Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy
have increased exponentially over the past decade. Integration of AI into epilepsy …

The fact that the rapid and definitive diagnosis of autism cannot be made today and that
autism cannot be treated provides an impetus to look into novel technological solutions. 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 …

Early seizure prediction is crucial for epilepsy patients to reduce accidental injuries and
improve their quality of life. Identifying pre-ictal EEG from the inter-ictal state is particularly …

Epilepsy, a complex pathology with various etiological origins, is characterized by producing
hyperexcitability in the brain, which can have multiple disruptive symptoms. It impacts about …

Automatic seizure detection plays a key role in assisting clinicians for rapid diagnosis and
treatment of epilepsy. In view of the parallelism of temporal convolutional network (TCN) and …

Repetitive Transcranial Magnetic Stimulation (rTMS) is proposed as an effective treatment
for major depressive disorder (MDD). However, because of the suboptimal treatment …

Facial expression recognition (FER) plays a vital role in the field of human-computer
interaction. To achieve automatic FER, various approaches based on deep learning (DL) …

Deep learning-based epileptic seizure recognition via electroencephalogram signals has
shown considerable potential for clinical practice. Although deep learning algorithms can …

Seizure prediction can improve the quality of life for patients with drug-resistant epilepsy.
With the rapid development of deep learning, lots of seizure prediction methods have been …