Data-driven frequency bands selection in EEG-based brain-computer interface

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this paper, we propose a novel method of frequency bands selection based on the analysis of a channel-frequency map, which we call 'channel-frequency map'. The spatial filtering, feature extraction, and classification processes are operated in each frequency band in parallel. We determine a class label for an input EEG based on the outputs from the multi-streams with a two-step decision strategy at the end. From our experiments on a public dataset of BCI Competition IV (2008) II-a that includes four motor imagery tasks from 9 subjects, the proposed algorithm outperformed the Common Spatial Pattern (CSP) algorithm and a filter bank CSP algorithm on average in terms of a session-to-session transfer rate using one session for training and the other session for test. 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 single-trial EEG classification that is based on modulations of brain rhythms.

Original languageEnglish
Title of host publicationProceedings - International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011
Pages25-28
Number of pages4
DOIs
Publication statusPublished - 2011 Aug 29
EventInternational Workshop on Pattern Recognition in NeuroImaging, PRNI 2011 - Seoul, Korea, Republic of
Duration: 2011 May 162011 May 18

Other

OtherInternational Workshop on Pattern Recognition in NeuroImaging, PRNI 2011
CountryKorea, Republic of
CitySeoul
Period11/5/1611/5/18

Fingerprint

Brain-Computer Interfaces
Brain computer interface
Electroencephalography
Frequency bands
Imagery (Psychotherapy)
Filter banks
Feature extraction
Labels
Brain
Modulation
Experiments

Keywords

  • Brain-computer interfaces
  • Electroencephalography
  • Event-related (de)synchronization (ERD/ERS)
  • Frequency bands selection
  • Machine learning
  • Motor imagery classification

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Suk, H-I., & Lee, S. W. (2011). Data-driven frequency bands selection in EEG-based brain-computer interface. In Proceedings - International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011 (pp. 25-28). [5961312] https://doi.org/10.1109/PRNI.2011.19

Data-driven frequency bands selection in EEG-based brain-computer interface. / Suk, Heung-Il; Lee, Seong Whan.

Proceedings - International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011. 2011. p. 25-28 5961312.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Suk, H-I & Lee, SW 2011, Data-driven frequency bands selection in EEG-based brain-computer interface. in Proceedings - International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011., 5961312, pp. 25-28, International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011, Seoul, Korea, Republic of, 11/5/16. https://doi.org/10.1109/PRNI.2011.19
Suk H-I, Lee SW. Data-driven frequency bands selection in EEG-based brain-computer interface. In Proceedings - International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011. 2011. p. 25-28. 5961312 https://doi.org/10.1109/PRNI.2011.19
Suk, Heung-Il ; Lee, Seong Whan. / Data-driven frequency bands selection in EEG-based brain-computer interface. Proceedings - International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011. 2011. pp. 25-28
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