School of Psychology, Qufu Normal University, Shandong, China.
College of Electronic and Information Engineering, Southwest University, Chongqing, China.
Sci Rep. 2024 Jul 31;14(1):17735. doi: 10.1038/s41598-024-68772-2.
Recognizing an individual's preference state for potential romantic partners based on electroencephalogram (EEG) signals holds significant practical value in enhancing matchmaking success rates and preventing romance fraud. Despite some progress has been made in this field, challenges such as high-dimensional feature space and channel redundancy limited the technology's practical application. The aim of this study is to explore the most discriminative EEG features and channels, in order to enhance the recognition performance of the system, while maximizing the portable and practical value of EEG-based systems for recognizing romantic attraction. To achieve this goal, we first conducted an interesting simulated dating experiment to collect the necessary data. Next, EEG features were extracted from various dimensions, including band power and asymmetry index features. Then, we introduced a novel method for EEG feature and channel selection that combines the sequential forward selection (SFS) algorithm with the frequency-based feature subset integration (FFSI) algorithm. Finally, we used the random forest classifier (RFC) to determine a person's preference state for potential romantic partners. Experimental results indicate that the optimal feature subset, selected using the SFS-FFSI method, attained an average classification accuracy of 88.42%. Notably, these features were predominantly sourced from asymmetry index features of electrodes situated in the frontal, parietal, and occipital lobes.
基于脑电图 (EEG) 信号识别个体对潜在浪漫伴侣的偏好状态，在提高配对成功率和防止浪漫欺诈方面具有重要的实际价值。尽管在这一领域已经取得了一些进展，但高维特征空间和通道冗余等挑战限制了该技术的实际应用。本研究旨在探索最具区分性的 EEG 特征和通道，以提高系统的识别性能，同时最大限度地提高基于 EEG 的系统识别浪漫吸引力的便携性和实用性。为了实现这一目标，我们首先进行了一项有趣的模拟约会实验，以收集必要的数据。接下来，从多个维度提取 EEG 特征，包括频带功率和不对称指数特征。然后，我们引入了一种新的 EEG 特征和通道选择方法，该方法结合了顺序前向选择 (SFS) 算法和基于频率的特征子集集成 (FFSI) 算法。最后，我们使用随机森林分类器 (RFC) 来确定一个人对潜在浪漫伴侣的偏好状态。实验结果表明，使用 SFS-FFSI 方法选择的最优特征子集的平均分类准确率为 88.42%。值得注意的是，这些特征主要来自额叶、顶叶和枕叶电极的不对称指数特征。
