Relevant bibliographies by topics / Blind Source Separation (BSS)

Academic literature on the topic 'Blind Source Separation (BSS)'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Blind Source Separation (BSS)"

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Zhang, Chao Zhu, Ahmed Kareem Abdullah, and Ali Abdullabs Abdullah. "Electroencephalogram-Artifact Extraction Enhancement Based on Artificial Intelligence Technique." Journal of Biomimetics, Biomaterials and Biomedical Engineering 27 (May 2016): 77–91. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.27.77.

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Blind source separation (BSS) is an important technique used to recover isolated independent sources signals from mixtures. This paper proposes two blind artificial intelligent separation algorithms based on hybridization between artificial intelligent techniques with classical blind source separation algorithms to enhance the separation process. Speedy genetic algorithm SGA directly guesses the optimal coefficients of the separating matrix based on candidate initial from classical BSS algorithms also the separation criteria based on minimization of mutual information between the separating independent components. The proposed algorithms are tested by real Electroencephalogram (EEG) data, the experimental results indicate that the algorithms can quickly and effectively get optimum solution to linear blind source separation compared to classical BSS techniques, the proposed works are described by high accuracy and robustness.
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Harmeling, Stefan, Andreas Ziehe, Motoaki Kawanabe, and Klaus-Robert Müller. "Kernel-Based Nonlinear Blind Source Separation." Neural Computation 15, no. 5 (May 1, 2003): 1089–124. http://dx.doi.org/10.1162/089976603765202677.

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We propose kTDSEP, a kernel-based algorithm for nonlinear blind source separation (BSS). It combines complementary research fields: kernel feature spaces and BSS using temporal information. This yields an efficient algorithm for nonlinear BSS with invertible nonlinearity. Key assumptions are that the kernel feature space is chosen rich enough to approximate the nonlinearity and that signals of interest contain temporal information. Both assumptions are fulfilled for a wide set of real-world applications. The algorithm works as follows: First, the data are (implicitly) mapped to a high (possibly infinite)—dimensional kernel feature space. In practice, however, the data form a smaller submanifold in feature space—even smaller than the number of training data points—a fact that has already been used by, for example, reduced set techniques for support vector machines. We propose to adapt to this effective dimension as a preprocessing step and to construct an orthonormal basis of this submanifold. The latter dimension-reduction step is essential for making the subsequent application of BSS methods computationally and numerically tractable. In the reduced space, we use a BSS algorithm that is based on second-order temporal decorrelation. Finally, we propose a selection procedure to obtain the original sources from the extracted nonlinear components automatically. Experiments demonstrate the excellent performance and efficiency of our kTDSEP algorithm for several problems of nonlinear BSS and for more than two sources.
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Ye, Ji-Min, Xiao-Long Zhu, and Xian-Da Zhang. "Adaptive Blind Separation with an Unknown Number of Sources." Neural Computation 16, no. 8 (August 1, 2004): 1641–60. http://dx.doi.org/10.1162/089976604774201622.

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The blind source separation (BSS) problem with an unknown number of sources is an important practical issue that is usually skipped by assuming that the source number n is known and equal to the number m of sensors. This letter studies the general BSS problem satisfying m ≥ n. First, it is shown that the mutual information of outputs of the separation network is a cost function for BSS, provided that the mixing matrix is of full column rank and the m×m separating matrix is nonsingular. The mutual information reaches its local minima at the separation points, where the m outputs consist of n desired source signals and m−n redundant signals. Second, it is proved that the natural gradient algorithm proposed primarily for complete BSS (m n) can be generalized to deal with the overdetermined BSS problem (m>n), but it would diverge inevitably due to lack of a stationary point. To overcome this shortcoming, we present a modified algorithm, which can perform BSS steadily and provide the desired source signals at specified channels if some matrix is designed properly. Finally, the validity of the proposed algorithm is confirmed by computer simulations on artificially synthesized data.
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Zi, Jiali, Danju Lv, Jiang Liu, Xin Huang, Wang Yao, Mingyuan Gao, Rui Xi, and Yan Zhang. "Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation." Sensors 22, no. 1 (December 24, 2021): 118. http://dx.doi.org/10.3390/s22010118.

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In recent years, separating effective target signals from mixed signals has become a hot and challenging topic in signal research. The SI-BSS (Blind source separation (BSS) based on swarm intelligence (SI) algorithm) has become an effective method for the linear mixture BSS. However, the SI-BSS has the problem of incomplete separation, as not all the signal sources can be separated. An improved algorithm for BSS with SI based on signal cross-correlation (SI-XBSS) is proposed in this paper. Our method created a candidate separation pool that contains more separated signals than the traditional SI-BSS does; it identified the final separated signals by the value of the minimum cross-correlation in the pool. Compared with the traditional SI-BSS, the SI-XBSS was applied in six SI algorithms (Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Sine Cosine Algorithm (SCA), Butterfly Optimization Algorithm (BOA), and Crow Search Algorithm (CSA)). The results showed that the SI-XBSS could effectively achieve a higher separation success rate, which was over 35% higher than traditional SI-BSS on average. Moreover, SI-SDR increased by 14.72 on average.
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Yin, Hong Wei, Guo Lin Li, and Cui Hua Lu. "Step Adaptive Normalization Blind Source Separation Algorithm." Advanced Materials Research 1049-1050 (October 2014): 1407–12. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1407.

