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

Jeong Hun Kim, Felix Bießmann, Seong Whan Lee

Research output: Contribution to conferencePaperpeer-review

11 Citations (Scopus)


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
Publication statusPublished - 2014
Event2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 - Gangwon, Korea, Republic of
Duration: 2014 Feb 172014 Feb 19


Other2014 International Winter Workshop on Brain-Computer Interface, BCI 2014
Country/TerritoryKorea, Republic of


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

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Human Factors and Ergonomics


Dive into the research topics of 'Reconstruction of hand movements from EEG signals based on non-linear regression'. Together they form a unique fingerprint.

Cite this