Multivariate fast iterative filtering based automated system for grasp motor imagery identification using EEG signals
One of the most crucial use of hands in daily life is grasping. Sometimes people with
neuromuscular disorders become incapable of moving their hands. This article proposes a …
neuromuscular disorders become incapable of moving their hands. This article proposes a …
Automated Grasp Recognition using sEMG: Recent Advances, Challenges and Future Developments
Surface electromyography (sEMG)-based automated grasp recognition (AGR) has emerged
as a vital technology in the field of automatic control, human-machine interfaces, prosthetics …
as a vital technology in the field of automatic control, human-machine interfaces, prosthetics …
Subject-independent trajectory prediction using pre-movement EEG during grasp and lift task
Electroencephalogram (EEG) based motor trajectory decoding for efficient control of brain–
computer interface (BCI) systems has been an active area of research. The systems include …
computer interface (BCI) systems has been an active area of research. The systems include …
NeuroAiR: Deep Learning Framework for Airwriting Recognition from Scalp-recorded Neural Signals
A Tripathi, A Gupta, AP Prathosh… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Airwriting recognition is a task that involves identifying letters written in free space using
finger movement. It is a special case of gesture recognition, where gestures correspond to …
finger movement. It is a special case of gesture recognition, where gestures correspond to …
BiCurNet: Pre-movement EEG based neural decoder for biceps curl trajectory estimation
Kinematic parameter (KP) estimation from early electroencephalogram (EEG) signals is
essential for positive augmentation using wearable robots. However, surface EEG-based …
essential for positive augmentation using wearable robots. However, surface EEG-based …
ESI-GAL: EEG source imaging-based trajectory estimation for grasp and lift task
Abstract Background: Electroencephalogram (EEG) signals-based motor kinematics
prediction (MKP) has been an active area of research to develop Brain–computer interface …
prediction (MKP) has been an active area of research to develop Brain–computer interface …
EEG Cortical Source Feature based Hand Kinematics Decoding using Residual CNN-LSTM Neural Network
Motor kinematics decoding (MKD) using brain signal is essential to develop Brain-computer
interface (BCI) system for rehabilitation or prosthesis devices. Surface …
interface (BCI) system for rehabilitation or prosthesis devices. Surface …
Portable Fabric-Based Soft Glove Controlled with Single-Channel Electroencephalography
Brain-computer interface (BCI) has been widely used to capture electrical signals generated
from the brain. One of the most commonly used methods in the BCI system is the …
from the brain. One of the most commonly used methods in the BCI system is the …
ESI-GAL: EEG Source Imaging-based Kinematics Parameter Estimation for Grasp and Lift Task
Objective: Electroencephalogram (EEG) signals-based motor kinematics prediction (MKP)
has been an active area of research to develop brain-computer interface (BCI) systems such …
has been an active area of research to develop brain-computer interface (BCI) systems such …
Subject-Independent 3D Hand Kinematics Reconstruction using Pre-Movement EEG Signals for Grasp And Lift Task
Brain-computer interface (BCI) systems can be utilized for kinematics decoding from scalp
brain activation to control rehabilitation or power-augmenting devices. In this study, the hand …
brain activation to control rehabilitation or power-augmenting devices. In this study, the hand …