Title :
Person identification based on parametric processing of the EEG
Author :
Poulos, M. ; Rangoussi, M. ; Chrissikopoulos, V. ; Evangelou, A.
Author_Institution :
Dept. of Inf., Univ. of Piraeus, Greece
Abstract :
Person identification based on parametric spectral analysis of the EEG signal is addressed in this work-a problem that has not yet been seen in a signal-processing framework, to the best of our knowledge. AR parameters are estimated from a signal containing only the alpha, rhythm activity of the EEG. These parameters are used as features in the classification step, which employs a learning vector quantizer network. The proposed method was applied on a set of real EEG recordings made on healthy individuals, in an attempt to experimentally investigate the connection between a person´s EEG and genetically-specific information. Correct classification scores at the range of 72% to 84% show the potential of our approach for person classification/identification and are in agreement with previous research showing evidence that the EEG carries genetic information
Keywords :
autoregressive processes; biometrics (access control); electroencephalography; pattern classification; spectral analysis; vector quantisation; AR parameters; EEG; classification step; genetic information; learning vector quantizer network; parametric processing; person classification; person identification; rhythm activity; spectral analysis; Computational geometry; Data mining; Electroencephalography; Encoding; Feature extraction; Genetics; Informatics; Information security; Physiology; Rhythm;
Conference_Titel :
Electronics, Circuits and Systems, 1999. Proceedings of ICECS '99. The 6th IEEE International Conference on
Conference_Location :
Pafos
Print_ISBN :
0-7803-5682-9
DOI :
10.1109/ICECS.1999.812278
