29 citations · 41 across the 14 of their papers we have counts for
14 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…
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…
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…
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…
ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers +2
Objective: Electroencephalography (EEG) and electromyography (EMG) are two non-invasive bio-signals, which are widely used in human machine interface (HMI) technologies (EEG-HMI an…
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…
Anchored-STFT and GNAA: An extension of STFT in conjunction with an adversarial data augmentation technique for the decoding of neural signals
Omair Ali, Muhammad Saif-ur-Rehman, Susanne Dyck +3
Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one…
SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm
Muhammad Saif-ur-Rehman, Omair Ali, Robin Lienkaemper +11
Objective. Recent advancements in electrode designs and micro-fabrication technology has allowed existence of microelectrode arrays with hundreds of channels for single-cell record…
Universal Spike Classifier
Muhammad Saif-ur-Rehman, Robin Lienkämper, Yaroslav Parpaley +8
In electrophysiology, microelectrodes are the primary source for recording neural data of single neurons (single unit activity). These microelectrodes can be implanted individually…