Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG
Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG
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DOI：
10.1093/cercor/bht355
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发表时间：
2015-07-01
期刊：
影响因子：
3.7
通讯作者：
Lalor, Edmund C.
中科院分区：
文献类型：
--
作者：
O'Sullivan, James A.;Power, Alan J.;Lalor, Edmund C.
人类如何解决鸡尾酒会问题仍是未知数。然而，由于意识到皮层活动跟踪语音的幅度包络，最近已经取得了进展。这导致了研究连续语音神经生理学的回归方法的发展。一种这样的方法，被称为刺激重建，已成功地用于皮层表面记录和脑磁图（MEG）。然而，前者是侵入性的，并给出了一个相对有限的看法处理沿着听觉层次，而后者是昂贵的和罕见的。因此，它将是非常有用的研究，在许多人群中，如果刺激重建是有效的使用脑电图（EEG），广泛使用和廉价的技术。在这里，我们表明，单次试验（约60秒）的非平均EEG数据可以解码，以确定在自然的多扬声器环境中的注意力选择。此外，我们显示了一个显着的相关性，我们的EEG为基础的措施，注意力和性能的高层次的注意力任务。此外，通过尝试解码注意力在个人lavelet，我们确定神经处理在类似的200毫秒是解决鸡尾酒会问题的关键。这些发现为使用EEG研究认知的持续动态以及开发有效和自然的脑机接口开辟了新的途径。
How humans solve the cocktail party problem remains unknown. However, progress has been made recently thanks to the realization that cortical activity tracks the amplitude envelope of speech. This has led to the development of regression methods for studying the neurophysiology of continuous speech. One such method, known as stimulus-reconstruction, has been successfully utilized with cortical surface recordings and magnetoencephalography (MEG). However, the former is invasive and gives a relatively restricted view of processing along the auditory hierarchy, whereas the latter is expensive and rare. Thus it would be extremely useful for research in many populations if stimulus-reconstruction was effective using electroen-cephalography (EEG), a widely available and inexpensive technology. Here we show that single-trial (approximate to 60 s) unaveraged EEG data can be decoded to determine attentional selection in a naturalistic multi-speaker environment. Furthermore, we show a significant correlation between our EEG-based measure of attention and performance on a high-level attention task. In addition, by attempting to decode attention at individual latencies, we identify neural processing at similar to 200 ms as being critical for solving the cocktail party problem. These findings open up new avenues for studying the ongoing dynamics of cognition using EEG and for developing effective and natural brain-computer interfaces.