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

Xu HUANG; Wanli MA

doi:10.1007/978-3-642-42042-9_78

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

Xu HUANG, Wanli MA

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

16 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 language	English
Title of host publication	International Conference on Neural Information Processing (ICONIP 2013)
Subtitle of host publication	Lecture Notes in Computer Science
Editors	Minho Lee, Akira Hirose, Zeng-Guang Hou, Rhee Man Kil
Place of Publication	Berlin Heidelberg
Publisher	Springer
Pages	632-639
Number of pages	8
Volume	8227
ISBN (Print)	9783642420412
DOIs	https://doi.org/10.1007/978-3-642-42042-9_78
Publication status	Published - 2013
Event	20th International Conference on Neural Information Processing (ICONIP 2013) - Daegu, Daegu, Korea, Republic of Duration: 3 Nov 2013 → 7 Nov 2013

Conference

Conference	20th International Conference on Neural Information Processing (ICONIP 2013)
Abbreviated title	ICONIP 2013
Country/Territory	Korea, Republic of
City	Daegu
Period	3/11/13 → 7/11/13

Access to Document

10.1007/978-3-642-42042-9_78

Cite this

@inproceedings{2db4932fa01944249a486dea9a67af99,

title = "EEG-Based Age and Gender Recognition Using Tensor Decomposition and Speech Feature",

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.",

keywords = "Age and Gender Classification, Tensor Decomposition",

author = "Xu HUANG and Wanli MA",

year = "2013",

doi = "10.1007/978-3-642-42042-9_78",

language = "English",

isbn = "9783642420412",

volume = "8227",

pages = "632--639",

editor = "Minho Lee and Akira Hirose and Zeng-Guang Hou and Kil, {Rhee Man}",

booktitle = "International Conference on Neural Information Processing (ICONIP 2013)",

publisher = "Springer",

address = "Netherlands",

note = "20th International Conference on Neural Information Processing (ICONIP 2013), ICONIP 2013 ; Conference date: 03-11-2013 Through 07-11-2013",

}

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 & RM Kil (eds), International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. vol. 8227, Springer, Berlin Heidelberg, pp. 632-639, 20th International Conference on Neural Information Processing (ICONIP 2013), Daegu, Korea, Republic of, 3/11/13. https://doi.org/10.1007/978-3-642-42042-9_78

EEG-Based Age and Gender Recognition Using Tensor Decomposition and Speech Feature. / HUANG, Xu ; MA, Wanli.
International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. ed. / Minho Lee; Akira Hirose; Zeng-Guang Hou; Rhee Man Kil. Vol. 8227 Berlin Heidelberg: Springer, 2013. p. 632-639.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - EEG-Based Age and Gender Recognition Using Tensor Decomposition and Speech Feature

AU - HUANG, Xu

AU - MA, Wanli

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Age and Gender Classification

KW - Tensor Decomposition

U2 - 10.1007/978-3-642-42042-9_78

DO - 10.1007/978-3-642-42042-9_78

M3 - Conference contribution

SN - 9783642420412

VL - 8227

SP - 632

EP - 639

BT - International Conference on Neural Information Processing (ICONIP 2013)

A2 - Lee, Minho

A2 - Hirose, Akira

A2 - Hou, Zeng-Guang

A2 - Kil, Rhee Man

PB - Springer

CY - Berlin Heidelberg

T2 - 20th International Conference on Neural Information Processing (ICONIP 2013)

Y2 - 3 November 2013 through 7 November 2013

ER -