CN102894966B - 用于评估媒体的方法和系统 - Google Patents
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Abstract
本发明公开了一种评估媒体的方法和系统,该系统包括:数据收集器,用于获得基本上在个体接触媒体的同时从个体的大脑产生的第一信号以及产生的来自个体的心脏的第二信号,第一信号包括α频率、β频率、δ频率或θ频率中的至少两个;以及处理器,用于:基于第一信号的α频率、β频率、δ频率或θ频率中的一个和α频率、β频率、δ频率或θ频率中的不同的一个之间的第一数学关系与第二信号和第一数学关系的结果之间的第二数学关系来计算参与度值,第一数学关系根据以下公式中的至少一个计算:δ/α;β/α;α/β;α/δ;或者(α-θ)/(α+θ);将参与度值与参考值比较以确定差;以及基于参与度值与参考值之间的差来评估媒体。
Description
本发明申请为2009年11月9日提交的发明名称为“用于对参与反应进行测量并评级的方法和系统”的第200780052893.2号发明专利申请的分案申请。
技术领域
本发明涉及媒体评估领域,更具体地,涉及一种基于生理信号来评估媒体的方法和系统。
背景技术
有创意的人设计出激励个体并使其保持参与的交互式媒体、活动和产品(“媒体”)。媒体通常在高度竞争的市场上被销售给消费者,在这样的市场中激励参与(engagement)的能力决定了价值。有创意的人愿意知道其消费者是否参与到媒体中,以便通过改进媒体来更好地激励个体而将价值最大化。如果媒体的价值没有被最大化,则消费者会购买提供更好的激励的竞争产品。如果竞争产品被售出,则利润会因为销量减小而受损。于是,问题是提供关于对交互式媒体、活动和产品引起的激励的反应的准确信息。对反应的测量需要交互式媒体、活动和产品的创作者了解目标市场。
在了解人类想法时,神经生物学、心理生理学和心理学的研究人员发现了发源于大脑的生理信号。使用脑电图(EEG),研究人员通过附着于头部的电极记录了生理信号。生理信号具有在30赫兹以下的四个主要成分。在1-4赫兹之间的频率是德尔塔波(δ),在4和8赫兹之间的频率是西塔(θ)波,在8-13赫兹之间的频率是阿尔法(α)脑波,并且在13和20赫兹之间的频率是贝塔(β)脑波。
此外,用来收集来自身体的数据的工具包括光体积描记器(PPG)和心电图(ECG或对应于德文elektrokardiogramm的EKG)。光体积描记器(PPG)是可以用来发现心动周期的以光学方式得到的度量。PPG使用脉搏氧饱和度仪,以观察在血液被泵到末梢时与心动周期相关的来自皮肤的氧弥散(oxygen omission)的变化。这样,可以基于氧弥散的变化来记录心动周期。心率的另一种度量是ECG。心电图(ECG)经由附着于胸部的电极来测量心跳。传统上,ECG产生心电描记图,或者产生关于时间示出心跳的图像。可替选地,记录由心脏产生的信号。
相关技术的前述示例和与之相关的限制意图是说明性的而非排他性的。基于对说明书的阅读和对附图的学习,对本领域技术人员而言,相关技术的其他限制将变得清楚。
发明内容
结合意在示例性和阐示性的而非限制范围的系统、工具和方法,描述并阐述以下实施例及其各方面。在各种实施例中,减少或排除了一个或更多上述问题,同时其他实施例针对其他改进。
新技术测量个体对媒体的“参与”反应。该技术使用源于大脑和身体的生理信号来测定参与反应。参与度值是对参与反应的客观度量,该度量量化了用户无意识动作的量。有利的是,可以使用参与反应以便在创作媒体时有效地改进媒体。在非限制性示例中,评级确定了个体是否发现电视比文档展示了更引人参与的刺激。此外,个体组可以具有能被测量并汇总的参与反应,以确定对媒体的总体人群反应。这样,该媒体的人群图可以用于对媒体评级,这是响应于媒体的生理变化的新的用途。
