无需标注数据，直接从脑电波识别听觉注意力状态
Unsupervised EEG-based decoding of absolute auditory attention with canonical correlation analysis
- 用无监督的CCA提取特征，结合MILDA分类器
- 在非平稳测试数据上表现优于有监督模型
- 适合无需训练的实时注意力监测场景
我们提出一种完全无监督的算法，通过脑电图（EEG）记录检测受试者是主动聆听声音，还是忽略声音。该问题称为绝对听觉注意解码（aAAD）。我们采用无监督判别CCA模型进行特征提取，并结合一种名为最小知情线性判别分析（MILDA）的无监督分类器完成aAAD分类。显著的是，所提无监督算法性能明显优于现有最先进的有监督模型。关键原因在于，该算法能以低计算成本有效适应非平稳测试数据。这为利用EEG信号分析个体听觉注意力提供了新可能：无需事先繁琐的有监督训练，模型即可自动适配个体。
原文摘要 · Abstract (English)
We propose a fully unsupervised algorithm that detects from encephalography (EEG) recordings when a subject actively listens to sound, versus when the sound is ignored. This problem is known as absolute auditory attention decoding (aAAD). We propose an unsupervised discriminative CCA model for feature extraction and combine it with an unsupervised classifier called minimally informed linear discriminant analysis (MILDA) for aAAD classification. Remarkably, the proposed unsupervised algorithm performs significantly better than a state-of-the-art supervised model. A key reason is that the unsupervised algorithm can successfully adapt to the non-stationary test data at a low computational cost. This opens the door to the analysis of the auditory attention of a subject using EEG signals with a model that automatically tunes itself to the subject without requiring an arduous supervised training session beforehand.
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