Subject and class specific frequency bands selection for multiclass motor imagery classification

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77 Citations (Scopus)


EEG-based discrimination among motor imagery states has been widely studied for brain-computer interfaces (BCIs) due to the great potential for real-life applications. However, in terms of designing a motor imagery-based BCI system, a lot of research in the literature either uses a frequency band of interest selected manually based on the visual analysis of EEG data or is set to a general broad band, causing performance degradation in classification. In this article, we propose a novel method of selecting subject and class specific frequency bands based on the analysis of a channel-frequency matrix, which we call a channel-frequency map. We operate the classification process for each frequency band individually, i.e., spatial filtering, feature extraction, and classification, and determine a class label for an input EEG by considering the outputs from multiple classifiers together at the end. From our experiments on a public dataset of BCI Competition IV (2008) II-a that includes four motor imagery tasks from nine subjects, the proposed algorithm outperformed the common spatial pattern (CSP) algorithm in a broad band and a filter bank CSP algorithm on average in terms of cross-validation and session-to-session transfer rate. Furthermore, a considerable increase of classification accuracy has been achieved for certain subjects. We also would like to note that the proposed data-driven frequency bands selection method is applicable to other kinds of single-trial EEG classifications that are based on modulations of brain rhythms, by no means limited to motor imagery-based BCI applications.

Original languageEnglish
Pages (from-to)123-130
Number of pages8
JournalInternational Journal of Imaging Systems and Technology
Issue number2
Publication statusPublished - 2011 Jun


  • brain-computer interface
  • electroencephalography
  • frequency bands selection
  • motor imagery classification

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Software
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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