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

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An integrated transport service fare system, supported by an agreement for ticket revenue sharing among service providers, is an essential component to improve the experience of the users who can find single tickets for the integrated transport services they look for. A challenge is to find a model to share the revenue which all providers agree on. A solution is to adopt data-driven approaches where user-generated data are collected to extract information on the extent each transport service was used. This is consistently used. However, it suffers from incomplete data, as not all users always validate their ticket when checking out or when switching lines. We studied all technologies available to support automatic ticket validation in order to record when the users access and exit each service line. The contributions of this work are the following: we give an in-depth description of the inner workings of this novel approach describing how we take advantage of each technology; we present the developed solution (Beep4Me), which adds new functionalities to an existing mobile ticketing platform; and we describe our testing framework, which includes most cases users might encounter during a trip. Our results demonstrate how it is possible to collect key data related to validations which can be used first for clearing purposes and then for network planning/fleet optimization.