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 …
Seizure prediction—ready for a new era
Epilepsy is a common disorder characterized by recurrent seizures. An overwhelming
majority of people with epilepsy regard the unpredictability of seizures as a major issue …
majority of people with epilepsy regard the unpredictability of seizures as a major issue …
A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals
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 …
capture changes in electrical brain activity that could declare an imminent seizure. In this …
A wearable motion capture suit and machine learning predict disease progression in Friedreich's ataxia
B Kadirvelu, C Gavriel, S Nageshwaran, JPK Chan… - Nature medicine, 2023 - nature.com
Friedreichʼs ataxia (FA) is caused by a variant of the Frataxin (FXN) gene, leading to its
downregulation and progressively impaired cardiac and neurological function. Current gold …
downregulation and progressively impaired cardiac and neurological function. Current gold …
Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies
A Shoeibi, N Ghassemi, M Khodatars… - … Signal Processing and …, 2022 - Elsevier
Epileptic seizures are one of the most crucial neurological disorders, and their early
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
Deep convolutional neural network-based epileptic electroencephalogram (EEG) signal classification
Electroencephalogram (EEG) signals contain vital information on the electrical activities of
the brain and are widely used to aid epilepsy analysis. A challenging element of epilepsy …
the brain and are widely used to aid epilepsy analysis. A challenging element of epilepsy …
Epileptic seizures prediction using deep learning techniques
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 …
people worldwide. In more than 30% of the cases, people affected by this disease cannot be …
Deep learning based efficient epileptic seizure prediction with EEG channel optimization
R Jana, I Mukherjee - Biomedical Signal Processing and Control, 2021 - Elsevier
A seizure is an unstable situation in epilepsy patients due to excessive electrical discharge
by brain cells. An efficient seizure prediction method is required to reduce the lifetime risk of …
by brain cells. An efficient seizure prediction method is required to reduce the lifetime risk of …
Automated seizure prediction
In the past two decades, significant advances have been made on automated
electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number …
electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number …
EEG seizure detection and prediction algorithms: a survey
Epilepsy patients experience challenges in daily life due to precautions they have to take in
order to cope with this condition. When a seizure occurs, it might cause injuries or endanger …
order to cope with this condition. When a seizure occurs, it might cause injuries or endanger …