根据本发明的一个方面,公开了一种用于评估媒体的方法,所述方法包括:获得基本上在个体接触所述媒体的同时从所述个体的大脑产生的第一信号,第一信号包括α频率α、β频率β、δ频率δ或θ频率θ中的至少两个;获得基本上与来自所述大脑的所述第一信号同时产生的来自所述个体的心脏的第二信号;基于第一信号的α频率、β频率、δ频率或θ频率中的一个和α频率、β频率、δ频率或θ频率中的不同的一个之间的第一数学关系与第二信号和第一数学关系的结果之间的第二数学关系来计算参与度值,其中第一数学关系根据以下公式中的至少一个计算:δ/α;β/α;α/β;α/δ;或者(α-θ)/(α+θ);将所述参与度值与参考值进行比较以确定差;以及基于所述参与度值与所述参考值之间的所述差来评估所述媒体。
根据本发明的另一个方面,公开了一种用于评估媒体的系统,所述系统包括:数据收集器,用于获得基本上在个体接触所述媒体的同时从所述个体的大脑产生的第一信号以及基本上在所述个体接触所述媒体的同时产生的来自所述个体的心脏的第二信号,第一信号包括α频率α、β频率β、δ频率δ或θ频率θ中的至少两个;以及处理器,用于:基于第一信号的α频率、β频率、δ频率或θ频率中的一个和α频率、β频率、δ频率或θ频率中的不同的一个之间的第一数学关系与第二信号和第一数学关系的结果之间的第二数学关系来计算参与度值,其中第一数学关系根据以下公式中的至少一个计算:δ/α;β/α;α/β;α/δ;或者(α-θ)/(α+θ);将所述参与度值与参考值进行比较以确定差;以及基于所述参与度值与所述参考值之间的所述差来评估所述媒体。
附图说明
在附图中阐示了本发明的实施例。但是,实施例和附图是说明性的而非限制性的:它们提供了本发明的示例。
图1是用于计算参与度值的系统100的示例的视图。
图2示出了用于基于个体无意识动作的量来计算参与度值的方法的示例的流程图200。
图3示出了用于基于参与度来对照第二媒体对第一媒体评级的方法的示例300的流程图。
图4示出了基于参与度值来对多个媒体评级的示例的图400。
图5示出了与为参与度评级相关的公式500的多个示例。
图6示出了在心率和可用于计算参与值的多个示例公式中的一个之间的关系600。
图7示出了以媒体来激励个体同时计算与个体对媒体的参与度相关的参与度值的示例的图700。
图8示出了以媒体来激励多个个体并基于他们对媒体的参与度来计算相关值的示例的图800。
图9示出了在时间上与事件相关的参与度的变化的示例的图900。
图10示出了以媒体(在该示例中是游戏)来激励个体并记录心率、思考和参与度的相关水平的示例的图1000和数据图表。
图11示出了包含电极的头戴式装置1100,其中电极有助于收集来自个体头部的信号以及心脏信号。
具体实施方式
在以下描述中,展示了若干特定细节以提供对本发明的实施例的全面理解。但是,相关领域的技术人员应认识到,本发明的实施可以在没有一个或更多个特定细节的情况下,或者可以与其他部件组合等来实施。在其他情形中,未详细示出或描述公知的实现或操作,以免模糊本发明的各个实施例的各方面。
测量“参与”反应以供用以评估媒体的新系统和方法使用生理信号。个体对媒体做出反应,同时生理传感器记录该反应。处理部件通过生理传感器来收集生理信号,并且基本上同时为个体无意识动作的量赋予参与度值。“基本上同时”意思是反应与激励是同时的或者在时间上接近。在反应中可能有延迟。因此,借助以下理解来计算参与度值:如果反应与激励不是精确地同时,则反应可能紧随激励之后。
