EEG Functional Connectivity Reveals Neural Mechanisms of Design Creativity in Engineering Students
Bibliographic record
Abstract
Design creativity is essential in engineering education, fostering innovation. This study examines its neural mechanisms across four cognitive states—idea generation (IDG), idea evolution (IDE), idea rating (IDR), and rest (RST). We hypothesize that brain dynamics differ across these states and can be analyzed using functional connectivity. EEG signals were recorded in loosely controlled experiments and analyzed via the weighted phase lag index (wPLI). Strength and Betweenness were extracted as graph-based features and evaluated through statistical analyses and classification models. Results showed significant features – Strength features in the left hemisphere (central, parietal, and temporal lobes) acting as key hubs, while Betweenness in the right hemisphere (frontal and central lobes) indicated network information flow. The proposed method achieved high classification accuracy (≥87%), with SVM (~92%) outperforming MLP and KNN. This computationally efficient approach advances neural studies of design creativity, offering insights into cognitive processes and real-world applications such as brain-computer interfaces.
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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) | 0.002 | 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".