Browsing by Author "Lang, E.W."
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- Blind source separation using time-delayed signalsPublication . Tomé, A.M.; Teixeira, Ana; Lang, E.W.; Stadlthanner, K.; Rocha, A.P.; Almeida, R.In this work a modified version of AMUSE, called MMUSE, is proposed. The main modification consists in increasing the dimension of the data vectors by joining delayed versions of the observed mixed signals. With the new data a matrix pencil is computed and its generalized eigendecomposition is performed as in AMUSE. We will show that in this case the output (or independent) signals are filtered versions of the source signals. Some numerical simulations using artificially mixed signals as well as biological data (RR and QT intervals of Electrocardiogram) are presented.
- dAMUSE : a new tool for denoising and blind source separationPublication . Tomé, A.M.; Teixeira, Ana; Lang, E.W.; Stadlthanner, K.; Rocha, A.P.; Almeida, R.In this work a generalized version of AMUSE, called dAMUSE is proposed. The main modification consists in embedding the observed mixed signals in a high-dimensional feature space of delayed coordinates. With the embedded signals a matrix pencil is formed and its generalized eigendecomposition is computed similar to the algorithm AMUSE. We show that in this case the uncorrelated output signals are filtered versions of the unknown source signals. Further, denoising the data can be achieved conveniently in parallel with the signal separation. Numerical simulations using artificially mixed signals are presented to show the performance of the method. Further results of a heart rate variability (HRV) study are discussed showing that the output signals are related with LF (low frequency) and HF (high frequency) fluctuations. Finally, an application to separate artifacts from 2D NOESY NMR spectra and to denoise the reconstructed artefact-free spectra is presented also.
- Denoising using local projective subspace methodsPublication . Gruber, P.; Stadlthanner, K.; Böhm, M.; Theis, F.J.; Lang, E.W.; Tomé, A.M.; Teixeira, Ana; Puntonet, C.G.; Gorriz Saéz, J.M.In this paper we present denoising algorithms for enhancing noisy signals based on Local ICA (LICA), Delayed AMUSE (dAMUSE) and Kernel PCA (KPCA). The algorithm LICA relies on applying ICA locally to clusters of signals embedded in a high-dimensional feature space of delayed coordinates. The components resembling the signals can be detected by various criteria like estimators of kurtosis or the variance of autocorrelations depending on the statistical nature of the signal. The algorithm proposed can be applied favorably to the problem of denoising multi-dimensional data. Another projective subspace denoising method using delayed coordinates has been proposed recently with the algorithm dAMUSE. It combines the solution of blind source separation problems with denoising efforts in an elegant way and proofs to be very efficient and fast. Finally, KPCA represents a non-linear projective subspace method that is well suited for denoising also. Besides illustrative applications to toy examples and images, we provide an application of all algorithms considered to the analysis of protein NMR spectra.
- Exploiting low-rank approximations of kernel matrics in denoising applicationSPublication . Teixeira, Ana; Tomé, A. M.; Lang, E.W.The eigendecomposition of a kernel matrix can present a computational burden in many kernel methods. Nevertheless only the largest eigenvalues and corresponding eigenvectors need to be computed. In this work we discuss the Nystrom low-rank approximations of the kernel matrix and its applications in KPCA denoising tasks. Furthermore, the low-rank approximations have the advantage of being related with a smaller subset of the training data which constitute then a basis of a subspace. In a common algebraic framework we discuss the different approaches to compute the basis. Numerical simulations concerning the denoising are presented to compare the discussed approaches.
- Feature Extraction and Classification of Biosignals - Emotion Valence Detection from EEG SignalsPublication . Tomé, A. M.; Hidalgo-Muñoz, A.R.; López, M.M.; Teixeira, Ana; Santos, I.M.; Pereira, A.T.; Vázquez-Marrufo, M.; Lang, E.W.In thisworkavalencerecognitionsystembasedonelectroencephalogramsispresented.Theperformanceof the systemisevaluatedfortwosettings:singlesubjects(intra-subject)andbetweensubjects(inter-subject). The featureextractionisbasedonmeasuresofrelativeenergiescomputedinshorttimeintervalsandcertain frequencybands.Thefeatureextractionisperformedeitheronsignalsaveragedoveranensembleoftrialsor on single-trialresponsesignals.Thesubsequentclassificationstageisbasedonanensembleclassifier,i.e.a random forestoftreeclassifiers.Theclassificationisperformedconsideringtheensembleaverageresponsesof all subjects(inter-subject)orconsideringthesingle-trialresponsesofsinglesubjects(intra-subject).Applying a properimportancemeasureoftheclassifier,featureeliminationhasbeenusedtoidentifythemostrelevant features of the decision making.
