LAUSR

Electroencephalography (EEG) is used in the diagnosis and prognosis of mental disorders because it provides brain biomarkers. However, only highly trained doctors can interpret EEG signals due to its complexity. Machine learning has been successfully trained with EEG signals for classifying mental disorders, but a time consuming and disorder-dependant feature engineering (FE) and subsampling process is required over raw EEG data. Deep Learning (DL) is positioned as a prominent research field to process EEG data because (i) it features automated FE by taking advantage of raw EEG signals improving results and (ii) it can be trained over the vast amount of data generated by EEG. In this work, a systematic mapping study has been performed with 46 carefully selected primary studies. Our goals were (i) to provide a clear view of which are the most prominent study topics in diagnosis and prognosis of mental disorders by using EEG with DL, and (ii) to give some recommendations for future works. Some results are: epilepsy was the predominant mental disorder present in around half of the studies, convolutional neural networks also appear in approximate 50% of the works. The main conclusions are (i) processing EEG with DL to detect mental disorders is a promising research field and (ii) to objectively compare performance between studies: public datasets, intra-subject validation, and standard metrics should be used. Additionally, we suggest to pay more attention to ease the reproducibility, and to use (when possible) an available framework to explain the results of the created DL models.