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 “” »
Author: jannis
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” »
New paper: Performance Boundaries for BCIs Using Error-Related Potentials and Reinforcement Learning
Aline Xavier Fidêncio; Felix Grün; Christian Klaes; Ioannis Iossifidis Non-invasive BCIs are promising assistive technologies, but real-world EEG is non-stationary—often causing performance to degrade over time. One route to robustness is to adapt the system online using feedback signals. In this work we study the limits of a closed-loop BCI framework that uses error-related potentials … Read More “New paper: Performance Boundaries for BCIs Using Error-Related Potentials and Reinforcement Learning” »
Stephan Johann Lehmler; Muhammad Saif-ur-Rehman; Tobias Glasmachers; Ioannis Iossifidis How can we tell whether a neural network is learning generalizable structure versus memorizing rare patterns? In this paper, we study distributional signatures inside networks trained under memorization-heavy regimes. Our starting point is the idea that memorization corresponds to learning “rare” input features—an effect that can … Read More “New paper: Distributional Properties of ReLU-Activations in Neural Networks That Learn by Memorization” »
Felix Grün; Muhammad Saif-ur-Rehman; Tobias Glasmachers; Ioannis Iossifidis Distributional reinforcement learning has delivered strong results in discrete-action benchmarks, largely driven by different ways of representing value distributions (e.g., via quantiles) and comparing them during training. In this work, we bring three prominent distributional methods—QR-DQN, IQN, and FQF—into the continuous action domain by integrating distributional critics … Read More “New paper: Invariance to Quantile Selection in Distributional Continuous Control” »
🚀 Wir freuen uns, bekannt zu geben, dass das Iossifidis Lab an der Bernstein-Konferenz teilgenommen und dort spannende Forschungsarbeiten unserer talentierten Teammitglieder Marie Dominique Schmidt, Aline Xavier Fidencio, Felix Grün und Stephan Lehmler präsentiert hat. Unsere Beiträge umfassten:🔹 Error-related potentials and reinforcement learning for neuroadaptive systems Fidêncio AX, Iossifidis I 🔹 A Framework to Model Your Model-Based / … Read More “Iossifidis Lab auf der Bernstein Konferenz 2025” »
Value Iteration in Grid World Value Iteration in Grid World Grid Größe: Gamma: Theta: Initialisieren Positionen der Hindernisse (x,y Paare): Zielposition (x,y): Bellman-Gleichung für Value Iteration: V(s) = maxa Σs’ [P(s’|s,a) * (R(s,a,s’) + γ * V(s’))] Wo: V(s) der Wert des Zustands s ist P(s’|s,a) die Übergangswahrscheinlichkeit ist (hier angenommen als deterministisch, daher = … Read More “Gridworld RL” »
Visualisation-animation of BSTs, B-Trees and Red-Black-Trees. A modified version of David Galles’ visualisation of BSTs, B-Trees and Red-Black-Trees tree-visualisation A modification of David Galles’ visualisation Manual Insert: Enter one or more numbers between 0 and 999. Separate them with your favorite non-digit character sequence. Press Insert. Delete: Enter a number. Press Delete. Find: Enter a … Read More “Algorithms – Visualisation” »
