Efficient feature selection and linear discrimination of EEG signals

Journal ar
  • Volumen: 115
  • Fecha: 04 September 2013
  • Páginas: 161-165
  • ISSN: 09252312 18728286
  • Source Type: Journal
  • DOI: 10.1016/j.neucom.2013.01.001
  • Document Type: Article
Brain-Computer Interface systems (BCIs) based on Electroencephalogram (EEG) signal processing allow us to translate the subject's brain activities into control commands for computer devices. This paper presents an efficient embedded approach for feature selection and linear discrimination of EEG signals. In the first stage, four well-known feature extraction methods are used: Power spectral features, Hjorth parameters, Autoregressive modelling and Wavelet transform. From all the obtained features, the proposed method efficiently selects and combines the most useful features for classification with less computational requirements. Least Angle Regression (LARS) is used for properly ranking each feature and, then, an efficient Leave-One-Out (LOO) estimation based on the PRESS statistic is used to choose the most relevant features. Experimental results on motor-imagery BCIs problems are provided to illustrate the competitive performance of the proposed approach against other conventional methods. © 2013 Elsevier B.V.

Author keywords

    Indexed keywords

      Funding details