CN101815467A - 神经反应刺激和刺激属性谐振估计器 - Google Patents

神经反应刺激和刺激属性谐振估计器 Download PDF

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CN101815467A
CN101815467A CN200880101500A CN200880101500A CN101815467A CN 101815467 A CN101815467 A CN 101815467A CN 200880101500 A CN200880101500 A CN 200880101500A CN 200880101500 A CN200880101500 A CN 200880101500A CN 101815467 A CN101815467 A CN 101815467A
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stimulus
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CN101815467B (zh
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A·普拉迪普
R·T·奈特
R·古鲁穆尔蒂
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Abstract

一种系统确定神经反应刺激和刺激属性谐振。使用用诸如事件相关电位(ERP)、脑电图(EEG)、皮肤电反应(GSR)、心电图(EKG)、眼电图(EOG)、眼跟踪、以及面部情感编码等机制收集的神经反应数据来评估刺激材料和刺激材料属性，诸如通信、构思、体验、消息、图像、音频、定价、以及封装。分析神经反应数据以确定刺激和刺激属性谐振。

Description

神经反应刺激和刺激属性谐振估计器
相关申请的交叉引用
本申请要求由Anantha Pradeep、Robert T.Knight、以及Ramachandran Gurumoorthy于2007年7月30日提交的题为Neuro-Behavioral Stimulus And Stimulus Attribute Resonance Estimator的临时专利申请60/952,723(档案号No.2007NF12)的优先权。
技术领域
本公开涉及刺激和刺激属性谐振估计器(stimulus attributeresonance estimator)。
背景技术
用于估计刺激和刺激属性谐振的传统系统受到限制。某些受众(audience)谐振测量系统是基于人口统计信息、统计数据、以及基于调查的反应(response)收集。然而，传统系统遭遇语义、句法、比喻、文化、以及解释错误的影响。
因此，期望提供用于估计刺激和刺激属性谐振的改进方法和装置。
附图说明
通过参考结合附图进行的以下说明，可以最透彻地理解本公开，附图举例说明特定的示例性实施例。
图1举例说明用于估计刺激和刺激属性谐振的系统的一个示例。
图2举例说明可以包括在刺激属性贮存库(repository)中的刺激属性的示例。
图3举例说明可以与刺激和反应贮存库一起使用的数据模型的示例。
图4举例说明可以与谐振估计系统一起使用的查询的一个示例。
图5举例说明使用谐振估计系统生成的报告的一个示例。
图6举例说明用于执行数据分析的技术的一个示例。
图7举例说明用于估计刺激和刺激属性谐振的技术的一个示例。
图8提供可以用来实现一种或多种机制的系统的一个示例。
具体实施方式
现在将对本发明的某些特定示例进行详细参考，所述特定示例包括本发明人预期的用于执行本发明的最佳方式。那些特定实施例的示例在附图中示出。虽然结合这些特定实施例来描述本发明，但应理解的是并不意图使本发明局限于所述实施例。相反，意图是涵盖包括在随附权利要求所定义的本发明的精神和范围内的替换、修改、以及等价物。
例如，将在诸如中枢神经系统、自主神经系统、以及效应器(effector)数据等特定类型的数据的背景下描述本发明的技术和机制。然而，应注意的是本发明的技术和机制适用于多种不同类型的数据。应注意的是可以将各种机制和技术应用于任何类型的刺激。在以下说明中，阐述了许多特定细节以便提供对本发明的透彻理解。可以在没有某些或全部这些特定细节的情况下实现本发明的特定示例性实施例。在其它实例中，未详细描述众所周知的过程操作以免不必要地使本发明含糊难懂。
为了明了起见，有时将以单数形式来描述本发明的各种技术和机制。然而，应注意的是除非另外说明，否则某些实施例包括技术的多次重复或机制的多次实例化。例如，系统在多种背景下使用处理器。然而，应认识到的是除非另有说明，否则系统可以在保持在本发明的范围内的同时使用多个处理器。例如，本发明的技术和机制有时将描述两个实体之间的连接。应注意的是两个实体之间的连接不一定意指直接、无阻碍的连接，因为在两个实体之间可能存在多种其它实体。例如，可以将处理器连接到存储器，但应认识到的是在处理器与存储器之间可以存在多种桥接器和控制器。因此，除非另有说明，连接不一定意指直接、无阻碍的连接。
概述
