The Problem with Time: Application of Partial Least Squares Analysis on Time-Frequency Plots to Account for Varying Time Intervals with Applied EEG Data
Bibliographic record
Abstract
Background/Objectives: When attempting to study neurocognitive mechanisms with electroencephalography (EEG) in applied ecologically valid settings, responses to stimuli may differ in time, which presents challenges to traditional EEG averaging methods. In this proof-of-concept paper, we present a method to normalize time over unequal trial lengths while preserving frequency content. Methods: Epochs are converted to time-frequency space where they are resampled to contain an equal number of timepoints representing the proportion of trial complete rather than true time. To validate this method, we used EEG data recorded from 8 novices and 4 experts in veterinary medicine while completing decision-making tasks using two question types: multiple-choice and script concordance questions used in veterinary school exams. Results: The resulting resampled time-frequency data were analyzed with partial least squares (PLS), a multivariate technique that extracts patterns of data that support a contrast between conditions and groups while controlling for Type I error. We found a significant latent variable representing a difference between question types for experts only. Conclusions: Despite within and between subject differences in timing, we found consistent differences between question types in experts in gamma and beta bands that are consistent with changes resulting from increased information load and decision-making. This novel analysis method may be a viable path forward to preserve ecological validity in EEG studies.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".