Advances in EEG-Based biometry

Authors:
António Ferreira;Carlos Almeida;Pétia Georgieva;Ana Tomé;Filipe Silva
Affiliations:
Department of Electronics, Telecommunications and Informatics/IEETA, University of Aveiro, Aveiro, Portugal;Department of Electronics, Telecommunications and Informatics/IEETA, University of Aveiro, Aveiro, Portugal;Department of Electronics, Telecommunications and Informatics/IEETA, University of Aveiro, Aveiro, Portugal;Department of Electronics, Telecommunications and Informatics/IEETA, University of Aveiro, Aveiro, Portugal;Department of Electronics, Telecommunications and Informatics/IEETA, University of Aveiro, Aveiro, Portugal
Venue:
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
Year:
2010

Citing 2
Cited 4

Biometrics from Brain Electrical Activity: A Machine Learning Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Evolving spiking neural networks and neurogenetic systems for spatio- and spectro-temporal data modelling and pattern recognition

WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
NeuCube evospike architecture for spatio-temporal modelling and pattern recognition of brain signals

ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Person authentication from neural activity of face-specific visual self-representation

Pattern Recognition
2013 Special Issue: Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition

Neural Networks

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Abstract

This paper is focused on proving the concept that the EEG signals collected during a perception or mental task can be used for discrimination of individuals. The viability of the EEG-based person identification was successfully tested for a data base of 13 persons. Among various classifiers tested, Support Vector Machine (SVM) with Radial Basis Function (RBF) exhibits the best performance. The problem of static classification that does not take into account the temporal nature of the EEG sequence was considered by an empirical post classifier procedure. The algorithm proposed has an effect of introducing a memory into the classifier without increasing its complexity. Control of a classified access into restricted areas security systems, health disorder identification in medicine, gaining more understanding of the cognitive human brain processes in neuroscience are some of the potential applications of EEG-based biometry.