Assessing Mild Cognitive Impairment Using Portable Electroencephalography: The P300 Component
Bibliographic record
Abstract
Increased prevalence of mild cognitive impairments (MCIs) and dementias are a growing concern as the population ages, which produces a need for an objective, accessible, and cost-effective tool to facilitate early detection and intervention. This article investigates whether a portable electroencephalography (EEG) system can provide an effective measure of MCI using a visual oddball task to target the memory and attention event-related potential (ERP) component called the P300. In this study, 40 participants were separated into two groups: individuals with a diagnosed cognitive impairment and a healthy age-matched control group. Participants completed two typical pen-and-paper MCI assessments to gather behavioural data, which were followed by a perceptual EEG oddball task to gather brain data. Results show that the MCI group demonstrated decreased behavioural task performance in the pen-and-paper assessments and a modulated brain response during the oddball task when compared to healthy controls, which the portable EEG system revealed to be a decreased P300 peak amplitude. These results indicate the capability of portable EEGs to identify biomarkers for MCI and their potential use in the diagnostic process. This capability could provide major benefits to patients, their families, and physicians, and would also assist with Alzheimer’s research. Future research could expand on these findings by applying a lifespan or disease-span approach to investigate P300 changes in the course of a healthy individual’s life compared to P300 changes in individuals with MCI over the entire course of their disease. This research could also cultivate a greater understanding of how MCI progresses, which could improve diagnostic or treatment development.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".