4 papers
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…
Deep-learning-based identification of individual motion characteristics from upper-limb trajectories towards disorder stage evaluation
Tim Sziburis, Susanne Blex, Tobias Glasmachers +1
The identification of individual movement characteristics sets the foundation for the assessment of personal rehabilitation progress and can provide diagnostic information on level…
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…