Browsing by Author "Lang, E."
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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.
- On the Use of KPCA to Extract Artifacts in One-Dimensional Biomedical SignalsPublication . Teixeira, Ana; Tome, A.; Lang, E.; Schachtner, R.; Stadlthanner, K.Kernel principal component analysis(KPCA) is a nonlinear projective technique that can be applied to decompose multi-dimensional signals and extract informative features as well as reduce any noise contributions. In this work we extend KPCA to extract and remove artifact-related contributions as well as noise from one-dimensional signal recordings. We introduce an embedding step which transforms the one-dimensional signal into a multi-dimensional vector. The latter is decomposed in feature space to extract artifact related contaminations. We further address the preimage problem and propose an initialization procedure to the fixed-point algorithm which renders it more efficient. Finally we apply KPCA to extract dominant Electrooculogram (EOG) artifacts contaminating Electroencephalogram (EEG) recordings in a frontal channel.