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An algorithm of step adaptive normalization BSS(SAN-BSS) is proposed to solve the problem that the traditional switching BSS algorithms are sensitive to the types and the number of the source signals. The proposed algorithm improves the original ones’ stability by making use of the normalization mechanism to modify the cost functions, and realizes the adaptive updating of the step size by combining the signals’ separation process with the summation of the edge negentropy. The simulation results show that when the number of the source signals improves or the types of the signals change, the proposed algorithm can keep good separation effect. Compared with the original ones, the separation accuracy of our proposed algorithm improved 98%, and the number of iterations reduced nearly 60%, which improved the stability and the separation speed of the algorithm greatly.
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Li, Ning, Hai Ting Chen, and Shao Peng Liu. "Rotating Machine Monitoring Based on Blind Source Separation of Correlated Source Signals." Applied Mechanics and Materials 321-324 (June 2013): 1299–302. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1299.

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Blind source separation (BSS) which separate the unknown sources from the observed signals is a new signal processing technique. The most methods for solving this problem rely on assumptions of independence or uncorrelation of source signals at least. However, the observed signal is always interfered by signals with common frequency in the rotating machine, and difficult to be separated by the conventional BSS method. In this paper, it is proved that the source signals with common frequencies are correlative, and the separating error brought by the cross-correlation of the source signals is analyzed. A new separating method for the correlated source signals with frequency overlapping is presented and it is successfully applied to separate the monitoring signals of rotor test stand.
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Gao, Tao, and Jincan Li. "The Research and Simulation of Blind Source Separation Algorithm." International Journal of Advanced Pervasive and Ubiquitous Computing 8, no. 3 (July 2016): 1–36. http://dx.doi.org/10.4018/ijapuc.2016070101.

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When the original source signals and input channel are unknown, blind source separation (BSS) tries decomposing the mixed signals observed to obtain the original source signals, as seems mysterious. BSS has found many applications in biomedicine science, image processing, wireless communication and speech enhancement. In this paper the basic theory of blind source separation is described, which consists of the mathematical model, knowledge, performance evaluation index, and so on. And a further research on blind source separation algorithm has done when the number of source signals is more than (equal) the number of the signals observed, including the traditional ways of BSS—fast independent component analysis (FastICA) algorithm and equivariant adaptive separation via independence (EASI) algorithm, as well as the SOBI algorithm which is based on the joint diagonalization of matrices.
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Qian, Si Chong, and Yang Xiang. "The Relationship between Frequency Domain Blind Source Separation and Frequency Domain Adaptive Beamformer." Applied Mechanics and Materials 490-491 (January 2014): 654–62. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.654.

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As two important methods of array signal processing, blind source separation and beamforming can extract the target signal and suppress interference by using the received information of the array element. In the case of convolution mixture of sources, frequency domain blind source separation and frequency domain adaptive beamforming have similar signal model. To find the relationship between them, comparison between the minimization of the off-diagonal components in the BSS update equation and the minimization of the mean square error in the ABF had been made from the perspective of mathematical expressions, and find that the unmixing matrix of the BSS and the filter coefficients of the ABF converge to the same solution in the mean square error sense under the condition that the two source signals are ideally independent. With MATLAB, the equivalence in the frequency domain have been verified and the causes affecting separation performance have been analyzed, which was achieved by simulating instantaneous and convolution mixtures and separating mixture speech in frequency-domain blind source separation and frequency domain adaptive beamforming way.
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Theis, Fabian J. "A New Concept for Separability Problems in Blind Source Separation." Neural Computation 16, no. 9 (September 1, 2004): 1827–50. http://dx.doi.org/10.1162/0899766041336404.

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The goal of blind source separation (BSS) lies in recovering the original independent sources of a mixed random vector without knowing the mixing structure. A key ingredient for performing BSS successfully is to know the indeterminacies of the problem—that is, to know how the separating model relates to the original mixing model (separability). For linear BSS, Comon (1994) showed using the Darmois-Skitovitch theorem that the linear mixing matrix can be found except for permutation and scaling. In this work, a much simpler, direct proof for linear separability is given. The idea is based on the fact that a random vector is independent if and only if the Hessian of its logarithmic density (resp. characteristic function) is diagonal everywhere. This property is then exploited to propose a new algorithm for performing BSS. Furthermore, first ideas of how to generalize separability results based on Hessian diagonalization to more complicated nonlinear models are studied in the setting of postnonlinear BSS.
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MÜLLER, KLAUS-ROBERT, RICARDO VIGÁRIO, FRANK MEINECKE, and ANDREAS ZIEHE. "BLIND SOURCE SEPARATION TECHNIQUES FOR DECOMPOSING EVENT-RELATED BRAIN SIGNALS." International Journal of Bifurcation and Chaos 14, no. 02 (February 2004): 773–91. http://dx.doi.org/10.1142/s0218127404009466.

