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Browsing by Author "Bohm, M."

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  • On the use of simulated annealing to automatically assign decorrelated components in second-order blind source separation
    Publication . 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.
    2006Journal article Open access Show more
  • Single-channel electroencephalogram analysis using non-linear subspace techniques
    Publication . Teixeira, A.R.; Alves, N.; Tome, A.M.; Bohm, M.; Lang, E.W.; Puntonet, C.G.
    In this work, we propose the correction of univariate, single channel EEGs using projective subspace techniques. The biomedical signals which often represent one dimensional time series, need to be transformed to multi-dimensional signal vectors for the latter techniques to be applicable. The transformation can be achieved by embedding an observed signal in its delayed coordinates. We propose the application of two non-linear subspace techniques to the obtained multidimensional signal. One of the techniques consists in a modified version of Singular Spectrum Analysis (SSA) and the other is kernel Principal Component Analysis (KPCA) implemented using a reduced rank approximation of the kernel matrix. Both nonlinear subspace projection techniques are applied to an electroencephalogram (EEG) signal recorded in the frontal channel to extract its prominent electrooculogram (EOG) interference.
    2007Conference object Open access Show more