一种系统确定神经反应刺激和刺激属性谐振。使用用诸如事件相关电位(Event Related Potential，ERP)、脑电图(Electroencephalography，EEG)、皮肤电反应(Galvanic SkinResponse，GSR)、心电图(Electrocardiogram，EKG)、眼电图(Electrooculography，EOG)、眼跟踪、以及面部(facial)情感编码等机制收集的神经反应数据来评估刺激材料和刺激材料属性，诸如通信(communication)、构思(concept)、体验、消息、图像、音频、定价(pricing)、以及封装(packaging)。分析神经反应数据以确定刺激和刺激属性谐振。
示例性实施例
刺激和刺激属性谐振估计器已经使用多年。通常，刺激和刺激属性谐振估计器是基于通常依赖于人口统计信息、统计信息、以及基于调查的反应收集的受众谐振测量。传统刺激和刺激属性谐振估计器的一个问题是传统谐振估计器不测量可归因于刺激的固有消息谐振。它们还易于发生语义、句法、比喻、文化、以及解释性错误，从而妨碍受众的准确和可重复目标确定(targeting)。
传统系统在评估用户反应时不使用神经行为和神经生理反应混合表现(manifestation)，且不引出对刺激的单独自定义神经生理和/或神经行为反应。
传统设备还未能跨越多个数据集、个体和模态(modality)而将多个数据集和多模态(multi-modal)反应的混合表现混合以显示并确认对刺激和刺激属性的偏好和谐振的引出测量。
在这点上，根据本发明的神经生理和神经行为刺激和刺激属性谐振估计器(estimator)基本上脱离现有技术的传统构思和设计，并在这种情况下提供一种装置，该装置主要是为了提供一种方法和系统而开发的，所述方法和系统用于对营销(marketing)、广告及其它音频/视觉/触觉/嗅觉刺激的属性的受众反应和谐振的基于神经生理和神经行为反应的测量，所述属性包括但不限于通信、构思、体验、消息、图像、音频、定价、封装。
本发明的技术和机制使用诸如中枢神经系统、自主神经系统和效应器测量等神经反应测量来改善谐振估计。中枢神经系统测量机制的某些实例包括功能性磁谐振成像(Functional Magnetic ResonanceImaging，fMRI)和脑电图(EEG)。fMRI测量大脑中的与增加的神经活动相关的血液氧合(blood oxygenation)。然而，fMRI的当前实施方式具有几秒钟的不良时间分辨率。EEG测量与在几毫秒范围内发生的突触后(post synaptic)电流相关的电活动。颅下EEG以最大的准确度来测量电活动，因为骨头和真皮层削弱大范围的频率的传播。尽管如此，如果经过适当分析，表面EEG提供大量的电生理学信息。即使具有干电极的便携式EEG也提供大量的神经反应信息。
自主神经系统测量机制包括皮肤电反应(GSR)、心电图(EKG)、瞳孔扩大等。效应器测量机制包括眼电图(EOG)、眼跟踪、面部情感编码、反应时间等等。
根据各种实施例，本发明的技术和机制智能地将认知前神经签名(precognitive neural signature)的多个模式和表现与认知(cognitive)神经签名和认知后(post cognitive)神经生理学表现混合以便更准确地执行谐振估计。在某些示例中，使用自主神经系统测量本身来确认中枢神经系统测量。效应器和行为反应被与其它测量混合且组合。根据各种实施例，将中枢神经系统、自主神经系统、以及效应器系统测量聚合(aggregate)成允许确定刺激和刺激属性谐振的测量。
在特定实施例中，使对象(subject)暴露于刺激材料并在暴露期间收集诸如中枢神经系统、自主神经系统、效应器数据等数据。根据各种实施例，收集数据是为了确定对评估谐振数据的多种分量测量(component measure)进行聚合的谐振测量。在特定实施例中，在使对象暴露于刺激之前和每当使对象暴露于刺激之后针对大脑的不同区域来评估特定事件相关电位(ERP)分析和/或事件相关功率谱扰动(event related power spectral perturbation，ERPSP)。
确定刺激前和刺激后差别(differential)以及脑的多个区域处的ERP时域分量的目标和干扰物(distracter)差别测量(DERP)。执行差别反应的事件相关事件-频率分析以跨越包括但不限于θ(theta)、α(alpha)、β(beta)、γ(gamma)和高γ的多个频带来评估注意力、情绪和记忆力保持力(memory retention，DERPSP)。在特定实施例中，可以使用单次试验和/或平均DERP和/或DERPSP来加强谐振测量。
谐振估计还可以结合关系评估，该关系评估使用与实体/关系有关的刺激的片断(segment)的脑区域一致性(coherence)测量、基于包括EEG测量的时间-频率分析的神经生理学测量来对注意力、情绪参与(engagement)和记忆力保持力估计进行合成处理的片断有效测量、以及与具有未耦合(non-coupled)交互作用的片断相比在出现耦合/关系模式的片断期间的差别扫视相关神经签名(differential saccaderelated neural signature)。
根据各种实施例，谐振估计器可以包括具有或没有用于引出潜在物体(object)/个体分组的人为干预的自动化系统。例如，这些还可以包括模式识别和物体识别技术。这些子系统可以包括硬件实施方式和/或软件实施方式。
