Background Data augmentation (DA) has recently been demonstrated to achieve
considerable performance gains for deep learning (DL)—increased accuracy and stability …

The identification and classification of human brain activities are essential for many medical
and Brain-Computer Interface (BCI) systems, saving human lives and time …

Epilepsy is a widespread neurological disorder characterized by recurring seizures that
have a significant impact on individuals' lives. Accurately recognizing epileptic seizures is …

The detection of seizures in epileptic patients via Electroencephalography (EEG) is an
essential key to medical treatment. With the advances in deep learning, many approaches …

A seizure is an unstable situation in epilepsy patients due to excessive electrical discharge
by brain cells. An efficient seizure prediction method is required to reduce the lifetime risk of …

Graph theory analysis using electroencephalogram (EEG) signals is currently an advanced
technique for seizure prediction. Recent deep learning approaches, which fail to fully …

Recently, most seizure prediction methods mainly utilize pure CNN or Transformer model,
which cannot extract local and global features simultaneously. To this end, we propose an …

Epilepsy is one of the most prevalent neurological diseases among humans and can lead to
severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to …

Objective: Epilepsy prediction algorithms offer patients with drug-resistant epilepsy a way to
reduce unintended harm from sudden seizures. The purpose of this study is to investigate …

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