This study decodes consumers' preference levels using a convolutional neural network (CNN) in neuromarketing. The classification accuracy in neuromarketing is a critical factor in evaluating the intentions of the consumers. Functional near-infrared spectroscopy (fNIRS) is utilized as a neuroimaging modality to measure the cerebral hemodynamic responses. In this study, a specific decoding structure, called CNN-based fNIRS-data analysis, was designed to achieve a high classification accuracy. Compared to other methods, the automated characteristics, constant training of the dataset, and learning efficiency of the proposed method are the main advantages. The experimental procedure required eight healthy participants (four female and four male) to view commercial advertisement videos of different durations (15, 30, and 60 s). The cerebral hemodynamic responses of the participants were measured. To compare the preference classification performances, CNN was utilized to extract the most common features, including the mean, peak, variance, kurtosis, and skewness. Considering three video durations, the average classification accuracies of 15, 30, and 60 s videos were 84.3, 87.9, and 86.4%, respectively. Among them, the classification accuracy of 87.9% for 30 s videos was the highest. The average classification accuracies of three preferences in females and males were 86.2 and 86.3%, respectively, showing no difference in each group. By comparing the classification performances in three different combinations (like vs. so-so, like vs. dislike, and so-so vs. dislike) between two groups, male participants were observed to have targeted preferences for commercial advertising, and the classification performance 88.4% between “like” vs. “dislike” out of three categories was the highest. Finally, pairwise classification performance are shown as follows: For female, 86.1% (like vs. so-so), 87.4% (like vs. dislike), 85.2% (so-so vs. dislike), and for male 85.7, 88.4, 85.1%, respectively.

中文翻译：

---

使用卷积神经网络解码消费者的三种不同偏好水平：功能性近红外光谱研究

本研究使用神经营销学中的卷积神经网络 (CNN) 解码消费者的偏好水平。神经营销学中的分类准确性是评估消费者意图的关键因素。功能性近红外光谱（fNIRS）被用作测量脑血流动力学反应的神经成像方式。在这项研究中，设计了一种称为基于 CNN 的 fNIRS 数据分析的特定解码结构，以实现高分类精度。与其他方法相比，该方法的主要优点是自动化特性、数据集的持续训练和学习效率。实验过程要求八名健康参与者（四名女性和四名男性）观看不同时长（15、30 和 60 秒）的商业广告视频。测量了参与者的脑血流动力学反应。为了比较偏好分类性能，利用 CNN 提取最常见的特征，包括平均值、峰值、方差、峰度和偏度。考虑三个视频时长，15、30和60秒视频的平均分类准确率分别为84.3、87.9和86.4%。其中，30s视频的分类准确率最高，为87.9%。女性和男性三种偏好的平均分类准确率分别为86.2%和86.3%，各组之间没有差异。通过比较两组之间三种不同组合（喜欢与一般、喜欢与不喜欢、一般与不喜欢）的分类表现，观察到男性参与者对商业广告有针对性的偏好，并且分类表现三个类别中“喜欢”与“不喜欢”的比率最高，为88.4%。最后，成对分类表现如下：女性为 86.1%（喜欢与一般）、87.4%（喜欢与不喜欢）、85.2%（一般与不喜欢），男性为 85.7、88.4 ，分别为 85.1%。