可以分析多种刺激材料，诸如娱乐和营销材料、媒体流、广告牌、打印广告、文本流、音乐、演奏、感觉体验等等。根据各种实施例，使用执行模态内(intra-modality)测量加强和跨模态测量加强两者的数据分析器来生成加强的神经反应数据。根据各种实施例，测量大脑活动不仅是为了确定活动的区域，而且是为了确定各种区域之间的交互和交互的类型。本发明的技术和机制认识到神经区域之间的交互作用支持相配合(orchestrated)且有组织的行为。注意力、情绪、记忆力、及其它能力不仅仅基于大脑的一部分，而且替代地依赖于大脑区域之间的网络交互。
本发明的技术和机制还认识到用于多区域通信的不同频带可以表示刺激的有效性。在特定实施例中，将评估校准至每个对象并在对象之间同步。在特定实施例中，为对象创建模板以创建用于测量刺激前和刺激后差别的基线。根据各种实施例，刺激发生器是智能的且自适应地针对正在分析的每个对象来修改诸如暴露长度和持续时间等特定参数。
可以使用多种模态，包括EEG、GSR、EKG、瞳孔扩大、EOG、眼跟踪、面部情感编码、反应时间等等。通过智能地识别神经区域通信路径来加强诸如EEG等各个模态。使用中枢神经系统、自主神经系统、以及效应器签名的合成和分析混合来加强跨模态分析。通过诸如时移和相移、关联、以及确认模态内确定等机制进行的合成和分析允许生成表征各种数据反应的重要性的复合输出以有效地执行谐振估计。
图1举例说明用于使用中枢神经系统、自主神经系统、和/或效应器测量来执行谐振估计的系统的一个示例。根据各种实施例，谐振估计系统包括刺激呈现设备101。在特定实施例中，刺激呈现设备101仅仅是向用户显示刺激材料的显示器、监视器、屏幕等等。所述刺激材料可以是媒体剪辑、商业广告、文本页面、商标图像、演奏、杂志广告、电影、音频呈现，且甚至可以包括特定的味道、气味、纹理和/或声音。所述刺激可以包括多种感觉且在有或没有人工监督的情况下发生。支持连续和离散模式。根据各种实施例，刺激呈现设备101还具有允许提供给不同市场中的多个对象的刺激的智能个性化的协议生成能力。
根据各种实施例，刺激呈现设备101可以包括诸如电视、电缆控制台、计算机和监视器、投影系统、显示设备、扬声器、触摸表面等用于呈现包括但不限于来自不同网络、本地网络、电缆频道、企业联合来源(syndicated source)、网站、因特网内容聚合器、门户、服务提供商等的广告和娱乐表演的刺激的设备。
根据各种实施例，将对象连接到数据收集设备105。数据收集设备105可以包括多种神经反应测量机制，包括神经学和神经生理学测量系统，诸如EEG、EOG、GSR、EKG、瞳孔扩大、眼跟踪、面部情感编码、以及反应时间设备等。根据各种实施例，神经反应数据包括中枢神经系统、自主神经系统、以及效应器数据。在特定实施例中，数据收集设备105包括EEG 111、EOG 113、以及GSR 115。在某些情况下，只使用数据收集设备。数据收集可以在有或没有人工监督的情况下进行。
数据收集设备105从多个来源收集神经反应数据。这包括诸如中枢神经系统源(EEG)、自主神经系统源(GSR、EKG、瞳孔扩大)、以及效应器源(EOG、眼跟踪、面部情感编码、反应时间)等设备的组合。在特定实施例中，对所收集的数据进行数字取样并存储以供稍后分析。在特定实施例中，可以实时地分析所收集的数据。根据特定实施例，基于正在测量的神经生理学和神经学数据来自适应地选择数字取样率。
在一个特定实施例中，谐振估计系统包括使用头皮级电极(scalplevel electrode)进行的EEG 111测量、使用屏护(shielded)电极来跟踪眼睛数据而进行的EOG 113测量、使用差别测量系统而执行的GSR 115测量、通过设置在面部上的特定位置处的屏护电极进行的面部肌肉测量、以及针对每个个体而自适应地导出的面部表情图形和视频分析器
在特定实施例中，数据收集设备与刺激呈现设备101时钟同步。在特定实施例中，数据收集设备105还包括提供自动触发器、警报和连续地监视对象、正在收集的数据、和数据收集仪器的状态的状态监视和可视化组件的条件评估子系统。所述条件评估子系统还可以呈现视觉警报并自动地触发补救动作。根据各种实施例，所述数据收集设备不仅包括用于监视对象对刺激材料的神经反应的机制，而且包括用于识别和监视刺激材料的机制。例如，数据收集设备105可以与机顶盒同步以便监视频道变化。在其它示例中，当对象不再注意刺激材料时，可以使数据收集设备105定向地与监视器同步。在其它示例中，数据收集设备105可以接收并存储对象通常观看的刺激材料，无论该刺激是节目、商业广告、打印材料、还是窗户外面的景色。所收集的数据允许分析神经反应信息并将该信息与实际刺激材料而不仅仅是对象注意力分散相关联。
根据各种实施例，谐振估计系统还包括数据清理器(cleanser)设备121。在特定实施例中，数据清理器设备121使用固定和自适应滤波、加权平均、高级分量提取(advanced component extraction)(例如PCA、ICA)、矢量和分量分离法等来对所收集的数据进行滤波以去除噪声、赝像(artifact)、及其它无关数据。此设备通过去除外来噪声(其中源在对象的心理之外，例如在对象观看视频的同时响起的电话)和内在赝像(其中源可以是神经生理学上的，例如肌肉运动、眨眼等)来清理(clean)数据。
赝像去除子系统包括选择性地隔离并检查反应数据且识别历元(epoch)的机制，该历元具有对应于诸如行频(line frequency)、眨眼、以及肌肉运动等赝像的时域和/或频域属性。赝像去除子系统随后通过忽略这些历元或通过用基于其它清理数据的估计(例如EGG最近邻加权平均法)替换这些历元数据来清理赝像。
