  Many research works indicate that EEG bands, specifically the alpha and theta
bands, have been potentially helpful cognitive load indicators. However,
minimal research exists to validate this claim. This study aims to assess and
analyze the impact of the alpha-to-theta and the theta-to-alpha band ratios on
supporting the creation of models capable of discriminating self-reported
perceptions of mental workload. A dataset of raw EEG data was utilized in which
48 subjects performed a resting activity and an induced task demanding exercise
in the form of a multitasking SIMKAP test. Band ratios were devised from
frontal and parietal electrode clusters. Building and model testing was done
with high-level independent features from the frequency and temporal domains
extracted from the computed ratios over time. Target features for model
training were extracted from the subjective ratings collected after resting and
task demand activities. Models were built by employing Logistic Regression,
Support Vector Machines and Decision Trees and were evaluated with performance
measures including accuracy, recall, precision and f1-score. The results
indicate high classification accuracy of those models trained with the
high-level features extracted from the alpha-to-theta ratios and theta-to-alpha
ratios. Preliminary results also show that models trained with logistic
regression and support vector machines can accurately classify self-reported
perceptions of mental workload. This research contributes to the body of
knowledge by demonstrating the richness of the information in the temporal,
spectral and statistical domains extracted from the alpha-to-theta and
theta-to-alpha EEG band ratios for the discrimination of self-reported
perceptions of mental workload.
