arXiv subjects

Jordy Thielen

Publications and source records attributed to Jordy Thielen.

4 recordsLinked to original sources

arXiv2026

Beyond Flickering: Introducing Code-Modulated Motion Visual Evoked Potentials for Brain-Computer Interfacing

This study presents a novel code-modulated motion visual evoked potential (c-MVEP) paradigm for brain-computer interfacing (BCI). To avoid the visual discomfort and fatigue associated with traditional flickering stimuli, this paradigm uses pseudo-random sequences to visually stimulate objects using motion. We conducted offline and online experiments, to investigate signal characteristics and evaluate feasibility, respectively. In the offline experiment, EEG data were recorded and compared during sequential stimulation of a single target under four conditions: c-MVEP, code-modulated visual evoked potential (c-VEP), steady-state motion visual evoked potential (SSMVEP), and steady-state visual evoked potential (SSVEP). The c-MVEP evoked similar temporal and broadband spectral responses as c-VEP, with a comparable signal-to-noise ratio (SNR), although c-MVEP responses were more focused in the lower frequency range. While SSMVEP and SSVEP both showed clear harmonic oscillations, SSVEP yielded higher SNRs. Spatially, both motion-based stimulations peaked at Oz but spread across multiple electrodes, whereas both flicker-based stimulations were more localized at Oz. In the online experiment, we evaluated a four-target BCI using the same four conditions, testing the practical feasibility of the c-MVEP paradigm. The c-MVEP BCI reached a mean accuracy of 85.67% with an average selection time of 2.61s, which was significantly lower than c-VEP (97.81%; 1.15s) and SSVEP (93.42%; 1.94s), but significantly higher than SSMVEP (64.91%; 4.18s). The subjective ratings revealed no clear preference between the motion- and flicker-based paradigms, indicating comparable user comfort. Overall, this study demonstrates the strong potential of the c-MVEP paradigm. By providing an effective, non-flickering alternative to traditional c-VEP and SSVEP, c-MVEP offers a viable approach for user-friendly BCI applications.

q-bio.NC↗

arXiv2025

Dareplane: A modular open-source software platform for BCI research with application in closed-loop deep brain stimulation

Objective - This work introduces Dareplane, a modular and broad technology-agnostic open source software platform for brain-computer interface research with an application focus on adaptive deep brain stimulation (aDBS). One difficulty for investigating control approaches for aDBS resides with the complex setups required for aDBS experiments, a challenge Dareplane tries to address. Approach - The key features of the platform are presented and the composition of modules into a full experimental setup is discussed in the context of a Python-based orchestration module. The performance of a typical experimental setup on Dareplane for aDBS is evaluated in three benchtop experiments, covering (a) an easy-to-replicate setup using an Arduino microcontroller, (b) a setup with hardware of an implantable pulse generator, and (c) a setup using an established and CE certified external neurostimulator. The full technical feasibility of the platform in the aDBS context is demonstrated in a first closed-loop session with externalized leads on a patient with Parkinson's disease receiving DBS treatment and further in a non-invasive BCI speller application using code-modulated visual evoked responses (c-VEP). Main results - The platform is implemented and open-source accessible on https://github.com/bsdlab/Dareplane. Benchtop results show that performance of the platform is sufficient for current aDBS latencies, and the platform could successfully be used in the aDBS experiment. The timing-critical c-VEP speller could be successfully implemented on the platform achieving expected information transfer rates. Significance - The Dareplane platform supports aDBS setups, and more generally the research on neurotechnological systems such as brain-computer interfaces. It provides a modular, technology-agnostic, and easy-to-implement software platform to make experimental setups more resilient and replicable.

q-bio.OT↗

arXiv2024

A Bayesian dynamic stopping method for evoked response brain-computer interfacing

As brain-computer interfacing (BCI) systems transition from assistive technology to more diverse applications, their speed, reliability, and user experience become increasingly important. Dynamic stopping methods enhance BCI system speed by deciding at any moment whether to output a result or wait for more information. Such approach leverages trial variance, allowing good trials to be detected earlier, thereby speeding up the process without significantly compromising accuracy. Existing dynamic stopping algorithms typically optimize measures such as symbols per minute (SPM) and information transfer rate (ITR). However, these metrics may not accurately reflect system performance for specific applications or user types. Moreover, many methods depend on arbitrary thresholds or parameters that require extensive training data. We propose a model-based approach that takes advantage of the analytical knowledge that we have about the underlying classification model. By using a risk minimisation approach, our model allows precise control over the types of errors and the balance between precision and speed. This adaptability makes it ideal for customizing BCI systems to meet the diverse needs of various applications. We validate our proposed method on a publicly available dataset, comparing it with established static and dynamic stopping methods. Our results demonstrate that our approach offers a broad range of accuracy-speed trade-offs and achieves higher precision than baseline stopping methods.

cs.HC↗

arXiv2016

Brains on Beats

We developed task-optimized deep neural networks (DNNs) that achieved state-of-the-art performance in different evaluation scenarios for automatic music tagging. These DNNs were subsequently used to probe the neural representations of music. Representational similarity analysis revealed the existence of a representational gradient across the superior temporal gyrus (STG). Anterior STG was shown to be more sensitive to low-level stimulus features encoded in shallow DNN layers whereas posterior STG was shown to be more sensitive to high-level stimulus features encoded in deep DNN layers.

q-bio.NC↗