Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain–Computer …

MH Kabir, NI Akhtar, N Tasnim… - Sensors (Basel …, 2024 - pmc.ncbi.nlm.nih.gov
The accuracy of classifying motor imagery (MI) activities is a significant challenge when
using brain–computer interfaces (BCIs). BCIs allow people with motor impairments to control …

[HTML][HTML] EEG channel selection for stroke patient rehabilitation using BAT optimizer

MA Al-Betar, ZAA Alyasseri, NK Al-Qazzaz… - Algorithms, 2024 - mdpi.com
Stroke is a major cause of mortality worldwide, disrupts cerebral blood flow, leading to
severe brain damage. Hemiplegia, a common consequence, results in motor task loss on …

A brief survey on human activity recognition using motor imagery of EEG signals

SP Mahalungkar, R Shrivastava… - … Biology and Medicine, 2024 - Taylor & Francis
Human being's biological processes and psychological activities are jointly connected to the
brain. So, the examination of human activity is more significant for the well-being of humans …

3-Channel Motor Imagery Classification using Conventional Classifiers and Deep Learning Models

M Rehman, IK Mirza, F Nasim, MA Jaffar - Journal of Computing & …, 2024 - jcbi.org
Brain-computer interfaces (BCIs) are one of the important applications based on motor
imagery classification using EEG signals. BCIs are designed to help patients afflicted with …

[HTML][HTML] An Empirical Model-Based Algorithm for Removing Motion-Caused Artifacts in Motor Imagery EEG Data for Classification Using an Optimized CNN Model

RK Megalingam, KS Sankardas, SK Manoharan - Sensors, 2024 - mdpi.com
Electroencephalography (EEG) is a non-invasive technique with high temporal resolution
and cost-effective, portable, and easy-to-use features. Motor imagery EEG (MI-EEG) data …

Multimodal brain-controlled system for rehabilitation training: Combining asynchronous online brain–computer interface and exoskeleton

L Liu, J Li, R Ouyang, D Zhou, C Fan, W Liang… - Journal of Neuroscience …, 2024 - Elsevier
Background: Traditional therapist-based rehabilitation training for patients with movement
impairment is laborious and expensive. In order to reduce the cost and improve the …

Toward calibration-free motor imagery brain–computer interfaces: a VGG-based convolutional neural network and WGAN approach

AG Habashi, AM Azab, S Eldawlatly… - Journal of Neural …, 2024 - iopscience.iop.org
Objective. Motor imagery (MI) represents one major paradigm of Brain–computer interfaces
(BCIs) in which users rely on their electroencephalogram (EEG) signals to control the …

[PDF][PDF] Multimodal Explainability Using Class Activation Maps and Canonical Correlation for MI-EEG Deep Learning Classification

M Loaiza, AM Álvarez-Meza, D Cárdenas-Peña… - 2024 - preprints.org
Brain-Computer Interfaces (BCIs) are essential in advancing medical diagnosis and
treatment by providing non-invasive tools to assess neurological states. Among these, Motor …

A Baseline Electroencephalography Motor Imagery Brain-Computer Interface System Using Artificial Intelligence and Deep Learning

FE Ekpar - European Journal of Electrical Engineering and …, 2024 - ejece.org
This paper presents a baseline or reference (single channel, single subject, single trial)
electroencephalography (EEG) motor imagery (MI) brain computer interface (BCI) that …

CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation

W Yao, S Wang - arxiv preprint arxiv:2408.00777, 2024 - arxiv.org
Multi-modal neuroimaging analysis is crucial for a comprehensive understanding of brain
function and pathology, as it allows for the integration of different imaging techniques, thus …