EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning

A Kumari, DR Edla, RR Reddy, S Jannu… - Journal of Neuroscience …, 2024 - Elsevier
Brain–computer interface (BCI) technology holds promise for individuals with profound
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 …

EEG-based finger movement classification with intrinsic time-scale decomposition

M Degirmenci, YK Yuce, M Perc, Y Isler - Frontiers in Human …, 2024 - frontiersin.org
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 …

An efficient deep learning framework for P300 evoked related potential detection in EEG signal

P Havaei, M Zekri, E Mahmoudzadeh… - Computer Methods and …, 2023 - Elsevier
Background Incorporating the time-frequency localization properties of Gabor transform
(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 …

EEG channel selection using Gramian Angular Fields and spectrograms for energy data visualization

OF Kucukler, A Amira, H Malekmohamadi - Engineering Applications of …, 2024 - Elsevier
Abstract Electroencephalography (EEG)-based brain-computer interfaces (BCIs) have a
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 …

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 …

Optimal sensor set for decoding motor imagery from EEG

A Dillen, F Ghaffari, O Romain, B Vanderborght… - Applied Sciences, 2023 - mdpi.com
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 …

Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures

T Mwata-Velu, E Zamora, JI Vasquez-Gomez… - Sensors, 2024 - mdpi.com
This work addresses the challenge of classifying multiclass visual EEG signals into 40
classes for brain–computer interface applications using deep learning architectures. The …