Ablation Study: EEG Data for Alzheimer's and MCI Diagnosis

A 5.5 KB Excel dataset by Ashik Mostafa Alvi, last updated in April 2026, presents results from a study on diagnosing Alzheimer's disease (AD) and mild cognitive impairment (MCI). The research proposes the Cognitive Decline Recognition Network (CDR-Net) framework using electroencephalography (EEG) data to classify AD, MCI, and healthy subjects.

Use Cases

• Benchmarking multiclass classification models for neurological disorders based on EEG-derived features.
• Training neural networks for cognitive decline recognition based on the proposed CDR-Net architecture.
• Analyzing model performance metrics like accuracy, sensitivity, and specificity for diagnostic tools.
• Studying data preprocessing techniques for EEG signals, including down-sampling and noise cleaning.

Strengths

• Reported model performance metrics are high, with 99.25% multiclass accuracy.
• The dataset is openly licensed under CC-BY-4.0.
• The study addresses stability and overfitting concerns using 10-fold and leave-one-out cross-validation.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating limited scope, likely containing summary results rather than raw EEG data.

Provenance

Source

Ashik Mostafa Alvi via figshare

Collection Method

Likely contains results from an experimental framework employing EEG data preprocessing and a custom CDR-Net model.

Time Range

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Freshness

Last updated 2026-04-20 17:58:13; freshness should be verified.

Geography

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