such hybrid mixtures. Simulations show that our approach outperforms competing algorithms designed for separating similar mixtures.

A framework for blind modal identification using joint approximate diagonalization

In this paper, a second-order statistical method employed in blind source separation (BSS) is adapted for use in modal parameter identification. Modal responses and mode shapes are estimated by the use of second-order blind identification (SOBI) on an expanded and pre-treated dataset. Frequency and damping can be obtained from the modal responses by simple single degree of freedom methods. Using this approach, a class of new non-parametric output-only modal identification algorithms is proposed and examples of its use are provided. It is demonstrated that the proposed methodology provides a novel and robust approach to modal identification. For the example shown, it is deduced that quality of the modal parameters produced by the method is competitive with the state of the art parametric methods.

Second-order criterion for blind source extraction

A second-order criterion for blind signal extraction in instantaneous linear mixtures has recently been proposed. It is proved that, with an adequate choice of autocorrelation time lags, the criterion leads indeed to a successful source extraction in the noiseless case. Using this criterion, the source identifiability conditions turn out to be the same as in the popular second-order blind identification method for blind source separation.

Using an Eye Tracker for Accurate Eye Movement Artifact Correction

We present a new method to correct eye movement artifacts in electroencephalogram (EEG) data. By using an eye tracker, whose data cannot be corrupted by any electrophysiological signals, an accurate method for correction is developed. The eye-tracker data is used in a Kalman filter to estimate which part of the EEG is of ocular origin. The main assumptions for optimal correction are summed and their validity is proven. The eye-tracker-based correction method is objectively evaluated on simulated data of four different types of eye movements and visually evaluated on experimental data. Results are compared to three established correction methods: Regression, Principal Component Analysis, and Second-Order Blind Identification. A comparison of signal to noise ratio after correction by these methods is given in Table II and shows that our method is consistently superior to the other three methods, often by a large margin. The use of a reference signal without electrophysiological influences, as provided by an eye tracker, is essential to achieve optimal eye movement artifact removal.

Blind source separation based vibration mode identification

In this paper, a novel method for linear normal mode (LNM) identification based on blind source separation (BSS) is introduced. Modal coordinates are considered as a specific case of sources that have certain time structure. This structure makes modal coordinates identifiable by many BSS algorithms. However, algorithms based on second-order statistics are particularly suited for extracting LNMs of a vibration system. Two well-known BSS algorithms are considered. First, algorithm for multiple unknown signals extraction (AMUSE) is used to illustrate the similarity with Ibrahim time domain (ITD) modal identification method. Second, second order blind identification (SOBI) is used to demonstrate noise robustness of BSS-based mode shape extraction. Numerical simulations and experimental results from these BSS algorithms and ITD method are presented.