English(EN) Torus Graphs for Large Scale Neural Phase Analysis

环面图可实现大规模神经相位分析

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-02 04:00

研究人员开发了一种名为环面图（Torus Graphs, TG）的新方法来分析神经信号中的相位关系，该方法可以处理数千个变量，而之前的限制约为100个。这一进展使得对脑电图（EEG）和局部场电位（LFP）记录等复杂数据进行更全面的分析成为可能。新方法还能够创建用于状态依赖耦合的TG隐马尔可夫模型（TG Hidden Markov Models）以及用于方向性相互作用的自回归TG（autoregressive TGs），从而揭示不同认知状态下的动态相位模式。 AI

影响能够对神经数据进行更复杂的分析，可能为神经科学和脑机接口带来新的见解。

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arXiv cs.LG TIER_1 English(EN) · Jack Goffinet, Casey Hanks, David E. Carlson · 2026-06-02 04:00

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. Modern recordings capture hundreds of channels across man…

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Torus Graphs for Large Scale Neural Phase Analysis

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