Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography (EEG)-based brain-computer interface (BCI) research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we employed discrete wavelet transform (DWT) and power spectral density (PSD), which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF). We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network (DNN) outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset.

中文翻译：

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通过分析和分类 EEG 信号识别消费者偏好

神经营销在弥合传统营销研究与基于脑电图 (EEG) 的脑机接口 (BCI) 研究之间的差距方面受到了关注。它通过偏好预测来确定客户的实际需求。基于EEG的偏好检测系统的性能取决于特征提取技术和机器学习算法的合适选择。在这项研究中，我们使用不同的 EEG 指数特征组合和不同的特征提取和分类算法检查了神经营销数据集的偏好检测。对于 EEG 特征提取，我们采用离散小波变换 (DWT) 和功率谱密度 (PSD) 来测量基于 EEG 的偏好指数，从而提高偏好检测的准确性。而且，我们将深度学习与其他传统分类器进行了比较，例如 k 最近邻 (KNN)、支持向量机 (SVM) 和随机森林 (RF)。我们还研究了偏好指标对分类算法性能的影响。通过严格的离线分析，我们研究了用于偏好检测和分类的计算智能。所提出的深度神经网络 (DNN) 的性能在准确率、准确率和召回率方面优于 KNN 和 SVM；然而，对于相同的数据集，RF 取得了与 DNN 相似的结果。我们研究了用于偏好检测和分类的计算智能。所提出的深度神经网络 (DNN) 的性能在准确率、准确率和召回率方面优于 KNN 和 SVM；然而，对于相同的数据集，RF 取得了与 DNN 相似的结果。我们研究了用于偏好检测和分类的计算智能。所提出的深度神经网络 (DNN) 的性能在准确率、准确率和召回率方面优于 KNN 和 SVM；然而，对于相同的数据集，RF 取得了与 DNN 相似的结果。