EGAN: An Ensemble Adversarial Network for Topology-Preserving EEG Data Generation for Predicting Cognitive Load Levels
Felix Havugimana,
Kazi Ashraf Moinuddin,
Mohammed Yeasin
Abstract:Learning representations and extracting meaningful patterns from Electroen- cephalogram (EEG) recordings is critical for analyzing cognitive events (e.g., predicting cognitive load). The primary challenges include individual variability, technical noise from unreliable sensor-skin contacts, and rapid temporal changes in the EEG recordings. Given the multi-factorial nature of the problems, deep learning models are natural choices for learning representations from the data. However, the extensive time required f… Show more
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