29 citations · 40 across the 9 of their papers we have counts for
9 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…
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…
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…