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MultiMindNet: AI-based mental health analysis using hybrid deep learning approach and Hybrid Ant-Grey Wolf

Sunil Kumar Sharma^1,2, Ahmad Raza Khan³, Ghanshyam G Tejani^4,5

¹Department of Information Systems, College of Computer and Information Sciences, Majmaah University, Majmaah, Saudi Arabia.

PLOS Digital Health

|April 13, 2026

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This study introduces NeuroHAGWO-Net, an AI framework using electroencephalogram (EEG) and text data for accurate mental health detection. The model achieves high accuracy, offering a robust tool for early screening and clinical support.

Area of Science:

Neuroscience
Artificial Intelligence
Computational Psychiatry

Background:

Mental health disorders present significant global challenges, necessitating accurate and non-invasive diagnostic methods.
Current diagnostic approaches rely on subjective and time-consuming self-reports or clinical evaluations.
There is a critical need for automated, objective tools for early mental health screening.

Purpose of the Study:

To propose NeuroHAGWO-Net, an advanced AI framework for automated mental health status detection using multimodal data.
To integrate electroencephalogram (EEG) signals and behavioral textual data for enhanced diagnostic accuracy.
To develop a robust system for early and reliable mental health screening.

Main Methods:

Utilized Empirical Mode Decomposition (EMD) for EEG signal noise reduction.

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Employed Bidirectional Encoder Representations from Transformers (BERT) for behavioral text data embedding.

Developed a hybrid BiLSTM-CNN architecture for EEG analysis and integrated behavioral embeddings for multimodal fusion.

Implemented a novel Hybrid Ant-Grey Wolf Optimization (HAGWO) for feature selection.

Performed AI-based detection using NeuroVisionNet, combining EfficientNetV2 and Temporal CNNs (T-CNNs).

Main Results:

Achieved high performance metrics on behavioral and EEG datasets.
Demonstrated accuracy of 0.9945, precision of 0.9874, and sensitivity of 0.9935 on behavioral data.
Reported excellent overall performance, including F1-Score of 0.9909 and MCC of 0.9925.
Showcased low False Positive Rate (FPR) of 0.0151 and False Negative Rate (FNR) of 0.0092.

Conclusions:

NeuroHAGWO-Net is a highly accurate and efficient AI framework for mental health detection.
The multimodal approach integrating EEG and text data enhances screening capabilities.
The model offers a robust solution for early mental health screening and clinical support, leveraging advanced optimization and deep learning.