Veröffentlichungen

@article{fidencioErrorrelatedPotentialDriven2025,

title = {Error-Related Potential Driven Reinforcement Learning for Adaptive Brain-Computer Interfaces},

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

url = {http://arxiv.org/abs/2502.18594},

doi = {10.48550/arXiv.2502.18594},

year  = {2025},

date = {2025-02-25},

urldate = {2025-02-25},

journal = {Arxiv},

abstract = {Brain-computer interfaces (BCIs) provide alternative communication methods for individuals with motor disabilities by allowing control and interaction with external devices. Non-invasive BCIs, especially those using electroencephalography (EEG), are practical and safe for various applications. However, their performance is often hindered by EEG non-stationarities, caused by changing mental states or device characteristics like electrode impedance. This challenge has spurred research into adaptive BCIs that can handle such variations. In recent years, interest has grown in using error-related potentials (ErrPs) to enhance BCI performance. ErrPs, neural responses to errors, can be detected non-invasively and have been integrated into different BCI paradigms to improve performance through error correction or adaptation. This research introduces a novel adaptive ErrP-based BCI approach using reinforcement learning (RL). We demonstrate the feasibility of an RL-driven adaptive framework incorporating ErrPs and motor imagery. Utilizing two RL agents, the framework adapts dynamically to EEG non-stationarities. Validation was conducted using a publicly available motor imagery dataset and a fast-paced game designed to boost user engagement. Results show the framework's promise, with RL agents learning control policies from user interactions and achieving robust performance across datasets. However, a critical insight from the game-based protocol revealed that motor imagery in a high-speed interaction paradigm was largely ineffective for participants, highlighting task design limitations in real-time BCI applications. These findings underscore the potential of RL for adaptive BCIs while pointing out practical constraints related to task complexity and user responsiveness.},

keywords = {BCI, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning, EEG, Quantitative Biology - Neurons and Cognition, Reinforcement learning},

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}

}

@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}

}

@article{xavierfidencioErrorrelated,

title = {Error-Related Potentials in Reinforcement Learning-Based Brain-Machine Interfaces},

author = {Aline Xavier Fidencio and Christian Klaes and Ioannis Iossifidis},

url = {https://www.frontiersin.org/article/10.3389/fnhum.2022.806517},

doi = {https://doi.org/10.3389/fnhum.2022.806517},

year  = {2022},

date = {2022-06-24},

urldate = {2022-06-24},

journal = {Frontiers in Human Neuroscience},

volume = {16},

abstract = {The human brain has been an object of extensive investigation in different fields. While several studies have focused on understanding the neural correlates of error processing, advances in brain-machine interface systems using non-invasive techniques further enabled the use of the measured signals in different applications. The possibility of detecting these error-related potentials (ErrPs) under different experimental setups on a single-trial basis has further increased interest in their integration in closed-loop settings to improve system performance, for example, by performing error correction. Fewer works have, however, aimed at reducing future mistakes or learning. We present a review focused on the current literature using non-invasive systems that have combined the ErrPs information specifically in a reinforcement learning framework to go beyond error correction and have used these signals for learning.},

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

pubstate = {published},

tppubtype = {article}

}

@article{aliAnchoredSTFTGNAAExtension2021a,

title = {Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method},

author = {Omair Ali and Muhammad Saif-ur-Rehman and Susanne Dyck and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},

url = {https://www.nature.com/articles/s41598-022-07992-w},

doi = {https://doi.org/10.1038/s41598-022-07992-w},

issn = {2045-2322},

year  = {2022},

date = {2022-03-10},

urldate = {2022-03-10},

journal = {Nature Scientific Reports},

volume = {12},

issue = {1},

pages = {4245},

abstract = {Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs is pivotal. Here, we propose a novel feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a novel augmentation method, called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a new CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms all state-of-the-art methods and yields an average classification accuracy of 90.7 % and 89.54 % on BCI competition II dataset III and BCI competition IV dataset 2b, respectively.},

keywords = {Adversarial NN, BCI, computer science, EEG, Machine Learning, Quantitative Biology, Quantitative Methods},

pubstate = {published},

tppubtype = {article}

}

@article{ali2020improving,

title = {Improving the performance of EEG decoding using anchored-STFT in conjunction with gradient norm adversarial augmentation},

author = {Omair Ali and Muhammad Saif-ur-Rehman and Susanne Dyck and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {arXiv preprint arXiv:2011.14694},

keywords = {Adversarial NN, BCI, EEG, Machine Learning},

pubstate = {published},

tppubtype = {article}

}