Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/43979
Título: Electroencephalogram subject identification: A review
Autores/as: Del Pozo-Banos, Marcos
Alonso, Jesús B. 
Ticay-Rivas, Jaime R.
Travieso, Carlos M. 
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Electroencephalogram, Biometrics, Genetics, Identification
Fecha de publicación: 2014
Editor/a: 0957-4174
Publicación seriada: Expert Systems with Applications 
Resumen: This is, to the best of the authors knowledge, the first complete research on the state of the art on EEG based subject identification. As well as covering the full story of this field (from 1980 to 2013), an overview of the findings made in genetic and neurophysiology areas, from which it is based, is also provided. After a comprehensive search, 109 biometric publications were found and studied, from which 88 were finally included in this document. A categorization of papers is proposed based on the recording paradigm. The most used databases, some of them public, have been identified and named to allow the comparison of results from these and future works. The findings of this work show that, although basic questions remain to be answered, the EEG, and specially its power spectrum in the range of the alpha rhythm, contains subject specific information that can be used for classification. Moreover, approaches such as a multi-day-session training, the fusion of information from different electrodes and bands, and Support Vector Machines are recommended to maximize the system’s performance. All in all, the problem of subject identification by means of their EEG is harder than initially expected, as it relies on information extracted from complex heterogeneous EEG traits which are the results of elaborated models of inheritance, which in turn makes the problem very sensitive to its variables (time, frequency, space, recording paradigm and algorithms).
URI: http://hdl.handle.net/10553/43979
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2014.05.013
Fuente: Expert Systems with Applications[ISSN 0957-4174],v. 41, p. 6537-6554
Colección:Reseña
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