Publications

@inproceedings{schmidtSpatialTemporalResolution2026,

title = {The Spatial and~Temporal Resolution of~Motor Intention in~Multi-target Prediction},

author = {Marie D. Schmidt and Ioannis Iossifidis},

editor = {Philipp Neumann and Michael J. Puma and Michael H. Lees and Derek Groen and Jack J. Dongarra and Peter M. A. Sloot},

doi = {10.1007/978-3-032-29924-6_36},

isbn = {978-3-032-29924-6},

year  = {2026},

date = {2026-06-27},

urldate = {2026-06-27},

booktitle = {Computational Science – ICCS 2026},

pages = {421–428},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Reaching, grasping, and object manipulation are essential motor functions in everyday life. This study predicts movement direction and target location from multichannel electromyography (EMG) signals, examining how spatially and temporally accurate intentions can be detected relative to movement onset. A computational pipeline combining data-driven temporal segmentation with Random Forest model is applied to EMG data across planning, execution, and contact phases of a reaching task.},

keywords = {BCI, EMG, HMI, Motor control, Random Forest},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{grunInvarianceQuantileSelection2026,

title = {Invariance to~Quantile Selection in~Distributional Continuous Control},

author = {Felix Grün and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},

editor = {Giuseppe Nicosia and Varun Ojha and Sven Giesselbach and M. Panos Pardalos and Renato Umeton and La Malfa Emanuele and La Malfa Gabriele},

doi = {10.1007/978-3-032-21477-5_12},

isbn = {978-3-032-21477-5},

year  = {2026},

date = {2026-06-01},

urldate = {2026-06-01},

booktitle = {Machine Learning, Optimization, and Data Science},

pages = {175–190},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {In recent years, distributional reinforcement learning has produced many state-of-the-art results in typical reinforcement learning benchmarks, such as the suite of Atari games. Increasingly sample-efficient distributional algorithms for the discrete action domain have been developed over time, which vary primarily in the way they parameterize their approximations of value distributions, and how they quantify the differences between those distributions. In this work, we transfer three of those algorithms - Quantile Regression Deep Q-Network (QR-DQN), Implicit Quantile Networks (IQN) and Fully Parameterized Quantile Function (FQF) - to the continuous action domain by extending two powerful actor-critic algorithms - Twin Delayed Deep Deterministic policy gradient (TD3) and Soft Actor-Critic (SAC) - with distributional critics. We investigate whether the relative performance of the methods for the discrete action space translates to the continuous case. To that end, we compare them empirically on a set of continuous control tasks (Ant, HalfCheetah, Hopper, Humanoid and Walker2D). Our results indicate qualitative invariance regarding the number and placement of distributional atoms in the deterministic, continuous action setting.},

keywords = {Actor-critic, BCI, Continuous control, Distributional reinforcement learning, Machine Learning, Quantile regression, reinforcement learning (RL)},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lehmlerDistributionalPropertiesReLUActivations2026,

title = {Distributional Properties of~ReLU-Activations in~Artificial Neural Networks That~Learn by~Memorization},

author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},

editor = {Giuseppe Nicosia and Varun Ojha and Sven Giesselbach and M. Panos Pardalos and Renato Umeton and La Malfa Emanuele and La Malfa Gabriele},

doi = {10.1007/978-3-032-21477-5_27},

isbn = {978-3-032-21477-5},

year  = {2026},

date = {2026-06-01},

urldate = {2026-06-01},

booktitle = {Machine Learning, Optimization, and Data Science},

pages = {410–423},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {We investigate the distributional properties of layers in Artificial Neural Network (ANN) that can be used to distinguish between networks learning by generalization and memorizing networks. Starting from the notion of memorization being essentially definable as learning ‘rare’ features of the input data, we propose the activation probability of Rectified Linear Units (ReLU)-neurons as an important indicator of memorization. Building on this idea, we show how hypotheses about distributional properties over whole networks structures can be derived from the activation probability of singular neurons in memorizing ANNs. We such extend previous work on using Poisson process models of activations in ANN by considering the correlation between neurons. Using this approach, we further simulate the effect of memorizing neurons on distributional properties of weight matrices and activation magnitudes and find a connection between L1/L2-norm regularization of weight matrices. We empirically evaluate the distributions of activation rate, correlation structure and weight matrices in memorizing and generalizing ANNs on a simple MNIST-classification task. Our initial findings show how the activation frequency and intra-layer correlation structure can be used to distinguish generalizing from memorizing networks and for inferring distributional properties on affected parts of the networks. This work presents a building block to later derive online metrics for memorization in ANNs.},

keywords = {Artificial neural networks, BCI, Machine Learning, Memorization, Statistical Modeling},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{xavierfidencioPerformanceBoundariesBrainComputer2026,

title = {Performance Boundaries for~Brain-Computer Interfaces Using Error-Related Potentials and~Reinforcement Learning},

