Performance Comparison by EEG Signals Data for Mental Health Detection

Sunil Kumar Sharma published a dataset on figshare in April 2026 containing performance metrics for an AI model designed for mental health detection. The NeuroHAGWO-Net model integrates electroencephalogram (EEG) signals and behavioral textual data, achieving high accuracy and other metrics as reported in the study description. The dataset is stored in an XLS file with a size of 5.5 KB.

Use Cases

• Benchmarking AI model performance for mental health detection based on reported accuracy, precision, and F1-score metrics.
• Analyzing the effectiveness of hybrid optimization techniques like HAGWO for feature selection in multimodal data.
• Studying the integration of EEG signal processing with behavioral text embeddings for early mental health screening.

Strengths

• Model performance metrics are reported with high specificity, including an accuracy of 0.9945 and an F1-Score of 0.9909 on behavioral data.
• The dataset is associated with a detailed methodological description involving advanced techniques like EMD, BERT, BiLSTM-CNN, and HAGWO.
• It is openly licensed under CC-BY-4.0.

Limitations

• Row count and column-level documentation are unknown; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating limited scope, likely containing only summary performance metrics.
• The geographic and temporal coverage of the underlying EEG and behavioral data is not specified.

Provenance

Source

Sunil Kumar Sharma via figshare.

Collection Method

Likely generated from the validation of the NeuroHAGWO-Net AI model on two datasets (behavioral and EEG signals).

Time Range

The temporal coverage of the source EEG and behavioral data is not specified.

Freshness

Last updated 2026-04-13 17:44:14; freshness should be verified.

Geography

The spatial coverage of the source EEG and behavioral data is not specified.