Data Sheet 1_EEG biomarkers of the sense of embodiment: methodological gaps and evidence-based recommendations from a systematic review.pdf

IntroductionThe 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.
MethodsThis 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.
ResultsAcross 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.
DiscussionCritically, 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.