Neuropsychology Lab, Department of Psychology, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany.
Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany.
J Neural Eng. 2022 Jan 6;18(6). doi: 10.1088/1741-2552/ac42b5.
Neuro-steered assistive technologies have been suggested to offer a major advancement in future devices like neuro-steered hearing aids. Auditory attention decoding (AAD) methods would in that case allow for identification of an attended speaker within complex auditory environments, exclusively from neural data. Decoding the attended speaker using neural information has so far only been done in controlled laboratory settings. Yet, it is known that ever-present factors like distraction and movement are reflected in the neural signal parameters related to attention.Thus, in the current study we applied a two-competing speaker paradigm to investigate performance of a commonly applied electroencephalography-based AAD model outside of the laboratory during leisure walking and distraction. Unique environmental sounds were added to the auditory scene and served as distractor events.. The current study shows, for the first time, that the attended speaker can be accurately decoded during natural movement. At a temporal resolution of as short as 5 s and without artifact attenuation, decoding was found to be significantly above chance level. Further, as hypothesized, we found a decrease in attention to the to-be-attended and the to-be-ignored speech stream after the occurrence of a salient event. Additionally, we demonstrate that it is possible to predict neural correlates of distraction with a computational model of auditory saliency based on acoustic features.Taken together, our study shows that auditory attention tracking outside of the laboratory in ecologically valid conditions is feasible and a step towards the development of future neural-steered hearing aids.
神经引导辅助技术被认为是未来神经引导助听器等设备的重大进步。在这种情况下，听觉注意解码 (AAD) 方法将允许仅从神经数据中识别复杂听觉环境中的关注说话者。迄今为止，使用神经信息解码关注说话者仅在受控的实验室环境中完成。然而，众所周知，干扰和运动等始终存在的因素会反映在与注意力相关的神经信号参数中。因此，在当前的研究中，我们应用了双竞争说话人范式，在休闲散步和分心期间，在实验室外研究一种常用的基于脑电图的 AAD 模型的性能。独特的环境声音被添加到听觉场景中，并作为干扰事件。当前的研究首次表明，在自然运动期间可以准确解码关注说话者。在 5 秒的时间分辨率内，并且没有伪影衰减，解码被发现明显高于随机水平。此外，正如假设的那样，我们发现，在发生突出事件后，对要关注的和要忽略的语音流的注意力会降低。此外，我们证明，基于听觉显着性的声学特征的计算模型，可以预测分心的神经相关性。综上所述，我们的研究表明，在生态有效条件下进行实验室外的听觉注意力跟踪是可行的，并且是朝着开发未来神经引导助听器的方向迈出的一步。
