Brain–Computer Interface Innovations for Virtual Reality Interaction

Abstract

The technology of Brain-Computer Interfaces (BCIs) provides a revolutionary approach to the idea of direct communication between the human brain and other devices, and it has a great potential to improve virtual reality (VR) experiences. BCIs can be used to interpret neural signals, enabling users to control VR through interaction without any standard input device, enabling users to have a highly immersive and intuitive control (Lebedev and Nicolelis, 2006). As of 2012, there is a wide range of new technologies in non-invasive electroencephalography (EEG) and invasive neural recording methods, greatly enhancing the accuracy and responsiveness of BCIs in VR applications, including gaming, education, rehabilitation, and assistive technologies (Wolpaw et al., 2002; Nicolas-Alonso and Gomez-Gil, 2012). The present study focuses on the recent developments in the BCI hardware and signal processing algorithms, such as machine learning-based neural decoding and adaptive calibration algorithms to maximize VR interaction. The research design includes a literature review, which will be conducted systematically, a set of simulations which will execute BCI-controlled VR activities, and the evaluation of user performance and experience indicators. The results indicate that the BCI integration would contribute greatly to immersion, efficiency, and accessibility of VR, but there are still issues associated with the reliability of signals, computational burden, and individual differences in neural reaction. The research gives the suggestions of the future research directions, which is multi-modal sensory integration, hybrid BCI systems, and adaptive interfaces to the realization of more natural and efficient VR interactions.