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BioMed Research International
Volume 2017, Article ID 6820482, 11 pages
https://doi.org/10.1155/2017/6820482
Research Article

Evaluation of a Compact Hybrid Brain-Computer Interface System

1Machine Learning Group, Berlin Institute of Technology, Berlin, Germany
2Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
3NIRx Medizintechnik GmbH, Berlin, Germany
4Department of Biomedical Engineering, Chonnam National University, Yeosu, Republic of Korea
5Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea

Correspondence should be addressed to Do-Won Kim; ed.nilreb-ut.supmac@mik.now-od and Han-Jeong Hwang; rk.ca.homuk@j2h

Received 28 July 2016; Accepted 20 October 2016; Published 8 March 2017

Academic Editor: Maria G. Knyazeva

Copyright © 2017 Jaeyoung Shin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We realized a compact hybrid brain-computer interface (BCI) system by integrating a portable near-infrared spectroscopy (NIRS) device with an economical electroencephalography (EEG) system. The NIRS array was located on the subjects’ forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involved a Stroop word-picture matching test in combination with mental arithmetic (MA) and baseline (BL) tasks, in which the subjects were asked to perform either MA or BL in response to congruent or incongruent conditions, respectively. We compared the classification accuracies of each of the modalities (NIRS or EEG) with that of the hybrid system. We showed that the hybrid system outperforms the unimodal EEG and NIRS systems by 6.2% and 2.5%, respectively. Since the proposed hybrid system is based on portable platforms, it is not confined to a laboratory environment and has the potential to be used in real-life situations, such as in neurorehabilitation.