Electroencephalogram Experimentation to Understand Creativity of Mechanical Engineering Students
Electroencephalogram Experimentation to Understand Creativity of Mechanical Engineering Students
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DOI：
10.1115/1.4056473
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发表时间：
2023-01
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影响因子：
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通讯作者：
Md Tanvir Ahad;T. Hartog;Amin G. Alhashim;M. Marshall;Z. Siddique
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作者：
Md Tanvir Ahad;T. Hartog;Amin G. Alhashim;M. Marshall;Z. Siddique
脑电（EEG）α功率（8-13 Hz）是各种创造性任务条件的特征，并且参与创造性思维。阿尔法功率随着与创造力相关的任务需求而变化。本研究采用事件相关电位（ERPs）、α功率激活和潜在机器学习（ML）对工科学生创造性任务的神经反应进行分类。所有的参与者都进行了修改的交替使用任务（AUT），在该任务中，参与者将日常物品的功能（或用途）归类为创造性的，无意义的或常见的。本研究首先探讨了中央和顶枕颞区的基本ERP。工程专业学生理解创造力的生物反应表明，在300-500 ms的窗口上，无意义和创造性刺激引起的N400振幅（分别为-1.107 mV和-0.755 mV）比普通用途（0.0859 mV）更大。N400效应在300-500 ms窗口上从每个感兴趣电极的总平均波形观察到。方差分析发现了一个显着的主要效应：创造性思维过程中的阿尔法功率下降，特别是在（O 1/2，P7/8）顶枕颞区。机器学习用于对特定时间区域数据的神经反应（创造性，无意义和常见）进行分类。使用k-最近邻（kNN）分类器，并使用从参与者收集的数据集在准确度，精确度，召回率和F1分数方面对结果进行评估。总体准确率为99.92%，曲线下面积为0.9995，kNN分类器成功地对参与者的神经反应进行了分类。这些结果对于机器学习技术在创造力研究中的更广泛应用具有巨大的潜力。
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.