A Continuously Updated, Computationally Efficient Stress Recognition Framework Using Electroencephalogram (EEG) by Applying Online Multitask Learning Algorithms (OMTL)

Houtan Jebelli, Mohammad Mahdi Khalili, Sanghyun Lee

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Recognizing the factors that cause stress is a crucial step toward early detection of stressors. In this regard, several studies make an effort to recognize individuals' stress using an Electroencephalogram (EEG). However, current EEG-based stress recognition frameworks have several drawbacks. First, they are mostly designed to recognize individuals' stress only in a controlled laboratory environment. Second, they do not take into account the changes in the EEG signals of different subjects under the same stressors. Third, most of the current stress recognition algorithms occur in an offline setting. To address these issues, this study proposes an EEG-based stress recognition framework that takes into account each subject's brainwave patterns to train the stress recognition classifier and continuously update its classifier based on new input signals in near real-time. The proposed framework first removes EEG signal artifacts, then extracts a broad range of EEG signal features, and finally applies different online multitask learning (OMTL) algorithms to recognize individuals' stress in near real time. The proposed framework was applied on the EEG collected in two environments-first on the EEG collected in a controlled lab environment using a wired-EEG and second on the EEG collected at in the field using a wearable EEG device. The OMTL-VonNeuman method resulted in the best prediction accuracy on both datasets (71.14% on the first dataset and 77.61% on second) among all tested algorithms. The proposed stress recognition framework continuously updates its classifier and therefore contributes to stress recognition for new stressful situations that are beyond the range of predefined stressful conditions in near real time both in a controlled lab environment and at real job sites.

Original languageEnglish (US)
Article number8467324
Pages (from-to)1928-1939
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Volume23
Issue number5
DOIs
StatePublished - Sep 2019

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Electroencephalography
Learning algorithms
Learning
Controlled Environment
Classifiers
Recognition (Psychology)
Brain Waves
Workplace
Artifacts
Equipment and Supplies

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

Cite this

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title = "A Continuously Updated, Computationally Efficient Stress Recognition Framework Using Electroencephalogram (EEG) by Applying Online Multitask Learning Algorithms (OMTL)",
abstract = "Recognizing the factors that cause stress is a crucial step toward early detection of stressors. In this regard, several studies make an effort to recognize individuals' stress using an Electroencephalogram (EEG). However, current EEG-based stress recognition frameworks have several drawbacks. First, they are mostly designed to recognize individuals' stress only in a controlled laboratory environment. Second, they do not take into account the changes in the EEG signals of different subjects under the same stressors. Third, most of the current stress recognition algorithms occur in an offline setting. To address these issues, this study proposes an EEG-based stress recognition framework that takes into account each subject's brainwave patterns to train the stress recognition classifier and continuously update its classifier based on new input signals in near real-time. The proposed framework first removes EEG signal artifacts, then extracts a broad range of EEG signal features, and finally applies different online multitask learning (OMTL) algorithms to recognize individuals' stress in near real time. The proposed framework was applied on the EEG collected in two environments-first on the EEG collected in a controlled lab environment using a wired-EEG and second on the EEG collected at in the field using a wearable EEG device. The OMTL-VonNeuman method resulted in the best prediction accuracy on both datasets (71.14{\%} on the first dataset and 77.61{\%} on second) among all tested algorithms. The proposed stress recognition framework continuously updates its classifier and therefore contributes to stress recognition for new stressful situations that are beyond the range of predefined stressful conditions in near real time both in a controlled lab environment and at real job sites.",
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