On the use of EEG features towards person identification via neural networks
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Poulos, M.
Rangoussi, M.
Alexandris, N.
Evangelou, A.
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peer-reviewed
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Med Inform Internet Med
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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.
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Keywords
Adult, Alpha Rhythm, Anthropology, Physical/*methods, Beta Rhythm, Electroencephalography/*methods, False Negative Reactions, False Positive Reactions, Female, Fourier Analysis, Humans, Male, Medical Informatics Applications, Medical Informatics Computing, Middle Aged, *Neural Networks (Computer), Patient Identification Systems/*methods, Pedigree, Sensitivity and Specificity, *Signal Processing, Computer-Assisted, Theta Rhythm
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http://www.ncbi.nlm.nih.gov/pubmed/11583407
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en
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Πανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικής