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Computational Intelligence and Neuroscience
Volume 2017, Article ID 3524208, 24 pages
Research Article

Mental Task Evaluation for Hybrid NIRS-EEG Brain-Computer Interfaces

Energy, Materials, and Telecommunications, Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC, Canada

Correspondence should be addressed to Hubert Banville; ac.srni.tme@ellivnab.trebuh

Received 6 April 2017; Revised 27 July 2017; Accepted 29 August 2017; Published 18 October 2017

Academic Editor: Manuel Rosa-Zurera

Copyright © 2017 Hubert Banville 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.


Based on recent electroencephalography (EEG) and near-infrared spectroscopy (NIRS) studies that showed that tasks such as motor imagery and mental arithmetic induce specific neural response patterns, we propose a hybrid brain-computer interface (hBCI) paradigm in which EEG and NIRS data are fused to improve binary classification performance. We recorded simultaneous NIRS-EEG data from nine participants performing seven mental tasks (word generation, mental rotation, subtraction, singing and navigation, and motor and face imagery). Classifiers were trained for each possible pair of tasks using () EEG features alone, () NIRS features alone, and () EEG and NIRS features combined, to identify the best task pairs and assess the usefulness of a multimodal approach. The NIRS-EEG approach led to an average increase in peak kappa of 0.03 when using features extracted from one-second windows (equivalent to an increase of 1.5% in classification accuracy for balanced classes). The increase was much stronger (0.20, corresponding to an 10% accuracy increase) when focusing on time windows of high NIRS performance. The EEG and NIRS analyses further unveiled relevant brain regions and important feature types. This work provides a basis for future NIRS-EEG hBCI studies aiming to improve classification performance toward more efficient and flexible BCIs.