Lehmler, Stephan Johann; Saif-ur-Rehman, Muhammad; Glasmachers, Tobias; Iossifidis, Ioannis In: Neurocomputing, S. 128473, 2024, ISSN: 0925-2312. Abstract | Links | BibTeX | Schlagwörter: Artificial neural networks, Generalization, Machine Learning, Memorization, Poisson process, Stochastic modeling Pilacinski, Artur; Christ, Lukas; Boshoff, Marius; Iossifidis, Ioannis; Adler, Patrick; Miro, Michael; Kuhlenkötter, Bernd; Klaes, Christian In: Frontiers in Neurorobotics, Bd. 18, 2024, ISSN: 1662-5218. Links | BibTeX | Schlagwörter: brain-machine interfaces, EEG, Human action recognition, human-robot collaboration, Sensor Fusion Lehmler, Stephan Johann; Iossifidis, Ioannis 3D Movement Analysis of the Ruhr Hand Motion Catalog of Human Center-Out Transport Trajectories Proceedings Article In: BC24 : Computational Neuroscience & Neurotechnology Bernstein Conference 2024, BCCN Bernstein Network Computational Networkvphantom, 2024. Abstract | Links | BibTeX | Schlagwörter: Fidencio, Aline Xavier; Klaes, Christian; Iossifidis, Ioannis Adaptive Brain-Computer Interfaces Based on Error-Related Potentials and Reinforcement Learning Proceedings Article In: BC24 : Computational Neuroscience & Neurotechnology Bernstein Conference 2024, BCCN Bernstein Network Computational Networkvphantom, 2024. Abstract | Links | BibTeX | Schlagwörter: BCI, Machine Learning Grün, Felix; Iossifidis, Ioannis Controversial Opinions on Model Based and Model Free Reinforcement Learning in the Brain Proceedings Article In: BCCN Bernstein Network Computational Networkvphantom, 2024. Abstract | Links | BibTeX | Schlagwörter: Machine Learning, Reinforcement learning Schmidt, Marie Dominique; Iossifidis, Ioannis Decoding Upper Limb Movements Proceedings Article In: BCCN Bernstein Network Computational Networkvphantom, 2024. Abstract | Links | BibTeX | Schlagwörter: BCI, Machine Learning, Muscle activity Lehmler, Stephan Johann; Iossifidis, Ioannis Stochastic Process Model Derived Indicators of Overfitting for Deep Architectures: Applicability to Small Sample Recalibration of sEMG Decoders Proceedings Article In: BC24 : Computational Neuroscience & Neurotechnology Bernstein Conference 2024, BCCN Bernstein Network Computational Networkvphantom, 2024. Abstract | Links | BibTeX | Schlagwörter: Machine Learning Fidêncio, Aline Xavier; Klaes, Christian; Iossifidis, Ioannis A Generic Error-Related Potential Classifier Based on Simulated Subjects Artikel In: Frontiers in Human Neuroscience, Bd. 18, S. 1390714, 2024, ISSN: 1662-5161. Abstract | Links | BibTeX | Schlagwörter: adaptive brain-machine (computer) interface, BCI, EEG, Error-related potential (ErrP), ErrP classifier, Generic decoder, Machine Learning, SEREEGA, Simulation Ali, Omair; Saif-ur-Rehman, Muhammad; Metzler, Marita; Glasmachers, Tobias; Iossifidis, Ioannis; Klaes, Christian GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals Artikel In: arXiv:2406.03115 [q-bio], 2024. Abstract | Links | BibTeX | Schlagwörter: BCI, EEG, Machine Learning, Quantitative Biology - Neurons and Cognition Büttner, Sabine; Handmann, Uwe; Irrek, Wolfgang Transformation zur Circular Economy - Kleine und mittlere Unternehmen im Wandel begleiten Buch Springer, 2024, (in print). Links | BibTeX | Schlagwörter: Arntz, Alexander; Helgert, André; Straßmann, Carolin; Eimler, Sabrina C. Enhancing Human-Robot Interaction Research by Using a Virtual Reality Lab Approach Proceedings Article In: 2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR), S. 340-344, 2024. Abstract | Links | BibTeX | Schlagwörter: Technological innovation;Solid modeling;Runtime;Human-robot interaction;Virtual environments;Physiology;Robots;Virtual Reality;Human-Robot Interaction;Empirical Studies;Research Platform;Study Tool;Wizard of Oz Heilmann, Dan; Helgert, André; Eimler, Sabrina C. Virtual Reality for Tinnitus Education: Inspiring Awareness and Proactive Behavioral Changes Proceedings Article In: 2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR), S. 294-300, 2024. Abstract | Links | BibTeX | Schlagwörter: Visualization;Solid modeling;Sociology;Psychology;Virtual reality;Human factors;Statistics;virtual reality;tinnitus;sensitization;immersion;simulation;health promotion;awareness Helgert, André; Straßmann, Carolin; Eimler, Sabrina C. Unlocking Potentials of Virtual Reality as a Research Tool in Human-Robot Interaction: A Wizard-of-Oz Approach Proceedings Article In: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, S. 535–539, Association for Computing Machinery, Boulder, CO, USA, 2024, ISBN: 9798400703232. Abstract | Links | BibTeX | Schlagwörter: accessibility, social robots, virtual reality, Wizard-of-Oz Albrecht-Gansohr, Carina; Timm, Lara; Eimler, Sabrina C.; Geisler, Stefan In: Virtual Worlds, Bd. 3, Nr. 2, S. 208–229, 2024, ISSN: 2813-2084. Abstract | Links | BibTeX | Schlagwörter: Arntz, Alexander Enabling Safe Empirical Studies for Human-Robot Collaboration: Implementation of a Sensor Array Driven Control Interface Proceedings Article In: Kurosu, Masaaki; Hashizume, Ayako (Hrsg.): Human-Computer Interaction, S. 42–57, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-60412-6. Abstract | BibTeX | Schlagwörter: Arntz, Alexander; Dia, Agostino Di; Riebner, Tim; Straßmann, Carolin; Eimler, Sabrina C. Teamwork Makes the Dream Work: A Virtual Reality-based Human-Robot Collaboration Sandbox Simulating Multiple Teams Proceedings Article In: 2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR), S. 335-339, 2024, ISSN: 2771-7453. Abstract | Links | BibTeX | Schlagwörter: Robot kinematics;Virtual assistants;Virtual environments;Industrial robots;Libraries;Teamwork;Task analysis;Human-Robot Collaboration;Virtual Reality;Machine Learning;Artificial Intelligence Ali, Omair; Saif-ur-Rehman, Muhammad; Glasmachers, Tobias; Iossifidis, Ioannis; Klaes, Christian In: Computers in Biology and Medicine, S. 107649, 2023, ISSN: 0010-4825. Abstract | Links | BibTeX | Schlagwörter: BCI, Brain computer interface, Deep learning, EEG decoding, EMG decoding, Machine Learning Erle, Lukas; Timm, Lara; Straßmann, Carolin; Eimler, Sabrina C. Using Focus Group Interviews to Examine Biased Experiences in Human-Robot-Interaction Artikel In: S. 4, 2023. Abstract | Links | BibTeX | Schlagwörter: Helgert, André; Eimler, Sabrina C.; Straßmann, Carolin In: S. 4, 2023. Abstract | Links | BibTeX | Schlagwörter: Sziburis, Tim; Blex, Susanne; Iossifidis, Ioannis Variability Study of Human Hand Motion during 3D Center-out Tasks Captured for the Diagnosis of Movement Disorders Proceedings Article In: BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2023. Abstract | BibTeX | Schlagwörter: movement model Fidencio, Aline Xavier; Klaes, Christian; Iossifidis, Ioannis Exploring Error-related Potentials in Adaptive Brain-Machine Interfaces: Challenges and Investigation of Occurrence and Detection Ratios Proceedings Article In: BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2023. Abstract | BibTeX | Schlagwörter: BCI, EEG, Machine Learning Grün, Felix; Iossifidis, Ioannis Investigation of the Interplay of Model-Based and Model-Free Learning Using Reinforcement Learning Proceedings Article In: BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2023. Abstract | BibTeX | Schlagwörter: Machine Learning, Reinforcement learning Schmidt, Marie Dominique; Iossifidis, Ioannis The Link between Muscle Activity and Upper Limb Kinematics Proceedings Article In: BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2023. Abstract | BibTeX | Schlagwörter: BCI, Machine Learning Hussain, Muhammad Ayaz; Iossifidis, Ioannis In: arXiv:2309.04698 [cs.RO], 2023. Abstract | Links | BibTeX | Schlagwörter: Autonomous robotics, BCI, Computer Science - Artificial Intelligence, Computer Science - Information Theory, Computer Science - Machine Learning, Exoskeleton Börsting, Johanna; Schwarze, Veronica; Eimler, Sabrina C. Tell me why - Combating racism on social media with knowledge Proceedings Article In: Melzer, André; Wagener, Gary Lee (Hrsg.): Proceedings of the 13th Conference of the Media Psychology Division (DGPs), Melusina Press, 2023. Links | BibTeX | Schlagwörter: Erle, Lukas; Timm, Lara; Straßmann, Carolin; Eimler, Sabrina C. Algorithmic Bias and Digital Divide – An Examination of Discrimination Experiences in Human-System Interactions (Poster) Proceedings Article In: Melzer, André; Wagener, Gary Lee (Hrsg.): Proceedings of the 13th Conference of the Media Psychology Division (DGPs), Melusina Press, 2023. Links | BibTeX | Schlagwörter: Eimler, Sabrina C.; Börsting, Johanna; Schwarze, Veronica Imagine it was you - Empathy as the key for reducing cyberbullying on social media Proceedings Article In: Melzer, André; Wagener, Gary Lee (Hrsg.): Proceedings of the 13th Conference of the Media Psychology Division (DGPs), Melusina Press, 2023. Links | BibTeX | Schlagwörter: Schwarze, Veronica; Eimler, Sabrina C.; Krämer, Nicole C. Picturing diversity: Exploring children’s perception of intergroup differences Proceedings Article In: Melzer, André; Wagener, Gary Lee (Hrsg.): Proceedings of the 13th Conference of the Media Psychology Division (DGPs), 2023. Links | BibTeX | Schlagwörter: Schweizer, Anne-Marie; Börsting, Johanna Keep calm - The role of resilience in the interplay between neuroticism and phubbing Proceedings Article In: Melzer, André; Wagener, Gary Lee (Hrsg.): Proceedings of the 13th Conference of the Media Psychology Division (DGPs), Melusina Press, 2023. Links | BibTeX | Schlagwörter: Schmidt, Marie D.; Glasmachers, Tobias; Iossifidis, Ioannis The Concepts of Muscle Activity Generation Driven by Upper Limb Kinematics Artikel In: BioMedical Engineering OnLine, Bd. 22, Nr. 1, S. 63, 2023, ISSN: 1475-925X. Abstract | Links | BibTeX | Schlagwörter: Artificial generated signal, BCI, Electromyography (EMG), Generative model, Inertial measurement unit (IMU), Machine Learning, Motion parameters, Muscle activity, Neural networks, transfer learning, Voluntary movement Saif-ur-Rehman, Muhammad; Ali, Omair; Klaes, Christian; Iossifidis, Ioannis In: arXiv:2304.01355 [cs, math, q-bio], 2023. Links | BibTeX | Schlagwörter: BCI, Machine Learning, Spike Sorting Irrek, Wolfgang; Handmann, Uwe Sauber getrennt ist halb verwertet - Recycling mittels KI Artikel In: IM+io Best & Next Practices aus Digitalisierung, Management, Wissenschaft, Bd. 2023, Nr. 01, S. 26-29, 2023, ISSN: 1616-1017. Links | BibTeX | Schlagwörter: Malzahn, Nils; Schwarze, Veronica; Eimler, Sabrina C.; Aprin, Farbod; Moder, Sarah; Hoppe, H. Ulrich How to measure disagreement as a premise for learning from controversy in a social media context Artikel In: Research and Practice in Technology Enhanced Learning, Bd. 18, S. 012, 2023. Abstract | Links | BibTeX | Schlagwörter: Rohrschneider, David; Baker, Nermeen Abou; Handmann, Uwe Double Transfer Learning to detect Lithium-Ion batteries on X-Ray images Konferenz 17th International Work-Conference on Artificial Neural Networks (IWANN 2023), Ponta Delgada, Portugal, June 19 - 21, 2023, Proceedings, Part I, Bd. 14134, Lecture Notes in Computer Science (LNCS) Springer Nature, Switzerland, 2023. Links | BibTeX | Schlagwörter: Baker, Nermeen Abou; Handmann, Uwe Don't waste SAM Konferenz The 31th European Symposium on Artificial Neural Networks (ESANN 2023), Bruges, Belgium, 2023. Links | BibTeX | Schlagwörter: Straßmann, Carolin; Helgert, André; Breil, Valentin; Settelmayer, Lina; Diehl, Inga Exploring the Use of Colored Ambient Lights to Convey Emotional Cues With Conversational Agents: An Experimental Study Proceedings Article In: 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), S. 99-105, 2023. Abstract | Links | BibTeX | Schlagwörter: Ethics;Emotion recognition;Virtual assistants;Games;Behavioral sciences;Robots Straßmann, Carolin; Helgert, André; Lingnau, Andreas Psychological Outcomes and Effectiveness of a Collaborative Video-Based Learning Tool for Synchronous Discussions Proceedings Article In: Fulantelli, Giovanni; Burgos, Daniel; Casalino, Gabriella; Cimitile, Marta; Bosco, Giosuè Lo; Taibi, Davide (Hrsg.): Higher Education Learning Methodologies and Technologies Online, S. 691–705, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-29800-4. Abstract | BibTeX | Schlagwörter: Theophilou, Emily; Schwarze, Veronica; Börsting, Johanna; Sánchez-Reina, Roberto; Scifo, Lidia; Lomonaco, Francesco; Aprin, Farbod; Ognibene, Dimitri; Taibi, Davide; Hernández-Leo, Davinia; Eimler, Sabrina C. Empirically Investigating Virtual Learning Companions to Enhance Social Media Literacy Proceedings Article In: Fulantelli, Giovanni; Burgos, Daniel; Casalino, Gabriella; Cimitile, Marta; Bosco, Giosuè Lo; Taibi, Davide (Hrsg.): Higher Education Learning Methodologies and Technologies Online, S. 345–360, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-29800-4. Abstract | BibTeX | Schlagwörter: Taibi, Davide; Börsting, Johanna; Hoppe, Ulrich; Ognibene, Dimitri; Hernández-Leo, Davinia; Eimler, Sabrina C.; Kruschwitz, Udo The Role of Educational Interventions in Facing Social Media Threats: Overarching Principles of the COURAGE Project Proceedings Article In: Fulantelli, Giovanni; Burgos, Daniel; Casalino, Gabriella; Cimitile, Marta; Bosco, Giosuè Lo; Taibi, Davide (Hrsg.): Higher Education Learning Methodologies and Technologies Online, S. 315–329, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-29800-4. Abstract | BibTeX | Schlagwörter: Ognibene, Dimitri; Wilkens, Rodrigo; Taibi, Davide; Hernández-Leo, Davinia; Kruschwitz, Udo; Donabauer, Gregor; Theophilou, Emily; Lomonaco, Francesco; Bursic, Sathya; Lobo, Rene Alejandro; Sánchez-Reina, J. Roberto; Scifo, Lidia; Schwarze, Veronica; Börsting, Johanna; Hoppe, Ulrich; Aprin, Farbod; Malzahn, Nils; Eimler, Sabrina C. In: Frontiers in Artificial Intelligence, Bd. 5, 2023, ISSN: 2624-8212. Abstract | Links | BibTeX | Schlagwörter: Dia, Agostino Di; Riebner, Tim; Arntz, Alexander; Jansen, Marc Prototyping a Smart Contract Application for Fair Reward Distribution in Software Development Projects Proceedings Article In: Prieto, Javier; Martínez, Francisco Luis Benítez; Ferretti, Stefano; Guardeño, David Arroyo; Nevado-Batalla, Pedro Tomás (Hrsg.): Blockchain and Applications, 4th International Congress, S. 131–141, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-21229-1. Abstract | BibTeX | Schlagwörter: Baker, Nermeen Abou; Handmann, Uwe E-Waste Recycling Gets Smarter with Digitalization Konferenz 10th IEEE Conference on Technologies for Sustainability (SUSTECH 2023), IEEE, Portland, Oregon USA, 2023. BibTeX | Schlagwörter: Handmann, Uwe; Baker, Nermeen Abou Digitalization & Circular Economy (Poster) Konferenz HRW, 2023. BibTeX | Schlagwörter: Grün, Felix; Saif-ur-Rehman, Muhammad; Glasmachers, Tobias; Iossifidis, Ioannis Invariance to Quantile Selection in Distributional Continuous Control Artikel In: arXiv:2212.14262 [cs.LG], 2022. Links | BibTeX | Schlagwörter: Artificial Intelligence (cs.AI), FOS: Computer and information sciences, I.2.6, I.2.8, Machine Learning, Machine Learning (cs.LG) Lehmler, Stephan Johann; Saif-ur-Rehman, Muhammad; Glasmachers, Tobias; Iossifidis, Ioannis Deep transfer learning compared to subject-specific models for sEMG decoders Artikel In: Journal of Neural Engineering, Bd. 19, Nr. 5, 2022. Abstract | Links | BibTeX | Schlagwörter: BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning Grün, Felix; Iossifidis, Ioannis Exploring Distribution Parameterizations for Distributional Continuous Control Proceedings Article In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022. Links | BibTeX | Schlagwörter: Machine Learning, Reinforcement learning Lehmler, Stephan Johann; Saif-ur-Rehman, Muhammad; Iossifidis, Ioannis Modeling Subject Specfic Surface EMG Features by Means of Deep Learning Proceedings Article In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022. Links | BibTeX | Schlagwörter: BCI, Machine Learning Schmidt, Marie Dominique; Iossifidis, Ioannis Linking Muscle Activity and Motion Trajectory Proceedings Article In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022. Links | BibTeX | Schlagwörter: BCI, Machine Learning Sziburis, Tim; Blex, Susanne; Iossifidis, Ioannis A Dataset of 3D Hand Transport Trajectories Determined by Inertial Measurements from a Single Sensor Proceedings Article In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022. Links | BibTeX | Schlagwörter: BCI, Machine Learning Fidencio, Aline Xavier; Klaes, Christian; Iossifidis, Ioannis Closed-Loop Adaptation of Brain-Machine Interfaces Using Error-Related Potentials and Reinforcement Learning Proceedings Article In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022. Links | BibTeX | Schlagwörter: BCI, Machine Learning2024
@article{lehmlerUnderstandingActivationPatterns2024,
title = {Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes: Discriminating Generalization from Memorization},
author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},
editor = {Elsevier},
url = {https://www.sciencedirect.com/science/article/pii/S092523122401244X},
doi = {10.1016/j.neucom.2024.128473},
issn = {0925-2312},
year = {2024},
date = {2024-09-19},
urldate = {2024-09-19},
journal = {Neurocomputing},
pages = {128473},
abstract = {To gain a deeper understanding of the behavior and learning dynamics of artificial neural networks, mathematical abstractions and models are valuable. They provide a simplified perspective and facilitate systematic investigations. In this paper, we propose to analyze dynamics of artificial neural activation using stochastic processes, which have not been utilized for this purpose thus far. Our approach involves modeling the activation patterns of nodes in artificial neural networks as stochastic processes. By focusing on the activation frequency, we can leverage techniques used in neuroscience to study neural spike trains. Specifically, we extract the activity of individual artificial neurons during a classification task and model their activation frequency. The underlying process model is an arrival process following a Poisson distribution.We examine the theoretical fit of the observed data generated by various artificial neural networks in image recognition tasks to the proposed model’s key assumptions. Through the stochastic process model, we derive measures describing activation patterns of each network. We analyze randomly initialized, generalizing, and memorizing networks, allowing us to identify consistent differences in learning methods across multiple architectures and training sets. We calculate features describing the distribution of Activation Rate and Fano Factor, which prove to be stable indicators of memorization during learning. These calculated features offer valuable insights into network behavior. The proposed model demonstrates promising results in describing activation patterns and could serve as a general framework for future investigations. It has potential applications in theoretical simulation studies as well as practical areas such as pruning or transfer learning.},
keywords = {Artificial neural networks, Generalization, Machine Learning, Memorization, Poisson process, Stochastic modeling},
pubstate = {published},
tppubtype = {article}
}
@article{pilacinskiHumanCollaborativeLoop2024,
title = {Human in the Collaborative Loop: A Strategy for Integrating Human Activity Recognition and Non-Invasive Brain-Machine Interfaces to Control Collaborative Robots},
author = {Artur Pilacinski and Lukas Christ and Marius Boshoff and Ioannis Iossifidis and Patrick Adler and Michael Miro and Bernd Kuhlenkötter and Christian Klaes},
url = {https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2024.1383089/full},
doi = {10.3389/fnbot.2024.1383089},
issn = {1662-5218},
year = {2024},
date = {2024-09-18},
urldate = {2024-09-18},
journal = {Frontiers in Neurorobotics},
volume = {18},
publisher = {Frontiers},
keywords = {brain-machine interfaces, EEG, Human action recognition, human-robot collaboration, Sensor Fusion},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{3DMovementAnalysis2024,
title = {3D Movement Analysis of the Ruhr Hand Motion Catalog of Human Center-Out Transport Trajectories},
author = {Stephan Johann Lehmler and Ioannis Iossifidis},
url = {https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/719bca6f-9fb9-4e53-96a5-a7b36b67c012},
year = {2024},
date = {2024-09-18},
urldate = {2024-09-18},
booktitle = {BC24 : Computational Neuroscience & Neurotechnology Bernstein Conference 2024},
publisher = {BCCN Bernstein Network Computational Networkvphantom},
abstract = {The Ruhr Hand Motion Catalog of Human Center-Out Transport Trajectories [1] is a compilation of three-dimensional task-space motion data simultaneously measured by two motion tracking systems. The first one, an optical motion capture system, provided robust reference data. The second recording system consisted of a single state-of-the-art IMU to demonstrate the feasibility of portable applications. The transport object was moved in 3D space from a unified start position to one of nine target positions, equidistantly aligned on a semicircle. Ten trials were performed per target and hand, resulting in 180 trials per participant in total. 31 participants (11 female, 20 male, age 21-78) without known movement disorders took part in the experiment. Based on those experimental data, we analyze several characteristics of upper-limb trajectories. All data are rotated so that the straight connection of the defined start and target positions composes the y-axis. By doing so, we explore properties which are independent of the directly measured target location for each task and focus on common properties shared between all target movements. Particularly, we investigate how individual or target-dependent differences can still be quantified after rotation. Furthermore, we model the measured movements by means of dynamical systems (extended attractor dynamics). Differences between the transportation movements to different targets would result in varying parameter sets. The investigated motion characteristics include the symmetry of velocity peaks and the polynomial target dependence of planarity attributes. To compare the diversity of trajectories in time and space, we introduce a novel variability measure for the planarity of hand paths regarding plane angles and path amplitudes within the plane. These aspects can expose differences between trials (intra-subject) and participants (inter-subject), explored in the modelling process and applied as a methodological framework for pathological analysis. For this, further measurements with patients experiencing movement disorders are planned for future examination. The separability can also be evaluated by machine learning of task classification and user identification. This can provide information on the potential of data-driven pathological analysis to extend the model-based approach since the described experiment and study are conducted in the context of developing a portable glove for the diagnosis of movement disorders.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{AdaptiveBraincomputerInterfaces2024,
