EEG based Major Depressive disorder and Bipolar disorder detection using\n Neural Networks: A review
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Abstract
Mental disorders represent critical public health challenges as they are\nleading contributors to the global burden of disease and intensely influence\nsocial and financial welfare of individuals. The present comprehensive review\nconcentrate on the two mental disorders: Major depressive Disorder (MDD) and\nBipolar Disorder (BD) with noteworthy publications during the last ten years.\nThere is a big need nowadays for phenotypic characterization of psychiatric\ndisorders with biomarkers. Electroencephalography (EEG) signals could offer a\nrich signature for MDD and BD and then they could improve understanding of\npathophysiological mechanisms underling these mental disorders. In this review,\nwe focus on the literature works adopting neural networks fed by EEG signals.\nAmong those studies using EEG and neural networks, we have discussed a variety\nof EEG based protocols, biomarkers and public datasets for depression and\nbipolar disorder detection. We conclude with a discussion and valuable\nrecommendations that will help to improve the reliability of developed models\nand for more accurate and more deterministic computational intelligence based\nsystems in psychiatry. This review will prove to be a structured and valuable\ninitial point for the researchers working on depression and bipolar disorders\nrecognition by using EEG signals.\n