Brain–Computer Interface Based on PLV-Spatial Filter and LSTM Classification for Intuitive Control of Avatars

Abstract

This study researches the combination of the brain–computer interface (BCI) and virtual reality (VR) in order to improve user experience and facilitate control learning in a safe environment. In addition, it assesses the applicability of the phase-locking value spatial filtering (PLV-SF) method and the Short-Term Memory Network (LSTM) in a real-time EEG-based BCI. The PLV-SF has been shown to improve signal quality, and the LSTM exhibits more stable and accurate behavior. Ten healthy volunteers, six men and four women aged 22 to 37 years, participated in tasks inside a virtual house, using their EEG states to direct their movements and actions through a commercial, low-cost wireless EEG device together with a virtual reality system. A BCI and VR can be used effectively to enable the intuitive control of virtual environments by immersing users in real-life situations, making the experience engaging, fun, and safe. Control test times decreased significantly from 3.65 min and 7.79 min in the first and second quartiles, respectively, to 2.56 min and 4.28 min. In addition, a free route was performed for the three best volunteers who finished in an average time of 6.30 min.