Computational Intelligence Techniques applied to Electrophysiological Data Analysis

Computational Intelligence Techniques applied to Electrophysiological Data AnalysisPhD of Alejandro Riera. This work contains the efforts I have made in the last years in the field of Electrophysiological data analysis. Most of the work has been done at Starlab Barcelona S. L. and part of it at the Neurodinamics Laboratory of the Department of Psychiatry and Clinical Psychobiology of the University of Barcelona. The main work deals with the analysis of EEG signals, although other signals have also been used. Several data sets have

PhD of Alejandro Riera. This work contains the efforts I have made in the last years in the field of Electrophysiological data analysis. Most of the work has been done at Starlab Barcelona S.L. and part of it at the Neurodinamics Laboratory of the Department of Psychiatry and Clinical Psychobiology of the University of Barcelona.
The main work deals with the analysis of EEG signals, although other signals have also been used. Several data sets have been collected and analysed applying advanced Signal Processing techniques. On a later stage Computational Intelligence techniques, such as Machine Learning and Genetic Algorithms, have been applied, mainly to classify the different conditions from the EEG data sets. 3 applications involving EEG and classification are proposed corresponding to each one of the 3 case studies presented in this thesis:
¥ Analysis of Electrophysiological signals for biometric purposes.
¥ EEG differences in First Psychotic Episode Patients.
¥ Markers of stress in the EEG signal.