The Experts below are selected from a list of 33 Experts worldwide ranked by ideXlab platform
J Salz - One of the best experts on this subject based on the ideXlab platform.
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an iterative algorithm for computing a Spatial Whitening filter
International Workshop on Signal Processing Advances in Wireless Communications, 2004Co-Authors: Sivarama Venkatesan, L Mailaender, J SalzAbstract:On a wireless link with multiple antennas at both transmitter and receiver, the interference at the receiving antenna array can exhibit a strong Spatial coloring in the presence of a small number of dominant interferers. In such a situation, a receiver whose detection algorithms are designed for Spatially white interference could exhibit degraded performance. We propose an iterative algorithm to compute a Spatial Whitening filter for a given covariance matrix. Unlike well-known techniques for computing such a Whitening filter (e.g., inverting the lower-triangular Cholesky factor of the covariance), the algorithm we propose requires only matrix additions and multiplications and no nontrivial division or square root operations, making it well suited to VLSI implementation. By analyzing the dynamics of the proposed algorithm, we provide simple conditions under which it is guaranteed to converge to a desired solution. We also describe a simple technique to estimate the Spatial covariance of the interference at the receiver, using pilot signals from the transmitter. We demonstrate by simulation that the proposed iterative Whitening algorithm achieves virtually the same BER-versus-SNR performance as an exact Whitening filter, with a small number of iterations.
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an iterative algorithm for computin g a Spatial Whitening filter
2004Co-Authors: Sivarama Venkatesan, L Mailaender, J SalzAbstract:On a wireless link with multiple antennas at both transmitter and receiver, the interference at the receiv ing antenna array can exhibit a strong spa tial coloring in the presence of a small number of dom inant interferers. In such a situation, a receiver whose detection algorithms are designed for Spatially white interference could exhibit degraded performance. In this paper, we propose an iterative algorithm to com pute a Spatial Whitening filter for a given covariance ma trix. Unlike well-known techniques for computing such a Whitening filter (e.g., inverting the lower-triangular Cholesky factor of the covariance), the algorithm we propose requires only matrix additions and multipli cations, and no nontrivial division or square root op erations, making it well suited to VLSI implementa tion. By analyzing the dynamics of the proposed al gorithm, we provide simple conditions under which it is guaranteed to converge to a desired solution. We also describe a simple technique to estimate the spa tial covariance of the interference at the receiver, us ing pilot signals from the transmitter. We demonstrate by simulation that the proposed iterative Whitening al gorithm achieves virtually the same BER-versus-SNR performance as an exact Whitening filter, with a small number of iterations.
Alexandre Gramfort - One of the best experts on this subject based on the ideXlab platform.
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automated model selection in covariance estimation and Spatial Whitening of meg and eeg signals
NeuroImage, 2015Co-Authors: Denis A Engemann, Alexandre GramfortAbstract:Abstract Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electromagnetic fields induced by post-synaptic neural currents. The estimation of the Spatial covariance of the signals recorded on M/EEG sensors is a building block of modern data analysis pipelines. Such covariance estimates are used in brain–computer interfaces (BCI) systems, in nearly all source localization methods for Spatial Whitening as well as for data covariance estimation in beamformers. The rationale for such models is that the signals can be modeled by a zero mean Gaussian distribution. While maximizing the Gaussian likelihood seems natural, it leads to a covariance estimate known as empirical covariance (EC). It turns out that the EC is a poor estimate of the true covariance when the number of samples is small. To address this issue the estimation needs to be regularized. The most common approach downweights off-diagonal coefficients, while more advanced regularization methods are based on shrinkage techniques or generative models with low rank assumptions: probabilistic PCA (PPCA) and factor analysis (FA). Using cross-validation all of these models can be tuned and compared based on Gaussian likelihood computed on unseen data. We investigated these models on simulations, one electroencephalography (EEG) dataset as well as magnetoencephalography (MEG) datasets from the most common MEG systems. First, our results demonstrate that different models can be the best, depending on the number of samples, heterogeneity of sensor types and noise properties. Second, we show that the models tuned by cross-validation are superior to models with hand-selected regularization. Hence, we propose an automated solution to the often overlooked problem of covariance estimation of M/EEG signals. The relevance of the procedure is demonstrated here for Spatial Whitening and source localization of MEG signals.
N J Hill - One of the best experts on this subject based on the ideXlab platform.