在一些实施例中,计算参与度值的示例性方式是考虑个体有多少无意识动作。三个可用于此的信号包括来自头脑的α波和θ波,以及心率(HR)。存在其他有用信号,并且将会后面讨论其中的某些信号。通常来说,增大的心率表示更多的参与,增大的θ表示更高水平的思考,因此表示更少的参与,以及增大的α表示更低水平的思考,因此表示更多的参与。在示出与为参与度评级相关的公式的示例的图4的讨论中,更深入地研究了这些精确的关系。
图1是系统100的示例视图。在图1的示例中,系统100包括媒体102、个体104、传感器106和处理部件108。如所示,由媒体102激励个体104,同时由处理部件108使用传感器106来监视个体的参与度水平。此处,媒体可以是如下之一或更多个:电影、视频、电视节目、广播片、广告、视频游戏、交互式在线媒体、印刷品或任何其他可激励个体的媒体。传感器106可以是如下之一或更多个:加速度计、血液氧传感器、检流计、脑电图、肌电描记器和任何其他生理传感器。
图2示出了用于计算参与度值的方法的示例的流程图200。按流程图200中的模块序列来组织所述方法。但是,应理解的是,可以对这些模块和与在此描述的其他方法相关联的模块进行重新排列以用于并行执行,或者重新排列成为不同的模块序列。在图2的示例中,流程图在模块202处开始以包含事件的媒体激励个体。
在图2的示例中,流程图200继续到模块204,在以媒体来激励个体时基本上同时采样来自个体大脑的第一信号。来自心脏的信号可以包括心率。同时使用为了该目的所附着的电极来收集来自心脏的信号。在计算心率或者每分钟的心跳数时,可以通过找到心脏信号的峰顶到心脏信号的低谷来确定心跳。可以使用参照图11讨论的示例性头戴式装置来记录心率和脑波。
在图2的示例中,随着在采样来自大脑的第一信号时基本上同时采样来自个体心脏的第二信号,流程图200继续到模块206。来自大脑的信号可以包括α波和θ波。此外,来自大脑的信号可以包括其他脑波,诸如Δ波和θ波。这些波的频率为大约1-4赫兹(δ)、4-8赫兹(θ)、8-13赫兹(α)、13-20赫兹(β)。例如在α和β之间的频率范围的分界点,例如在13赫兹处分界,是近似的;本领域技术人员可以依据心理生理学科学中的各种学派中的某些关注点来应用所述范围。在此所提供的算法的示例可以通过在使用心率之外还使用在1赫兹和100赫兹之间的任何频率或频率集合来确定参与度。有可能使用图11的示例所示的头戴式装置来将电极附着到个体头部。胸电极可以是简单的电极,其具有确保其附着于皮肤上的粘性和将其连接到用于收集心率的设备的电线。
在图2的实施例中,流程图200继续到模块208,将第一信号和第二信号分解到频域中。在该示例中,使用在数字信号处理领域中公知的快速傅里叶变换(FFT)或者小波分析来分解。FFT是计算离散傅里叶变换(DFT)的有效方法;也可以使用DFT以及计算傅里叶分析的其他方法。在可替选方案中,可以使用小波分析来将信号划分为不同频率成分,使得它们可以被认为是独立的。特别地,morlet小波、墨西哥帽小波、daubechies小波、β小波或者coiflet小波会可用于这样做。其他的小波同样可以是可用的。
在一些实施例中,从信号中分离出频率并将其存储进贮存器(bin)。在存储来自信号的频率时,贮存器保存来自频域的经采样的信号。可以通过计算n点DFT来定义DFT贮存器。特别地,从X(0)到X(n-1)生成n个不同的采样值。在i的值为0到n-1的情况下,X(i)是保存相关采样值的贮存器。α贮存器可以保存在8-13赫兹之间的任何频率,但不必然包括该范围内的所有频率。θ贮存器可以保存在4-8赫兹之间的任何频率,但不必包括所有频率。相似地,可以在δ贮存器和β贮存器中保存δ波和β波。此外,可以调整频率分布图来移除信号中噪音譬,如白噪音或粉红噪音。
在图2的示例中,流程图200继续到模块210,使用来自第一信号的一个或更多个频率和来自第二信号的频率来计算用以定义个体响应于事件激励无意识动作的量的参与度值,以便与参考值进行比较,从而基于在媒体的参考值和参与度值之间的差来评估媒体。
在一些实施例中,有可能对比于心率,只使用α或者只使用θ来感测参与度。总EEG功率也是有用的。可以使用单个公式来计算参与度值,其中x/EEG表示x比总EEG功率。此外,可以使用优化的θ乘法器,譬如采用θ的自然对数并乘以比例因子。在非限制性示例中,可以将θ优化为:优化的θ=s·ln(θ),其中s是比例因子,并且ln(x)表示求x的自然对数的函数。可以与其结合使用θ或优化的θ。
在一些实施例中,α脑波与认知活动相反地相关。随着α功率增加,思考减少;相反随皮层处理增加,通常被称为α抑制的α功率减少。使用这些基础,通过使用寻找增大的心率、减小的α功率和增大的θ功率的公式来确定参与度值。这样的公式的示例是:
该公式使用心率、α值和θ值的组合。特别地,从调整的心率中减去α值和θ值的组合,其中调整的心率通过将其除以50来调整得到。调整以及α值和θ值的组合是非限制性的,并且公式可以对于特定应用依需要而被重写。后面关于图5讨论了其他可以使用的公式,图5示出了在心率和可用于计算参与度值的多个示例公式之一之间的关系。
在一些实施例中,使用媒体中的一个或更多个事件来针对该媒体定义参与度值。事件是媒体的可识别的部分。它可以是笑话的妙语或者电影的重要场景。媒体的事件是可测量的并且可以具有与其相关联的参与度值。大量事件会具有大量参与度值。通过考虑媒体包含的事件和与这些事件相关联的参与度值,可以将媒体作为整体来评级。
在一些实施例中,在特定时间点计算参与度值。基于参考媒体激励来计算的多个参与度值,示例性系统产生个体参与度的时变图。
在一些实施例中,可以计算导数以确定表示对激励的反应的参与度的变化。在非限制性示例中,媒体的事件引人参与,导致通过正导数识别的参与反应。正导数表示参与度增加,而负导数表示参与度减少。媒体的创作者可以使用该信息来按照创作者的期望创作更引人参与或更不引人参与的媒体制品。
在一些实施例中,可以基于参与度值来对媒体评级。图3示出了用于对比第二媒体为第一媒体评级的方法的示例的流程图300。流程图300在模块302处开始,计算个体对媒体的事件的参与度值。在获得第一参与度值时,可以使第一个体接触媒体,并且获得的数据可以包括心率、α波、θ波、δ波和β波。在时间上同时地汇集这些值。数据点包括在该时间点的参与度值。反应可能存在延迟,因此借助以下理解来采样信号:如果反应不是与激励精确地同时,则反应紧随激励之后,因此基本上与激励同时发生。
在一些实施例中,使用参考值来将对事件的用户参与反应与事件的预定参与度值进行比较。参考值可以是为提供比较值的目的而产生的任何值,根据比较值来确定在用户的参与度值和事件之间的差。媒体的开发者可以生成其自己的参考值。参考值可以是理想值,即所期望的目标。参考值可以是为了产生参考值的目的而单独计算的大量不同用户参与度值的平均,根据参考值来比较其他个体。
在图3的示例中,流程图300前进到模块304,将参与度值与参考值比较以确定在个体参与媒体的量和媒体的参考值之间的差。可以用与第一数据点相同的方式获得参考值。可替选地,由媒体的创作者提供参考值。
在图3的示例中,流程图300前进到模块306,保存所述比较作为定义对媒体事件的评估的度量。可以使用在参与度值和参考值之间的相对差来确定个体比参考值相对更多地参与或是更少地参与媒体。可以使用相对差来为诸如在图4中所示的多个不同媒体评级。
在一些实施例中,根据参与度值来为多个媒体评级。在图4的示例中,为多个媒体评级的示例图400包含了n个不同媒体,并且按关于与个体相关的参与度值的顺序来为它们评级。这可以推广到关于图8所讨论的个体组,其中可以使用平均参与度值或最高参与度值或者其他在统计学上有根据的参与度的值,来在媒体间进行比较。在图400的顶部的原顺序中,媒体是未经组织的:游戏402、然后是运动404、然后是广告406、以及然后是电影408。曾根据相关的参与度值E1、E2…EN为媒体进行了评级。可以按照经评级的电影412、然后是经评级的运动414、然后是经评级的游戏416、以及然后是经评级的广告418,来查看相关评级。可以使用评级来确定多个媒体中哪个媒体最引人参与,哪个最不引人参与,以及个体对媒体的相关参与度的其他统计度量。
