A high-performance brain-machine interface (BMI) using image information

Hong Gi Yeom, June Sic Kim, Chun Kee Chung

Research output: Contribution to conferencePaperResearchpeer-review

1 Citation (Scopus)

Abstract

Sensory feedback is very important for movement control. However the feedback information has not been considered in the brain-machine interface (BMI) studies. Here, we propose a novel prediction method, the feedback-prediction algorithm (FPA), to generate feedback information from the positions of objects and using the feedback to predict movements. The FPA modifies the predicted direction toward the target and modulates the magnitude of the predicted vector to easily reach the target by combining feedback information. We demonstrated that combining feedback information for movement prediction considerably improves prediction accuracy. The proposed method, FPA, will promote the development of a practical BMI system.

Original languageEnglish
DOIs
StatePublished - 1 Jan 2014
Event2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 - Gangwon, Korea, Republic of
Duration: 17 Feb 201419 Feb 2014

Other

Other2014 International Winter Workshop on Brain-Computer Interface, BCI 2014
CountryKorea, Republic of
CityGangwon
Period17/02/1419/02/14

Fingerprint

Brain
brain
Feedback
performance
Sensory feedback

Keywords

  • Brain-machine interface
  • component
  • Kalman Filter
  • Movement trajectory prediction
  • Sensory feedback

Cite this

Yeom, H. G., Kim, J. S., & Chung, C. K. (2014). A high-performance brain-machine interface (BMI) using image information. Paper presented at 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014, Gangwon, Korea, Republic of. https://doi.org/10.1109/iww-BCI.2014.6782564
Yeom, Hong Gi ; Kim, June Sic ; Chung, Chun Kee. / A high-performance brain-machine interface (BMI) using image information. Paper presented at 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014, Gangwon, Korea, Republic of.
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Yeom, HG, Kim, JS & Chung, CK 2014, 'A high-performance brain-machine interface (BMI) using image information' Paper presented at 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014, Gangwon, Korea, Republic of, 17/02/14 - 19/02/14, . https://doi.org/10.1109/iww-BCI.2014.6782564

A high-performance brain-machine interface (BMI) using image information. / Yeom, Hong Gi; Kim, June Sic; Chung, Chun Kee.

2014. Paper presented at 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014, Gangwon, Korea, Republic of.

Research output: Contribution to conferencePaperResearchpeer-review

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T1 - A high-performance brain-machine interface (BMI) using image information

AU - Yeom, Hong Gi

AU - Kim, June Sic

AU - Chung, Chun Kee

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N2 - Sensory feedback is very important for movement control. However the feedback information has not been considered in the brain-machine interface (BMI) studies. Here, we propose a novel prediction method, the feedback-prediction algorithm (FPA), to generate feedback information from the positions of objects and using the feedback to predict movements. The FPA modifies the predicted direction toward the target and modulates the magnitude of the predicted vector to easily reach the target by combining feedback information. We demonstrated that combining feedback information for movement prediction considerably improves prediction accuracy. The proposed method, FPA, will promote the development of a practical BMI system.

AB - Sensory feedback is very important for movement control. However the feedback information has not been considered in the brain-machine interface (BMI) studies. Here, we propose a novel prediction method, the feedback-prediction algorithm (FPA), to generate feedback information from the positions of objects and using the feedback to predict movements. The FPA modifies the predicted direction toward the target and modulates the magnitude of the predicted vector to easily reach the target by combining feedback information. We demonstrated that combining feedback information for movement prediction considerably improves prediction accuracy. The proposed method, FPA, will promote the development of a practical BMI system.

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Yeom HG, Kim JS, Chung CK. A high-performance brain-machine interface (BMI) using image information. 2014. Paper presented at 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014, Gangwon, Korea, Republic of. https://doi.org/10.1109/iww-BCI.2014.6782564