Online impiementation of Top-Down SSVEP-BMI

Min Hee Ahn, Byoung-Kyong Min

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

ßrain machine interfaces (BMls) enable us to control extern al devices using our brain signals. Using a grid-shaped flicke ring line-Array and a shrink-rLDA c1assifier, top-down information could recently be decoded in a steady-state visual evoked potential (SSVEP)-based BMI pamdigm. The present study tested its feasibility in online implementation. We found that within reasonable computing time (0.114 s on average) its online system was successfully accomplished with a decoding accuracy of 53.7% on average. The accuracy was 3.2 times significantly high er than the accuracy by mndom-shuffled data (16.7%). Therefore, using the grid-shaped SSVEP-based BMl, one's muIticlass (at least 6 c1asses) intention can be online decoded and subsequently control extemal devices.

Original languageEnglish
Title of host publication5th International Winter Conference on Brain-Computer Interface, BCI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages27-29
Number of pages3
ISBN (Electronic)9781509050963
DOIs
Publication statusPublished - 2017 Feb 16
Event5th International Winter Conference on Brain-Computer Interface, BCI 2017 - Gangwon Province, Korea, Republic of
Duration: 2017 Jan 92017 Jan 11

Other

Other5th International Winter Conference on Brain-Computer Interface, BCI 2017
CountryKorea, Republic of
CityGangwon Province
Period17/1/917/1/11

Keywords

  • Brain-machine interface
  • Component
  • Shrink rLDA
  • Top-Down SSVEP

ASJC Scopus subject areas

  • Signal Processing
  • Human-Computer Interaction

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  • Cite this

    Ahn, M. H., & Min, B-K. (2017). Online impiementation of Top-Down SSVEP-BMI. In 5th International Winter Conference on Brain-Computer Interface, BCI 2017 (pp. 27-29). [7858149] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2017.7858149