The Experts below are selected from a list of 296550 Experts worldwide ranked by ideXlab platform
Matti Kajola - One of the best experts on this subject based on the ideXlab platform.
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applications of the signal space Separation Method
IEEE Transactions on Signal Processing, 2005Co-Authors: Samu Taulu, Juha Simola, Matti KajolaAbstract:The reliability of biomagnetic measurements is traditionally challenged by external interference signals, movement artifacts, and comparison problems caused by different positions of the subjects or different sensor configurations. The Signal Space Separation Method (SSS) idealizes magnetic multichannel signals by transforming them into device-independent idealized channels representing the measured data in uncorrelated form. The transformation has separate components for the biomagnetic and external interference signals, and thus, the biomagnetic signals can be reconstructed simply by leaving out the contribution of the external interference. The foundation of SSS is a basis spanning all multichannel signals of magnetic origin. It is based on Maxwell's equations and the geometry of the sensor array only, with the assumption that the sensors are located in a current free volume. SSS is demonstrated to provide suppression of external interference signals, standardization of different positions of the subject, standardization of different sensor configurations, compensation for distortions caused by movement of the subject (even a subject containing magnetic impurities), suppression of sporadic sensor artifacts, a tool for fine calibration of the device, extraction of biomagnetic DC fields, and an aid for realizing an active compensation system. Thus, SSS removes many limitations of traditional biomagnetic measurements.
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presentation of electromagnetic multichannel data the signal space Separation Method
Journal of Applied Physics, 2005Co-Authors: Samu Taulu, Matti KajolaAbstract:Measurement of external magnetic fields provides information on electric current distribution inside an object. For example, in magnetoencephalography modern measurement devices sample the magnetic field produced by the brain in several hundred distinct locations around the head. The signal space Separation (SSS) Method creates a fundamental linear basis for all measurable multichannel signal vectors of magnetic origin. The SSS basis is based on the fact that the magnetic field can be expressed as a combination of two separate and rapidly converging expansions of harmonic functions with one expansion for signals arising from inside of the measurement volume of the sensor array and another for signals arising from outside of this volume. The Separation is based on the different convergence volumes of the two expansions and on the fact that the sensors are located in a source current-free volume between the interesting and interfering sources. Individual terms of the expansions are shown to contain uncorrelated information of the underlying source distribution. SSS provides a stable decomposition of the measurement into a fundamental device-independent form when used with an accurately calibrated multichannel device. The external interference signals are elegantly suppressed by leaving the interference components out from the reconstruction based on the decomposition. Representation of multichannel data with the SSS basis is shown to provide a large variety of applications for improved analysis of multichannel data.
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suppression of interference and artifacts by the signal space Separation Method
Brain Topography, 2003Co-Authors: Samu Taulu, Matti Kajola, Juha SimolaAbstract:Multichannel measurement with hundreds of channels oversamples a curl-free vector field, like the magnetic field in a volume free of sources. This is based on the constraint caused by the Laplace's equation for the magnetic scalar potential; outside of the source volume the signals are spatially band limited. A functional solution of Laplace's equation enables one to separate the signals arising from the sphere enclosing the interesting sources, e.g. the currents in the brain, from the magnetic interference. Signal space Separation (SSS) is accomplished by calculating individual basis vectors for each term of the functional expansion to create a signal basis covering all measurable signal vectors. Because the SSS basis is linearly independent for all practical sensor arrangements, any signal vector has a unique SSS decomposition with separate coefficients for the interesting signals and signals coming from outside the interesting volume. Thus, SSS basis provides an elegant Method to remove external disturbances. The device-independent SSS coefficients can be used in transforming the interesting signals to virtual sensor configurations. This can also be used in compensating for distortions caused by movement of the object by modeling it as movement of the sensor array around a static object. The device-independence of the decomposition also enables physiological DC phenomena to be recorded using voluntary head movements. When used with properly designed sensor array, SSS does not affect the morphology or the signal-to-noise ratio of the interesting signals.
Samu Taulu - One of the best experts on this subject based on the ideXlab platform.
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spatiotemporal signal space Separation Method for rejecting nearby interference in meg measurements
Physics in Medicine and Biology, 2006Co-Authors: Samu Taulu, Juha SimolaAbstract:Limitations of traditional magnetoencephalography (MEG) exclude some important patient groups from MEG examinations, such as epilepsy patients with a vagus nerve stimulator, patients with magnetic particles on the head or having magnetic dental materials that cause severe movement-related artefact signals. Conventional interference rejection Methods are not able to remove the artefacts originating this close to the MEG sensor array. For example, the reference array Method is unable to suppress interference generated by sources closer to the sensors than the reference array, about 20-40 cm. The spatiotemporal signal space Separation Method proposed in this paper recognizes and removes both external interference and the artefacts produced by these nearby sources, even on the scalp. First, the basic Separation into brain-related and external interference signals is accomplished with signal space Separation based on sensor geometry and Maxwell's equations only. After this, the artefacts from nearby sources are extracted by a simple statistical analysis in the time domain, and projected out. Practical examples with artificial current dipoles and interference sources as well as data from real patients demonstrate that the Method removes the artefacts without altering the field patterns of the brain signals.
