The primary goal of any company is to increase its profits by improving both the quality of its products and how they are advertised. In this context, neuromarketing seeks to enhance the promotion of products and generate a greater acceptance on potential buyers. Traditionally, neuromarketing studies have relied on a single biosignal to obtain feedback from presented stimuli. However, thanks to new devices and technological advances studying this area of knowledge, recent trends indicate a shift towards the fusion of diverse biosignals. An example is the usage of electroencephalography for understanding the impact of an advertisement at the neural level and visual tracking to identify the stimuli that induce such impacts. This emerging pattern determines which biosignals to employ for achieving specific neuromarketing objectives. Furthermore, the fusion of data from multiple sources demands advanced processing methodologies. Despite these complexities, there is a lack of literature that adequately collates and organizes the various data sources and the applied processing techniques for the research objectives pursued. To address these challenges, the current paper conducts a comprehensive analysis of the objectives, biosignals, and data processing techniques employed in neuromarketing research. This study provides both the technical definition and a graphical distribution of the elements under revision. Additionally, it presents a categorization based on research objectives and provides an overview of the combinatory methodologies employed. After this, the paper examines primary public datasets designed for neuromarketing research together with others whose main purpose is not neuromarketing, but can be used for this matter. Ultimately, this work provides a historical perspective on the evolution of techniques across various phases over recent years and enumerates key lessons learned.

翻译：任何公司的首要目标都是通过提高产品质量和广告宣传效果来增加利润。在此背景下，神经营销学致力于增强产品推广效果，并促使潜在买家产生更高的接受度。传统上，神经营销研究依赖单一生物信号来获取对呈现刺激的反馈。然而，得益于该知识领域研究的新设备与技术进步，近期趋势表明，研究方向正转向多种生物信号的融合。例如，利用脑电图从神经层面理解广告的影响，并借助视觉追踪来识别引发此类影响的刺激。这种新兴模式决定了为实现特定神经营销目标应选用哪些生物信号。此外，来自多个来源的数据融合需要先进的处理方法。尽管存在这些复杂性，目前仍缺乏能够充分整理和归类各种数据源及其处理技术的文献，以服务于所追求的研究目标。为应对这些挑战，本文对神经营销研究中采用的目标、生物信号和数据处理技术进行了全面分析。本研究提供了所审视要素的技术定义和图形分布。此外，它还基于研究目标进行了分类，并概述了所采用的组合方法。随后，本文考察了专为神经营销研究设计的主要公开数据集，以及其他主要目的并非神经营销但可用于此目的的数据集。最终，本工作提供了近年来各阶段技术演变的历史视角，并列举了关键经验教训。