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E’ coordinatore della Divisione Biometria del Pattern Recognition and Applications Laboratory (PRA Lab) diretto dal Prof. Fabio Roli. L’attività di ricerca è incentrata sulle tecnologie biometriche per la sicurezza informatica. In particolare si occupa di classificazione e verifica di impronte digitali e volti, rilevazione di contraffazioni e sistemi multimodali. Ha al suo attivo oltre ottanta pubblicazioni fra riviste, atti di conferenze e congressi, capitoli di libro, tutte di impatto internazionale.

E’ revisore di progetti, riviste e conferenze internazionali.

E’ team leader e responsabile di progetti di ricerca internazionali pubblici (FP-European Union) e privati (Crossmatch) nonché progetti nazionali (PRIN, RAS) e locali (“Giovani Ricercatori”) e di collaborazione con il RaCIS di Cagliari.

L’elenco completo delle sue pubblicazioni, delle tesi di laurea e dottorato delle quali è stato co-relatore è nel suo curriculum_vitae e nella pagina personale del sito PRA Lab.

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He is team leader of the Biometric Unit of the Pattern Recognition and Applications Laboratory (PRA Lab) leaded by Prof. Fabio Roli. His research activity is focused on the biometric Technologies for information security. In particular, identification, verification and vulnerability analysis of fingerprint and face, multi-modal biometric systems. He has co-authored more than one hundred of publications in journal, conference proceedings and books chapters. He also co-authored the voice “Antispoofing: Multimodal” in the last edition of Encyclopedia of Biometrics.

He acts as referee for international projects, journals and conferences.

He is in charge of national and international research projects.

The complete publication list and activities is in his curriculum_vitae and in his personal webpage in the PRA Lab website.

Titolo: Brain network analysis of EEG functional connectivity during imagery hand movements
Autori: 
Data di pubblicazione: 2013
Rivista: 
JOURNAL OF INTEGRATIVE NEUROSCIENCE  
Abstract: The characterization of human neural activity during imaginary movement tasks represent an important challenge in order to develop er effective applications that allow the control of a machine. Yet methods based on brain network analysis of functional connectivity have been scarcely investigated. As a result we use graph theoretic methods to investigate the functional connectivity and brain network measures in order to characterize imagery hand movements in a set of healthy subjects. The results of the present study show that functional connectivity analysis and minimum spanning tree (MST) parameters allow to successfully discriminate between imagery hand movements (both right and left) and resting state conditions. In conclusion, this paper shows that brain network analysis of EEG functional connectivity could represent an efficient alternative to more classical local activation based approaches. Furthermore, it also suggests the shift toward methods based on the characterization of a limited set of fundamental functional connections that disclose salient network topological features.
Handle: http://hdl.handle.net/11584/105488
Tipologia:1.1 Articolo in rivista

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