  Windowing is a common technique in EEG machine learning classification and
other time series tasks. However, a challenge arises when employing this
technique: computational expense inhibits learning global relationships across
an entire recording or set of recordings. Furthermore, the labels inherited by
windows from their parent recordings may not accurately reflect the content of
that window in isolation. To resolve these issues, we introduce a multi-stage
model architecture, incorporating meta-learning principles tailored to
time-windowed data aggregation. We further tested two distinct strategies to
alleviate these issues: lengthening the window and utilizing overlapping to
augment data. Our methods, when tested on the Temple University Hospital
Abnormal EEG Corpus (TUAB), dramatically boosted the benchmark accuracy from
89.8 percent to 99.0 percent. This breakthrough performance surpasses prior
performance projections for this dataset and paves the way for clinical
applications of machine learning solutions to EEG interpretation challenges. On
a broader and more varied dataset from the Temple University Hospital EEG
Corpus (TUEG), we attained an accuracy of 86.7%, nearing the assumed
performance ceiling set by variable inter-rater agreement on such datasets.
