IEEE Trans Neural Syst Rehabil Eng. 2020 Sep;28(9):1908-1920. doi: 10.1109/TNSRE.2020.3003342. Epub 2020 Jun 18.
Multi-view learning improves the learning performance by utilizing multi-view data: data collected from multiple sources, or feature sets extracted from the same data source. This approach is suitable for primate brain state decoding using cortical neural signals. This is because the complementary components of simultaneously recorded neural signals, local field potentials (LFPs) and action potentials (spikes), can be treated as two views. In this paper, we extended broad learning system (BLS), a recently proposed wide neural network architecture, from single-view learning to multi-view learning, and validated its performance in decoding monkeys' oculomotor decision from medial frontal LFPs and spikes. We demonstrated that medial frontal LFPs and spikes in non-human primate do contain complementary information about the oculomotor decision, and that the proposed multi-view BLS is a more effective approach for decoding the oculomotor decision than several classical and state-of-the-art single-view and multi-view learning approaches.
从多个来源收集的数据，或从同一数据源提取的特征集。这种方法适用于使用皮质神经信号进行灵长类动物大脑状态解码。这是因为同时记录的神经信号（局部场电位 (LFP) 和动作电位 ( spikes)）的补充成分可以视为两个视图。在本文中，我们将最近提出的广泛神经网络架构——广义学习系统 (BLS) 从单视图学习扩展到多视图学习，并验证了其在从内侧额 LFPs 和 spikes 解码猴子眼球运动决策中的性能。我们证明了非人类灵长类动物的内侧额 LFP 和 spikes 确实包含有关眼球运动决策的互补信息，并且与几种经典和最先进的单视图和多视图学习方法相比，所提出的多视图 BLS 是一种更有效的解码眼球运动决策的方法。
