  Objective: To investigate the effects of different approaches to EEG
preprocessing, channel montage selection, and model architecture on the
performance of an online-capable stress detection algorithm in a classroom
scenario. Methods: This analysis used EEG data from a longitudinal stress and
fatigue study conducted among university students. Their self-reported stress
ratings during each class session were the basis for classifying EEG recordings
into either normal or elevated stress states. We used a data-processing
pipeline that combined Artifact Subspace Reconstruction (ASR)and an Independent
Component Analysis (ICA)-based method to achieve online artifact removal. We
compared the performance of a Linear Discriminant Analysis (LDA) and a 4-layer
neural network as classifiers. We opted for accuracy, balanced accuracy, and F1
score as the metrics for assessing performance. We examined the impact of
varying numbers of input channels using different channel montages.
Additionally, we explored different window lengths and step sizes during online
evaluation. Results: Our online artifact removal method achieved performance
comparable to the offline ICA method in both offline and online evaluations. A
balanced accuracy of 77% and 78% in an imbalanced binary classification were
observed when using the 11-frontal-channel LDA model with the proposed artifact
removal method. Moreover, the model performance remained intact when changing
the channel montage from 30 full-scalp channels to just 11 frontal channels.
During the online evaluation, we achieved the highest balanced accuracy (78%)
with a window length of 20 seconds and a step size of 1 second. Significance:
This study comprehensively investigates the deployment of stress detection in
real-world scenarios. The findings of this study provide insight into the
development of daily mental stress monitoring.
