  Classifying EEG data is integral to the performance of Brain Computer
Interfaces (BCI) and their applications. However, external noise often
obstructs EEG data due to its biological nature and complex data collection
process. Especially when dealing with classification tasks, standard EEG
preprocessing approaches extract relevant events and features from the entire
dataset. However, these approaches treat all relevant cognitive events equally
and overlook the dynamic nature of the brain over time. In contrast, we are
inspired by neuroscience studies to use a novel approach that integrates
feature selection and time segmentation of EEG data. When tested on the
EEGEyeNet dataset, our proposed method significantly increases the performance
of Machine Learning classifiers while reducing their respective computational
complexity.
