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Classifying EEG data is integral to the performance of Brain ComputerInterfaces (BCI) and their applications. However, external noise oftenobstructs EEG data due to its biological nature and complex data collectionprocess. Especially when dealing with classification tasks, standard EEGpreprocessing approaches extract relevant events and features from the entiredataset. However, these approaches treat all relevant cognitive events equallyand overlook the dynamic nature of the brain over time. In contrast, we areinspired by neuroscience studies to use a novel approach that integratesfeature selection and time segmentation of EEG data. When tested on theEEGEyeNet dataset, our proposed method significantly increases the performanceof Machine Learning classifiers while reducing their respective computationalcomplexity.

Two Heads are Better than One: A Bio-inspired Method for Improving Classification on EEG-ET Data