Identification of Relationships Between Electroencephalography (EEG) Bands and Design Activities
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Bibliographic record
Abstract
Electroencephalography (EEG) study of design activities has been drawing increasing attentions in design cognition research. The aim of this present paper is to identify EEG bands that are associated with design activities through principal component analysis (PCA). Based on the analysis of the data on 32 subjects collected from experiments conducted in the Design Lab at Concordia University, it was found that resting, problem solving and evaluation activities have relations to specific EEG bands. EEG powers of beta-2 (20–30Hz), gamma-1 (20–30Hz), and gamma-2 (30–40Hz) are mostly associated to the design activities whereas resting is mostly associated to alpha band (8–14Hz). In addition, there are differences in frequency above 20Hz between the resting before and after design activities. The work presented in this paper can be used to further quantify designer’s cognitive activities, which will ultimately improve the development of effective design tools and methods.
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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 | 0.000 |
| 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) | 0.000 | 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 it