  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.
