Holtze Björn, Rosenkranz Marc, Jaeger Manuela, Debener Stefan, Mirkovic Bojana
Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany.
Neurophysiology of Everyday Life Group, Department of Psychology, University of Oldenburg, Oldenburg, Germany.
Front Neurosci. 2022 May 3;16:869426. doi: 10.3389/fnins.2022.869426. eCollection 2022.
Auditory attention is an important cognitive function used to separate relevant from irrelevant auditory information. However, most findings on attentional selection have been obtained in highly controlled laboratory settings using bulky recording setups and unnaturalistic stimuli. Recent advances in electroencephalography (EEG) facilitate the measurement of brain activity outside the laboratory, and around-the-ear sensors such as the cEEGrid promise unobtrusive acquisition. In parallel, methods such as speech envelope tracking, intersubject correlations and spectral entropy measures emerged which allow us to study attentional effects in the neural processing of natural, continuous auditory scenes. In the current study, we investigated whether these three attentional measures can be reliably obtained when using around-the-ear EEG. To this end, we analyzed the cEEGrid data of 36 participants who attended to one of two simultaneously presented speech streams. Speech envelope tracking results confirmed a reliable identification of the attended speaker from cEEGrid data. The accuracies in identifying the attended speaker increased when fitting the classification model to the individual. Artifact correction of the cEEGrid data with artifact subspace reconstruction did not increase the classification accuracy. Intersubject correlations were higher for those participants attending to the same speech stream than for those attending to different speech streams, replicating previously obtained results with high-density cap-EEG. We also found that spectral entropy decreased over time, possibly reflecting the decrease in the listener's level of attention. Overall, these results support the idea of using ear-EEG measurements to unobtrusively monitor auditory attention to continuous speech. This knowledge may help to develop assistive devices that support listeners separating relevant from irrelevant information in complex auditory environments.
听觉注意力是一种重要的认知功能，用于区分相关和不相关的听觉信息。然而，大多数关于注意力选择的研究结果是在高度受控的实验室环境中，使用笨重的记录设备和非自然的刺激获得的。脑电图（EEG）的最新进展有助于在实验室外测量大脑活动，而诸如cEEGrid之类的耳周传感器有望实现不引人注意的数据采集。与此同时，出现了诸如语音包络跟踪、受试者间相关性和频谱熵测量等方法，这些方法使我们能够研究自然、连续听觉场景的神经处理中的注意力效应。在本研究中，我们调查了使用耳周脑电图时，这三种注意力测量方法是否能够可靠地获得。为此，我们分析了36名参与者的cEEGrid数据，这些参与者关注同时呈现的两个语音流之一。语音包络跟踪结果证实，从cEEGrid数据中可以可靠地识别出被关注的说话者。将分类模型应用于个体时，识别被关注说话者的准确率会提高。使用伪迹子空间重建对cEEGrid数据进行伪迹校正并没有提高分类准确率。与关注不同语音流的参与者相比，关注相同语音流的参与者之间的受试者间相关性更高，这重复了之前使用高密度帽状脑电图获得的结果。我们还发现频谱熵随时间下降，这可能反映了听众注意力水平的下降。总体而言，这些结果支持使用耳周脑电图测量来不引人注意地监测对连续语音的听觉注意力这一观点。这些知识可能有助于开发辅助设备，以帮助听众在复杂的听觉环境中区分相关和不相关的信息。
