Original Article

Medical & Biological Engineering & Computing

, 45:403

First online:

A comparison approach toward finding the best feature and classifier in cue-based BCI

  • R. BoostaniAffiliated withDepartment of Computer Science and Engineering, School of Engineering, Shiraz University Email author 
  • , B. GraimannAffiliated withLaboratory of Brain–Computer Interfaces, Institute for Knowledge Discovery at the Graz, University of Technology
  • , M. H. MoradiAffiliated withDepartment of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic)
  • , G. PfurtschellerAffiliated withLaboratory of Brain–Computer Interfaces, Institute for Knowledge Discovery at the Graz, University of Technology

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Abstract

In this paper, a comparative evaluation of state-of-the art feature extraction and classification methods is presented for five subjects in order to increase the performance of a cue-based Brain–Computer interface (BCI) system for imagery tasks (left and right hand movements). To select an informative feature with a reliable classifier features containing standard bandpower, AAR coefficients, and fractal dimension along with support vector machine (SVM), Adaboost and Fisher linear discriminant analysis (FLDA) classifiers have been assessed. In the single feature-classifier combinations, bandpower with FLDA gave the best results for three subjects, and fractal dimension and FLDA and SVM classifiers lead to the best results for two other subjects. A genetic algorithm has been used to find the best combination of the features with the aforementioned classifiers and led to dramatic reduction of the classification error and also best results in the four subjects. Genetic feature combination results have been compared with the simple feature combination to show the performance of the Genetic algorithm.

Keywords

Brain–Computer interface (BCI) Adaboost Support vector machine (SVM) Adaptive auto regressive (AAR) Fisher linear discriminate analysis (FLDA) Fractal dimension (FD) Bandpower (BP) Genetic algorithm