Abstract

Introduction:

The sense of embodiment (SoE), describing the experience of owning, controlling, and being located within a body, underpins virtual reality (VR) interaction, brain-computer interfaces (BCIs), and multisensory body-illusion research. Although SoE is typically assessed through subjective questionnaires, their variability and limited validity have motivated the search for objective neural markers. Electroencephalography (EEG) has become the most widely used technique given its portability and high temporal resolution; however, the existence of a consistent EEG correlate of embodiment remains unclear.

Methods:

This systematic review summarizes 35 EEG studies (2010–June 2025) identified through structured database searches, examining SoE across immersive and non-immersive VR, augmented reality, and non-VR paradigms. We analyze EEG features including spectral power, event-related desynchronization/synchronization (ERD/ERS), connectivity, and temporal dynamics, and examine methodological variability in illusion induction and SoE assessment.

Results:

Across studies, the reduction of the alpha-band over central-parietal regions emerges as the most recurrent correlate of embodiment. Beta-band decreases and gamma-band increases appear in several studies but lack consistent replication, while findings in Delta and Theta bands remain sparse and contradictory. Considerable heterogeneity is found in VR paradigms, EEG setups, preprocessing, and psychometric tools, contributing to inconsistent results and limiting cross-study comparability.

Discussion:

Critically, no EEG feature demonstrates sufficient reproducibility to qualify as a universal biomarker of SoE, and no standardized protocol for EEG-based embodiment assessment currently exists. Overall, this review highlights both the promise and current limitations of EEG-based approaches to measuring embodiment. We conclude by identifying methodological gaps and outlining recommendations to support the development of reliable EEG markers for future applications in VR rehabilitation, MI-BCIs, cognitive neuroscience, and clinical interventions.

1 Introduction

Sense of Embodiment (SoE) refers to the subjective perception that a non-biological body or body part, such as a virtual avatar or prosthetic limb, feels like one's own body. This arises from the alignment of sensations of being within, owning, and controlling the artificial body part (Kilteni et al., 2012a). The concept was first introduced by Botvinick and Cohen (1998) through the well-known Rubber Hand Illusion (RHI), which demonstrated that tactile sensations could be transferred to an artificial limb. In this paradigm, participants observed a rubber hand being stroked while their real, hidden hand was touched synchronously. This multisensory congruence led participants to perceive the rubber hand as their own, making the RHI a foundational experiment for exploring body ownership and the brain's construction of self-perception.

Following the original RHI, numerous studies have replicated and extended this phenomenon using different sensory feedback modalities. Among the most powerful tools for inducing SoE illusion is Virtual Reality (VR), since it enables the replacement of the user's real body with a virtual avatar, allowing the controlled manipulation of SoE (Guy et al., 2023). VR can simulate realistic or fantastical computer-generated scenarios in which users can interact with and experience a virtual body, making it an ideal platform for controlled embodiment illusions. This effect is further enhanced by VR's capacity to evoke a strong sense of immersion, typically achieved through head-mounted displays (HMDs). Immersive VR, characterized by 3D environments, is distinguished from non-immersive VR by its ability to create a sense of physical presence, which is the feeling of being within the virtual world, independent of body perception (Dincelli and Yayla, 2022; Kilteni et al., 2012a; Guy et al., 2023). These controlled environments make VR an ideal setting for inducing embodiment illusions through multisensory feedback, often involving visuoproprioceptive cues, motion tracking, and haptic interaction. Visual feedback, in particular, plays a crucial role, especially when the virtual body responds synchronously to the user's voluntary movements (Choi et al., 2020b; Škola and Liarokapis, 2023). This coupling of sensory input and motor output enhances SoE, creating a rich and immersive experience (Ferreira, 2006; Vourvopoulos et al., 2022, 2016). Early studies extended the RHI into VR (Lenggenhager et al., 2007), later advancing to full-body ownership illusions with applications in rehabilitation, gaming, and neuroscience (Slater, 2009; Petkova and Ehrsson, 2008; Vagaja et al., 2024; Guy et al., 2023).