author = {Aline Xavier Fidêncio and Felix Grün and Christian Klaes and Ioannis Iossifidis},

editor = {Giuseppe Nicosia and Varun Ojha and Sven Giesselbach and M. Panos Pardalos and Renato Umeton and La Malfa Emanuele and La Malfa Gabriele},

doi = {10.1007/978-3-032-21480-5_23},

isbn = {978-3-032-21480-5},

year  = {2026},

date = {2026-06-01},

urldate = {2026-06-01},

booktitle = {Machine Learning, Optimization, and Data Science},

pages = {335–349},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Non-invasive brain-computer interfaces (BCIs) can improve quality of life for individuals with motor disabilities. However, input data non-stationarity often leads to performance degradation over time. Adaptive BCIs aim to address this challenge. Recent studies have proposed leveraging error-related potentials (ErrPs) - neural signals elicited during self-made or observed errors - as a natural feedback mechanism for closed-loop systems. While these approaches demonstrate potential performance gains, their effectiveness relies heavily on accurate ErrP detection, which remains challenging. In a previous study, we introduced a novel reinforcement learning-based BCI framework incorporating ErrPs as reward signals. We systematically examine how misclassification rates in ErrP detection in terms of false positives (FPs) and false negatives (FNs) influence closed-loop performance. We use both synthetic and real datasets to evaluate two contextual bandit algorithms (LinUCB and NeuralUCB), trained to map motor imagery-related time-frequency modulations to actions in a binary task. Firstly, our findings show that the sensitivity of agents to FPs and FNs depends on baseline accuracies. In some conditions, such as insufficient exploration, false negatives might be more detrimental than false positives, but this needs further investigation. Proper agent parametrization and enough data samples can compensate the negative effects to some extent.},

keywords = {adaptive brain-computer interface, BCI, electroencephalography (EEG), error-related potentials (ErrPs), Machine Learning, motor imagery (MI), reinforcement learning (RL)},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{grunFrameworkModelYour2025,

title = {A Framework to Model Your Model Based / Model Free Action Selection Hypothesis},

author = {Felix Grün and Ioannis Iossifidis},

url = {https://abstracts.g-node.org/conference/BC25/abstracts#/uuid/e3090016-2d82-4107-b587-1474f97be0b3},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-01},

booktitle = {BC25 : Bernstein Conference 2025,},

publisher = {BCCN Bernstein Network Computational Neuroscience & Neurotechnology},

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 = {BCI, Machine Learning, Reinforcement learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{schmidtPredictingUpperLimb2025,

title = {Predicting Upper Limb Muscle Activity from Kinematics Using LSTM Networks},

author = {Marie Dominique Schmidt and Ioannis Iossifidis},

url = {https://abstracts.g-node.org/conference/BC25/abstracts#/uuid/a8fa799f-bf8e-4192-8ba9-3b1e3ad078ea},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-01},

booktitle = {Bernstein Conference 2025},

publisher = {BCCN Bernstein Network Computational Neuroscience & Neurotechnology},

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},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{xavierfidencioErrorrelatedPotentialsReinforcement2025,

title = {Error-Related Potentials and Reinforcement Learning for Neuroadaptive Systems},

author = {Aline Xavier Fidencio and Ioannis Iossifidis},

url = {https://abstracts.g-node.org/conference/BC25/abstracts#/uuid/84a56344-585c-4d7c-992d-1f34489770f5},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-01},

booktitle = {BC25 : Bernstein Conference 2025,},

publisher = {BCCN Bernstein Network Computational Neuroscience & Neurotechnology},

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{lehmlerActivationBasedIndicatorsMemorization2025,

title = {Activation-Based Indicators of Memorization in ReLU Artificial Neural Networks: A Computational Perspective},

author = {Stephan Johann Lehmler and Muhammad Saif Rehman and Ioannis Iossifidis},

url = {https://abstracts.g-node.org/conference/BC25/abstracts#/uuid/cfc1121c-9d2c-4017-a781-15e9ec615053},

year  = {2025},

date = {2025-09-30},

urldate = {2025-09-30},

booktitle = {BC25 : Bernstein Conference 2025},

publisher = {BCCN Bernstein Network Computational Neuroscience & Neurotechnology},

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}

}

@inproceedings{icnr2024,

title = {Deep-learning-based identification of individual motion characteristics from upper-limb trajectories towards disorder stage evaluation},

author = {Tim Sziburis and Susanne Blex and Tobias Glasmachers and Ioannis Iossifidis},

editor = {Jose L. Pons and Jesus Tornero and Metin Akay},

year  = {2024},

date = {2024-11-30},

urldate = {2024-11-30},

booktitle = {Converging Clinical and Engineering Research on Neurorehabilitation V - Proceedings of the 6th International Conference on Neurorehabilitation (ICNR2024)},

publisher = {Springer International Publishing},

address = {La Granja, Spain},

series = {Biosystems and Biorobotics},

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},

doi = {DOI: 10.12751/nncn.bc2024.257},

year  = {2024},

date = {2024-09-18},

urldate = {2024-09-18},

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}

}

@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},

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 = {},

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},

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 = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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},

