用连续时间模型更精准预测脑电动态变化。
ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks
- 基于神经微分方程建模脑电时空频特征的连续动态
- 在多个数据集上显著优于传统离散递归方法
- 适合研究脑网络动态或开发脑机接口的科研人员
建模神经群体动态对基础神经科学研究和临床应用至关重要。传统隐变量方法通常通过递归结构离散化时间来模拟连续脑动态，导致累积预测误差，并难以捕捉脑电的瞬时非线性特性。我们提出 ODEBRAIN，一种基于神经微分方程的隐动态预测框架，将时空频特征融合至谱图节点，再通过神经微分方程建模连续隐状态动态。该设计使隐表示能在任意时间点捕捉复杂脑状态的随机变化。大量实验验证，ODEBRAIN 在预测脑电动态方面显著优于现有方法，具备更强的鲁棒性和泛化能力。
原文摘要 · Abstract (English)
Modeling neural population dynamics is crucial for foundational neuroscientific research and various clinical applications. Conventional latent variable methods typically model continuous brain dynamics through discretizing time with recurrent architecture, which necessarily results in compounded cumulative prediction errors and failure of capturing instantaneous, nonlinear characteristics of EEGs. We propose ODEBRAIN, a Neural ODE latent dynamic forecasting framework to overcome these challenges by integrating spatio-temporal-frequency features into spectral graph nodes, followed by a Neural ODE modeling the continuous latent dynamics. Our design ensures that latent representations can capture stochastic variations of complex brain states at any given time point. Extensive experiments verify that ODEBRAIN can improve significantly over existing methods in forecasting EEG dynamics with enhanced robustness and generalization capabilities.
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