Thesis - Instituto de Telecomunicações

2.3. BEHAVIORAL BIOMETRICS 29of signal are near the 3% EER.A very generic patent [198] proposes a system that would include the capability ofidentifying the user by his ECG signal.A different way to sense the heart is via its sound. In [186], 7 subjects where classifiedaccording to the sound of theirs hearts with an equal error rate of 4%.The already mentioned work of Crosby (et al.) [43] has used blood pressure as one ofthe traits in the multimodal system. The blood pressure is a variable that contains identityinformation from the heart beat dynamics and from the vessel’s structure where the bloodflows to the place where it is sensed.Advantages and Disadvantages To acquire the signals from the heart some intrusionwill occur. The measured electrical signals need adequate electrical contact with the skin.In a circumstance where the ECG is already being monitored the implementation couldeasily lead to the implementation of a continuous verification system.2.3.8 BrainOur brain is the most complex organ and it has been noted that the interior morphologyof the gray matter in the brain varies among individuals [45].Given that the signals from the brain have brought information concerning some pathologies[142, 251], it was the pursuit of some researches to find differences between normalsubject’s EEG signals [154].The tools we are able to use to study the brain are relatively rudimentary when comparedto the object of study, even in the case of recent imagery systems brought by ComputorizedTomography (CT), functional Magnetic Resonance Imaging (fMRI), and Positron EmissionTomography (PET). In the particular case of EEG, the signal is captured at a relativelylarge distance from its source, so most of the spatial information is lost [171].Even so, using only one position in the head, P4 (a position named based on the 10-20EEG electrodes position system [101]) in a study with 40 subjects [182], results showed thatthe system error is 16%. The authors based their work in the estimation of autoregressivemodels [239] for EEG signal dynamics, after the removal of Electromiography (EMG) noisevia the analysis of a trained neurologist. The subjects were performing open eye and closed