20 citations · 25 across the 9 of their papers we have counts for
5 papers · 1 filter
Hand Gesture Recognition Using Temporal Convolutions and Attention Mechanism
Elahe Rahimian, Soheil Zabihi, Amir Asif +3
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 Interfac…
Deep Metric Learning with Locality Sensitive Angular Loss for Self-Correcting Source Separation of Neural Spiking Signals
Alexander Kenneth Clarke, Dario Farina
Neurophysiological time series, such as electromyographic signal and intracortical recordings, are typically composed of many individual spiking sources, the recovery of which can…
TEMGNet: Deep Transformer-based Decoding of Upperlimb sEMG for Hand Gestures Recognition
Elahe Rahimian, Soheil Zabihi, Amir Asif +3
There has been a surge of recent interest in Machine Learning (ML), particularly Deep Neural Network (DNN)-based models, to decode muscle activities from surface Electromyography (…
Human movement augmentation and how to make it a reality
Jonathan Eden, Mario Bräcklein, Jaime Ibáñez Pereda +5
Augmenting the body with artificial limbs controlled concurrently to the natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances h…
Modelling and Analysis of Magnetic Fields from Skeletal Muscle for Valuable Physiological Measurements
Siming Zuo, Kianoush Nazarpour, Dario Farina +2
MagnetoMyoGraphy (MMG) is a method of studying muscle function via weak magnetic fields generated from human active organs and tissues. The correspondence between MMG and electromy…