EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning
Brain–computer interface (BCI) technology holds promise for individuals with profound
motor impairments, offering the potential for communication and control. Motor imagery (MI) …
motor impairments, offering the potential for communication and control. Motor imagery (MI) …
Brain age prediction/classification through recurrent deep learning with electroencephalogram recordings of seizure subjects
K Jusseaume, I Valova - Sensors, 2022 - mdpi.com
With modern population growth and an increase in the average lifespan, more patients are
becoming afflicted with neurodegenerative diseases such as dementia and Alzheimer's …
becoming afflicted with neurodegenerative diseases such as dementia and Alzheimer's …
EEG-based finger movement classification with intrinsic time-scale decomposition
Introduction Brain-computer interfaces (BCIs) are systems that acquire the brain's electrical
activity and provide control of external devices. Since electroencephalography (EEG) is the …
activity and provide control of external devices. Since electroencephalography (EEG) is the …
An efficient deep learning framework for P300 evoked related potential detection in EEG signal
Background Incorporating the time-frequency localization properties of Gabor transform
(GT), the complexity understandings of convolutional neural network (CNN), and histogram …
(GT), the complexity understandings of convolutional neural network (CNN), and histogram …
EEG Motor Imagery Classification by Feature Extracted Deep 1D-CNN and Semi-Deep Fine-Tuning
M Taghizadeh, F Vaez, M Faezipour - IEEE Access, 2024 - ieeexplore.ieee.org
The main goal of this paper is to introduce a Motor Imagery (MI) classification system for
electroencephalography (EEG) that is extremely precise. To achieve this goal, we propose …
electroencephalography (EEG) that is extremely precise. To achieve this goal, we propose …
EEG channel selection using Gramian Angular Fields and spectrograms for energy data visualization
Abstract Electroencephalography (EEG)-based brain-computer interfaces (BCIs) have a
wide range of applications in affect recognition. The usage of irrelevant information channels …
wide range of applications in affect recognition. The usage of irrelevant information channels …
An Intelligent Athlete Signal Processing Methodology for Balance Control Ability Assessment with Multi-Headed Self-Attention Mechanism
N Xu, X Cui, X Wang, W Zhang, T Zhao - Mathematics, 2022 - mdpi.com
In different kinds of sports, the balance control ability plays an important role for every
athlete. Therefore, coaches and athletes need accurate and efficient assessments of the …
athlete. Therefore, coaches and athletes need accurate and efficient assessments of the …
Motor Imagery Multi-Tasks Classification for BCIs Using the NVIDIA Jetson TX2 Board and the EEGNet Network
T Mwata-Velu, E Niyonsaba-Sebigunda… - Sensors, 2023 - mdpi.com
Nowadays, Brain–Computer Interfaces (BCIs) still captivate large interest because of
multiple advantages offered in numerous domains, explicitly assisting people with motor …
multiple advantages offered in numerous domains, explicitly assisting people with motor …
Optimal sensor set for decoding motor imagery from EEG
Brain–computer interfaces (BCIs) have the potential to enable individuals to interact with
devices by detecting their intention from brain activity. A common approach to BCI is to …
devices by detecting their intention from brain activity. A common approach to BCI is to …
Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures
This work addresses the challenge of classifying multiclass visual EEG signals into 40
classes for brain–computer interface applications using deep learning architectures. The …
classes for brain–computer interface applications using deep learning architectures. The …