Felix Leonardo B, Gonçalves Moisés C, Zanotelli Tiago, Miranda de Sá Antonio M F L, Simpson David M
Department of Electrical Engineering, Federal University of Viçosa, Viçosa, MG, Brazil; Graduate Program in Electrical Engineering, Federal University of São João del rei, São João del rei, MG, Brazil; Institute of Sound and Vibration and Research, University of Southampton, Southampton, Hampshire, UK.
Graduate Program in Electrical Engineering, Federal University of São João del rei, São João del rei, MG, Brazil.
Comput Methods Programs Biomed. 2020 Oct;195:105550. doi: 10.1016/j.cmpb.2020.105550. Epub 2020 May 24.
There are many phenomena that lead to changes in the power spectrum of a given signal, and their detection has been a challenge that has received considerable attention over the years. Objective Response Detection (ORD) techniques are a set of tools that perform automated tests for such a task, allowing thus to automatically track changes in the spectrum. The performance of these detectors is affected by the signal-to-noise ratio (SNR) of the recorded signal as well as the length of the available data. The Global F Test (GFT) is a promising detector that can be used to test whether there is a statistically significant difference between the spectrum before and during an event. In fact, this detector has proved useful in the detection of event-related desynchronization/synchronization (ERD/ERS), where only amplitude, but not the phase, changes are locked to the stimulus. In order to improve the statistical power of the GFT (for the same length of recording), multiple channels recorded simultaneously can be included. This concept is called Multivariate Response Detection. The aim of the current work is to extend the GFT to the multivariate (multichannel) case.
Firstly, the single channel normalization of the GFT is presented as a new ORD detector - the global Beta test (GBT). After that, three multivariate extensions of this new test are derived. The critical values used in the detection of spectral changes are obtained by using theoretical distributions, and where this is intractable, by means of Monte Carlo simulations. The probability of detection (PD) of each technique was estimated using simulation and was used in order to compare the detectors performance. A practical example with the electroencephalogram (EEG) from 10 volunteers under intermittent photic stimulation was also provided.
The statistics under both the null and alternative hypothesis could be obtained for all detectors. Simulated results for PD demonstrate the strong potential of the proposed method and the performances in EEG data are always improved with increasing number of signals.
If more than one signal is available, then the multivariate extensions may provide significant benefit compared to the original GFT.
存在许多导致给定信号功率谱变化的现象，多年来对其进行检测一直是备受关注的挑战。客观反应检测（ORD）技术是执行此类任务自动化测试的一组工具，从而能够自动跟踪频谱变化。这些检测器的性能受记录信号的信噪比（SNR）以及可用数据长度的影响。全局F检验（GFT）是一种有前景的检测器，可用于测试事件发生前和发生期间的频谱之间是否存在统计学上的显著差异。事实上，该检测器已被证明在检测事件相关去同步化/同步化（ERD/ERS）方面很有用，其中只有幅度而非相位的变化与刺激相关。为了提高GFT的统计功效（对于相同的记录长度），可以纳入同时记录的多个通道。这个概念称为多变量反应检测。当前工作的目的是将GFT扩展到多变量（多通道）情况。
首先，将GFT的单通道归一化作为一种新的ORD检测器——全局贝塔检验（GBT）提出。之后，推导了这种新检验的三种多变量扩展。检测频谱变化时使用的临界值通过理论分布获得，在难以处理的情况下，则通过蒙特卡罗模拟获得。使用模拟估计每种技术的检测概率（PD），并用于比较检测器的性能。还提供了一个来自10名志愿者在间歇性光刺激下的脑电图（EEG）的实际例子。
所有检测器均可获得原假设和备择假设下的统计数据。PD的模拟结果证明了所提方法的强大潜力，并且在EEG数据中的性能总是随着信号数量的增加而提高。
如果有多个信号可用，那么与原始GFT相比，多变量扩展可能会带来显著益处。
