With the advancement in artificial intelligence (AI) and machine learning (ML) techniques,
researchers are striving towards employing these techniques for advancing clinical practice …

Personal and ubiquitous sensing technologies such as smartphones have allowed the
continuous collection of data in an unobtrusive manner. Machine learning methods have …

Objective. Brain–computer interfaces (BCI) enable direct communication with a computer,
using neural activity as the control signal. This neural signal is generally chosen from a …

The electroencephalogram (EEG) is the most prominent means to study epilepsy and
capture changes in electrical brain activity that could declare an imminent seizure. In this …

Deep Convolutional Neural Network (CNN) has achieved remarkable results in computer
vision tasks for end-to-end learning. We evaluate here the power of a deep CNN to learn …

Background People with epilepsy are burdened with the apparent unpredictability of
seizures. In the past decade, converging evidence from studies using chronic EEG (cEEG) …

Background Seizure prediction would be clinically useful in patients with epilepsy and could
improve safety, increase independence, and allow acute treatment. We did a multicentre …

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 a very common neurological disease that has affected more than 65 million
people worldwide. In more than 30% of the cases, people affected by this disease cannot be …

Diagnosing depression in the early curable stages is very important and may even save the
life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating …