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 …
Deep learning for medical anomaly detection–a survey
Machine learning–based medical anomaly detection is an important problem that has been
extensively studied. Numerous approaches have been proposed across various medical …
extensively studied. Numerous approaches have been proposed across various medical …
Hybrid CNN-LSTM model for short-term individual household load forecasting
M Alhussein, K Aurangzeb, SI Haider - Ieee Access, 2020 - ieeexplore.ieee.org
Power grids are transforming into flexible, smart, and cooperative systems with greater
dissemination of distributed energy resources, advanced metering infrastructure, and …
dissemination of distributed energy resources, advanced metering infrastructure, and …
Deep learning for electroencephalogram (EEG) classification tasks: a review
Objective. Electroencephalography (EEG) analysis has been an important tool in
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …
[HTML][HTML] Epileptic seizures detection using deep learning techniques: a review
A Shoeibi, M Khodatars, N Ghassemi, M Jafari… - International journal of …, 2021 - mdpi.com
A variety of screening approaches have been proposed to diagnose epileptic seizures,
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
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 …
Data augmentation for deep-learning-based electroencephalography
Background Data augmentation (DA) has recently been demonstrated to achieve
considerable performance gains for deep learning (DL)—increased accuracy and stability …
considerable performance gains for deep learning (DL)—increased accuracy and stability …
Deep learning-based electroencephalography analysis: a systematic review
Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …
of training, as well as advanced signal processing and feature extraction methodologies to …
EEG based multi-class seizure type classification using convolutional neural network and transfer learning
Recognition of epileptic seizure type is essential for the neurosurgeon to understand the
cortical connectivity of the brain. Though automated early recognition of seizures from …
cortical connectivity of the brain. Though automated early recognition of seizures from …
Epileptic seizure detection based on EEG signals and CNN
M Zhou, C Tian, R Cao, B Wang, Y Niu, T Hu… - Frontiers in …, 2018 - frontiersin.org
Epilepsy is a neurological disorder that affects approximately fifty million people according to
the World Health Organization. While electroencephalography (EEG) plays important roles …
the World Health Organization. While electroencephalography (EEG) plays important roles …