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。当人聆听连续语音时，大脑会产生相应反应，可通过脑电图（EEG）记录。目前常使用线性模型将EEG记录与对应语音信号相关联。线性模型寻找两种信号之间映射的能力被用作衡量神经跟踪语音的指标。此类模型存在局限性，因其假设EEG与语音之间呈线性关系，而忽视了大脑的非线性动力学特性。作为替代方案，近期深度学习模型已被用于关联EEG与连续语音，尤其在听觉注意力解码（AAD）和单语音源范式领域。方法。本文回顾并评述了在AAD和单语音源范式中基于深度学习将EEG与连续语音关联的研究。我们指出了反复出现的方法论陷阱，以及建立标准化模型分析基准的必要性。主要结果。我们汇总了28项研究。发现的主要方法论问题包括：有偏的交叉验证、导致模型过拟合的数据泄漏，以及数据规模与模型复杂度不匹配。此外，我们提出了标准化基准模型分析的要求，例如公开数据集、通用评估指标以及匹配-不匹配任务的规范实践。意义。我们是首个发表综述论文的研究，系统总结了基于深度学习将EEG与语音关联的主要研究，同时指出了这一新兴领域中方法论陷阱及重要考量。鉴于深度学习在EEG-语音解码中的应用日益增长，本研究具有特殊意义。