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Authors: N. S. Dias 1 ; M. Kamrunnahar 2 ; P. M. Mendes 1 ; S. J. Schiff 2 and J. H. Correia 3

Affiliations: 1 University of Minho, Portugal ; 2 The Pennsylvania State University, United States ; 3 Industrial Electronics Department, University of Minho, Portugal

ISBN: 978-989-8111-65-4

Keyword(s): BCI, EEG, feature selection.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Informatics in Control, Automation and Robotics ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing, Sensors, Systems Modeling and Control ; Soft Computing ; Time and Frequency Response ; Time-Frequency Analysis

Abstract: A new formulation of principal component analysis (PCA) that considers group structure in the data is proposed as a Variable Subset Selection (VSS) method. Optimization of electrode channels is a key problem in brain-computer interfaces (BCI). BCI experiments generate large feature spaces compared to the sample size due to time limitations in EEG sessions. It is essential to understand the importance of the features in terms of physical electrode channels in order to design a high performance yet realistic BCI. The VSS produces a ranked list of original variables (electrode channels or features), according to their ability to discriminate between tasks. A linear discrimination analysis (LDA) classifier is applied to the selected variable subset. Evaluation of the VSS method using synthetic datasets selected more than 83% of relevant variables. Classification of imagery tasks using real BCI datasets resulted in less than 16% classification error.

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Paper citation in several formats:
Dias N.; Kamrunnahar M.; Mendes P.; Schiff S.; Correia J. and (2009). VARIABLE SUBSET SELECTION FOR BRAIN-COMPUTER INTERFACE - PCA-based Dimensionality Reduction and Feature Selection.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009) ISBN 978-989-8111-65-4, pages 35-40. DOI: 10.5220/0001533200350040

@conference{biosignals09,
author={N. S. Dias and M. Kamrunnahar and P. M. Mendes and S. J. Schiff and J. H. Correia},
title={VARIABLE SUBSET SELECTION FOR BRAIN-COMPUTER INTERFACE - PCA-based Dimensionality Reduction and Feature Selection},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)},
year={2009},
pages={35-40},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001533200350040},
isbn={978-989-8111-65-4},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)
TI - VARIABLE SUBSET SELECTION FOR BRAIN-COMPUTER INTERFACE - PCA-based Dimensionality Reduction and Feature Selection
SN - 978-989-8111-65-4
AU - Dias, N.
AU - Kamrunnahar, M.
AU - Mendes, P.
AU - Schiff, S.
AU - Correia, J.
PY - 2009
SP - 35
EP - 40
DO - 10.5220/0001533200350040

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