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Category: scientific publication

New paper: Performance Boundaries for BCIs Using Error-Related Potentials and Reinforcement Learning

Posted on June 7, 2026 By jannis
New paper: Performance Boundaries for BCIs Using Error-Related Potentials and Reinforcement Learning
publication, scientific publication

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” »

New paper: Distributional Properties of ReLU-Activations in Neural Networks That Learn by Memorization

Posted on June 7, 2026June 7, 2026 By jannis
New paper: Distributional Properties of ReLU-Activations in Neural Networks That Learn by Memorization
publication, scientific publication

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” »

New paper: Invariance to Quantile Selection in Distributional Continuous Control

Posted on June 7, 2026June 7, 2026 By jannis
New paper: Invariance to Quantile Selection in Distributional Continuous Control
publication, scientific publication, teaching

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” »

Neurocomputing: Exploring Neural Activation Dynamics

Posted on October 14, 2024March 30, 2025 By jannis
Neurocomputing: Exploring Neural Activation Dynamics
publication, scientific publication

Published in Journal of Neurocomputing: Understanding activation patterns in artificial neural networks by exploring stochastic processes: Discriminating generalization from memorization Stephan Johann Lehmler, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis for more details: https://doi.org/10.1016/j.neucom.2024.128473

A generic error-related potential classifier based on simulated subjects-Frontiers in Human Neuroscience

Posted on September 16, 2024 By jannis
A generic error-related potential classifier based on simulated subjects-Frontiers in Human Neuroscience
scientific publication

https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2024.1390714/full Aline Xavier Fidêncio1,2,3*Christian Klaes3Ioannis Iossifidis2 Error-related potentials (ErrPs) are brain signals known to be generated as a reaction to erroneous events. Several works have shown that not only self-made errors but also mistakes generated by external agents can elicit such event-related potentials. The possibility of reliably measuring ErrPs through non-invasive techniques has increased the … Read More “A generic error-related potential classifier based on simulated subjects-Frontiers in Human Neuroscience” »

BCCN23: Investigation of the Interplay of Model-Based and Model-Free Learning Using Reinforcement Learning

Posted on September 27, 2023September 28, 2023 By jannis
BCCN23: Investigation of the Interplay of Model-Based and Model-Free Learning Using Reinforcement Learning
Conference participation, scientific publication

Investigation of the Interplay of Model-Based and Model-Free Learning Using Reinforcement Learning The reward prediction error hypothesis of dopamine in the brain states that activity of dopaminergic neurons in certain brain regions correlates with the reward prediction error that corresponds to the temporal difference error, often used as a learning signal in model free reinforcement … Read More “BCCN23: Investigation of the Interplay of Model-Based and Model-Free Learning Using Reinforcement Learning” »

Meet Iossifidis Lab at Bernstein Conference 2023

Posted on September 26, 2023September 16, 2024 By jannis
Meet Iossifidis Lab at Bernstein Conference 2023
Conference participation, press release, scientific publication

Iossifidis Lab is participating with 4 publications at the Bernstein Conference 2023 in Berlin. Meet us from 27.09 to 29.09.2023 at the Humboldt Universität zu Berlin and Charité

BCCN23: Exploring Error-related Potentials in Adaptive Brain-Machine Interfaces: Challenges and Investigation of Occurrence and Detection Ratios

Posted on September 26, 2023September 28, 2023 By jannis
BCCN23: Exploring Error-related Potentials in Adaptive Brain-Machine Interfaces: Challenges and Investigation of Occurrence and Detection Ratios
Conference participation, scientific publication

Non-invasive techniques like EEG can record error-related potentials (ErrPs), neural signals associated with error processing and awareness. ErrPs are generated in response to self-made and external errors, including those produced by the BMI. Since ErrPs are implicitly elicited and don’t add extra workload for the subject, they serve as a natural and intrinsic feedback source … Read More “BCCN23: Exploring Error-related Potentials in Adaptive Brain-Machine Interfaces: Challenges and Investigation of Occurrence and Detection Ratios” »

The concepts of muscle activity generation driven by upper limb kinematics

Posted on June 26, 2023September 5, 2023 By jannis
The concepts of muscle activity generation driven by upper limb kinematics
publication, scientific publication

Published at BioMedical Engineering OnLineMarie D. Schmidt*, Tobias Glasmachers and Ioannis Iossifidis Abstract Background: The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the end-effector instead), which are similarly represented … Read More “The concepts of muscle activity generation driven by upper limb kinematics” »

Bernstein Conference 2022:Closed-loop adaptation of brain-machine interfaces using error-related potentials and reinforcement learning

Posted on September 29, 2022September 29, 2022 By jannis
Bernstein Conference 2022:Closed-loop adaptation of brain-machine interfaces using error-related potentials and reinforcement learning
scientific publication

Closed-loop adaptation of brain-machine interfaces using error-related potentials and reinforcement learning Aline Xavier Fidêncio1, 2, 3 , Christian Klaes1 , Ioannis Iossifidis2 University Hospital Knappschaftskrankenhaus, Ruhr University Bochum, Bochum, Germany Institute of Computer Science, Ruhr West University of Applied Sciences, Mülheim an der Ruhr, Germany Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum, … Read More “Bernstein Conference 2022:Closed-loop adaptation of brain-machine interfaces using error-related potentials and reinforcement learning” »

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Recent Posts

  • (no title) June 18, 2026
  • New journal article: Predicting Muscle Activity from Upper Limb Kinematics with LSTM Networks June 7, 2026
  • New preprint: The Spatial and Temporal Resolution of Motor Intention in Multi-Target Prediction June 7, 2026
  • New paper: Performance Boundaries for BCIs Using Error-Related Potentials and Reinforcement Learning June 7, 2026
  • New paper: Distributional Properties of ReLU-Activations in Neural Networks That Learn by Memorization June 7, 2026

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