Neuroadaptive Haptics: Comparing Reinforcement Learning from Explicit Ratings and Neural Signals for Adaptive XR Systems
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908720572661760 |
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| author | Gehrke, Lukas Koselevs, Aleksandrs Klug, Marius Gramann, Klaus |
| author_facet | Gehrke, Lukas Koselevs, Aleksandrs Klug, Marius Gramann, Klaus |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15984 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neuroadaptive Haptics: Comparing Reinforcement Learning from Explicit Ratings and Neural Signals for Adaptive XR Systems Gehrke, Lukas Koselevs, Aleksandrs Klug, Marius Gramann, Klaus Human-Computer Interaction 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. |
| title | Neuroadaptive Haptics: Comparing Reinforcement Learning from Explicit Ratings and Neural Signals for Adaptive XR Systems |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.15984 |