Transformers in biosignal analysis: A review
Transformer architectures have become increasingly popular in healthcare applications.
Through outstanding performance in natural language processing and superior capability to …
Through outstanding performance in natural language processing and superior capability to …
Smart epidermal electrophysiological electrodes: Materials, structures, and algorithms
Y Ye, H Wang, Y Tian, K Gao, M Wang… - Nanotechnology and …, 2023 - pubs.aip.org
Epidermal electrophysiological monitoring has garnered significant attention for its potential
in medical diagnosis and healthcare, particularly in continuous signal recording. However …
in medical diagnosis and healthcare, particularly in continuous signal recording. However …
On lightmyography based muscle-machine interfaces for the efficient decoding of human gestures and forces
Conventional muscle-machine interfaces like Electromyography (EMG), have significant
drawbacks, such as crosstalk, a non-linear relationship between the signal and the …
drawbacks, such as crosstalk, a non-linear relationship between the signal and the …
MITNet: a fusion transformer and convolutional neural network architecture approach for T-cell epitope prediction
Classifying epitopes is essential since they can be applied in various fields, including
therapeutics, diagnostics and peptide-based vaccines. To determine the epitope or peptide …
therapeutics, diagnostics and peptide-based vaccines. To determine the epitope or peptide …
A novel approach to surface EMG-based gesture classification using a vision transformer integrated with convolutive blind source separation
A robust pattern recognition framework is required for ideal real-time human-machine
interface (HMI) applications. Convolutional neural networks and recurrent neural networks …
interface (HMI) applications. Convolutional neural networks and recurrent neural networks …
Eeg-based epileptic seizure prediction using temporal multi-channel transformers
Epilepsy is one of the most common neurological diseases, characterized by transient and
unprovoked events called epileptic seizures. Electroencephalogram (EEG) is an auxiliary …
unprovoked events called epileptic seizures. Electroencephalogram (EEG) is an auxiliary …
Electromyography based gesture decoding employing few-shot learning, transfer learning, and training from scratch
Over the last decade several machine learning (ML) based data-driven approaches have
been used for Electromyography (EMG) based control of prosthetic hands. However, the …
been used for Electromyography (EMG) based control of prosthetic hands. However, the …
[HTML][HTML] Movement recognition via channel-activation-wise sEMG attention
Context: Surface electromyography (sEMG) signals contain rich information recorded from
muscle movements and therefore reflect the user's intention. sEMG has seen dominant …
muscle movements and therefore reflect the user's intention. sEMG has seen dominant …
Explainable deep learning for sEMG-based similar gesture recognition: A Shapley-value-based solution
Surface electromyography (sEMG) based gesture recognition shows promise in enhancing
human-robot interaction. However, accurately recognizing similar gestures is a challenging …
human-robot interaction. However, accurately recognizing similar gestures is a challenging …
Improving bionic limb control through reinforcement learning in an interactive game environment
Enhancing the accuracy and robustness of bionic limb controllers that decode motor intent is
a pressing challenge in the field of prosthetics. State-of-the-art research has mostly focused …
a pressing challenge in the field of prosthetics. State-of-the-art research has mostly focused …