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Recently blind source separation (BSS) methods have been highly successful when applied to biomedical data. This paper reviews the concept of BSS and demonstrates its usefulness in the context of event-related MEG measurements. In a first experiment we apply BSS to artifact identification of raw MEG data and discuss how the quality of the resulting independent component projections can be evaluated. The second part of our study considers averaged data of event-related magnetic fields. Here, it is particularly important to monitor and thus avoid possible overfitting due to limited sample size. A stability assessment of the BSS decomposition allows to solve this task and an additional grouping of the BSS components reveals interesting structure, that could ultimately be used for gaining a better physiological modeling of the data.
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Dissertations / Theses on the topic "Blind Source Separation (BSS)"

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Vikram, Anil Babu. "Tracking in wireless sensor network using blind source separation algorithms." Cleveland, Ohio : Cleveland State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=csu1259959597.

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Thesis (M.S.)--Cleveland State University, 2009.
Abstract. Title from PDF t.p. (viewed on Dec. 2, 2009). Includes bibliographical references (p. 65-72). Available online via the OhioLINK ETD Center and also available in print.
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Ziehe, Andreas. "Blind source separation based on joint diagonalization of matrices with applications in biomedical signal processing." Phd thesis, [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=976710331.

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Marin, Jorge I. "Robust binaural noise-reduction strategies with binaural-hearing-aid constraints: design, analysis and practical considerations." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44747.

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The objective of the dissertation research is to investigate noise reduction methods for binaural hearing aids based on array and statistical signal processing and inspired by a human auditory model. In digital hearing aids, wide dynamic range compression (WDRC) is the most successful technique to deal with monaural hearing losses. This WDRC processing is usually performed after a monaural noise reduction algorithm. When hearing losses are present in both ears, i.e., a binaural hearing loss, independent monaural hearing aids have been shown not to be comfortable for most users, preferring a processing that involves synchronization between both hearing devices. In addition, psycho-acoustical studies have identified that under hostile environments, e.g., babble noise at very low SNR conditions, users prefer to use linear amplification rather than WDRC. In this sense, the noise reduction algorithm becomes an important component of a digital hearing aid to provide improvement in speech intelligibility and user comfort. Including a wireless link between both hearing aids offers new ways to implement more efficient methods to reduce the background noise and coordinate processing for the two ears. This approach, called binaural hearing aid, has been recently introduced in some commercial products but using very simple processing strategies. This research analyzes the existing binaural noise-reduction techniques, proposes novel perceptually-inspired methods based on blind source separation (BSS) and multichannel Wiener filter (MWF), and identifies different strategies for the real-time implementation of these methods. The proposed methods perform efficient spatial filtering, improve SNR and speech intelligibility, minimize block processing artifacts, and can be implemented in low-power architectures.
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Naik, Ganesh Ramachandra, and ganesh naik@rmit edu au. "Iterative issues of ICA, quality of separation and number of sources: a study for biosignal applications." RMIT University. Electrical and Computer Engineering, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090320.115103.

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This thesis has evaluated the use of Independent Component Analysis (ICA) on Surface Electromyography (sEMG), focusing on the biosignal applications. This research has identified and addressed the following four issues related to the use of ICA for biosignals: • The iterative nature of ICA • The order and magnitude ambiguity problems of ICA • Estimation of number of sources based on dependency and independency nature of the signals • Source separation for non-quadratic ICA (undercomplete and overcomplete) This research first establishes the applicability of ICA for sEMG and also identifies the shortcomings related to order and magnitude ambiguity. It has then developed, a mitigation strategy for these issues by using a single unmixing matrix and neural network weight matrix corresponding to the specific user. The research reports experimental verification of the technique and also the investigation of the impact of inter-subject and inter-experimental variations. The results demonstrate that while using sEMG without separation gives only 60% accuracy, and sEMG separated using traditional ICA gives an accuracy of 65%, this approach gives an accuracy of 99% for the same experimental data. Besides the marked improvement in accuracy, the other advantages of such a system are that it is suitable for real time operations and is easy to train by a lay user. The second part of this thesis reports research conducted to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The work proposes the use of value of the determinant of the Global matrix generated using sparse sub band ICA for identifying the number of active sources. The results indicate that the technique is successful in identifying the number of active muscles for complex hand gestures. The results support the applications such as human computer interface. This thesis has also developed a method of determining the number of independent sources in a given mixture and has also demonstrated that using this information, it is possible to separate the signals in an undercomplete situation and reduce the redundancy in the data using standard ICA methods. The experimental verification has demonstrated that the quality of separation using this method is better than other techniques such as Principal Component Analysis (PCA) and selective PCA. This has number of applications such as audio separation and sensor networks.
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Laruelo, Fernandez Andrea. "Integration of magnetic resonance spectroscopic imaging into the radiotherapy treatment planning." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30126/document.

