Key result
Decision fusion of EEG and fNIRS signals significantly improved the detection rate of mental stress by 7.76% (p<0.001) and 10.57% (p<0.0005) compared to EEG and fNIRS alone, respectively.
Why the study?
Does decision fusion of EEG and fNIRS improve the detection rate of mental stress compared to single modalities?
Does decision fusion of EEG and fNIRS improve the detection rate of mental stress compared to single modalities?
Combining EEG and fNIRS signals using a decision fusion technique significantly improves the detection rate of mental stress compared to using either modality alone.
Answers are grounded in this paper and cite their sources.
Fusion technique for EEG-fNIRS classifiers developed; leaves open clinical utility in neurological assessment pending validation.
emergingFusion 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.
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Al-Shargie et al. (2017) studied Mental stress (n=20). Decision fusion of EEG and fNIRS signals vs. Sole modality of EEG and fNIRS was evaluated on Detection rate of mental stress (+7.76% vs EEG, +10.57% vs fNIRS, p=<0.001 and <0.0005). Decision fusion of EEG and fNIRS signals significantly improved the detection rate of mental stress by 7.76% (p<0.001) and 10.57% (p<0.0005) compared to EEG and fNIRS alone, respectively.
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