A portable brainwave technology in detecting functional brain changes in aging and dementia: A pilot study on feasibility of the application in residential care older adults
Bibliographic record
Abstract
Abstract Background A portable brainwave technology based on electroencephalography (EEG) / event‐related potential (ERP) has been established; i.e., the so called Brain Vital Signs ‐ BVS. Previous studies reported the development of the BVS framework and the characterization of BVS features in healthy younger adults. In the present study, we investigated the feasibility of application of this novel brainwave technology in older adult residents in long‐term care, and analyzed the relationship of the BVS scores in relation to age and dementia diagnosis. Method Fifty‐one residents in long‐term care (mean age=81.4±9.5 years; range =56‐98; 66% female) with or without dementia (31:20) participated in the study. Each participant was tested, in their residence facility, at baseline and followed for up to 12 months during which two more tests were conducted. At each test time, participants were scanned twice using the BVS device, with which the electrodes placed on the frontal, central, and posterior sites along the midline of the head. Three ERP components, N100 (perception), P300 (attention), and N400 (cognition) were identified and converted into normalized comparative scores, based on which BVS sub‐scores assessing the amplitude, latency, and the area under the curve of each component were computed. The BVS total score was generated based on the sub‐scores applying a weighted algorithm. Inter‐rater reliability was examined using Intraclass Correlation Coefficient. The BVS scores were correlated with age, and compared for dementia and cognitive status. Result Data were consistently collected from residential‐care older adults at each of the three time points with 100% success rate, suggesting the feasible application of the portably brainwave technology. Inter‐rater reliability rate was as high as 0.991‐1.000. The BVS total scores and several of the component scores were moderately related to age; e.g., Spearman correlation coefficient r=.262, p=.027 for P300 amplitude and r=‐.286, p=.016 for N400 latency. Some of the component scores differed by dementia status (e.g., t=‐2.03, p‐value= .046 for P300 latency). Conclusion The study suggests that BVS can be feasibly applied in studying older residents in long‐term care. Ongoing analysis effort is to better understand age‐ and dementia‐ associated changes of the BVS scores for practical result interpretation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".