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 = {},

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},

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 = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@inproceedings{doliwaBiologicallyInspiredModel2022,

title = {Biologically Inspired Model for Timed Motion in Robotic Systems},

author = {Sebastian Doliwa and Muhammad Ayaz Hussain and Tim Sziburis and Ioannis Iossifidis},

year  = {2022},

date = {2022-08-12},

urldate = {2022-08-12},

booktitle = {9th IEEE RAS/EMBS International Conference on Biomedical Robotics & Biomechatronics},

publisher = {IEEE},

address = {Seoul, South Korea},

keywords = {dynamical systems, Robotics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{doliwaDevelopmentScalableAnalogacceptedb,

title = {Development of a Scalable Analog Front-End for Brain-Computer Interfaces},

author = {Sebastian Doliwa and Andreas Erbeslöh and Karsten Seidl and Ioannis Iossifidis},

year  = {2022},

date = {2022-06-15},

urldate = {2022-06-15},

booktitle = {17th International Conference on PhD Research in Microelectronics and Electronics},

publisher = {IEEE Prime 2022},

address = {Sardinia, Italy},

keywords = {BCI, Implantable BCI},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{doliwaDevelopmentScalableAnalogaccepted,

title = {Development of a Scalable Analog Front-End for Brain-Computer Interfaces},

author = {Sebastian Doliwa and Andreas Erbeslöh and Karsten Seidl and Ioannis Iossifidis},

doi = {10.1109/PRIME55000.2022.9816757},

year  = {2022},

date = {2022-06-15},

urldate = {2022-06-15},

booktitle = {17th International Conference on PhD Research in Microelectronics and Electronics},

publisher = {IEEE Prime 2022},

address = {Sardinia, Italy},

keywords = {BCI, Implantable BCI},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{fidencioErrorrelatedPotentialsDetection2022b,

title = {Error-Related Potentials Detection with Dry- and Wet-Electrode EEG},

author = {Aline Xavier Fidencio and Tobias Glasmachers and Ioannis Iossifidis},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {FENS, Forum 2022},

publisher = {FENS, Federation of European Neuroscience Societies},

abstract = {Electroencephalography (EEG) is a non-invasive technique for measuring brain electrical activity from electrodes placed on the scalp surface. Improvements in this technology are particularly relevant because they also boost brain-machine interfaces (BMI) development. Commonly, gel-based electrodes are used since they guarantee a high-quality signal. Alternatively, dry electrodes have been introduced, more suitable for daily use. In this work, we compare conventional dry and wet electrode systems specifically for the detection of error-related potentials (ErrPs). ErrPs are elicited as a reaction to both self-made and external errors. There has been increased interest in the integration of these signals into BMIs to improve their performance since they provide a convenient source of feedback to the system with no extra workload for the subject. These signals can be used, e.g., to correct errors or even for system adaptation. ErrP-based BMIs in the literature have consistently used wet electrodes. Therefore, even though both electrodes types have been compared for other event-related potentials (e.g., P300), it is relevant to know whether the signal quality for the detection of ErrPs is comparable among them. In this work, we implement a simple game to elicit ErrPs and compare the quality of the measured signals. We tested the feasibility of the experimental protocol to elicit ErrP and the measured ErrP displayed a similar waveshape in terms of observed peaks. However, differences exist in both latencies as well as in their amplitude. These variations and other relevant characteristics have to be further verified with more subjects},

keywords = {BCI, EEG, error-related potentials, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{schmidtMotionIntentionPrediction2022ab,

title = {Motion Intention Prediction},

author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {FENS, Forum 2022},

publisher = {FENS, Federation of European Neuroscience Societies},

abstract = {Motion intention prediction is the key to robot-assisted rehabilitation systems. These can rely on various biological signals. One commonly used signal is the muscle activity measured by an electromyogram that occurs between 50-100 milliseconds before the actual movement, allowing a real-world application to assist in time. We show that upper limb motion can be estimated from the corresponding muscle activity. To this end, eight-arm muscles are mapped to the joint angle, velocity, and acceleration of the shoulder, elbow, and wrist. For this purpose, we specifically develop an artificial neural network that estimates complex motions involving multiple upper limb joints. The network model is evaluated concerning its ability to generalize across subjects as well as for new motions. This is achieved through training on multiple subjects and additional transfer learning methods so that the prediction for new subjects is significantly improved. In particular, this is beneficial for a robust real-world application. Furthermore, we investigate the importance of the different parameters such as angle, velocity, and acceleration for simple and complex motions. Predictions for simple motions along with the main components of complex motions achieve excellent accuracy while joints that do not play a dominant role during the motion have comparatively lower accuracy.},

keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},

pubstate = {published},

tppubtype = {inproceedings}

}