CN115153554B - Cognitive load assessment method and system - Google Patents

Abstract

Translated fromChinese

本发明提供一种认知负荷评估方法及系统，方法包括：获取近红外光强信号和心电信号；第一设定算法将近红外光强信号转换为氧合血红蛋白浓度信号和脱氧血红蛋白浓度信号，计算脑氧饱和度；用莫莱小波对述氧合血红蛋白浓度信号进行小波变换，提取脑血氧相位信号；用莫莱小波对心电信号进行小波变换，提取心电相位信号；第二设定算法计算脑血氧相位信号和心电相位信号间的心脑耦合强度；频域分析提取心率变异性；对脑氧饱和度、心脑耦合强度和心率变异性加权求和得到认知负荷指数。系统包括：信息采集模块、信号处理分析模块、指标融合模块、认知负荷评级模块和交互模块。本发明能够将生理信号转化为认知负荷指数，直观地将训练的负荷强度反馈给用户。

The present invention provides a cognitive load assessment method and system, the method comprising: acquiring a near-infrared light intensity signal and an electrocardiogram signal; a first setting algorithm converts the near-infrared light intensity signal into an oxygenated hemoglobin concentration signal and a deoxygenated hemoglobin concentration signal, Calculate cerebral oxygen saturation; use Morley wavelet to perform wavelet transformation on the oxyhemoglobin concentration signal to extract the cerebral blood oxygen phase signal; use Morley wavelet to perform wavelet transformation on the ECG signal to extract the ECG phase signal; the second setting The algorithm calculates the heart-brain coupling strength between the cerebral blood oxygen phase signal and the ECG phase signal; the frequency domain analysis extracts the heart rate variability; the weighted sum of the cerebral oxygen saturation, the heart-brain coupling strength and the heart rate variability obtains the cognitive load index. The system includes: information collection module, signal processing and analysis module, indicator fusion module, cognitive load rating module and interaction module. The invention can transform the physiological signal into the cognitive load index, and intuitively feed back the training load intensity to the user.

Description

Translated fromChinese

一种认知负荷评估方法及系统A cognitive load assessment method and system

技术领域Technical Field

本发明涉及认知神经科学技术领域，尤其涉及一种认知负荷评估方法及系统。The present invention relates to the field of cognitive neuroscience technology, and in particular to a cognitive load assessment method and system.

背景技术Background Art

脑中风、脑外伤患者经常会出现认知障碍，因此在康复过程中会对患者进行认知训练。适当强度的认知负荷可以提高训练者的认知训练效率。然而在传统的认知训练过程中，不能直观的判断训练者对训练内容的认知负荷，过高的认知负荷会降低训练者的记忆力，反应能力和操作能力，过低的认知负荷会导致认知训练效果下降。因此，亟需一种能直观反映训练者认知训练过程中认知负荷高低的方法，以提高训练者的认知训练效率。Patients with stroke and brain trauma often have cognitive impairment, so cognitive training is performed on them during rehabilitation. Appropriate cognitive load can improve the efficiency of cognitive training for trainees. However, in traditional cognitive training, it is not possible to intuitively judge the cognitive load of trainees on the training content. Too high cognitive load will reduce the trainees' memory, reaction ability and operation ability, while too low cognitive load will lead to a decrease in the effect of cognitive training. Therefore, there is an urgent need for a method that can intuitively reflect the level of cognitive load during the cognitive training of trainees, so as to improve the efficiency of cognitive training for trainees.

发明内容Summary of the invention

本发明实施例提供了一种认知负荷评估方法及系统，以消除或改善现有技术中存在的一个或更多个缺陷，解决了现有技术无法直观反映训练者认知训练过程中认知负荷高低的问题，提高了训练者认知训练的效率。The embodiments of the present invention provide a cognitive load assessment method and system to eliminate or improve one or more defects in the prior art, solve the problem that the prior art cannot intuitively reflect the level of cognitive load during the trainee's cognitive training, and improve the efficiency of the trainee's cognitive training.

本发明的一个方面提供了一种认知负荷评估方法，该方法包括以下步骤：One aspect of the present invention provides a method for evaluating cognitive load, the method comprising the following steps:

获取针对用户大脑前额叶区域采集的近红外光强信号以及用户的心电信号；Acquire near-infrared light intensity signals collected from the frontal lobe area of the user's brain and the user's electrocardiogram signals;

根据第一设定算法将所述近红外光强信号转换为氧合血红蛋白浓度信号和脱氧血红蛋白浓度信号，并根据所述氧合血红蛋白浓度信号和所述脱氧血红蛋白浓度信号计算脑氧饱和度指标；converting the near-infrared light intensity signal into an oxyhemoglobin concentration signal and a deoxyhemoglobin concentration signal according to a first set algorithm, and calculating a brain oxygen saturation index according to the oxyhemoglobin concentration signal and the deoxyhemoglobin concentration signal;

使用复数域的莫莱小波对所述氧合血红蛋白浓度信号进行连续小波变换，以提取其在第一设定频段的脑血氧相位信号；使用复数域的莫莱小波对所述心电信号进行连续小波变换，以提取其在第二设定频段的心电相位信号；根据第二设定算法计算所述脑血氧相位信号和所述心电相位信号之间的心脑耦合强度指标；Performing a continuous wavelet transform on the oxygenated hemoglobin concentration signal using a Morlet wavelet in the complex domain to extract its cerebral blood oxygen phase signal in a first set frequency band; performing a continuous wavelet transform on the electrocardiogram signal using a Morlet wavelet in the complex domain to extract its electrocardiogram phase signal in a second set frequency band; calculating a heart-brain coupling strength index between the cerebral blood oxygen phase signal and the electrocardiogram phase signal according to a second set algorithm;

通过频域分析提取所述心电信号在第三设定频段的心率变异性指标；Extracting the heart rate variability index of the electrocardiogram signal in a third set frequency band through frequency domain analysis;

对所述脑氧饱和度指标、所述心脑耦合强度指标和所述心率变异性指标进行加权求和得到认知负荷指数，以通过所述认知负荷指数表征用户的认知负荷。The brain oxygen saturation index, the heart-brain coupling strength index and the heart rate variability index are weightedly summed to obtain a cognitive load index, so as to characterize the user's cognitive load through the cognitive load index.

在一些实施例中，所述近红外光强信号是通过多个近红外信号采集探头对用户大脑前额叶区域采集获得的，各近红外信号采集探头之间的间距为30mm。In some embodiments, the near-infrared light intensity signal is obtained by collecting the prefrontal lobe area of the user's brain through multiple near-infrared signal collection probes, and the distance between each near-infrared signal collection probe is 30 mm.

在一些实施例中，所述第一设定算法为比尔朗伯特定律，所述比尔朗伯特定律先将所述近红外光强信号转化为光密度数据，再将所述光密度数据转化为所述氧合血红蛋白浓度信号和所述脱氧血红蛋白浓度信号。In some embodiments, the first setting algorithm is the Beer-Lambert law, which first converts the near-infrared light intensity signal into optical density data, and then converts the optical density data into the oxygenated hemoglobin concentration signal and the deoxygenated hemoglobin concentration signal.

在一些实施例中，根据所述氧合血红蛋白浓度信号和所述脱氧血红蛋白浓度信号计算脑氧饱和度指标，包括：In some embodiments, calculating the brain oxygen saturation index according to the oxygenated hemoglobin concentration signal and the deoxygenated hemoglobin concentration signal includes:

所述脑氧饱和度指标计算公式为：The calculation formula of the cerebral oxygen saturation index is:

其中，c为所述脑氧饱和度指标，a为所述氧合血红蛋白浓度信号，b为所述脱氧血红蛋白浓度信号。Among them, c is the brain oxygen saturation index, a is the oxygenated hemoglobin concentration signal, and b is the deoxygenated hemoglobin concentration signal.

