Not Logged In

Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals

Recognition of motor imagery tasks (MI) from electroencephalographic (EEG) signals is crucial for developing rehabilitation and motor assisted devices based on brain-computer interfaces (BCI). Here we consider the challenge of learning a classifier, based on relevant patterns of the EEG signals; this learning step typically involves both feature selection, as well as a base learning algorithm. However, in many cases it is not clear what combination of these methods will yield the best classifier. This paper contributes a detailed assessment of feature selection techniques, viz., squared Pearson’s correlation (R2), principal component analysis (PCA), kernel principal component analysis (kPCA) and fast correlation-based filter (FCBF); and the learning algorithms: linear discriminant analysis (LDA), support vector machines (SVM), and Feed Forward Neural Network (NN). A systematic evaluation of the combinations of these methods was performed in three two-class classification scenarios: rest vs. movement,upper vs. lower limb movement and right vs. left hand movement. FCBF in combination with SVM achieved the best results witha classification accuracy of 81.45%, 77.23% and 68.71% in the three scenarios, respectively. Importantly, FCBF determines, based on the feature set, whether a classifier can be learned, and if so, automatically identifies the subset of relevant and non-correlated features. This suggests that FCBF is a powerful method for BCI systems based on MI. Knowledge gained here about procedural combinations has the potential to produce useful BCI tool, that can provide effective motor control for the users.

Citation

R. Vega, T. Sajed, K. Mathewson, K. Khare, P. Pilarski, R. Greiner, G. Sanchez-Ante, J. Antelis. "Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals". Artificial Intelligence Research, 6(1), September 2016.

Keywords: machine learning, feature selection, EEG, biomedical application
Category: In Journal
Web Links: DOI
  Journal

BibTeX

@article{Vega+al:AI-R16,
  author = {Roberto Vega and Touqir Sajed and Kory Wallace Mathewson and Kriti
    Khare and Patrick Pilarski and Russ Greiner and Gildardo Sanchez-Ante and
    Javier M. Antelis},
  title = {Assessment of feature selection and classification methods for
    recognizing motor imagery tasks from electroencephalographic signals},
  Volume = "6",
  Number = "1",
  journal = {Artificial Intelligence Research},
  year = 2016,
}

Last Updated: July 19, 2018
Submitted by Russ Greiner

University of Alberta Logo AICML Logo