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Abstract Electroencephalogram (EEG) alpha power (8–13 Hz) is a characteristic of various creative task conditions and is involved in creative ideation. Alpha power varies as a function of creativity-related task demands. This study investigated the event-related potentials (ERPs), alpha power activation, and potential machine learning (ML) to classify the neural responses of engineering students involved with creativity task. All participants performed a modified alternate uses task (AUT), in which participants categorized functions (or uses) for everyday objects as either creative, nonsense, or common. At first, this study investigated the fundamental ERPs over central and parietooccipital temporal areas. The bio-responses to understand creativity in engineering students demonstrates that nonsensical and creative stimuli elicit larger N400 amplitudes (−1.107 mV and −0.755 mV, respectively) than common uses (0.0859 mV) on the 300–500 ms window. N400 effect was observed on 300–500 ms window from the grand average waveforms of each electrode of interest. ANOVA analysis identified a significant main effect: decreased alpha power during creative ideation, especially over (O1/2, P7/8) parietooccipital temporal area. Machine learning is used to classify the specific temporal area data’s neural responses (creative, nonsense, and common). A k-nearest neighbors (kNN) classifier was used, and results were evaluated in terms of accuracy, precision, recall, and F1- score using the collected datasets from the participants. With an overall 99.92% accuracy and area under the curve at 0.9995, the kNN classifier successfully classified the participants’ neural responses. These results have great potential for broader adaptation of machine learning techniques in creativity research. 

Investigations of creativity have been an intriguing topic for a long time, but assessing creativity is extremely complex. Creativity is a cornerstone of engineering disciplines, so understanding creativity and how to enhance creative abilities through engineering education has received substantial attention. Fields outside of engineering are no stranger to neuro-investigations of creativity and although some neuro-response studies have been conducted to understand creativity in engineering, these studies need to map the engineering design and concept generation processes better. Using neuroimaging techniques alongside engineering design and concept generation processes is necessary for understanding how to improve creative idea generation and creativity studies in engineering. In this paper, a survey is provided of the literature for the different neurological approaches that have been used to study the engineering design process and creative processes. Also presented are proposed strategies to apply these neurological approaches to engineering design to understand the creative process in greater detail. Furthermore, results from a pilot study investigating neuro-responses of engineers are presented. 

Assessing creativity is not an easy task, but that has not stopped researchers from exploring it. Because creativity is essential to engineering disciplines, knowing how to enhance creative abilities through engineering education has been a topic of interest. In this paper, the event related potential (ERP) technique is used to study the neural responses of engineers via a modified alternative uses task (AUT). Though only a pilot study testing two participants, the preliminary results of this study indicate general neuro-responsiveness to novel or unusual stimuli. These findings also suggest that a scaled-up study along these lines would enable better understanding and modeling of neuroresponses of engineers and creative thinking, as well as contribute to the growing field of ERP research in the field of engineering. 

Creativity is the driver of innovation in engineering. Hence, assessing the effectiveness of a curriculum, a method, or a technique in enhancing the creativity of engineering students is no doubt important. In this paper, the process involved in quantifying creativity when measured through the alternative uses task (AUT) is explained in detail. The AUT is a commonly used test for divergent thinking ability, which is a main aspect of creativity. Although it is commonly used, the processes used to score this task are far from standardized and tend to differ across studies. In this paper, we introduce these problems and move towards a standardized process by providing a detailed account of our quantification process. This quantification process takes into consideration four commonly used dimensions of creativity: originality, flexibility, fluency, and elaboration. AUT data from a preliminary case study were used to illustrate how the AUT and the quantification process can be used. The study was performed to understand the effect of the stereotype threat on the creativity of 25 female engineering students. The results indicate that after the stereotype threat intervention, participants generated more diverse and original ideas.