Demographic Data for Subjects in an Alzheimer's Disease EEG Study

A small demographic dataset for subjects participating in a study on Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy controls. The data was uploaded by Ashik Mostafa Alvi to figshare and last updated on April 20, 2026. The associated research proposes a deep learning framework, CDR-Net, using electroencephalography (EEG) for diagnosis.

Use Cases

• Training classifiers to differentiate between Alzheimer's disease, mild cognitive impairment, and healthy subjects based on demographic and EEG-derived features.
• Validating the performance of the proposed CDR-Net architecture or similar models for cognitive decline recognition.
• Conducting secondary analysis on the relationship between demographic factors and EEG-based diagnostic outcomes.

Strengths

• Dataset is openly licensed under CC-BY-4.0, permitting broad reuse.
• Associated research reports high model performance metrics (e.g., 99.25% multiclass accuracy) for the proposed diagnostic framework.

Limitations

• Dataset is very small at 5.5 KB, indicating limited scope and likely a small number of subjects.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for model training.

Provenance

Source

Ashik Mostafa Alvi via figshare.

Collection Method

Likely collected as part of a clinical research study involving EEG recordings and subject assessments.

Time Range

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Freshness

Last updated 2026-04-20 17:58:06; freshness should be verified.

Geography

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