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Evaluation of EEG features in decoding individual finger movements from one hand.

Publication ,  Journal Article
Xiao, R; Ding, L
Published in: Computational and mathematical methods in medicine
January 2013

With the advancements in modern signal processing techniques, the field of brain-computer interface (BCI) is progressing fast towards noninvasiveness. One challenge still impeding these developments is the limited number of features, especially movement-related features, available to generate control signals for noninvasive BCIs. A few recent studies investigated several movement-related features, such as spectral features in electrocorticography (ECoG) data obtained through a spectral principal component analysis (PCA) and direct use of EEG temporal data, and demonstrated the decoding of individual fingers. The present paper evaluated multiple movement-related features under the same task, that is, discriminating individual fingers from one hand using noninvasive EEG. The present results demonstrate the existence of a broadband feature in EEG to discriminate individual fingers, which has only been identified previously in ECoG. It further shows that multiple spectral features obtained from the spectral PCA yield an average decoding accuracy of 45.2%, which is significantly higher than the guess level (P < 0.05) and other features investigated (P < 0.05), including EEG spectral power changes in alpha and beta bands and EEG temporal data. The decoding of individual fingers using noninvasive EEG is promising to improve number of features for control, which can facilitate the development of noninvasive BCI applications with rich complexity.

Duke Scholars

Published In

Computational and mathematical methods in medicine

DOI

EISSN

1748-6718

ISSN

1748-670X

Publication Date

January 2013

Volume

2013

Start / End Page

243257

Related Subject Headings

  • Young Adult
  • Signal Processing, Computer-Assisted
  • Principal Component Analysis
  • Movement
  • Models, Neurological
  • Humans
  • Hand
  • Fingers
  • Electroencephalography
  • Computational Biology
 

Citation

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MLA
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Xiao, R., & Ding, L. (2013). Evaluation of EEG features in decoding individual finger movements from one hand. Computational and Mathematical Methods in Medicine, 2013, 243257. https://doi.org/10.1155/2013/243257
Xiao, Ran, and Lei Ding. “Evaluation of EEG features in decoding individual finger movements from one hand.Computational and Mathematical Methods in Medicine 2013 (January 2013): 243257. https://doi.org/10.1155/2013/243257.
Xiao R, Ding L. Evaluation of EEG features in decoding individual finger movements from one hand. Computational and mathematical methods in medicine. 2013 Jan;2013:243257.
Xiao, Ran, and Lei Ding. “Evaluation of EEG features in decoding individual finger movements from one hand.Computational and Mathematical Methods in Medicine, vol. 2013, Jan. 2013, p. 243257. Epmc, doi:10.1155/2013/243257.
Xiao R, Ding L. Evaluation of EEG features in decoding individual finger movements from one hand. Computational and mathematical methods in medicine. 2013 Jan;2013:243257.

Published In

Computational and mathematical methods in medicine

DOI

EISSN

1748-6718

ISSN

1748-670X

Publication Date

January 2013

Volume

2013

Start / End Page

243257

Related Subject Headings

  • Young Adult
  • Signal Processing, Computer-Assisted
  • Principal Component Analysis
  • Movement
  • Models, Neurological
  • Humans
  • Hand
  • Fingers
  • Electroencephalography
  • Computational Biology