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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 …
Recognition of imbalanced epileptic EEG signals by a graph‐based extreme learning machine
Epileptic EEG signal recognition is an important method for epilepsy detection. In essence,
epileptic EEG signal recognition is a typical imbalanced classification task. However …
epileptic EEG signal recognition is a typical imbalanced classification task. However …
Human-computer interaction for brain-inspired computing based on machine learning and deep learning: A review
The continuous development of artificial intelligence has a profound impact on biomedicine
and other fields, providing new research ideas and technical methods. Brain-inspired …
and other fields, providing new research ideas and technical methods. Brain-inspired …
An epileptic seizures diagnosis system using feature selection, fuzzy temporal naive Bayes and T-CNN
Today's hospitals make use of state-of-the-art methods such as magnetic resonance
imaging (MRI) and electroencephalogram (EEG) signal predictions in order to predict the …
imaging (MRI) and electroencephalogram (EEG) signal predictions in order to predict the …
Epileptic EEG patterns recognition through machine learning techniques and relevant time–frequency features
Objectives The present study is designed to explore the process of epileptic patterns'
automatic detection, specifically, epileptic spikes and high-frequency oscillations (HFOs), via …
automatic detection, specifically, epileptic spikes and high-frequency oscillations (HFOs), via …
[Retracted] Automatic Detection of Epilepsy Based on Entropy Feature Fusion and Convolutional Neural Network
Y Sun, X Chen - Oxidative Medicine and Cellular Longevity, 2022 - Wiley Online Library
Epilepsy is a neurological disorder, caused by various genetic and acquired factors.
Electroencephalogram (EEG) is an important means of diagnosis for epilepsy. Aiming at the …
Electroencephalogram (EEG) is an important means of diagnosis for epilepsy. Aiming at the …
LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring
Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers
of their drowsiness status, thereby reducing the probability of traffic accidents. Graph …
of their drowsiness status, thereby reducing the probability of traffic accidents. Graph …
A novel multivariate approach for the detection of epileptic seizure using BCS-WELM
This paper proposes a novel weighted extreme learning machine (WELM) classifier using
binary cuckoo search (BCS) optimization algorithm for a fast and efficient detection of the …
binary cuckoo search (BCS) optimization algorithm for a fast and efficient detection of the …
Supervised machine learning models to identify early-stage symptoms of sars-cov-2
The coronavirus disease (COVID-19) pandemic was caused by the SARS-CoV-2 virus and
began in December 2019. The virus was first reported in the Wuhan region of China. It is a …
began in December 2019. The virus was first reported in the Wuhan region of China. It is a …
Utilizing Eeg Signals for Epilepsy Seizure Detection
HG Alfughi, AH Maamar… - 2024 IEEE 4th …, 2024 - ieeexplore.ieee.org
Epilepsy is a central nervous system disease causing abnormal brain activity, odd behavior,
and coma. It affects individuals of various ages and ethnicities, making seizure detection …
and coma. It affects individuals of various ages and ethnicities, making seizure detection …