根据各种实施例，使用硬件、固件、和/或软件来实现数据清理器设备121。应注意的是虽然数据清理器设备121被示为位于数据收集设备105之后和数据分析器181之前，但数据清理器设备121如同其它组件一样可以具有基于系统实施方式而变的位置和功能。例如，某些系统可能不使用任何自动化数据清理器设备，而在其它系统中，数据清理器设备可能被集成到各个数据收集设备中。
根据各种实施例，可选刺激属性贮存库131提供关于被呈现给多个对象的刺激材料的信息。根据各种实施例，刺激属性包括刺激材料的性质以及目的、呈现属性、报告生成属性等。在特定实施例中，刺激属性包括时间跨度、频道、等级(rating)、媒体、类型等。刺激属性还可以包括各种帧中的实体的位置、物体关系、物体的位置和显示的持续时间。目的属性包括刺激的渴望(aspiration)和物体，包括激动、记忆力保持力、联想等。呈现属性包括加强或避免所需的音频、视频、形象化(imagery)、以及消息。其它属性可以被也可以不被包括在刺激属性贮存库或其它贮存库中。
数据清理器设备121和刺激属性贮存库131将数据传递至数据分析器181。数据分析器181使用多种机制来分析系统中的基础数据以确定谐振。根据各种实施例，数据分析器在每种模态下针对每个个体定制并提取独立的神经学和神经生理学参数，并将模态内以及跨模态的估计混合以引出(elicit)对所呈现的刺激材料的加强反应。在特定实施例中，数据分析器181将跨越对象的反应测量聚合在数据集中。
根据各种实施例，使用时域分析和频域分析来测量神经学和神经生理学签名。此类分析使用跨越个体而通用的参数以及为每个个体所独有的参数。所述分析还可以包括根据合成反应的时间和频率分量两者进行的统计参数提取和基于模糊逻辑的属性估计。
在某些示例中，在混合有效性估计中所使用的统计参数包括歪斜(skew)、峰值、第一和第二瞬间(moment)、人口分布的评估、以及注意力、情绪参与和记忆力保持力反应的模糊估计。
根据各种实施例，数据分析器101可以包括模态内反应合成器和跨模态反应合成器。在特定实施例中，模态内反应合成器被配置为在每种模态下针对每个个体来定制并提取独立的神经学和神经生理学参数并在分析上将模态内的估计混合以引出对所呈现的刺激的加强反应。在特定实施例中，模态内反应合成器还将来自不同对象的数据聚合在数据集中。
根据各种实施例，跨模态反应合成器或融合设备将不同的模态内反应混合，包括原始信号和信号输出。信号的组合加强模态内的有效性测量。跨模态反应融合设备还可以将来自不同对象的数据聚合在数据集中。
根据各种实施例，数据分析器181还包括复合加强有效性估计器(composite enhanced effectiveness estimator，CEEE)，其将来自每种模态的加强反应和估计组合以提供有效性的混合估计。在特定实施例中，针对对象每次暴露于刺激材料而提供混合估计。随着时间的推移来评估该混合估计以评估谐振特性。根据各种实施例，对每个混合估计赋予数值。该数值可以对应于神经反应测量的强度、峰值的重要性、峰值之间的变化等。较高的数值可以对应于神经反应强度的较高重要性。较低的数值可以对应于较低的重要性、乃至无意义的神经反应活动。在其它示例中，对每个混合估计赋予多个值。在其它示例中，用图形来表示神经反应重要性的混合估计以显示重复暴露之后的变化。
根据各种实施例，数据分析器181将经分析和加强的反应数据提供给数据通信设备183。应注意的是在特定情况下，不需要数据通信设备183。根据各种实施例，数据通信设备183提供原始和/或经分析的数据和认知。在特定实施例中，数据通信设备183可以包括用于对数据进行压缩和加密以便进行安全的存储和通信的机制。
根据各种实施例，数据通信设备183使用诸如文件传输协议(FTP)、超文本传输协议(HTTP)以及多种传统总线、有线网络、无线网络、卫星、和专用通信协议来发送数据。所发送的数据可以包括整体的数据、数据的摘录、经转换的数据、和/或引出的反应测量。根据各种实施例，所述数据通信设备是将从数据收集设备获得的数据发送到谐振估计器185的机顶盒、无线设备、计算机系统等。在特定实施例中，所述数据通信设备甚至可以在数据清理或数据分析之前发送数据。在其它示例中，所述数据通信设备可以在数据清理和分析之后发送数据。
在特定实施例中，数据通信设备183将数据发送到谐振估计器185。根据各种实施例，谐振估计器185评估并提取谐振模式。在特定实施例中，谐振估计器185确定各种刺激片断中的实体位置并在将扫视与注意力、记忆力保持力以及情绪参与的神经评估相关联的同时使位置信息与眼跟踪路径匹配。在特定实施例中，谐振估计器185还收集用户行为和调查反应并将其与所分析的反应数据结合以便更有效地估计谐振。
可以存储多种数据以供稍后分析、管理、操作、和检索。在特定实施例中，使用贮存库来跟踪刺激属性和呈现属性、受众反应且可选地还可以用来将受众测量信息相结合。
如所述系统中的多种组件的情况一样，可以使谐振估计器与系统的其余部分及用户共位(co-locate)，或者可以将其在远程位置处实现。还可以可选地将其分成可以集中于或分布在刺激材料的(一个或多个)提供商处的评估贮存库系统。在其它示例中，将谐振估计器容纳在刺激材料提供商和/或用户可访问的第三方服务提供商的设施处。
