Detection of error in static and dynamic visual stimulation via electroencephalogram and eye-tracking systems
Bibliographic record
Abstract
Human responses such as electroencephalogram (EEG), eye-tracking, and heart rate have been studied for error detection during visual stimulation, often in controlled settings with single-target fixation. This study delves into constructing machine learning (ML) models for binary error classification across diverse visual stimulation conditions, including static, dynamic, and single or multiple targets, using EEG and eye-tracking data. When constructing these models, using gaze fixation data for epoch extraction can enhance the ability to extract salient, stimulus-induced responses from EEG and eye-tracking data. These features can be strongly associated with changes in visual stimulation. Among 30 ML models tested, the best-performing ML models built on a personalized approach consistently achieved over 90 % accuracy across conditions. For feature importance, we integrate a repetition approach with the Boruta SHapley Additive exPlanations (BorutaSHAP) algorithm to enhance the legitimacy of key feature selection. Feature analysis revealed distinct patterns, e.g., eye-tracking features like log energy entropy being particularly prominent under dynamic conditions, EEG features from the delta, and theta bands being significant across all conditions. Interestingly, an increase in the number of visual targets led to a reduction in the importance of EEG features, especially during dynamic stimulations. These insights have the potential to enhance the ML models through tailored feature selection. While this study acknowledges certain limitations concerning real-time applicability, generalizability, etc, the novelty of our models p[presents opportunities for various applications in human-computer/robot interaction (HCI/HRI), monitoring systems, rehabilitation systems, assistive technologies for individuals with limited mobility and driving, etc.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".