arXiv (All)
open access
CoupleFlow: Null-Calibrated Auditory Attention Decoding With Time-Locked Brain-Speech Coupling and Flow-Matching Likelihood Ratios
Auditory attention decoding (AAD) seeks to identify the attended talker from brief noninvasive brain recordings by comparing neural responses with competing speech streams. Recent electroencephalography (EEG) decoders report nearly perfect accuracy from one second of data, yet we show that this perf
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
- MOV-AAD: A Large-Scale Multimodal Dataset for Auditory Attention Decoding During Moving Conversations#1199454
- Cross-subject decoding of imagined speech from scalp EEG: a leakage-controlled evaluation on three public corpora#1356290
- Decoding Chinese speech across multiple neural conditions via EEG: dataset construction and interpretability driven spatial optimization.#446004
- Multiscale Gaussian-Mixture Modeling for HMM Post-Processing in Selective Auditory Attention Decoding#1109492
- Do EEG Foundation Models Transfer to Speech? A Benchmark on Overt and Imagined Speech Decoding#984206
Record · ID 1448465
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