[HTML][HTML] A multi-branch convolutional neural network with squeeze-and-excitation attention blocks for EEG-based motor imagery signals classification
Electroencephalography-based motor imagery (EEG-MI) classification is a critical
component of the brain-computer interface (BCI), which enables people with physical …
component of the brain-computer interface (BCI), which enables people with physical …
AutoEncoder filter bank common spatial patterns to decode motor imagery from EEG
The present paper introduces a novel method, named AutoEncoder-Filter Bank Common
Spatial Patterns (AE-FBCSP), to decode imagined movements from electroencephalography …
Spatial Patterns (AE-FBCSP), to decode imagined movements from electroencephalography …
EEGANet: Removal of ocular artifacts from the EEG signal using generative adversarial networks
P Sawangjai, M Trakulruangroj… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
The elimination of ocular artifacts is critical in analyzing electroencephalography (EEG) data
for various brain-computer interface (BCI) applications. Despite numerous promising …
for various brain-computer interface (BCI) applications. Despite numerous promising …
Fusion of EEG and eye blink analysis for detection of driver fatigue
M Shahbakhti, M Beiramvand, E Nasiri… - … on Neural Systems …, 2023 - ieeexplore.ieee.org
Objective: The driver fatigue detection using multi-channel electroencephalography (EEG)
has been extensively addressed in the literature. However, the employment of a single …
has been extensively addressed in the literature. However, the employment of a single …
EEG-based motor imagery recognition framework via multisubject dynamic transfer and iterative self-training
H Wang, P Chen, M Zhang, J Zhang… - … on Neural Networks …, 2023 - ieeexplore.ieee.org
A robust decoding model that can efficiently deal with the subject and period variation is
urgently needed to apply the brain–computer interface (BCI) system. The performance of …
urgently needed to apply the brain–computer interface (BCI) system. The performance of …
A GAN guided parallel CNN and transformer network for EEG denoising
Electroencephalography (EEG) signals are often contaminated with various physiological
artifacts, seriously affecting the quality of subsequent analysis. Therefore, removing artifacts …
artifacts, seriously affecting the quality of subsequent analysis. Therefore, removing artifacts …
Sleep signal analysis for early detection of Alzheimer's disease and related dementia (ADRD)
S Khosroazad, A Abedi… - IEEE journal of biomedical …, 2023 - ieeexplore.ieee.org
Objective: Alzheimer's Disease and Related Dementia (ADRD) is growing at alarming rates,
putting research and development of diagnostic methods at the forefront of the biomedical …
putting research and development of diagnostic methods at the forefront of the biomedical …
Graph signal processing based cross-subject mental task classification using multi-channel EEG signals
Classification of mental tasks from electroencephalogram (EEG) signals play a crucial role in
designing various brain-computer interface (BCI) applications. Most of the current …
designing various brain-computer interface (BCI) applications. Most of the current …
A transfer learning-based CNN deep learning model for unfavorable driving state recognition
J Chen, H Wang, E He - Cognitive Computation, 2024 - Springer
The detection of unfavorable driving states (UDS) of drivers based on electroencephalogram
(EEG) measures has received continuous attention from extensive scholars on account of …
(EEG) measures has received continuous attention from extensive scholars on account of …
[HTML][HTML] Visual preference of plant features in different living environments using eye tracking and EEG
N Ding, Y Zhong, J Li, Q Xiao, S Zhang, H Xia - Plos one, 2022 - journals.plos.org
Plants play a very important role in landscape construction. In order to explore whether
different living environment will affect people's preference for the structural features of plant …
different living environment will affect people's preference for the structural features of plant …