Single Channel EEG Based Score Generation to Monitor the Severity and Progression of Mild Cognitive Impairment
Bibliographic record
Abstract
Mild Cognitive Impairment (MCI) is a preliminary stage of Dementia. MCI is determined by behavioral screening measures such as Montreal Cognitive Assessment (MoCA) and Mini-Mental Status Examination (MMSE). Therefore, monitoring the progression of MCI and predicting MoCA scores from objective physiological measures like the EEG is crucial as it will not only help to improve the mental healthcare of the aging population but also to reduce healthcare costs. In this study, we demonstrate a single channel EEG based MoCA score generation method, which is cost-effective and suitable for continuous patient monitoring in the longitudinal study. We collected scalp EEG data while subjects were stimulated with five auditory speech signals. We extracted 590 features from Event-Related Brain Potentials (ERPs), which included time and spectral domain characteristics of the response. The top 11 features, ranked by mutual information, were used for building regression models to generate MoCA scores of the subjects. Robustness of our model was tested using R-squared value, mean square error (MSE), residual's quantile plot, and cook's distance. The analysis shows R-squared=0.78 with MSE=1.63, and residual analysis suggests that the model is acceptable in terms of quantile plot, leverage, and Cook's distance. The outcomes indicate that single-channel based EEG can be used to estimate cognitive scores automatically for severity detection and progression monitoring, which will help us to efficaciously assess the mental health status of elderly people to improve the prognosis and rehabilitation of age-related cognitive impairments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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".