Decoding lower-limb kinematic parameters during pedaling tasks using deep learning approaches and EEG

CF Blanco-Diaz, CD Guerrero-Mendez… - Medical & Biological …, 2024 - Springer
Stroke is a neurological condition that usually results in the loss of voluntary control of body
movements, making it difficult for individuals to perform activities of daily living (ADLs). Brain …

A Method to Extract Task-Related EEG Feature Based on Lightweight Convolutional Neural Network

Q Huang, J Ding, X Wang - Neuroscience Bulletin, 2024 - Springer
Unlocking task-related EEG spectra is crucial for neuroscience. Traditional convolutional
neural networks (CNNs) effectively extract these features but face limitations like overfitting …

Gait pattern recognition based on electroencephalogram signals with common spatial pattern and graph attention networks

Y Lu, H Wang, Z Lu, J Niu, C Liu - Engineering Applications of Artificial …, 2025 - Elsevier
Assisting human locomotion in various gait patterns is one of the challenges in the
interaction between human and lower limb exoskeleton. In this paper, we propose the graph …

Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals

CF Blanco-Díaz, CD Guerrero-Mendez… - Biomedical Physics …, 2024 - iopscience.iop.org
Stroke is a neurological syndrome that usually causes a loss of voluntary control of
lower/upper body movements, making it difficult for affected individuals to perform Activities …

Optimizing prediction accuracy in dynamic systems through neural network integration with Kalman and alpha-beta filters

J Khan, U Zaman, E Lee, AS Balobaid, RY Aburasain… - PloS one, 2024 - journals.plos.org
In the realm of dynamic system analysis, the Kalman filter and the alpha-beta filter are widely
recognized for their tracking and prediction capabilities. However, their performance is often …

EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study

R Dong, X Zhang, H Li, G Masengo, A Zhu… - Frontiers in …, 2024 - frontiersin.org
Introduction Active rehabilitation requires active neurological participation when users use
rehabilitation equipment. A brain-computer interface (BCI) is a direct communication …

Deep Learning Approach for EEG Classification in Lower-Limb Movement Phases: Towards Enhanced Brain-Computer Interface Control

CF Blanco-Diaz, CD Guerrero-Mendez… - 2024 20th IEEE …, 2024 - ieeexplore.ieee.org
Embedded Brain-Computer Interfaces (BCI) have emerged as an alternative for
rehabilitation/assistance of people with neuromotor impairments. Data-driven methods are …

Detection of pedaling tasks through EEG using extreme learning machine for lower-limb rehabilitation brain-computer interfaces

CF Blanco-Díaz, CD Guerrero-Méndez… - … on Applications of …, 2023 - ieeexplore.ieee.org
Brain-Computer Interfaces (BCI) are systems that may function as communication channels
between people and external devices through brain information. BCIs using …

EEG-based 90-Degree Turn Intention Detection for Brain-Computer Interface

P Anand, A Jain, SP Muthukrishnan, S Bhasin… - arxiv preprint arxiv …, 2024 - arxiv.org
Electroencephalography (EEG)--based turn intention prediction for lower limb movement is
important to build an efficient brain-computer interface (BCI) system. This study investigates …

EEG-Based Multi-Class Classification for Recognizing Pedaling Velocities: A Promising Approach for Brain-Computer Interface-Enhanced Lower-Limb Robotic …

AX Gonzalez-Cely, CF Blanco-Diaz… - 2024 10th IEEE RAS …, 2024 - ieeexplore.ieee.org
Lower-limb robotic rehabilitation devices have shown promising results in restoring
functional activities of individuals with motor impairments. Motorized Mini-Exercise Bikes …