Variable down-selection for brain-computer interfaces

Nuno S. Dias, Mst Kamrunnahar, Paulo M. Mendes, Steven Schiff, Jose H. Correia

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations


A new formulation of principal component analysis (PCA) that considers group structure in the data is proposed as a variable down-selection 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 proposed algorithm produces a ranked list of original variables (electrode channels or features), according to their ability to discriminate movement imagery tasks. A linear discrimination analysis (LDA) classifier is applied to the selected variable subset. Evaluation of the down-selection method using synthetic datasets selected more than 83% of relevant variables. Classification of imagery tasks using real BCI datasets resulted in less than 19% classification error. Across-Group Variance (AGV) showed the best classification performance with the largest dimensionality reduction in comparison with other algorithms in common use.

Original languageEnglish (US)
Title of host publicationBiomedical Engineering Systems and Technologies
Subtitle of host publicationInternational Joint Conference, BIOSTEC 2009 Porto, Portugal, January 14-17, 2009, Revised Selected Papers
EditorsAna Fred, Hugo Gamboa, Joaquim Filipe
Number of pages15
StatePublished - Apr 8 2010

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929

All Science Journal Classification (ASJC) codes

  • General Computer Science


Dive into the research topics of 'Variable down-selection for brain-computer interfaces'. Together they form a unique fingerprint.

Cite this