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Age and gender classification using EEG paralinguistic features
conference contribution
posted on 2013-01-01, 00:00 authored by Phuoc NguyenPhuoc Nguyen, D Tran, X Huang, W MaThe effects of age and gender on EEG signal have been investigated in clinical psychophysiology. However extracting age and gender information from EEG data has not been addressed. This information is useful in building automatic systems that can classify a person in to gender or age groups based on EEG characteristics of that person, index EEG data for searching, identify or verify a person, and improve brain-computer interface systems. We propose in this paper a framework of automatic age and gender classification system using EEG data. We also propose a speech-based method to extract paralinguistic features in EEG signal that contain rich age and gender information and apply these features to improve performance of our age and gender classification system. Experimental results for system evaluation and comparison are also presented. © 2013 IEEE.
History
Event
Neural Engineering. International Conference (6th : 2013, San Diego, California)Pagination
1295 - 1298Publisher
IEEELocation
San Diego, CaliforniaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2013-11-06End date
2013-11-08ISSN
1948-3546eISSN
1948-3554ISBN-13
9781467319690Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2013, IEEETitle of proceedings
NER 2013 : 6th International IEEE/EMBS Conference on Neural EngineeringUsage metrics
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