Please use this identifier to cite or link to this item:
Title: Computational Intelligence Techniques for Electro-Physiological Data Analysis
Author: Riera Sardà, Alexandre
Director/Tutor: Grau Fonollosa, Carles
Ruffini i Fores, Giulio
Keywords: Intel·ligència computacional
Estrès (Fisiologia)
Aprenentatge automàtic
Computational intelligence
Stress (Physiology)
Machine learning
Issue Date: 9-Nov-2012
Publisher: Universitat de Barcelona
Abstract: 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 Neurodynamics Laboratory of the Department of Psychiatry and Clinical Psychobiology of the University of Barcelona. The main work deals with the analysis of electroencephalography (EEG) signals, although other signals, such as electrocardiography (ECG), electroculography (EOG) and electromiography (EMG) 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: We demonstrate the potential of using EEG signals for biometric purposes. Using the ENOBIO EEG amplifier, and using only two frontal EEG channels, we are able to authenticate subjects with a performance up to 96.6%. We also looked for features extracted from the ECG signals and in that case the performance was equal to 97.9%. We also fused the results of both modalities achieving a perfect performance. Our system is ready to use and since it only uses 4 channels (2 for EEG, 1 for ECG in the left wrist and 1 as active reference in the right ear lobe), the wireless ENOBIO sensor is perfectly suited for our application. EEG differences in First Psychotic Episode (FPE) Patients: From an EEG data set of 15 FPE patients and the same number of controls, we studied the differences in their EEG signals in order to train a classifier able to recognise to which group an EEG sample comes from. The feature we use are extracted from the EEG by computing the Synchronization Likelihood feature between all possible pairs of channels. The next step is to build a graph and from that graph we extracted the Mean Path Length and the Clustering Coefficient. Those features as a function of the connectivity threshold are then used in our classifiers. We then create several classification problems and we reach up to 100% of classification in some cases. Markers of stress in the EEG signal: In this research, we designed a protocol in which the participants where asked to perform different tasks, each one with a different stress level. Among these tasks we can find the Stroop Test, Mathematical arithmetics and also a fake blood sample test. By extracting the alpha asymmetry and the beta/alpha ration, we where able to discriminate between the different tasks with performances up to 88%. This application can be used with only 3 EEG electrodes, and it can also work in real time. Finally this application can also be used as a neurofeedback training to learn how to cope with stress.
Este trabajo contiene los esfuerzos que he realizado en los últimos años en el campo del análisis de datos electro-fisiológicos. La mayor parte del trabajo se ha hecho en Starlab Barcelona SL y otra parte en el Laboratorio de Neurodinámica del Departamento de Psiquiatría y Psicobiología Clínica de la Universidad de Barcelona. La parte central de esta tesis está relacionado con el análisis de la señales de electroencefalografía (EEG), aunque otras señales, tales como electrocardiografía (ECG), electroculografía (EOG) y electromiografía (EMG) también se han utilizado. Varios conjuntos de datos se han recogido y analizado aplicando técnicas avanzadas de procesamiento de señales. En una fase posterior, técnicas de inteligencia computacional, tales como 'Machine Learning' y algoritmos genéticos, se han aplicado, principalmente para clasificar las diferentes condiciones de los conjuntos de datos de EEG. Las 3 aplicaciones, que involucran EEG y técnicas de clasificación, que se presentan en esta tesis son: -Análisis de señales electro-fisiológicas para aplicaciones de biometría -Diferencias en las características del EEG en pacientes de primer brote psicótico -Marcadores de estrés en la señal de EEG
Appears in Collections:Tesis Doctorals - Facultat - Medicina

Files in This Item:
File Description SizeFormat 
ARS_PhD_THESIS.pdf8.79 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.