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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 Ma
The 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

Pagination

1295-1298

Location

San Diego, California

Start date

2013-11-06

End date

2013-11-08

ISSN

1948-3546

eISSN

1948-3554

ISBN-13

9781467319690

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2013, IEEE

Title of proceedings

NER 2013 : 6th International IEEE/EMBS Conference on Neural Engineering

Event

Neural Engineering. International Conference (6th : 2013, San Diego, California)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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