Concealed personally familiar face with EEG in rapid serial visual presentation

Classical concealed information tests (CITs) can in some circumstances detect concealed information, but are vulnerable to countermeasures that participants can use to evade detection. Rapid serial visual presentation (RSVP) has demonstrated effectiveness against such countermeasures and can thus significantly reduce type-II errors. This study examined the effectiveness of an RSVP-based CIT combined with EEG in detecting ‘concealed knowledge’ of personally familiar faces. We compared the sensitivity of traditional univariate analyses, regional multichannel analyses and multivariate decoding analyses. A total of 29 participants performed an RSVP task in which they searched for a target face while a personally familiar face (one of their parents), or one of two control faces appeared in the stream. Using univariate cluster-based permutation tests on the P300 and P600 components at Pz, personally familiar faces were detected in 18 out of 29 participants, yielding a detection rate of 62.1%. Additionally, increased theta power was observed in response to personally familiar faces, allowing detection in 14 participants (48.3%). Regional multichannel analyses indicated that Pz and surrounding electrodes exhibited the largest familiarity effect, successfully detecting 13 participants (44.8%). Multivariate decoding analyses detected personally familiar faces at the group level, though individual variability remained high. It suggests that multivariate decoding is promising but requires larger datasets than traditional analyses and should focus on central and frontal electrodes to avoid the influence of low-level visual features. Overall, our results highlight the potential of RSVP-based CIT as an effective tool when paired with an optimized EEG experimental paradigms and data-analysis techniques.经典隐蔽信息测试（Concealed Information Test, CIT）虽可在部分场景下识别被隐藏的信息，但极易受到被试采用的反检测手段影响，从而无法侦测到隐藏信息。快速序列视觉呈现（Rapid Serial Visual Presentation, RSVP）已被证实可有效抵御此类反检测手段，从而显著降低II类错误（Type-II Error）的发生概率。本研究针对结合脑电图（Electroencephalogram, EEG）的基于RSVP的CIT方案，在识别个体熟悉面孔相关“隐藏知识”方面的有效性展开了探究。本研究对比了传统单变量分析、区域多通道分析以及多变量解码分析的检测灵敏度。总计29名被试参与了RSVP任务：被试需在快速序列流中搜寻目标面孔，同时序列中会随机呈现个体熟悉面孔（即被试的父母之一），或是两张对照面孔中的一张。通过对Pz电极位点的P300与P600成分进行基于聚类的置换检验单变量分析，本研究在29名被试中成功识别出18名对熟悉面孔有明显反应的个体，识别率达62.1%。此外，针对熟悉面孔的刺激可引发θ频段功率提升，基于此可识别出14名被试，识别率为48.3%。区域多通道分析结果显示，Pz电极及其周边电极位点呈现出最显著的熟悉度效应，成功识别出13名被试，识别率为44.8%。多变量解码分析可在组水平上识别出熟悉面孔相关的神经信号，但个体间差异依然较大。这表明多变量解码分析具有良好应用前景，但相较于传统分析方法，其需要更大规模的数据集，且应聚焦于中央与额叶电极位点，以规避低层次视觉特征带来的干扰。总体而言，本研究结果证实，结合优化后的EEG实验范式与数据分析技术，基于RSVP的CIT方案可作为高效的检测工具，具备良好的应用潜力。