activity
20212024
most citedDeep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification

3 citations · 6 across the 6 of their papers we have counts for

collaborators

6 papers

q-bio.NC2024

GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals

Omair Ali, Muhammad Saif-ur-Rehman, Marita Metzler +3

Generating continuous electroencephalography (EEG) signals through advanced artificial neural networks presents a novel opportunity to enhance brain-computer interface (BCI) techno…

q-bio.QM20231 cited

Ruhr Hand Motion Catalog of Human Center-Out Transport Trajectories in 3D Task-Space Captured by a Redundant Measurement System

Tim Sziburis, Susanne Blex, Tobias Glasmachers +1

Neurological conditions are a major source of movement disorders. Motion modelling and variability analysis have the potential to identify pathology but require profound data. We i…

cs.RO20231 cited

Advancements in Upper Body Exoskeleton: Implementing Active Gravity Compensation with a Feedforward Controller

Muhammad Ayaz Hussain, Ioannis Iossifidis

In this study, we present a feedforward control system designed for active gravity compensation on an upper body exoskeleton. The system utilizes only positional data from internal…

cs.LG2023

Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes

Stephan Johann Lehmler, Muhammad Saif-ur-Rehman, Tobias Glasmachers +1

To gain a deeper understanding of the behavior and learning dynamics of (deep) artificial neural networks, it is valuable to employ mathematical abstractions and models. These tool…

q-bio.NC20231 cited

Adaptive SpikeDeep-Classifier: Self-organizing and self-supervised machine learning algorithm for online spike sorting

Muhammad Saif-ur-Rehman, Omair Ali, Christian Klaes +1

Objective. Research on brain-computer interfaces (BCIs) is advancing towards rehabilitating severely disabled patients in the real world. Two key factors for successful decoding of…

cs.LG20213 cited

Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification

Stephan Johann Lehmler, Muhammad Saif-ur-Rehman, Tobias Glasmachers +1

Accurate decoding of surface electromyography (sEMG) is pivotal for muscle-to-machine-interfaces (MMI) and their application for e.g. rehabilitation therapy. sEMG signals have high…