图5示出了与为参与度评级相关的多个公式500。如关于图1所讨论的,在θ、α和心率之间的关系确定了个体无意识动作的量,从而确定了该个体的参与度。减小的θ波表示降低的参与度水平。增加的α波表示较低的参与度水平。该α的增加和/或相关的θ的减少表示参与度水平的变化。如在个体的心跳响应令人兴奋的事件而“加速”时所经历的,增加的心率与兴奋水平相关联。其组合是用于发现个体参与正在与其互动的媒体的基础。这并不足以表明单独的θ、α和心率变化足以确定参与度的变化,但是,这些值之一的变化与参与度值的变化相关联,并且所涉及的所有值的相关联的变化可以表示参与度的变化。
图6示出了在心率和可用于计算参与度值的多个示例公式之一之间的关系600的示例。关系600包括心率(HR)602和公式604到公式628。公式626表示来自小波分析的占优势的脉冲宽度。在计算参与度值时,公式可以考虑HR和一个或更多个公式。公式和HR都或分别乘以或除以恒定值,以针对特定应用对其进行调整。可以相似地通过恒定值来调整一个或更多个公式中的各变量,而无需相似地调整公式中的其他变量。使用在HR和公式之间的关系来确定参与度。相似地包括未述及的但符合这些指导的精神的公式。
图7示出了以媒体来激励个体的同时计算与个体对媒体的参与度相关的参与度值的示例的图700。图700包括媒体702、处理装置704、心脏706、电极708、个体710和头戴式装置712。如所示,个体712观看好莱坞电影(媒体702),同时由处理装置704监视其参与度水平。从头部和心脏706经由电极708和头戴式装置712收集信号。将这些信号发送到处理装置704用于处理为参与度值。特别地,δ波、α波、θ波和β波都由头戴式装置712接收,并且被发送到处理装置704,无论实际是否使用它们。
在一些实施例中,生成从生理反应中得出的大量个体参与度值的汇总,以确定对媒体的组群反应。可以通过大量个体的平均反应或者通过高阶近似来得到汇总。
图8示出了以媒体来激励多个个体并基于他们对媒体的参与度来计算相关值的示例的图800。图800包括媒体802、第一个体804、第二个体806、第三个体808、处理装置810、总反应向量812、平均参与度814、最高参与度816和在某时刻的参与度818。在图8的示例中,个体804、806和810参与到媒体中,在该示例中他们观看电影。处理装置810根据图1的讨论接收信号并计算参与度值。使用这些参与度值来基于收集到的参与度值产生关于媒体的统计学信息。例如,总反应向量812可以接受每个个体804、806和810的参与度值并确定对所参与的媒体做出反应的个体的数量。在该示例中,可以得到对媒体的组群反应。可以使用附加统计学信息譬如平均参与度814、最高参与度816和在某时刻的参与度818来基于对媒体的组群反应为媒体评级。
在一些实施例中,通过使用数学变换将事件分类为特定的事件类型,以将该事件与其他事件进行比较。这样的数学变换可以包括但不限于平均、一阶导数、二阶导数、多项式近似、相对于平均值的标准差、导数相对于平均值的标准差、和生理反应的分布图,所述分布图可以借助考虑以下一个或更多个方面的卷积或其他方法来实现:在中部剧烈增加、在开始处形成尖峰、平坦等。
