University of Arkansas, Department of Psychological Science, 480 Campus Drive, Fayetteville, AR, 72701, USA.
University of Arkansas, Department of Psychological Science, 480 Campus Drive, Fayetteville, AR, 72701, USA.
Neuroimage. 2020 Oct 1;219:116990. doi: 10.1016/j.neuroimage.2020.116990. Epub 2020 May 28.
Prior research has shown that greater EEG alpha power (8-13 Hz) is characteristic of more creative individuals, and more creative task conditions. The present study investigated the potential for machine learning to classify more and less creative brain states. Participants completed an Alternate Uses Task, in which they thought of Normal or Uncommon (more creative) uses for everyday objects (e.g., brick). We hypothesized that alpha power would be greater for Uncommon (vs. Common) uses, and that a machine learning (ML) approach would enable the reliable classification data from the two conditions. Further, we expected that ML would be successful at classifying more (vs. less) creative individuals. As expected, alpha power was significantly greater for the Uncommon than for the Normal condition. Using spectrally weighted common spatial patterns to extract EEG features, and quadratic discriminant analysis, we found that classification accuracy for the two conditions varied widely among individuals, with a mean of 63.9%. For more vs. less creative individuals, 82.3% classification accuracy was attained. These findings indicate the potential for broader adoption of machine learning in creativity research.
先前的研究表明，更大的脑电图阿尔法功率（8-13 赫兹）是更具创造力的个体的特征，也是更具创造性的任务条件的特征。本研究探讨了机器学习对分类更多和更少创造性大脑状态的潜力。参与者完成了交替用途任务，他们需要思考日常物体（例如砖头）的正常或不常见（更具创造性）用途。我们假设，阿尔法功率对于不常见（与常见相比）用途会更大，并且机器学习（ML）方法将能够可靠地对两种情况下的数据进行分类。此外，我们预计 ML 将能够成功地对更多（与更少）创造性个体进行分类。正如预期的那样，阿尔法功率对于不常见条件显著大于常见条件。使用频谱加权的公共空间模式来提取 EEG 特征和二次判别分析，我们发现两种条件的分类准确性在个体之间差异很大，平均值为 63.9%。对于更多与更少创造性的个体，达到了 82.3%的分类准确性。这些发现表明机器学习在创造力研究中的更广泛应用的潜力。
