Derzsi Zoltan
Department of Psychology, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom.
Front Neurosci. 2021 Feb 18;15:600543. doi: 10.3389/fnins.2021.600543. eCollection 2021.
In EEG studies, one of the most common ways to detect a weak periodic signal in the steady-state visual evoked potential (SSVEP) is spectral evaluation, a process that detects peaks of power present at notable temporal frequencies. However, the presence of noise decreases the signal-to-noise ratio (SNR), which in turn lowers the probability of successful detection of these spectral peaks. In this paper, using a single EEG channel, we compare the detection performance of four different metrics to analyse the SSVEP: two metrics that use spectral power density, and two other metrics that use phase coherency. We employ these metrics find weak signals with a known temporal frequency hidden in the SSVEP, using both simulation and real data from a stereoscopic apparent depth movement perception task. We demonstrate that out of these metrics, the phase coherency analysis is the most sensitive way to find weak signals in the SSVEP, provided that the phase information of the stimulus eliciting the SSVEP is preserved.
在脑电图研究中，检测稳态视觉诱发电位（SSVEP）中微弱周期性信号最常用的方法之一是频谱评估，该过程可检测在显著时间频率处出现的功率峰值。然而，噪声的存在会降低信噪比（SNR），进而降低成功检测这些频谱峰值的概率。在本文中，我们使用单个脑电图通道，比较四种不同指标对SSVEP的检测性能：两种使用频谱功率密度的指标，以及另外两种使用相位相干性的指标。我们使用这些指标，通过模拟和来自立体表观深度运动感知任务的真实数据，来寻找隐藏在SSVEP中具有已知时间频率的微弱信号。我们证明，在这些指标中，相位相干性分析是在SSVEP中寻找微弱信号最敏感的方法，前提是引发SSVEP的刺激的相位信息得以保留。
