TY  - JOUR
SN  - 21693536
N2  - Fusion of electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) is an emerging approach in the field of psychological and neurological studies. We developed a decision fusion technique to combine the output probabilities of the EEG and fNIRS classifiers. The fusion explored support vector machine as classifier for each modality, and optimized the classifiers based on their receiver operating characteristic curve values. EEG and fNIRS signal were acquired simultaneously while performing mental arithmetic task under control and stress conditions. Experiment results from 20 subjects demonstrated significant improvement in the detection rate of mental stress by +7.76 ( p<0.001) and +10.57 ( p<0.0005), compared with sole modality of EEG and fNIRS, respectively. © 2013 IEEE.
KW  - Curve fitting; Electroencephalography; Electrophysiology; Infrared devices; Near infrared spectroscopy; Neuroimaging; Probes; Stresses; Support vector machines
KW  -  Atmospheric measurement; Decision fusion; Functional near-infrared spectroscopy (fnirs); Mental arithmetic; Particle measurement; Receiver operating characteristic curves; Spatial resolution; Stress assessment
KW  -  Functional neuroimaging
TI  - Stress Assessment Based on Decision Fusion of EEG and fNIRS Signals
ID  - scholars8383
EP  - 19896
PB  - Institute of Electrical and Electronics Engineers Inc.
SP  - 19889
AV  - none
Y1  - 2017///
N1  - cited By 60
A1  - Al-Shargie, F.
A1  - Tang, T.B.
A1  - Kiguchi, M.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030634505&doi=10.1109%2fACCESS.2017.2754325&partnerID=40&md5=8f187f3d8b0e0df4305a465b1637a258
JF  - IEEE Access
VL  - 5
ER  -