[HTML][HTML] Review of public motor imagery and execution datasets in brain-computer interfaces
The demand for public datasets has increased as data-driven methodologies have been
introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are …
introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are …
EEG motor imagery classification using deep learning approaches in naïve BCI users
CD Guerrero-Mendez, CF Blanco-Diaz… - Biomedical Physics …, 2023 - iopscience.iop.org
Abstract Motor Imagery (MI)-Brain Computer-Interfaces (BCI) illiteracy defines that not all
subjects can achieve a good performance in MI-BCI systems due to different factors related …
subjects can achieve a good performance in MI-BCI systems due to different factors related …
Classification of motor-imagery tasks using a large EEG dataset by fusing classifiers learning on wavelet-scattering features
TD Pham - IEEE Transactions on Neural Systems and …, 2023 - ieeexplore.ieee.org
Brain-computer or brain-machine interface technology allows humans to control machines
using their thoughts via brain signals. In particular, these interfaces can assist people with …
using their thoughts via brain signals. In particular, these interfaces can assist people with …
Exploring the Frontier: Transformer-Based Models in EEG Signal Analysis for Brain-Computer Interfaces
MA Pfeffer, SSH Ling, JKW Wong - Computers in Biology and Medicine, 2024 - Elsevier
This review systematically explores the application of transformer-based models in EEG
signal processing and brain-computer interface (BCI) development, with a distinct focus on …
signal processing and brain-computer interface (BCI) development, with a distinct focus on …
[HTML][HTML] Bridging the BCI illiteracy gap: a subject-to-subject semantic style transfer for EEG-based motor imagery classification
Introduction Brain-computer interfaces (BCIs) facilitate direct interaction between the human
brain and computers, enabling individuals to control external devices through cognitive …
brain and computers, enabling individuals to control external devices through cognitive …
[HTML][HTML] Spatiotemporal characteristics of cortical activities of REM sleep behavior disorder revealed by explainable machine learning using 3D convolutional neural …
Isolated rapid eye movement sleep behavior disorder (iRBD) is a sleep disorder
characterized by dream enactment behavior without any neurological disease and is …
characterized by dream enactment behavior without any neurological disease and is …
A strong and simple deep learning baseline for bci mi decoding
We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor
Imagery decoding in BCI. Our main motivation is to propose a very simple baseline to …
Imagery decoding in BCI. Our main motivation is to propose a very simple baseline to …
EEG-based motor imagery classification with quantum algorithms
Developing efficient algorithms harnessing the power of current quantum processors has
sparked the emergence of techniques that combine soft computing with quantum computing …
sparked the emergence of techniques that combine soft computing with quantum computing …
[HTML][HTML] Kcs-fcnet: Kernel cross-spectral functional connectivity network for eeg-based motor imagery classification
DG García-Murillo, AM Álvarez-Meza… - Diagnostics, 2023 - mdpi.com
This paper uses EEG data to introduce an approach for classifying right and left-hand
classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity …
classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity …
[HTML][HTML] Motor imagery EEG signal classification with a multivariate time series approach
Background Electroencephalogram (EEG) signals record electrical activity on the scalp.
Measured signals, especially EEG motor imagery signals, are often inconsistent or distorted …
Measured signals, especially EEG motor imagery signals, are often inconsistent or distorted …