论文标题
基于Riemannian几何形状的解码，使用EEG对听觉注意力的方向重点进行解码
Riemannian geometry-based decoding of the directional focus of auditory attention using EEG
论文作者
论文摘要
听觉注意力解码（AAD）算法解释了脑电图（EEG）信号的听觉关注，这些信号捕获了听众的神经活动。据信，这种AAD方法是对所谓神经启动辅助听力设备的重要组成部分。例如，传统的AAD解码器允许检测听众正在关注的多个扬声器中的哪个，通过重建来自EEG信号的演讲信号的振幅信封。最近，提出了这种刺激重建方法的替代范式，其中使用常见的空间模式过滤器（CSP）确定了听觉注意力的方向焦点。在这里，我们建议基于Riemannian几何分类（RGC）作为这种CSP方法的替代方法，其中新EEG段的协方差矩阵在考虑其Riemannian结构时直接进行了分类。虽然提出的RGC方法的性能与CSP方法相似，以实现短期决策长度（即用来做出决定的EEG样本的数量），但我们表明，它在更长的决策窗口长度方面大大优于它。
Auditory attention decoding (AAD) algorithms decode the auditory attention from electroencephalography (EEG) signals that capture the listener's neural activity. Such AAD methods are believed to be an important ingredient towards so-called neuro-steered assistive hearing devices. For example, traditional AAD decoders allow detecting to which of multiple speakers a listener is attending to by reconstructing the amplitude envelope of the attended speech signal from the EEG signals. Recently, an alternative paradigm to this stimulus reconstruction approach was proposed, in which the directional focus of auditory attention is determined instead, solely based on the EEG, using common spatial pattern filters (CSP). Here, we propose Riemannian geometry-based classification (RGC) as an alternative for this CSP approach, in which the covariance matrix of a new EEG segment is directly classified while taking its Riemannian structure into account. While the proposed RGC method performs similarly to the CSP method for short decision lengths (i.e., the amount of EEG samples used to make a decision), we show that it significantly outperforms it for longer decision window lengths.
