Background and purpose In recent years, the development of machine learning (ML)
frameworks for automatic diagnosis of unipolar depression has escalated to a next level of …

Clinical depression is a neurological disorder that can be identified by analyzing the
Electroencephalography (EEG) signals. However, the major drawback in using EEG to …

Depression affects large number of people across the world today and it is considered as
the global problem. It is a mood disorder which can be detected using …

In recent years, advanced neurocomputing and machine learning techniques have been
used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In …

Depression is a common reason for an increase in suicide cases worldwide. Thus, to
mitigate the effects of depression, accurate diagnosis and treatment are needed. An …

Depression is one of the significant contributors to the global burden disease, affecting
nearly 264 million people worldwide along with the increasing rate of suicidal deaths …

Background Modern day's society is engaged in commitment-based and time-bound jobs.
This invites tension and mental depression among many people who are not able to cope …

Electroencephalography (EEG)–based studies focus on depression recognition using data
mining methods, while those on mild depression are yet in infancy, especially in effective …

With the advent of greater computing power each year, computer-based disease/condition
diagnosis have been gaining significant importance recently. In this paper, an extensive …

Background Major depressive disorder (MDD) is a prevalent mental illness that is diagnosed
through questionnaire-based approaches; however, these methods may not lead to an …