title = {Adaptive Brain-Computer Interfaces Based on Error-Related Potentials and Reinforcement Learning},
author = {Aline Xavier Fidencio and Christian Klaes and Ioannis Iossifidis},
url = {https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/03d3dd16-4c50-43d8-b878-abcfa7857386},
year = {2024},
date = {2024-09-18},
urldate = {2024-09-24},
booktitle = {BC24 : Computational Neuroscience & Neurotechnology Bernstein Conference 2024},
publisher = {BCCN Bernstein Network Computational Networkvphantom},
abstract = {Error-related potentials (ErrPs) represent the neural signature of error processing in the brain and numerous studies have demonstrated their reliable detection using non-invasive techniques such as electroencephalography (EEG). Over recent decades, the brain-computer interface (BCI) community has shown growing interest in leveraging these intrinsic feedback signals to enhance system performance. However, the effective use of ErrPs in a closed-loop setup crucially depends on accurate single-trial detection, which is typically achieved using a subject-specific classifier (or decoder) trained on samples recorded during extensive calibration sessions before the BCI system can be deployed. In our research, we explore the potential of simulated EEG data for training a truly generic ErrP classifier. Utilizing the SEREEGA simulator, we demonstrate that EEG data can be generated in a cost-effective manner, allowing for controlled and systematic variations in data distribution to accommodate uncertainties in ErrP generation. A classifier trained solely on the generated data exhibits promising generalization capabilities across different datasets and performs comparably to a leave-one-subject-out approach trained on real data (Xavier Fidêncio et al., 2024). In our experiments, we deliberately provoked ErrPs when the BCI misinterpreted the user's intention, resulting in incorrect actions. Subjects engaged in a game controlled via keyboard and/or motor imagery (imagining hand movements), with EEG data recorded using various EEG systems for comparison. Considering the challenges in obtaining clear ErrP signals for all subjects and the limitations identified in existing literature (Xavier Fidêncio et al., 2022), we hypothesize whether a measurable error signal is consistently generated at the scalp level when subjects encounter erroneous conditions, and how this influences closed-loop setups that incorporate ErrPs for improved BCI performance. To address these questions, we assess the effects of the occurrence-to-detection ratio of ErrPs in the classification pipeline using simulated data and explore the impact of error misclassification rates in an ErrP-based learning framework, which employs reinforcement learning to enhance BCI performance.},
keywords = {BCI, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{ControversialOpinionsModel2024,
title = {Controversial Opinions on Model Based and Model Free Reinforcement Learning in the Brain},
author = {Felix Grün and Ioannis Iossifidis},
url = {https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/18e92e07-e4b1-43af-b2ac-ea282f4e81e7},
year = {2024},
date = {2024-09-18},
urldate = {2024-09-24},
publisher = {BCCN Bernstein Network Computational Networkvphantom},
abstract = {Dopaminergic Reward Prediction Errors (RPEs) are a key motivation and inspiration for model free, temporal difference reinforcement learning methods. Originally, the correlation of RPEs with model free temporal difference errors was seen as a strong indicator for model free reinforcement learning in brains. The standard view was that model free learning is the norm and more computationally expensive model based decision-making is only used when it leads to outcomes that are good enough to justify the additional effort. Nowadays, the landscape of opinions, models and experimental evidence, both electrophysiological and behavioral, paints a more complex picture, including but not limited to mechanisms of arbitration between the two systems. Model based learning or hybrid models better capture experimental behavioral data, and model based signatures are found in RPEs that were previously thought to be model free or hybrid [1]. The evidence for clearly model free learning is scarce [2]. On the other hand, multiple approaches show how model based behavior and RPEs can be produced with fundamentally model free reinforcement learning methods [3, 4, 5]. We point out findings that seem to contradict each other, others that complement each other, speculate which ideas are compatible with each other and give our opinions on ways forward, towards understanding if and how model based and model free learning from rewards coexist and interact in the brain.},
keywords = {Machine Learning, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{DecodingUpperLimb2024,
title = {Decoding Upper Limb Movements},
author = {Marie Dominique Schmidt and Ioannis Iossifidis},
url = {https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/4725140f-ce7c-4ac5-b694-c627ceeb8d98},
year = {2024},
date = {2024-09-18},
urldate = {2024-09-24},
publisher = {BCCN Bernstein Network Computational Networkvphantom},
abstract = {The upper limbs are essential for performing everyday tasks that require a wide range of motion and precise coordination. Planning and timing are crucial to achieve coordinated movement. Sensory information about the target and current body state is critical, as is the integration of prior experience represented by prelearned inverse dynamics that generate the associated muscle activity. We propose a generative model that uses a recurrent neural network to predict upper limb muscle activity during various simple and complex everyday movements. By identifying movement primitives within the signal, our model enables the decomposition of these movements into a fundamental set, facilitating the reconstruction of muscle activity patterns. Our approach has implications for the fundamental understanding of movement control and the rehabilitation of neuromuscular disorders with myoelectric prosthetics and functional electrical stimulation.},
keywords = {BCI, Machine Learning, Muscle activity},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{StochasticProcessModel2024,
title = {Stochastic Process Model Derived Indicators of Overfitting for Deep Architectures: Applicability to Small Sample Recalibration of sEMG Decoders},
author = {Stephan Johann Lehmler and Ioannis Iossifidis},
url = {https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/72f03ff1-61dc-443c-92c2-b623d672ce15},
year = {2024},
date = {2024-09-18},
urldate = {2024-09-24},
booktitle = {BC24 : Computational Neuroscience & Neurotechnology Bernstein Conference 2024},
publisher = {BCCN Bernstein Network Computational Networkvphantom},
abstract = {Our recent work presents a stochastic process model of the activations within an ANN and shows a promising indicator to distinguish memorizing from generalizing ANNs. The average λ, or mean firing rate (MFR), of a hidden layer, shows stable differences between memorizing and generalizing networks, comparatively independent of the underlying data used for evaluation. We first show the performance of this indicator during training on benchmark computer vision datasets such as MNIST and CIFAR-10. In a second step, we extend the work to the real-life use case of calibrating a pre-trained model to a new user. We focus on decoding surface electromyographic (sEMG) signals, which are highly variable within and between users, and therefore necessitate frequent user calibration. Especially in situations when user calibration has to only rely on a small number of samples, degradation in performance overtime due to memorization and overfitting is a not unlikely outcome. In those cases, traditional regularization methods that function by observing the performance on a validation set, such as early stopping, don’t necessarily work, because they are evaluated on data from the same subject and set of movements, which features are being memorized. Our new indicators of memorization could help as stable indicators for model performance and give live insights during model calibration when more samples from the new users would be necessary. We evaluate the usefulness of the MFR-indicator for identifying the moment a pre-trained sEMG decoder starts to memorize given inputs},
keywords = {Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{xavierfidencioGenericErrorrelatedPotential2024,
title = {A Generic Error-Related Potential Classifier Based on Simulated Subjects},
author = {Aline Xavier Fidêncio and Christian Klaes and Ioannis Iossifidis},
editor = {Frontiers Media SA},
url = {https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2024.1390714/full},
doi = {10.3389/fnhum.2024.1390714},
issn = {1662-5161},
year = {2024},
date = {2024-07-19},
urldate = {2024-07-17},
journal = {Frontiers in Human Neuroscience},
volume = {18},
pages = {1390714},
publisher = {Frontiers},
abstract = {$<$p$>$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 interest in the brain-computer interface (BCI) community in using such signals to improve performance, for example, by performing error correction. Extensive calibration sessions are typically necessary to gather sufficient trials for training subject-specific ErrP classifiers. This procedure is not only time-consuming but also boresome for participants. In this paper, we explore the effectiveness of ErrPs in closed-loop systems, emphasizing their dependency on precise single-trial classification. To guarantee the presence of an ErrPs signal in the data we employ and to ensure that the parameters defining ErrPs are systematically varied, we utilize the open-source toolbox SEREEGA for data simulation. We generated training instances and evaluated the performance of the generic classifier on both simulated and real-world datasets, proposing a promising alternative to conventional calibration techniques. Results show that a generic support vector machine classifier reaches balanced accuracies of 72.9%, 62.7%, 71.0%, and 70.8% on each validation dataset. While performing similarly to a leave-one-subject-out approach for error class detection, the proposed classifier shows promising generalization across different datasets and subjects without further adaptation. Moreover, by utilizing SEREEGA, we can systematically adjust parameters to accommodate the variability in the ErrP, facilitating the systematic validation of closed-loop setups. Furthermore, our objective is to develop a universal ErrP classifier that captures the signal's variability, enabling it to determine the presence or absence of an ErrP in real EEG data.$<$/p$>$},
keywords = {adaptive brain-machine (computer) interface, BCI, EEG, Error-related potential (ErrP), ErrP classifier, Generic decoder, Machine Learning, SEREEGA, Simulation},
pubstate = {published},
tppubtype = {article}
}
@article{aliGETGenerativeEEG2024,
title = {GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals},
author = {Omair Ali and Muhammad Saif-ur-Rehman and Marita Metzler and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},
url = {http://arxiv.org/abs/2406.03115},
doi = {10.48550/arXiv.2406.03115},
year = {2024},
date = {2024-06-09},
urldate = {2024-06-09},
journal = {arXiv:2406.03115 [q-bio]},