在一些实施例中，所述第一设定频段为0.02～0.07Hz频段；所述第二设定频段为0.6～2Hz频段；所述第三设定频率为0.04～0.15Hz频段。In some embodiments, the first set frequency band is a frequency band of 0.02-0.07 Hz; the second set frequency band is a frequency band of 0.6-2 Hz; and the third set frequency band is a frequency band of 0.04-0.15 Hz.

在一些实施例中，所述第二设定算法的计算步骤包括：In some embodiments, the calculation step of the second setting algorithm includes:

选取所述第一设定频段的脑血氧相位信号和所述第二设定频段的心电相位信号，由随机微分方程构建所述脑血氧相位信号和所述心电相位信号间的耦合函数模型；Selecting a cerebral blood oxygen phase signal of the first set frequency band and an electrocardiogram phase signal of the second set frequency band, and constructing a coupling function model between the cerebral blood oxygen phase signal and the electrocardiogram phase signal by a stochastic differential equation;

根据贝叶斯定理建立所述耦合函数模型的二次型负对数形式的似然函数，计算所述似然函数的驻点坐标，得到所述第一设定频段的脑血氧相位信号和所述第二设定频段的心电相位信号之间相位振子模型的耦合系数和耦合矩阵，采用所述二次型负对数形式的似然函数对得到的所述耦合系数和所述耦合矩阵递归计算，直至稳定状态下得到耦合系数矩阵；A likelihood function in the form of a quadratic negative logarithm of the coupling function model is established according to the Bayesian theorem, the stationary point coordinates of the likelihood function are calculated, the coupling coefficient and coupling matrix of the phase oscillator model between the cerebral blood oxygen phase signal of the first set frequency band and the electrocardiogram phase signal of the second set frequency band are obtained, and the coupling coefficient and the coupling matrix obtained are recursively calculated using the likelihood function in the form of a quadratic negative logarithm until a coupling coefficient matrix is obtained in a stable state;

根据所述耦合系数矩阵计算第一设定频段的所述脑血氧相位信号与第二设定频段的心电相位信号的耦合强度。The coupling strength between the cerebral blood oxygen phase signal in the first set frequency band and the electrocardiogram phase signal in the second set frequency band is calculated according to the coupling coefficient matrix.

在一些实施例中，所述脑血氧相位信号和所述心电相位信号间的耦合函数模型为：In some embodiments, the coupling function model between the cerebral blood oxygen phase signal and the electrocardiogram phase signal is:

其中，i≠j，i,j＝{1,2}，φ_i表示所述脑血氧相位信号的相位振子模型，φ_j表示所述心电相位信号的相位振子模型，t为时变参数，ω(t)为固有频率参数，ξ(t)为高斯白噪声，q(φ_i,φ_j,t)为基函数；Wherein, i≠j, i,j＝{1,2}, φ_i represents the phase oscillator model of the cerebral blood oxygen phase signal, φ_j represents the phase oscillator model of the electrocardiogram phase signal, t is a time-varying parameter, ω(t) is a natural frequency parameter, ξ(t) is Gaussian white noise, and q(φ_i ,φ_j ,t) is a basis function;

所述基函数通过傅里叶级数形式表示后，所述耦合函数模型可解构为：After the basis function is expressed in Fourier series form, the coupling function model can be deconstructed as:

其中，φ_i表示所述脑血氧相位信号的相位振子模型，φ_j表示所述心电相位信号的相位振子模型，i＝0时，

即固有频率；

和Φ_i,k为傅里叶分量成分，K为傅里叶级数的最高阶数；Wherein, φ_i represents the phase oscillator model of the cerebral blood oxygen phase signal, φ_j represents the phase oscillator model of the electrocardiogram phase signal, when i=0,

