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. 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 learned connectome, and a metriplectic formulation places the twin at a non- equilibrium steady state sustained by a metabolic port. Fitted to 'FitTrainN' phasor samples from the PhysioNet EEG Motor Movement/Imagery database under a leakage-free split, the twin reaches a held-out reconstruction error of 'FitTestMSE'. Scored free-running against invariants it did not author, the verdict is mixed: it reproduces near-critical avalanche branching (σ≈1) but not the aperiodic 1/f slope or the long-range temporal correlations of the recordings. Skew-symmetry and non-negative dissipation hold by construction rather than by penalty, making the twin a structure-preserving substrate on which closed-loop neuromodulation can be designed and tested.