Enhanced performance by a hybrid NIRS-EEG brain computer interface
- Authors
- Klaus-Robert Muller; Siamac Fazli
- Issue Date
- 1월-2012
- Publisher
- ACADEMIC PRESS INC ELSEVIER SCIENCE
- Keywords
- Combined NIRS-EEG; Hybrid BCI; Meta-classifier
- Citation
- NEUROIMAGE, v.59, pp.519 - 529
- Indexed
- SCIE
SCOPUS
- Journal Title
- NEUROIMAGE
- Volume
- 59
- Start Page
- 519
- End Page
- 529
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/84562
- ISSN
- 1053-8119
- 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. (C) 2011 Elsevier Inc. All rights reserved.
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Collections - Graduate School > Department of Brain and Cognitive Engineering > 1. Journal Articles
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