Electroencephalography signal processing: A comprehensive review and analysis of methods and techniques
The electroencephalography (EEG) signal is a noninvasive and complex signal that has
numerous applications in biomedical fields, including sleep and the brain–computer …
numerous applications in biomedical fields, including sleep and the brain–computer …
A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification
In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable
interest in the field of time series analysis, due to their ability to capture dependencies …
interest in the field of time series analysis, due to their ability to capture dependencies …
Electroencephalography signals-based sparse networks integration using a fuzzy ensemble technique for depression detection
Today, depression is a psychological condition that affects many individuals globally and, if
untreated, can negatively impact one's emotions and lifestyle quality. Machine learning (ML) …
untreated, can negatively impact one's emotions and lifestyle quality. Machine learning (ML) …
Dynamical graph neural network with attention mechanism for epilepsy detection using single channel EEG
Y Li, Y Yang, Q Zheng, Y Liu, H Wang, S Song… - Medical & Biological …, 2024 - Springer
Epilepsy is a chronic brain disease, and identifying seizures based on
electroencephalogram (EEG) signals would be conducive to implement interventions to help …
electroencephalogram (EEG) signals would be conducive to implement interventions to help …
Diffmdd: A diffusion-based deep learning framework for mdd diagnosis using eeg
Major Depression Disorder (MDD) is a common yet destructive mental disorder that affects
millions of people worldwide. Making early and accurate diagnosis of it is very meaningful …
millions of people worldwide. Making early and accurate diagnosis of it is very meaningful …
EEG Signal Epilepsy Detection with a Weighted Neighbour Graph Representation and Two-stream Graph-based Framework
Epilepsy is one of the most common neurological diseases. Clinically, epileptic seizure
detection is usually performed by analyzing electroencephalography (EEG) signals. At …
detection is usually performed by analyzing electroencephalography (EEG) signals. At …
You only acquire sparse-channel (yoas): A unified framework for dense-channel eeg generation
H Chen, W Zeng, L Cai, L Wang, J Lu, Y Li… - arXiv preprint arXiv …, 2024 - arxiv.org
High-precision acquisition of dense-channel electroencephalogram (EEG) signals is often
impeded by the costliness and lack of portability of equipment. In contrast, generating dense …
impeded by the costliness and lack of portability of equipment. In contrast, generating dense …
A Review of Graph Theory-Based Diagnosis of Neurological Disorders Based on EEG and MRI
Graph theory analysis, as a mathematical tool, has been widely employed in studying the
connectivity of the brain to explore the structural organization. Through the computation of …
connectivity of the brain to explore the structural organization. Through the computation of …
A Novel Framework for Epileptic Seizure Detection Using Electroencephalogram Signals Based on the Bat Feature Selection Algorithm
M Pouryosef, R Abedini-Nassab, SMR Akrami - Neuroscience, 2024 - Elsevier
The precise electroencephalogram (EEG) signal classification with the highest possible
accuracy is a key goal in the brain-computer interface (BCI). Considering the complexity and …
accuracy is a key goal in the brain-computer interface (BCI). Considering the complexity and …
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 …