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Found 9 papers, 1 papers with code
Tackling Electrode Shift In Gesture Recognition with HD-EMG Electrode Subsets
sEMG pattern recognition algorithms have been explored extensively in decoding movement intent, yet are known to be vulnerable to changing recording conditions, exhibiting significant drops in performance across subjects, and even across sessions.
A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training
Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel ``copy/new'' feedback paradigm to help shape the signal generation of the subject toward the optimal distribution; 2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process.
Conditional Generative Models for Simulation of EMG During Naturalistic Movements
As a solution to this problem, we propose a transfer learning approach, in which a conditional generative model is trained to mimic the output of an advanced numerical model.
Common Synaptic Input, Synergies, and Size Principle: Control of Spinal Motor Neurons for Movement Generation
Understanding how movement is controlled by the central nervous system remains a major challenge, with ongoing debate about basic features underlying this control.
Hand Gesture Recognition Using Temporal Convolutions and Attention Mechanism
Advances in biosignal signal processing and machine learning, in particular Deep Neural Networks (DNNs), have paved the way for the development of innovative Human-Machine Interfaces for decoding the human intent and controlling artificial limbs.
Deep Metric Learning with Locality Sensitive Angular Loss for Self-Correcting Source Separation of Neural Spiking Signals
Neurophysiological time series, such as electromyographic signal and intracortical recordings, are typically composed of many individual spiking sources, the recovery of which can give fundamental insights into the biological system of interest or provide neural information for man-machine interfaces.
TEMGNet: Deep Transformer-based Decoding of Upperlimb sEMG for Hand Gestures Recognition
We propose a novel Vision Transformer (ViT)-based neural network architecture (referred to as the TEMGNet) to classify and recognize upperlimb hand gestures from sEMG to be used for myocontrol of prostheses.
Modelling and Analysis of Magnetic Fields from Skeletal Muscle for Valuable Physiological Measurements
Here, upon briefly describing the principles of voltage distribution inside skeletal muscles due to the electrical stimulation, we provide a protocol to determine the effects of the magnetic field generated from a time-changing action potential propagating in a group of skeletal muscle cells.
FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography
This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their widespread applications in human-machine interfaces.
