Koopman-based linearization of preparatory EEG dynamics in Parkinson’s disease during galvanic vestibular stimulation
Bibliographic record
Abstract
Introduction: Parkinson's disease (PD) impairs motor preparation due to basal ganglia dysfunction, contributing to motor deficits. Galvanic Vestibular Stimulation (GVS), a non-invasive neuromodulation technique, shows promise in enhancing motor function in PD, but its underlying neural mechanisms are poorly understood. This study employs a Deep Koopman model to linearize and analyze preparatory EEG dynamics in PD, hypothesizing that GVS restores cortical activity patterns critical for motor planning. Methods: EEG data from 18 PD participants (on/off medication) and 18 healthy controls were collected during a preparatory phase of a motor task under three conditions: sham, GVS1 (50-100 Hz multi-sine), and GVS2 (100-150 Hz multi-sine). A Deep Koopman framework mapped EEG signals into a three-dimensional latent space for linear dynamical analysis. Temporal dynamics were assessed via eigenvalue analysis, spatial contributions via regression-based scalp mapping, and motor performance correlations via Pearson's coefficients. A Linear Quadratic Regulator (LQR) simulated control of PD dynamics toward healthy patterns. Results: The Deep Koopman model accurately captured EEG dynamics, with eigenvalue analysis showing no significant temporal dynamic differences across groups. Spatial contribution analysis revealed that PD-Off sham conditions deviated most from healthy control EEG patterns, while GVS and medication significantly reduced these deviations, aligning PD patterns closer to controls. Closer alignment correlated with improved motor performance metrics, including reduced reaction and squeeze times. LQR control effectively guided PD neural dynamics toward healthy trajectories in the latent space. Discussion: GVS enhances motor preparation in PD by restoring healthy cortical EEG patterns, with additive benefits from dopaminergic medication. The Deep Koopman framework offers a powerful approach for dissecting complex EEG dynamics and designing targeted neuromodulation strategies. These findings elucidate GVS's therapeutic mechanisms and highlight its potential for personalized PD interventions, warranting further exploration in larger cohorts and varied stimulation protocols.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".