Enhanced performance by a hybrid NIRS-EEG brain computer interface

Siamac Fazli, Jan Mehnert, Jens Steinbrink, Gabriel Curio, Arno Villringer, Klaus Robert Müller, Benjamin Blankertz

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322 Citations (Scopus)

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 (p < 0:01). 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.

Original languageEnglish
Pages (from-to)519-529
Number of pages11
JournalNeuroImage
Volume59
Issue number1
DOIs
Publication statusPublished - Jan 2 2012
Externally publishedYes

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Keywords

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

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

Cite this

Fazli, S., Mehnert, J., Steinbrink, J., Curio, G., Villringer, A., Müller, K. R., & Blankertz, B. (2012). Enhanced performance by a hybrid NIRS-EEG brain computer interface. NeuroImage, 59(1), 519-529. https://doi.org/10.1016/j.neuroimage.2011.07.084