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L'objectif de cette thèse est de proposer de nouveaux algorithmes pour surmonter les limitations actuelles et de relever les défis ouverts dans le traitement de l'imagerie spectroscopique par résonance magnétique (ISRM). L'ISRM est une modalité non invasive capable de fournir la distribution spatiale des composés biochimiques (métabolites) utilisés comme biomarqueurs de la maladie. Les informations fournies par l'ISRM peuvent être utilisées pour le diagnostic, le traitement et le suivi de plusieurs maladies telles que le cancer ou des troubles neurologiques. Cette modalité se montre utile en routine clinique notamment lorsqu'il est possible d'en extraire des informations précises et fiables. Malgré les nombreuses publications sur le sujet, l'interprétation des données d'ISRM est toujours un problème difficile en raison de différents facteurs tels que le faible rapport signal sur bruit des signaux, le chevauchement des raies spectrales ou la présence de signaux de nuisance. Cette thèse aborde le problème de l'interprétation des données d'ISRM et la caractérisation de la rechute des patients souffrant de tumeurs cérébrales. Ces objectifs sont abordés à travers une approche méthodologique intégrant des connaissances a priori sur les données d'ISRM avec une régularisation spatio-spectrale. Concernant le cadre applicatif, cette thèse contribue à l'intégration de l'ISRM dans le workflow de traitement en radiothérapie dans le cadre du projet européen SUMMER (Software for the Use of Multi-Modality images in External Radiotherapy) financé par la Commission européenne (FP7-PEOPLE-ITN)
The aim of this thesis is to propose new algorithms to overcome the current limitations and to address the open challenges in the processing of magnetic resonance spectroscopic imaging (MRSI) data. MRSI is a non-invasive modality able to provide the spatial distribution of relevant biochemical compounds (metabolites) commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow-up of several diseases such as cancer or neurological disorders. Obtaining accurate and reliable information from in vivo MRSI signals is a crucial requirement for the clinical utility of this technique. Despite the numerous publications on the topic, the interpretation of MRSI data is still a challenging problem due to different factors such as the low signal-to-noise ratio (SNR) of the signals, the overlap of spectral lines or the presence of nuisance components. This thesis addresses the problem of interpreting MRSI data and characterizing recurrence in tumor brain patients. These objectives are addressed through a methodological approach based on novel processing methods that incorporate prior knowledge on the MRSI data using a spatio-spectral regularization. As an application, the thesis addresses the integration of MRSI into the radiotherapy treatment workflow within the context of the European project SUMMER (Software for the Use of Multi-Modality images in External Radiotherapy) founded by the European Commission (FP7-PEOPLE-ITN framework)
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Toumi, Ichrak. "Decomposition methods of NMR signal of complex mixtures : models ans applications." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4351/document.

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L'objectif de ce travail était de tester des méthodes de SAS pour la séparation des spectres complexes RMN de mélanges dans les plus simples des composés purs. Dans une première partie, les méthodes à savoir JADE et NNSC ont été appliqué es dans le cadre de la DOSY , une application aux données CPMG était démontrée. Dans une deuxième partie, on s'est concentré sur le développement d'un algorithme efficace "beta-SNMF" . Ceci s'est montré plus performant que NNSC pour beta inférieure ou égale à 2. Etant donné que dans la littérature, le choix de beta a été adapté aux hypothèses statistiques sur le bruit additif, une étude statistique du bruit RMN de la DOSY a été faite pour obtenir une image plus complète de nos données RMN étudiées
The objective of the work was to test BSS methods for the separation of the complex NMR spectra of mixtures into the simpler ones of the pure compounds. In a first part, known methods namely JADE and NNSC were applied in conjunction for DOSY , performing applications for CPMG were demonstrated. In a second part, we focused on developing an effective algorithm "beta- SNMF ". This was demonstrated to outperform NNSC for beta less or equal to 2. Since in the literature, the choice of beta has been adapted to the statistical assumptions on the additive noise, a statistical study of NMR DOSY noise was done to get a more complete picture about our studied NMR data
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Korczowski, Louis. "Méthodes pour l'électroencéphalographie multi-sujet et application aux interfaces cerveau-ordinateur." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAT078/document.

