Category: teaching

Reference Saif-ur-Rehman, M., Ali, O., Klaes, C., & Iossifidis, I. (2025). Adaptive SpikeDeep-classifier: Self-organizing and self-supervised machine learning algorithm for online spike sorting. Neurocomputing, 655, 131370. DOI: 10.1016/j.neucom.2025.131370 Online Spike Sorting That Adapts Itself: Why Ada‑SpikeDeepClassifier Is a Big Step Toward Practical Invasive BCIs TL;DR: Ada‑SpikeDeepClassifier is a semi‑online, fully automatic spike sorting pipeline that … Read More “” »

Marie D. Schmidt; Tobias Glasmachers; Ioannis Iossifidis Understanding how movement relates to muscle activation is a core problem in motor control and a practical challenge for rehabilitation technology. In this article, we train a Long Short-Term Memory (LSTM) model on upper-limb end-effector kinematics to predict activity across eight muscles. The model predicts muscle activity accurately … Read More “New journal article: Predicting Muscle Activity from Upper Limb Kinematics with LSTM Networks” »

Marie Dominique Schmidt; Ioannis Iossifidis Decoding motor intention early and reliably is central to responsive assistive devices and adaptive rehabilitation. In this study, we investigate how precisely movement direction and target location can be inferred from multichannel EMG signals, and how early this information becomes available relative to movement onset. We present a pipeline combining … Read More “New preprint: The Spatial and Temporal Resolution of Motor Intention in Multi-Target Prediction” »

The upper limbs are crucial in performing daily tasks that require strength, a wide range of motion, and precision. To achieve coordinated motion, planning and timing are critical. Sensory information about the target and the current body state is essential, as well as integrating past experiences, represented by pre-learned inverse dynamics that generate associated muscle … Read More “BCCN23: The link between muscle activity and upper limb kinematics” »