title: On the use of EEG features towards person identification via neural networks.
abstract: Person identification based on spectral information extracted from the EEG is addressed in this work a problem that has not yet been seen in a signal processing framework. Spectral features are extracted non-parametrically from real EEG data recorded from healthy individuals. Neural network classification is applied on these features using a Learning Vector Quantizer in an attempt to experimentally investigate the connection between a person's EEG and genetically specific information. The proposed method, compared with previously proposed methods, has yielded encouraging correct classification scores in the range of 80% to 100% (case-dependent). These results are in agreement with previous research showing evidence that the EEG carries genetic information.
authors.affiliation: Department of Informatics, University of Piraeus, Greece.
authors.forename: M
authors.initials: M
authors.lastname: Poulos
authors.forename: M
authors.initials: M
authors.lastname: Rangoussi
authors.forename: N
authors.initials: N
authors.lastname: Alexandris
authors.forename: A
authors.initials: A
authors.lastname: Evangelou
mesh_terms: D000328:Adult; D000513:Alpha Rhythm; D000885:Anthropology, Physical / Q000379:methods*; D001611:Beta Rhythm; D004569:Electroencephalography / Q000379:methods*; D005188:False Negative Reactions; D005189:False Positive Reactions; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D008297:Male; D008491:Medical Informatics Applications; D008492:Medical Informatics Computing; D008875:Middle Aged; D016571:Neural Networks, Computer; D010355:Patient Identification Systems / Q000379:methods*; D010375:Pedigree; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013826:Theta Rhythm