Variable subset selection for brain-computer interface: PCA-based dimensionality reduction and feature selection

N. S. Dias, M. Kamrunnahar, P. M. Mendes, S. J. Schiff, J. H. Correia

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

7 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationBIOSIGNALS 2009 - Proceedings of the 2nd International Conference on Bio-Inspired Systems and Signal Processing
Pages35-40
Number of pages6
StatePublished - Jul 21 2009
Event2nd International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2009 - Porto, Portugal
Duration: Jan 14 2009Jan 17 2009

Publication series

NameBIOSIGNALS 2009 - Proceedings of the 2nd International Conference on Bio-Inspired Systems and Signal Processing

Other

Other2nd International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2009
CountryPortugal
CityPorto
Period1/14/091/17/09

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All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Control and Systems Engineering

Cite this

Dias, N. S., Kamrunnahar, M., Mendes, P. M., Schiff, S. J., & Correia, J. H. (2009). Variable subset selection for brain-computer interface: PCA-based dimensionality reduction and feature selection. In BIOSIGNALS 2009 - Proceedings of the 2nd International Conference on Bio-Inspired Systems and Signal Processing (pp. 35-40). (BIOSIGNALS 2009 - Proceedings of the 2nd International Conference on Bio-Inspired Systems and Signal Processing).