With the increasing accessibility of immersive VR technologies, virtual embodiment has been applied across multiple healthcare domains. Pérez-Marcos et al. (2009) were the first to demonstrate that SoE over a virtual hand could be induced using motor imagery (MI) via a brain-computer interface (BCI). MI-BCI systems work by recording brain activity generated during MI tasks, that is, when individuals mentally rehearse movements without physically executing them (Chen et al., 2023; Pichiorri et al., 2015; Gu et al., 2021). This neural information is then translated into commands for external devices, such as robotic arms or, in this case, a virtual avatar, allowing them to act on the user's behalf (Wolpaw et al., 2020; Chen et al., 2023; Daly and Huggins, 2015). In the study by Pérez-Marcos et al. (2009), neurofeedback was provided by rendering a virtual hand that performed the imagined movement in synchrony with the user's intention, effectively reinforcing the SoE over the virtual limb. This integration of MI and VR opens promising pathways for immersive, user-centered rehabilitation therapies. Based on this, in neurorehabilitation, integrating embodiment into MI-BCI training has been shown to enhance patients'ability to modulate brain activity and improve recovery outcomes (Vagaja et al., 2024; Batista et al., 2024; Juliano et al., 2020; Choi et al., 2020a), leading to a growing interest in SoE. Virtual embodiment and embodiment have been used in psychiatric and psychological treatments, including exposure therapy for phobias (Tardif et al., 2019), therapy for eating disorders (Cesa et al., 2013), treatment of post-traumatic stress disorder (PTSD) (Reger and Gahm, 2008), and pain management for burn patients (Hoffman et al., 2000). Embodiment-based feedback has also been employed in psychological self-counseling programs (Osimo et al., 2015). Moreover, SoE provides insights into neurological disorders. For instance, in anarchic hand syndrome, patients perceive their limb as acting independently of their will, reflecting a breakdown in SoE (Kilteni et al., 2012a).

Given its broad potential in multiple healthcare areas, embodiment illusion has grown considerably and, as a result, so has its conceptual framework. The most recent and widely adopted model defines SoE as comprising three interrelated components: Sense of Ownership (SoO), Sense of Agency (SoA), and Sense of Self-location (SoSL). When these three components are aligned, a strong SoE toward a virtual body or object is typically established (Kilteni et al., 2012a; Vagaja et al., 2024; Guy et al., 2023).

SoO refers to self-attribution of the body, of the belief that the experienced sensations are being applied to the individual's body. In the VR community, the distinction between ownership of the whole body or specific body parts is less pronounced since the focus often lies on embodying full-body avatars (Kilteni et al., 2012a; Segil et al., 2022; Guy et al., 2023). SoO was the primary effect observed during the RHI and it became the first component of the SoE to be extensively studied (Guy et al., 2023). It arises from bottom-up sensory inputs (e.g., visual, tactile, and proprioceptive) and top-down cognitive expectations (e.g., internal body maps). Essentially, it refers to how the brain processes and interprets sensory input based on expectations. SoO only occurs when both these influences are aligned, creating a coherent perception of the body. However, how these processes interact is still unclear (Kilteni et al., 2012a; Guy et al., 2023). Additionally, research suggests that stronger SoO is induced when external objects resemble the real body, requiring anatomical plausibility and spatial alignment. VR enhances this by enabling customizable avatars that closely match users' real bodies (Guy et al., 2023; Segil et al., 2022).

SoA is the experience of controlling one's movements and their outcomes. It includes the feeling of agency, an implicit, low-level, non-reflective sense tied to action initiation, and the judgment of agency, which is a higher-level, reflective reasoning of the action based on sensory feedback. Thus, this sense arises from sensorimotor integration and cognitive processes. SoA is triggered when predicted and actual sensory feedback align, with temporal and spatial congruence being critical (Kilteni et al., 2012a; Segil et al., 2022; Guy et al., 2023). In VR, immersive tracking and low-latency feedback enhance SoA, ensuring seamless control of virtual avatars (Guy et al., 2023).