That is, the natural frequency;

and Φ_i,k are the Fourier component components, K is the highest order of the Fourier series;

所述似然函数表达式为：The likelihood function expression is:

其中，n＝1,2,...,N，N为相位信息序列点数，h为采样步长，E为噪声矩阵，c_k为耦合系数矩阵，Φ_k为傅里叶分量成分，φ.为所求耦合关系的通道，φ_l中的l代表i或者j，φ_i表示所述脑血氧相位信号的相位振子模型，φ_j表示所述心电相位信号的相位振子模型；Wherein, n=1, 2, ..., N, N is the number of phase information sequence points, h is the sampling step, E is the noise matrix, c_k is the coupling coefficient matrix, Φ_k is the Fourier component, φ. is the channel of the coupling relationship to be sought, l in φ_l represents i or j, φ_i represents the phase oscillator model of the cerebral blood oxygen phase signal, and φ_j represents the phase oscillator model of the electrocardiogram phase signal;

所述耦合系数矩阵表达式为：The coupling coefficient matrix expression is:

c_k＝Ξ^-1r；c_k =Ξ^-1 r;

其中，Ξ为密度矩阵，r为中间矩阵变量；Among them, Ξ is the density matrix, r is the intermediate matrix variable;

根据所述耦合系数矩阵计算第一设定频段的所述脑血氧相位信号与第二设定频段的所述心电相位信号的耦合强度，计算式为：The coupling strength between the cerebral blood oxygen phase signal of the first set frequency band and the electrocardiogram phase signal of the second set frequency band is calculated according to the coupling coefficient matrix, and the calculation formula is:

其中，CS_i,j表示第一设定频段的所述脑血氧相位信号与第二设定频段的心电相位信号的耦合强度，C_k^(i：j)表示脑血氧相位信号的相位振子φ_i与心电相位信号的相位振子φ_j间的耦合系数，K为傅里叶级数的最高阶数。Among them, CS_i,j represents the coupling strength between the cerebral blood oxygen phase signal of the first set frequency band and the electrocardiogram phase signal of the second set frequency band, C_k^(i:j) represents the coupling coefficient between the phase oscillator φ_i of the cerebral blood oxygen phase signal and the phase oscillator φ_j of the electrocardiogram phase signal, and K is the highest order of the Fourier series.

在一些实施例中，所述系统包括：In some embodiments, the system comprises:

信息采集模块，包括近红外脑血氧采集模块和心电采集模块，所述红外脑血氧用于采集模块采集用户大脑前额叶区域的所述近红外光强信号，所述心电采集模块用于采集所述用户的心电信号；An information collection module, including a near-infrared cerebral blood oxygen collection module and an electrocardiogram collection module, wherein the infrared cerebral blood oxygen collection module is used to collect the near-infrared light intensity signal of the user's frontal lobe area, and the electrocardiogram collection module is used to collect the user's electrocardiogram signal;

信息处理分析模块，连接所述信息采集模块，所述信息处理模块根据第一设定算法将所述近红外光强信号转换为氧合血红蛋白浓度信号和脱氧血红蛋白浓度信号，并根据所述氧合血红蛋白浓度信号和所述脱氧血红蛋白浓度信号计算脑氧饱和度指标；使用复数域的莫莱小波对所述氧合血红蛋白浓度信号进行连续小波变换，以提取其在第一设定频段的脑血氧相位信号；使用复数域的莫莱小波对所述心电信号进行连续小波变换，以提取其在第二设定频段的心电相位信号；根据第二设定算法计算所述脑血氧相位信号和所述心电相位信号之间的心脑耦合强度指标；通过频域分析提取所述心电信号在第三设定频段的心率变异性指标，将得到的所述心脑耦合强度指标；An information processing and analysis module is connected to the information acquisition module. The information processing module converts the near-infrared light intensity signal into an oxygenated hemoglobin concentration signal and a deoxygenated hemoglobin concentration signal according to a first set algorithm, and calculates a brain oxygen saturation index according to the oxygenated hemoglobin concentration signal and the deoxygenated hemoglobin concentration signal; performs continuous wavelet transform on the oxygenated hemoglobin concentration signal using a complex domain Morlet wavelet to extract its brain blood oxygen phase signal in a first set frequency band; performs continuous wavelet transform on the electrocardiogram signal using a complex domain Morlet wavelet to extract its electrocardiogram phase signal in a second set frequency band; calculates a heart-brain coupling strength index between the brain blood oxygen phase signal and the electrocardiogram phase signal according to a second set algorithm; extracts a heart rate variability index of the electrocardiogram signal in a third set frequency band through frequency domain analysis, and obtains the heart-brain coupling strength index;

