Brain Connectivity on Health and Disease
One of the main lines of the group is the study of brain function with analytical tools derived from dynamical systems theory. From the data analysis point of view, we combine nonlinear measures of connectivity from time series and complex graph methods to analysis brain activiy using (mainly) EEG and MEG. The data comes mainly from external collaborations in the framework of different funded research projects (see the Links section here), but we have recently bought a wireless EEG system, which we plan to use in different, BCI-like applications using real time brain connectivity analysis. We also use the connectivity patterns to classify subjects into different groups using machine learning algorithms, which is an emerging scientific field with potential applications in both basic and applied research, and even in clinical applications in the medium term.
Analysis of spatially extended dynamical systems from the signals they generate; Estimation of functional and effective brain connectivity from EEG and MEG data; Time series analysis and forecasting.
Matlab / C++ programming. HPC from both CPU / GPU applied to time series analysis. Advanced multivariate statistical analysis. Montecarlo methods for time series resampling.
Our interest is to understand a little more how the brain works, for that reason, we are studying biophysical properties, electrical signals and its processing in neurons. Then, different tools are used to achieve these targets.
In our background, we have experience designed and developed techniques to study biological phenomena (action potential, calcium signal, etc.) using electrophysiological methods (current clamp, voltage clamp, capacitance measurements in isolated cells and dynamic clamp) and image techniques (epifluorescence and two-photon excited fluorescence laser-scanning microscopy). In addition, mathematical modeling is used to study the electrical and biophysical properties of neurons in the brain.
Modeling single-neuron dynamics and information processing; Biophysics, plasticity and memory in single neurons.
Multicompartmental modeling of single neuron. The NEURON simulation environment.
Renewable Energy Systems
Renewable energies have been acquiring a major relevance in the energetic mixes in developed countries.
In our group we have been researching in solar photovoltaic energy, methods to improve the solar cell efficiencies at low cost and its grid integration. Recently we have started to research in wind power energy and its electrical penetration in isolated systems.
Renewable energies production analysis; Renewable energy prototype evaluation; Analysis of the grid integration; Technical advices in renewable energy projects.
FTIR; XPS; MW-PCD; SEM; Van der Pauw Method; Four-Probe System; Solar Simulator; Reflectance; Photoluminescence; Optical Absorption; EQE; IQE; Spectral Response;