## 6 Neurophysiological Characterization of Embodiment in Mixed Reality
### Neurophysiological Measurements  
This section summarizes the neurophysiological measures identified in the scientific literature for assessing embodiment in MR. As discussed earlier in this review, the literature consistently focuses on using EEG, ECG, PPG, and EDA to objectively characterize embodiment, providing insights into brain, cardiac, and electrodermal system activities. Concerning the brain activity evaluation, i.e., the EEG analysis, the scientific literature contains a consistent number of works based on the time-related EEG features. More specifically, the Error-Related Negativity (ERN), the Error-Positivity (PE), and the N400 were the most selected parameters by the researchers for the neurophysiological evaluation of embodiment [48; 49; 57]. The ERN was defined as a component of brain electrical activity that occurs after a person makes an error during a task. It is characterized by a negative deflection in brain electrical activity, usually within the first 100 milliseconds after the error is detected. ERN was often observed primarily in the fronto-central regions of the brain and has been associated with error perception and recognition. In the context of embodiment, ERN can be studied to better understand how the brain reacts to errors during interaction with MR environments and to assess the participant's experience. PE was defined as a component of brain electrical activity that occurs after a person has consciously detected an error [48]. It was characterized by a positive deflection in brain electrical activity, usually around 200-400 milliseconds after an error was committed. PE was often observed primarily in the parietal region of the brain, with a prominent peak at electrode Pz. In research contexts of embodiment assessment, PE can be studied to better understand how the brain perceives and responds to errors during interaction with virtual environments. Its measurement provides valuable information about subjective error perception and the participant's experience in the MR environment. Finally, the N400 was defined as a component of brain electrical activity that typically occurs around 400 milliseconds after the presentation of a linguistic stimulus, such as a word or phrase [48]. It is characterized by a negative deflection in brain electrical activity, usually observed in central-parietal EEG channels. In the context of the neurophysiological characterization of embodiment, it was demonstrated that the N400 can be studied to better understand how the brain processes concepts and information related to body and action during interaction with virtual environments [48; 57]. Concerning the cardiac (i.e., PPG and ECG) and electrodermal (i.e., EDA) activity monitoring for the neurophysiological embodiment assessment, the scientific literature highlighted that the most common selected parameters consisted of the Heart Rate (HR), the Heart Rate Variability (HRV), the Skin Conductance Level (SCL), and the Skin Conductance Response (SCR). Regarding the first two parameters, related to the cardiac activity collected through ECG or PPG tracking systems, the works proposed by [PERSON] and colleagues [48], [PERSON] and colleagues [43], and [PERSON] and colleagues [38] applied the [PERSON]'s method for the frequency analysis of the ECG and PPG signals. This approach estimated the HR and HRV parameters. More specifically, the HR parameter was estimated from the ECG or PPG signal by preprocessing the signal for identifying and correcting the signal artifacts [78] and, subsequently, by identifying the R-peaks for computing the RR interval. Similarly, the HRV parameter was defined as the variation in the time intervals between heartbeats (i.e., RR intervals). Its computation relies on the identification of Very Low Frequency (VLF) [<0.04 Hz], Low Frequency (LF) [0.04-0.15 Hz], and High Frequency (HF) [0.12-0.4 Hz] spectral components of the ECG/PPG signal. Several scientific approaches defined HRV as the ratio between the LF and HF [38; 43].