指标融合模块，连接所述信息处理分析模块，所述指标融合模块对所述信息处理分析模块得到的所述脑氧饱和度指标、所述心脑耦合强度指标和所述心率变异性指标进行加权求和得到认知负荷指数；an index fusion module, connected to the information processing and analysis module, wherein the index fusion module performs weighted summation on the brain oxygen saturation index, the heart-brain coupling strength index and the heart rate variability index obtained by the information processing and analysis module to obtain a cognitive load index;

认知负荷评级模块，连接所述指标融合模块，所述认知负荷评级模块根据用户认知能力对所述指标融合模块传递来的所述认知负荷指数进行认知负荷评级，同时根据评级结果对用户认知训练过程中的参数进行实时调整；A cognitive load rating module, connected to the index fusion module, wherein the cognitive load rating module rates the cognitive load index transmitted by the index fusion module according to the user's cognitive ability, and adjusts the parameters of the user's cognitive training process in real time according to the rating result;

交互模块，连接所述认知负荷评级模块，所述交互模块用于显示并存储所述认知负荷评级结果。An interaction module is connected to the cognitive load rating module, and the interaction module is used to display and store the cognitive load rating result.

在一些实施例中，所述近红外光强信号是通过多个近红外信号采集探头对用户大脑前额叶区域采集获得的，各近红外信号采集探头之间的间距为30mm。In some embodiments, the near-infrared light intensity signal is obtained by collecting the prefrontal lobe area of the user's brain through multiple near-infrared signal collection probes, and the spacing between each near-infrared signal collection probe is 30 mm.

在一些实施例中，所述第一设定算法为比尔朗伯特定律，所述比尔朗伯特定律先将所述近红外光强信号转化为光密度数据，再将所述光密度数据转化为所述氧合血红蛋白浓度信号和所述脱氧血红蛋白浓度信号。In some embodiments, the first setting algorithm is the Beer-Lambert law, which first converts the near-infrared light intensity signal into optical density data, and then converts the optical density data into the oxygenated hemoglobin concentration signal and the deoxygenated hemoglobin concentration signal.

本发明的有益效果至少是：The beneficial effects of the present invention are at least:

本发明所述认知负荷评估方法及系统，将认知训练与脑血氧信号以及心电信号相结合，通过系统设定算法将脑血氧信号以及心电信号转化为认知负荷指数，得到的认知负荷指数能够直观的反映训练者认知训练过程中的认知负荷高低。训练者根据认知负荷评估结果调整认知训练强度，确保了训练过程中的认知负荷强度不会过高或过低，提高了训练者的认知训练效率。The cognitive load assessment method and system of the present invention combines cognitive training with cerebral blood oxygen signals and electrocardiogram signals, and converts cerebral blood oxygen signals and electrocardiogram signals into cognitive load indexes through a system-set algorithm. The obtained cognitive load index can intuitively reflect the cognitive load level of the trainee during cognitive training. The trainee adjusts the cognitive training intensity according to the cognitive load assessment results, ensuring that the cognitive load intensity during the training process is not too high or too low, thereby improving the trainee's cognitive training efficiency.

