Stress assessment based on EEG univariate features and functional connectivity measures

Area of Science:

• Neuroscience
• Physiology
• Biomedical Engineering

Background:

• Stress response involves complex brain and physiological changes.
• Current stress assessment relies on hormones and physiological signals (ECG, BP, GSR).
• Electroencephalography (EEG) offers a potential non-invasive method for stress evaluation.

Purpose of the Study:

• To assess stress using EEG-derived variables from univariate and functional connectivity analyses.
• To identify common EEG patterns associated with stress induction in volunteers.
• To explore the utility of EEG features as objective stress biomarkers.

Main Methods:

• Applied two distinct stressors: Stroop test and sleep deprivation.
• Recorded EEG data from 30 healthy volunteers.
• Analyzed EEG using univariate metrics (power, entropy) and functional connectivity (coherence, cross-mutual information).

Main Results:

• Observed decreased high alpha power (11-12 Hz) and approximate entropy.
• Detected increased high beta band power (23-36 Hz), indicative of heightened cognitive activity.
• Found significant increases in high beta coherence and interhemispheric nonlinear couplings under stress.

Conclusions:

• Common EEG patterns reliably reflect stress responses.
• Changes in specific EEG frequency bands and connectivity metrics can serve as stress indicators.
• EEG-based stress assessment shows promise for clinical and research applications.