DEEP LEARNING-BASED ELECTROENCEPHALOGRAPHY ANALYSIS: A SYSTEMATIC REVIEW Yannick Roy∗ Hubert Banville∗ Isabela Albuquerque Alexandre Gramfort Faubert Lab Inria MuSAE Lab Inria Université de Montréal Université Paris-Saclay INRS-EMT Université Paris-Saclay Montréal, Canada Paris, France & Université du Québec Paris, France
[email protected] InteraXon Inc. Montréal, Canada Toronto, Canada Tiago H. Falk Jocelyn Faubert MuSAE Lab Faubert Lab INRS-EMT Université de Montréal Université du Québec Montréal, Canada Montréal, Canada ABSTRACT Context. Electroencephalography (EEG) is a complex signal and can require several years of train- ing, as well as advanced signal processing and feature extraction methodologies 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. Objective. In this work, we review 156 papers that apply DL to EEG, published between Jan- uary 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 inter- esting approaches from this large body of literature in order to inform future research and formulate recommendations. Methods. Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. arXiv:1901.05498v2 [cs.LG] 20 Jan 2019 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.