abstract = {Generating continuous electroencephalography (EEG) signals through advanced artificial neural networks presents a novel opportunity to enhance brain-computer interface (BCI) technology. This capability has the potential to significantly enhance applications ranging from simulating dynamic brain activity and data augmentation to improving real-time epilepsy detection and BCI inference. By harnessing generative transformer neural networks, specifically designed for EEG signal generation, we can revolutionize the interpretation and interaction with neural data. Generative AI has demonstrated significant success across various domains, from natural language processing (NLP) and computer vision to content creation in visual arts and music. It distinguishes itself by using large-scale datasets to construct context windows during pre-training, a technique that has proven particularly effective in NLP, where models are fine-tuned for specific downstream tasks after extensive foundational training. However, the application of generative AI in the field of BCIs, particularly through the development of continuous, context-rich neural signal generators, has been limited. To address this, we introduce the Generative EEG Transformer (GET), a model leveraging transformer architecture tailored for EEG data. The GET model is pre-trained on diverse EEG datasets, including motor imagery and alpha wave datasets, enabling it to produce high-fidelity neural signals that maintain contextual integrity. Our empirical findings indicate that GET not only faithfully reproduces the frequency spectrum of the training data and input prompts but also robustly generates continuous neural signals. By adopting the successful training strategies of the NLP domain for BCIs, the GET sets a new standard for the development and application of neural signal generation technologies.},
keywords = {BCI, EEG, Machine Learning, Quantitative Biology - Neurons and Cognition},
pubstate = {published},
tppubtype = {article}
}
@book{CE-IrrHanBue2024,
title = {Transformation zur Circular Economy - Kleine und mittlere Unternehmen im Wandel begleiten},
author = {Sabine Büttner and Uwe Handmann and Wolfgang Irrek},
editor = {Sabine Büttner and Uwe Handmann and Wolfgang Irrek},
url = {https://link.springer.com/book/9783658433376},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
publisher = {Springer},
note = {in print},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
@inproceedings{10445600,
title = {Enhancing Human-Robot Interaction Research by Using a Virtual Reality Lab Approach},
author = {Alexander Arntz and André Helgert and Carolin Straßmann and Sabrina C. Eimler},
doi = {10.1109/AIxVR59861.2024.00058},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)},
pages = {340-344},
abstract = {Human-robot interaction (HRI) research often faces limitations in real-world environments due to uncontrollable external factors. This applies in particular to field study setups in public spaces, as these can limit the validity of the study results, e.g. due to unpredictable and unsystematic changes in the environment, noise, people passing, etc. Especially for interdisciplinary studies involving psychological perspectives, virtual reality (VR) has emerged as a promising solution, offering realistic, controlled, and reproducible environments. Also, recent technological advancements enable detailed observation of human behavior and physiological responses via eye tracking, physiological assessments, and motion capture. To effectively add value by using VR as a tool, immersion, and presence in the virtual environment are essential preconditions. Besides, the manipulability of the VR environment during runtime is a bonus in exploring human behavior in interaction with robot-enriched spaces. As a methodological innovation in HRI studies, this paper presents a VR lab as a research tool that provides a virtual model of the robot Pepper along with interfaces for easy navigation and adaptive robot behavior. Moreover, the presented Wizard of Oz dashboard allows to flexibly react to the scenery by allowing the manipulation of several robot parameters during runtime. With the help of the VR lab, a framework for a variety of interdisciplinary research purposes in human-robot interaction (not only) in public spaces is provided.},
keywords = {Technological innovation;Solid modeling;Runtime;Human-robot interaction;Virtual environments;Physiology;Robots;Virtual Reality;Human-Robot Interaction;Empirical Studies;Research Platform;Study Tool;Wizard of Oz},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{10445553,
title = {Virtual Reality for Tinnitus Education: Inspiring Awareness and Proactive Behavioral Changes},
author = {Dan Heilmann and André Helgert and Sabrina C. Eimler},
doi = {10.1109/AIxVR59861.2024.00049},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)},
pages = {294-300},
abstract = {Tinnitus is a prevalent symptom in otorhinolaryngology affecting a substantial portion of the population. Factors contributing to tinnitus include acoustic stress, sensory overload, and neurological and psychological disturbances. Although associated comorbidities can be treated, tinnitus lacks a definitive cure. Consequently, preventive measures and awareness about its consequences are crucial. Virtual Reality (VR) emerges as a potential tool for assistance as it offers (often appealing) immersive simulations, combining visual and auditory perceptual inputs supporting a feeling of being in a real physical environment. As it could help to effectively recreate the experience of tinnitus in everyday situations, VR may facilitate a deep sense of empathy and comprehension. As a first step in designing the application, a pre-study collected scenarios among people (N = 32) with tinnitus symptoms. These were subsequently implemented in the VR application and evaluated by students (N = 22) without tinnitus symptoms. The application aims to recreate distressing scenarios, to elicit empathy and educate unaffected individuals.},
keywords = {Visualization;Solid modeling;Sociology;Psychology;Virtual reality;Human factors;Statistics;virtual reality;tinnitus;sensitization;immersion;simulation;health promotion;awareness},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{10.1145/3610978.3640741,
title = {Unlocking Potentials of Virtual Reality as a Research Tool in Human-Robot Interaction: A Wizard-of-Oz Approach},
author = {André Helgert and Carolin Straßmann and Sabrina C. Eimler},
url = {https://doi.org/10.1145/3610978.3640741},
doi = {10.1145/3610978.3640741},
isbn = {9798400703232},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction},
pages = {535–539},
publisher = {Association for Computing Machinery},
address = {Boulder, CO, USA},
series = {HRI '24},
abstract = {Wizard-of-Oz (WoZ) systems represent a widespread method in HRI research. While they are cost-effective, flexible and are often preferred over developing autonomous dialogs in experimental settings, they are typically tailored to specific use cases. In addition, WoZ systems are mainly used in lab studies that deviate from real world scenarios. Here, virtual reality (VR) can be used to immerse the user in a real world interaction scenario with robots. This article highlights the necessity for a modularized and customizable WoZ system, using the benefits of VR. The proposed system integrates well-established features like speech and gesture control, while expanding functionality to encompass a data dashboard and dynamic robot navigation using VR technology. The discussion emphasizes the importance of developing technical systems, like the WoZ system, in a modularized and customizable way, particularly for non-technical researchers. Overcoming usability hurdles is crucial to establishing this tool's role in the HRI research field.},
keywords = {accessibility, social robots, virtual reality, Wizard-of-Oz},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{virtualworlds3020011,
title = {An Augmented Reality Application for Wound Management: Enhancing Nurses’ Autonomy, Competence and Connectedness},
author = {Carina Albrecht-Gansohr and Lara Timm and Sabrina C. Eimler and Stefan Geisler},
url = {https://www.mdpi.com/2813-2084/3/2/11},
doi = {10.3390/virtualworlds3020011},
issn = {2813-2084},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Virtual Worlds},
volume = {3},
number = {2},
pages = {208–229},
abstract = {The use of Augmented Reality glasses opens up many possibilities in hospital care, as they facilitate treatments and their documentation. In this paper, we present a prototype for the HoloLens 2 supporting wound care and documentation. It was developed in a participatory process with nurses using the positive computing paradigm, with a focus on the improvement of the working conditions of nursing staff. In a qualitative study with 14 participants, the factors of autonomy, competence and connectedness were examined in particular. It was shown that good individual adaptability and flexibility of the system with respect to the work task and personal preferences lead to a high degree of autonomy. The availability of the right information at the right time strengthens the feeling of competence. On the one hand, the connection to patients is increased by the additional information in the glasses, but on the other hand, it is hindered by the unusual appearance of the device and the lack of eye contact. In summary, the potential of Augmented Reality glasses in care was confirmed, and approaches for a well-being-centered system design were identified but, at the same time, a number of future research questions, including the effects on patients, were also identified.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{10.1007/978-3-031-60412-6_4,
title = {Enabling Safe Empirical Studies for Human-Robot Collaboration: Implementation of a Sensor Array Driven Control Interface},
author = {Alexander Arntz},
editor = {Masaaki Kurosu and Ayako Hashizume},
isbn = {978-3-031-60412-6},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Human-Computer Interaction},
pages = {42–57},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {In response to the growing relevance of collaborative robots, the need for empirical user studies in the domain of Human-Robot Collaboration become increasingly important. While collaborative robots incorporate internal safety features, their usage for user studies remains associated with inherent safety risks. This project addresses these challenges by introducing a toolbox for a robot arm to conduct Wizard-of-Oz studies by using advanced controls complemented by a sophisticated security system leveraging microcontrollers and human presence detection sensors. This approach unifies both control systems within a single application, seamlessly monitoring and synchronizing their respective inputs. The gamepad control scheme offers Wizard-of-Oz study supervisors an intuitive means of interacting with the robot, enabling precise and responsive control while maintaining safety. Meanwhile, the security system, built on microcontroller technology and human presence detection sensors, acts as a vigilant guardian, continuously assessing the robot's surroundings for potential risks. This integrated application not only empowers users with effective control over the xArm 7 but also provides real-time feedback on the security status, enhancing the overall safety and usability of collaborative robots in various industrial settings. By bridging the gap between human operators and robots, this project contributes to the evolution of safer and more user-friendly human-robot collaboration.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{10445597,
