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Enhanced performance by a hybrid NIRS–EEG brain computer interface

MPS-Authors
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Mehnert,  Jan
Berlin Neuroimaging Center, Charité University Medicine Berlin, Germany;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Villringer,  Arno
Berlin Neuroimaging Center, Charité University Medicine Berlin, Germany;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Berlin School of Mind and Brain, Humboldt University Berlin, Germany;

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Fazli_2012_Enhanced.pdf
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Citation

Fazli, S., Mehnert, J., Steinbrink, J., Curio, G., Villringer, A., Mueller, K.-R., et al. (2012). Enhanced performance by a hybrid NIRS–EEG brain computer interface. NeuroImage, 59(1), 519-529. doi:10.1016/j.neuroimage.2011.07.084.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0012-13FF-0
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.