Abstract In the last century, 10–20 lead EEG recordings became the gold standard of surface
EEG recordings, and the 10–20 system provided comparability between international …

The development of seizure prediction models is often based on long-term scalp
electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive …

Artificial intelligence (AI) allows data analysis and integration at an unprecedented
granularity and scale. Here we review the technological advances, challenges, and future …

Despite considerable advancement of first choice treatment (pharmacological, physical
therapy, etc.) over many decades, neurological disorders still represent a major portion of …

A lot of mileage has been made recently on the long and winding road toward seizure
forecasting. Here we briefly review some selected milestones passed along the way, which …

Objective Recently, a deep learning artificial intelligence (AI) model forecasted seizure risk
using retrospective seizure diaries with higher accuracy than random forecasts. The present …

Objective Previous studies suggested that patients with epilepsy might be able to forecast
their own seizures. This study aimed to assess the relationships between premonitory …

According to the literature, seizure prediction models should be developed following a
patient-specific approach. However, seizures are usually very rare events, meaning the …

Background Seizure risk forecasting could reduce injuries and even deaths in people with
epilepsy. There is great interest in using non-invasive wearable devices to generate …

Epilepsy affects around 1% of the population worldwide. Anti-epileptic drugs are an
excellent option for controlling seizure occurrence but do not work for around one-third of …