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Combining multiple features for error detection and its application in brain-computer interface.

Publication ,  Journal Article
Tong, J; Lin, Q; Xiao, R; Ding, L
Published in: Biomedical engineering online
February 2016

Brain-computer interface (BCI) is an assistive technology that conveys users' intentions by decoding various brain activities and translating them into control commands, without the need of verbal instructions and/or physical interactions. However, errors existing in BCI systems affect their performance greatly, which in turn confines the development and application of BCI technology. It has been demonstrated viable to extract error potential from electroencephalography recordings.This study proposed a new approach of fusing multiple-channel features from temporal, spectral, and spatial domains through two times of dimensionality reduction based on neural network. 26 participants (13 males, mean age = 28.8 ± 5.4, range 20-37) took part in the study, who engaged in a P300 speller task spelling cued words from a 36-character matrix. In order to evaluate the generalization ability across subjects, the data from 16 participants were used for training and the rest for testing.The total classification accuracy with combination of features is 76.7 %. The receiver operating characteristic (ROC) curve and area under ROC curve (AUC) further indicate the superior performance of the combination of features over any single features in error detection. The average AUC reaches 0.7818 with combined features, while 0.7270, 0.6376, 0.7330 with single temporal, spectral, and spatial features respectively.The proposed method combining multiple-channel features from temporal, spectral, and spatial domain has better classification performance than any individual feature alone. It has good generalization ability across subject and provides a way of improving error detection, which could serve as promising feedbacks to promote the performance of BCI systems.

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Published In

Biomedical engineering online

DOI

EISSN

1475-925X

ISSN

1475-925X

Publication Date

February 2016

Volume

15

Start / End Page

17

Related Subject Headings

  • Young Adult
  • Spatio-Temporal Analysis
  • Signal Processing, Computer-Assisted
  • Research Design
  • ROC Curve
  • Nerve Net
  • Male
  • Humans
  • Female
  • Electroencephalography
 

Citation

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Tong, J., Lin, Q., Xiao, R., & Ding, L. (2016). Combining multiple features for error detection and its application in brain-computer interface. Biomedical Engineering Online, 15, 17. https://doi.org/10.1186/s12938-016-0134-9
Tong, Jijun, Qinguang Lin, Ran Xiao, and Lei Ding. “Combining multiple features for error detection and its application in brain-computer interface.Biomedical Engineering Online 15 (February 2016): 17. https://doi.org/10.1186/s12938-016-0134-9.
Tong J, Lin Q, Xiao R, Ding L. Combining multiple features for error detection and its application in brain-computer interface. Biomedical engineering online. 2016 Feb;15:17.
Tong, Jijun, et al. “Combining multiple features for error detection and its application in brain-computer interface.Biomedical Engineering Online, vol. 15, Feb. 2016, p. 17. Epmc, doi:10.1186/s12938-016-0134-9.
Tong J, Lin Q, Xiao R, Ding L. Combining multiple features for error detection and its application in brain-computer interface. Biomedical engineering online. 2016 Feb;15:17.
Journal cover image

Published In

Biomedical engineering online

DOI

EISSN

1475-925X

ISSN

1475-925X

Publication Date

February 2016

Volume

15

Start / End Page

17

Related Subject Headings

  • Young Adult
  • Spatio-Temporal Analysis
  • Signal Processing, Computer-Assisted
  • Research Design
  • ROC Curve
  • Nerve Net
  • Male
  • Humans
  • Female
  • Electroencephalography