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

Yongkoo Park, Wonzoo Chung

Research output: Contribution to journalArticlepeer-review

58 Citations (Scopus)

Abstract

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
Volume27
Issue number7
DOIs
Publication statusPublished - 2019 Jul

Bibliographical note

Funding Information:
Manuscript received January 24, 2019; revised April 22, 2019 and May 28, 2019; accepted June 10, 2019. Date of publication June 13, 2019; date of current version July 4, 2019. This work was supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451; Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning). (Corresponding author: Wonzoo Chung.) The authors are with the Division of Computer and Communications Engineering, Korea University, Seoul 02841, South Korea (e-mail: shoutme1@korea.ac.kr; wchung@korea.ac.kr). Digital Object Identifier 10.1109/TNSRE.2019.2922713

Publisher Copyright:
© 2001-2011 IEEE.

Keywords

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

ASJC Scopus subject areas

  • Internal Medicine
  • General Neuroscience
  • Biomedical Engineering

Fingerprint

Dive into the research topics of 'Frequency-Optimized Local Region Common Spatial Pattern Approach for Motor Imagery Classification'. Together they form a unique fingerprint.

Cite this