University of Malta

An EEG-Based Biometric System
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An EEG-Based Biometric System

Lead Investigator: Mr Andrea Lia
Supervisor: Dr Owen Falzon, Centre for Biomedical Cybernetics


Electroencephalography (EEG) is the non-invasive way of recording the electrical activity in the brain by placing electrodes on different parts of the human scalp. EEG signals are used in two main areas: medical area and brain computer interfacing (BCI). EEG is used as a diagnostic tool for identifying medical abnormalities such as epilepsy and brain tumours. Recent studies contributed in the use of EEG as a biometric trait. The EEG is unique making it a good candidate to use as a biometric trait. Moreover, an EEG-based biometric system offers several benefits over other biometric systems. Since the EEG is a summation of the electrical potentials caused by the neurons, it is very difficult for an attacker to acquire the EEG data and feed it to the system. This will result in a more robust system against sensor spoofing offering a higher level of security. This system can be also used by people with severe disabilities such as missing hands and fingertips.

The EEG-based biometric system proposed in this project uses a new experimental protocol that elicits a type of response in the brain that has never been analysed before in terms of biometry. This response is known as the steady state visually evoked potential (SSVEP). A literature review was carried out to investigate the techniques used for biometry in terms of feature extraction and classification algorithms. Comparing the methods used in the different studies led to a selection of the appropriate methods to implement in this project. 


Since SSVEPs in biometry has never been used so far, the appropriate frequencies and duty cycle had to be analysed within each individual before discriminating between the subjects. After selecting the best frequencies and duty cycle which performed best across all subjects, the discrimination of the different subjects follows using different types of feature vectors. This was done to study which elements have the most relevant information to discriminate between the subjects.

Other Links

Biomedical Engineering Research at the Dept. of Systems and Control Engineering [Link



Funding Award for BrainApp

CBC awarded research funding under the FUSION Technology Development Programme (TDP) 2017

New peer-reviewed journal article

CBC publishes in Biomedical Physics & Engineering Express

CBC attends EMBC 2017

Three Papers Accepted for Presentation at the Annual Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017)

Research at UoM: Brain Computer Interface

Communicating using brain signals

Last Updated: 11 May 2017

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