arXiv subjects

Marc M. Van Hulle

Publications and source records attributed to Marc M. Van Hulle.

7 recordsLinked to original sources

arXiv2026

Mindspeller Neuroprofiling. How task performance, EEG, and association evidence support O*NET-based role guidance

Mindspeller produces a Neuroprofile from three sources: rational self-report, association-based semantic positioning, and performance recorded during cognitive tasks together with EEG. The task-and-EEG stream is the only source used to create occupational evidence. A result can enter role matching only after the participant's task performance supports the intended construct and the corresponding EEG data pass the required quality and evidence checks. The current pilot uses four EEG electrodes, twelve scored tasks, 23 O*NET abilities, and an internal bank of 376 occupations. Self-report and association evidence help explain motivation, preference, and alignment, but do not generate roles. The output is intended to support discussion about cognitive fit. It is not a hiring decision, a measure of practical job skill, or a prediction of job performance. Role confidence is currently capped at Moderate, and external psychometric and job-outcome validity have not yet been established.

cs.CY↗

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↗

arXiv2025

BTTDA: Block-Term Tensor Discriminant Analysis for Brain-Computer Interfacing

Brain-computer interfaces (BCIs) allow direct communication between the brain and external devices, frequently using electroencephalography (EEG) to record neural activity. Dimensionality reduction and structured regularization are essential for effectively classifying task-related brain signals, including event-related potentials (ERPs) and motor imagery (MI) rhythms. Current tensor-based approaches, such as Tucker and PARAFAC decompositions, often lack the flexibility needed to fully capture the complexity of EEG data. This study introduces Block-Term Tensor Discriminant Analysis (BTTDA): a novel tensor-based and supervised feature extraction method designed to enhance classification accuracy by providing flexible multilinear dimensionality reduction. Extending Higher Order Discriminant Analysis (HODA), BTTDA uses a novel and interpretable forward model for HODA combined with a deflation scheme to iteratively extract discriminant block terms, improving feature representation for classification. BTTDA and a sum-of-rank-1-terms variant PARAFACDA were evaluated on publicly available ERP (second-order tensors) and MI (third-order tensors) EEG datasets from the MOABB benchmarking framework. Benchmarking revealed that BTTDA and PARAFACDA significantly outperform the traditional HODA method in ERP decoding, resulting in state-of-the art performance (ROC-AUC = 91.25%). For MI, decoding results of HODA, BTTDA and PARAFACDA were subpar, but BTTDA still significantly outperformed HODA (64.52% > 61.00%). The block-term structure of BTTDA enables interpretable and more efficient dimensionality reduction without compromising discriminative power. This offers a promising and adaptable approach for feature extraction in BCI and broader neuroimaging applications.

eess.SP↗

arXiv2025

CoMET: A Contrastive-Masked Brain Foundation Model for Universal EEG Representation

Electroencephalography (EEG) is a non-invasive technique for recording brain activity, widely used in brain-computer interfaces, clinic, and healthcare. Traditional EEG deep models typically focus on specific dataset and task, limiting model size and generalization. Recently, self-supervised brain foundation models have emerged and been applied to various downstream tasks. Nevertheless, these models still have limitations: current SOTA models typically rely on masked reconstruction strategy; however, EEG features of adjacent channels are highly correlated, which causes the pre-training to overly focus on low-dimensional signal-similarity features in local regions and neglect the global discriminative patterns vital for downstream tasks. To address these limitations, we propose a brain foundation model called CoMET. Specifically, we employ the masked autoencoder with redesigned patching and embedding for EEG as backbone and devise a novel contrastive learning framework with mirror-scale augmentation to strengthen the global discrimination ability. CoMET is pre-trained on mixed EEG datasets over 3000 subjects with over one million samples. It is evaluated on ten different downstream datasets, and the SOTA results demonstrate CoMET's superior ability in extracting universal EEG representations and strong clinical potential.

eess.SP↗

arXiv2024

Alterations of electrocortical activity during hand movements induced by motor cortex glioma

Glioma cells can reshape functional neuronal networks by hijacking neuronal synapses, leading to partial or complete neurological dysfunction. These mechanisms have been previously explored for language functions. However, the impact of glioma on sensorimotor functions is still unknown. Therefore, we recruited a control group of patients with unaffected motor cortex and a group of patients with glioma-infiltrated motor cortex, and recorded high-density electrocortical signals during finger movement tasks. The results showed that glioma suppresses task-related synchronization in the high-gamma band and reduces the power across all frequency bands. The resulting atypical motor information transmission model with discrete signaling pathways and delayed responses disrupts the stability of neuronal encoding patterns for finger movement kinematics across various temporal-spatial scales. These findings demonstrate that gliomas functionally invade neural circuits within the motor cortex. This result advances our understanding of motor function processing in chronic disease states, which is important to advance the surgical strategies and neurorehabilitation approaches for patients with malignant gliomas.

q-bio.NC↗