Objective. When a person listens to continuous speech, a corresponding response is elicited in the brain and can be recorded using electroencephalography (EEG). Linear models are presently used to relate the EEG recording to the corresponding speech signal. The ability of linear models to find a mapping between these two signals is used as a measure of neural tracking of speech. Such models are limited as they assume linearity in the EEG-speech relationship, which omits the nonlinear dynamics of the brain. As an alternative, deep learning models have recently been used to relate EEG to continuous speech, especially in auditory attention decoding (AAD) and single-speech-source paradigms. Approach. This paper reviews and comments on deep-learning-based studies that relate EEG to continuous speech in AAD and single-speech-source paradigms. We point out recurrent methodological pitfalls and the need for a standard benchmark of model analysis. Main results. We gathered 28 studies. The main methodological issues we found are biased cross-validations, data leakage leading to over-fitted models, or disproportionate data size compared to the model's complexity. In addition, we address requirements for a standard benchmark model analysis, such as public datasets, common evaluation metrics, and good practices for the match-mismatch task. Significance. We are the first to present a review paper summarizing the main deep-learning-based studies that relate EEG to speech while addressing methodological pitfalls and important considerations for this newly expanding field. Our study is particularly relevant given the growing application of deep learning in EEG-speech decoding.

翻译：目的。当一个人聆听连续语音时，大脑中会引发相应的反应，并且可以通过脑电图（EEG）进行记录。目前，线性模型被用于将EEG记录与相应的语音信号相关联。线性模型找到这两类信号之间映射的能力被用作神经跟踪语音的衡量标准。此类模型具有局限性，因为它们假设EEG与语音关系中的线性关系，这忽略了大脑的非线性动态特性。作为一种替代方法，深度学习模型最近被用于将EEG与连续语音相关联，特别是在听觉注意力解码（AAD）和单语音源范式中。方法。本文回顾并评述了基于深度学习的研究，这些研究将EEG与AAD和单语音源范式中的连续语音相关联。我们指出了反复出现的方法学陷阱以及对模型分析标准基准的需求。主要结果。我们收集了28项研究。我们发现的主要方法学问题包括有偏的交叉验证、导致模型过拟合的数据泄露，或者与模型复杂度相比不成比例的数据规模。此外，我们还讨论了标准基准模型分析的要求，例如公共数据集、通用评估指标以及匹配-不匹配任务的良好实践。意义。我们是首个提出综述文章的研究团队，总结了将EEG与语音相关联的主要基于深度学习的研究，同时指出了这一新兴领域的方法学陷阱和重要考量。鉴于深度学习在EEG-语音解码中的应用日益增长，我们的研究尤为相关。