Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis

Abstract

The global prevalence of Major Depressive Disorder (MDD) is increasing at an alarming rate, underscoring the urgent need for timely and accurate diagnoses to facilitate effective interventions and treatments. Electroencephalography remains a widely used neuroimaging technique in psychiatry, due to its non-invasive nature and cost-effectiveness. With the rise of computational psychiatry, the integration of EEG with artificial intelligence has yielded remarkable results in diagnosing depression. This review offers a comparative analysis of two predominant methodologies in research: traditional machine learning and deep learning methods. Furthermore, this review addresses key challenges in current research and suggests potential solutions. These insights aim to enhance diagnostic accuracy for depression and also foster further development in the area of computational psychiatry.

Keywords: artificial intelligence; deep learning; major depressive disorder; resting-state electroencephalography; traditional machine learning.

Figure 1

General framework for depression diagnosis based on TML and DL methods.

Figure 2

The 10–20 system electrode position diagram, taken from Ref. [68].

Figure 3

Schematic diagram of brain regions.

Figure 4

Frequency of different preprocessing methods used in studies of depression diagnosis.

Figure 5

Frequency distribution of TML-based algorithms for depression diagnosis.

Figure 6

Frequency distribution of deep learning-based algorithms for depression diagnosis.

Figure 7

Frequency distribution of different validation methods in depression diagnostic studies.