Neuromarketing exploits neuroimaging techniques so as to reinforce the predictive power of conventional marketing tools, like questionnaires and focus groups. Electroencephalography (EEG) is the most commonly encountered neuroimaging technique due to its non-invasiveness, low-cost, and its very recent embedding in wearable devices. The transcription of brainwave patterns to consumer attitude is supported by various signal descriptors, while the quest for profitable novel ways is still an open research question. Here, we suggest the use of sample covariance matrices as alternative descriptors, that encapsulate the coordinated neural activity from distinct brain areas, and the adoption of Riemannian geometry for their handling. We first establish the suitability of Riemannian approach for neuromarketing-related problems and then suggest a relevant decoding scheme for predicting consumers’ choices (e.g., willing to buy or not a specific product). Since the decision-making process involves the concurrent interaction of various cognitive processes and consequently of distinct brain rhythms, the proposed decoder takes the form of an ensemble classifier that builds upon a multi-view perspective, with each view dedicated to a specific frequency band. Adopting a standard machine learning procedure, and using a set of trials (training data) in conjunction with the associated behavior labels (“buy”/ “not buy”), we train a battery of classifiers accordingly. Each classifier is designed to operate in the space recovered from the inter-trial distances of SCMs and to cast a rhythm-depended decision that is eventually combined with the predictions of the rest ones. The demonstration and evaluation of the proposed approach are performed in 2 neuromarketing-related datasets of different nature. The first is employed to showcase the potential of the suggested descriptor, while the second to showcase the decoder’s superiority against popular alternatives in the field.

中文翻译：

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RNeuMark：神经营销的黎曼脑电图分析框架

神经营销利用神经成像技术来增强传统营销工具（如问卷和焦点小组）的预测能力。脑电图 (EEG) 是最常见的神经影像学技术，因为它具有非侵入性、低成本以及最近嵌入可穿戴设备的特点。各种信号描述符支持将脑电波模式转录为消费者态度，而寻求有利可图的新方法仍然是一个悬而未决的研究问题。在这里，我们建议使用样本协方差矩阵作为替代描述符，封装来自不同大脑区域的协调神经活动，并采用黎曼几何来处理它们。我们首先确定黎曼方法对神经营销相关问题的适用性，然后提出用于预测消费者选择（例如，愿意购买或不购买特定产品）的相关解码方案。由于决策过程涉及各种认知过程的并发交互，因此涉及不同的大脑节律，因此所提出的解码器采用基于多视图视角的集成分类器的形式，每个视图专用于特定频段。采用标准的机器学习程序，并结合相关的行为标签（“购买”/“不购买”）使用一组试验（训练数据），我们相应地训练了一组分类器。每个分类器都设计为在从 SCM 的试验间距离恢复的空间中运行，并根据节奏做出最终与其余分类器的预测相结合的决定。所提出方法的演示和评估在 2 个不同性质的神经营销相关数据集中进行。第一个用于展示建议描述符的潜力，而第二个用于展示解码器相对于该领域流行替代方案的优势。