Zacksenhouse Miriam, Nemets Simona
The advance of BMIs was largely motivated by investigations of velocity encoding in single neurons during stereotypical reaching experiments. However, BMIs are designed to decode neural activity from an ensemble of neurons and direct general reaching movements. Hence, neural data analysis strategies for BMIs are required to: (1) analyze the neural activity from ensemble of neurons, (2) account for the dynamical nature of the neural activity associated with general reaching movements, and (3) explore and exploit other relevant modulating signals. Decoding neural activity can be performed either in a single stage or two stages. Two-stage decoding relies on a preliminary encoding stage to determine how the neurons are tuned to the relevant biological signals. Based on the estimated tuning curves, the neural activity across an ensemble of neurons can be decoded using either a population-vector, maximum likelihood estimation, or Bayesian inference (Pouget et al., 2003). The population-vector approach results in a linear relationship between the spike counts and the estimated biological signal, which can be estimated directly in a single stage using linear regression (Brown et al., 2004). This chapter focuses on single-stage decoding with linear regression, and in particular on two special challenges facing the application of linear regression to neural ensemble decoding during reaching movements (see “Movement Prediction”). First, given the dynamic nature of the decoded signal, it is necessary to include the history of the neural activity, rather than just its current spike count. Second, due to the correlation between the activities of different neurons (see “Ensemble Analysis”) and the activities in different time lags, the resulting regression problem is ill-posed and requires regularization techniques (see “Linear Regression”). Although neural decoding can be performed in a single stage, neural encoding is still important for investigating which signals are encoded in the neural activity. For this purpose, the notion of tuning curves is generalized to characterize how the neural activity represents the spatiotemporal profile of the movement. This analysis quantifies both the spatiotemporal tuning curves and the percent variance of the neural activity that is accounted by the movement profile (see “Neuronal Encoding and Tuning Curves”). For comparison, the percent variance in the neural activity that might be related to general neural modulations is assessed independently under the Poisson assumption (see “Neuronal Modulations”). These two-faced variance analyses provide a viable tool for quantifying the extent to which the neural code is effectively decoded, and the potential contribution of yet undecoded modulating signals. The strategies and algorithms described in this chapter are demonstrated using the neural activity recorded from an ensemble of cortical neurons in different brain areas during a typical target-hitting experiment with pole control as described in Carmena et al., 2003.
脑机接口（BMI）的发展很大程度上是由在刻板的伸手实验中对单个神经元的速度编码研究推动的。然而，BMI旨在从一组神经元中解码神经活动并指导一般的伸手动作。因此，BMI的神经数据分析策略需要：（1）分析一组神经元的神经活动，（2）考虑与一般伸手动作相关的神经活动的动态性质，以及（3）探索和利用其他相关的调制信号。解码神经活动可以分一个阶段或两个阶段进行。两阶段解码依赖于一个初步的编码阶段来确定神经元如何被调整以适应相关的生物信号。基于估计的调谐曲线，可以使用群体向量、最大似然估计或贝叶斯推理（Pouget等人，2003年）对一组神经元的神经活动进行解码。群体向量方法导致尖峰计数与估计的生物信号之间存在线性关系，可以使用线性回归在单个阶段直接估计（Brown等人，2004年）。本章重点介绍使用线性回归的单阶段解码，特别是在伸手动作期间将线性回归应用于神经群体解码时面临的两个特殊挑战（见“运动预测”）。首先，考虑到解码信号的动态性质，有必要纳入神经活动的历史，而不仅仅是其当前的尖峰计数。其次，由于不同神经元活动之间的相关性（见“群体分析”）以及不同时间滞后的活动，由此产生的回归问题是不适定的，需要正则化技术（见“线性回归”）。尽管神经解码可以在一个阶段进行，但神经编码对于研究神经活动中编码了哪些信号仍然很重要。为此，调谐曲线的概念被推广以表征神经活动如何代表运动的时空轮廓。这种分析量化了时空调谐曲线以及由运动轮廓解释的神经活动的方差百分比（见“神经元编码和调谐曲线”）。为了进行比较，在泊松假设下独立评估可能与一般神经调制相关的神经活动的方差百分比（见“神经元调制”）。这两种方差分析提供了一个可行的工具，用于量化神经编码被有效解码的程度以及尚未解码的调制信号的潜在贡献。本章中描述的策略和算法使用在如Carmena等人，2003年所述的典型的带杆控制的目标击中实验期间从不同脑区的一组皮层神经元记录的神经活动进行了演示。
