arXiv (All)
open access
Time Window Optimization for Motor Imagery Classification via Temporal Structure Embedded Reinforcement Learning
Online motor imagery-based brain-computer interfaces (MI-BCIs) typically decode continuous EEG using fixed-length sliding windows that may be misaligned with neural events. Existing window optimization methods often evaluate windows independently, overlooking temporal dependencies and potentially pr
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
- Advancing stroke rehabilitation: the potential and challenges of closed-loop brain-computer interface technology.#354645
- Replaying the Movie in Your Mind Decoding Dynamic Visual Perception and Recall from EEG#1297472
- Brain-computer interface: an update for the clinicians.#159972
- Decoding Chinese speech across multiple neural conditions via EEG: dataset construction and interpretability driven spatial optimization.#446004
- Toward Robust, Reproducible, and Widely Accessible Intracranial Speech Brain-Computer Interfaces: A Comprehensive Narrative Review of Neural Mechanisms, Hardware, Algorithms, Evaluation, Clinical Pathways and Future Directions#1129194
Record · ID 1341803
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