Feature embeddings derived from continuous map** using the deep neural network are
critical for accurate classification in seizure prediction tasks. However, the embeddings of …

Domain adaptation (DA) techniques are frequently utilized to enhance seizure prediction
accuracy by leveraging the labeled electroencephalogram data of existing patients on new …

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 …

The superiority of life for people with epilepsy can be greatly improved with the assistance of
accurate seizure prediction and early warning. An automatic prediction model is required to …

To decrease the interference in the process of epileptic feature extraction caused by
insufficient detection capability in partial channels of focal epilepsy, this paper proposes a …

In this paper, we propose a novel neural network architecture called CTCNet. First, we adopt
a multi-scale convolutional neural network (MSCNN) to extract low and high-frequency …

Existing domain adaptation (DA) methods typically require access to source domain data,
which raises privacy concerns due to the sensitive information contained in …

Data augmentation (DA) can significantly strengthen the electroencephalogram (EEG)-
based seizure prediction methods. However, existing DA approaches are just the linear …

Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and
accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make …

When building seizure prediction systems, the typical research scenario is patient-specific.
In this scenario, the model is limited to performing well for individual patients and cannot …