Investigating different stress-relief methods using Electroencephalogram (EEG).

Area of Science:

• Neuroscience
• Psychology
• Biomedical Engineering

Background:

• Mental stress is a widespread societal issue linked to numerous physical and psychological ailments.
• Electroencephalography (EEG) offers a potential non-invasive method for monitoring brain activity related to stress.

Purpose of the Study:

• To investigate the feasibility of using EEG to detect varying levels of mental stress.
• To evaluate the efficacy of different stress-relief techniques.

Main Methods:

• EEG data were collected from 25 participants under resting and induced stress conditions (mental arithmetic tasks).
• Nine stress-correlated features were extracted, and discriminative features were selected using Fisher Ratio.
• A Linear Discriminant Analysis classifier was trained and evaluated using 10-fold cross-validation.

Main Results:

• High classification accuracies were achieved: 85.6% (stress vs. rest), 71.2% (two stress levels vs. rest), and 58.4% (three stress levels vs. rest).
• Beta brain waves (13-30Hz) and prefrontal relative Gamma power were identified as key discriminative features.
• Hugging a pillow was the most effective stress-relief method identified, reducing stress levels detected by EEG.

Conclusions:

• EEG shows significant promise for detecting mental stress levels.
• Specific EEG features, particularly Beta and Gamma wave activity, are highly indicative of stress.
• Simple interventions like hugging a pillow can effectively mitigate stress, suggesting potential for integrated stress management systems.