Finally, SoSL refers to the subjective experience of perceiving oneself as situated within one's body, with a clear awareness of spatial position and the physical boundaries separating the body from the surrounding environment. It is shaped by visuospatial perspective (viewing the world from within the body or from an external vantage point), vestibular signals (providing information about motion, rotation, balance, and orientation), and tactile input. Tactile stimulation operates across three spatial zones: personal space (the skin and the body's boundaries), peripersonal space (within arm's reach), and extrapersonal space (beyond arm's reach). Interestingly, studies have shown that individuals can experience self-location in two different places simultaneously, adding further complexity to the understanding of SoSL (Kilteni et al., 2012a; Guy et al., 2023).

Research has shown that the three components of SoE are distinct and can be experienced independently, yet they remain deeply interconnected. VR provides unique opportunities to examine SoO, SoA, and SoSL in isolation; however, these components are not easily disentangled at the neurophysiological level, which complicates the understanding of SoE. Moreover, little is known about how each subcomponent contributes to the overall experience or how they interact with one another. Importantly, the relative significance of each component appears to vary across experimental contexts (Kilteni et al., 2012a; Guy et al., 2023; Segil et al., 2022).

The induction of SoE relies on sensory cues that align with SoO, SoA, and SoSL. In VR, researchers typically use three primary types of sensory triggers: visuomotor (synchronization between visual feedback and motor execution), visuotactile (alignment between the tactile sensations perceived and the visual feedback received from the virtual body), and visuoproprioceptive (visuospatial perspective) (Vagaja et al., 2024; Guy et al., 2023). Additional influences include task goals, emotions, personality traits, and social or cultural factors. Users often adjust their behavior and elements of a virtual avatar (control, appearance, and perspective) based on their task goals and personal inclinations. This effect, called the Proteus effect, leads to users modifying their actions and cognitive performance based on the characteristics of their avatars (Yee and Bailenson, 2007; Peck et al., 2013; Banakou et al., 2016, 2018). However, it's essential to note that these embodiment effects are not universal. While some individuals may experience profound behavioral changes, such as the Proteus effect, or actions influenced by social stereotypes, others may show little to no modification in their behavior or perception during SoE (Guy et al., 2023), further highlighting the complexity of SoE.

In terms of assessment methods, the most common method to evaluate SoE is through questionnaires and subjective reports, typically using Likert scales where participants rate their agreement with statements about SoE over a fake body or body part (Guy et al., 2023; Segil et al., 2022). However, variations of these questionnaires are often used across studies, making comparisons difficult and imposing a considerable limitation in the SoE assessment. Efforts to standardize questionnaires, such as the 16-item version by Peck and Gonzalez-Franco (2021), have improved comparability across experiments, but new questionnaires continue to emerge, adding to the complexity (Guy et al., 2023). Furthermore, there are criticisms regarding their reliability due to personal subjectivity, participant interpretation, and scale biases. Therefore, they are not considered the gold standard for SoE assessment despite their usefulness, leading researchers to explore more objective methods.

Behavioral measures like proprioceptive drift, pain perception, intentional binding, and sensory attenuation are often combined with questionnaires for a more comprehensive evaluation.

Proprioceptive drift measures a person's ability to locate a hidden limb without visual feedback. It is calculated as the difference between the actual and perceived positions of the hidden limb after the embodiment illusion, with increased drift indicating a shift in perception toward the fake limb. Thus, it tracks changes in body representation and can be repeated over time to track long-term changes. While linked to ownership illusions, it focuses more on body representation than visuotactile aspects. The SoSL is considered modified when participants point toward the fake limb, suggesting an update in their peripersonal space (Guy et al., 2023; Segil et al., 2022).

Pain perception is based on the hypothesis that disownership reduces pain severity when a painful stimuli are applied to the disowned limb. Pain is typically induced using thermal skin conduction or infrared laser probes, with participants rating pain intensity, and the comparison of pain ratings across conditions gives insight into SoE changes (Segil et al., 2022).

