MultiMindNet: AI-based mental health analysis using hybrid deep learning approach and Hybrid Ant-Grey Wolf Optimization (HAGWO) algorithm.

Overview

1. Department of Information Systems, College of Computer and Information Sciences, Majmaah University, Majmaah, Saudi Arabia
2. King Salman Center for Disability Research, Riyadh, Saudi Arabia
3. Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Majmaah, Saudi Arabia
4. Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India
5. Applied Science Research Center, Applied Science Private University, Amman, Jordan
6. Smart Structural Health Monitoring and Control Laboratory, DGUT-CNAM, Dongguan University of Technology, China
7. ENS -Paris-Saclay University, Centre Borelli, UMR CNRS, Gif-sur-Yvette, France
8. Institute of Engineering (IoE), Thapathali Campus, Tribhuvan University, Kathmandu, Nepal

Abstract

Mental health disorders like depression and anxiety pose global challenges, requiring accurate, non-invasive detection methods. Classical modes of diagnosis are typically based on self-reported symptoms or clinical evaluation, which could be subjective and protracted in time. To address these limitations, this study proposes NeuroHAGWO-Net, an advanced artificial intelligence-based framework for automated mental health status detection using multimodal data. The proposed model integrates electroencephalogram (EEG) signals and behavioral textual data to enable early and reliable mental health screening. EEG signals are pre-processed with Empirical Mode Decomposition (EMD) for noise removal, while behavioral text data is transformed into embeddings using Bidirectional Encoder Representations from Transformers (BERT) models. The hybrid BiLSTM-CNN architecture captures temporal dependencies and spatial patterns in EEG data, enhanced by integrating behavioral embeddings for multimodal analysis. Features are selected using a novel Hybrid Ant-Grey Wolf Optimization (HAGWO) approach, combining Ant Colony Optimization (ACO) and Modified Grey Wolf Optimization (mGWO), respectively. The AI-based mental health detection is performed using NeuroVisionNet, integrating EfficientNetV2 and Temporal CNNs (T-CNNs). The model’s performance is validated on two datasets: behavioral data and EEG signals data. On behavioral data, it achieves an accuracy of 0.9945, precision of 0.9874, sensitivity of 0.9935, specificity of 0.9915, F1-Score of 0.9909, Matthews Correlation Coefficient (MCC) of 0.9925, Negative Predictive Value (NPV) of 0.9905, False Positive Rate (FPR) of 0.0151, and False Negative Rate (FNR) of 0.0092. With its strong accuracy and efficiency in detecting mental health situations under diverse data modalities, NeuroHAGWO-Net Model proves to be a robust tool for early mental health screening and clinical support using modern optimization techniques and deep learning architectures.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/ayushtibrewal, at Kaggle; found in the references
• kaggle.com/datasets/cid007, at Kaggle; found in the references

Data Availability

Dataset 1: https://www.kaggle.com/datasets/ayushtibrewal/eeg-features-dataset-for-stress-classification- Ayush Tibrewal’s “EEG Features Dataset for Stress Classification” on Kaggle is a particular dataset designed for stress detection using EEG features that are already pre-analyzed. Dataset 2: https://www.kaggle.com/datasets/cid007/mental-disorder-classification. The dataset enables researchers to detect specific psychological patterns which makes it optimal for developing machine learning and deep learning models that aid in early mental disorder diagnosis. The research of clinical decision support systems and mental health analytics will benefit from these data.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 1 funder, 28 references.

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This paper

Sharma, S. K., Khan, A. R., Tejani, G. G., Bassir, D., & Tripathi, S. (2026). MultiMindNet: AI-based mental health analysis using hybrid deep learning approach and Hybrid Ant-Grey Wolf Optimization (HAGWO) algorithm. PLOS digital health, 5(4), e0001158. https://doi.org/10.1371/journal.pdig.0001158

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