Real-Time Tracking of Magnetoencephalographic Neuromarkers during a Dynamic Attention-Switching Task.
Real-Time Tracking of Magnetoencephalographic Neuromarkers during a Dynamic Attention-Switching Task.
复制标题
动态注意力切换任务期间脑磁图神经标记物的实时跟踪。
DOI：
10.1109/embc.2019.8857953
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Simon,JonathanZ
中科院分区：
文献类型：
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作者：
Presacco,Alessandro;Miran,Sina;Babadi,Behtash;Simon,JonathanZ
在过去的几年里，大量的实验已经集中在探索使用非侵入性技术的可能性，如脑电图（EEG）和脑磁图（MEG），以确定与注意有关的神经标记物。结果从几个研究中，参与者听一个扬声器讲述的故事，而试图忽略一个不同的故事叙述的竞争对手的扬声器，建议提取的神经标记，表现出增强相位锁定出席语音流的可行性。这些有希望的发现有可能用于临床应用，例如EEG驱动的助听器。实现这一目标的一个主要挑战是需要设计一种算法，能够实时跟踪这些神经标记物，当个体被赋予自由，可以随意在说话者之间反复切换注意力时。在这里，我们提出了一个算法流水线，旨在有效地识别动态注意力切换任务期间神经语音跟踪的变化，并将它们用作近实时状态空间模型的输入，该模型将这些神经标记转换为具有最小延迟的注意力状态估计。该算法管道使用从参与者收集的MEG数据进行了测试，这些参与者可以自由地在两个说话者之间改变他们的注意力焦点。结果表明，使用我们的算法流水线跟踪的注意力的变化在一个动态的听觉场景中的近实时的可行性。
In the last few years, a large number of experiments have been focused on exploring the possibility of using non-invasive techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), to identify auditory-related neuromarkers which are modulated by attention. Results from several studies where participants listen to a story narrated by one speaker, while trying to ignore a different story narrated by a competing speaker, suggest the feasibility of extracting neuromarkers that demonstrate enhanced phase locking to the attended speech stream. These promising findings have the potential to be used in clinical applications, such as EEG-driven hearing aids. One major challenge in achieving this goal is the need to devise an algorithm capable of tracking these neuromarkers in real-time when individuals are given the freedom to repeatedly switch attention among speakers at will. Here we present an algorithm pipeline that is designed to efficiently recognize changes of neural speech tracking during a dynamic-attention switching task and to use them as an input for a near real-time state-space model that translates these neuromarkers into attentional state estimates with a minimal delay. This algorithm pipeline was tested with MEG data collected from participants who had the freedom to change the focus of their attention between two speakers at will. Results suggest the feasibility of using our algorithm pipeline to track changes of attention in near-real time in a dynamic auditory scene.