The body recognition process includes complex visual processing, the sensation, perception, and distinction stages of the stimulus. This study examined this process by using the time–frequency analysis of EEG signals and analyzed the obtained data by using the event-related oscillations method. This study aimed to examine the oscillatory brain responses and distinguish one’s own body from other’s body. In the present study, 17 young adults were included and the EEGs were recorded with 32 electrodes placed in different locations. Event-related power spectrum and phase-locking analyzes were performed. ITC and ERSP data were analyzed using 2 (condition) × 11 (location) × 2 (hemisphere) ANOVA Design. As we observed a prolonged response in the theta band in the grand averages, we included the time variable in the overall model. As a result, we found that the phase-locking and the event-related power spectrum of the theta response in recognizing one’s own body were higher when compared to the phase-locking and the event-related power spectrum of the theta response in recognizing others’ body (p < 0.05). When the time variable was included, the early theta response was more phase-locked and had a higher power spectrum compared to the late theta response (p < 0.05). As a result of the power spectrum analysis, the condition × hemisphere interaction effect in the beta band was higher in the left hemisphere regarding increased responses in recognizing one’s own body (p < 0.05). As a result of ITC, the main effect of the condition was higher in the recognition of the stimulus of one’s own body (p < 0.05). Finally, the theta oscillator response stood out in distinguishing one’s own body from other’s body. Similarly, the power spectrum in the beta response was higher in the left hemisphere, and this finding is consistent with the literature.

身体识别过程包括复杂的视觉处理、刺激的感觉、知觉和区分阶段。本研究通过对脑电信号的时频分析来检验这一过程，并通过事件相关振荡方法分析获得的数据。这项研究旨在检查大脑的振荡反应，并将自己的身体与他人的身体区分开来。在本研究中，包括了 17 名年轻人，并用放置在不同位置的 32 个电极记录了脑电图。进行了与事件相关的功率谱和锁相分析。使用 2（条件）×11（位置）×2（半球）ANOVA 设计分析 ITC 和 ERSP 数据。由于我们观察到在总平均值中的 theta 波段有一个延长的响应，我们将时间变量包含在整个模型中。因此，p  < 0.05)。当包括时间变量时，与晚期θ响应相比，早期θ响应更锁相并且具有更高的功率谱（p  <0.05）。作为功​​率谱分析的结果，β波段的条件×半球相互作用效应在左半球较高，因为识别自己身体的反应增加（p  <0.05）。作为ITC的结果，条件的主效应在识别自己的身体刺激方面更高（p  <0.05）。最后，θ振荡器反应在区分自己的身体和他人的身体方面很突出。同样，左半球 β 响应的功率谱较高，这一发现与文献一致。