Reconstruction of hand movements from EEG signals based on non-linear regression

Jeong Hun Kim, Felix Bießmann, Seong Whan Lee

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

4 Citations (Scopus)

Abstract

Brain-Computer Interface (BCI) systems allow users to control external devices using their thoughts. In particular, brain signals can be used to decode the trajectory of hand movements for neurorehabilitation or control of arm prostheses. Previous studies have decoded hand movement velocity during simple tasks. However, under real world conditions, patients need to control artificial limbs with more degrees of freedom in order to accomplish everyday tasks such as drinking water or eating food. In this work we decode hand movement velocity from electroencephalography (EEG) signals based on linear and nonlinear regression during complex trajectories. We considered two types of movement trajectories: one with low variation in movement velocity and one with high variation in hand movement velocity. Two decoding strategies are compared, linear and non-linear regression. Our results show that linear models can yield state-of-the-art decoding performance on the simple task with low variations in movement velocity, in the more difficult task with large variations in movement velocity, nonlinear regression techniques can improve decoding of movement trajectories.

Original languageEnglish
Title of host publication2014 International Winter Workshop on Brain-Computer Interface, BCI 2014
PublisherIEEE Computer Society
DOIs
Publication statusPublished - 2014 Jan 1
Event2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 - Gangwon, Korea, Republic of
Duration: 2014 Feb 172014 Feb 19

Other

Other2014 International Winter Workshop on Brain-Computer Interface, BCI 2014
CountryKorea, Republic of
CityGangwon
Period14/2/1714/2/19

Fingerprint

Electroencephalography
reconstruction
regression
brain
Trajectories
Decoding
linear model
eating behavior
Artificial limbs
food
water
Brain computer interface
Prosthetics
Potable water
performance
Brain

Keywords

  • Arm movement trajectory
  • BCI
  • EEG
  • Kernel ridge regression
  • Upper limb rehabilitation

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Human Factors and Ergonomics

Cite this

Kim, J. H., Bießmann, F., & Lee, S. W. (2014). Reconstruction of hand movements from EEG signals based on non-linear regression. In 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 [6782572] IEEE Computer Society. https://doi.org/10.1109/iww-BCI.2014.6782572

Reconstruction of hand movements from EEG signals based on non-linear regression. / Kim, Jeong Hun; Bießmann, Felix; Lee, Seong Whan.

2014 International Winter Workshop on Brain-Computer Interface, BCI 2014. IEEE Computer Society, 2014. 6782572.

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

Kim, JH, Bießmann, F & Lee, SW 2014, Reconstruction of hand movements from EEG signals based on non-linear regression. in 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014., 6782572, IEEE Computer Society, 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014, Gangwon, Korea, Republic of, 14/2/17. https://doi.org/10.1109/iww-BCI.2014.6782572
Kim JH, Bießmann F, Lee SW. Reconstruction of hand movements from EEG signals based on non-linear regression. In 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014. IEEE Computer Society. 2014. 6782572 https://doi.org/10.1109/iww-BCI.2014.6782572
Kim, Jeong Hun ; Bießmann, Felix ; Lee, Seong Whan. / Reconstruction of hand movements from EEG signals based on non-linear regression. 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014. IEEE Computer Society, 2014.
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