Abbaspourazad Hamidreza, Wong Yan, Pesaran Bijan, Shanechi Maryam M
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:3778-3781. doi: 10.1109/EMBC.2018.8513242.
A key element needed in a brain-machine interface (BMI) decoder is the encoding model, which relates the neural activity to intended movement. The vast majority of work have used a representational encoding model, which assumes movement parameters are directly encoded in neural activity. Recent work have in turn suggested the existence of neural dynamics that represent behavior. This recent evidence motivates developing dynamical encoding models with hidden states that encode movement. Regardless of their type, encoding models have vastly characterized a single scale of activity, e.g., either spikes or local field potentials (LFP). In our recent work we developed a multiscale representational encoding model to simultaneously characterize and decode discrete spikes and continuous field activity. However, learning a multiscale dynamical model from simultaneous spike-field recordings in the presence of hidden states is challenging. Here we present an unsupervised learning algorithm for estimating a multiscale state-space model with hidden states and validate it using spike-LFP activity during a reaching movement. We use the learned multiscale statespace model and a corresponding decoder to identify hidden states from spike-LFP activity. We then decode the movement trajectories using these hidden states. We find that the identified states can accurately decode the trajectories. Moreover, we demonstrate that adding LFP to spikes improves the decoding accuracy, suggesting that our unsupervised learning algorithm incorporates information across scales. This learning algorithm could serve as a new tool to study encoding across scales and to enhance future BMI systems.
脑机接口（BMI）解码器所需的一个关键要素是编码模型，它将神经活动与预期运动联系起来。绝大多数工作都使用了代表性编码模型，该模型假设运动参数直接编码在神经活动中。最近的研究转而表明存在表征行为的神经动力学。这一最新证据促使人们开发具有隐藏状态的动态编码模型来编码运动。无论其类型如何，编码模型大多只表征单一尺度的活动，例如，要么是尖峰信号，要么是局部场电位（LFP）。在我们最近的工作中，我们开发了一种多尺度代表性编码模型，以同时表征和解码离散的尖峰信号和连续的场活动。然而，在存在隐藏状态的情况下，从同时记录的尖峰信号和场信号中学习多尺度动态模型具有挑战性。在这里，我们提出一种无监督学习算法，用于估计具有隐藏状态的多尺度状态空间模型，并在伸手运动期间使用尖峰-LFP活动对其进行验证。我们使用学习到的多尺度状态空间模型和相应的解码器从尖峰-LFP活动中识别隐藏状态。然后，我们使用这些隐藏状态解码运动轨迹。我们发现识别出的状态能够准确地解码轨迹。此外，我们证明将LFP添加到尖峰信号中可以提高解码精度，这表明我们的无监督学习算法整合了跨尺度的信息。这种学习算法可以作为一种新工具，用于研究跨尺度编码并增强未来的BMI系统。