Intentional binding measures time compression between voluntary actions and their sensory outcomes. This phenomenon is closely linked to SoA and is measured by comparing perceived time intervals between self-initiated actions and external events, with greater binding indicates a stronger SoA. However, its reliability is limited by factors like attention and external conditions (Guy et al., 2023; Segil et al., 2022).

Finally, sensory attenuation refers to the decreased perceived intensity of self-generated touch compared to externally generated touch. The brain distinguishes non-threatening self-touch from potentially threatening external touch. Therefore, the attenuation effect indicates whether an individual feels agency over the actions of a fake body part, being again a measure closely related to SoA. It can be tested across multiple sensory modalities, including auditory (e.g., judging sound intensity), visual (e.g., perceiving visual stimuli), and tactile (e.g., applying force to one's own hand and measuring perceived intensity) (Guy et al., 2023; Segil et al., 2022).

While behavioral measures are useful, they alone are insufficient for a complete SoE assessment, and contradictory reports on their reliability further introduce uncertainties regarding these behavioral changes as SoE measurement methods. Thus, physiological measures have been pointed out for a more objective evaluation of SoE. For example, skin temperature, where findings indicate that a decreased skin temperature indicates a feeling of disownership, while increased temperature relates to SoE due to the relationship between skin temperature and autonomic responses. Nonetheless, results are inconsistent, which could stem from uncontrolled factors (e.g., room temperature) (Segil et al., 2022). Similarly, skin conductance measures autonomic responses like sweating. For example, when individuals feel a SoE over an artificial hand, their sympathetic response (such as sweating) is heightened when the hand is threatened. Yet, it is also affected by external factors, such as participant variability and repeated exposure, limiting its reliability over time (Segil et al., 2022). More controlled research is needed to establish these measures as reliable SoE indicators.

Moreover, research into neurophysiological biomarkers using techniques such as Electroencephalography (EEG), Magnetoencephalography (MEG), Functional Magnetic Resonance Imaging (fMRI), Magnetic Resonance Imaging (MRI), Electromyography (EMG), and Near-Infrared Spectroscopy (NIRS) is ongoing, though no definitive conclusions have been reached yet. These non-invasive neuroimaging methods aim to measure brain activity during SoE experiences, to link neurological responses to SoE. This would help to better assess SoE, but also expand the understanding of how one perceives their body and the theoretical framework of embodiment experiences (Segil et al., 2022). Of all techniques, EEG arises as one of the most promising due to its notorious advantages, namely, being a low-cost, portable, and non-invasive procedure that can be applied repeatedly to both adults and children, its high temporal resolution (millisecond level), and the existence of standardized electrode placement and signal processing techniques, tailored to specific application goals, allowing reproducibility and increasing the flexibility of this technique (Teplan, 2022; Gu et al., 2021).

In short, at present, there is no universally accepted objective method for evaluating SoE, and consequently, subjective questionnaires remain the primary tool until more reliable metrics are developed. However, questionnaires are inherently subjective and prone to bias, highlighting the need for robust and objective biomarkers of SoE. Identifying an EEG-based biomarker would provide a more objective means of assessing the illusion, with potential applications in fields such as neurorehabilitation through MI-BCI training and psychological interventions for phobias or trauma. Such a biomarker could enable real-time monitoring to ensure that subjects experience SoE during therapy, facilitating the design of more effective and immersive VR environments. Moreover, physiological assessment methods are essential for advancing our understanding of the underlying neural mechanisms and ensuring consistent evaluation across studies and domains. In particular, EEG-based MI-BCI applications in VR rehabilitation stand to benefit greatly from such developments, motivating the need for a systematic review.

Therefore, the review aims to synthesize EEG findings related to SoE and assess whether consistent EEG changes associated with embodiment have been identified across virtual and non-virtual contexts. It also examines the methodological diversity of existing studies, with the goal of identifying effective approaches and promoting greater consistency and validity in future SoE research. Understanding variability in experimental methods is especially important, as it may significantly influence outcomes (Falcone et al., 2023; Guy et al., 2023).