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L'étude par neuro-imagerie de l'activité de plusieurs cerveaux en interaction (hyperscanning) permet d'étendre notre compréhension des neurosciences sociales. Nous proposons un cadre pour l'hyperscanning utilisant les interfaces cerveau-ordinateur multi-utilisateur qui inclut différents paradigmes sociaux tels que la coopération ou la compétition. Les travaux de cette thèse comportent trois contributions interdépendantes. Notre première contribution est le développement d'une plateforme expérimentale sous la forme d'un jeu vidéo multijoueur, nommé Brain Invaders 2, contrôlé par la classification de potentiels évoqués visuels enregistrés par électroencéphalographie (EEG). Cette plateforme est validée par deux protocoles expérimentaux comprenant dix-neuf et vingt-deux paires de sujets et utilise différentes approches de classification adaptative par géométrie riemannienne. Ces approches sont théoriquement et expérimentalement comparées et nous montrons la supériorité de la fusion des classifieurs indépendants sur la classification d'un hypercerveau durant la seconde contribution. L'analyse de coïncidence des signaux entre les individus est une approche classique pour l'hyperscanning, elle est pourtant difficile quand les signaux EEG concernés sont transitoires avec une grande variabilité (intra- et inter-sujet) spatio-temporelle et avec un faible rapport signal-à-bruit. En troisième contribution, nous proposons un nouveau modèle composite de séparation aveugle de sources physiologiquement plausibles permettant de compenser cette variabilité. Une solution par diagonalisation conjointe approchée est proposée avec une implémentation d'un algorithme de type Jacobi. A partir des données de Brain Invaders 2, nous montrons que cette solution permet d'extraire simultanément des sources d'artéfacts, des sources d'EEG évoquées et des sources d'EEG continues avec plus de robustesse et de précision que les modèles existants
The study of several brains interacting (hyperscanning) with neuroimagery allows to extend our understanding of social neurosciences. We propose a framework for hyperscanning using multi-user Brain-Computer Interfaces (BCI) that includes several social paradigms such as cooperation or competition. This dissertation includes three interdependent contribution. The first contribution is the development of an experimental platform consisting of a multi-player video game, namely Brain Invaders 2, controlled by classification of visual event related potentials (ERP) recorded by electroencephalography (EEG). The plateform is validated through two experimental protocols including nineteen and twenty two pairs of subjects while using different adaptive classification approaches using Riemannian geometry. Those approaches are theoretically and experimentally compared during the second contribution ; we demonstrates the superiority in term of accuracy of merging independent classifications over the classification of the hyperbrain during the second contribution. Analysis of inter-brain synchronizations is a common approach for hyperscanning, however it is challenging for transient EEG waves with an great spatio-temporal variability (intra- and inter-subject) and with low signal-to-noise ratio such as ERP. Therefore, as third contribution, we propose a new blind source separation model, namely composite model, to extract simultaneously evoked EEG sources and ongoing EEG sources that allows to compensate this variability. A solution using approximate joint diagonalization is given and implemented with a fast Jacobi-like algorithm. We demonstrate on Brain Invaders 2 data that our solution extracts simultaneously evoked and ongoing EEG sources and performs better in term of accuracy and robustness compared to the existing models
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Boulais, Axel. "Méthodes de séparation aveugle de sources et application à l'imagerie hyperspectrale en astrophysique." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30318/document.

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Ces travaux de thèse concernent le développement de nouvelles méthodes de séparation aveugle de mélanges linéaires instantanés pour des applications à des données hyperspectrales en astrophysique. Nous avons proposé trois approches pour effectuer la séparation des données. Une première contribution est fondée sur l'hybridation de deux méthodes existantes de séparation aveugle de source (SAS) : la méthode SpaceCORR nécessitant une hypothèse de parcimonie et une méthode de factorisation en matrices non négatives (NMF). Nous montrons que l'utilisation des résultats de SpaceCORR pour initialiser la NMF permet d'améliorer les performances des méthodes utilisées seules. Nous avons ensuite proposé une première méthode originale permettant de relâcher la contrainte de parcimonie de SpaceCORR. La méthode MASS (pour \textit{Maximum Angle Source Separation}) est une méthode géométrique basée sur l'extraction de pixels mono-sources pour réaliser la séparation des données. Nous avons également étudié l'hybridation de MASS avec la NMF. Enfin, nous avons proposé une seconde approche permettant de relâcher la contrainte de parcimonie de SpaceCORR. La méthode originale SIBIS (pour \textit{Subspace-Intersection Blind Identification and Separation}) est une méthode géométrique basée sur l'identification de l'intersection de sous-espaces engendrés par des régions de l'image hyperspectrale. Ces intersections permettent, sous une hypothèse faible de parcimonie, de réaliser la séparation des données. L'ensemble des approches proposées dans ces travaux ont été validées par des tests sur données simulées puis appliquées sur données réelles. Les résultats obtenus sur ces données sont très encourageants et sont comparés à ceux obtenus par des méthodes de la littérature
This thesis deals with the development of new blind separation methods for linear instantaneous mixtures applicable to astrophysical hyperspectral data sets. We propose three approaches to perform data separation. A first contribution is based on hybridization of two existing blind source separation (BSS) methods: the SpaceCORR method, requiring a sparsity assumption, and a non-negative matrix factorization (NMF) method. We show that using SpaceCORR results to initialize the NMF improves the performance of the methods used alone. We then proposed a first original method to relax the sparsity constraint of SpaceCORR. The method called MASS (Maximum Angle Source Separation) is a geometric method based on the extraction of single-source pixels to achieve the separation of data. We also studied the hybridization of MASS with the NMF. Finally, we proposed an approach to relax the sparsity constraint of SpaceCORR. The original method called SIBIS (Subspace-Intersection Blind Identification and Separation) is a geometric method based on the identification of intersections of subspaces generated by regions of the hyperspectral image. Under a sparsity assumption, these intersections allow one to achieve the separation of the data. The approaches proposed in this manuscript have been validated by experimentations on simulated data and then applied to real data. The results obtained on our data are very encouraging and are compared with those obtained by methods from the literature
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Congedo, Marco. "EEG Source Analysis." Habilitation à diriger des recherches, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00880483.