title = {Teamwork Makes the Dream Work: A Virtual Reality-based Human-Robot Collaboration Sandbox Simulating Multiple Teams},
author = {Alexander Arntz and Agostino Di Dia and Tim Riebner and Carolin Straßmann and Sabrina C. Eimler},
doi = {10.1109/AIxVR59861.2024.00057},
issn = {2771-7453},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)},
pages = {335-339},
abstract = {We present a virtual reality-based Human-Robot Collaboration sandbox that allows the representation of multiple teams composed of humans and robots. Within the sandbox, virtual robots and humans can collaborate with their respective partners and interact with other teams to coordinate the required procedures while accomplishing a shared task. For this purpose, the virtual reality sandbox is equipped with a variety of interaction mechanics that enable a range of different shared tasks. The network integration allows for multiple users within the virtual environment. The VR application contains a library of different industrial robots that can act autonomously controlled by machine learning agents and interact with the user through verbal commands. The sandbox is specifically designed to serve as a research tool to explore new concepts and validate existing approaches in the domain of Human-Robot Collaboration involving autonomous robots in a series of upcoming studies.},
keywords = {Robot kinematics;Virtual assistants;Virtual environments;Industrial robots;Libraries;Teamwork;Task analysis;Human-Robot Collaboration;Virtual Reality;Machine Learning;Artificial Intelligence},
pubstate = {published},
tppubtype = {inproceedings}
}
2023
@article{aliConTraNetHybridNetwork2023,
title = {ConTraNet: A Hybrid Network for Improving the Classification of EEG and EMG Signals with Limited Training Data},
author = {Omair Ali and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},
url = {https://www.sciencedirect.com/science/article/pii/S0010482523011149},
doi = {10.1016/j.compbiomed.2023.107649},
issn = {0010-4825},
year = {2023},
date = {2023-11-02},
urldate = {2023-11-02},
journal = {Computers in Biology and Medicine},
pages = {107649},
abstract = {Objective Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals is however non-trivial because of the time-varying and non-stationary characteristics. Furthermore, existence of short- and long-range dependencies in these time-series signal makes the decoding even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures for the classification of these bio-signals, which are proven useful to learn spatial representations. However, CNNs because of the fixed size convolutional kernels and shared weights pay only uniform attention and are also suboptimal in learning short-long term dependencies, simultaneously, which could be pivotal in decoding EEG and EMG signals. Therefore, it is important to address these limitations of CNNs. To learn short- and long-range dependencies simultaneously and to pay more attention to more relevant part of the input signal, Transformer neural network-based architectures can play a significant role. Nonetheless, it requires a large corpus of training data. However, EEG and EMG decoding studies produce limited amount of the data. Therefore, using standalone transformers neural networks produce ordinary results. In this study, we ask a question whether we can fix the limitations of CNN and transformer neural networks and provide a robust and generalized model that can simultaneously learn spatial patterns, long-short term dependencies, pay variable amount of attention to time-varying non-stationary input signal with limited training data. Approach In this work, we introduce a novel single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that contains the strengths of both CNN and Transformer neural networks. ConTraNet uses a CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the short- and long-range or global dependencies in the signal and learn to pay different attention to different parts of the signals. Main results We evaluated and compared the ConTraNet with state-of-the-art methods on four publicly available datasets (BCI Competition IV dataset 2b, Physionet MI-EEG dataset, Mendeley sEMG dataset, Mendeley sEMG V1 dataset) which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, 7-class, and 10-class decoding tasks). Significance With limited training data ConTraNet significantly improves classification performance on four publicly available datasets for 2, 3, 4, 7, and 10-classes compared to its counterparts.},
keywords = {BCI, Brain computer interface, Deep learning, EEG decoding, EMG decoding, Machine Learning},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Using Focus Group Interviews to Examine Biased Experiences in Human-Robot-Interaction},
author = {Lukas Erle and Lara Timm and Carolin Straßmann and Sabrina C. Eimler},
editor = {ArXiv},
doi = {10.48550/arXiv.2310.01421},
year = {2023},
date = {2023-09-27},
pages = {4},
abstract = {When deploying interactive agents like (social) robots in public spaces they need to be able to interact with a diverse audience, with members each having individual diversity characteristics and prior experiences with interactive systems. To cater for these various predispositions, it is important to examine what experiences citizens have made with interactive systems and how these experiences might create a bias towards such systems. To analyze these bias-inducing experiences, focus group interviews have been conducted to learn of citizens individual discrimination experiences, their attitudes towards and arguments for and against the deployment of social robots in public spaces. This extended abstract focuses especially on the method and measurement of diversity.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Virtual Reality as a Tool for Studying Diversity and Inclusion in Human-Robot Interaction: Advantages and Challenges},
author = {André Helgert and Sabrina C. Eimler and Carolin Straßmann},
editor = {ArXiv},
doi = {10.48550/arXiv.2309.14937},
year = {2023},
date = {2023-09-23},
pages = {4},
abstract = {This paper investigates the potential of Virtual Reality (VR) as a research tool for studying diversity and inclusion characteristics in the context of human-robot interactions (HRI). Some exclusive advantages of using VR in HRI are discussed, such as a controllable environment, the possibility to manipulate the variables related to the robot and the human-robot interaction, flexibility in the design of the robot and the environment, and advanced measurement methods related e.g. to eye tracking and physiological data. At the same time, the challenges of researching diversity and inclusion in HRI are described, especially in accessibility, cyber sickness and bias when developing VR-environments. Furthermore, solutions to these challenges are being discussed to fully harness the benefits of VR for the studying of diversity and inclusion.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{sziburisVariabilityStudyHuman2023,
title = {Variability Study of Human Hand Motion during 3D Center-out Tasks Captured for the Diagnosis of Movement Disorders},
author = {Tim Sziburis and Susanne Blex and Ioannis Iossifidis},
year = {2023},
date = {2023-09-15},
urldate = {2023-09-15},
booktitle = {BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},
publisher = {BCCN Bernstein Network Computational Network},
abstract = {Variability analysis bears the potential to differentiate between healthy and pathological human movements [1]. Our study is conducted in the context of developing a portable glove for the diagnosis of movement disorders. This proposal has methodical as well as technical requirements. Generally, the identification of movement disorders via an analysis of motion data needs to be confirmed within the given setup. Typically, rhythmic movements like gait or posture control are examined for their variability, but here, the characteristic pathological traits of arm movement like tremors are under observation. In addition, the usability of a portable sensor instead of a stationary tracking system has to be validated. In this part of the project, human motion data are recorded redundantly by both an optical tracking system and an IMU. In our setup, a small cylinder is transported in three-dimensional space from a unified start position to one of nine target positions, which are equidistantly aligned on a semicircle. 10 trials are performed per target and hand, resulting in 180 trials per participant in total. 31 participants (11 female and 20 male) without known movement disorders, aged between 21 and 78 years, took part in the study. In addition, the 10-item EHI is used. The purpose of the analysis is to compare different variability measures to uncover differences between trials (intra-subject variability) and participants (inter-subject variability), especially in terms of age and handedness effects. Particularly, a novel variability measure is introduced which makes use of the characteristic planarity of the examined hand paths [2]. For this, the angle of the plane which best fits the travel phase of the trajectory is determined. In addition to neurological motivation, the advantage of this measure is that it allows the comparison of trials of different time spans and to different target directions without depending on trajectory warping. In the future, measurements of the same experimental setup with patients experiencing movement disorders are planned. For the subsequent pathological analysis, this study provides a basis in terms of methodological considerations and ground truth data of healthy participants. In parallel, the captured motion data are modelled utilizing dynamical systems (extended attractor dynamics approach). For this approach, the recorded and modelled data can be compared by the variability measures examined in this study.},
keywords = {movement model},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{xavierfidencioExploringErrorrelatedPotentials2023,
title = {Exploring Error-related Potentials in Adaptive Brain-Machine Interfaces: Challenges and Investigation of Occurrence and Detection Ratios},
author = {Aline Xavier Fidencio and Christian Klaes and Ioannis Iossifidis},
year = {2023},
date = {2023-09-15},
urldate = {2023-09-15},
booktitle = {BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},
publisher = {BCCN Bernstein Network Computational Network},
