logo SBA

ETD

Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-04072020-102537


Tipo di tesi
Tesi di laurea magistrale
Autore
CAVALIERE, LAURA
URN
etd-04072020-102537
Titolo
Characterization of upper limb movement-related cortical dynamics through fractional integrated autoregressive modeling of electroencephalographic signals
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
INGEGNERIA BIOMEDICA
Relatori
relatore Valenza, Gaetano
correlatore Ing. Catrambone, Vincenzo
correlatore Prof.ssa Rocha, Ana Paula
Parole chiave
  • autocorrelation
  • autoregressive-model
  • electroencephalogram
  • intransitives
  • long-memory
  • task
  • tool mediated;
  • transitives
  • upper limb
Data inizio appello
24/04/2020
Consultabilità
Non consultabile
Data di rilascio
24/04/2090
Riassunto
EEG is a neuroimaging technique widely used for the study of the brain dynamics involved in the execution of various tasks, such as motor ones. To this end, there are several methods used in the literature to characterize the EEG signal, among which the autoregressive models. These include univariate, multivariate and fractionally integrated models. In this work, the use of ARFI models was proposed with the aim of extracting the long memory component from the EEG signal, described by parameter d. These models have proved useful for the analysis of various time series, but have never been applied to the EEG signal. The application of these models to the EEG signal proved to be very useful as significant differences were found in the parameter d between movement and rest. This implies that this parameter contributes to the discrimination between movement and rest. Furthermore, significant differences were also found between different categories of movement. Since the results obtained are very promising, this analysis could be successfully used in applications such as robot control, brain computer interface, rehabilitation.
File