arXiv (All)
open access
Cross-subject decoding of imagined speech from scalp EEG: a leakage-controlled evaluation on three public corpora
Objective. A decoder of imagined speech that works for a new user without calibration would make a non-invasive speech interface practical. We tested whether imagined-speech content recorded with scalp electroencephalography (EEG) transfers across people, and examined why accuracy within a single pe
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
- Band-Gated Identity-Disentangled Training for Cross-Subject Auditory Attention Decoding#970618
- Do EEG Foundation Models Transfer to Speech? A Benchmark on Overt and Imagined Speech Decoding#984206
- Decoding Imagined Speech: A Strictly Subject-Independent Approach Using EEG#1077747
- Mechanistic Interpretability Reveals Shared Causal Subspaces in Brain-to-Speech Decoders#1115948
- MAESTRO: a Multimodal Auditory-attention Egocentric Speech-TRacking Open corpus#1115854
Record · ID 1356290
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