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. Approach. This paper reviews and comments on deep-learning-based studies that relate EEG to continuous speech in single- or multiple-speakers 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 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，大脑会诱发相应反应，可通过脑电图记录。当前采用线性模型关联脑电记录与对应语音信号，利用线性模型在两者间建立映射的能力作为神经追踪语音的度量指标。此类模型存在局限性，因其假设脑电-语音关系具有线性特征，忽略了大脑的非线性动力学特性。作为替代方案，深度学习模型近年来被用于关联脑电与连续语音。方法。本文综述并评述了基于深度学习研究单说话人或多人说话人范式下脑电与连续语音关联的工作，指出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：有偏交叉验证、导致过拟合模型的数据泄露、以及模型复杂度与数据规模不匹配。此外，我们针对匹配-不匹配任务提出了基准模型分析所需的标准，如公开数据集、通用评估指标及良好实践规范。意义。本文系统总结了将脑电与语音关联的主要深度学习研究，同时指出这一新兴领域的方法学陷阱与重要考量因素。鉴于深度学习在脑电解码语音领域的应用日益增长，本研究具有重要参考价值。