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（Classical concealed information tests, CITs）在部分场景下可检测隐蔽信息，但易受被试采用的反检测手段干扰。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类错误率。本研究探讨了基于RSVP的CIT结合脑电图（electroencephalogram, EEG）在检测个体熟悉面孔的“隐蔽知识”方面的有效性。我们对比了传统单变量分析、区域多通道分析与多变量解码分析的灵敏度。共有29名被试完成了一项RSVP任务：任务中被试需在快速序列视觉流中搜寻目标面孔，同时序列中会呈现个体熟悉面孔（其父母之一）或两张对照面孔之一。通过对Pz电极处的P300及P600成分进行基于聚类的置换单变量检验，29名被试中有18人成功被检测出识别出熟悉面孔，检测率达62.1%。此外，针对个体熟悉面孔的θ波段功率显著升高，据此可检测出14名被试（占比48.3%）。区域多通道分析结果显示，Pz电极及周边电极的熟悉效应最为显著，成功检测出13名被试（占比44.8%）。多变量解码分析可在组水平上检测出个体熟悉面孔，但个体间差异仍较大。研究表明，多变量解码分析颇具应用前景，但相较于传统分析方法需要更大的数据集，且应聚焦于中央区与额叶区电极，以规避低水平视觉特征的干扰。综上，本研究结果凸显了基于RSVP的CIT在配合优化的EEG实验范式与数据分析技术时，可作为一项高效检测工具的潜力。