29 citations · 41 across the 14 of their papers we have counts for
6 papers · 1 filter
Applicability of memorization indicators for early spotting of overfitting while recalibrating sEMG-decoders on low sample sizes
Stephan J. Lehmler, Tobias Glasmachers, Ioannis Iossifidis
Deep learning models for surface electromyography (sEMG) can benefit substantially from subject-specific (re-)calibration, since no sufficiently large and diverse datasets are avai…
Error-related Potential driven Reinforcement Learning for adaptive Brain-Computer Interfaces
Aline Xavier Fidêncio, Felix Grün, Christian Klaes +1
Brain-computer interfaces (BCIs) provide alternative communication methods for individuals with motor disabilities by allowing control and interaction with external devices. Non-in…
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…
Invariance to Quantile Selection in Distributional Continuous Control
Felix Grün, Muhammad Saif-ur-Rehman, Tobias Glasmachers +1
In recent years distributional reinforcement learning has produced many state of the art results. Increasingly sample efficient Distributional algorithms for the discrete action do…
From Motion to Muscle
Marie D. Schmidt, Tobias Glasmachers, Ioannis Iossifidis
Voluntary human motion is the product of muscle activity that results from upstream motion planning of the motor cortical areas. We show that muscle activity can be artificially ge…
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…