Enhanced performance by a hybrid NIRS – EEG brain computer interface

  • Fazli S
  • Mehnert J
  • Steinbrink J
 et al. 
  • 2

    Readers

    Mendeley users who have this article in their library.
  • N/A

    Citations

    Citations of this article.

Abstract

Noninvasive Brain Computer Interfaces (BCI) have been promoted to be used for neuroprosthetics. However, reports on applications with electroencephalography (EEG) show a demand for a better accuracy and stability. Here we investigate whether near-infrared spectroscopy (NIRS) can be used to enhance the EEG approach. In our study both methods were applied simultaneously in a real-time Sensory Motor Rhythm (SMR)-based BCI paradigm, involving executed movements as well as motor imagery. We tested how the classification of NIRS data can complement ongoing real-time EEG classification. Our results show that simultaneous measurements of NIRS and EEG can significantly improve the classification accuracy of motor imagery in over 90% of considered subjects and increases performance by 5% on average (pb0:01). However, the long time delay of the hemodynamic response may hinder an overall increase of bit-rates. Furthermore we find that EEG and NIRS complement each other in terms of information content and are thus a viable multimodal imaging technique, suitable for BCI.

Author-supplied keywords

  • Combined NIRS-EEG
  • Hybrid BCI
  • Meta-classifier

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Siamac Fazli

  • Jan Mehnert

  • Jens Steinbrink

  • Gabriel Curio

  • Arno Villringer

  • Klaus-Robert Müller

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free