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

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. The port-Hamiltonian structure is a modelling choice - it buys passivity, a certified steady-state power balance, and a clean separation of storage, routing and dissipation - not a claim about what the brain is. The state pairs each channel's instantaneous phase with its angular frequency, and stored energy decomposes over the five canonical frequency bands. A phase-locking prior measured from the same recordings gates the lear...

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