Stress Monitoring Using Low-Cost Electroencephalogram Devices: A Systematic Literature Review
<|reference_start|>Stress Monitoring Using Low-Cost Electroencephalogram Devices: A Systematic Literature Review: Introduction. Low-cost health monitoring devices are increasingly being used for mental health related studies including stress. While cortisol response magnitude remains the gold standard indicator for stress assessment, a growing number of studies have started to use low-cost EEG devices as primary recorders of biomarker data.   Methods. This study reviews published works contributing and/or using EEG devices for detecting stress and their associated machine learning methods. The reviewed works are selected to answer three general research questions and are then synthesized into four categories of stress assessment using EEG, low-cost EEG devices, available datasets for EEG-based stress measurement, and machine learning techniques for EEG-based stress measurement.   Results. A number of studies were identified where low-cost EEG devices were utilized to record brain function during phases of stress and relaxation. These studies generally reported a high predictive accuracy rate, verified using a number of different machine learning validation methods and statistical approaches. Of these studies, 60% can be considered low-powered studies based on the small number of test subjects used during experimentation.   Conclusion. Low-cost consumer grade wearable devices including EEG and wrist-based monitors are increasingly being used in stress-related studies. Standardization of EEG signal processing and importance of sensor location still requires further study, and research in this area will continue to provide improvements as more studies become available.<|reference_end|>
@article{vos2024stress,
    title={Stress Monitoring Using Low-Cost Electroencephalogram Devices: A
  Systematic Literature Review},
    author={Gideon Vos, Maryam Ebrahimpour, Liza van Eijk, Zoltan Sarnyai, Mostafa
  Rahimi Azghadi},
    journal={arXiv preprint arXiv:2403.05577},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05577},
    primaryClass={cs.HC eess.SP}
}