A review on mental stress assessment methods using eeg signals

Rateb Katmah, Fares Al-Shargie, Usman Tariq, Fabio Babiloni, Fadwa Al-Mughairbi, Hasan Al-Nashash

Research output: Contribution to journalReview articlepeer-review

19 Citations (Scopus)

Abstract

Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contains rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Accordingly, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.

Original languageEnglish
Article number5043
JournalSensors
Volume21
Issue number15
DOIs
Publication statusPublished - Aug 1 2021

Keywords

  • Connectivity network
  • Data analysis
  • EEG
  • Machine Learning
  • Mental stress

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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