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applications of the signal space Separation Method
IEEE Transactions on Signal Processing, 2005Co-Authors: Samu Taulu, Juha Simola, Matti KajolaAbstract:The reliability of biomagnetic measurements is traditionally challenged by external interference signals, movement artifacts, and comparison problems caused by different positions of the subjects or different sensor configurations. The Signal Space Separation Method (SSS) idealizes magnetic multichannel signals by transforming them into device-independent idealized channels representing the measured data in uncorrelated form. The transformation has separate components for the biomagnetic and external interference signals, and thus, the biomagnetic signals can be reconstructed simply by leaving out the contribution of the external interference. The foundation of SSS is a basis spanning all multichannel signals of magnetic origin. It is based on Maxwell's equations and the geometry of the sensor array only, with the assumption that the sensors are located in a current free volume. SSS is demonstrated to provide suppression of external interference signals, standardization of different positions of the subject, standardization of different sensor configurations, compensation for distortions caused by movement of the subject (even a subject containing magnetic impurities), suppression of sporadic sensor artifacts, a tool for fine calibration of the device, extraction of biomagnetic DC fields, and an aid for realizing an active compensation system. Thus, SSS removes many limitations of traditional biomagnetic measurements.
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presentation of electromagnetic multichannel data the signal space Separation Method
Journal of Applied Physics, 2005Co-Authors: Samu Taulu, Matti KajolaAbstract:Measurement of external magnetic fields provides information on electric current distribution inside an object. For example, in magnetoencephalography modern measurement devices sample the magnetic field produced by the brain in several hundred distinct locations around the head. The signal space Separation (SSS) Method creates a fundamental linear basis for all measurable multichannel signal vectors of magnetic origin. The SSS basis is based on the fact that the magnetic field can be expressed as a combination of two separate and rapidly converging expansions of harmonic functions with one expansion for signals arising from inside of the measurement volume of the sensor array and another for signals arising from outside of this volume. The Separation is based on the different convergence volumes of the two expansions and on the fact that the sensors are located in a source current-free volume between the interesting and interfering sources. Individual terms of the expansions are shown to contain uncorrelated information of the underlying source distribution. SSS provides a stable decomposition of the measurement into a fundamental device-independent form when used with an accurately calibrated multichannel device. The external interference signals are elegantly suppressed by leaving the interference components out from the reconstruction based on the decomposition. Representation of multichannel data with the SSS basis is shown to provide a large variety of applications for improved analysis of multichannel data.
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suppression of interference and artifacts by the signal space Separation Method
Brain Topography, 2003Co-Authors: Samu Taulu, Matti Kajola, Juha SimolaAbstract:Multichannel measurement with hundreds of channels oversamples a curl-free vector field, like the magnetic field in a volume free of sources. This is based on the constraint caused by the Laplace's equation for the magnetic scalar potential; outside of the source volume the signals are spatially band limited. A functional solution of Laplace's equation enables one to separate the signals arising from the sphere enclosing the interesting sources, e.g. the currents in the brain, from the magnetic interference. Signal space Separation (SSS) is accomplished by calculating individual basis vectors for each term of the functional expansion to create a signal basis covering all measurable signal vectors. Because the SSS basis is linearly independent for all practical sensor arrangements, any signal vector has a unique SSS decomposition with separate coefficients for the interesting signals and signals coming from outside the interesting volume. Thus, SSS basis provides an elegant Method to remove external disturbances. The device-independent SSS coefficients can be used in transforming the interesting signals to virtual sensor configurations. This can also be used in compensating for distortions caused by movement of the object by modeling it as movement of the sensor array around a static object. The device-independence of the decomposition also enables physiological DC phenomena to be recorded using voluntary head movements. When used with properly designed sensor array, SSS does not affect the morphology or the signal-to-noise ratio of the interesting signals.
Juha Simola - One of the best experts on this subject based on the ideXlab platform.
