SecNet: A second order neural network for MI-EEG
Motor imagery-based brain-computer interfaces (MI-BCIs) hold significant potential for
individuals with severe paralysis who are aware and alert by but unable to reliably control …
individuals with severe paralysis who are aware and alert by but unable to reliably control …
Efficient one-step multi-trial electroencephalograph spectral clustering via unsupervised covariance-based representations
T Luo - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
As an important research branch of artificial intelligence, decoding motor imagery
electroencephalograph (MI-EEG) is notoriously famous in engineering of constructing …
electroencephalograph (MI-EEG) is notoriously famous in engineering of constructing …
A Spatial-Spectral and Temporal Dual Prototype Network for Motor Imagery Brain-Computer Interface
C Han, C Liu, Y Wang, C Cai, J Wang… - ar**
motor imagery brain-computer interfaces (MI-BCIs). However, decoding intentions from MI …
motor imagery brain-computer interfaces (MI-BCIs). However, decoding intentions from MI …
A multi‐feature fusion graph attention network for decoding motor imagery intention in spinal cord injury patients
J Leng, L Gao, X Jiang, Y Lou, Y Sun… - Journal of Neural …, 2024 - iopscience.iop.org
Objective. Electroencephalogram (EEG) signals exhibit temporal–frequency–spatial multi-
domain feature, and due to the nonplanar nature of the brain surface, the electrode …
domain feature, and due to the nonplanar nature of the brain surface, the electrode …
Multiscale spatial-temporal feature fusion neural network for motor imagery brain-computer interfaces
Motor imagery, one of the main brain-computer interface (BCI) paradigms, has been
extensively utilized in numerous BCI applications, such as the interaction between disabled …
extensively utilized in numerous BCI applications, such as the interaction between disabled …
3-Channel Motor Imagery Classification using Conventional Classifiers and Deep Learning Models
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 …
imagery classification using EEG signals. BCIs are designed to help patients afflicted with …
A Novel Multi-Sensor Fusion System with a Machine Learning-Based Human-Machine Interface for Automating Industrial Robots
Y Behbehani, T Messay-Kebede - NAECON 2024-IEEE …, 2024 - ieeexplore.ieee.org
This paper presents a novel method to control an industrial robotic arm using multiple
sensors. This system consists of a hybrid brain activity and vision sensors that convey a …
sensors. This system consists of a hybrid brain activity and vision sensors that convey a …
Enhancing motor imagery classification: a novel CNN with self-attention using local and global features of filtered EEG data
Motor imagery (MI)-based brain computer interfaces (BCIs) frequently use convolutional
neural networks (CNNs) to analyse electroencephalography (EEG) signals. In this study, we …
neural networks (CNNs) to analyse electroencephalography (EEG) signals. In this study, we …
MCMTNet: Advanced network architectures for EEG-based motor imagery classification
Y Yang, X Zhang, X Zhang, C Yu - Neurocomputing, 2025 - Elsevier
Brain–computer interface (BCI) technology converts electroencephalogram (EEG) signals
into control commands to help patients with motor disorders, such as stroke and amyotrophic …
into control commands to help patients with motor disorders, such as stroke and amyotrophic …
FACT-Net: a Frequency Adapter CNN with Temporal-periodicity Inception for Fast and Accurate MI-EEG Decoding
S Ke, B Yang, Y Qin, F Rong, J Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Motor imagery brain-computer interface (MI-BCI) based on non-invasive
electroencephalogram (EEG) signals is a typical paradigm of BCI. However, existing …
electroencephalogram (EEG) signals is a typical paradigm of BCI. However, existing …