Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance

Omurtag, A. ORCID: 0000-0002-3773-8506, Aghajani, H. and Keles, H.O., 2017. Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance. Journal of Neural Engineering, 14 (6): 066003. ISSN 1741-2560

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Abstract

Objective: Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system's ability to decode mental states and compare it with unimodal systems.

Approach: We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data.

Main results: EEG+fNIRS's decoding accuracy was greater than that of its subsystems, partly due to the new type of neurovascular features made available by hybrid data.

Significance: Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics .

Item Type: Journal article
Publication Title: Journal of Neural Engineering
Creators: Omurtag, A., Aghajani, H. and Keles, H.O.
Publisher: Institute of Physics
Date: 31 October 2017
Volume: 14
Number: 6
ISSN: 1741-2560
Identifiers:
NumberType
10.1088/1741-2552/aa814bDOI
Divisions: Schools > School of Science and Technology
Depositing User: Jonathan Gallacher
Date Added: 15 Feb 2018 15:33
Last Modified: 16 Apr 2018 11:48
URI: http://irep.ntu.ac.uk/id/eprint/32726

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