Many state‐of‐the‐art methods for seizure prediction, using the electroencephalogram, are
based on machine learning models that are black boxes, weakening the trust of clinicians in …

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

Objective Epilepsy is characterized by spontaneous seizures that recur at unexpected times.
Nonetheless, using years‐long electroencephalographic (EEG) recordings, we previously …

Catamenial epilepsy is the best described and most researched sex steroid‐specific seizure
exacerbation. Yet despite this there are no current evidence‐based treatments, nor an …

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 …

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 …

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 …

Objective Interictal epileptiform discharges (IED) are hallmark biomarkers of epilepsy which
are typically detected through visual analysis. Deep learning has shown potential in …

Nearly 20 million people with epilepsy suffer from seizures that are poorly controlled by
medication. 1 For many of these individuals, seizures originate from 1 or more focal areas …

Background: The ability to predict coming seizures will improve the quality of life of patients
with epilepsy. Analysis of brain electrical activity using electroencephalogram (EEG) signals …