图2举例说明可以连同刺激属性贮存库一起提供的数据模型的示例。根据各种实施例，刺激属性数据模型201包括频道203、媒体类型205、时间跨度207、受众209、以及人口统计信息211。刺激目的数据模块216可以包括意图(intent)217和目标(objective)219。根据各种实施例，刺激属性数据模块201还包括关于实体及实体之间出现的关系的空间和时间信息221。
根据各种实施例，另一刺激属性数据模型221包括创建(creation)属性223、所有权属性225、广播属性227、以及用于自动地将神经生理反应和神经行为反应跟与刺激相关的其它属性和元信息结合的基于统计学、人口统计和/或调查的标识符。
图3举例说明可以用于与谐振的跟踪和测量相关的信息的存储的数据模型的示例。根据各种实施例，数据集数据模型301包括实验名称303和/或标识符、客户(client)属性305、对象池307、诸如测试的位置、日期以及时间的后勤(logistics)信息309、以及包括刺激材料属性的刺激材料311。
在特定实施例中，对象属性数据模型315包括对象名称317和/或标识符、联系信息321、以及可以用于检查神经学和神经生理学数据的人口统计属性319。相关人口统计属性的某些示例包括婚姻状况、就业状况、职业、家庭收入、家庭大小和组成、民族、地理位置、性别、种族。在数据模型315中可以包括的其它字段可以包括购物偏好、娱乐偏好、以及金融偏好。购物偏好可以包括喜欢的商店、购物频率、购买的种类、喜欢的品牌。娱乐偏好包括网络/电缆/卫星接入能力、喜欢的节目、喜欢的流派、以及喜欢的演员。金融偏好包括喜欢的保险公司、优选投资习惯、储蓄偏好、以及喜欢的在线金融工具。在对象属性数据模型315中可以包括多种对象属性且可以预置或自定义地生成数据模型以适应特定的目的。
根据各种实施例，用于神经反馈关联325的数据模型识别实验协议327、诸如EEG、EOG、GSR等所包括的模态329、所执行的调查、以及诸如片断和片断属性等实验设计参数333。其它字段可以包括实验呈现脚本、片断长度、例如所使用的刺激材料等片断细节、对象间变化、对象内变化、指示、呈现顺序、所使用的调查问题等。其它数据模型可以包括数据收集数据模型337。根据各种实施例，数据收集数据模型337包括记录属性339，诸如站位(station)和位置标识符、记录的日期和时间、以及操作员细节。在特定实施例中，设备属性341包括放大器标识符和传感器标识符。
所记录的模态343可以包括特定于模态的属性，例如EEG帽布局(cap layout)、活动信道、取样频率、以及所使用的滤波器。特定于EOG的属性包括所使用的传感器的数目和类型、应用的传感器的位置等。特定于眼跟踪的属性包括所使用的跟踪器的类型、数据记录频率、正在记录的数据、记录格式等。根据各种实施例，数据存储属性345包括文件存储惯例(格式、命名惯例、日期惯例)、存储位置、档案属性、终止属性等。
预置查询数据模型349包括查询名称351和/或标识符、诸如所涉及的数据段等所访问的数据集合353(模型、数据库/立方体(cube)、表格等)、所包括的谁进行什么类型的访问的访问安全属性355、以及诸如查询终止、刷新频率等的刷新属性357。还可以包括诸如推挽式偏好等其它字段以识别自动推式报告驱动器或用户驱动报告检索系统。
图4举例说明可以执行以便获得与谐振估计相关的数据的查询的示例。根据各种实施例，根据一般或自定义脚本语言和构成、视觉机制、预置查询库、包括向下钻取(drill-down)诊断的诊断查询、以及根据情况进行引出来定义查询。根据各种实施例，对象属性查询415可以被配置为使用位置417或地理信息、诸如测试时间和日期等会话信息421、以及人口统计属性419来从神经信息学贮存库获得数据。人口统计属性包括家庭收入、家庭大小和状况、教育水平、儿童年龄等。
其它查询可以基于对象参与者的购物偏好、面容(countenance)、生理评估、完成(completion)状况来检索刺激材料。例如，用户可以对与产品种类、购买的产品、时常出入的商店、对象眼睛矫正状态、色盲、对象状态、测量反应的信号强度、α频带振铃器(ringer)、肌肉运动评估、完成的片断等相关的数据进行查询。基于实验设计的查询可以基于实验协议427、产品分类429、所包括的调查431、以及所提供的刺激433来从神经信息学贮存库获得数据。可以使用的其它字段包括所使用的协议重复的数目、所使用的协议的组合、以及调查的使用配置。
基于客户和行业的查询可以基于包括在测试中的行业的类型、所测试的特定种类、所涉及的客户公司、以及正在测试的品牌来获得数据。基于反应评估的查询437可以包括注意力评分439、情绪评分、441、保持力评分443、以及有效性评分445。此类查询可以获得引出特定评分的材料。
基于反应测量简档的查询可以使用平均测量阈值、差异测量、所检测的峰值的数目等。群组反应查询可以包括群组统计信息，例如平均值、差异、峰度、p值等、群组大小、以及逸出值(outlier)评估测量。其它查询可以涉及测试属性，例如试验位置、时间段、试验重复计数、试验站、以及试验操作员领域。可以使用多种类型的查询和组合类型的查询来高效地提取数据。
图5举例说明可以生成的报告的示例。根据各种实施例，客户评估概要报告501包括有效性测量503、分量评估测量505、以及谐振测量507。有效性评估测量包括(多个)复合评估测量、行业/种类/客户专有位置(百分点(percentile)、排行等)、诸如去除材料、修改片断、或微调特定元素等可控告(actionable)分组评估测量、以及有效性简档随时间推移的演进。在特定实施例中，分量评估报告包括分量评估测量，例如注意力、情绪参与评分、百分点位置、排行等。分量简档测量包括分量测量和简档统计学评估的基于时间的演进。根据各种实施例，报告包括评估材料的次数、所使用的多次呈现的属性、反应评估测量与多次呈现相比的演进、以及使用推荐。