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Electroencephalographic data recorded on the human scalp can be modeled as a linear mixture of underlying dipolar source generators. The characterization of such generators is the aim of several families of signal processing methods. In this HDR we consider in several details three of such families, namely 1) EEG distributed inverse solutions, 2) diagonalization methods, including spatial filtering and blind source separation and 3) Riemannian geometry. We highlight our contributions in each of this family, we describe algorithms reporting all necessary information to make purposeful use of these methods and we give numerous examples with real data pertaining to our published studies. Traditionally only the single-subject scenario is considered; here we consider in addition the extension of some methods to the simultaneous multi-subject recording scenario. This HDR can be seen as an handbook for EEG source analysis. It will be particularly useful to students and other colleagues approaching the field.
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Toumi, Ichrak. "Decomposition methods of NMR signal of complex mixtures : models ans applications." Electronic Thesis or Diss., Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4351.

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L'objectif de ce travail était de tester des méthodes de SAS pour la séparation des spectres complexes RMN de mélanges dans les plus simples des composés purs. Dans une première partie, les méthodes à savoir JADE et NNSC ont été appliqué es dans le cadre de la DOSY , une application aux données CPMG était démontrée. Dans une deuxième partie, on s'est concentré sur le développement d'un algorithme efficace "beta-SNMF" . Ceci s'est montré plus performant que NNSC pour beta inférieure ou égale à 2. Etant donné que dans la littérature, le choix de beta a été adapté aux hypothèses statistiques sur le bruit additif, une étude statistique du bruit RMN de la DOSY a été faite pour obtenir une image plus complète de nos données RMN étudiées
The objective of the work was to test BSS methods for the separation of the complex NMR spectra of mixtures into the simpler ones of the pure compounds. In a first part, known methods namely JADE and NNSC were applied in conjunction for DOSY , performing applications for CPMG were demonstrated. In a second part, we focused on developing an effective algorithm "beta- SNMF ". This was demonstrated to outperform NNSC for beta less or equal to 2. Since in the literature, the choice of beta has been adapted to the statistical assumptions on the additive noise, a statistical study of NMR DOSY noise was done to get a more complete picture about our studied NMR data
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Books on the topic "Blind Source Separation (BSS)"

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Xiang, Yong, Dezhong Peng, and Zuyuan Yang. Blind Source Separation. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-227-2.

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Naik, Ganesh R., and Wenwu Wang, eds. Blind Source Separation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4.

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Yu, Xianchuan, Dan Hu, and Jindong Xu. Blind Source Separation. Singapore: John Wiley & Sons, Singapore Pte. Ltd, 2014. http://dx.doi.org/10.1002/9781118679852.

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Deville, Yannick, Leonardo Tomazeli Duarte, and Shahram Hosseini. Nonlinear Blind Source Separation and Blind Mixture Identification. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64977-7.

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Bourgeois, Julien, and Wolfgang Minker, eds. Time-Domain Beamforming and Blind Source Separation. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-68836-7.

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Shi, Xizhi. Blind signal processing: Theory and practice. Shanghai: Shanghai Jiao Tong University Press, 2011.

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Comon, Pierre. Handbook of Blind Source Separation: Independent Component Analysis and Applications. Burlington: Elsevier, 2010.

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author, Sun Jiande, and Xu Hongji author, eds. Mang xin hao chu li li lun yu ying yong. Beijing: Ke xue chu ban she, 2013.

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Mang xin hao chu li ji chu ji qi ying yong: Blind signal processing foundation and it's applications. Beijing Shi: Guo fang gong ye chu ban she, 2010.

