iLab: From Brain to Machine and Back
iossifidis Lab
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
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” »
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” »
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
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” »
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” »
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” »
Marie Dominique Schmidt1 , Ioannis Iossifidis1 Institute of Computer Science, University of Applied Science Ruhr West, Duisburger Str. 100, 45479 Mülheim an der Ruhr, Germany The upper limbs enable us to perform a variety of tasks in everyday life that require strength and a wide range of motion as well as precision. For coordinated motion, … Read More “Bernstein Conference 2022: Linking muscle activity and motion trajectory” »
Felix Grün1, 2 , Muhammed Saif-ur-Rehman1 , Ioannis Iossifidis1 Department for Computer Science, Ruhr-West University of Applied Sciences, Lützowstraße 5, 46236 Bottrop, Germany Institut für Neuroinformatik, Ruhr-University Bochum, Universitätsstraße 150, 44801 Bochum, Germany The relation between the activity of dopaminergic neurons and the temporal difference error in Reinforcement Learning (RL) problems [1] is well-known to … Read More “Bernstein Conference 2022: Exploring Distribution Parameterizations for Distributional Continuous Control” »
Stephan Johann Lehmler, Muhammad Saif-Ur-Rehman, Ioannis Iossifidis Computer Science, Ruhr West University of Applied Science, 45407 Mülheim an der Ruhr, Germany Institute for Neural Computation, Ruhr-University Bochum, 44801 Bochum, Germany Bioelectrical signals gathered via surface electromyography (sEMG) are the basis of muscle-machine-interfaces (MMI), which makes accurate decoding of those signals an important step in aplications … Read More “Bernstein Conference 2022: Modeling subject specfic surface EMG features by means of deep learning” »
