Mixed Reality (MR) environments hold immense potential for inducing a sense of embodiment, where users feel like their bodies are present within the virtual space. This subjective experience has been traditionally assessed using subjective reports and behavioral measures. However, neurophysiological approaches offer unique advantages in objectively characterizing embodiment. This review article explores the current state of the art in utilizing neurophysiological techniques, particularly Electroencephalography (EEG), Photoplethysmography (PPG), and Electrodermal activity (EDA), to investigate the neural and autonomic correlates of embodiment in MR for out-of-the-lab applications. More specifically, it was investigated how EEG, with its high temporal resolution, PPG, and EDA, can capture transient brain activity associated with specific aspects of embodiment, such as visuomotor synchrony, visual feedback of a virtual body, and manipulations of virtual body parts. The potential of such neurophysiological signals to differentiate between subjective experiences of embodiment was discussed, with a particular regard to identify the neural and autonomic markers of early embodiment formation during MR exposure in real settings. Finally, the strengths and limitations of the neurophysiological approach in the context of MR embodiment research were discussed, in order to achieve a more comprehensive understanding of this multifaceted phenomenon.

Objective: Objective evaluation of operational training is challenging, especially when comparing physical training with virtual reality (VR). Behavioural and subjective measures capture performance and workload but provide limited insight into neurocognitive adaptation. We propose and validate an EEG-derived Cognitive Training Index (CTI) integrating multiple training-sensitive neurometrics to (i) track training progression and (ii) compare VR- versus physical-based training during subsequent real-environment execution. Approach: Participants performed three repetitions of a realistic confined-space industrial maintenance procedure with Entrance, Rolling shutter, and Pipe replacement phases. Execution time, procedural errors, and NASA-TLX were collected after each trial. Wearable EEG was recorded continuously; six neurometrics were computed from cleaned EEG using band-specific Global Field Power over predefined electrode sets. CTI was obtained via Principal Component Analysis at trial level, then analysed segment-wise. Temporal specificity was tested with phase-randomized surrogate analyses. Main results: Execution time decreased across trials (F = 14.82, p < .001, η²p = 0.35), errors decreased (F = 10.31, p < .001, η²p = 0.27), and NASA-TLX declined (F = 11.94, p < .001, η²p = 0.30). CTI increased with training (F = 18.21, p < .001, η²p = 0.39) and was higher in learning-relevant phases (Entrance and Pipe replacement) while remaining near baseline in Rolling shutter; surrogate analyses confirmed CTI exceeded null expectations. Repeated-measures correlations linked higher CTI to fewer errors (R = −0.42, p < .01) and lower NASA-TLX (R = −0.38, p < .01), with no association with execution time (R = 0.18, p = .12). Significance: CTI provides a psychologically grounded, multivariate EEG marker that tracks training progression and aligns with operationally meaningful outcomes (accuracy and perceived workload) rather than speed, enabling objective comparison of cognitive adaptation across training modalities.