The standard approach to analyzing brain electrical activity is to examine the spectral density function (SDF) and identify predefined frequency bands that have the most substantial relative contributions to the overall variance of the signal. However, a limitation of this approach is that the precise frequency and bandwidth of oscillations vary with cognitive demands. Thus they should not be arbitrarily defined a priori in an experiment. In this paper, we develop a data-driven approach that identifies (i) the number of prominent peaks, (ii) the frequency peak locations, and (iii) their corresponding bandwidths (or spread of power around the peaks). We propose a Bayesian mixture auto-regressive decomposition method (BMARD), which represents the standardized SDFas a Dirichlet process mixture based on a kernel derived from second-order auto-regressive processes which completely characterize the location (peak)and scale (bandwidth) parameters. We present a Metropolis-Hastings within Gibbs algorithm to sample from the posterior distribution of the mixture parameters. Simulation studies demonstrate the robustness and performance of the BMARD method. Finally, we use the proposed BMARD method to analyze local field potential (LFP) activity from the hippocampus of laboratory rats across different conditions in a non-spatial sequence memory experiment to identify the most interesting frequency bands and examine the link between specific patterns of activity and trial-specific cognitive demands.
翻译:分析大脑电子活动的标准方法是检查光谱密度函数(SDF),并查明对信号总体差异具有最实质性相对贡献的预定义频带(SDF),但这一方法的一个局限性是,振动的精确频率和带宽随认知需求而变化。因此,不应在实验中任意确定它们的一个先验性。在本文中,我们开发了一种数据驱动方法,确定(一) 突出峰值的数量,(二) 频率峰值位置,和(三) 相应的带宽(或峰值周围的权力分布)。我们建议采用一种巴耶斯混合混合物混合物混合物混合物的自动递减脱位法(BARD),该方法代表基于二级自动递减过程(Paak)和比例(带宽)参数参数参数参数完全特征的内部位(peak)和比例(bandwewth)参数参数的参数样本。我们用一种Meopolopolis-His, 模拟研究表明BMAR-C-BMAR-C-C-C-C-CRestal Restal Restal Breal Adal Adal Adal Aracts) 和MAR-C-C-C-Sleval Areval Aral Agal Arence-S,我们使用了一种拟议的实验室-C-BMAL-C-C-C-C-C-C-C-C-C-C-BML 活动在实验室-C-CReval-C-C-C-BMAL-C-C-C-C-C-C-C-C-C-CL 和ML-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-CL 和MFPAdal-C-C-C-C-C-C-C-C-C-C-C-C-I-C-C-C-I-C-C-C-C-C-CL-C-C-C-C-C-C-C-C-C-C-C