EEG channel location estimation using CNN–based depression classifier

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ID: 284318
2026
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Abstract
Depression is a mental disorder that results to deteriorating effects in the lives of many people around the world. Traditional methods of diagnosing depression are based on interviews and questionaries. However, there were studies that have shown the possibilities of detecting depression biomarkers and perform classification. Electroencephalogram (EEG) analysis is one way of doing this. A series of signal processing techniques were used to streamline EEG data into a form which are recognizable by a machine learning algorithm. In this study, a convolutional neural network (CNN) –based depression classifier was used to classify depression using three EEG systems with 5, 16, and 128 channel locations. The aim is to estimate if there are differences in terms of classification performance. Results show that a system with locations for 5 and 16 EEG channels can achieve 98% accuracy like a 128-channel system. Hence, EEG systems with fewer number of electrodes can be utilized for depression classification applications.
Reference Key
persistent_1760652292_68f16c04da740 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Awang, Saidatul Ardeenawatie
Journal Malay Journal
Year 2026
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/faculty_research/13812
Keywords Keywords not found

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