图9示出了与事件相关的参与度在时间上的变化的示例的图表的图900。图900包括令人兴奋的参与事件902和不令人兴奋的非参与事件904。在图9的示例中,令人兴奋的参与事件902导致参与度增加。紧跟在令人兴奋的参与事件902之后的参与度向量的导数明显是正的,直到兴奋消失导致稳定的参与度为止。相反地,兴奋的参与个体体验了不令人兴奋的非参与事件904,非参与事件904在紧随其后的时间内,导致个体的参与度降至个体对事件的参与比之前显著少的程度。
图10示出了以媒体(此处是游戏)来激励个体并记录心率、思考和参与度的相关水平的示例的图1000和数据图表。图1000包括游戏1002、个体1004、头戴式装置1006、心脏1008、电极1010、处理装置1012、第一图表1014和第二图表1016。第一图表1014和第二图表1016并不对应于来自同一个体的同一数据,而是来自于不同的实验。在图10的示例中,个体1004玩游戏,同时由头戴式装置1006收集他的脑波并且由处理装置1012收集他的心脏信号。根据图1的讨论来分析得到的信号,并跟踪参与度。在图表1014中,绘制出脑波和心率。在实验中观察个体并生成图表。通过随着时间比较个体1004的动作和图表1014,可以得出:在高强度周期中,在个体识别了他已经开始参与时,他的心率升高而他的思考水平(通过α和θ的变化来识别)下降。在发现该参与度如所讨论地对应于心率和脑波后,产生了图表1016。图表1016示出了个体对游戏的参与度,并记录了对应于非参与的关键点。该非参与对应于如在图9的讨论中所描述的非参与,尽管图表是通过实际实验产生的。
在一些实施例中,可以将集成的头戴式装置放置在观众头部,用于在观众观看媒体的事件时测量他/她的生理数据。可以在允许观众在佩戴头戴式装置时与媒体互动的计算机上的程序中记录数据。
图11示出了包含可用于收集来自个体头部的信号的电极的头戴式装置1100。头戴式装置1100包括处理单元1101、三轴加速度计1102、硅稳定带1103、右EEG电极1104、心率传感器1105、左EEG电极1106、电池模块1107和可调整带1108。图11从不同角度描绘了与本发明的一个实施例一起使用的集成的头戴式装置的示例。处理单元1101是将生理数据数字化并能够将数据处理为生理反应的微处理器,所述生理反应包括但不限于思考、参与、沉浸、身体参与度、反感、活力及其他。三轴加速度计1102感测头部的运动。硅稳定带1103通过将运动最小化的头戴式装置的稳定来允许更鲁棒的感测。右EEG电极1104和左EEG电极1106是使用前无需做准备的前额干电极。电极与皮肤之间需要接触但不需要过多压迫。心率传感器1105是位于前额中央附近的强健血容量脉冲传感器,并且可充电或可替换电池模块1107位于一只耳朵上方。后部的可调节带1108用于针对许多不同头部尺寸而将头戴式装置调节到舒适的松紧设定。
在一些实施例中,集成的头戴式装置可以通过按钮接通,并立即测量和记录观众的生理数据。数据传送可以通过头戴式装置链接的计算机接口以无线方式来处理。在观众身上无需皮肤制剂或凝胶以获得准确测量,并且头戴式装置可以轻松地从观众身上取下并可以立即被另一名观众使用。头戴式装置在使用期间不出现老化,并且头戴式装置可以重复使用上千次。
本领域技术人员应理解的是,前述示例和实施例是示例性的并且不限制本发明的范围。意味着,对于本领域技术人员而言通过阅读说明书和研究附图而变得明显的所有对本发明的置换、强化、等同物和改进包括在本发明的真正精神和范围内。因此,意味着,以下的权利要求包括落在本发明的真正精神和范围内的所有这样的修改、置换和等同物。
Claims (18)
1.一种用于评估媒体的方法,所述方法包括:
获得基本上在个体接触所述媒体的同时从所述个体的大脑产生的第一信号,所述第一信号包括α频率α、β频率β、δ频率δ或θ频率θ中的至少两个;
获得基本上与来自所述大脑的所述第一信号同时产生的来自所述个体的心脏的第二信号;
