神经营销中的数据融合：生物信号的多模态分析、生命周期阶段、当前进展、数据集、趋势与挑战
Data Fusion in Neuromarketing: Multimodal Analysis of Biosignals, Lifecycle Stages, Current Advances, Datasets, Trends, and Challenges
AI总结：
本文针对神经营销领域多生物信号融合趋势下缺乏系统梳理的问题，全面分析了该领域的研究目标、生物信号类型与数据处理技术，分类梳理了组合方法与可用数据集，并呈现技术演进脉络与关键经验。
摘要：
任何企业的核心目标都是通过提升产品质量与广告宣传效果来增加利润。在此背景下，神经营销旨在优化产品推广效果，提升潜在买家的接受度。传统的神经营销研究依赖单一生物信号来获取受试者对呈现刺激的反馈。然而，随着该研究领域相关新设备与技术的进步，近期趋势显示研究正转向多种生物信号的融合——例如结合脑电图（EEG）从神经层面理解广告的影响，同时利用眼动追踪识别引发这些影响的刺激因素。这种新兴研究模式需要明确针对特定神经营销目标应选用哪些生物信号。此外，多源数据的融合需要先进的处理方法。尽管存在这些复杂性，但目前缺乏足够的文献对各类数据源、对应研究目标所采用的处理技术进行充分梳理与整合。为应对这些挑战，本文对神经营销研究中涉及的研究目标、生物信号与数据处理技术开展了全面分析。本研究既给出了所梳理要素的技术定义，也呈现了这些要素的图形化分布情况；此外，研究基于研究目标对相关内容进行了分类，并概述了所采用的各类组合方法。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，以及其他虽核心用途并非神经营销但可用于该领域研究的数据集。最终，本研究梳理了近年来各阶段技术演进的历史脉络，并总结了关键经验教训。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
