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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publication2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)
PublisherIEEE
ISBN (Electronic)9781509008452
DOIs
Publication statusPublished - 19 Jan 2017
Event6th International Conference on Intelligent and Advanced Systems 2016 - Kuala Lumpur, Malaysia
Duration: 15 Aug 201617 Aug 2016

Conference

Conference6th International Conference on Intelligent and Advanced Systems 2016
Abbreviated titleICIAS 2016
CountryMalaysia
CityKuala Lumpur
Period15/08/1617/08/16

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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  • Cite this

    Al-Shargie, F., Tang, T. B., Badruddin, N., Dass, S. C., & 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. https://doi.org/10.1109/ICIAS.2016.7824060