alexa Enhanced performance by a hybrid NIRS-EEG brain computer interface.
Engineering

Engineering

Journal of Biomimetics Biomaterials and Tissue Engineering

Author(s): Fazli S, Mehnert J, Steinbrink J, Curio G, Villringer A,

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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. Copyright © 2011 Elsevier Inc. All rights reserved. This article was published in Neuroimage and referenced in Journal of Biomimetics Biomaterials and Tissue Engineering

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