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.
Soroush et al. (Thu,) studied this question.
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