Gatsmote: Improving imbalanced node classification on graphs via attention and homophily
In recent decades, non-invasive neuroimaging techniques and graph theories have enabled
a better understanding of the structural patterns of the human brain at a macroscopic level …
a better understanding of the structural patterns of the human brain at a macroscopic level …
An Approach for EEG Denoising Based on Wasserstein Generative Adversarial Network
Electroencephalogram (EEG) recordings often contain artifacts that would lower signal
quality. Many efforts have been made to eliminate or at least minimize the artifacts, and most …
quality. Many efforts have been made to eliminate or at least minimize the artifacts, and most …
A multi-artifact EEG denoising by frequency-based deep learning
Electroencephalographic (EEG) signals are fundamental to neuroscience research and
clinical applications such as brain-computer interfaces and neurological disorder diagnosis …
clinical applications such as brain-computer interfaces and neurological disorder diagnosis …
From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces
Objective. Brain-computer interfaces (BCI) have been extensively researched in controlled
lab settings where the P300 event-related potential (ERP), elicited in the rapid serial visual …
lab settings where the P300 event-related potential (ERP), elicited in the rapid serial visual …
Task-oriented EEG denoising generative adversarial network for enhancing SSVEP-BCI performance
P Zeng, L Fan, Y Luo, H Shen… - Journal of Neural …, 2024 - iopscience.iop.org
Objective. The quality of electroencephalogram (EEG) signals directly impacts the
performance of brain–computer interface (BCI) tasks. Many methods have been proposed to …
performance of brain–computer interface (BCI) tasks. Many methods have been proposed to …
[HTML][HTML] Seamless Optimization of Wavelet Parameters for Denoising LFM Radar Signals: An AI-Based Approach
Linear frequency modulation (LFM) signals are pivotal in radar systems, enabling high-
resolution measurements and target detection. However, these signals are often degraded …
resolution measurements and target detection. However, these signals are often degraded …
AnEEG: leveraging deep learning for effective artifact removal in EEG data
In neuroscience and clinical diagnostics, electroencephalography (EEG) is a crucial
instrument for capturing neural activity. However, this signal is polluted by different artifacts …
instrument for capturing neural activity. However, this signal is polluted by different artifacts …
[HTML][HTML] DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN–Transformer Network
Y Cai, Z Meng, D Huang - Sensors, 2025 - mdpi.com
Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain
activity studies, cognitive mechanism research, and the diagnosis and treatment of …
activity studies, cognitive mechanism research, and the diagnosis and treatment of …