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Found 10 papers, 0 papers with code
Predicting Multi-Joint Kinematics of the Upper Limb from EMG Signals Across Varied Loads with a Physics-Informed Neural Network
The PINN model is constructed by combining a feed-forward Artificial Neural Network (ANN) with a joint torque computation model.
Insights into Age-Related Functional Brain Changes during Audiovisual Integration Tasks: A Comprehensive EEG Source-Based Analysis
We aimed to understand how aging affects brain activation and functional connectivity among hubs during audio-visual tasks.
Brain Connectivity Features-based Age Group Classification using Temporal Asynchrony Audio-Visual Integration Task
A correlation-based functional connectivity analysis is done, and the changes among different age groups including: young (18-25 years), transition from young to middle age (25-33 years), and medium (33-41 years), are observed.
EEG Cortical Source Feature based Hand Kinematics Decoding using Residual CNN-LSTM Neural Network
In this work, the feasibility of hand kinematics decoding using EEG cortical source signals has been explored for grasp and lift task.
BiCurNet: Pre-Movement EEG based Neural Decoder for Biceps Curl Trajectory Estimation
The feasibility of early estimation of KPs is demonstrated using brain source imaging.
Subject-Independent 3D Hand Kinematics Reconstruction using Pre-Movement EEG Signals for Grasp And Lift Task
In this study, the hand kinematics decoding for grasp and lift task is performed in three-dimensional (3D) space using scalp electroencephalogram (EEG) signals.
Reorganization of resting state brain network functional connectivity across human brain developmental stages
The changes in overall resting-state brain functional connectivity with changes in brain developmental stages have also been explored using BrainNet Viewer.
PreMovNet: Pre-Movement EEG-based Hand Kinematics Estimation for Grasp and Lift task
Low-frequency signals recorded from non-invasive electroencephalography (EEG) are associated with the neural motor correlation utilised for motor trajectory decoding (MTD).
Adaptive area-preserving parameterization of open and closed anatomical surfaces
In this work, we propose adaptive area-preserving parameterization methods for simply-connected open and closed surfaces with the target of the parameterization being a spherical cap.
Source Aware Deep Learning Framework for Hand Kinematic Reconstruction using EEG Signal
The ability to reconstruct the kinematic parameters of hand movement using non-invasive electroencephalography (EEG) is essential for strength and endurance augmentation using exosuit/exoskeleton.
