Classification of motor imagery tasks by means of time-frequency-spatial analysis for Brain-Computer Interface applications

L. Qin, B. Kamousi, Z. M. Liu, L. Ding, Bin He

Research output: Contribution to conferencePaper

3 Scopus citations

Abstract

We have developed new algorithms for classification of motor imagery tasks for Brain-Computer Interface applications by analyzing single trial scalp EEG signals in the time-, frequency-, and space- domains. These new algorithms have been evaluated using a publically available dataset. The results are promising, suggesting that the newly developed algorithms may provide useful alternative for noninvaisve Brain-Computer Interface applications.

Original languageEnglish (US)
Pages374-376
Number of pages3
DOIs
StatePublished - Dec 1 2005
Event2nd International IEEE EMBS Conference on Neural Engineering, 2005 - Arlington, VA, United States
Duration: Mar 16 2005Mar 19 2005

Other

Other2nd International IEEE EMBS Conference on Neural Engineering, 2005
CountryUnited States
CityArlington, VA
Period3/16/053/19/05

Keywords

  • Brain-computer interface
  • EEG
  • Inverse solutions
  • Motor imagery
  • Spatial analysis
  • Time-frequency analysis

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    Qin, L., Kamousi, B., Liu, Z. M., Ding, L., & He, B. (2005). Classification of motor imagery tasks by means of time-frequency-spatial analysis for Brain-Computer Interface applications. 374-376. Paper presented at 2nd International IEEE EMBS Conference on Neural Engineering, 2005, Arlington, VA, United States. https://doi.org/10.1109/CNE.2005.1419636