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 Fidencio, Aline Xavier; Klaes, Christian; Iossifidis, Ioannis Error-Related Potentials in Reinforcement Learning-Based Brain-Machine Interfaces Artikel In: Frontiers in Human Neuroscience, Bd. 16, 2022. Abstract | Links | BibTeX | Schlagwörter: BCI, EEG, error-related potentials, Machine Learning, Reinforcement learning Ali, Omair; Saif-ur-Rehman, Muhammad; Dyck, Susanne; Glasmachers, Tobias; Iossifidis, Ioannis; Klaes, Christian In: Nature Scientific Reports, Bd. 12, Ausg. 1, S. 4245, 2022, ISSN: 2045-2322. Abstract | Links | BibTeX | Schlagwörter: Adversarial NN, BCI, computer science, EEG, Machine Learning, Quantitative Biology, Quantitative Methods Fidencio, Aline Xavier; Glasmachers, Tobias; Iossifidis, Ioannis Error-Related Potentials Detection with Dry- and Wet-Electrode EEG Proceedings Article In: FENS, Forum 2022, FENS, Federation of European Neuroscience Societies, 2022. Abstract | BibTeX | Schlagwörter: BCI, EEG, error-related potentials, Machine Learning Ali, Omair; Saif-ur-Rehman, Muhammad; Dyck, Susanne; Glasmachers, Tobias; Iossifidis, Ioannis; Klaes, Christian Improving the performance of EEG decoding using anchored-STFT in conjunction with gradient norm adversarial augmentation Artikel In: arXiv preprint arXiv:2011.14694, 2020. BibTeX | Schlagwörter: Adversarial NN, BCI, EEG, Machine Learning2023
@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}
}
2022
@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}
}
@inproceedings{fidencioErrorrelatedPotentialsDetection2022,
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}
}
2020
@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}
}