arXiv AI
Backspace as a Natural Experiment: An Accelerated Failure Time Model of Selective Post-Error Motor Impairment in Parkinsons Disease
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arXiv AI
Jul 29
Selective Impairment of Motor Recovery from Typing Errors in Parkinson's Disease: A Survival Analysis
arXiv:2607. 24796v1 Announce Type: cross Abstract: Parkinson's disease (PD) affects multiple, dissociable stages of motor and cognitive control.
arXiv Machine Learning
Jun 25
Inverse Reinforcement Learning for Interpretable Keystroke Biomarkers in Parkinson's Disease
arXiv:2606. 25270v1 Announce Type: new Abstract: Keystroke dynamics have been explored extensively as a passive digital biomarker for Parkinson's disease (PD), typically by extracting summary statistics from typing timing and training a classifier to discriminate PD from healthy controls.
arXiv Machine Learning
Jul 14
From Observed Viability to Internal Predictive Approximation: A Single-Subject Latent-Space Analysis of Gait Dynamics Under Occlusal Constraint
arXiv:2605. 15862v2 Announce Type: replace Abstract: Understanding adaptive biomechanical systems requires distinguishing observable performance, static multivariate representation, longitudinal displacement, and internal approximation of observed change.
arXiv Machine Learning
Aug 4
Cross-Cohort Spectral-Temporal Dissociation in Frozen EEG Foundation-Model Representations
arXiv:2607. 24834v2 Announce Type: replace-cross Abstract: Objective.
arXiv AI
Aug 24
Aggregate, Don't Adapt: Subject-Level Posterior Aggregation and Transductive Calibration for Cross-Site Parkinsonian Gait Severity
The paper reports the winning solution to the MoCha 2026 Parkinsonian Gait Benchmark, achieving a macro‑F1 score of 0.6945 on unseen clinical sites. The approach relies on a frozen public motion encoder followed by a single 4×512 linear layer, and gains are largely attributed to three key steps: exact replication of the benchmark’s head recipe, averaging per‑walk posteriors at the subject level, and a label‑free transductive calibration of feature means and decision thresholds. Extensive ablation studies show that fine‑tuning the encoder or using alternative encoders does not improve performance, and the subject‑level aggregation is identified as the primary contributor to the top score.
arXiv Machine Learning
Jul 14
Observable Performance Does Not Fully Reflect Adaptive System Organization: A Multi-Level Analysis of Gait Dynamics Under Occlusal Constraint
arXiv:2605. 00778v2 Announce Type: replace Abstract: In biomechanical systems, observable performance is often used as a proxy for underlying organization, although similar outputs may arise from different adaptive configurations.