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Cited by 107EEG and Brain-Computer InterfacesMuscle activation and electromyography studiesEmotion and Mood Recognition

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Multi-class filter bank common spatial pattern for four-class motor imagery BCI

Zheng Yang Chin, Kai Keng Ang, Chuanchu Wang, Cuntai Guan, Haihong Zhang·2009·2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
DOI 10.1109/iembs.2009.5332383
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Abstract

This paper investigates the classification of multi-class motor imagery for electroencephalogram (EEG)-based Brain-Computer Interface (BCI) using the Filter Bank Common Spatial Pattern (FBCSP) algorithm. The FBCSP algorithm classifies EEG measurements from features constructed using subject-specific temporal-spatial filters. However, the FBCSP algorithm is limited to binary-class motor imagery. Hence, this paper proposes 3 approaches of multi-class extension to the FBCSP algorithm: One-versus-Rest, Pair-Wise and Divide-and-Conquer. These approaches decompose the multi-class problem into several binary-class problems. The study is conducted on the BCI Competition IV dataset IIa, which comprises single-trial EEG data from 9 subjects performing 4-class motor imagery of left-hand, right-hand, foot and tongue actions. The results showed that the multi-class FBCSP algorithm could extract features that matched neurophysiological knowledge, and yielded the best performance on the evaluation data compared to other international submissions.

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Crossref · 10.1109/iembs.2009.5332383

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1 of 1 references verified0 citation errors · 0 not checkable

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  • FBCSP is a widely used method to decode oscillatory EEG data, for example, with respect to movement-related information.

    “FBCSP [Ang et al., 2008; Chin et al., 2009] is a widely used method to decode oscillatory EEG data, for example, with respect to movement‐related information, that is, the decoding problem we focus on in this study.”

    From Deep learning with convolutional neural networks for EEG decoding and visualization (2017) · cites this paper as Chin et al., 2009

  • FBCSP was the best-performing method for the BCI competition IV dataset 2a.

    “FBCSP was the best‐performing method for the BCI competition IV dataset 2a, which we use in this study”

    From Deep learning with convolutional neural networks for EEG decoding and visualization (2017) · cites this paper as Chin et al., 2009

Citations

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Flagged first, then by citations

Research works printed but not linked: 13

  1. blankertz, 2008, BCI Competition IV
  2. 10.1155/2007/39714 · DOI
  3. 10.1109/IEMBS.2008.4649325 · DOI
  4. 10.1016/j.patrec.2007.01.002 · DOI
  5. 10.1016/j.neuroimage.2005.12.003 · DOI
  6. 10.1016/j.clinph.2008.08.013 · DOI
  7. dornhege, 2004, Increase information transfer rates in BCI by CSP extension to multi-class, Proc Advances in Neural Information Processing Systems, 733
  8. 10.1016/S1388-2457(98)00038-8 · DOI · Paper page
  9. 10.1109/86.895946 · DOI · Paper page
  10. 10.1109/TBME.2008.921154 · DOI
  11. 10.1152/jn.01113.2002 · DOI
  12. 10.1109/TBME.2004.827088 · DOI
  13. 10.1145/1394622 · DOI

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Metadata

Authors
Chin, Z. Y., Ang, K. K., Wang, C., Guan, C., & Zhang, H.
Affiliations
Agency for Science, Technology and Research (SG)
ORCID
Venue
2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publisher
IEEE
Published
2009-09
Volume
2009
Pages
571-574
ISSN
2375-7477 (ISSN-L)
DOI
10.1109/iembs.2009.5332383
OpenAlex
W2098391699
Cited by
107
References
1
Citations by year
2025: 7 · 2024: 10 · 2023: 10 · 2022: 10 · 2021: 10 · 2020: 12 · 2019: 4 · 2018: 10 · 2017: 6 · 2016: 7 · 2015: 2 · 2014: 4 · 2013: 4 · 2012: 7
FWCI
6.79
Type
conference paper
Language
en
Open access
closed
Locations
  • Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Keywords
filter bank common spatial pattern, EEG-based brain-computer interface, multi-class classification, pairwise classification, divide-and-conquer, single-trial EEG, motor imagery classification
MeSH terms
Algorithms, Brain Mapping, Electroencephalography, Humans, Imagination, Motor Cortex, Pattern Recognition, Automated, Sensitivity and Specificity, Signal Processing, Computer-Assisted, User-Computer Interface, Reproducibility of Results, Evoked Potentials, Motor

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