Recognition of human emotions using EEG signals: A review
Assessment of the cognitive functions and state of clinical subjects is an important aspect of
e-health care delivery, and in the development of novel human-machine interfaces. A …
e-health care delivery, and in the development of novel human-machine interfaces. A …
Deep learning for neurodegenerative disorder (2016 to 2022): A systematic review
J Chaki, M Woźniak - Biomedical Signal Processing and Control, 2023 - Elsevier
A neurodegenerative disorder, such as Parkinson's, Alzheimer's, epilepsy, stroke, and
others, is a type of disease in which central nervous system cells stop working or die …
others, is a type of disease in which central nervous system cells stop working or die …
Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals
Background Epilepsy is one of the most commonly seen neurologic disorders worldwide
and has generally caused seizures. Electroencephalography (EEG) is widely used in …
and has generally caused seizures. Electroencephalography (EEG) is widely used in …
Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits
The neurological condition epilepsy is demanding and even fatal. Electroencephalogram
(EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and …
(EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and …
Znet: deep learning approach for 2D MRI brain tumor segmentation
Background: Detection and segmentation of brain tumors using MR images are challenging
and valuable tasks in the medical field. Early diagnosing and localizing of brain tumors can …
and valuable tasks in the medical field. Early diagnosing and localizing of brain tumors can …
An analysis of explainability methods for convolutional neural networks
Deep learning models have gained a reputation of high accuracy in many domains.
Convolutional Neural Networks (CNN) are specialized towards image recognition and have …
Convolutional Neural Networks (CNN) are specialized towards image recognition and have …
Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning
M Varlı, H Yılmaz - Journal of Computational Science, 2023 - Elsevier
Epilepsy stands out as one of the common neurological diseases. The neural activity of the
brain is observed using electroencephalography (EEG), which allows the diagnosis of …
brain is observed using electroencephalography (EEG), which allows the diagnosis of …
On the use of wavelet domain and machine learning for the analysis of epileptic seizure detection from EEG signals
Epileptic patients suffer from an epileptic brain seizure caused by the temporary and
unpredicted electrical interruption. Conventionally, the electroencephalogram (EEG) signals …
unpredicted electrical interruption. Conventionally, the electroencephalogram (EEG) signals …
Epileptic seizure detection based on bidirectional gated recurrent unit network
Y Zhang, S Yao, R Yang, X Liu, W Qiu… - … on Neural Systems …, 2022 - ieeexplore.ieee.org
Visual inspection of long-term electroencephalography (EEG) is a tedious task for
physicians in neurology. Based on bidirectional gated recurrent unit (Bi-GRU) neural …
physicians in neurology. Based on bidirectional gated recurrent unit (Bi-GRU) neural …
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 …