NeuroHAGWO-Net: Multimodal EEG and Text Data for Mental Health Detection

A 5.5 KB Excel file containing system configuration and software requirements for the NeuroHAGWO-Net AI model. The model, authored by Sunil Kumar Sharma and last updated in April 2026, integrates electroencephalogram (EEG) signals and behavioral textual data for automated mental health status detection. It employs a hybrid BiLSTM-CNN architecture and a novel Hybrid Ant-Grey Wolf Optimization (HAGWO) feature selection method.

Use Cases

• Training multimodal AI models for mental health detection based on integrated EEG and text data.
• Benchmarking feature selection algorithms based on the described Hybrid Ant-Grey Wolf Optimization (HAGWO) approach.
• Reproducing the NeuroHAGWO-Net framework's performance metrics, which include accuracy of 0.9945 on behavioral data.
• Studying the integration of temporal CNNs (T-CNNs) and EfficientNetV2 architectures for health data analysis.

Strengths

• Model performance on behavioral data is quantified with specific metrics, including an accuracy of 0.9945 and an F1-Score of 0.9909.
• The dataset is openly licensed under CC-BY-4.0.
• The underlying methodology is described in detail, including the use of Empirical Mode Decomposition (EMD) and BERT embeddings.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The 5.5 KB size suggests the file contains configuration parameters, not the primary EEG or text data used for model training.

Provenance

Source

Sunil Kumar Sharma via figshare

Collection Method

Likely compiled as part of the NeuroHAGWO-Net AI model development study.

Time Range

The associated research and model were likely current as of the 2026 update.

Freshness

Last updated 2026-04-13 17:44:10; freshness should be verified.

Geography

Geographic coverage of the underlying EEG and text data is unknown.