arXiv AI

A Physics-Inspired Classical Digital Twin of Cortical Dynamics: A Band-Stratified Metriplectic Port-Hamiltonian Neural Network Learned from Brain-Computer-Interface EEG

arXiv:2607. 10439v3 Announce Type: replace-cross Abstract: We present a physics-inspired classical digital twin of brain-computer- interface (BCI) data: a graph neural network constrained to a band-stratified, metriplectic port-Hamiltonian form, with parameters learned from scalp EEG recorded during rest and motor imagery.

arXiv AI
Jul 20

Learning the Brain's Dynamics as a Port-Hamiltonian System: A GNN-Surrogate Metriplectic Twin for Non-Equilibrium Cortical Dynamics and Closed-Loop Neuromodulation

arXiv:2607. 10439v2 Announce Type: replace-cross Abstract: We model human motor cortex, recorded during rest and motor-imagery BCI conditions, as a port-Hamiltonian system: a conservative interconnection (skew-symmetric coupling between band-limited neural phasors) together with a dissipative port whose state-dependent decay is set by a graph-neural-network surrogate.

By Dibakar Sigdel
arXiv AI
Jul 14

Learning the Brain's Dynamics as a Port-Hamiltonian System

arXiv:2607. 10439v1 Announce Type: cross Abstract: We model human motor cortex during a wrist-extension BCI task as a port-Hamiltonian system (pHS): a conservative interconnection (gyroscopic coupling between neural phasors) plus a dissipative port (power-law energy decay driven by a GNN surrogate).

By Dibakar Sigdel
arXiv Machine Learning
Jun 2

Torus Graphs for Large Scale Neural Phase Analysis

arXiv:2606. 00496v1 Announce Type: new Abstract: Oscillatory neural signals such as electroencephalography (EEG) and local field potentials (LFPs) show phase relationships that coordinate communication across brain regions.

By Jack Goffinet, Casey Hanks, David E. Carlson
arXiv Machine Learning
Jul 2

Timesynth: A Temporal Fidelity Framework for Health Signal Digital Twins

arXiv:2607. 00431v1 Announce Type: new Abstract: Forecasting models for health-signal digital twins must preserve the oscillatory, frequency, phase, and state-transition dynamics of physiological signals, yet the pointwise metrics used to benchmark them cannot detect when these fundamental properties are lost.

By Md Rakibul Haque, Shireen Elhabian, Warren Woodrich Pettine
arXiv AI
4d ago

PHASE: A Physiology-Guided Hierarchical Foundation Model for Intracranial EEG

arXiv:2609.36087v1 Announce Type: cross Abstract: Clinicians and neuroscientists have long analyzed intracranial electroencephalography (iEEG) through directly measurable physiological characteristic...

By Yipeng Zhang, Chenda Duan, Yuanyi Ding, Tianyi Wang, Atsuro Daida, Masaki Izumi, Yuta Tanoue, Naoto Kuroda, Shaun A. Hussain, Nishant Sinha, Eishi Asano. Hiroki Nariai, Vwani Roychowdhury
arXiv Machine Learning
Sep 25

Orbital Error Dynamics: Self-Organized Criticality, Ephemeral Parameter Resonance, and Non-Linear Biological Ontologies in Zero-Storage Neural Synthesis

The paper introduces Orbital Error Dynamics (OED), an analytical framework that reinterprets neural network weights as transient topological resonances rather than static matrices, derived from a complex quadratic polynomial map. It proposes the Bent Sine Wave Hypothesis to explain non‑equilibrium living systems, defines Observer Horizon Geometry in parameter space, and presents a heavy‑tailed Biomimetic Perturbed Jump Operator inspired by biological processes. Empirical tests on the Two‑Moons manifold show that procedural parameterization from a 24‑byte seed yields competitive accuracy compared to a conventional gradient baseline, while also aligning conceptually with an analog optical co‑processor.

By Volkan Da\u{g}l{\i}, Zerrin Da\u{g}l{\i}, Da\u{g}han Da\u{g}l{\i}
arXiv AI
Jul 13

PHINN-EEG: Topological Time-Series Analysis of Dream-State EEG -- Dynamic Betti Curves for Dream Content Classification and Topology-Conditioned Neural Signal Synthesis

arXiv:2607. 09662v1 Announce Type: cross Abstract: Current electroencephalography (EEG)-based dream detection relies on power spectral density (PSD) and statistical moment features, achieving a state-of-the-art area under the receiver operating characteristic curve (AUC) of approximately 0.

By Ren Takahashi, Emre Yusuf, Jayabrata Bhaduri
arXiv AI
Sep 15

MANAS-2: Constrained Reconstruction for EEG Foundation Models

MANAS-2 is a new EEG foundation model that integrates a Raw‑Band Hybrid masked autoencoder with a physics‑motivated Constrained Reconstruction (ConRec) regularizer. ConRec penalizes RMS energy differences in short temporal windows, guiding the encoder toward oscillatory‑envelope organization. Across seven held‑out EEG datasets, adding ConRec improves spectral‑power recovery (R² from 0.860 to 0.906) and band‑energy dynamics (R² from 0.283 to 0.354), while maintaining strong temporal waveform recoverability and outperforming leading EEG models on downstream tasks.

By Arvasu Kulkarni, Aditya Ray Mishra, Mahir Jain, Parshva Runwal, Lakshya Saini, Siddharth Panwar, Sandeep Singh