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Auflistung nach Autor:in "Rocca, Daria La"

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  • Konferenzbeitrag
    EEG based user recognition using BUMP modelling
    (BIOSIG 2013, 2013) Rocca, Daria La; Campisi, Patrizio; Solé-Casals, Jordi
    In this paper the use of electroencephalogram (EEG) as biometric identifier is investigated. The use of EEG within the biometric framework has already been introduced in the recent past although it has not been extensively analyzed. In this contribution we apply the “bump” modelling analysis for the feature extraction stage within an identification framework, in order to reduce the huge amount of data recorded through EEG. For the purpose of this study we rely on the “resting state with eyes closed” protocol. The employed database is composed of 36 healthy subjects whose EEG signals have been acquired in an ad hoc laboratory. Different electrodes configurations pertinent with the employed protocol have been considered. A classifier based on Mahalanobis distance have been tested for the enrollment of the subjects and their identification. An information fusion performed at the score level has shown to improve correct classification performance. The obtained results show that an identification accuracy of 99.69% can be achieved. It represents an high degree of accuracy, given the current state of research on EEG biometrics.
  • Konferenzbeitrag
    EEG biometrics for user recognition using visually evoked potentials
    (BIOSIG 2015, 2015) Das, Rig; Maiorana, Emanuele; Rocca, Daria La; Campisi, Patrizio
    Electroencephalographic signals (EEG) have been long supposed to contain features characteristic of each individual, yet a substantial interest for exploiting them as a potential biometrics for people recognition has only recently grown. The biggest advantages of EEG-based biometrics lie in its universality and security, while its major concerns are related to the acquisition protocol that can be inconvenient and time consuming. This paper investigates the use of EEG signals, elicited using visual stimuli, for the purpose of biometric recognition, and evaluates the performance obtained considering various frequency bands, different number of visual stimuli, and various subsets of time intervals after the stimuli presentation. An exhaustive set of experimental tests has been performed by employing EEG data of 50 different healthy subjects acquired in two different sessions, separated by one week time.