abstract = {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 for developing adaptive BMIs. In our study, we assess the occurrence of interaction ErrPs in an adaptive BMI that combines ErrPs and reinforcement learning. We intentionally provoke ErrPs when the BMI misinterprets the user’s intention and performs an incorrect action. Subjects participated in a game controlled by a keyboard and/or motor imagery (imagining hand movements), and EEG data were recorded using an eight-electrode gel-based EEG system. Results reveal that obtaining a distinct ErrPs signal for each subject is more challenging than anticipated. Current practices report the ErrP in terms of over all subjects and trials difference grand average (error minus correct). This approach has, however, the limitation of masking the inter-trial and subject variability, which are relevant for the online single-trial detection of such signals. Moreover, the reported ErrPs waveshape exhibit differences in terms of components observed, as well as their respective latencies, even when very similar tasks are used. Consequently, we conducted additional individualized data analysis to gain deeper insights into the single-trial nature of the ErrPs. As a result, we determined the need for a better understanding and further investigation of how effectively the ErrPs waveforms generalize across subjects, tasks, experimental protocols, and feedback modalities. Given the challenges in obtaining a clear signal for all subjects and the limitations found in existing literature (Xavier Fidêncio et al., 2022), we hypothesize whether an error signal measurable at the scalp level is consistently generated when subjects encounter erroneous conditions. To address this question, we will assess the occurrence-to-detection ratio of ErrPs using invasive and non-invasive recording techniques, examining how uncertainties regarding error generation in the brain impact the learning pipeline.},
keywords = {BCI, EEG, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{grunInvestigationInterplayModelBased2023,
title = {Investigation of the Interplay of Model-Based and Model-Free Learning Using Reinforcement Learning},
author = {Felix Grün and Ioannis Iossifidis},
year = {2023},
date = {2023-09-15},
urldate = {2023-09-15},
booktitle = {BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},
publisher = {BCCN Bernstein Network Computational Network},
abstract = {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 learning (RL). This suggests that some form of reinforcement learning is used in animal and human brains when learning a task. On the other hand, it is clear that humans are capable of building an internal model of a task, or environment, and using it for planning, especially in sequential tasks. In RL, these two learning approaches, model-driven and reward-driven, are known as model based and model-free RL approaches. Both systems were previously thought to exist in parallel, with some higher process choosing which to use. A decade ago, research suggested both could be used concurrently, with some subject-specific weight assigned to each [1]. Still, the prevalent belief appeared to be that model-free learning is the default mechanism used, replaced or assisted by model-based planning only when the task demands it, i.e. higher rewards justify the additional cognitive effort. Recently, Feher da Silva et al. [2] questioned this belief, presenting data and analyses that indicate model-based learning may be used on its own and can even be computationally more efficient. We take a RL perspective, consider different ways to combine model-based and model-free approaches for modeling and for performance, and discuss how to further study this interplay in human behavioral experiments.},
keywords = {Machine Learning, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{schmidtLinkMuscleActivity2023,
title = {The Link between Muscle Activity and Upper Limb Kinematics},
author = {Marie Dominique Schmidt and Ioannis Iossifidis},
year = {2023},
date = {2023-09-15},
urldate = {2023-09-15},
booktitle = {BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},
publisher = {BCCN Bernstein Network Computational Network},
abstract = {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 activity. We propose a generative model that predicts upper limb muscle activity from a variety of simple and complex everyday motions by means of a recurrent neural network. The model shows promising results, with a good fit for different subjects and abstracts well for new motions. We handle the high inter-subject variation in muscle activity using a transfer learning approach, resulting in a good fit for new subjects. Our approach has implications for fundamental movement control understanding and the rehabilitation of neuromuscular diseases using myoelectric prostheses and functional electrical stimulation. Our model can efficiently predict both muscle activity and motion trajectory, which can assist in developing more effective rehabilitation techniques.},
keywords = {BCI, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{ayazhussainAdvancementsUpperBody2023,
title = {Advancements in Upper Body Exoskeleton: Implementing Active Gravity Compensation with a Feedforward Controller},
author = {Muhammad Ayaz Hussain and Ioannis Iossifidis},
url = {https://doi.org/10.48550/arXiv.2309.04698},
doi = {10.48550/arXiv.2309.04698},
year = {2023},
date = {2023-09-09},
urldate = {2023-09-09},
journal = {arXiv:2309.04698 [cs.RO]},
abstract = {In this study, we present a feedforward control system designed for active gravity compensation on an upper body exoskeleton. The system utilizes only positional data from internal motor sensors to calculate torque, employing analytical control equations based on Newton-Euler Inverse Dynamics. Compared to feedback control systems, the feedforward approach offers several advantages. It eliminates the need for external torque sensors, resulting in reduced hardware complexity and weight. Moreover, the feedforward control exhibits a more proactive response, leading to enhanced performance. The exoskeleton used in the experiments is lightweight and comprises 4 Degrees of Freedom, closely mimicking human upper body kinematics and three-dimensional range of motion. We conducted tests on both hardware and simulations of the exoskeleton, demonstrating stable performance. The system maintained its position over an extended period, exhibiting minimal friction and avoiding undesired slewing.},
keywords = {Autonomous robotics, BCI, Computer Science - Artificial Intelligence, Computer Science - Information Theory, Computer Science - Machine Learning, Exoskeleton},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Börsting2023,
title = {Tell me why - Combating racism on social media with knowledge},
author = {Johanna Börsting and Veronica Schwarze and Sabrina C. Eimler},
editor = {André Melzer and Gary Lee Wagener},
url = {https://doi.org/10.26298/1981-5555},
year = {2023},
date = {2023-09-06},
booktitle = {Proceedings of the 13th Conference of the Media Psychology Division (DGPs)},
publisher = {Melusina Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Erle2023,
title = {Algorithmic Bias and Digital Divide – An Examination of Discrimination Experiences in Human-System Interactions (Poster)},
author = {Lukas Erle and Lara Timm and Carolin Straßmann and Sabrina C. Eimler},
editor = {André Melzer and Gary Lee Wagener},
url = {https://doi.org/10.26298/1981-5555},
year = {2023},
date = {2023-09-06},
booktitle = {Proceedings of the 13th Conference of the Media Psychology Division (DGPs)},
publisher = {Melusina Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{nokey,
title = {Imagine it was you - Empathy as the key for reducing cyberbullying on social media},
author = {Sabrina C. Eimler and Johanna Börsting and Veronica Schwarze},
editor = {André Melzer and Gary Lee Wagener},
url = {https://doi.org/10.26298/1981-5555},
year = {2023},
date = {2023-09-06},
booktitle = {Proceedings of the 13th Conference of the Media Psychology Division (DGPs)},
publisher = {Melusina Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Schwarze2023,
title = {Picturing diversity: Exploring children’s perception of intergroup differences },
author = {Veronica Schwarze and Sabrina C. Eimler and Nicole C. Krämer},
editor = {André Melzer and Gary Lee Wagener},
url = {https://doi.org/10.26298/1981-5555},
year = {2023},
date = {2023-09-06},
booktitle = {Proceedings of the 13th Conference of the Media Psychology Division (DGPs)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Schweizer2023,
title = {Keep calm - The role of resilience in the interplay between neuroticism and phubbing },
author = {Anne-Marie Schweizer and Johanna Börsting},
editor = {André Melzer and Gary Lee Wagener},
url = {https://doi.org/10.26298/1981-5555},
year = {2023},
date = {2023-09-06},
urldate = {2023-09-06},
booktitle = {Proceedings of the 13th Conference of the Media Psychology Division (DGPs)},
publisher = {Melusina Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{schmidtConceptsMuscleActivity2023,
title = {The Concepts of Muscle Activity Generation Driven by Upper Limb Kinematics},
author = {Marie D. Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
url = {https://doi.org/10.1186/s12938-023-01116-9},
doi = {10.1186/s12938-023-01116-9},
issn = {1475-925X},
year = {2023},
date = {2023-06-24},
urldate = {2023-06-24},
journal = {BioMedical Engineering OnLine},
volume = {22},
number = {1},
pages = {63},
abstract = {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 in our brains. This model is motivated by the known motion planning process in the central nervous system. That process incorporates the current body state from sensory systems and previous experiences, which might be represented as pre-learned inverse dynamics that generate associated muscle activity.},
keywords = {Artificial generated signal, BCI, Electromyography (EMG), Generative model, Inertial measurement unit (IMU), Machine Learning, Motion parameters, Muscle activity, Neural networks, transfer learning, Voluntary movement},
pubstate = {published},
tppubtype = {article}
}
@article{saifurrehman2023adaptive,
title = {Adaptive SpikeDeep-Classifier: Self-organizing and self-supervised machine learning algorithm for online spike sorting},
author = {Muhammad Saif-ur-Rehman and Omair Ali and Christian Klaes and Ioannis Iossifidis},
doi = {10.48550/arXiv.2304.01355},
year = {2023},
date = {2023-05-02},
urldate = {2023-05-02},
journal = {arXiv:2304.01355 [cs, math, q-bio]},
keywords = {BCI, Machine Learning, Spike Sorting},
pubstate = {published},
tppubtype = {article}
}
@article{IMio-IrrHan2023,
title = {Sauber getrennt ist halb verwertet - Recycling mittels KI},
author = {Wolfgang Irrek and Uwe Handmann},
url = {https://www.im-io.de/wegeausdemmangel/sauber-getrennt-ist-halb-verwertet/},
issn = {1616-1017},
year = {2023},
date = {2023-03-01},
urldate = {2023-03-01},
journal = {IM+io Best & Next Practices aus Digitalisierung, Management, Wissenschaft},
volume = {2023},
number = {01},
pages = {26-29},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Malzahn_Schwarze_Eimler_Aprin_Moder_Hoppe_2023,
title = {How to measure disagreement as a premise for learning from controversy in a social media context},
author = {Nils Malzahn and Veronica Schwarze and Sabrina C. Eimler and Farbod Aprin and Sarah Moder and H. Ulrich Hoppe},
url = {https://rptel.apsce.net/index.php/RPTEL/article/view/2023-18012},
doi = {10.58459/rptel.2023.18012},
year = {2023},
date = {2023-02-01},
urldate = {2023-02-01},
journal = {Research and Practice in Technology Enhanced Learning},
volume = {18},
pages = {012},
abstract = {Learning scenarios building on disagreement in a learning group or a whole classroom are well established in modern pedagogy. In the specific tradition of collaborative learning, such approaches have been traced back to theories of socio-cognitive conflict and have been associated with argumentative learning interactions. An important premise for these types of learning scenarios is the identification of disagreement. In the spirit of learning analytics, this calls for analytic tools and mechanisms to detect and measure disagreement in learning groups.
