## 2 Material and Methods
### Research on VR-BCI Systems  
Research on BCI with VR employs a variety of methodologies, often combining several approaches (Figure 3). Common techniques used in this field include neuroimaging techniques, virtual reality (VR) systems, experimental paradigms, data analysis and classification, and user experience evaluation. Figure 1: Bibliometric analysis procedure. The methodologies used in VR-BCI systems, especially in neuroimaging, play a key role in determining the quality, applicability, and effectiveness of the systems in specific applications. EEG is one of the most commonly used methods due to its non-invasive nature, relatively low cost, and high temporal resolution, making it ideal for applications that require real-time processing of brain signals, such as responsive games or instantaneous feedback in VR training environments. However, EEG suffers from low spatial resolution, which limits its ability to capture complex spatial patterns of brain activity, making it less suitable for tasks requiring detailed brain mapping. In contrast, functional near-infrared spectroscopy (fNIRS) offers higher spatial resolution by detecting changes in cerebral blood  
Figure 3: General architecture of BCI-based VR system for IoT/smart home control. Figure 2: A PRISMA flow diagram of the review process using selected PRISMA 2020 guidelines. flow, which can provide more precise information on specific areas involved in tasks or emotional responses in VR. Despite this spatial advantage, fNIRS has lower temporal resolution compared to EEG because it measures hemodynamic responses, which change more slowly than electrical brain signals, making it more suitable for applications that do not require immediate feedback. EEG is also more sensitive to electrical noise and artifacts from physical movements, which can be a challenge in VR environments where users frequently interact and move around. To overcome this, some VR-BCI systems combine EEG with fNIRS to take advantage of both high temporal and spatial resolution, capturing richer brain data while minimizing the limitations of each individual modality. Another factor is the practicality of wearing the equipment: EEG caps and fNIRS headsets can be cumbersome, especially in immersive VR setups, so advances in wireless and lightweight devices are key to increasing user comfort and realism. For highly interactive VR applications, the real-time capabilities of EEG make it preferable, while for applications focused on monitoring cognitive load, mental fatigue, or emotional states, fNIRS may offer more meaningful data. Other neuroimaging techniques, such as MEG (magnetoencephalography) or fMRI (functional magnetic resonance imaging), provide more detailed information but are less practical for VR-BCI systems due to their high cost, bulk, and the need for immobilization, which limits natural interaction. As a result, the choice of neuroimaging method depends largely on the specific goals of the VR-BCI application, balancing the need for spatial and temporal accuracy, user comfort, and interaction fidelity. Practical guidelines for VR-BCI development often suggest choosing EEG for real-time interactive applications and fNIRS for detailed location-specific monitoring, especially where time is less of a concern. Methodological studies comparing results from different neuroimaging modalities in VR-BCI contexts can help establish clearer guidelines, ultimately optimizing system performance and user experience across applications. Neuroimaging techniques include the following:  
1. Electroencephalography [52; 53] constitutes the most common method due to its non-invasiveness, portability, and affordability.