arXiv CS
open access
Removing Timing Shortcuts Improves Non-Invasive Brain-to-Text
We find that major reported improvements in decoding words from non-invasive brain recordings are largely reproducible without any brain data. In the influential work of d'Ascoli et al. (2025), time series of brain activity from subjects perceiving continuous speech are segmented into fixed-length w
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
- Removing Timing Shortcuts Improves Non-Invasive Brain-to-Text#1154618
- MEG state dynamics of sentence generation: evidence for a compensatory segmentation mechanism in healthy aging#377957
- Decoding Order Matters in Autoregressive Speech Synthesis#1049740
- Beyond Sub-Gaussian Detector Scores: Robust Weighted Profile-Loss Change Point Detection for Human-LLM Text Segmentation#1132089
- Residual Vector-based Reconstruction as Long-Context Recall Regardless of Context Window Size#1070167
Record · ID 1136807
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