Biomedical subjects

P A Valdes-Sosa

Publications and source records attributed to P A Valdes-Sosa.

3 recordsLinked to original sources

PubMed2001

A comparison of non-linear non-parametric models for epilepsy data.

EEG spike and wave (SW) activity has been described through a non-parametric stochastic model estimated by the Nadaraya-Watson (NW) method. In this paper the performance of the NW, the local linear polynomial regression and support vector machines (SVM) methods were compared. The noise-free realizations obtained by the NW and SVM methods reproduced SW better than as reported in previous works. The tuning parameters had to be estimated manually. Adding dynamical noise, only the NW method was capable of generating SW similar to training data. The standard deviation of the dynamical noise was estimated by means of the correlation dimension.

Data Interpretation, Statistical↗

PubMed1988

A parametric model for multichannel EEG spectra.

The EEG is modelled as the superposition of two component processes: the xi and the alpha processes. In the frequency domain, the xi process is always present and appears as a spectral peak with maximum amplitude at very low frequencies, while the alpha process is characterized by a spectral peak with its maximum located in the traditional alpha band (7-13 Hz), and is not necessarily always present. The multivariate properties of EEG spectra are adequately modelled with frequency independent coherence matrices for each process. Multichannel EEG studies reveal interesting properties: (1) the generalized coherence for alpha is much larger than for xi, indicating increased functional coupling for the alpha process; (2) the alpha coherence matrix has reduced dimensionality, possibly related to a small number of generators; (3) xi coherences are zero phase with magnitudes that decrease exponentially with interelectrode distance; and (4) alpha coherences have significant nonzero phase shifts.

Algorithms↗

PubMed1988

Current source density estimation and interpolation based on the spherical harmonic Fourier expansion.

A method for the spatial analysis of EEG and EP data, based on the spherical harmonic Fourier expansion (SHE) of scalp potential measurements, is described. This model provides efficient and accurate formulas for: (1) the computation of the surface Laplacian and (2) the interpolation of electrical potentials, current source densities, test statistics and other derived variables. Physiologically based simulation experiments show that the SHE method gives better estimates of the surface Laplacian than the commonly used finite difference method. Cross-validation studies for the objective comparison of different interpolation methods demonstrate the superiority of the SHE over the commonly used methods based on the weighted (inverse distance) average of the nearest three and four neighbor values.

Brain↗