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Mental disorders represent critical public health challenges as they areleading contributors to the global burden of disease and intensely influencesocial and financial welfare of individuals. The present comprehensive reviewconcentrate on the two mental disorders: Major depressive Disorder (MDD) andBipolar Disorder (BD) with noteworthy publications during the last ten years.There is a big need nowadays for phenotypic characterization of psychiatricdisorders with biomarkers. Electroencephalography (EEG) signals could offer arich signature for MDD and BD and then they could improve understanding ofpathophysiological mechanisms underling these mental disorders. In this review,we focus on the literature works adopting neural networks fed by EEG signals.Among those studies using EEG and neural networks, we have discussed a varietyof EEG based protocols, biomarkers and public datasets for depression andbipolar disorder detection. We conclude with a discussion and valuablerecommendations that will help to improve the reliability of developed modelsand for more accurate and more deterministic computational intelligence basedsystems in psychiatry. This review will prove to be a structured and valuableinitial point for the researchers working on depression and bipolar disordersrecognition by using EEG signals.

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review