Usman Syed Muhammad, Khalid Shehzad, Tanveer Aimen, Imran Ali Shariq, Zubair Muhammad
Department of Computer Science, Bahria School of Engineering and Applied Science, Bahria University, Islamabad, Pakistan.
Department of Computer Engineering, Bahria School of Engineering and Applied Science, Bahria University, Islamabad, Pakistan.
Front Comput Neurosci. 2025 Jan 8;18:1516440. doi: 10.3389/fncom.2024.1516440. eCollection 2024.
Marketing plays a vital role in the success of a business, driving customer engagement, brand recognition, and revenue growth. Neuromarketing adds depth to this by employing insights into consumer behavior through brain activity and emotional responses to create more effective marketing strategies. Electroencephalogram (EEG) has typically been utilized by researchers for neuromarketing, whereas Eye Tracking (ET) has remained unexplored. To address this gap, we propose a novel multimodal approach to predict consumer choices by integrating EEG and ET data. Noise from EEG signals is mitigated using a bandpass filter, Artifact Subspace Reconstruction (ASR), and Fast Orthogonal Regression for Classification and Estimation (FORCE). Class imbalance is handled by employing the Synthetic Minority Over-sampling Technique (SMOTE). Handcrafted features, including statistical and wavelet features, and automated features from Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM), have been extracted and concatenated to generate a feature space representation. For ET data, preprocessing involved interpolation, gaze plots, and SMOTE, followed by feature extraction using LeNet-5 and handcrafted features like fixations and saccades. Multimodal feature space representation was generated by performing feature-level fusion for EEG and ET, which was later fed into a meta-learner-based ensemble classifier with three base classifiers, including Random Forest, Extended Gradient Boosting, and Gradient Boosting, and Random Forest as the meta-classifier, to perform classification between buy vs. not buy. The performance of the proposed approach is evaluated using a variety of performance metrics, including accuracy, precision, recall, and F1 score. Our model demonstrated superior performance compared to competitors by achieving 84.01% accuracy in predicting consumer choices and 83% precision in identifying positive consumer preferences.
市场营销在企业成功中起着至关重要的作用，推动客户参与度、品牌认知度和收入增长。神经营销通过利用对消费者行为的洞察，借助大脑活动和情感反应来创造更有效的营销策略，从而为市场营销增添深度。脑电图（EEG）通常被研究人员用于神经营销，而眼动追踪（ET）尚未得到探索。为了填补这一空白，我们提出了一种新颖的多模态方法，通过整合EEG和ET数据来预测消费者的选择。使用带通滤波器、伪迹子空间重构（ASR）以及用于分类和估计的快速正交回归（FORCE）来减轻EEG信号中的噪声。通过采用合成少数过采样技术（SMOTE）来处理类别不平衡问题。已经提取并连接了手工制作的特征，包括统计特征和小波特征，以及来自卷积神经网络和长短期记忆（CNN-LSTM）的自动特征，以生成特征空间表示。对于ET数据，预处理包括插值、注视点图和SMOTE，随后使用LeNet-5和诸如注视和扫视等手工制作的特征进行特征提取。通过对EEG和ET进行特征级融合生成多模态特征空间表示，随后将其输入到一个基于元学习器的集成分类器中，该分类器具有三个基本分类器，包括随机森林、扩展梯度提升和梯度提升，以及作为元分类器的随机森林，以执行购买与不购买之间的分类。使用各种性能指标，包括准确率、精确率、召回率和F1分数，来评估所提出方法的性能。与竞争对手相比，我们的模型表现出卓越的性能，在预测消费者选择方面达到了84.01%的准确率，在识别积极的消费者偏好方面达到了83%的精确率。
