用深度学习解码听觉注意力，实现单个词的精准识别。
Single-word Auditory Attention Decoding Using Deep Learning Model
- 基于EEGNet构建深度模型，捕捉大脑对特定词汇的认知响应。
- 在最真实的竞争语音场景中，跨被试准确率达58%以上。
- 首次解决单词级听觉注意力解码问题，适合脑机接口研究者。
通过对比听觉刺激与对应脑电反应来识别听觉注意力，称为听觉注意力解码（AAD）。现有方法多依赖声波包络同步机制，即通过听觉流包络驱动脑电（EEG）信号变化来判断注意力。但神经活动也可基于内源性认知反应解码，例如对语流中特定词汇的注意所引发的神经反应。这一思路在AAD领域尚未充分探索，形成了单词级听觉注意力解码问题：将与特定词汇时间对齐的EEG片段标记为注意或未注意。本文提出一种基于EEGNet的深度学习方法解决该问题。在事件相关AAD数据集上，采用三种范式进行被试无关评估：词类奇异性、含竞争说话人词类任务、目标语音竞争任务。结果表明，该模型能有效利用与认知相关的时空脑电特征，在最真实的竞争语音范式中对未见被试实现至少58%的准确率。据我们所知，这是首个针对该问题的研究。
原文摘要 · Abstract (English)
Identifying auditory attention by comparing auditory stimuli and corresponding brain responses, is known as auditory attention decoding (AAD). The majority of AAD algorithms utilize the so-called envelope entrainment mechanism, whereby auditory attention is identified by how the envelope of the auditory stream drives variation in the electroencephalography (EEG) signal. However, neural processing can also be decoded based on endogenous cognitive responses, in this case, neural responses evoked by attention to specific words in a speech stream. This approach is largely unexplored in the field of AAD but leads to a single-word auditory attention decoding problem in which an epoch of an EEG signal timed to a specific word is labeled as attended or unattended. This paper presents a deep learning approach, based on EEGNet, to address this challenge. We conducted a subject-independent evaluation on an event-based AAD dataset with three different paradigms: word category oddball, word category with competing speakers, and competing speech streams with targets. The results demonstrate that the adapted model is capable of exploiting cognitive-related spatiotemporal EEG features and achieving at least 58% accuracy on the most realistic competing paradigm for the unseen subjects. To our knowledge, this is the first study dealing with this problem.
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