2 Methodology

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines (Page et al., 2021) were followed to ensure a rigorous and transparent framework for addressing the research questions (the PRISMA 2020 checklist is provided in the Supplementary material). Selection criteria were defined, and the identified papers were systematically screened for inclusion or exclusion, with the aim of capturing studies reporting EEG-based biomarkers of SoE. The search strategy is described in detail, and the included papers were examined with respect to reported EEG biomarkers, data collection procedures, and methodological approaches.

2.1 Inclusion criteria

Articles were included if they explored interventions or exposures related to SoE, such as VR experiences, RHI, MI training, or multisensory integration tasks, while recording and analyzing EEG patterns during illusion. Including and excluding criteria were defined according to the PICOS framework (Table 1). Within this framework, no explicit comparison condition was predefined, as the aim of this review was to identify consistent reports of EEG-based biomarkers associated with SoE across different experimental paradigms, rather than to compare specific control conditions. Studies that did not meet these inclusion criteria, lacked sufficient information, contained redundant data, or did not directly contribute to understanding SoE were excluded from this review.

CategoryInclusion criteriaExclusion criteriaPopulation (P)Human participants.Animals or pure simulation studies.Intervention (I)Tasks or paradigms designed to explicit induce or modulate SoE, including VR, AR, RHI, MI, and multisensory integration tasks.Studies not evaluate SoE, or did not explicitly state it.Comparison (C)Not applicable (no explicit comparison condition defined).Outcome (O)EEG-derived measures related or accessed during SoE induction.Studies assessing SoE exclusively through behavioral, subjective, or questionnaire-based measures without EEG.Study design (S)Peer-reviewed journal articles and conference proceedings published between 2010 and June 2025, in English.Reviews, meta-analyses, editorials, theses, non–peer-reviewed articles, or abstracts only. Studies published before 2010, or in non-English language.

Summary of inclusion and exclusion criteria according to the PICOS framework.

2.2 Search strategy

The search for an EEG biomarker of SoE was conducted across multiple databases, including ScienceDirect, PubMed, ACM Digital Library, and IEEE Xplore. These databases were selected to ensure comprehensive coverage of the multidisciplinary literature relevant to SoE, EEG, and immersive technologies. ScienceDirect was chosen for its broad coverage of neuroscience and engineering research; PubMed was included to capture studies with biomedical and clinical relevance; the ACM Digital Library was selected to emphasize literature from VR, Human-Computer Interaction (HCI), and game-related research; and IEEE Xplore was included due to its extensive coverage of BCIs, signal processing, and VR/AR technologies.

A common core search strategy was defined and adapted to the specific syntax of each database. The search utilized the following keyword combinations: (“Sense of Embodiment” OR “Body Ownership”) AND (“Measure” OR “Biomarker” OR “Assessment”), with or without (“Virtual Reality” OR “VR” OR “Illusion”). No exclusion keywords were applied. Filters were applied during this initial search to ensure that the retrieved papers met the predefined inclusion criteria (Table 1). Additionally, reference list screening was not conducted as part of the formal systematic review procedure.

2.3 Study selection and data extraction

All screening and eligibility assessments of the included studies were based on their titles and abstracts to determine whether they were relevant to the SoE. Duplicate or not accessible articles were then removed, and the remaining papers were reviewed to ensure they met the inclusion criteria. The second phase of the screening procedure focused on the papers' abstracts, methods, and conclusions to confirm that they focused on modulating SoE and included at least one related physiological measure. Articles that discussed physiological biomarkers of SoE were selected and categorized by the technique used. Then, EEG-related studies were further divided into Augmented Reality (AR), immersive VR, non-immersive VR, and no-VR settings, and were moved to further analysis. Lastly, the EEG-related articles were analyzed in detail, focusing on the methods and results reported.