根据各种实施例，客户累计报告511包括所评估的所有刺激的媒体分组报告513、所评估的刺激的运动分组报告515、以及所评估的刺激的时间/位置分组报告517。根据各种实施例，行业累计和企业联合报告521包括聚合评估反应测量523、顶部执行者列表525、底部执行者列表527、逸出值529、以及趋势报告531。在特定实施例中，跟踪和报告包括特定产品、种类、公司、品牌。
图6举例说明谐振估计的一个示例。在601处，向多个地理市场中的多个对象提供刺激材料。根据各种实施例，刺激包括通过诸如广播电视、有线电视、卫星等机制提供的流视频和音频。可以在相同或变化的时间向不同地理市场中的用户呈现刺激。在特定实施例中，对象以分组或单独的设定(setting)在其自己家中观看刺激。在603处，使用多种模态来收集对象反应，诸如EEG、ERP、EOG、GSR等。在某些示例中，还可以收集口头和书面反应并将其与神经学和神经生理学反应相关联。在605处，使数据通过数据清理器以去除可能使得数据更难以解释的噪声和赝像。根据各种实施例，数据清理器去除与闪烁及其它内在/外在赝像相关的EEG电活动。
根据各种实施例，执行数据分析。数据分析可以包括模态内反应合成和跨模态反应合成以加强有效性测量。应注意的是在某些特定情况下，可以执行一种类型的合成而不执行其它类型的合成。例如，可以在有或没有模态内合成的情况下执行跨模态反应合成。
可以使用多种机制来执行数据分析。在特定实施例中，访问刺激属性贮存库131以获得刺激材料的属性和特性、以及目的、意图、目标等。在特定实施例中，将EEG反应数据合成以提供加强的有效性评估。根据各种实施例，EEG测量从与大脑的不同部分相关的数千个同时神经过程得到的电活动。可以在各种频带中将EEG数据分类。根据各种实施例，脑波频率包括δ(delta)、θ、α、β、和γ频率范围。δ波被分类为小于4Hz的那些波但在深度睡眠期间突出。θ波具有3.5至7.5Hz之间的频率且与记忆力、注意力、情绪、以及感觉相关。θ波通常在内部关注(internal focus)的状态期间突出。
α频率在7.5与13Hz之间且峰值通常在10Hz左右。α波在放松状态下突出。β波具有在14与30Hz之间的频率范围。β波在运动控制、大脑区域之间的长距离同步、分析问题解决、判断、以及进行判定的状态期间突出。γ波发生在30与60Hz之间且涉及将不同群体的神经元一起绑定成网络以便执行某种认知或运动机能、以及如在注意力和记忆力中一样。由于颅骨和真皮层使此频率范围内的波衰减，所以难以检测75～80Hz以上的脑波且常常不将其用于刺激反应评估。
然而，本发明的技术和机制认识到除θ、α、β、以及低γ频段测量之外还分析高γ频段(k(kappa)频段：在60Hz以上)可加强神经注意力、情绪参与和保持力分量估计。在特定实施例中，获得、加强并评估EEG测量，其包括难以检测高γ或k频段测量。识别θ、α、β、γ和k频段中的对象和任务专有签名子频段以提供加强的反应估计。根据各种实施例，在对刺激的频率反应的基于反演模型(inverse model-based)的加强中可以使用80Hz以上(通常可用颅下EEG和/或脑磁图(magnetoencephalograophy)来检测)的高γ波(k频段)。
本发明的各种实施例认识到每个频率范围内的特定子频段在某些活动期间具有特定的突出性。特定频段中的频率的子集在本文中称为子频段。例如，子频段可以包括γ频段内的40～45Hz范围。在特定实施例中，选择不同频段内的多个子频段，同时对其余频率进行带通滤波。在特定实施例中，可以加强多个子频段反应，同时可以使其余频率反应衰减。
使用基于信息论的频段加权模型来进行选择性数据集专用、对象专用、任务专用频段的自适应提取以加强有效性测量。可以使用模糊缩放(fuzzy scaling)来执行自适应提取。可以呈现刺激且确定加强的测量多次以确定跨多次呈现的变化简档。确定各种简档提供初次(primary)反应的加强评估以及营销和娱乐刺激的寿命(耗尽)。测量多个个体对音乐会中呈现的刺激的同时反应以确定加强的跨对象同步有效性测量。根据各种实施例，可以针对位于单独位置上的多个对象或针对位于同一位置上的多个对象来确定同步反应。
虽然描述了多种合成机制，但应认识到可以依次或并行地或在机制之间有或没有相互作用的情况下应用任何数目的机制。
虽然模态内合成机制提供加强的重要性数据，但也可以使用附加跨模态合成机制。诸如EEG、眼跟踪、GSR、EOG、以及面部情感编码等多种机制被连接到跨模态合成机制。还可以包括其它机制以及对现有机制的修改和加强。根据各种实施例，可以使用来自一个或多个其它模态的数据来加强来自特定模态的数据。在特定实施例中，EEG通常在例如α、β和γ的不同频段中进行频率测量以提供重要性的估计。然而，本发明的技术认识到另外可以使用来自其它模态的信息来力口强重要性测量。
