Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning
Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its diverse and often overlapping clinical manifestations. In this context, electroencephalography (EEG) has gained attention as a potential objective tool...
Saved in:
| Main Authors: | Damir Mulc, Jaksa Vukojevic, Eda Kalafatic, Mario Cifrek, Domagoj Vidovic, Alan Jovic |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/2/409 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Impact of Feature Selection Techniques on the Performance of Machine Learning Models for Depression Detection Using EEG Data
by: Marwa Hassan, et al.
Published: (2024-11-01) -
Association of resting-state EEG with suicidality in depressed patients: a systematic review
by: Fatemeh Shamsi, et al.
Published: (2025-01-01) -
Stacked ensemble machine learning approach for electroencephalography based major depressive disorder classification using temporal statistics
by: Nader Nisar Ahmed, et al.
Published: (2024-12-01) -
Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device
by: Kei Suzuki, et al.
Published: (2024-10-01) -
Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis
by: Haijun Lin, et al.
Published: (2024-10-01)