Similarly, data extraction from these EEG-related studies was performed. The primary outcomes of interest were EEG-derived measures associated with the SoE. As this was an exploratory literature review, no outcome selection process was applied, and all reported EEG outcomes related to SoE were extracted. Additional variables were extracted to examine methodological variability, including sample characteristics, VR paradigms, EEG setups, EEG metrics, and psychometric tools. Due to the exploratory nature of the review and the substantial methodological heterogeneity across experimental paradigms and EEG outcome measures, a narrative synthesis approach was adopted. Studies were grouped by VR modality (AR, immersive VR, non-immersive VR, and no-VR) and EEG feature to facilitate qualitative comparison. Tables were used to report results, supporting their coherent synthesis. Missing or unclear information was not included.

2.4 Quality assessment

Study quality and risk of bias were assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Analytical Cross-Sectional Studies (Moola, 2024), a method that evaluates methodological quality and potential sources of bias in study design, conduct, and analysis. Although originally developed for clinical and epidemiological research, key appraisal domains apply to the experimental studies included in this review, to provide a structured, transparent approach to assessing methodological rigor across heterogeneous study designs. The first author assessed each study according to the eight questions from the JBI checklist that covered participant selection, confounders, outcome measurement, and statistical analysis. For the purposes of this review, the JBI checklist items were interpreted as it follows: Q1 (“Were the criteria for inclusion in the sample clearly defined?”) was interpreted as the explicit definition of inclusion/exclusion criteria of participants beyond vague descriptions; Q2 (“Were the study subjects and the setting described in detail?”) was assessed based on whether the sample was adequately described in terms of demographic characteristics (e.g., age, sex/gender, and sample size); Q3 (“Was the exposure measured in a valid and reliable way?”) was interpreted in relation to whether the experimental conditions were clearly defined and appropriately implemented to manipulate SoE; Q4 (“Were objective, standard criteria used for measurement of the condition?”) was evaluated based on whether the authors clearly defined what aspects of SoE were being assessed and how these were measured, using standardized methods; Q5 (“Were confounding factors identified?”) focused on whether potential confounders related to study design, SoE implementation, or EEG analysis, were acknowledged; Q6 (“Were strategies to deal with confounding factors stated?”) was assessed based on whether at least some of the identified confounders were addressed or explicitly reported as study limitations; Q7 (“Were the outcomes measured in a valid and reliable way?”) was interpreted in the context of EEG recording, preprocessing, analysis, and outcome definition; and Q8 (“Was appropriate statistical analysis used?”) was assessed based on whether the statistical analyses were suitable for the study design and were adequately reported. The responses were scored as “yes,” “no,” “unclear,” or “not applicable,” and the results were summarized descriptively to characterize common sources of bias across studies.

3 Results

After the search, 602 papers were initially identified across the databases. After removing duplicates and excluding articles without access, 478 studies remained for further analysis to determine if they met the inclusion criteria. Ultimately, 134 papers were found to meet the criteria, addressing SoE biomarkers. Of these, 20 were reviews, 80 focused on different physiological techniques (such as skin temperature, EMG, MEG, fMRI, or MRI, skin conductance, cardiac and blood perfusion, eye tracking, NIRS, enzyme and molecular studies, physiological activity in deep visceral organs, etc.), and 35 focused on EEG. Overall, EEG was the most common physiological technique for identifying a SoE biomarker in the last fifteen years.

In the end, those 35 articles specifically related to EEG biomarkers of SoE were included for analysis. These articles were further categorized into AR (1 article), immersive VR (15 articles), non-immersive VR (5 articles), and no-VR settings (14 articles). The screening process for the selected articles is illustrated in Figure 1.

Flowchart illustrating the identification, screening, and selection process of studies included in the analysis of EEG biomarkers associated with the SoE.

Figure 2 presents a histogram showing the number of articles included in this review per year. An increase in studies was expected due to the growing interest in SoE across various fields, particularly in restorative MI-BCI research. This trend was further driven by the rapid adoption of VR, especially following recent developments in HMD that have made immersive VR more accessible and user-friendly. Consequently, an increase in SoE-focused studies using VR settings was anticipated. As shown in the histogram, the number of studies indeed grew over time.

