  Electroencephalography (EEG) is a complex signal and can require several
years of training to be correctly interpreted. Recently, deep learning (DL) has
shown great promise in helping make sense of EEG signals due to its capacity to
learn good feature representations from raw data. Whether DL truly presents
advantages as compared to more traditional EEG processing approaches, however,
remains an open question. In this work, we review 156 papers that apply DL to
EEG, published between January 2010 and July 2018, and spanning different
application domains such as epilepsy, sleep, brain-computer interfacing, and
cognitive and affective monitoring. We extract trends and highlight interesting
approaches in order to inform future research and formulate recommendations.
Various data items were extracted for each study pertaining to 1) the data, 2)
the preprocessing methodology, 3) the DL design choices, 4) the results, and 5)
the reproducibility of the experiments. Our analysis reveals that the amount of
EEG data used across studies varies from less than ten minutes to thousands of
hours. As for the model, 40% of the studies used convolutional neural networks
(CNNs), while 14% used recurrent neural networks (RNNs), most often with a
total of 3 to 10 layers. Moreover, almost one-half of the studies trained their
models on raw or preprocessed EEG time series. Finally, the median gain in
accuracy of DL approaches over traditional baselines was 5.4% across all
relevant studies. More importantly, however, we noticed studies often suffer
from poor reproducibility: a majority of papers would be hard or impossible to
reproduce given the unavailability of their data and code. To help the field
progress, we provide a list of recommendations for future studies and we make
our summary table of DL and EEG papers available and invite the community to
contribute.
