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 …
Artificial intelligence techniques for automated diagnosis of neurological disorders
Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
Epileptic seizure detection in EEG signals using a unified temporal-spectral squeeze-and-excitation network
The intelligent recognition of epileptic electro-encephalogram (EEG) signals is a valuable
tool for the epileptic seizure detection. Recent deep learning models fail to fully consider …
tool for the epileptic seizure detection. Recent deep learning models fail to fully consider …
Machine learning-based EEG signals classification model for epileptic seizure detection
The detection of epileptic seizures by classifying electroencephalography (EEG) signals into
ictal and interictal classes is a demanding challenge, because it identifies the seizure and …
ictal and interictal classes is a demanding challenge, because it identifies the seizure and …
Spatio-temporal-spectral hierarchical graph convolutional network with semisupervised active learning for patient-specific seizure prediction
Graph theory analysis using electroencephalogram (EEG) signals is currently an advanced
technique for seizure prediction. Recent deep learning approaches, which fail to fully …
technique for seizure prediction. Recent deep learning approaches, which fail to fully …
[HTML][HTML] Multimodal detection of epilepsy with deep neural networks
Epilepsy constitutes a chronic noncommunicable disease of the brain affecting
approximately 50 million people around the world. Most of the existing research initiatives …
approximately 50 million people around the world. Most of the existing research initiatives …
A novel deep learning approach with data augmentation to classify motor imagery signals
Brain-computer interface provides a new communication bridge between the human mind
and devices, depending largely on the accurate classification and identification of non …
and devices, depending largely on the accurate classification and identification of non …
A decision support system for automated identification of sleep stages from single-channel EEG signals
A decision support system for automated detection of sleep stages can alleviate the burden
of medical professionals of manually annotating a large bulk of data, expedite sleep disorder …
of medical professionals of manually annotating a large bulk of data, expedite sleep disorder …
Epileptic-seizure classification using phase-space representation of FBSE-EWT based EEG sub-band signals and ensemble learners
Electroencephalogram (EEG) signals are non-linear and non-stationary in nature. The
phase-space representation (PSR) method is useful for analysing the non-linear …
phase-space representation (PSR) method is useful for analysing the non-linear …
Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise
Background: Epileptic seizure detection is traditionally performed by visual observation of
Electroencephalogram (EEG) signals. Owing to its onerous and time-consuming nature …
Electroencephalogram (EEG) signals. Owing to its onerous and time-consuming nature …