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interactions between pre processing and classification methods for event related potential classification best practice guidelines for brain computer interfacing
Neuroinformatics, 2013Co-Authors: J D R Farquhar, N J HillAbstract:Detecting event related potentials (ERPs) from single trials is critical to the operation of many stimulus-driven brain computer interface (BCI) systems. The low strength of the ERP signal compared to the noise (due to artifacts and BCI irrelevant brain processes) makes this a challenging signal detection problem. Previous work has tended to focus on how best to detect a single ERP type (such as the visual oddball response). However, the underlying ERP detection problem is essentially the same regardless of stimulus modality (e.g. visual or tactile), ERP component (e.g. P300 oddball response, or the error-potential), measurement system or electrode layout. To investigate whether a single ERP detection method might work for a wider range of ERP BCIs we compare detection performance over a large corpus of more than 50 ERP BCI datasets whilst systematically varying the electrode montage, spectral filter, Spatial filter and classifier training methods. We identify an interesting interaction between Spatial Whitening and regularised classification which made detection performance independent of the choice of spectral filter low-pass frequency. Our results show that pipeline consisting of spectral filtering, Spatial Whitening, and regularised classification gives near maximal performance in all cases. Importantly, this pipeline is simple to implement and completely automatic with no expert feature selection or parameter tuning required. Thus, we recommend this combination as a “best-practice” method for ERP detection problems.
Sivarama Venkatesan - One of the best experts on this subject based on the ideXlab platform.
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an iterative algorithm for computing a Spatial Whitening filter
International Workshop on Signal Processing Advances in Wireless Communications, 2004Co-Authors: Sivarama Venkatesan, L Mailaender, J SalzAbstract:On a wireless link with multiple antennas at both transmitter and receiver, the interference at the receiving antenna array can exhibit a strong Spatial coloring in the presence of a small number of dominant interferers. In such a situation, a receiver whose detection algorithms are designed for Spatially white interference could exhibit degraded performance. We propose an iterative algorithm to compute a Spatial Whitening filter for a given covariance matrix. Unlike well-known techniques for computing such a Whitening filter (e.g., inverting the lower-triangular Cholesky factor of the covariance), the algorithm we propose requires only matrix additions and multiplications and no nontrivial division or square root operations, making it well suited to VLSI implementation. By analyzing the dynamics of the proposed algorithm, we provide simple conditions under which it is guaranteed to converge to a desired solution. We also describe a simple technique to estimate the Spatial covariance of the interference at the receiver, using pilot signals from the transmitter. We demonstrate by simulation that the proposed iterative Whitening algorithm achieves virtually the same BER-versus-SNR performance as an exact Whitening filter, with a small number of iterations.
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an iterative algorithm for computin g a Spatial Whitening filter
2004Co-Authors: Sivarama Venkatesan, L Mailaender, J SalzAbstract:On a wireless link with multiple antennas at both transmitter and receiver, the interference at the receiv ing antenna array can exhibit a strong spa tial coloring in the presence of a small number of dom inant interferers. In such a situation, a receiver whose detection algorithms are designed for Spatially white interference could exhibit degraded performance. In this paper, we propose an iterative algorithm to com pute a Spatial Whitening filter for a given covariance ma trix. Unlike well-known techniques for computing such a Whitening filter (e.g., inverting the lower-triangular Cholesky factor of the covariance), the algorithm we propose requires only matrix additions and multipli cations, and no nontrivial division or square root op erations, making it well suited to VLSI implementa tion. By analyzing the dynamics of the proposed al gorithm, we provide simple conditions under which it is guaranteed to converge to a desired solution. We also describe a simple technique to estimate the spa tial covariance of the interference at the receiver, us ing pilot signals from the transmitter. We demonstrate by simulation that the proposed iterative Whitening al gorithm achieves virtually the same BER-versus-SNR performance as an exact Whitening filter, with a small number of iterations.
Denis A Engemann - One of the best experts on this subject based on the ideXlab platform.
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automated model selection in covariance estimation and Spatial Whitening of meg and eeg signals
NeuroImage, 2015Co-Authors: Denis A Engemann, Alexandre GramfortAbstract:Abstract Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electromagnetic fields induced by post-synaptic neural currents. The estimation of the Spatial covariance of the signals recorded on M/EEG sensors is a building block of modern data analysis pipelines. Such covariance estimates are used in brain–computer interfaces (BCI) systems, in nearly all source localization methods for Spatial Whitening as well as for data covariance estimation in beamformers. The rationale for such models is that the signals can be modeled by a zero mean Gaussian distribution. While maximizing the Gaussian likelihood seems natural, it leads to a covariance estimate known as empirical covariance (EC). It turns out that the EC is a poor estimate of the true covariance when the number of samples is small. To address this issue the estimation needs to be regularized. The most common approach downweights off-diagonal coefficients, while more advanced regularization methods are based on shrinkage techniques or generative models with low rank assumptions: probabilistic PCA (PPCA) and factor analysis (FA). Using cross-validation all of these models can be tuned and compared based on Gaussian likelihood computed on unseen data. We investigated these models on simulations, one electroencephalography (EEG) dataset as well as magnetoencephalography (MEG) datasets from the most common MEG systems. First, our results demonstrate that different models can be the best, depending on the number of samples, heterogeneity of sensor types and noise properties. Second, we show that the models tuned by cross-validation are superior to models with hand-selected regularization. Hence, we propose an automated solution to the often overlooked problem of covariance estimation of M/EEG signals. The relevance of the procedure is demonstrated here for Spatial Whitening and source localization of MEG signals.