Browsing by Author "Tomé, A. M."
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- Clustering evoked potential signals using subspace methodsPublication . Tomé, A. M.; Teixeira, Ana; Figueiredo, N.; Georgieva, P.; Santos, I.M.; Lang, E.This work proposes a clustering technique to analyze evoked potential signals. The proposed method uses an orthogonal subspace model to enhance the single-trial signals of a session and simultaneously a subspace measure to group the trials into clusters. The ensemble averages of the signals of the different clusters are compared with ensemble averages of visually selected trials which are free of any artifact. Preliminary results consider recordings from an occipital channel where evoked response P100 wave is most pronounced.
- 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 this work a valence recognition system based on electroencephalograms is presented. The performance of the system is evaluated for two settings: single subjects (intra-subject) and between subjects (inter-subject). The feature extraction is based on measures of relative energies computed in short time intervals and certain frequency bands. The feature extraction is performed either on signals averaged over an ensemble of trials or on single-trial response signals. The subsequent classification stage is based on an ensemble classifier, i. e. a random forest of tree classifiers. The classification is performed considering the ensemble average responses of all subjects (inter-subject) or considering the single-trial responses of single subjects (intra-subject). Applying a proper importance measure of the classifier, feature elimination has been used to identify the most relevant 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.
- How to apply nonlinear subspace techniques to univariate biomedical tim e seriesPublication . Teixeira, Ana; Tomé, A. M.; Böhm, M.; Puntonet, Carlos G.; Lang, Elmar W.In this paper, we propose an embedding technique for univariate single-channel biomedical signals to apply projective subspace techniques. Biomedical signals are often recorded as 1-D time series; hence, they need to be transformed to multidimensional signal vectors for subspace techniques to be applicable. The transformation can be achieved by embedding an observed signal in its delayed coordinates. We propose the application of two nonlinear subspace techniques to embedded multidimensional signals and discuss their relation. The techniques consist of modified versions of singular-spectrum analysis (SSA) and kernel principal component analysis (KPCA). For illustrative purposes, both nonlinear subspace projection techniques are applied to an electroencephalogram (EEG) signal recorded in the frontal channel to extract its dominant electrooculogram (EOG) interference. Furthermore, to evaluate the performance of the algorithms, an experimental study with artificially mixed signals is presented and discussed.
- Nonlinear projective techniques to extract artifacts in biomedical signalsPublication . Teixeira, Ana; Tomé, A. M.; Stadlthanner, K.; Lang, E. W.Biomedical signals are generally contaminated with artifacts and noise. In case the artifacts dominate, the useful signal can easily be extracted with projective subspace techniques. Then, biomedical signals which often represent one dimensional time series, need to be transformed to multidimensional signal vectors for the latter techniques to be applicable. The transformation can be achieved by embedding an observed signal in its delayed coordinates. Using this embedding we propose to cluster the resulting feature vectors and apply a singular spectrum analysis (SSA) locally in each cluster to recover the undistorted signals. We also compare the reconstructed signals to results obtained with kernel-PCA. Both nonlinear subspace projection techniques are applied to artificial data to demonstrate the suppression of random noise signals as well as to an electroencephalogram (EEG) signal recorded in the frontal channel to extract its prominent electrooculogram (EOG) interference.