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Stress Assessment Based on Decision Fusion of EEG and fNIRS Signals

Al-Shargie, F. and Tang, T.B. and Kiguchi, M. (2017) Stress Assessment Based on Decision Fusion of EEG and fNIRS Signals. IEEE Access, 5 . pp. 19889-19896.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

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.

Item Type:Article
Impact Factor:cited By 0
Departments / MOR / COE:Centre of Excellence > Center for Intelligent Signal and Imaging Research
ID Code:19358
Deposited By: Ahmad Suhairi
Deposited On:20 Apr 2018 00:21
Last Modified:20 Apr 2018 00:21

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