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Updated: Jun 28, 2026

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Published on: July 7, 2023

CastNet: A three-channel EEG-based deep learning model for cross-subject depression detection.

Shuo Zhang¹, Bohao Zhang¹, Jiaming Cai¹

¹School of Electronic Information Engineering, Hebei University, Baoding 071002, China.

Artificial Intelligence in Medicine

|June 26, 2026

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This study introduces CastNet, a deep learning model for diagnosing depression using electroencephalogram (EEG) data. CastNet achieves high accuracy, demonstrating the potential of AI in mental health diagnostics.

Area of Science:

Neuroscience
Artificial Intelligence
Psychiatry

Background:

Depression is a prevalent mental health disorder impacting millions globally.
Deep learning models have shown promise in electroencephalogram (EEG)-based depression diagnosis.

Purpose of the Study:

To propose CastNet, a novel deep learning model for depression detection using three-channel EEG data.
To enhance feature learning and address computational complexity in EEG analysis for depression diagnosis.

Main Methods:

CastNet integrates Convolutional Neural Networks (CNN), Transformer, and Long Short-Term Memory (LSTM) for multi-level EEG feature learning.
A feature enhancement module, depthwise separable convolution, and a novel coupled bidirectional LSTM are employed.
Leave-One-Subject-Out (LOSO) cross-validation was used on MPHC and PRED+CT datasets.

Keywords:

Convolutional dynamic sparse attention (CDSA)Coupled BiLSTM Deep learning Depression Electroencephalography (EEG)Feature enhancement Three channel data

Related Experiment Videos

Last Updated: Jun 28, 2026

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Published on: July 7, 2023

Main Results:

CastNet achieved classification accuracies of 97.2% on the MPHC dataset and 87.5% on the PRED+CT dataset.
The model outperformed existing methods in EEG-based depression diagnosis.
The study highlights the effectiveness of multi-level feature learning and advanced LSTM fusion.

Conclusions:

CastNet demonstrates significant potential for accurate depression diagnosis using limited EEG channels.
The findings offer new insights into EEG signal patterns associated with depression.
This research supports the application of advanced deep learning techniques in clinical mental health assessment.