People suffering from hearing impairment often have difficulties participating in conversations in so-called cocktail party scenarios where multiple individuals are simultaneously talking. Although advanced algorithms exist to suppress background noise in these situations, a hearing device also needs information about which speaker a user actually aims to attend to. The voice of the correct (attended) speaker can then be enhanced through this information, and all other speakers can be treated as background noise. Recent neuroscientific advances have shown that it is possible to determine the focus of auditory attention through noninvasive neurorecording techniques, such as electroencephalography (EEG). Based on these insights, a multitude of auditory attention decoding (AAD) algorithms has been proposed, which could, combined with appropriate speaker separation algorithms and miniaturized EEG sensors, lead to so-called neurosteered hearing devices. In this article, we provide a broad review and a statistically grounded comparative study of EEG-based AAD algorithms and address the main signal processing challenges in this field.

中文翻译：

---

基于脑电图的听觉注意力解码：神经引导听力设备


患有听力障碍的人通常难以参与所谓的鸡尾酒会场景中的对话，其中多个人同时讲话。尽管存在先进的算法来抑制这些情况下的背景噪声，但听力设备还需要有关用户实际想要注意哪个说话者的信息。然后，可以通过此信息增强正确（有人值守）说话者的声音，并且可以将所有其他说话者视为背景噪声。最近的神经科学进展表明，可以通过脑电图（EEG）等无创神经记录技术来确定听觉注意力的焦点。基于这些见解，人们提出了多种听觉注意解码（AAD）算法，这些算法可以与适当的说话者分离算法和小型化脑电图传感器相结合，形成所谓的神经引导听力设备。在本文中，我们对基于 EEG 的 AAD 算法进行了广泛的回顾和统计比较研究，并解决了该领域的主要信号处理挑战。