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C, Loizou Philipos, ed. Advances in modern blind signal separation algorithms: Theory and applications. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool Publishers, 2010.

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Book chapters on the topic "Blind Source Separation (BSS)"

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Sanei, Saeid, Loukianos Spyrou, Wenwu Wang, and Jonathon A. Chambers. "Localization of P300 Sources in Schizophrenia Patients Using Constrained BSS." In Independent Component Analysis and Blind Signal Separation, 177–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30110-3_23.

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Hadi, Fatin Izzati Mohamad Abdul, Dzati Athiar Ramli, and Ahmad Saiful Azhar. "Passive Acoustic Monitoring (PAM) of Snapping Shrimp Sound Based on Blind Source Separation (BSS) Technique." In Lecture Notes in Electrical Engineering, 605–11. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8129-5_92.

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Antoni, J., and S. Chauhan. "Second Order Blind Source Separation techniques (SO-BSS) and their relation to Stochastic Subspace Identification (SSI) algorithm." In Structural Dynamics, Volume 3, 177–87. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9834-7_16.

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Massar, H., M. Miyara, T. Belhoussine Drissi, and B. Nsiri. "An Integrated Approach for Artifact Elimination in EEG Signals: Combining Variational Mode Decomposition with Blind Source Separation (VMD-BSS)." In Lecture Notes in Networks and Systems, 84–90. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-48573-2_13.

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Zarzoso, V., and A. K. Nandi. "Blind Source Separation." In Blind Estimation Using Higher-Order Statistics, 167–252. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-2985-6_4.

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Cong, Fengyu. "Blind Source Separation." In EEG Signal Processing and Feature Extraction, 117–40. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9113-2_7.

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Jang, Gil-Jin, and Te-Won Lee. "Monaural Source Separation." In Blind Speech Separation, 339–64. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-6479-1_12.

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Deville, Yannick, and Alain Deville. "Quantum-Source Independent Component Analysis and Related Statistical Blind Qubit Uncoupling Methods." In Blind Source Separation, 3–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4_1.

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Saruwatari, Hiroshi, and Ryoichi Miyazaki. "Statistical Analysis and Evaluation of Blind Speech Extraction Algorithms." In Blind Source Separation, 291–322. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4_10.

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Wang, Lin, Heping Ding, and Fuliang Yin. "Speech Separation and Extraction by Combining Superdirective Beamforming and Blind Source Separation." In Blind Source Separation, 323–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4_11.

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Conference papers on the topic "Blind Source Separation (BSS)"

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Shoker, L., and S. Sanei. "Artefact removal from EEGs using a hybrid BSS-SVM algorithm." In IEE Seminar on Blind Source Separation in Biomedicine. IEE, 2004. http://dx.doi.org/10.1049/ic:20040616.

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James, C. J. "Introduction and overview of the BSS/ICA problem - specifically when applied to biomedicine." In IEE Seminar on Blind Source Separation in Biomedicine. IEE, 2004. http://dx.doi.org/10.1049/ic:20040611.

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Hesse, C. "BSS for EEG signal pre-processing and feature extraction in seizure onset analysis." In IEE Seminar on Blind Source Separation in Biomedicine. IEE, 2004. http://dx.doi.org/10.1049/ic:20040618.

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Lei, Tianhu, and Jayaram K. Udupa. "Blind source separation (BSS) for fMRI analysis." In Medical Imaging 2001, edited by Chin-Tu Chen and Anne V. Clough. SPIE, 2001. http://dx.doi.org/10.1117/12.428151.

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Cedola, Luca, Mauro Villarini, Enzo Fioriti, and Maurizio Carlini. "Identification of Spatially Extended Pollution Sources by Means of Blind Sources Separation Algorithms." In ASME 8th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2006. http://dx.doi.org/10.1115/esda2006-95586.

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Blind Source Separation (BSS) is able to recover original source signals from their mixtures. Well-known sources are brain electric activity, acoustic phenomena, earthquakes. Here we apply BSS to air pollution analysis identificating gas emissions using only sensor measurements mutually and statistically independent. This application consents an independent, low cost and real time environmental monitoring system. Besides, a real-world pollution case from an industrial region of central Italy is presented and processed by BSS.
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Wang, Dongliang, Benshan Wang, Weipeng Zhang, Thomas Ferreira de Lima, Bhavin J. Shastri, Paul R. Prucnal, and Chaoran Huang. "Photonic Blind Source Separation for Multimode Optical Fiber Interconnects." In CLEO: Science and Innovations. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/cleo_si.2022.sth4n.4.

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We propose combining blind source separation (BSS) algorithm with photonic matrix processor to solve dynamic modal crosstalk in multimode fiber interconnects. The approach can solve DSP constraints and enable high-capacity and low-power data-center interconnects.
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Tse, Peter W., and Jinyu Zhang. "The Use of Blind-Source-Separation Algorithm for Mechanical Signal Separation and Machine Fault Diagnosis." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-55318.

