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Neural decoding of EEG signals with machine learning: a systematic review
Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
Machine learning and artificial intelligence applications to epilepsy: a review for the practicing epileptologist
Abstract Purpose of Review Machine Learning (ML) and Artificial Intelligence (AI) are data-
driven techniques to translate raw data into applicable and interpretable insights that can …
driven techniques to translate raw data into applicable and interpretable insights that can …
The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials
Seizures have a profound impact on quality of life and mortality, in part because they can be
challenging both to detect and forecast. Seizure detection relies upon accurately …
challenging both to detect and forecast. Seizure detection relies upon accurately …
Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy
Background This study sought to predict postsurgical seizure freedom from pre-operative
diagnostic test results and clinical information using a rapid automated approach, based on …
diagnostic test results and clinical information using a rapid automated approach, based on …
[HTML][HTML] Diagnostic delay in psychogenic seizures and the association with anti-seizure medication trials
Purpose The average delay from first seizure to diagnosis of psychogenic non-epileptic
seizures (PNES) is over 7 years. The reason for this delay is not well understood. We …
seizures (PNES) is over 7 years. The reason for this delay is not well understood. We …
Identifying psychogenic seizures through comorbidities and medication history
Objective Low‐cost evidence‐based tools are needed to facilitate the early identification of
patients with possible psychogenic nonepileptic seizures (PNES). Prior to accurate …
patients with possible psychogenic nonepileptic seizures (PNES). Prior to accurate …
Abnormal phase–amplitude coupling characterizes the interictal state in epilepsy
Objective. Diagnosing epilepsy still requires visual interpretation of electroencephalography
(EEG) and magnetoencephalography (MEG) by specialists, which prevents quantification …
(EEG) and magnetoencephalography (MEG) by specialists, which prevents quantification …
Transfer learning for the identification of paediatric EEGs with interictal epileptiform abnormalities
EEG is a test that helps in the clinical diagnosis of epilepsy. Epilepsy diagnosis is facilitated
by establishing the presence of interictal epileptiform abnormalities on EEG, which predict …
by establishing the presence of interictal epileptiform abnormalities on EEG, which predict …
Objective score from initial interview identifies patients with probable dissociative seizures
Abstract Objective To develop a Dissociative Seizures Likelihood Score (DSLS), which is a
comprehensive, evidence-based tool using information available during the first outpatient …
comprehensive, evidence-based tool using information available during the first outpatient …
Diagnosing epilepsy with normal interictal EEG using dynamic network models
Objective Whereas a scalp electroencephalogram (EEG) is important for diagnosing
epilepsy, a single routine EEG is limited in its diagnostic value. Only a small percentage of …
epilepsy, a single routine EEG is limited in its diagnostic value. Only a small percentage of …