The field of virtual reality (VR) and brain-computer interfaces (BCIs) is moving towards more immersive, interactive, and user-friendly experiences. Researchers are exploring innovative ways to detect user familiarity, visual fatigue, and cybersickness in VR environments, with a focus on developing practical tools for real-time evaluation and mitigation. Deep learning-based approaches are being used to analyze eye gaze patterns, hand movement biometrics, and video-based features to predict user experiences. Noteworthy papers include: BrainForm, a gamified BCI training system, which demonstrates the potential for scalable data collection and user engagement. Towards Cybersickness Severity Classification, which effectively leverages transfer learning and temporal modeling to predict cybersickness severity from VR gameplay videos. Behavioral Biometrics for Automatic Detection of User Familiarity in VR, which achieves high accuracy in detecting user familiarity using hand movement patterns. Deep Learning-Based Visual Fatigue Detection, which introduces a reliable and nonintrusive modality for continuous fatigue detection in immersive VR using eye-gaze dynamics.