3 citations · 4 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
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…