- Greedy kernel PCA applied to single-channel EEG eecordingsPublication . Tomé, A. M.; Teixeira, Ana; Lang, E.W.; Silva, A. MartinsIn this work, we propose the correction of univariate, single channel EEGs using a kernel technique. The EEG signal is embedded in its time-delayed coordinates obtaining a multivariate signal. A kernel subspace technique is used for denoising and artefact extraction. The proposed kernel method follows a greedy approach to use a reduced data set to compute a new basis onto which to project the mapped data in feature space. The pre-image of the reconstructed multivariate signal is computed and the embedding is reverted. The resultant signal is the high amplitude artifact which must be subtracted from the original signal to obtain a corrected version of the underlying signal.
- Kernel-PCA denoising of artifact-free protein NMR spectraPublication . Stadlthanner, K.; Lang, E.W.; Gruber, P.; Theis, E J.; Tomé, A.M.; Teixeira, Ana; Puntonet, C. G.Multidimensional 'H NMR spectra of hiomolecules dissolved in light water are contaminated by an intense water artifact. Generalized eigenvalue decomposition methods using congruent matrix pencils are used to separate the water artefact from the protein spectra. Due to the statistical separation process, however, noise is introduced into the reconstructed spectra. Hence Kernel - based denoising techniques are discussed lo obtain noise- and artifact - free 2D NOESY NMR spectra of proteins.
- KPCA denoising and the pre-image problem revisitedPublication . Teixeira, Ana; Tomé, A.M.; Stadlthanner, K.; Lang, E.W.Kernel principal component analysis (KPCA) is widely used in classification, feature extraction and denoising applications. In the latter it is unavoidable to deal with the pre-image problem which constitutes the most complex step in the whole processing chain. One of the methods to tackle this problem is an iterative solution based on a fixed-point algorithm. An alternative strategy considers an algebraic approach that relies on the solution of an under-determined system of equations. In this work we present a method that uses this algebraic approach to estimate a good starting point to the fixed-point iteration. We will demonstrate that this hybrid solution for the pre-image shows better performance than the other two methods. Further we extend the applicability of KPCA to one-dimensional signals which occur in many signal processing applications. We show that artefact removal from such data can be treated on the same footing as denoising. We finally apply the algorithm to denoise the famous USPS data set and to extract EOG interferences from single channel EEG recordings.
- Mining EEG scalp maps of independent components related to HCT tasksPublication . Teixeira, Ana; Santos, I.M.; Lang, E.W.; Tome, A.M.This work presents an unsupervised mining strat- egy, applied to an independent component analysis (ICA) of segments of data collected while participants are answering to the items of the Halstead Category Test (HCT). This new methodology was developed to achieve signal components at trial level and therefore to study signal dynamics which are not available within participants’ ensemble average signals. The study will be focused on the signal component that can be elicited by the binary visual feedback which is part of the HCT protocol. The experimental study is conducted using a cohort of 58 participants.
- On the use of simulated annealing to automatically assign decorrelated components in second-order blind source separationPublication . Bohm, M.; Stadlthanner, K.; Gruber, P.; Theis, F.J.; Lang, E.W.; Tome, A.M.; Teixeira, Ana; Gronwald, W.; Kalbitzer, H.R.—In this paper, an automatic assignment tool, called BSS-AutoAssign, for artifact-related decorrelated components within a second-order blind source separation (BSS) is presented. The latter is based on the recently proposed algorithm dAMUSE, which provides an elegant solution to both the BSS and the denoising problem simultaneously. BSS-AutoAssign uses a local principal component analysis (PCA)to approximate the artifact signal and defines a suitable cost function which is optimized using simulated annealing. The algorithms dAMUSE plus BSS-AutoAssign are illustrated by applying them to the separation of water artifacts from two-dimensional nuclear overhauser enhancement (2-D NOESY) spectroscopy signals of proteins dissolved in water.