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Jordy Thielen

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Published work

2 published item(s)

preprint2026arXiv

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

A code-modulated motion visual evoked potential (c-MVEP) for brain-computer interfacing (BCI) is presented in this study. This paradigm uses pseudo-random sequences to visually stimulate objects using motion as an alternative to flickering. In an offline experiment of this study, EEG data were recorded and compared during sequential stimulation of a single object 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). c-MVEP showed similar time-domain characteristics as c-VEP, and also in the frequency domain c-MVEP evoked a broadband response similar to c-VEP, with a comparable signal-to-noise ratio (SNR), albeit more focused in the lower frequency range. Both SSMVEP and SSVEP showed clear oscillatory responses at the stimulation frequency and harmonics, with a higher SNR for SSVEP than SSMVEP. The spatial distribution of c-MVEP showed the main activation at Oz and spread across multiple electrodes, whereas c-VEP showed less spreading and was more focused at Oz. Similar observations were made for SSMVEP and SSVEP. From subjective ratings, there was no clear preference for the motion-based stimulation of SSMVEP or c-MVEP over flicker-based stimulation of SSVEP or c-VEP. The online experiment of this study, evaluated a 4-class BCI with 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). Overall, this study shows the great potential of the newly proposed c-MVEP paradigm using motion stimulation for BCI applications, providing a valuable alternative to the c-VEP paradigm using flickering stimulation.

preprint2016arXiv

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.