Tesi etd-09192016-170443 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
CATRAMBONE, VINCENZO
Indirizzo email
catrambone.vincenzo@gmail.com
URN
etd-09192016-170443
Titolo
Computational modeling and analysis of ECoG data through Support Vector Machines algorithms.
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
INGEGNERIA BIOMEDICA
Relatori
relatore Prof. Vanello, Nicola
controrelatore Prof. Landini, Luigi
relatore Prof. Formisano, Elia
controrelatore Prof. Landini, Luigi
relatore Prof. Formisano, Elia
Parole chiave
- ECoG
- Machine Learning
- Multivariate Analysis
- SVM
Data inizio appello
07/10/2016
Consultabilità
Non consultabile
Data di rilascio
07/10/2086
Riassunto
In this work some ElectoCorticography data has been taken in account, a multivariate analysis has been proposed considering spatial and time-frequency features. The Dataset has been recorded following the Recalibration and Adaptation paradigm about ambigous and not-ambigous audio-visual stimulation. A classification between these two conditions through a Support Vector Machine algorithm using a RBF kernel has been implemented. An intra- and inter- subject discussion of the results will close the dissertation.
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