Loading...
7 results
Search Results
Now showing 1 - 7 of 7
- 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.
- 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.
- Identifying evoked potential response patterns using independent component analysis and unsupervised learningPublication . Teixeira, Ana; Santos, Isabel M.; Tomé, A.M.Independent Component Analysis(ICA) is a pre-processing step widely used in brain studies. One of the most common problems in artifact elimination or brain activity related studies is the ordering and identification of the independent components(ICs). In this work, a novel procedure is proposed which combines ICA decomposition at trial level with an unsupervised learning algorithm (K-means) at participant level in order to enhance the related signal patterns which might represent interesting brain waves. The feasibility of this methodology is evaluated with EEG data acquired with participants performing on the Halstead Category Test. The analysis shows that it is possible to find the Feedback Error Negativity (FRN) Potential at single-trial level and relate its characteristics with the performance of the participant based on their knowledge of the abstract principle underlying the task.
- 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.
- ERP correlates of error processing during performance on the HalsteadCategory TestPublication . Santos, I.M.; Teixeira, Ana; Tomé, A.M.; Pereira, A.T.; Rodrigues, P.; Vagos, P.; Costa, J.; Carrito, M.L.; Oliveira, B.; DeFilippis, N.A.; Silva, C.F.The Halstead Category Test (HCT) is a neuropsychological test that measures a person's ability to formulate and apply abstract principles. Performance must be adjusted based on feedback after each trial and errors are common until the underlying rules are discovered. Event-related potential (ERP) studies associated with the HCT are lacking. This paper demonstrates the use of amethodology inspired on Singular SpectrumAnalysis (SSA) applied to EEG signals, to remove high amplitude ocular andmovement artifacts during performance on the test. This filtering technique introduces no phase or latency distortions, with minimum loss of relevant EEG information. Importantly, the test was applied in its original clinical format, without introducing adaptations to ERP recordings. After signal treatment, the feedback-related negativity (FRN) wave, which is related to error-processing, was identified. This component peaked around 250ms, after feedback, in fronto-central electrodes. As expected, errors elicited more negative amplitudes than correct responses. Results are discussed in terms of the increased clinical potential that coupling ERP informationwith behavioral performance data can bring to the specificity of theHCT in diagnosing different types of impairment in frontal brain function.
- 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.
- 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.