MPNet高效解码多节律脑电信号，速度提升10倍且省资源。
MPNet: A Robust and Efficient Manifold Pooling Network for Multi-Rhythm EEG Signal Decoding
- 通过自适应卷积提取多视角时频特征，生成多流形节点
- 首创流形节点池化层，将高维输入压缩为固定大小融合节点
- 在小数据下仍稳定高效，适合真实脑机接口场景
深度黎曼网络为脑电（EEG）解码提供了强大框架，但实际应用受限。准确解码需建模跨多个节律的复杂时间动态，导致黎曼输入维度高、计算成本大。为此，我们提出流形池化网络（MPNet）。MPNet采用节律自适应卷积前端，提取全面的时频表示并生成多视角黎曼节点；随后提出新颖的流形节点池化层，将这些节点聚合为固定尺寸的融合节点，使后续深层黎曼网络处理成本大幅降低。在两个公开脑电数据集上的实验表明，MPNet达到当前最优精度，运行速度比同类黎曼模型快达10倍，且在数据有限条件下仍保持稳健性能。这些发现凸显了MPNet在真实脑电应用中的实用性与效率。
原文摘要 · Abstract (English)
Deep Riemannian networks provide a powerful framework for Electroencephalography (EEG) decoding, but their practical applications are severely constrained. Accurately decoding EEG signals requires modeling complex temporal dynamics across multiple rhythms, which results in high-dimensional Riemannian inputs and significant computational costs. To address this, we propose the Manifold Pooling Network (MPNet). MPNet uses a rhythm-adaptive convolutional frontend to extract comprehensive time-frequency representations and generate multi-view Riemannian nodes. A novel manifold node pooling layer is then proposed to aggregate these nodes into a single fusion node with a fixed size, enabling the following deep Riemannian network to process it with greatly reduced costs. Experiments on two public EEG datasets show that MPNet achieves state-of-the-art accuracy, runs up to 10 times faster than the comparable Riemannian model, and maintains robust performance under limited-data conditions. These findings highlight MPNet's practicality and efficiency for real-world EEG applications.
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