Our mathematical analysis of several methods shows that methods of different origin are largely equivalent, only differing in the normalization factors and ensuing scaling properties. We have selected a measure that scales best and applied it to a target scenario in which learners judged types and levels of “toxicity” of social media content using an interactive tagging tool. Due restrictions imposed by the pandemic, we had to replace the originally envisaged classroom scenario by online experiments. We report on two consecutive experiments involving 42 students in the first and 89 subjects in the second instance. The results corroborate the adequacy of the measure in combination with the interactive, game-based approach to collecting judgements. We also saw that a revision of categories after the first study reduced the ambiguity. In addition to applying the disagreement measure to the learner judgements, we also assessed several personality traits, such as authoritarianism and social closeness. Regarding the dependency of the learner judgements on personality traits, we could only observe a weak influence of authoritarianism.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Our mathematical analysis of several methods shows that methods of different origin are largely equivalent, only differing in the normalization factors and ensuing scaling properties. We have selected a measure that scales best and applied it to a target scenario in which learners judged types and levels of “toxicity” of social media content using an interactive tagging tool. Due restrictions imposed by the pandemic, we had to replace the originally envisaged classroom scenario by online experiments. We report on two consecutive experiments involving 42 students in the first and 89 subjects in the second instance. The results corroborate the adequacy of the measure in combination with the interactive, game-based approach to collecting judgements. We also saw that a revision of categories after the first study reduced the ambiguity. In addition to applying the disagreement measure to the learner judgements, we also assessed several personality traits, such as authoritarianism and social closeness. Regarding the dependency of the learner judgements on personality traits, we could only observe a weak influence of authoritarianism.@conference{IWANN-RohBakHan2023,
title = {Double Transfer Learning to detect Lithium-Ion batteries on X-Ray images},
author = {David Rohrschneider and Nermeen Abou Baker and Uwe Handmann},
url = {https://link.springer.com/chapter/10.1007/978-3-031-43085-5_14},
doi = {10.1007/978-3-031-43085-5_14},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {17th International Work-Conference on Artificial Neural Networks (IWANN 2023), Ponta Delgada, Portugal, June 19 - 21, 2023, Proceedings, Part I},
volume = {14134},
pages = {175-188},
address = {Springer Nature, Switzerland},
series = {Lecture Notes in Computer Science (LNCS)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{ESANN-BakHan2023,
title = {Don't waste SAM},
author = {Nermeen Abou Baker and Uwe Handmann},
url = {https://www.esann.org/sites/default/files/proceedings/2023/ES2023-116.pdf},
doi = {10.14428/esann/2023.ES2023-116},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {The 31th European Symposium on Artificial Neural Networks (ESANN 2023)},
pages = {429-434},
address = {Bruges, Belgium},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@inproceedings{10309310,
title = {Exploring the Use of Colored Ambient Lights to Convey Emotional Cues With Conversational Agents: An Experimental Study},
author = {Carolin Straßmann and André Helgert and Valentin Breil and Lina Settelmayer and Inga Diehl},
doi = {10.1109/RO-MAN57019.2023.10309310},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
pages = {99-105},
abstract = {Conversational agents (CAs) lack of possibilities to enrich the interaction with emotional cues, although this makes the conversation more human-like and enhances user engagement. Thus, the potential of CAs is not fully exploit and possibilities to convey emotional cues are needed. In this work, CAs use colored ambient lights to display moral emotions during the interaction. To evaluate this approach, a between-subject lab experiment (N=64) was conducted. Participants played a cooperation game with Amazon’s Alexa. Depending on the experimental condition participants received different light expressions: no light, neutral light, or morally emotional light (yellow = joy, blue = sorrow, red = anger matching the game decisions). The effect of the light expressions on the perception of the CA, users’ empathy and cooperation behavior was tested. Against our assumptions, the results indicated no positive effect of the emotional light cues. Limitations, next steps, and implications are discussed.},
keywords = {Ethics;Emotion recognition;Virtual assistants;Games;Behavioral sciences;Robots},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{10.1007/978-3-031-29800-4_52,
title = {Psychological Outcomes and Effectiveness of a Collaborative Video-Based Learning Tool for Synchronous Discussions},
author = {Carolin Straßmann and André Helgert and Andreas Lingnau},
editor = {Giovanni Fulantelli and Daniel Burgos and Gabriella Casalino and Marta Cimitile and Giosuè Lo Bosco and Davide Taibi},
isbn = {978-3-031-29800-4},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Higher Education Learning Methodologies and Technologies Online},
pages = {691–705},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {In the context of digitization and the COVID-19 pandemic, online teaching comes more and more into focus of higher education. Various online learning methodologies such as using videos as a teaching method are promising, but can also create problems in the area of social relations or learning habits. The concept of collaborative learning, especially in an online environment, could counteract these problems. This paper presents a collaborative online learning tool that allows students to get together in learning groups to watch educational videos together. Various functions, such as a chat, help students to communicate with each other. A psychological evaluation was conducted to investigate the effects on students. The evaluation demonstrated positive effects of the tool, since it enhances important psychological processes (like flow and cognitive load) within the learning process. Moreover, its usability was rated as good and participants showed a high usage intention for the tool. Nevertheless, further investigations in long-term learning courses are needed to finally confirm the tool's effectiveness.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{10.1007/978-3-031-29800-4_27,
title = {Empirically Investigating Virtual Learning Companions to Enhance Social Media Literacy},
author = {Emily Theophilou and Veronica Schwarze and Johanna Börsting and Roberto Sánchez-Reina and Lidia Scifo and Francesco Lomonaco and Farbod Aprin and Dimitri Ognibene and Davide Taibi and Davinia Hernández-Leo and Sabrina C. Eimler},
editor = {Giovanni Fulantelli and Daniel Burgos and Gabriella Casalino and Marta Cimitile and Giosuè Lo Bosco and Davide Taibi},
isbn = {978-3-031-29800-4},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Higher Education Learning Methodologies and Technologies Online},
pages = {345–360},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Social media platforms provide opportunities for users across the world to connect and communicate between them and engage into acts of social support and entertainment. Yet it can also bring negative consequences as it has been associated with poor mental health and life dissatisfaction. This underlines the importance of delivering social media literacy (SML) interventions that raise awareness of the dangers and threats that are hidden within. To this date, SML initiatives have shown their benefits towards the acquisition of SML skills through the forms of school interventions and mini-games. However, studies on promoting SML through social media platforms need to be also encouraged and innovative approaches to provide interactive scenarios with hands-on experiences need to be formulated. Hence, the project COURAGE introduces a new approach towards SML by proposing the integration of educational opportunities within a controlled social media platform. To provide students the opportunity to learn whilst they naturally explore social media we propose the integration of virtual learning companions. In this paper we report seven empirical approaches towards SML skills acquisition powered by virtual learning companions. The paper concludes with a discussion towards the benefits and limitations of using this type of SML interventions.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{10.1007/978-3-031-29800-4_25,
title = {The Role of Educational Interventions in Facing Social Media Threats: Overarching Principles of the COURAGE Project},
author = {Davide Taibi and Johanna Börsting and Ulrich Hoppe and Dimitri Ognibene and Davinia Hernández-Leo and Sabrina C. Eimler and Udo Kruschwitz},
editor = {Giovanni Fulantelli and Daniel Burgos and Gabriella Casalino and Marta Cimitile and Giosuè Lo Bosco and Davide Taibi},
isbn = {978-3-031-29800-4},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Higher Education Learning Methodologies and Technologies Online},
pages = {315–329},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Social media are offering new opportunities for communication and interaction way beyond what was possible only a few years ago. However, social media are also virtual spaces where young people are exposed to a variety of threats. Digital addiction, discrimination, hate speech, misinformation, polarization as well as manipulative influences of algorithms, body stereotyping, and cyberbullying are examples of challenges that find fertile ground on social media. Educators and students are not adequately prepared to face these challenges. To this aim, the COURAGE project, presented in this paper, introduces new tools and learning methodologies that can be adopted within higher education learning paths to train educators to deal with social media threats. The overarching principles of the COURAGE project leverage the most recent advances in the fields of artificial intelligence and in the educational domain paired with social and media psychological insights to support the development of the COURAGE ecosystem. The results of the experiments currently implemented with teachers and students of secondary schools as well as the impact of the COURAGE project on societal changes and ethical questions are presented and discussed.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{10.3389/frai.2022.654930,
title = {Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion},
