Explainable AI-aided examination of saccade preparation in human EEG signals
Bibliographic record
Abstract
We investigated spatiotemporal patterns of neural processing underlying saccadic planning using explainable AI techniques. We recorded scalp EEG using a 64-channel BioSemi setup while participants (N = 20) completed pro-saccades to the left or right, randomly selected on each trial. A 3D adaptation of EEGNet trained with a novel data augmentation technique we developed to address the relatively small number of EEG trials (mean: 436.45 per participant, range: 273-492), successfully predicted saccade direction prior to onset with performance significantly above chance (mean AUC=0.77, p<.001, 95% CI: [0.67, 0.88]; mean accuracy=0.70, p<.001, 95% CI: [0.61, 0.79]) based on the held-out validation data. Model performance correlated strongly with dataset size for both AUC and accuracy (r=0.68, p=0.001), suggesting that sufficiency of training data was the primary limiting factor for model generalization performance. We applied a modified GradCAM algorithm to identify spatial and temporal features informing CNN predictions. Right frontal electrodes (FP2, AF4, F2, FC2, C2, CP2) were critical for predicting left saccades, while left electrodes (F1, FC1, C1, CP1) were important for right saccades. Of note, right frontal electrodes (FP2, AF4) remained critical for both directions, possibly reflecting a right-lateralized 'cognitive motor planning' signal. Temporal analysis of this signal revealed harmonic oscillations, peaking at 30Hz for both left and right saccades, consistent with low gamma band activity with a ~10ms lag for left saccades compared to right. A lateralized 'motor preparation signal' peaked during the final 32-16ms for right saccades at the left frontal electrodes. This motor preparation signal was more broadly distributed temporally for left saccades, appearing 36-24ms and 72-56ms prior to the onset of the saccade at the right frontal electrodes. We conclude that our explainable AI analysis can identify saccadic preparation signals and reveals significant asymmetries in the generation of left versus right saccades that require exploring.
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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 | 0.000 | 0.003 |
| 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".