Alejandra Figliola

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Since traditional electrical brain signal analysis is mostly qualitative, the development of new quantitative methods is crucial for restricting the subjectivity in the study of brain signals. These methods are particularly fruitful when they are strongly correlated with intuitive physical concepts that allow a better understanding of brain dynamics. Here,(More)
Wavelet-based informational tools for quantitative electroencephalogram (EEG) record analysis are reviewed. Relative wavelet energies, wavelet entropies and wavelet statistical complexities are used in the characterization of scalp EEG records corresponding to secondary generalized tonic-clonic epileptic seizures. In particular, we show that the epileptic(More)
S. Blanco,* A. Figliola, R. Quian Quiroga, O. A. Rosso, and E. Serrano Facultad de Ciencias Exactas y Naturales, Instituto de Cálculo, Universidad de Buenos Aires, Pabellón II, Ciudad Universitaria, 1428 Buenos Aires, Argentina Institute of Physiology, Medical University Lübeck, Ratzeburger Alle 160, D-23538 Lübeck 1, Germany Departamento de Matemática,(More)
EEG signals obtained during tonic-clonic epileptic seizures can be severely contaminated by muscle and physiological noise. Heavily contaminated EEG signals are hard to analyse quantitatively and also are usually rejected for visual inspection by physicians, resulting in a considerable loss of collected information. The aim of this work was to develop a(More)
Odor input evokes characteristic, time-evolving (non-stationary) events in the spontaneously active central ganglia of the snail Helix pomatia. Assuming stationarity for the signals, one could, as the first approach, apply the Fourier-based methods, frequency amplitude characteristics (FAC) measures, for analyzing such events. We could thus for the first(More)
By recourse to appropriate information theory quantifiers (normalized Shannon entropy and Martín-Plastino-Rosso intensive statistical complexity measure), we revisit the characterization of Gaussian self-similar stochastic processes from a Bandt-Pompe viewpoint. We show that the ensuing approach exhibits considerable advantages with respect to other(More)
Odorants evoke characteristic, but complex, local field potentials (LFPs) in the molluscan brain. Wavelet tools in combination with Fourier analysis can detect and characterize hitherto unknown discrete, slow potentials underlying the conspicuous oscillations. Ethanol was one of the odorants that we have extensively studied (J. Neurosci. Methods, 119 (2002)(More)