author = {Dimitri Ognibene and Rodrigo Wilkens and Davide Taibi and Davinia Hernández-Leo and Udo Kruschwitz and Gregor Donabauer and Emily Theophilou and Francesco Lomonaco and Sathya Bursic and Rene Alejandro Lobo and J. Roberto Sánchez-Reina and Lidia Scifo and Veronica Schwarze and Johanna Börsting and Ulrich Hoppe and Farbod Aprin and Nils Malzahn and Sabrina C. Eimler},
url = {https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2022.654930},
doi = {10.3389/frai.2022.654930},
issn = {2624-8212},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Frontiers in Artificial Intelligence},
volume = {5},
abstract = {<p>Social media have become an integral part of our lives, expanding our interlinking capabilities to new levels. There is plenty to be said about their positive effects. On the other hand, however, some serious negative implications of social media have been repeatedly highlighted in recent years, pointing at various threats to society and its more vulnerable members, such as teenagers, in particular, ranging from much-discussed problems such as digital addiction and polarization to manipulative influences of algorithms and further to more teenager-specific issues (e.g., body stereotyping). The impact of social media—both at an individual and societal level—is characterized by the complex interplay between the users' interactions and the intelligent components of the platform. Thus, users' understanding of social media mechanisms plays a determinant role. We thus propose a theoretical framework based on an adaptive “<italic>Social Media Virtual Companion</italic>” for educating and supporting an entire community, teenage students, to interact in social media environments in order to achieve desirable conditions, defined in terms of a community-specific and participatory designed measure of Collective Well-Being (CWB). This Companion combines automatic processing with expert intervention and guidance. The virtual Companion will be powered by a <italic>Recommender System</italic> (<italic>CWB-RS</italic>) that will optimize a <italic>CWB</italic> metric instead of engagement or platform profit, which currently largely drives recommender systems thereby disregarding any societal collateral effect. CWB-RS will optimize CWB both in the short term by balancing the level of social media threats the users are exposed to, and in the long term by adopting an <italic>Intelligent Tutor System</italic> role and enabling adaptive and personalized sequencing of playful learning activities. We put an emphasis on <italic>experts</italic> and <italic>educators</italic> in the <italic>educationally managed social media community</italic> of the Companion. They play five key roles: (a) use the Companion in classroom-based educational activities; (b) guide the definition of the CWB; (c) provide a hierarchical structure of learning strategies, objectives and activities that will support and contain the adaptive sequencing algorithms of the CWB-RS based on hierarchical reinforcement learning; (d) act as moderators of direct conflicts between the members of the community; and, finally, (e) monitor and address ethical and educational issues that are beyond the intelligent agent's competence and control. This framework offers a possible approach to understanding how to design social media systems and embedded educational interventions that favor a more healthy and positive society. Preliminary results on the performance of the Companion's components and studies of the educational and psychological underlying principles are presented.</p>},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{10.1007/978-3-031-21229-1_13,
title = {Prototyping a Smart Contract Application for Fair Reward Distribution in Software Development Projects},
author = {Agostino Di Dia and Tim Riebner and Alexander Arntz and Marc Jansen},
editor = {Javier Prieto and Francisco Luis Benítez Martínez and Stefano Ferretti and David Arroyo Guardeño and Pedro Tomás Nevado-Batalla},
isbn = {978-3-031-21229-1},
year = {2023},
date = {2023-01-01},
booktitle = {Blockchain and Applications, 4th International Congress},
pages = {131--141},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The following work describes the development of a project management platform based on the waves blockchain technology. The aim is to use the platform to optimally reward both fairness and the achievement of quality standards in software development. Due to the outsourcing of software projects and the associated processes, situations arise in which different project participants work on the same tasks, while paid differently and in most cases independent of the delivered software quality. This is based on the respective contract conditions an individual software developer has negotiated, without factoring in the actual quality of the code the developer provided. The proposal of this work is a prototypical software application that takes the requirements of a project and measures the corresponding contributions of the developers based on their software quality. Project sponsors can also pay out the enrolled project partners via the platform.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@conference{SUSTECH-BakHan2023,
title = {E-Waste Recycling Gets Smarter with Digitalization},
author = {Nermeen Abou Baker and Uwe Handmann},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {10th IEEE Conference on Technologies for Sustainability (SUSTECH 2023)},
publisher = {IEEE},
address = {Portland, Oregon USA},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{HN-NRW-HanBak2023,
title = {Digitalization & Circular Economy (Poster)},
author = {Uwe Handmann and Nermeen Abou Baker},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {HRW},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
2022
@article{grunInvarianceQuantileSelection2022,
title = {Invariance to Quantile Selection in Distributional Continuous Control},
author = {Felix Grün and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},
url = {https://arxiv.org/abs/2212.14262},
doi = {10.48550/ARXIV.2212.14262},
year = {2022},
date = {2022-12-29},
urldate = {2022-12-29},
journal = {arXiv:2212.14262 [cs.LG]},
keywords = {Artificial Intelligence (cs.AI), FOS: Computer and information sciences, I.2.6, I.2.8, Machine Learning, Machine Learning (cs.LG)},
pubstate = {published},
tppubtype = {article}
}
@article{lehmlerTransferLearningPatientSpecific2021bb,
title = {Deep transfer learning compared to subject-specific models for sEMG decoders},
author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},
editor = {{IOP Publishing},
url = {https://dx.doi.org/10.1088/1741-2552/ac9860},
doi = {10.1088/1741-2552/ac9860},
year = {2022},
date = {2022-10-28},
urldate = {2022-10-28},
journal = {Journal of Neural Engineering},
volume = {19},
number = {5},
abstract = {{Objective. Accurate decoding of surface electromyography (sEMG) is pivotal for muscle-to-machine-interfaces and their application e.g. rehabilitation therapy. sEMG signals have high inter-subject variability, due to various factors, including skin thickness, body fat percentage, and electrode placement. Deep learning algorithms require long training time and tend to overfit if only few samples are available. In this study, we aim to investigate methods to calibrate deep learning models to a new user when only a limited amount of training data is available. Approach. Two methods are commonly used in the literature, subject-specific modeling and transfer learning. In this study, we investigate the effectiveness of transfer learning using weight initialization for recalibration of two different pretrained deep learning models on new subjects data and compare their performance to subject-specific models. We evaluate two models on three publicly available databases (non invasive adaptive prosthetics database 2–4) and compare the performance of both calibration schemes in terms of accuracy, required training data, and calibration time. Main results. On average over all settings, our transfer learning approach improves 5%-points on the pretrained models without fine-tuning, and 12%-points on the subject-specific models, while being trained for 22% fewer epochs on average. Our results indicate that transfer learning enables faster learning on fewer training samples than user-specific models. Significance. To the best of our knowledge, this is the first comparison of subject-specific modeling and transfer learning. These approaches are ubiquitously used in the field of sEMG decoding. But the lack of comparative studies until now made it difficult for scientists to assess appropriate calibration schemes. Our results guide engineers evaluating similar use cases.},
keywords = {BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{grunExploringDistributionParameterizations2022,
title = {Exploring Distribution Parameterizations for Distributional Continuous Control},
author = {Felix Grün and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2022.112},
year = {2022},
date = {2022-09-15},
urldate = {2022-09-15},
booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},
publisher = {BCCN Bernstein Network Computational Network},
keywords = {Machine Learning, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{lehmlerModelingSubjectSpecfic2022,
title = {Modeling Subject Specfic Surface EMG Features by Means of Deep Learning},
author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2022.309},
year = {2022},
date = {2022-09-15},
urldate = {2022-09-15},
booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},
publisher = {BCCN Bernstein Network Computational Network},
keywords = {BCI, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{schmidtLinkingMuscleActivity2022,
title = {Linking Muscle Activity and Motion Trajectory},
author = {Marie Dominique Schmidt and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2022.191},
year = {2022},
date = {2022-09-15},
urldate = {2022-09-15},
booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},
publisher = {BCCN Bernstein Network Computational Network},
keywords = {BCI, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{sziburisDataset3DHand2022,
title = {A Dataset of 3D Hand Transport Trajectories Determined by Inertial Measurements from a Single Sensor},
author = {Tim Sziburis and Susanne Blex and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2022.186},
year = {2022},
date = {2022-09-15},
urldate = {2022-09-15},
booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},
publisher = {BCCN Bernstein Network Computational Network},
keywords = {BCI, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{xavierfidencioClosedloopAdaptationBrainmachine2022,
title = {Closed-Loop Adaptation of Brain-Machine Interfaces Using Error-Related Potentials and Reinforcement Learning},
author = {Aline Xavier Fidencio and Christian Klaes and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2022.136},
year = {2022},
date = {2022-09-15},
urldate = {2022-09-15},
booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},
publisher = {BCCN Bernstein Network Computational Network},
keywords = {BCI, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}