Electroencephalogram subject identification: A review

•The state-of-the-art on EEG based subject identification.•Research methodology on EEG for Biometrics.•The genotype–phenotype map of the EEG.•Published works on EEG identification. This is, to the best of the authors knowledge, the first complete research on the state of the art on EEG based subject...

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Vydáno v:	Expert systems with applications Ročník 41; číslo 15; s. 6537 - 6554
Hlavní autoři:	Del Pozo-Banos, Marcos, Alonso, Jesús B., Ticay-Rivas, Jaime R., Travieso, Carlos M.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier Ltd 01.11.2014 Elsevier
Témata:	Algorithms Applied sciences Biological and medical sciences Biometrics Computer science; control theory; systems Computer systems and distributed systems. User interface Data processing. List processing. Character string processing EEC Electrodes Electrodiagnosis. Electric activity recording Electroencephalogram Exact sciences and technology Genetics Identification Investigative techniques, diagnostic techniques (general aspects) Medical sciences Memory organisation. Data processing Nervous system Recording Rhythm Searching Software State of the art Support vector machines Biometrics Genetics Identification Electroencephalogram Performance evaluation Alpha rhythm Time frequency domain method Inheritance Electroencephalography Variable frequency Review Modeling Neurophysiology Electrodes Vector support machine Data fusion Genetic engineering Categorization Power spectrum
ISSN:	0957-4174, 1873-6793
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Popis
Shrnutí:	•The state-of-the-art on EEG based subject identification.•Research methodology on EEG for Biometrics.•The genotype–phenotype map of the EEG.•Published works on EEG identification. 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).
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0957-4174 1873-6793
DOI:	10.1016/j.eswa.2014.05.013