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 29 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：当人聆听连续语音时，大脑会引发相应的响应，该响应可通过脑电图（EEG）记录。当前常用线性模型关联EEG记录与相应语音信号。线性模型建立两种信号间映射的能力被用作衡量语音神经追踪的指标。此类模型存在局限性，因其假设EEG-语音关系呈线性，忽略了大脑的非线性动态特性。作为替代方案，深度学习模型近期被用于关联EEG与连续语音，尤其在听觉注意解码（AAD）和单语音源范式中。方法：本文综述并评述了在AAD和单语音源范式中基于深度学习的EEG与连续语音关联研究。我们指出了反复出现的方法学陷阱以及建立标准模型分析基准的必要性。主要结果：我们收集了29项研究。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、或数据规模与模型复杂度不相称。此外，我们提出了标准基准模型分析的要求，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：本文首次以综述形式总结了基于深度学习关联EEG与语音的主要研究，同时指出了这一新兴扩展领域的方法学陷阱及重要考量。鉴于深度学习在EEG-语音解码中日益广泛的应用，本研究具有特殊相关性。