Histogram showing the number of articles included in the analysis of EEG biomarkers associated with the SoE, categorized by year. Blue bars represent studies using immersive VR to induce the illusion, orange bars represent non-immersive VR settings, gray bars represent studies using other methods to induce the illusion (no-VR settings), and, lastly, the yellow bar represents the study using AR.

3.1 EEG biomarkers of SoE

Table 2 summarizes the most notable findings from the included papers, highlighting conclusions regarding potential EEG biomarkers of SoE. Overall, the literature indicates that embodiment illusions can modulate EEG signals, with several potential biomarkers reported across different frequency bands. However, there is also a considerable amount of conflicting evidence.

CategoryFindingsArticlesImmersive VRNo immersive VRNo-VRAROscillatory ProcessesThere is an increase of mid-frontal theta power in response to observing avatar errors when embodied.(Pavone et al., 2016)There is a stronger theta ERS in the left frontocentral areas during high levels of SoA in pre- movement of the hand, while this increase is observed in the right temporal areas during low SoA during hand movement.(Jeunet et al., 2018)Theta BandSoE is related to theta power modulation in the central region.(Esteves et al., 2025), (Ramírez-Campos et al., 2024)Greater theta bands activity in the temporal and adjacent areas occurs during incongruent visuotactile stimuli (low SoE), indicating a greater workload to assimilate the incongruent inputs.(Hansford et al., 2023)No changes in theta power during SoE.(Li et al., 2023; Raz et al., 2020)(Sciortino and Kayser, 2022b)SoE is related to alpha activity over central–parietal regions.(Ramírez-Campos et al., 2024)SoE is associated with stronger alpha band suppression in the frontal, parietal, and central regions, mainly in the sensorimotor cortex.(Raz et al., 2020; Nicolardi et al., 2025)(Kang et al., 2015), (Shibuya et al., 2021), (Shibuya et al., 2018) (Sciortino and Kayser, 2022b), (Faivre et al., 2017), (Rao and Kayser, 2017), (Della Longa et al., 2021), (Shibuya and Ohki, 2023)When mentally or physically controlling an avatar's gait, SoE is associated with higher alpha ERS in centrofrontal and centro-parietal areas.(Alchalabi et al., 2019)Alpha BandHigher frontal alpha power is associated with decreased susceptibility to SoE illusions.(Hsu et al., 2022), (Della Longa et al., 2021)SoSL is associated with a stronger alpha band over the mPFC. There is less alpha suppression over fronto-parietal areas during SoO illusions.(Evans and Blanke, 2013)(Lenggenhager et al., 2011)There is an increase of alpha band desynchronization over parietal areas in response to observing avatar errors when embodied.(Pavone et al., 2016)No lateralization of alpha power reduction was observed during SoE.(Sciortino and Kayser, 2022b)No changes in alpha band during SoE.(Li et al., 2023; Esteves et al., 2025)(Hansford et al., 2023)SoE is associated with beta power increases in the occipital region.(Esteves et al., 2025)SoE is associated with a decrease of beta power over frontal, central, and parietal, mainly sensorimotor brain areas.(Kang et al., 2015)(Sciortino and Kayser, 2022b), (Rao and Kayser, 2017), (Faivre et al., 2017)No lateralization of beta power reduction during SoE.(Sciortino and Kayser, 2022b)Beta BandBeta power reduction during RHI (SoE) emerges immediately after the illusion onset.(Sciortino and Kayser, 2022b)SoO is associated with attenuated beta ERD over contralateral sensorimotor brain areas.(Shibuya et al., 2021)Behavioral SoE measure (proprioceptive drift) is associated with stronger high beta power over fronto- temporal sites.(Faivre et al., 2017)No changes in beta power during SoE.(Alchalabi et al., 2019; Li et al., 2023; Evans and Blanke, 2013; Raz et al., 2020)(Hansford et al., 2023)SoE is associated to gamma activity over central–parietal regions.(Ramírez-Campos