Precise temporal localisation of M/EEG effects with Bayesian generalised additive multilevel models
Bibliographic record
Abstract
Abstract Time-resolved electrophysiological measurements such as those obtained through magneto- and electroencephalography (M/EEG) offer a unique window onto the neural activity underlying cognitive processes. Researchers are often interested in determining whether and when these signals differ across experimental conditions or participant groups. The conventional approach involves mass univariate statistical testing across time and space followed by corrections for multiple comparisons or some form of cluster-based inference. While effective for controlling error rates at the cluster-level, clusterbased inference comes with a significant limitation: by shifting the focus of inference from individual time points to clusters, it prevents drawing conclusions about the precise onset or offset of observed effects. Here, we present a model-based alternative for analysing M/EEG timeseries, such as event-related potentials or time-resolved decoding accuracy. Our approach leverages Bayesian generalised additive multilevel models, providing posterior odds that an effect exceeds zero (or chance) at each time point, while naturally accounting for temporal dependencies and between-subject variability. Using both simulated and empirical M/EEG datasets, we show that this approach substantially outperforms conventional methods in estimating the onset and offset of neural effects, yielding more precise and reliable estimates. We provide an open-source R package implementing the method and describe how it can be integrated into M/EEG analysis pipelines using MNE-Python.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".