Individualized <scp>real‐time</scp> prediction of working memory performance by classifying electroencephalography signals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract An individualized model that predicts trial‐by‐trial working memory performance is instrumental for personalized interventions. Here, we propose a single‐trial electroencephalography (EEG) classification process predicting individuals' responses, that is, target correct versus target non‐correct during a working memory task, N‐back. We used event‐related (de‐)synchronization (ERD and ERS) prior to an anticipatory cue as features. The proposed comprehensive process addresses single‐trial EEG classification challenges such as temporal overlap between training and testing datasets, feature selection's stability, and significance of the classification accuracy which have been often overlooked. Our model identified for the first time a few (ranged between 4 and 10) brain regions and oscillations where ERD and ERS predicted an individual's performance. Mean (SD) prediction accuracy across 50 participants (mean age [SD] = 28.56 [7.55]) was 69.51% (8.41). Accuracy was significantly above chance in 34 participants. This machine learning‐based approach provides a proof of principle for individualizing EEG targets for potential interventions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it