例如，可以使用面部情感编码测量来加强EEG情绪参与测量的效价(valence)。可以使用物体实体的EOG和眼跟踪扫视测量来加强包括但不限于注意力、情绪参与、以及记忆力保持力的重要性的EEG估计。根据各种实施例，跨模态合成机制执行数据的时移和相移以允许来自不同模态的数据对齐。在某些示例中，应认识到EEG反应将常常发生在面部情感测量变化之前的几百毫秒。可以绘制相关性并基于个体以及群组进行时移和相移。在其它示例中，可以将扫视眼运动确定为在特定的EEG反应之前和之后发生。根据各种实施例，使用经时间修正的GSR测量来缩放并加强包括注意力、情绪参与和记忆力保持力测量的重要性的EEG估计。
特定区域中的特定时域差别事件相关电位分量(例如DERP)的发生或未发生的证据与对象对特定刺激的反应性相关。根据各种实施例，响应于营销和娱乐刺激的呈现而使用EEG时间-频率测量(ERPSP)来加强ERP测量。提取并隔离特定部分以识别要执行的ERP、DERP和ERPSP分析。在特定实施例中，使用注意力、情绪和记忆力保持力(ERPSP)的EEG频率估计作为加强ERP、DERP和时域反应分析中的辅助因子(co-factor)。
EOG测量扫视以确定对刺激的特定物体的注意力的存在。眼跟踪测量对象的凝视路径、位置和在刺激的特定物体上的停顿。根据各种实施例，通过测量枕骨和纹外区(extra striate region)中的正在进行的EEG中的λ(lambda)波(扫视有效性的神经生理学指数)的存在来加强EOG和眼跟踪，其由扫视开始的斜率(slope)触发以估计EOG和眼跟踪测量的重要性。在特定实施例中，测量诸如缓慢电位漂移的活动的特定EEG签名和在扫视开始前的额页眼动区(FEF)区域处的时间-频率反应中的一致性的测量以加强扫视活动数据的有效性。
GSR通常响应于所呈现的刺激来测量一般激励的变化。根据各种实施例，通过使EEG/ERP反应与GSR测量相关联以获得对象投入的加强估计来加强GSR。在构造对刺激的时间修正GSR反应时使用GSR等待时间基线。所述时间修正GSR反应以EEG测量为辅助因子以加强GSR重要性测量。
根据各种实施例，面部情感编码使用通过在测试会话之前测量表达各种情绪的个体的面部肌肉位置和运动而生成的模板。使这些个体专有面部情感编码模板与个体反应匹配以识别对象情绪反应。在特定实施例中，通过评估在特定频带中的EEG反应的半球间(inter-hemispherical)不对称性并测量频带交互来加强这些面部情感编码测量。本发明的技术认识到不仅特定的频带范围在EEG反应中重要，而且用于大脑特定区域之间的通信的特定频带也很重要。因此，这些EEG反应加强基于EMG、图形和视频的面部情感识别。
根据各种实施例，在607处测量大脑的多个区域中的ERP时域分量的刺激后对比刺激前差别测量(DERP)。差别测量提供用于引出可归因于刺激的反应的机制。例如，使用谐振前和谐振后估计来确定可归因于广告的消息传递反应或可归因于多个品牌的品牌反应。
在609处，针对大脑的不同区域来确定目标对比干扰物刺激差别反应(DERP)。在611处，使用差别反应的事件相关时间-频率分析(DERPSP)来跨越多个频带估计注意力、情绪和记忆力保持力测量。根据各种实施例，所述多个频带包括θ、α、β、γ和高γ或k。在613处，执行多次试验以加强谐振测量。
在615处，将已处理数据提供给数据通信设备以便通过诸如能够发送数据的无线、有线、卫星、或其它类型的通信网络等网络来传输。在617处，将数据提供给谐振估计器。根据各种实施例，数据通信设备使用诸如文件传输协议(FTP)、超文本传输协议(HTTP)以及多种传统的总线、有线网络、无线网络、卫星、以及专有通信协议等协议来发送数据。所发送的数据可以包括整体的数据、数据的摘录、经转换的数据、和/或引出的反应测量。根据各种实施例，使用能够传递来自多个对象位置的信息以进行数据集成和分析的通信、无线、因特网、卫星、或任何其它通信机制来发送数据。可以将该机制集成在机顶盒、计算机系统、接收机、移动设备等中。
在特定实施例中，数据通信设备将数据发送到谐振估计器。根据各种实施例，谐振估计器在使用诸如各种实体和物体的位置、运动、加速度、以及空间关系等关于刺激材料属性的信息的同时将对刺激材料的经分析和加强反应组合。在特定实施例中，谐振估计器还收集用户行为和调查反应并将其与经分析和加强的反应数据结合以更有效地评估谐振模式。
图7举例说明用于估计谐振的技术的示例。根据各种实施例，在701处获得来自不同模态的测量。根据各种实施例，在703处，将包括差别事件相关电位(DERP)、差别事件相关功率谱扰动(DERPSP)、扩瞳反应等的测量混合以获得组合测量。在特定实施例中，必须使每次测量适当地对齐以便允许混合。根据各种实施例，谐振估计器包括使用通过跨模态来自数据分析器的不同测量并将其混合的机制。在特定实施例中，数据包括DERP测量、DERPSP、扩瞳反应、GSR、眼运动、一致性、耦合和基于λ波的反应。将跨越模态的测量混合以引出用户谐振的合成测量。
在705处，将诸如DERP、DERPSP、扩瞳反应等的神经反应测量与基于统计学、人口统计、和/或调查的信息组合。所述设备包含在713处将神经生理和神经行为反应与关于刺激的其它属性和元信息(基于统计学、人口统计和/或调查)集成以便选择目标以便进行附加刺激准备/呈现的机制。
谐振估计器还可以包括精简(refine)简档并随时间的推移跟踪对特定刺激或一系列刺激的变化反应的自适应学习组件。可以使得此信息可用于其它目的，诸如使用该信息进行呈现属性判定。根据各种实施例，谐振估计器生成指数以供评估使用。数据和测量结果被存储在贮存库中以供稍后检索和分析。
