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 …
An overview of EEG-based machine learning methods in seizure prediction and opportunities for neurologists in this field
B Maimaiti, H Meng, Y Lv, J Qiu, Z Zhu, Y Xie, Y Li… - Neuroscience, 2022 - Elsevier
The unpredictability of epileptic seizures is one of the most problematic aspects of the field of
epilepsy. Methods or devices capable of detecting seizures minutes before they occur may …
epilepsy. Methods or devices capable of detecting seizures minutes before they occur may …
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 …
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 …
Deep learning for patient-independent epileptic seizure prediction using scalp EEG signals
Epilepsy is one of the most prevalent neurological diseases among humans and can lead to
severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to …
severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to …
Seizure prediction using directed transfer function and convolution neural network on intracranial EEG
Automatic seizure prediction promotes the development of closed-loop treatment system on
intractable epilepsy. In this study, by considering the specific information exchange between …
intractable epilepsy. In this study, by considering the specific information exchange between …
EEGWaveNet: Multiscale CNN-based spatiotemporal feature extraction for EEG seizure detection
P Thuwajit, P Rangpong, P Sawangjai… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
The detection of seizures in epileptic patients via Electroencephalography (EEG) is an
essential key to medical treatment. With the advances in deep learning, many approaches …
essential key to medical treatment. With the advances in deep learning, many approaches …
EEG-based seizure prediction via Transformer guided CNN
Recently, most seizure prediction methods mainly utilize pure CNN or Transformer model,
which cannot extract local and global features simultaneously. To this end, we propose an …
which cannot extract local and global features simultaneously. To this end, we propose an …
A deep fourier neural network for seizure prediction using convolutional neural network and ratios of spectral power
P Peng, L Xie, H Wei - International Journal of Neural Systems, 2021 - World Scientific
Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-
resistant epilepsy. Conventional methods usually adopt handcrafted features and manual …
resistant epilepsy. Conventional methods usually adopt handcrafted features and manual …
Pediatric seizure prediction in scalp EEG using a multi-scale neural network with dilated convolutions
Objective: Epileptic seizure prediction based on scalp electroencephalogram (EEG) is of
great significance for improving the quality of life of patients with epilepsy. In recent years, a …
great significance for improving the quality of life of patients with epilepsy. In recent years, a …