A Data-Driven Eeg Framework For Multiclass Dementia Classification Via Spectral, Microstate, And Connectivity Features
Bibliographic record
Abstract
Accurate differential screening of Alzheimer’s disease (AD) and frontotemporal dementia (FTD) remains difficult, motivating scalable EEG-based decision support; however, prior work often benchmarks spectral, microstate, and connectivity features separately and uses heuristic dimensionality reduction. Using a public OpenNeuro eyes-closed resting-state EEG dataset (N=88; 10–20 montage; 500 Hz), we built a standardized pipeline including EEGLAB preprocessing (filtering, ICA artifact removal), temporal standardization (60–360 s), multi-domain feature extraction (spectral band power, theta–alpha ratio, aperiodic 1/f exponent, alpha peak frequency; wPLI-based connectivity with graph metrics; four-class microstate parameters), and leakage-controlled dimensionality reduction via PCA with parallel analysis to automatically determine retained components within each training fold. We compared Elastic Net, RBF-SVM, and Random Forest (500 trees) using repeated 5-fold cross-validation over 100 cycles, evaluating classwise precision/recall/F1 and an integrated radar-area index capturing balanced multi-class performance across AD/FTD/controls. Across models, FTD was frequently misclassified as AD, indicating limited intrinsic separability from resting EEG alone. Feature representation was the dominant determinant of performance: microstate features yielded the largest and most balanced radar areas, spectral features were intermediate, and connectivity features were weakest. The proposed workflow provides a reproducible benchmarking template and practical guidance for EEG/BCI-oriented dementia screening systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".