  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.