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Vibration based machine fault diagnosis is widely adopted in machine condition monitoring. Since a machine is usually composed of many mechanical components, during the machine running, each component will generate its vibration and transmit to other components thru the shaft or linkages. Hence, the vibration signal collected from a sensor is the aggregation of all generated vibrations. To enhance the accuracy in vibration based machine fault diagnosis, the vibration generated by each component must be isolated and identified. In this paper, the performance of blind-source-separation (BSS) in separating various mixed sources is discussed. The BSS based method of second order statistics (SOS) has been applied to separate the aggregated vibration signals generated from a number of mechanical components. To verify the effectiveness of the BSS based SOS, a number of experiments were conducted using both simulated data and vibration generated form the industrial machines. The results show that the BSS possesses the ability to separate both artificially and naturally mixed signals. Such ability is definitely welcome in the fields of condition monitoring and maintenance. Moreover, the paper also discusses the advantages and disadvantages of the algorithm in the applications of machine fault diagnosis and future improvements.
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Wu, J. B., J. Chen, Z. M. Zhong, and P. Zhong. "Application of Blind Source Separation Method in Mechanical Sound Signal Analysis." In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-39225.

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As the result of vibration emission in air, the mechanical noise signal carries affluent information about the working condition of machinery and it can be used in mechanical fault diagnosis. But in practice, the measured sound signal is usually the mixing of condition signal and other uncorrelated signals. And the signal received is usually of very low SNR. Therefore, to obtain the features of original signals, the mixed signals have to be separated and the uncorrelated signals have to be removed by means of the blind source separation technique. The BSS is a class of signal processing method that can recover the original signals according to the observed mixing signals. In application of BSS algorithms, it is generally supposed that the number of sources is known. But unfortunately, this is not the case in application. Then, before applying the BSS method, the singular-value analysis method is introduced to estimate the number of sound sources at first. On the other hand, to avoid the ill-conditioned problem caused by environment noise and/or measuring noise in applying BSS method, the partial singular-value analysis method is employed to select those signals with maximum information entropy from mixed signals. This method significantly reduces the distortion of separated signals. Afterward, the second order blind identification (SOBI) algorithm, one of the BSS methods, which only relies on the second order statistics of measuring signals, is utilized and it is modified, in this paper, especially for purpose of spectra separation. Finally, the spectra separation results obtained from the mixed signals measured in a semi-anechoic chamber demonstrate the availability of the presented method.
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Zhang, Linke, Lin He, and Yong Jiang. "Study on the Noise Source Identification Based on a Novel Variable Step-Size Algorithm of Blind Sources Separation." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34434.

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Some preknowledge of sources input signals or transmission paths were required in advance for traditional noise source identification. In this paper, a novel variable step-size algorithm of blind source separation (BSS) is proposed to identify noise source, which doesn’t need any preknowledge but some statistical assumptions about sources. Most BSS algorithms have been issued on fixed step-size, relatively little work has been focused on variable step-size. The output feedback of step-size update in the proposed algorithm is derived from the analysis of adaptive blind sources separation and adaptive filter. With the natural gradient algorithm based minimum mutual information, the iteration formula of separate matrix is also obtained. The availability of this algorithm is confirmed by simulations. Namely, the convergence rate of this algorithm is rapid, while ensuring low steady-state error.
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Leng, Yong-gang, Ting-ting Chen, Yue-ran Pan, and Zhi-hui Lai. "Blind Source Separation of a Single Channel Based on Repeated Independent Component Analysis." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47120.

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Independent component analysis (ICA) is a kind of steady algorithm for the separation of blind source (BSS). However, there are three assumptions for the ICA application, one of which is that the number of test channels must be more than that of the signal sources. The limitation brings much inconvenience to practical signal acquisition and processing. In this paper, we propose a new method of repeated independent component analysis (Re-ICA) to realize the separation of blind sources. Under a single test channel, we can increase test channels by means of constructing virtual signal channels and use ICA repeatedly to separate every source signal in turn. Numerical simulation and signal processing of practical data acquisition by one single test-channel show that the proposed method is simple and feasible for operation, and is of great potential in engineering application.
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Reports on the topic "Blind Source Separation (BSS)"

1

Zokay, Mustapha, and Hicham Saylani. Removing specular reflection in multispectral dermatological images using blind source separation. Peeref, June 2023. http://dx.doi.org/10.54985/peeref.2306p8383322.

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Xu, Pengfei, and Yinjie Jia. Blind Source Separation for Chirp Signals Based on the Local Quadratic Regression Smoothing. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, November 2020. http://dx.doi.org/10.7546/crabs.2020.11.13.

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Hoffman, Jeffrey. Using Blind Source Separation and a Compact Microphone Array to Improve the Error Rate of Speech Recognition. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5258.

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