Ensemble Methods with Statistics and Machine Learning on the Class Imbalance Problems of EEG data

Abstract

Class imbalance in EEG data sets is a significant issue that affects the quality of outputs. The uncertainty in the data sets, which can be small or large, can lead to class imbalance problems (CIP). This imbalance can lead to highly imbalanced predictive models. The selection of random samples for algorithms can result in high variation of classes. Data sets of EEG are generated as image data sets and are often random and never repeat, causing a high variation in classes. To address this issue, various sampling methods on the data and heuristics are being developed to develop predictive models which were in vogue based on the level of the uncertain state.