Mental stress assessment based on feature level fusion of fNIRS and EEG signals

Fares Al-Shargie; Tong Boon Tang; Nasreen Badruddin; Sarat C. Dass; Masashi Kiguchi

doi:10.1109/ICIAS.2016.7824060

Mental stress assessment based on feature level fusion of fNIRS and EEG signals

Fares Al-Shargie, Tong Boon Tang, Nasreen Badruddin, Sarat C. Dass, Masashi Kiguchi

School of Mathematical & Computer Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Citations (Scopus)

Abstract

This study aims to improve the detection rate of mental stress using the complementary nature of functional Near Infrared Spectroscopy (fNIRS) and Electroencephalogram (EEG). Simultaneous measurements of fNIRS and EEG signals were conducted on 12 subjects while solving arithmetic problems under two different conditions (control and stress). The stressors in this work were time pressure and negative feedback of individual performance. The study demonstrated significant reduction in the concentration of oxygenated haemoglobin (p=0.0032) and alpha rhythm power (p=0.0213) on the prefrontal cortex (PFC) under stress condition. Specifically, the right PFC and dorsolateral PFC were highly sensitive to mental stress. Using support vector machine (SVM), the mean detection rate of mental stress was 91%, 95% and 98% using fNIRS, EEG and fusion of fNIRS and EEG signals, respectively.

Original language	English
Title of host publication	2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)
Publisher	IEEE
ISBN (Electronic)	9781509008452
DOIs	https://doi.org/10.1109/ICIAS.2016.7824060
Publication status	Published - 19 Jan 2017
Event	6th International Conference on Intelligent and Advanced Systems 2016 - Kuala Lumpur, Malaysia Duration: 15 Aug 2016 → 17 Aug 2016

Conference

Conference	6th International Conference on Intelligent and Advanced Systems 2016
Abbreviated title	ICIAS 2016
Country/Territory	Malaysia
City	Kuala Lumpur
Period	15/08/16 → 17/08/16

ASJC Scopus subject areas

Electrical and Electronic Engineering
Control and Optimization
Energy Engineering and Power Technology
Artificial Intelligence
Computer Science Applications

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10.1109/ICIAS.2016.7824060

Cite this

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title = "Mental stress assessment based on feature level fusion of fNIRS and EEG signals",

abstract = "This study aims to improve the detection rate of mental stress using the complementary nature of functional Near Infrared Spectroscopy (fNIRS) and Electroencephalogram (EEG). Simultaneous measurements of fNIRS and EEG signals were conducted on 12 subjects while solving arithmetic problems under two different conditions (control and stress). The stressors in this work were time pressure and negative feedback of individual performance. The study demonstrated significant reduction in the concentration of oxygenated haemoglobin (p=0.0032) and alpha rhythm power (p=0.0213) on the prefrontal cortex (PFC) under stress condition. Specifically, the right PFC and dorsolateral PFC were highly sensitive to mental stress. Using support vector machine (SVM), the mean detection rate of mental stress was 91%, 95% and 98% using fNIRS, EEG and fusion of fNIRS and EEG signals, respectively.",

author = "Fares Al-Shargie and Tang, {Tong Boon} and Nasreen Badruddin and Dass, {Sarat C.} and Masashi Kiguchi",

year = "2017",

month = jan,

day = "19",

doi = "10.1109/ICIAS.2016.7824060",

language = "English",

booktitle = "2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)",

publisher = "IEEE",

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note = "6th International Conference on Intelligent and Advanced Systems 2016, ICIAS 2016 ; Conference date: 15-08-2016 Through 17-08-2016",

}

Al-Shargie, F, Tang, TB, Badruddin, N, Dass, SC & Kiguchi, M 2017, Mental stress assessment based on feature level fusion of fNIRS and EEG signals. in 2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)., 7824060, IEEE, 6th International Conference on Intelligent and Advanced Systems 2016, Kuala Lumpur, Malaysia, 15/08/16. https://doi.org/10.1109/ICIAS.2016.7824060

TY - GEN

T1 - Mental stress assessment based on feature level fusion of fNIRS and EEG signals

AU - Al-Shargie, Fares

AU - Tang, Tong Boon

AU - Badruddin, Nasreen

AU - Dass, Sarat C.

AU - Kiguchi, Masashi

PY - 2017/1/19

Y1 - 2017/1/19

N2 - This study aims to improve the detection rate of mental stress using the complementary nature of functional Near Infrared Spectroscopy (fNIRS) and Electroencephalogram (EEG). Simultaneous measurements of fNIRS and EEG signals were conducted on 12 subjects while solving arithmetic problems under two different conditions (control and stress). The stressors in this work were time pressure and negative feedback of individual performance. The study demonstrated significant reduction in the concentration of oxygenated haemoglobin (p=0.0032) and alpha rhythm power (p=0.0213) on the prefrontal cortex (PFC) under stress condition. Specifically, the right PFC and dorsolateral PFC were highly sensitive to mental stress. Using support vector machine (SVM), the mean detection rate of mental stress was 91%, 95% and 98% using fNIRS, EEG and fusion of fNIRS and EEG signals, respectively.

AB - This study aims to improve the detection rate of mental stress using the complementary nature of functional Near Infrared Spectroscopy (fNIRS) and Electroencephalogram (EEG). Simultaneous measurements of fNIRS and EEG signals were conducted on 12 subjects while solving arithmetic problems under two different conditions (control and stress). The stressors in this work were time pressure and negative feedback of individual performance. The study demonstrated significant reduction in the concentration of oxygenated haemoglobin (p=0.0032) and alpha rhythm power (p=0.0213) on the prefrontal cortex (PFC) under stress condition. Specifically, the right PFC and dorsolateral PFC were highly sensitive to mental stress. Using support vector machine (SVM), the mean detection rate of mental stress was 91%, 95% and 98% using fNIRS, EEG and fusion of fNIRS and EEG signals, respectively.

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U2 - 10.1109/ICIAS.2016.7824060

DO - 10.1109/ICIAS.2016.7824060

M3 - Conference contribution

AN - SCOPUS:85011982455

BT - 2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)

PB - IEEE

T2 - 6th International Conference on Intelligent and Advanced Systems 2016

Y2 - 15 August 2016 through 17 August 2016

ER -

Mental stress assessment based on feature level fusion of fNIRS and EEG signals

Abstract

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ASJC Scopus subject areas

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