arXiv subjects

Bob Van Dyck

Publications and source records attributed to Bob Van Dyck.

3 recordsLinked to original sources

arXiv2026

Characterization of Speech Imagery in Scalp EEG and Comparison with Motor Imagery

Speech imagery is an attractive brain-computer interface paradigm for communication because it is endogenous and intrinsically linguistic. Yet despite growing interest, its dominant scalp-EEG spatiotemporal characteristics remain poorly characterized. We investigated whether speech imagery, understood here as the motor imagery of articulatory movements, exhibits the motor-related mu/alpha and beta desynchronization expected from motor imagery. In $34$ participants, we compared speech imagery, finger motor imagery, and explicitly cued no-task trials recorded under the same trial structure, analyzing band-power dynamics across channels and time. Finger motor imagery showed the expected contralateral mu/alpha and beta desynchronization over sensorimotor areas, whereas speech imagery showed a weaker, more distributed increase in alpha power relative to no-task. A classifier discriminating imagery from no-task reached mean balanced accuracies of $0.563 \pm 0.071$ for speech imagery and $0.717 \pm 0.125$ for motor imagery, with band-ablation analyses showing larger, more robust alpha and beta effects for motor imagery. These results show that the dominant group-level scalp response to speech imagery did not resemble the canonical alpha/beta desynchronization associated with motor imagery.

eess.SP↗

arXiv2026

CORTEG: Foundation Models Enable Cross-Modality Representation Transfer from Scalp to Intracranial Brain Recordings

Intracranial electrocorticography (ECoG) offers high-signal-to-noise access to cortical activity for brain-computer interfaces, yet limited per-patient data has led most prior work to rely on small, subject-specific decoders that neglect information shared across patients. We investigate whether large pretrained scalp-EEG foundation models (EEG FMs) can be adapted to ECoG, enabling cross-patient learning and competitive decoding performance while calibrating to a held-out patient in 10-30 minutes on a single GPU. We introduce CORTEG, a cross-modality transfer framework that combines a pretrained EEG FM backbone, an electrode-aware KNNSoftFourier spatial adapter, a dual-stream tokenizer for low-frequency and high-gamma activity, and a leave-one-subject-out fine-tuning strategy. We evaluate CORTEG on two challenging regression tasks: public finger trajectory regression (n=9) and private audio envelope regression (n=16). CORTEG matches or exceeds the strongest task-specific baselines on both tasks: it reaches the highest mean correlation among compared methods on the public finger benchmark (gain not statistically significant on n=9 subjects), with larger and statistically significant gains on the audio task and in low-data per-patient calibration. Feature analyses align with neurophysiology, and latent manifolds capture low-dimensional finger-movement structure. CORTEG provides systematic evidence that scalp-EEG pretraining can be repurposed for ECoG decoding, enabling data-efficient intracranial BCIs that can adapt to new patients.

cs.AI↗

arXiv2026

An open-source implementation of a closed-loop electrocorticographic Brain-Computer Interface using Micromed, FieldTrip, and PsychoPy

We present an open-source implementation of a closed-loop Brain-Computer Interface (BCI) system based on electrocorticographic (ECoG) recordings. Our setup integrates FieldTrip for interfacing with a Micromed acquisition system and PsychoPy for implementing experiments. We open-source three custom Python libraries (psychopylib, pymarkerlib, and pyfieldtriplib) each covering different aspects of a closed-loop BCI interface: designing interactive experiments, sending event information, and real-time signal processing. Our modules facilitate the design and operation of a transparent BCI system, promoting customization and flexibility in BCI research, and lowering the barrier for researchers to translate advances in ECoG decoding into BCI applications.

cs.HC↗