arXiv (All)
open access
Augmenting Human Performance with an XR Agent Learning from Online Behavior and BCI Evidence
We present OLIVE, a framework for adapting a foundation model to provide real-time assistance in temporally demanding, high-stakes, and dynamic tasks. We show that passive EEG, fused online with behavioral evidence, can meaningfully extend the number of targets users detect and engage beyond their u
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Augmenting Human Performance with an XR Agent Learning from Online Behavior and BCI Evidence#798154
- Online Inference of Human Intention as a Latent Control State from Single-Trial EEG#1130620
- MotiVE BCI: motivation models including valence and expectancy in brain-computer interface use.#358136
- A Common Measure of Communication for Speech Brain-Computer Interfaces#925002
- A Common Measure of Communication for Speech Brain-Computer Interfaces#920768
Record · ID 799577
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive