Rapid EEG‐Based Detection of Attentional Deficits in Mild Cognitive Impairment
Bibliographic record
Abstract
Abstract Background Mild cognitive impairment (MCI) is a risk factor for dementia, where early detection improves outcomes through early lifestyle change interventions that may delay disease progression. Traditional methods for detecting MCI rely on clinical interviews, cognitive and functional assessments, and laboratory testing, which are resource‐intensive and require skilled administrators. As MCI prevalence grows with an aging population, there is a pressing need for accessible, efficient, and objective screening tools. Electroencephalography (EEG) is sensitive to cognitive dysfunction but is limited by the cost, time, and technical expertise required for traditional systems. Mobile EEG presents a promising alternative, offering quick and user‐friendly assessments. This study focuses on identifying attentional deficits, which are subtle in presentation but impactful on daily living. Method We evaluated 200 participants aged 64‐86, including 46 individuals diagnosed with MCI. Cognitive function was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Participants completed a simple mobile EEG task, the oddball paradigm, designed to elicit the N200 event‐related potential (ERP), which is associated with attentional processes. Multiple linear regression was used to examine the relationship between N200 amplitude and latency and RBANS attention index scores. Welch's t‐test was performed to assess group differences of N200 amplitude between MCI and healthy controls. Result N200 amplitude and latency were predictive of RBANS attention index scores ( R = 0.223). Additionally, MCI patients exhibited reduced N200 amplitude compared to healthy controls ( p < 0.05). Conclusion Our findings support the use of the N200 as a potential electrophysiological biomarker of attentional deficits in MCI. More importantly, they demonstrate the value of mobile EEG for efficient and accessible cognitive screening; our total testing time, including EEG setup, was less than seven minutes and required no technical expertise. This approach could enable scalable early detection of MCI, providing an opportunity for timely intervention in aging populations.
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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) | 0.000 | 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) | 0.002 | 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".