title: Using Combination of µ,β and γ Bands in Classification of EEG Signals.
abstract: BACKGROUND
  In most BCI articles which aim to separate movement imaginations, µ and β frequency bands have been used. In this paper, the effect of presence and absence of γ band on performance improvement is discussed since movement imaginations affect γ frequency band as well.
  
  
  METHODS
  In this study we used data set 2a from BCI Competition IV. In this data set, 9 healthy subjects have performed left hand, right hand, foot and tongue movement imaginations. Time and frequency intervals are computed for each subject and then are classified using Common Spatial Pattern (CSP) as a feature extractor. Finally, data is classified by LDA, RBF MLP, SVM and KNN methods. In all experiments, accuracy rate of classification is computed using 4 fold validation method.
  
  
  RESULTS
  It is seen that most of the time, combination of µ,β and γ bands would have better performance than just using combination of µ and β bands or γ band alone. In general, the improvement rate of the average classification accuracy is computed 2.91%.
  
  
  CONCLUSIONS
  In this study, it is shown that using combination of µ, β and γ frequency bands provides more information than only using combination of µ and β in movement imagination separations.
authors.affiliation: Brain and Intelligent Systems Research Laboratory (BISLab), Department of Electrical and Computer Engineering, ShahidRajaee Teacher Training University, Tehran, Iran.
authors.forename: Mina
authors.initials: M
authors.lastname: Mirnaziri
authors.affiliation: Brain and Intelligent Systems Research Laboratory (BISLab), Department of Electrical and Computer Engineering, ShahidRajaee Teacher Training University, Tehran, Iran.
authors.forename: Masoomeh
authors.initials: M
authors.lastname: Rahimi
authors.affiliation: Department of Computer Science, School of Mathematics, Statistics and Computer Science, University of Tehran, Iran.
authors.forename: Sepidehsadat
authors.initials: S
authors.lastname: Alavikakhaki
authors.affiliation: Brain and Intelligent Systems Research Laboratory (BISLab), Department of Electrical and Computer Engineering, ShahidRajaee Teacher Training University, Tehran, Iran.
authors.forename: Reza
authors.initials: R
authors.lastname: Ebrahimpour
keywords: Brain – Computer Interface (BCI); Common Spatial Pattern (CSP); Electroencephalogram (EEG); Linear Discriminant Analysis (LDA); Multi – Layer Perceptron (MLP); Radial Basis Function (RBF)