Transformer Architectures, Foundation Models and TPU-Accelerated Intelligence for Advances in EEG-Based Seizure Prediction: A Survey

Epilepsy is a chronic condition of the brain that manifests itself in the form of seizures and affects millions of people around the world. When it comes to predicting seizures correctly early on, it could make all the difference from a patient safety and clinical outcome point of view, using electroencephalography (EEG) signals. Generally, the traditional machine learning methods have shown poor capability because of the requirement of manually designing the features to extract them. One recent breakthrough in the deep learning field is the ability to learn complex representations of the spatiotemporal dynamics of EEG data automatically, especially with the use of transformer architectures. Moreover, the rise of the Foundation Models and computing using Tensor Processing Unit (TPU) has presented an opportunity to explore new directions for the development of scalable and efficient seizure prediction systems. This survey addresses the evolution of classical machine learning based models to Transformer inspired models, Foundation Models and intelligent systems based on TPUs for prediction of seizures. An existing comparative analysis to existing approaches is presented that shows strengths and weaknesses. Research gaps are discussed with regard to generalization, explainability, computational complexity and real-time deployment. Last but not least, future research directions related to self-supervised learning, Federated Learning, Explainable Artificial Intelligence (XAI) and TPU-accelerated Foundation Models are presented.