When multiple speakers talk simultaneously, a hearing device cannot identify which of these speakers the listener intends to attend to. Auditory attention decoding (AAD) algorithms can provide this information by, for example, reconstructing the attended speech envelope from electroencephalography (EEG) signals. However, these stimulus reconstruction decoders are traditionally trained in a supervised manner, requiring a dedicated training stage during which the attended speaker is known. Pre-trained subject-independent decoders alleviate the need of having such a per-user training stage but perform substantially worse than supervised subject-specific decoders that are tailored to the user. This motivates the development of a new unsupervised self-adapting training/updating procedure for a subject-specific decoder, which iteratively improves itself on unlabeled EEG data using its own predicted labels. This iterative updating procedure enables a self-leveraging effect, of which we provide a mathematical analysis that reveals the underlying mechanics. The proposed unsupervised algorithm, starting from a random decoder, results in a decoder that outperforms a supervised subject-independent decoder. Starting from a subject-independent decoder, the unsupervised algorithm even closely approximates the performance of a supervised subject-specific decoder. The developed unsupervised AAD algorithm thus combines the two advantages of a supervised subject-specific and subject-independent decoder: it approximates the performance of the former while retaining the ‘plug-and-play’ character of the latter. As the proposed algorithm can be used to automatically adapt to new users, as well as over time when new EEG data is being recorded, it contributes to more practical neuro-steered hearing devices.

中文翻译：

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无监督自适应听觉注意力解码


当多个说话者同时说话时，听力设备无法识别听者想要注意的是这些说话者中的哪一个。听觉注意解码 (AAD) 算法可以通过例如从脑电图 (EEG) 信号重建参与的语音包络来提供此信息。然而，这些刺激重建解码器传统上是以监督方式进行训练的，需要一个专门的训练阶段，在此阶段，参与的说话者是已知的。预训练的与主题无关的解码器减轻了对每个用户训练阶段的需求，但其性能比为用户定制的有监督的主题特定解码器要差得多。这激励了针对特定主题的解码器开发一种新的无监督自适应训练/更新程序，该解码器使用自己的预测标签在未标记的脑电图数据上迭代地改进自身。这种迭代更新过程可以实现自我杠杆效应，我们提供了数学分析来揭示潜在的机制。所提出的无监督算法从随机解码器开始，产生的解码器性能优于受监督的独立于主体的解码器。从与主题无关的解码器开始，无监督算法甚至非常接近有监督的主题特定解码器的性能。因此，所开发的无监督 AAD 算法结合了有监督的特定主题解码器和主题独立解码器的两个优点：它近似前者的性能，同时保留后者的“即插即用”特性。 由于所提出的算法可用于自动适应新用户，以及随着时间的推移记录新的脑电图数据，因此它有助于打造更实用的神经引导听力设备。