Straetmans Lisa, Adiloglu Kamil, Debener Stefan
Department of Psychology, Neuropsychology Lab, University of Oldenburg, Oldenburg, Germany.
Hörzentrum Oldenburg gGmbH, Oldenburg, Germany.
Front Hum Neurosci. 2024 Nov 13;18:1483024. doi: 10.3389/fnhum.2024.1483024. eCollection 2024.
In our complex world, the auditory system plays a crucial role in perceiving and processing our environment. Humans are able to segment and stream concurrent auditory objects, allowing them to focus on specific sounds, such as speech, and suppress irrelevant auditory objects. The attentional enhancement or suppression of sound processing is evident in neural data through a phenomenon called neural speech tracking. Previous studies have identified correlates of neural speech tracking in electroencephalography (EEG) data, but EEG measures are susceptible to motion artefacts, and the association between neural data and auditory objects is vulnerable to distraction.
The current study investigated EEG-based auditory attention decoding in realistic everyday scenarios. N=20 participants were exposed to the sound of a busy cafeteria or walked along busy and quiet streets while listening to one or two simultaneous speech streams. We also investigated the robustness of neural speech tracking estimates within subjects. Linear decoding models were used to determine the magnitude of neural speech tracking.
The results confirmed that neural speech tracking was strongest in single speaker scenarios. In dual speaker conditions, there was significantly stronger neural speech tracking for the attended speaker compared to the ignored speaker, even in complex environments such as a busy cafeteria or outdoor settings.
In conclusion, EEG-based attention decoding is feasible in highly complex and realistic everyday conditions while humans behave naturally.
在我们这个复杂的世界中，听觉系统在感知和处理我们周围的环境方面起着至关重要的作用。人类能够对同时出现的听觉对象进行分割和流处理，使他们能够专注于特定的声音，如语音，并抑制无关的听觉对象。通过一种称为神经语音跟踪的现象，声音处理的注意力增强或抑制在神经数据中很明显。先前的研究已经在脑电图（EEG）数据中确定了神经语音跟踪的相关因素，但EEG测量容易受到运动伪影的影响，并且神经数据与听觉对象之间的关联容易受到干扰。
本研究调查了在现实日常场景中基于EEG的听觉注意力解码。20名参与者在繁忙的自助餐厅环境中或沿着繁忙和安静的街道行走时，同时听一到两个语音流。我们还研究了受试者内部神经语音跟踪估计的稳健性。使用线性解码模型来确定神经语音跟踪的程度。
结果证实，在单说话者场景中神经语音跟踪最强。在双说话者条件下，与被忽略的说话者相比，对于被关注的说话者，神经语音跟踪明显更强，即使在繁忙的自助餐厅或户外等复杂环境中也是如此。
总之，在人类自然行为的高度复杂和现实的日常条件下，基于EEG的注意力解码是可行的。
