Deep learning for motor imagery EEG-based classification: A review
Objectives The availability of large and varied Electroencephalogram (EEG) datasets,
rapidly advances and inventions in deep learning techniques, and highly powerful and …
rapidly advances and inventions in deep learning techniques, and highly powerful and …
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 …
[HTML][HTML] An efficient CNN based epileptic seizures detection framework using encrypted EEG signals for secure telemedicine applications
Recently, the rapid development of Artificial Intelligence (AI) applied in the Medical Internet
of Things (MIoT) for the diagnosis of diseases such as epilepsy based on the investigation of …
of Things (MIoT) for the diagnosis of diseases such as epilepsy based on the investigation of …
A comprehensive survey on the detection, classification, and challenges of neurological disorders
Simple Summary This study represents a resourceful review article that can deliver
resources on neurological diseases and their implemented classification algorithms to …
resources on neurological diseases and their implemented classification algorithms to …
Epileptic seizure detection using machine learning: Taxonomy, opportunities, and challenges
Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent
unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many …
unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many …
An overview of machine learning methods in enabling IoMT-based epileptic seizure detection
ALN Al-Hajjar, AKM Al-Qurabat - The Journal of Supercomputing, 2023 - Springer
The healthcare industry is rapidly automating, in large part because of the Internet of Things
(IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of …
(IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of …
An efficient feature selection and explainable classification method for EEG-based epileptic seizure detection
Epilepsy is a prevalent neurological disorder that poses life-threatening emergencies. Early
electroencephalogram (EEG) seizure detection can mitigate the risks and aid in the …
electroencephalogram (EEG) seizure detection can mitigate the risks and aid in the …
Artificial neural network model using short-term fourier transform for epilepsy seizure detection
Epilepsy is a neurological illness that can strike anyone at any time in their life. However, a
person with epilepsy will experience frequent to uncommon seizures, resulting in death …
person with epilepsy will experience frequent to uncommon seizures, resulting in death …
Detection of epileptic seizures from EEG signals by combining dimensionality reduction algorithms with machine learning models
M Zubair, MV Belykh, MUK Naik… - IEEE Sensors …, 2021 - ieeexplore.ieee.org
Epilepsy is a neurological condition that affects the central nervous system. While its effects
are different for each person, they mostly include abnormal behaviour, periods of loss of …
are different for each person, they mostly include abnormal behaviour, periods of loss of …
Machine learning classification of maladaptive rumination and cognitive distraction in terms of frequency specific complexity
In this study, cognitive and behavioral emotion regulation strategies (ERS) are classified by
using machine learning models driven by a new local EEG complexity approach so called …
using machine learning models driven by a new local EEG complexity approach so called …