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.

脑电图（EEG）可用于精神疾病的诊断和预后，因为它提供了大脑生物标志物。然而，由于其复杂性，只有训练有素的医生才能解释脑电信号。机器学习已经成功地通过脑电信号进行了训练，以对精神障碍进行分类，但是需要对原始脑电数据进行耗时且依赖于障碍的特征工程（FE）和二次采样过程。深度学习（DL）被定位为处理EEG数据的重要研究领域，因为（i）它通过利用原始EEG信号来改善结果而具有自动FE功能，并且（ii）可以对EEG生成的大量数据进行训练。在这项工作中，已经对46项精心挑选的基础研究进行了系统的制图研究。我们的目标是（i）通过将EEG与DL结合使用，清楚地了解哪些是精神障碍的诊断和预后中最重要的研究主题，以及（ii）为以后的工作提供一些建议。一些结果是：在大约一半的研究中，癫痫症是主要的精神障碍，卷积神经网络也出现在大约50％的研究中。主要结论是（i）用DL处理脑电图以检测精神障碍是一个有前途的研究领域，并且（ii）客观比较研究之间的表现：应使用公共数据集，受试者内部验证和标准指标。此外，我们建议更加注意减轻可重复性，并使用（如果可能）使用可用的框架来解释创建的DL模型的结果。