Depression diagnosis: EEG-based cognitive biomarkers and machine learning.

Area of Science:

• Neuroscience
• Psychiatry
• Biomedical Engineering

Background:

• Depression significantly impacts individuals and society.
• Traditional depression diagnosis methods have limitations.
• Emerging biomarkers, particularly EEG-based ones, show promise.

Purpose of the Study:

• To review the significance of cognitive biomarkers in depression assessment.
• To investigate the neurophysiological effects of depression on brain regions.
• To explore the integration of machine learning (ML) and deep learning (DL) in EEG-based depression diagnosis.

Main Methods:

• Comprehensive literature review of cognitive biomarkers and EEG studies.
• Analysis of depression's effects on brain activity patterns.
• Examination of ML/DL algorithms applied to EEG data for diagnostic purposes.

Main Results:

• Cognitive biomarkers offer valuable insights into depression assessment.
• EEG data reveals neurophysiological alterations associated with depression.
• ML/DL models enhance diagnostic accuracy and personalized treatment planning using EEG.

Conclusions:

• Understanding the neurophysiological basis of depression is crucial.
• EEG-based biomarkers, analyzed with ML/DL, represent a significant advancement in depression diagnosis.
• This approach holds potential for optimizing personalized therapeutic protocols.