Below you can find a short list of selected research papers, my PhD Thesis, and other scientific publications. If you are interested, you can also see my full list of publications.
A close association was found between fluctuations in an autonomic correlate of vigilance state (i.e. the distal-to-proximal skin temperature gradient), and fluctuations in central nervous system correlates of vigilance (i.e. background EEG power and ERP features). The findings are of theoretical and practical relevance for the assessment and manipulation of vigilance.
An algorithm for the blind separation of mutually independent and/or temporally correlated sources is presented in this article.The algorithm is closely related to the maximum likelihood approach based on entropy rate minimization but uses a simpler contrast function that can be accurately and efficiently estimated using nearest-neighbor distances. The advantages of the new algorithm are highlighted using simulations and real EEG data.
A problem when studying functional brain connectivity with EEG is that electromagnetic volume conduction introduces spurious correlations between any pair of EEG sensors. The traditional solution is to map scalp potentials to brain space before computing connectivity indices. The fundamental pitfall of this approach is that the EEG inverse solution becomes unreliable when more than a single compact brain area is actively involved in EEG generation.
This thesis proposes an analysis methodology that partially overcomes volume conduction effects. Prospectively, new brain connectivity analysis techniques like the one proposed in this thesis could provide a rational basis for evaluating how new drugs affect neural networks in early degeneration, which might have far-reaching implications for therapeutic drug development.
Finding interdependency relations between (possibly multivariate) time series provides valuable knowledge about the processes that generate the signals. Information theory sets a natural framework for non-parametric measures of several classes of statistical dependencies. However, a reliable estimation from information-theoretic functionals is hampered when the dependency to be assessed is brief or evolves in time.
Here, we show that these limitations can be overcome when we have access to an ensemble of independent repetitions of the time series. In particular, we gear a data-efficient estimator of probability densities to make use of the full structure of trial-based measures. By doing so, we can obtain time-resolved estimates for a family of entropy combinations (including mutual information, transfer entropy, and their conditional counterparts) which are more accurate than the simple average of individual estimates over trials. We show with simulated and real data that the proposed approach allows to recover the time-resolved dynamics of the coupling between different subsystems.
The functional dynamics of thalamocortical networks (as measured by EEG) differentiate individuals at high risk of developing AD from healthy elderly subjects. These results support the hypothesis that neurodegeneration mechanisms are active years before the patient is clinically diagnosed with dementia.
Directional connectivity in the brain has been typically computed between scalp EEG signals, neglecting the fact that correlations between scalp measurements are partly caused by electrical conduction through the head volume. Although recently proposed techniques are able to identify causality relationships between EEG sources rather than between recording sites, most of them need a priori assumptions about the cerebral regions involved in the EEG generation.
In this paper we present a novel methodology based on VAR modeling and ICA able to determine the temporal activation of the intracerebral EEG sources as well as their approximate locations. The superiority of the proposed approach over several existing methods is demonstrated using simulations and a real EEG-alpha dataset.
Blind inversion of a linear and instantaneous mixture of source signals is a problem often encountered in many signal processing applications. Efficient fastICA (EFICA) offers an asymptotically optimal solution to this problem when all of the sources obey a generalized Gaussian distribution, at most one of them is Gaussian, and each is independent and identically distributed (i.i.d.) in time. Likewise, weights-adjusted second-order blind identification (WASOBI) is asymptotically optimal when all the sources are Gaussian and can be modeled as autoregressive (AR) processes with distinct spectra. Nevertheless, real-life mixtures are likely to contain both Gaussian AR and non-Gaussian i.i.d. sources, rendering WASOBI and EFICA severely suboptimal. In this paper, we propose a novel scheme for combining the strengths of EFICA and WASOBI in order to deal with such hybrid mixtures. Simulations show that our approach outperforms competing algorithms designed for separating similar mixtures.