发表机构
Mindverse Computing LLC（Mindverse Computing LLC）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究将人类运动皮层在脑机接口任务中建模为端口哈密顿系统，用辛积分器等方法，经训练达到一定测试均方误差并通过临界性阶梯，模型能生成闭环信号恢复锁相，为脑机接口解码器提供新思路。
AI 中文摘要
我们将手腕伸展脑机接口任务中的人类运动皮层建模为端口哈密顿系统（pHS）：一个保守互连（神经相量之间的陀螺耦合）加上一个耗散端口（由GNN代理驱动的幂律能量衰减）。一个辛积分器演化相量状态；一个涨落-耗散一致噪声通道在体温下产生随机轨迹。在\FitTrainN\个真实脑电图周期（PhysioNet EEGMMIDB，3个留出的受试者）上训练，测试均方误差达到\FitTestMSE\并通过三个无标度临界性阶梯：近临界分支比（\(\sigma\approx1\)）、\(1/f\)幂律谱和长程DFA相关性。该模型生成闭环神经调节信号，应用于去同步输入时可在计算机模拟中恢复锁相，为结构保留脑机接口解码器指明了一条道路。
英文摘要
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 $1{,}109{,}250$ phasor samples from the PhysioNet EEG Motor Movement/Imagery database under a leakage-free split, the twin reaches a held-out reconstruction error of $1.30\times10^{-4}$. Scored free-running against invariants it did not author, the verdict is mixed: it reproduces near-critical avalanche branching ($σ\approx1$) 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.
