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首页> 美国卫生研究院文献>Frontiers in Neuroinformatics >A Novel Fast Reliable and Data-Driven Method for Simultaneous Single-Trial Mining and Amplitude—Latency Estimation Based on Proximity Graphs and Network Analysis
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A Novel Fast Reliable and Data-Driven Method for Simultaneous Single-Trial Mining and Amplitude—Latency Estimation Based on Proximity Graphs and Network Analysis

机译：一种新的快速可靠和数据驱动的同时进行单次试验和振幅的方法-基于邻近图和网络分析的延迟估计

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Both amplitude and latency of single-trial EEG/MEG recordings provide valuable information regarding functionality of the human brain. In this article, we provided a data-driven graph and network-based framework for mining information from multi-trial event-related brain recordings. In the first part, we provide the general outline of the proposed methodological approach. In the second part, we provide a more detailed illustration, and present the obtained results on every step of the algorithmic procedure. To justify the proposed framework instead of presenting the analytic data mining and graph-based steps, we address the problem of response variability, a prerequisite to reliable estimates for both the amplitude and latency on specific N/P components linked to the nature of the stimuli. The major question addressed in this study is the selection of representative single-trials with the aim of uncovering a less noisey averaged waveform elicited from the stimuli. This graph and network-based algorithmic procedure increases the signal-to-noise (SNR) of the brain response, a key pre-processing step to reveal significant and reliable amplitude and latency at a specific time after the onset of the stimulus and with the right polarity (N or P). We demonstrated the whole approach using electroencephalography (EEG) auditory mismatch negativity (MMN) recordings from 42 young healthy controls. The method is novel, fast and data-driven succeeding first to reveal the true waveform elicited by MMN on different conditions (frequency, intensity, duration, etc.). The proposed graph-oriented algorithmic pipeline increased the SNR of the characteristic waveforms and the reliability of amplitude and latency within the adopted cohort. We also demonstrated how different EEG reference schemes (REST vs. average) can influence amplitude-latency estimation. Simulation results revealed robust amplitude-latency estimations under different SNR and amplitude-latency variations with the proposed algorithm.
机译：单次EEG / MEG记录的振幅和潜伏期都提供了有关人脑功能的有价值的信息。在本文中，我们提供了一个数据驱动的图形和基于网络的框架，用于从与多次试验相关的大脑记录中提取信息。在第一部分中，我们提供了所提出的方法论方法的概述。在第二部分中，我们提供了更详细的说明，并在算法过程的每个步骤中介绍了获得的结果。为了证明所提出的框架的合理性，而不是提出分析数据挖掘和基于图的步骤，我们解决了响应可变性的问题，这是对与刺激性质相关的特定N / P分量的振幅和潜伏期进行可靠估计的前提。这项研究中解决的主要问题是选择具有代表性的单项试验，目的是发现刺激产生的噪声较小的平均波形。这种基于图形和网络的算法程序可提高大脑反应的信噪比（SNR），这是关键的预处理步骤，可揭示刺激发生后特定时间的显着且可靠的振幅和潜伏期，并且随着正确极性（N或P）。我们使用来自42个年轻健康对照组的脑电图（EEG）听觉失配阴性（MMN）记录演示了整个方法。该方法新颖，快速且由数据驱动，首先成功揭示了MMN在不同条件（频率，强度，持续时间等）下引发的真实波形。所提出的面向图形的算法流水线提高了所采用队列中特征波形的信噪比以及幅度和等待时间的可靠性。我们还演示了不同的EEG参考方案（REST与平均值）如何影响振幅潜伏期估计。仿真结果表明，所提算法在不同信噪比和幅值延迟变化下具有鲁棒的幅值延迟估计。

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