Addressing class imbalance in traumatic brain injury prognostication: A survey of resampling approaches.

In recent years, the classification of electroencephalogram (EEG) signals has attracted considerable interest, particularly in clinical and cognitive neuroscience applications. However, the problem of class imbalance often undermines the performance of classification algorithms and leads to biased predictions in favor of the majority class. In this paper, I provide a review of data approaches that address the class imbalance problem in EEG classification. I explored methods such as oversampling, undersampling, and the application of advanced algorithms such as Generative Adversarial Networks (GANs), which generally showed better overall performance in dealing with class imbalance. In this paper, I reviewed work published in the last 5 years to evaluate the current state of the art in imbalance. In addition, I provide insight into potential gaps in EEG classification, especially in traumatic brain injury (TBI), and discuss the potential of data augmentation based on deep learning, i.e., GANs, which have shown promise in generating synthetic data to improve minority class representation in other application domains for the EEG-based classification model. By highlighting gaps in existing EEG classification methods and proposing GANs as a solution to class imbalance, I aim to contribute to the development of more robust prediction models that may lead to improved patient outcomes in TBI cases.