Haupt Thorge, Rosenkranz Marc, Bleichner Martin G
Neurophysiology of Everyday Life Group, Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany
Neurophysiology of Everyday Life Group, Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany.
eNeuro. 2025 Jan 17;12(1). doi: 10.1523/ENEURO.0287-24.2024. Print 2025 Jan.
A comprehensive analysis of everyday sound perception can be achieved using electroencephalography (EEG) with the concurrent acquisition of information about the environment. While extensive research has been dedicated to speech perception, the complexities of auditory perception within everyday environments, specifically the types of information and the key features to extract, remain less explored. Our study aims to systematically investigate the relevance of different feature categories: discrete sound-identity markers, general cognitive state information, and acoustic representations, including discrete sound onset, the envelope, and mel-spectrogram. Using continuous data analysis, we contrast different features in terms of their predictive power for unseen data and thus their distinct contributions to explaining neural data. For this, we analyze data from a complex audio-visual motor task using a naturalistic soundscape. The results demonstrated that the feature sets that explain the most neural variability were a combination of highly detailed acoustic features with a comprehensive description of specific sound onsets. Furthermore, it showed that established features can be applied to complex soundscapes. Crucially, the outcome hinged on excluding periods devoid of sound onsets in the analysis in the case of the discrete features. Our study highlights the importance to comprehensively describe the soundscape, using acoustic and non-acoustic aspects, to fully understand the dynamics of sound perception in complex situations. This approach can serve as a foundation for future studies aiming to investigate sound perception in natural settings.
通过脑电图（EEG）并同时获取有关环境的信息，可以实现对日常声音感知的全面分析。虽然已经有大量研究致力于语音感知，但日常环境中听觉感知的复杂性，特别是要提取的信息类型和关键特征，仍有待深入探索。我们的研究旨在系统地研究不同特征类别的相关性：离散声音识别标记、一般认知状态信息以及声学表征，包括离散声音起始、包络和梅尔频谱图。通过连续数据分析，我们根据不同特征对未见数据的预测能力以及它们对解释神经数据的不同贡献进行对比。为此，我们使用自然主义音景分析来自复杂视听运动任务的数据。结果表明，解释最多神经变异性的特征集是高度详细的声学特征与特定声音起始的全面描述的组合。此外，研究表明既定特征可应用于复杂音景。至关重要的是，在离散特征的分析中，结果取决于排除没有声音起始的时间段。我们的研究强调了利用声学和非声学方面全面描述音景对于充分理解复杂情况下声音感知动态的重要性。这种方法可为未来旨在研究自然环境中声音感知的研究奠定基础。
