Recently, the use of brain activity as biometric trait for automatic users recognition has been investigated. EEG (Electroencephalography) signal is more often used in the medical field for diagnostic purposes. However, early EEG studies adopted similar signal properties and processing tools to study individual distinctive characteristics. As a matter of fact, features related mostly to a single region of the scalp were used, thus losing information on possible links among brain areas. In this work we approached the investigation of the EEG signal as possible biometric by focusing on two recent methods based on functional connectivity, which, in contrast with previous approaches, tend to estimate the complex interactions between EEG signals by measuring the time-series statistical interdependence. Thanks to their potential complementary, we explored their fusion by feature-level and match score-level approaches. Experimental results have shown a performance improvement with respect to that of the individual systems.
['Marco Garau', 'Matteo Fraschini', 'Luca Didaci', 'Gian Luca Marcialis']
Experimental results on multi-modal fusion of EEG-based personal verification algorithms