Feature selection by independent component analysis and mutual information maximization in EEG signal classification

Tian Lan, Deniz Erdogmus, Andre Adami, Michael Pavel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

Feature selection and dimensionality reduction are important steps in pattern recognition. In this paper, we propose a scheme for feature selection using linear independent component analysis and mutual information maximization method. The method is theoretically motivated by the fact that the classification error rate is related to the mutual information between the feature vectors and the class labels. The feasibility of the principle is illustrated on a synthetic dataset and its performance is demonstrated using EEG signal classification. Experimental results show that this method works well for feature selection.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages3011-3016
Number of pages6
Volume5
DOIs
StatePublished - 2005
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: Jul 31 2005Aug 4 2005

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2005
CountryCanada
CityMontreal, QC
Period7/31/058/4/05

Fingerprint

Independent component analysis
Electroencephalography
Feature extraction
Pattern recognition
Labels

Keywords

  • Brain-Computer Interface
  • EEG
  • Entropy Estimation
  • Feature Selection
  • Independent Component Analysis
  • Mutual Information

ASJC Scopus subject areas

  • Software

Cite this

Lan, T., Erdogmus, D., Adami, A., & Pavel, M. (2005). Feature selection by independent component analysis and mutual information maximization in EEG signal classification. In Proceedings of the International Joint Conference on Neural Networks (Vol. 5, pp. 3011-3016). [1556405] https://doi.org/10.1109/IJCNN.2005.1556405

Feature selection by independent component analysis and mutual information maximization in EEG signal classification. / Lan, Tian; Erdogmus, Deniz; Adami, Andre; Pavel, Michael.

Proceedings of the International Joint Conference on Neural Networks. Vol. 5 2005. p. 3011-3016 1556405.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lan, T, Erdogmus, D, Adami, A & Pavel, M 2005, Feature selection by independent component analysis and mutual information maximization in EEG signal classification. in Proceedings of the International Joint Conference on Neural Networks. vol. 5, 1556405, pp. 3011-3016, International Joint Conference on Neural Networks, IJCNN 2005, Montreal, QC, Canada, 7/31/05. https://doi.org/10.1109/IJCNN.2005.1556405
Lan T, Erdogmus D, Adami A, Pavel M. Feature selection by independent component analysis and mutual information maximization in EEG signal classification. In Proceedings of the International Joint Conference on Neural Networks. Vol. 5. 2005. p. 3011-3016. 1556405 https://doi.org/10.1109/IJCNN.2005.1556405
Lan, Tian ; Erdogmus, Deniz ; Adami, Andre ; Pavel, Michael. / Feature selection by independent component analysis and mutual information maximization in EEG signal classification. Proceedings of the International Joint Conference on Neural Networks. Vol. 5 2005. pp. 3011-3016
@inproceedings{d10f9ee0f6fc4db7a6b25d7d3c3a6ef1,
title = "Feature selection by independent component analysis and mutual information maximization in EEG signal classification",
abstract = "Feature selection and dimensionality reduction are important steps in pattern recognition. In this paper, we propose a scheme for feature selection using linear independent component analysis and mutual information maximization method. The method is theoretically motivated by the fact that the classification error rate is related to the mutual information between the feature vectors and the class labels. The feasibility of the principle is illustrated on a synthetic dataset and its performance is demonstrated using EEG signal classification. Experimental results show that this method works well for feature selection.",
keywords = "Brain-Computer Interface, EEG, Entropy Estimation, Feature Selection, Independent Component Analysis, Mutual Information",
author = "Tian Lan and Deniz Erdogmus and Andre Adami and Michael Pavel",
year = "2005",
doi = "10.1109/IJCNN.2005.1556405",
language = "English (US)",
isbn = "0780390482",
volume = "5",
pages = "3011--3016",
booktitle = "Proceedings of the International Joint Conference on Neural Networks",

}

TY - GEN

T1 - Feature selection by independent component analysis and mutual information maximization in EEG signal classification

AU - Lan, Tian

AU - Erdogmus, Deniz

AU - Adami, Andre

AU - Pavel, Michael

PY - 2005

Y1 - 2005

N2 - Feature selection and dimensionality reduction are important steps in pattern recognition. In this paper, we propose a scheme for feature selection using linear independent component analysis and mutual information maximization method. The method is theoretically motivated by the fact that the classification error rate is related to the mutual information between the feature vectors and the class labels. The feasibility of the principle is illustrated on a synthetic dataset and its performance is demonstrated using EEG signal classification. Experimental results show that this method works well for feature selection.

AB - Feature selection and dimensionality reduction are important steps in pattern recognition. In this paper, we propose a scheme for feature selection using linear independent component analysis and mutual information maximization method. The method is theoretically motivated by the fact that the classification error rate is related to the mutual information between the feature vectors and the class labels. The feasibility of the principle is illustrated on a synthetic dataset and its performance is demonstrated using EEG signal classification. Experimental results show that this method works well for feature selection.

KW - Brain-Computer Interface

KW - EEG

KW - Entropy Estimation

KW - Feature Selection

KW - Independent Component Analysis

KW - Mutual Information

UR - http://www.scopus.com/inward/record.url?scp=33745701301&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33745701301&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2005.1556405

DO - 10.1109/IJCNN.2005.1556405

M3 - Conference contribution

AN - SCOPUS:33745701301

SN - 0780390482

SN - 9780780390485

VL - 5

SP - 3011

EP - 3016

BT - Proceedings of the International Joint Conference on Neural Networks

ER -