根据各种实施例，在多个设备上实现各种机制，诸如数据收集机制、模态内合成机制、跨模态合成机制等。然而，还可以在单个系统中以硬件、固件、和/或软件来实现各种机制。图8提供可以用来实现一个或多个机制的系统的一个示例。例如，图8所示的系统可以用来实现谐振测量系统。
根据特定的示例性实施例，适合于实现本发明的特定实施例的系统800包括处理器801、存储器803、接口811、以及总线815(例如PCI总线)。当在适当的软件或固件的控制下运行时，处理器801负责诸如模式生成的这样的任务。除处理器801之外或作为处理器801的替换，还可以使用各种特殊配置的设备。还可以在定制硬件中进行完整的实现。接口811通常被配置为通过网络来发送和接收数据分组或数据段。设备支持的接口的特定示例包括主机总线适配器(HBA)接口、以太网接口、帧中继接口、电缆接口、DSL接口、令牌环接口等等。
另外，可以提供各种超高速接口，诸如快速以太网接口、千兆比特以太网接口、ATM接口、HSSI接口、POS接口、FDDI接口等等。通常，这些接口可以包括适合于与适当媒体通信的端口。在某些情况下，它们还可以包括独立处理器，并且在某些情况下还包括易失性RAM。独立处理器可以控制诸如数据合成的通信密集型任务。
根据特定的示例性实施例，系统800使用存储器803来存储数据、算法和程序指令。例如，程序指令可以控制操作系统和/或一个或多个应用程序的操作。(一个或多个)存储器还可以被配置为存储接收到的数据并处理接收到的数据。
由于可以采用此类信息和程序指令来实现本文所述的系统/方法，所以本发明涉及有形、机器可读介质，其包括用于执行本文所述的各种操作的程序指令、状态信息等。机器可读介质的示例包括但不限于诸如硬盘、软盘、以及磁带等磁介质；诸如CD-ROM磁盘和DVD等光学介质；诸如光盘等磁光介质；以及特别地被配置为存储并执行程序指令的硬件设备，诸如只读存储器设备(ROM)和随机存取存储器(RAM)。程序指令的示例包括诸如由编译程序产生的机器代码和包含可以由计算机使用解释程序来执行的高级代码的文件两者。
虽然已出于透彻理解的目的而相当详细地描述了前述发明，但显然，在随附权利要求的范围内，可以实施某些变更和修改。因此，应将本实施例视为说明性而非限制性的，且本发明不限于本文所给出的细节，而是可以在随附权利要求和等价物的范围内进行修改。

Claims (20)

1.一种系统，包括：
刺激呈现设备，可用于向对象提供刺激材料；
数据收集设备，可用于获得包括刺激前和刺激后事件相关电位(ERP)测量的神经反应数据以确定大脑的多个区域处的ERP时域分量的差别测量(DERP)；
谐振估计器，可用于确定对刺激材料和刺激材料的属性的对象谐振测量。
2.权利要求1的系统，其中，所述数据收集设备还可用于获得目标和干扰物ERP测量以确定大脑的多个区域处的ERP时域分量的差别测量(DERP)。
3.权利要求1的系统，其中，所述数据收集设备还可用于获得差别反应的事件相关时间-频率分析以跨多个频带评估注意力、情绪和记忆力保持力(DERPSP)。
4.权利要求3的系统，其中，所述多个频带包括θ、α、β、γ和高γ。
5.权利要求1的系统，其中，刺激材料的属性包括通信、构思、体验、消息、图像、音频、定价、封装。
6.权利要求1的系统，其中，对象谐振测量用以识别用于附加营销、广告及其它音频/视觉/触觉/嗅觉刺激的选择性目标。
7.权利要求1的系统，还包括可用于确定DERP的数据分析器。
8.权利要求1的系统，其中，所述数据收集设备还可用于获得对象的瞳孔扩大、皮肤电反应(GSR)、和心率测量。
9.权利要求1的系统，其中，所述神经反应数据包括中枢神经系统和自主神经系统数据。
10.权利要求1的系统，其中，所述神经反应数据包括中枢神经系统和效应器数据。
11.权利要求1的系统，其中，使用包括注意力、情绪、和记忆力保持力的神经学和神经生理学测量的组合来执行谐振估计。
12.权利要求1的系统，其中，使用具有干电极的便携式脑电图(EEG)来从多个对象获得神经反应数据。
13.权利要求11的系统，其中，使来自第一模态的神经反应数据与来自第二模态的神经反应数据对齐并组合。
14.权利要求12的系统，其中，使来自第一模态的神经反应数据与来自第二模态的神经反应数据对齐包括时移和相移。
15.一种方法，包括：
向对象提供刺激材料；
获得包括刺激前和刺激后事件相关电位(ERP)测量的神经反应数据以确定大脑的多个区域处的ERP时域分量的差别测量(DERP)；
确定对刺激材料和刺激材料的属性的对象谐振测量。
16.权利要求15的方法，还包括获得目标和干扰物ERP测量以确定大脑的多个区域处的ERP时域分量的差别测量(DERP)。
17.权利要求15的方法，还包括获得差别反应的事件相关时间-频率分析以跨多个频带评估注意力、情绪和记忆力保持力(DERPSP)。
18.一种装置，包括：
用于向对象提供刺激材料的装置；
用于获得包括刺激前和刺激后事件相关电位(ERP)测量的神经反应数据以确定大脑的多个区域处的ERP时域分量的差别测量(DERP)的装置；
用于确定对刺激材料和刺激材料的属性的对象谐振测量的装置。
19.权利要求18的装置，还包括用于获得目标和干扰物ERP测量以确定大脑的多个区域处的ERP时域分量的差别测量(DERP)的装置。
20.权利要求18的装置，还包括用于获得差别反应的事件相关时间-频率分析以跨多个频带评估注意力、情绪和记忆力保持力(DERPSP)的装置。
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