Online Seizure Prediction via Fine-Tuning and Test-Time Adaptation

Privacy protection has become increasingly crucial in the field of epilepsy prediction. Some latest studies introduced the source free domain adaptation (SFDA), which only utilizes a pre-trained source model for protecting the source data privacy. However, the existing SFDA methods exist two shortcomings. (1) the offline setting, which is not suitable for real-world online scenarios (2) the poor performance, which is attributed to the absence of labeled calibration data during the adaptation phase. To this end, we proposed a online seizure prediction framework based on fine-tuning and test-time adaptation (FT3A). Specifically, FT3A employs one seizure event target data to fine-tune and continuously adapt pre-trained source model to unlabeled target data stream. In addition, the adaption and prediction is performed simultaneously. On the one hand, we design the task model as a multi-head structure to increase the confidence of the model and reduce error accumulation. On the other hand, a memory bank is introduced to store a small amount of historical EEG data, which helps handle the catastrophic forgetting concern of the model during online adaptation. Extensive experiments on public CHB-MIT dataset and the private freiburg hospital dataset indicate the superiority and generality of the proposed method.