A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data


PATTERN RECOGNITION LETTERS, vol.31, no.11, pp.1207-1215, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 31 Issue: 11
  • Publication Date: 2010
  • Doi Number: 10.1016/j.patrec.2010.04.009
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1207-1215
  • Keywords: Brain computer interface (BCI), Polynomial fitting, k-Nearest neighbor, Electroencephalogram (EEG), Feature extraction, Classification, BRAIN-COMPUTER INTERFACE, SINGLE-TRIAL EEG, COMPETITION 2003, FEATURE-EXTRACTION, SPATIAL-PATTERNS, INFORMATION, POTENTIALS, DISCRIMINATION, IMAGINATION, TRANSFORM
  • Karadeniz Technical University Affiliated: Yes


Speed and accuracy in classification of electroencephalographic (EEG) signals are key issues in brain computer interface (BCI) technology. In this paper, we propose a fast and accurate classification method for cursor movement imagery EEG data. A two-dimensional feature vector is obtained from coefficients of the second order polynomial applied to signals of only one channel. Then, the features are classified by using the k-nearest neighbor (k-NN) algorithm. We obtained significant improvement for the speed and accuracy of the classification for data set la, which is a typical representative of one kind of BCI competition 2003 data. Compared with the Multiple Layer Perceptron (MLP) and the Support Vector Machine (SVM) algorithms, the k-NN algorithm not only provides better classification accuracy but also needs less training and testing times. (C) 2010 Elsevier B.V. All rights reserved.