EEG-Based Age and Gender Recognition Using Tensor Decomposition and Speech Feature

Xu HUANG, Wanli MA

Research output: A Conference proceeding or a Chapter in BookConference contribution

5 Citations (Scopus)
6 Downloads (Pure)

Abstract

Extracting age and gender information from EEG data has not been investigated. This information is useful in building automatic systems that can classify a person into gender or age groups based on EEG characteristics of that person, index EEG data for searching, identify or verify a person, and improve performance of brain-computer interface systems. In this paper, we propose a framework based on PARAFAC and SVM that can automatically classify age and gender using EEG data. We also propose a method using N-PLS and SVM to improve the classification rate. Experimental results for the proposed method are presented.
Original languageEnglish
Title of host publication International Conference on Neural Information Processing (ICONIP 2013)
Subtitle of host publicationLecture Notes in Computer Science
EditorsMinho Lee, Akira Hirose, Zeng-Guang Hou, Rhee Man Kil
Place of PublicationBerlin Heidelberg
PublisherSpringer
Pages632-639
Number of pages8
Volume8227
ISBN (Print)9783642420412
DOIs
Publication statusPublished - 2013
Event20th International Conference on Neural Information Processing (ICONIP 2013) - Daegu, Daegu, Korea, Republic of
Duration: 3 Nov 20137 Nov 2013

Conference

Conference20th International Conference on Neural Information Processing (ICONIP 2013)
Abbreviated titleICONIP 2013
CountryKorea, Republic of
CityDaegu
Period3/11/137/11/13

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  • Cite this

    HUANG, X., & MA, W. (2013). EEG-Based Age and Gender Recognition Using Tensor Decomposition and Speech Feature. In M. Lee, A. Hirose, Z-G. Hou, & R. M. Kil (Eds.), International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science (Vol. 8227, pp. 632-639). Springer. https://doi.org/10.1007/978-3-642-42042-9_78