发表机构
The Hong Kong Polytechnic University（香港理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出SoundBubble-EEG多场景脑电数据集，包含128通道、30人、超25小时记录，用于在自然多说话人环境中解码听觉注意，支持神经助听技术发展。
AI 中文摘要
理解大脑在竞争性语音中如何选择性跟随相关语音，是听觉神经科学的核心挑战，也是迈向神经引导助听技术的关键一步。然而，大多数用于听觉注意解码（AAD）的开源脑电图（EEG）数据集采用理想化的单一竞争说话人范式，过度简化了日常交流中的声学、空间和语义结构。为捕捉这种生态复杂性，我们引入了SoundBubble-EEG数据集：一个高密度128通道脑电图资源，包含来自30名参与者的超过25小时的记录。该范式要求听者在三种现实场景（餐厅、家庭电视观看和会议讨论）中，在竞争性多说话人干扰气泡中，选择性注意一个动态的目标说话人组，即指定的“声音气泡”。通过弥合受限实验室协议与现实世界听觉场景之间的差距，该数据集支持多说话人语音理解、神经语音跟踪和跨场景泛化的研究。它还为AAD算法在现实声学和语义变异性下提供了基准，并可能支持听觉神经科学及神经引导助听技术的发展。
英文摘要
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.
