Deep learning in EEG-based BCIs: a comprehensive review of transformer models, advantages, challenges, and applications
Brain-computer interfaces (BCIs) have undergone significant advancements in recent years.
The integration of deep learning techniques, specifically transformers, has shown promising …
The integration of deep learning techniques, specifically transformers, has shown promising …
Deep learning in motor imagery EEG signal decoding: A Systematic Review
Thanks to the fast evolution of electroencephalography (EEG)-based brain-computer
interfaces (BCIs) and computing technologies, as well as the availability of large EEG …
interfaces (BCIs) and computing technologies, as well as the availability of large EEG …
Enhancing MI EEG Signal Classification with a Novel Weighted and Stacked Adaptive Integrated Ensemble Model: A Multi-Dataset Approach
Electroencephalography (EEG) based Brain-Computer Interfaces (BCIs) are vital for various
applications, yet achieving accurate EEG signal classification, particularly for Motor Imagery …
applications, yet achieving accurate EEG signal classification, particularly for Motor Imagery …
A systematic evaluation of euclidean alignment with deep learning for eeg decoding
Objective: Electroencephalography signals are frequently used for various Brain–Computer
interface (BCI) tasks. While deep learning (DL) techniques have shown promising results …
interface (BCI) tasks. While deep learning (DL) techniques have shown promising results …
[HTML][HTML] Ensemble Pretrained Convolutional Neural Networks for the Classification of Insulator Surface Conditions
Overhead transmission line insulators are non-conductive materials that separate
conductors from grounded transmission towers. Once in operation, they frequently …
conductors from grounded transmission towers. Once in operation, they frequently …
Subject-independent deep architecture for EEG-based motor imagery classification
Motor imagery (MI) classification based on electroencephalogram (EEG) is a widely-used
technique in non-invasive brain-computer interface (BCI) systems. Since EEG recordings …
technique in non-invasive brain-computer interface (BCI) systems. Since EEG recordings …
Compact convolutional transformer for subject-independent motor imagery EEG-based BCIs
Motor imagery electroencephalography (EEG) analysis is crucial for the development of
effective brain-computer interfaces (BCIs), yet it presents considerable challenges due to the …
effective brain-computer interfaces (BCIs), yet it presents considerable challenges due to the …
SCDAN: Learning Common Feature Representation of Brain Activation for Intersubject Motor Imagery EEG Decoding
An electroencephalogram (EEG)-based motor imagery (MI) brain–computer interface (BCI)
builds a direct communication channel between humans and computers by decoding EEG …
builds a direct communication channel between humans and computers by decoding EEG …
Data Constraints and Performance Optimization for Transformer-based Models in EEG-based Brain-Computer Interfaces: A Survey
A Keutayeva, B Abibullaev - IEEE Access, 2024 - ieeexplore.ieee.org
This work reviews the critical challenge of data scarcity in developing Transformer-based
models for Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) …
models for Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) …
Improved Motor Imagery EEG Inter-device Decoding by Reweighting Multi-source Domain Samples
Electroencephalogram (EEG)-based motor imagery brain-computer interface (MI BCI) has
exciting prospects in applications. Multisource domain problem of MI EEG decoding needs …
exciting prospects in applications. Multisource domain problem of MI EEG decoding needs …