Computer Vision And Pattern Analysis Laboratory Home Page  Home
People  People
Publications  Publications
Publications  Databases
Contact Information  Contact
Supported Research Projects  Supported Research Projects
Research Activites  Research Activites
Research Groups
SPIS - Signal Processing and Information Systems Lab.SPIS - Signal Processing and Information Systems Lab.
Medical Vision and Analysis Group  Medical Research Activities
Biometrics Research Group  Biometrics Research Group
SPIS - Signal Processing and Information Systems Lab.MISAM - Machine Intelligence for Speech Audio and Multimedia.
Knowledge Base
  Paper Library
  VPA Lab Inventory
  Databases in VPALAB
Design and Analysis of a Brain-computer Interface-based Robotic Rehabilitation System
Authors: Ela Koyas
Published in:
Publication year: 2014
Abstract: In this thesis, we have investigated the effect of brain-computer interfaces (BCI) which enable direct communication between a brain and a computer, to increase the patient's active involvement to his/her task in the robotic rehabilitation therapy. We have designed several experimental paradigms using electroencephalography (EEG) based BCIs which can be used to extract information about arm movement imagery
in the context of robotic rehabilitation experiments. In particular, we propose a protocol that extracts and uses information about the level of intention of the subject to control the robot continuously throughout a rehabilitation experiment. In this context we have developed and implemented EEG signal processing, learning and classification algorithms for offline and online decision-making.

We have used different types of controlling methods over the robotic system and examined the potential impact of BCI on rehabilitation, the effect of robotic haptic feedback on BCI, and information contained in EEG about the rehabilitation process. Our results verify that the use of haptic feedback through robotic movement improves BCI performance. We also observe that using BCI continuously in the experiment rather than only to trigger robotic movement may be preferable. Finally, our results indicate stronger motor imagery activity in BCI-based experiments over conventional experiments in which movement is performed by the robot without the subject's involvement.
  download full paper

Home Back