T-SNN：用于脑电信号解码的时间单纯复形神经网络
T-SNN: Temporal Simplicial Neural Network for EEG Decoding
- University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出T-SNN，将EEG表示为演变单纯复形序列，联合学习高阶交互与时间演变，在SEED-VII情绪识别任务上优于多种基线，并支持多模态解码。
AI中文摘要：
解码大脑状态需要能够同时捕捉神经活动的演变以及大脑区域群体之间相互作用的模型。现有的脑电（EEG）方法通常将记录视为多元时间序列，或用成对图表示功能连接，导致动态高阶相互作用在很大程度上未被建模。我们提出了时间单纯复形神经网络（Temporal Simplicial Neural Network, T-SNN），它将EEG记录表示为不断演变的单纯复形序列。通过将单纯复形卷积与循环更新相结合，T-SNN联合学习高阶相互作用及其时间演变。在七类SEED-VII情绪识别任务中，T-SNN在试验级和跨被试评估中均优于卷积、循环、基于图及Transformer方法。结合眼动特征可进一步提升性能，展示了该框架在多模态大脑状态解码方面的潜力。
英文摘要：
Decoding brain states requires models that capture both the evolution of neural activity and interactions among groups of brain regions. Existing EEG methods often treat recordings as multivariate time series or represent functional connectivity with pairwise graphs, leaving dynamic higher-order interactions largely unmodeled. We introduce the Temporal Simplicial Neural Network (T-SNN), which represents EEG recordings as sequences of evolving simplicial complexes. By combining simplicial convolutions with recurrent updates, T-SNN jointly learns higher-order interactions and their temporal evolution. On the seven-class SEED-VII emotion recognition task, T-SNN outperforms convolutional, recurrent, graph-based, and Transformer methods in both trial-wise and cross-subject evaluations. Incorporating eye-movement features further improves performance, demonstrating the framework's potential for multimodal brain-state decoding.
