arXiv (All)
open access
A multi-scenario EEG dataset for auditory attention decoding in naturalistic multi-talker environments
Understanding how the brain selectively follows relevant speech amid competing voices is a central challenge in auditory neuroscience and a key step toward neuro-steered hearing technologies. However, most open-source Electroencephalography (EEG) datasets for Auditory Attention Decoding (AAD) use id
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
- Band-Gated Identity-Disentangled Training for Cross-Subject Auditory Attention Decoding#970618
- MAESTRO: a Multimodal Auditory-attention Egocentric Speech-TRacking Open corpus#1115854
- A hidden Markov model for unsupervised EEG-based tracking of auditory attention to a single voice#1357346
- A High-Density EEG Dataset for Stimulus-Driven Auditory Attention#1169665
Record · ID 1233578
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