Bimanual arm movements decoding using hybrid method movement state Classification and PLS Regression

Hoseok Choi, Dong Pyo Jang, Kyoung Min Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

In arm movement BCI (brain-computer interface), the unimanual research has been weil. However, the bimanual brain state is known to be different from the unimanual one, so the conventional arm movement decoding method seems to be insufficient to decode bimanual movement. In this research, we suggested the hybrid method to improve the decoding accuracy for bimanual movement estimation. The method consists of two step; 1st step: The movement conditions classification, and 2nd step: The hand trajectory prediction algorithm. As a result, the hybrid method showed improved arm movement decoding performance and significant and stable decoding rate over several months for bimanual tasks. This technique could be applied to arm movement BCI in real world and the various neuro-prosthetics fields.

Original languageEnglish
Title of host publication5th International Winter Conference on Brain-Computer Interface, BCI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages60-62
Number of pages3
ISBN (Electronic)9781509050963
DOIs
StatePublished - 16 Feb 2017
Event5th International Winter Conference on Brain-Computer Interface, BCI 2017 - Gangwon Province, Korea, Republic of
Duration: 9 Jan 201711 Jan 2017

Other

Other5th International Winter Conference on Brain-Computer Interface, BCI 2017
CountryKorea, Republic of
CityGangwon Province
Period9/01/1711/01/17

Fingerprint

Decoding
Brain computer interface
Prosthetics
Brain
Trajectories

Keywords

  • Arm movement decoding
  • BCI
  • Bimanual
  • Unimanual

Cite this

Choi, H., Jang, D. P., & Lee, K. M. (2017). Bimanual arm movements decoding using hybrid method movement state Classification and PLS Regression. In 5th International Winter Conference on Brain-Computer Interface, BCI 2017 (pp. 60-62). [7858159] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2017.7858159
Choi, Hoseok ; Jang, Dong Pyo ; Lee, Kyoung Min. / Bimanual arm movements decoding using hybrid method movement state Classification and PLS Regression. 5th International Winter Conference on Brain-Computer Interface, BCI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 60-62
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abstract = "In arm movement BCI (brain-computer interface), the unimanual research has been weil. However, the bimanual brain state is known to be different from the unimanual one, so the conventional arm movement decoding method seems to be insufficient to decode bimanual movement. In this research, we suggested the hybrid method to improve the decoding accuracy for bimanual movement estimation. The method consists of two step; 1st step: The movement conditions classification, and 2nd step: The hand trajectory prediction algorithm. As a result, the hybrid method showed improved arm movement decoding performance and significant and stable decoding rate over several months for bimanual tasks. This technique could be applied to arm movement BCI in real world and the various neuro-prosthetics fields.",
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Choi, H, Jang, DP & Lee, KM 2017, Bimanual arm movements decoding using hybrid method movement state Classification and PLS Regression. in 5th International Winter Conference on Brain-Computer Interface, BCI 2017., 7858159, Institute of Electrical and Electronics Engineers Inc., pp. 60-62, 5th International Winter Conference on Brain-Computer Interface, BCI 2017, Gangwon Province, Korea, Republic of, 9/01/17. https://doi.org/10.1109/IWW-BCI.2017.7858159

Bimanual arm movements decoding using hybrid method movement state Classification and PLS Regression. / Choi, Hoseok; Jang, Dong Pyo; Lee, Kyoung Min.

5th International Winter Conference on Brain-Computer Interface, BCI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 60-62 7858159.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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N2 - In arm movement BCI (brain-computer interface), the unimanual research has been weil. However, the bimanual brain state is known to be different from the unimanual one, so the conventional arm movement decoding method seems to be insufficient to decode bimanual movement. In this research, we suggested the hybrid method to improve the decoding accuracy for bimanual movement estimation. The method consists of two step; 1st step: The movement conditions classification, and 2nd step: The hand trajectory prediction algorithm. As a result, the hybrid method showed improved arm movement decoding performance and significant and stable decoding rate over several months for bimanual tasks. This technique could be applied to arm movement BCI in real world and the various neuro-prosthetics fields.

AB - In arm movement BCI (brain-computer interface), the unimanual research has been weil. However, the bimanual brain state is known to be different from the unimanual one, so the conventional arm movement decoding method seems to be insufficient to decode bimanual movement. In this research, we suggested the hybrid method to improve the decoding accuracy for bimanual movement estimation. The method consists of two step; 1st step: The movement conditions classification, and 2nd step: The hand trajectory prediction algorithm. As a result, the hybrid method showed improved arm movement decoding performance and significant and stable decoding rate over several months for bimanual tasks. This technique could be applied to arm movement BCI in real world and the various neuro-prosthetics fields.

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Choi H, Jang DP, Lee KM. Bimanual arm movements decoding using hybrid method movement state Classification and PLS Regression. In 5th International Winter Conference on Brain-Computer Interface, BCI 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 60-62. 7858159 https://doi.org/10.1109/IWW-BCI.2017.7858159