We propose the Gaussian-Linear Hidden Markov model (GLHMM), a generalisation of different types of HMMs commonly used in neuroscience. In short, the GLHMM is a general framework where linear regression is used to flexibly parameterise the Gaussian state distribution, thereby accommodating a wide range of uses -including unsupervised, encoding and decoding models. GLHMM is implemented as a Python toolbox with an emphasis on statistical testing and out-of-sample prediction -i.e. aimed at finding and characterising brain-behaviour associations. The toolbox uses a stochastic variational inference approach, enabling it to handle large data sets at reasonable computational time. Overall, the approach can be applied to several data modalities, including animal recordings or non-brain data, and applied over a broad range of experimental paradigms. For demonstration, we show examples with fMRI, electrocorticography, magnetoencephalo-graphy and pupillometry.

翻译：我们提出了高斯-线性隐马尔可夫模型（GLHMM），这是神经科学中常用各类隐马尔可夫模型的一种推广形式。简言之，GLHMM是一个通用框架，通过线性回归灵活参数化高斯状态分布，从而适应广泛的应用场景——包括无监督模型、编码模型和解码模型。GLHMM实现为Python工具箱，重点支持统计检验和样本外预测，即旨在发现和表征大脑-行为关联。该工具箱采用随机变分推理方法，能够在合理计算时间内处理大规模数据集。总体而言，该方法可应用于多种数据模态，包括动物记录数据或非大脑数据，并适用于广泛的实验范式。为作展示，我们给出了在功能磁共振成像（fMRI）、皮层脑电图（ECoG）、脑磁图（MEG）和瞳孔测量术中的应用实例。