Xue Huang, Yang Jingmin, Zhang Wenjie, Yang Bokai
School of Computer Science and Engineering, Minnan Normal University, Zhangzhou, 363000, China.
Key Laboratory of Data Science and Intelligence Application, Zhangzhou, 363000, Fujian Province, China.
Med Biol Eng Comput. 2024 Feb;62(2):591-603. doi: 10.1007/s11517-023-02954-4. Epub 2023 Nov 12.
Decision-making plays a critical role in an individual's interpersonal interactions and cognitive processes. Due to the issue of strong subjectivity in the classification research of art design decisions, we utilize the relatively objective electroencephalogram (EEG) to explore design decision problems. However, different regions of the brain do not have the same influence on the design decision classification, so this paper proposes a spatial feature based convolutional neural network (space-CNN) to explore the problem of decision classification of EEG signals from different regions. We recruit 16 subjects to collect their EEG data while viewing four stimulation patterns. After noise reduction of the raw data by discrete wavelet transform (DWT), the EEG image is generated by combining it with the spatial features of the EEG signal, which is used as the input of CNN. Our experimental results show that the degree of influence of different brain regions on decision-making is parietal lobe > frontal lobe > occipital lobe > temporal lobe. In addition, the average accuracy of space-CNN reaches 86.13%, which is about 6% higher than similar studies.
决策在个体的人际互动和认知过程中起着关键作用。由于艺术设计决策分类研究中存在较强主观性的问题，我们利用相对客观的脑电图（EEG）来探索设计决策问题。然而，大脑的不同区域对设计决策分类的影响并不相同，因此本文提出了一种基于空间特征的卷积神经网络（space-CNN）来探索来自不同区域的脑电信号决策分类问题。我们招募了16名受试者，在他们观看四种刺激模式时收集他们的脑电数据。通过离散小波变换（DWT）对原始数据进行降噪后，将其与脑电信号的空间特征相结合生成脑电图像，作为卷积神经网络的输入。我们的实验结果表明，不同脑区对决策的影响程度为顶叶>额叶>枕叶>颞叶。此外，space-CNN的平均准确率达到86.13%，比同类研究高出约6%。
