29 citations · 34 across the 5 of their papers we have counts for
5 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…
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…
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…
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…