TY - JOUR
T1 - Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance
AU - Omurtag, Ahmet
AU - Aghajani, Haleh
AU - Keles, Hasan Onur
N1 - Funding Information:
This work is based partly on support by the National Science Foundation I/UCRC for Cyber-Physical Systems for the Hospital Operating Room under Grant no. IIP-1266334 and by industry partners.
Funding Information:
This work is based partly on support by the National Science Foundation I/UCRC for Cyber-Physical Systems for the Hospital Operating Room under Grant no. IIP-1266334 and by industry partners. The authors thank Dr Sridhar Madala for his helpful criticism during the preparation of this manuscript.
Publisher Copyright:
© 2017 IOP Publishing Ltd.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2017/10/31
Y1 - 2017/10/31
N2 - 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 its unimodal components. 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.
AB - 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 its unimodal components. 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.
KW - category fluency
KW - electroencephalography (EEG)
KW - functional near-infrared spectroscopy (fNIRS)
KW - hybrid functional neuroimaging
KW - mental state decoding
KW - multi-modal brain recording
KW - support vector machine
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U2 - 10.1088/1741-2552/aa814b
DO - 10.1088/1741-2552/aa814b
M3 - Article
C2 - 28730995
AN - SCOPUS:85036476484
SN - 1741-2560
VL - 14
JO - Journal of neural engineering
JF - Journal of neural engineering
IS - 6
M1 - 066003
ER -