Application of quantitative electroencephalography in digital screening for mild cognitive impairment
Bibliographic record
Abstract
Objective To explore the quantitative electroencephalography (qEEG) characteristics of the prefrontal cortex in patients with mild cognitive impairment (MCI) during digital screening tasks for MCI screening. Methods A total of 592 MCI patients (MCI group) and 317 normal cognitively elderly individuals (control group) were recruited from 40 communities in Nanjing, Jiangsu Province, from July to August, 2024. All participants were assessed using Montreal Cognitive Assessment-Beijing Version (MoCA-BJ). Prefrontal EEG data were collected using a portable EEG device, and power spectral analysis was performed via Fast Fourier Transform. An XG-Boost algorithm was employed to construct an MCI identification model based on qEEG power features, and the model's performance was evaluated using receiver operating characteristic (ROC) curve. Results Compared with the control group, prefrontal δ, α, and β band power increased during screening tasks in MCI group (P＜0.05); δ power was negatively correlated with MoCA-BJ total scores, and visuospatial/executive function, attention and delayed recall scores (r=-0.269, -0.169, -0.133, -0.171, P＜0.001); α power was negatively correlated with MoCA-BJ total scores, attention and delayed recall scores (r=-0.113, -0.075, -0.091, P＜0.05). The XGBoost model based on δ and α power was excellent in MCI identification, with an area under the curve of 0.91, accuracy of 0.81, precision of 0.89, F1 score of 0.84, recall of 0.80, and specificity of 0.81. Conclusion MCI patients exhibit increased power in the prefrontal δ and α frequency bands during digital screening tasks, which is associated with cognitive decline. An XGBoost model based on qEEG power features can enable early prediction of MCI.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".