Linear Independent Component Analysis (ICA) is a blind source separation technique that has been used in various domains to identify independent latent sources from observed signals. In order to obtain a higher signal-to-noise ratio, the presence of multiple views of the same sources can be used. In this work, we present MultiView Independent Component Analysis with Delays (MVICAD). This algorithm builds on the MultiView ICA model by allowing sources to be delayed versions of some shared sources: sources are shared across views up to some unknown latencies that are view- and source-specific. Using simulations, we demonstrate that MVICAD leads to better unmixing of the sources. Moreover, as ICA is often used in neuroscience, we show that latencies are age-related when applied to Cam-CAN, a large-scale magnetoencephalography (MEG) dataset. These results demonstrate that the MVICAD model can reveal rich effects on neural signals without human supervision.

翻译：线性独立成分分析（ICA）是一种盲源分离技术，已被广泛应用于从观测信号中识别独立潜在源。为获得更高的信噪比，可借助同一信源的多个观测视角。本文提出含时延的多视角独立成分分析（MVICAD）算法。该算法基于多视角ICA模型扩展，允许各信源为某些共享信源的延迟版本：信源在不同视角间共享，但存在视角和信源特定的未知延迟量。仿真实验表明，MVICAD能实现更优的信源分离。此外，鉴于ICA常用于神经科学领域，我们将其应用于大规模脑磁图（MEG）数据集Cam-CAN时发现，延迟量与年龄相关。这些结果表明，MVICAD模型可在无需人工监督的情况下揭示神经信号中的丰富效应。