A multi-scenario EEG dataset for auditory attention decoding in naturalistic multi-talker environments

Shu Peng; Rui Liu; Yufei Zhang; Wenlong You; Zhige Chen; Jiachen Xi; Qiyuan Sun; Yan Liu; Kay Chen Tan; Jibin Wu

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arXiv · 2610.09539

A multi-scenario EEG dataset for auditory attention decoding in naturalistic multi-talker environments

Shu Peng·Rui Liu·Yufei Zhang·Wenlong You·Zhige Chen·Jiachen Xi·Qiyuan Sun·Yan Liu·Kay Chen Tan·Jibin Wu

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Abstract

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 idealized single-competing-talker paradigms that oversimplify the acoustic, spatial, and semantic structure of everyday communication. To capture this ecological complexity, we introduce the SoundBubble-EEG dataset: a high-density 128-channel EEG resource comprising more than 25 hours of recordings from 30 participants. The paradigm requires listeners to selectively attend to a dynamic target speaker group, a designated "sound bubble", amid competing multi-speaker distractor bubbles across three realistic scenarios: a restaurant, a home TV viewing, and a meeting discussion. By bridging the gap between constrained laboratory protocols and real-world auditory scenes, this dataset enables investigations of multi-talker speech comprehension, neural speech tracking, and cross-scenario generalization. It also provides a benchmark for AAD algorithms under realistic acoustic and semantic variability and may support auditory neuroscience and the development of neuro-steered hearing technologies.

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Shu Peng, Rui Liu, Yufei Zhang, Wenlong You, Zhige Chen, Jiachen Xi, Qiyuan Sun, Yan Liu, Kay Chen Tan, Jibin Wu. 2026-10-07. A multi-scenario EEG dataset for auditory attention decoding in naturalistic multi-talker environments. https://arxiv.org/abs/2610.09539

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