  The differences in distributional patterns between benchmark data and
real-world data have been one of the main challenges of using
electroencephalogram (EEG) signals for eye-tracking (ET) classification.
Therefore, increasing the robustness of machine learning models in predicting
eye-tracking positions from EEG data is integral for both research and consumer
use. Previously, we compared the performance of classifiers trained solely on
finer-grain data to those trained solely on coarse-grain. Results indicated
that despite the overall improvement in robustness, the performance of the
fine-grain trained models decreased, compared to coarse-grain trained models,
when the testing and training set contained the same distributional patterns
\cite{vectorbased}. This paper aims to address this case by training models
using datasets of mixed data complexity to determine the ideal distribution of
fine- and coarse-grain data. We train machine learning models utilizing a mixed
dataset composed of both fine- and coarse-grain data and then compare the
accuracies to models trained using solely fine- or coarse-grain data. For our
purposes, finer-grain data refers to data collected using more complex methods
whereas coarser-grain data refers to data collected using more simple methods.
We apply covariate distributional shifts to test for the susceptibility of each
training set. Our results indicated that the optimal training dataset for
EEG-ET classification is not composed of solely fine- or coarse-grain data, but
rather a mix of the two, leaning towards finer-grain.

Signal Processing;Machine Learning