Neuroadaptive Haptics: Comparing Reinforcement Learning from Explicit Ratings and Neural Signals for Adaptive XR Systems
Journal:
arXiv
Published Date:
Apr 22, 2025
Abstract
Neuroadaptive haptics offers a path to more immersive extended reality (XR)
experiences by dynamically tuning multisensory feedback to user preferences. We
present a neuroadaptive haptics system that adapts XR feedback through
reinforcement learning (RL) from explicit user ratings and brain-decoded neural
signals. In a user study, participants interacted with virtual objects in VR
while Electroencephalography (EEG) data were recorded. An RL agent adjusted
haptic feedback based either on explicit ratings or on outputs from a neural
decoder. Results show that the RL agent's performance was comparable across
feedback sources, suggesting that implicit neural feedback can effectively
guide personalization without requiring active user input. The EEG-based neural
decoder achieved a mean F1 score of 0.8, supporting reliable classification of
user experience. These findings demonstrate the feasibility of combining
brain-computer interfaces (BCI) and RL to autonomously adapt XR interactions,
reducing cognitive load and enhancing immersion.
