A Novel Approach for Analysis and Detection of Depression Using Electroencephalogram (EEG) Signals

Abstract

Depression is a widespread mental health disorder that affects millions of individuals globally. Early and accurate detection of depression is essential for timely intervention and effective treatment. The abstract outlines the key steps involved in developing a depression detection system using EEG, starting with data collection from individuals with and without depression. Preprocessing techniques are applied to clean and normalize the EEG signals, ensuring the removal of artifacts and noise. Feature extraction is a critical phase where relevant information is derived from EEG signals to characterize brain activity patterns associated with depression. After that, the extracted features are used to train machine learning models for the categorization of depression, such as support vector machines (SVMs), random forests, or deep learning architectures (CNN). This highlights the importance of addressing challenges like small and imbalanced datasets, inter-subject variability, and generalizability across diverse populations. Additionally, the model emphasizes the importance of interpretability in machine learning models for depression detection, as it aids in understanding the underlying neural correlates of depression. The abstract gives underscoring the promising prospects of EEG-based depression detection in early diagnosis, personalized treatment, and improved management of depression, ultimately contributing to enhanced mental health care and patient well-being.