Frequency-Optimized Local Region Common Spatial Pattern Approach for Motor Imagery Classification

Yongkoo Park, Wonzoo Chung

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)


This paper presents a novel feature extraction approach for motor imagery classification overcoming the weakness of conventional common spatial pattern (CSP) methods, especially for small sample settings. We consider local CSPs generated from individual channels and their neighbors (termed 'local regions') rather than a global CSP generated from all channels. The novelty is to select a few good local regions using interquartile range (IQR) or an 'above the mean' rule based on variance ratio dispersion score (VRDS) and inter-class feature distance (ICFD); instead of computationally expensive cross-validation method. Furthermore, we develop frequency optimization using filter banks by extending the VRDS and ICFD to frequency-optimized local CSPs. The proposed methods are tested on three publicly available brain-computer interface (BCI) datasets: BCI competition III dataset IVa, BCI competition IV dataset I, and BCI competition IV dataset IIb. The proposed method exhibits substantially improved classification accuracy compared to recent related motor imagery (MI) classification methods.

Original languageEnglish
Article number8736402
Pages (from-to)1378-1388
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number7
Publication statusPublished - 2019 Jul


  • Brain-computer interfaces (BCIs)
  • Common spatial pattern (CSP)
  • Electroencephalography (EEG)
  • Local feature
  • Motor imagery (MI)

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering


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