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spatiotemporal signal space Separation Method for rejecting nearby interference in meg measurements
Physics in Medicine and Biology, 2006Co-Authors: Samu Taulu, Juha SimolaAbstract:Limitations of traditional magnetoencephalography (MEG) exclude some important patient groups from MEG examinations, such as epilepsy patients with a vagus nerve stimulator, patients with magnetic particles on the head or having magnetic dental materials that cause severe movement-related artefact signals. Conventional interference rejection Methods are not able to remove the artefacts originating this close to the MEG sensor array. For example, the reference array Method is unable to suppress interference generated by sources closer to the sensors than the reference array, about 20-40 cm. The spatiotemporal signal space Separation Method proposed in this paper recognizes and removes both external interference and the artefacts produced by these nearby sources, even on the scalp. First, the basic Separation into brain-related and external interference signals is accomplished with signal space Separation based on sensor geometry and Maxwell's equations only. After this, the artefacts from nearby sources are extracted by a simple statistical analysis in the time domain, and projected out. Practical examples with artificial current dipoles and interference sources as well as data from real patients demonstrate that the Method removes the artefacts without altering the field patterns of the brain signals.
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applications of the signal space Separation Method
IEEE Transactions on Signal Processing, 2005Co-Authors: Samu Taulu, Juha Simola, Matti KajolaAbstract:The reliability of biomagnetic measurements is traditionally challenged by external interference signals, movement artifacts, and comparison problems caused by different positions of the subjects or different sensor configurations. The Signal Space Separation Method (SSS) idealizes magnetic multichannel signals by transforming them into device-independent idealized channels representing the measured data in uncorrelated form. The transformation has separate components for the biomagnetic and external interference signals, and thus, the biomagnetic signals can be reconstructed simply by leaving out the contribution of the external interference. The foundation of SSS is a basis spanning all multichannel signals of magnetic origin. It is based on Maxwell's equations and the geometry of the sensor array only, with the assumption that the sensors are located in a current free volume. SSS is demonstrated to provide suppression of external interference signals, standardization of different positions of the subject, standardization of different sensor configurations, compensation for distortions caused by movement of the subject (even a subject containing magnetic impurities), suppression of sporadic sensor artifacts, a tool for fine calibration of the device, extraction of biomagnetic DC fields, and an aid for realizing an active compensation system. Thus, SSS removes many limitations of traditional biomagnetic measurements.
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suppression of interference and artifacts by the signal space Separation Method
Brain Topography, 2003Co-Authors: Samu Taulu, Matti Kajola, Juha SimolaAbstract:Multichannel measurement with hundreds of channels oversamples a curl-free vector field, like the magnetic field in a volume free of sources. This is based on the constraint caused by the Laplace's equation for the magnetic scalar potential; outside of the source volume the signals are spatially band limited. A functional solution of Laplace's equation enables one to separate the signals arising from the sphere enclosing the interesting sources, e.g. the currents in the brain, from the magnetic interference. Signal space Separation (SSS) is accomplished by calculating individual basis vectors for each term of the functional expansion to create a signal basis covering all measurable signal vectors. Because the SSS basis is linearly independent for all practical sensor arrangements, any signal vector has a unique SSS decomposition with separate coefficients for the interesting signals and signals coming from outside the interesting volume. Thus, SSS basis provides an elegant Method to remove external disturbances. The device-independent SSS coefficients can be used in transforming the interesting signals to virtual sensor configurations. This can also be used in compensating for distortions caused by movement of the object by modeling it as movement of the sensor array around a static object. The device-independence of the decomposition also enables physiological DC phenomena to be recorded using voluntary head movements. When used with properly designed sensor array, SSS does not affect the morphology or the signal-to-noise ratio of the interesting signals.
Ing Yann Soon - One of the best experts on this subject based on the ideXlab platform.
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partial Separation Method for solving permutation problem in frequency domain blind source Separation of speech signals
Neurocomputing, 2008Co-Authors: V G Reju, Soo Ngee Koh, Ing Yann SoonAbstract:This paper addresses the well known permutation problem in frequency domain blind source Separation. The proposed Method uses correlation between two signals in each DFT bin to solve the permutation problem. One of the signals is partially separated by a time domain blind source Separation Method and the other is obtained by the frequency domain blind source Separation Method. Two different ways of configuring the time and frequency domain blocks, i.e., in parallel or cascade, have been studied. The cascaded configuration not only achieves a better Separation performance but also reduces the computational cost as compared to the parallel configuration.
Piet C W Pcw C W Piet C W Pcw Sommen - One of the best experts on this subject based on the ideXlab platform.
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A frequency domain blind signal Separation Method based on decorrelation
IEEE Transactions on Signal Processing, 2002Co-Authors: Daniël W.e. Schobben, Piet C W Pcw C W Piet C W Pcw SommenAbstract:This paper addresses the issue of separating multiple speakers from mixtures of these that are obtained using multiple microphones in a room. An adaptive blind signal Separation algorithm, which is entirely based on second-order statistics, is derived. One of the advantages of this algorithm is that no parameters need to be tuned. Moreover, an extension of the algorithm that can simultaneously deal with blind signal Separation and echo cancellation is derived. Experiments with real recordings have been carried out, showing the effectiveness of the algorithm for real-world signals
