Novel Machine Learning Methods for ERP Analysis: A Validation From Research on Infants at Risk for Autism
Bibliographic record
Abstract
Machine learning and other computer intensive pattern recognition methods are successfully applied to a variety of fields that deal with high-dimensional data and often small sample sizes such as genetic microarray, functional magnetic resonance imaging (fMRI) and, more recently, electroencephalogram (EEG) data. The aim of this article is to discuss the use of machine learning and discrimination methods and their possible application to the analysis of infant event-related potential (ERP) data. The usefulness of two methods, regularized discriminant function analyses and support vector machines, will be demonstrated by reanalyzing an ERP dataset from infants (Elsabbagh et al., 2009 Elsabbagh, M., Volein, A., Csibra, G., Holmboe, K., Garwood, H., Tucker, L., … and Johnson, M. H. 2009. Neural correlates of eye gaze processing in the infant broader autism phenotype. Biological Psychiatry, 65: 31–38. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). Using cross-validation, both methods successfully discriminated above chance between groups of infants at high and low risk of a later diagnosis of autism. The suitability of machine learning methods for the use of single trial or averaged ERP data is discussed.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".