基于所述第一信号的所述α频率、β频率、δ频率或θ频率中的一个和所述α频率、β频率、δ频率或θ频率中不同的一个之间的第一数学关系与所述第二信号和所述第一数学关系的结果之间的第二数学关系来计算参与度值,其中,所述第一数学关系根据以下公式中的至少一个计算:
δ/α;
β/α;
α/β;
α/δ;或者
(α-θ)/(α+θ);
将所述参与度值与参考值进行比较以确定差;以及
基于所述参与度值与所述参考值之间的所述差来评估所述媒体。
2.根据权利要求1所述的方法,其中,所述参与度值与所述媒体中的事件相关联。
3.根据权利要求1所述的方法,其中,汇总与所述媒体中的事件相关联的来自多个个体的多个参与度值,以形成对所述事件的参与反应。
4.根据权利要求1所述的方法,其中,在总反应向量中包括来自多个个体的多个参与度值,所述总反应向量识别以参与而对所述媒体反应的人数。
5.根据权利要求1所述的方法,还包括使用快速傅里叶变换或者小波分析来识别所述一个或更多个频率。
6.根据权利要求5所述的方法,其中,使用从墨西哥帽小波、morlet小波、daubechies小波、β小波和coiflet小波中选择的小波来完成小波分析。
7.根据权利要求1所述的方法,还包括计算代表参与度随时间的变化的所述参与度值的导数。
8.根据权利要求1所述的方法,其中,监视每次心跳的长度。
9.根据权利要求1所述的方法,其中,所述第二信号包括心率,通过对比所述第二信号与所述第一数学关系的结果来计算所述参与度值。
10.根据权利要求9所述的方法,其中,所述参与度值通过计算[心率/50]-[(θ-α)/(θ+α)]来确定。
11.根据权利要求1所述的方法,其中,所述第二信号包括心跳,通过对比所述第二信号与所述第一数学关系的结果来计算所述参与度值。
12.根据权利要求1所述的方法,其中所述媒体包括电视广播、视频游戏、视听广告、棋盘游戏、卡片游戏、现场动作事件、印刷品广告和网络广告中的一个或更多个。
13.根据权利要求1所述的方法,还包括:
识别与所述参与度值对应的时间点;
识别所述媒体的与所述时间点基本上同时发生的事件;
将所述参与度值与所述事件相关;以及
将所述参与度值定位于所述媒体。
14.一种用于评估媒体的系统,所述系统包括:
数据收集器,用于获得基本上在个体接触所述媒体的同时从所述个体的大脑产生的第一信号以及基本上在所述个体接触所述媒体的同时产生的来自所述个体的心脏的第二信号,所述第一信号包括α频率α、β频率β、δ频率δ或θ频率θ中的至少两个;以及
处理器,用于:
基于所述第一信号的所述α频率、β频率、δ频率或θ频率中的一个和所述α频率、β频率、δ频率或θ频率中的不同的一个之间的第一数学关系与所述第二信号和所述第一数学关系的结果之间的第二数学关系来计算参与度值,其中,所述第一数学关系根据以下公式中的至少一个计算:
δ/α;
β/α;
α/β;
α/δ;或者
(α-θ)/(α+θ);
将所述参与度值与参考值进行比较以确定差;以及
基于所述参与度值与所述参考值之间的所述差来评估所述媒体。
15.根据权利要求14所述的系统,其中,所述数据收集器包括一个或更多个传感器,所述一个或更多个传感器被包括在用于测量来自由所述媒体激励的所述个体的信号的集成的传感器头戴式装置中。
16.根据权利要求14所述的系统,其中,所述第二信号包括心率,通过对比所述第二信号与所述第一数学关系的结果来计算所述参与度值。
17.根据权利要求16所述的系统,其中,所述参与度值通过计算[心率/50]-[(θ-α)/(θ+α)]来确定。
18.根据权利要求14所述的系统,其中,所述第二信号包括心跳,通过对比所述第二信号与所述第一数学关系的结果来计算所述参与度值。
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