Time-frequency selection in two bipolar channels for improving the classification of motor imagery EEG.

Yuan Yang*, Sylvain Chevallier, Joe Wiart, Isabelle Bloch

*Corresponding author for this work

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Time and frequency information is essential to feature extraction in a motor imagery BCI, in particular for systems based on a few channels. In this paper, we propose a novel time-frequency selection method based on a criterion called Time-frequency Discrimination Factor (TFDF) to extract discriminative event-related desynchronization (ERD) features for BCI data classification. Compared to existing methods, the proposed approach generates better classification performances (mean kappa coefficient= 0.62) on experimental data from the BCI competition IV dataset IIb, with only two bipolar channels.

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Imagery (Psychotherapy)
Electroencephalography
Feature extraction

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

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title = "Time-frequency selection in two bipolar channels for improving the classification of motor imagery EEG.",
abstract = "Time and frequency information is essential to feature extraction in a motor imagery BCI, in particular for systems based on a few channels. In this paper, we propose a novel time-frequency selection method based on a criterion called Time-frequency Discrimination Factor (TFDF) to extract discriminative event-related desynchronization (ERD) features for BCI data classification. Compared to existing methods, the proposed approach generates better classification performances (mean kappa coefficient= 0.62) on experimental data from the BCI competition IV dataset IIb, with only two bipolar channels.",
author = "Yuan Yang and Sylvain Chevallier and Joe Wiart and Isabelle Bloch",
year = "2012",
month = "12",
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language = "English (US)",
pages = "2744--2747",
journal = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",
issn = "1557-170X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

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AU - Yang, Yuan

AU - Chevallier, Sylvain

AU - Wiart, Joe

AU - Bloch, Isabelle

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AB - Time and frequency information is essential to feature extraction in a motor imagery BCI, in particular for systems based on a few channels. In this paper, we propose a novel time-frequency selection method based on a criterion called Time-frequency Discrimination Factor (TFDF) to extract discriminative event-related desynchronization (ERD) features for BCI data classification. Compared to existing methods, the proposed approach generates better classification performances (mean kappa coefficient= 0.62) on experimental data from the BCI competition IV dataset IIb, with only two bipolar channels.

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