进一步的，通过对训练者的心电信号和脑电信号分别进行监测，增加了对训练者认知负荷评估的准确性。Furthermore, by monitoring the trainee's electrocardiogram (ECG) and electroencephalogram (EEG) signals separately, the accuracy of the trainee's cognitive load assessment is increased.

进一步的，交互模块通过显示器将训练过程中的认知负荷评级结果反馈给训练者，使得训练者可以对训练强度进行自适应性调整。Furthermore, the interactive module feeds back the cognitive load rating results during the training process to the trainer through a display, so that the trainer can adaptively adjust the training intensity.

本发明的附加优点、目的，以及特征将在下面的描述中将部分地加以阐述，且将对于本领域普通技术人员在研究下文后部分地变得明显，或者可以根据本发明的实践而获知。本发明的目的和其它优点可以通过在说明书以及附图中具体指出的结构实现到并获得。Additional advantages, purposes, and features of the present invention will be described in part in the following description, and will become apparent to those skilled in the art after studying the following, or may be learned from the practice of the present invention. The purposes and other advantages of the present invention may be achieved and obtained by the structures specifically indicated in the specification and the accompanying drawings.

本领域技术人员将会理解的是，能够用本发明实现的目的和优点不限于以上具体所述，并且根据以下详细说明将更清楚地理解本发明能够实现的上述和其他目的。Those skilled in the art will appreciate that the objectives and advantages that can be achieved with the present invention are not limited to the above specific description, and the above and other objectives that can be achieved by the present invention will be more clearly understood from the following detailed description.

附图说明BRIEF DESCRIPTION OF THE DRAWINGS

此处所说明的附图用来提供对本发明的进一步理解，构成本申请的一部分，并不构成对本发明的限定。附图中的部件不是成比例绘制的，而只是为了示出本发明的原理。为了便于示出和描述本发明的一些部分，附图中对应部分可能被放大，即，相对于依据本发明实际制造的示例性装置中的其它部件可能变得更大。在附图中：The drawings described herein are used to provide a further understanding of the present invention, constitute a part of this application, and do not constitute a limitation of the present invention. The components in the drawings are not drawn to scale, but are only for illustrating the principles of the present invention. In order to facilitate the illustration and description of some parts of the present invention, the corresponding parts in the drawings may be enlarged, that is, they may become larger relative to other parts in the exemplary device actually manufactured according to the present invention. In the drawings:

图1为本发明一实施例所述认知负荷评估方法的逻辑示意图。FIG1 is a logic diagram of a cognitive load assessment method according to an embodiment of the present invention.

图2为本发明一实施例所述认知负荷评估系统的结构示意图。FIG. 2 is a schematic diagram of the structure of a cognitive load assessment system according to an embodiment of the present invention.

附图标记说明：Description of reference numerals:

100：信息采集模块； 200：信息处理分析模块；100: information collection module; 200: information processing and analysis module;

300：指标融合模块； 400：认知负荷评级模块；300: indicator fusion module; 400: cognitive load rating module;

500：交互模块； 110：近红外光强信号采集模块；500: interaction module; 110: near-infrared light intensity signal acquisition module;

120：心电信号采集模块； 510：显示器；120: ECG signal acquisition module; 510: display;

520：存储器。520: Memory.

具体实施方式DETAILED DESCRIPTION

为使本发明的目的、技术方案和优点更加清楚明白，下面结合实施方式和附图，对本发明做进一步详细说明。在此，本发明的示意性实施方式及其说明用于解释本发明，但并不作为对本发明的限定。In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

在此，还需要说明的是，为了避免因不必要的细节而模糊了本发明，在附图中仅仅示出了与根据本发明的方案密切相关的结构和/或处理步骤，而省略了与本发明关系不大的其他细节。It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and/or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

应该强调，术语“包括/包含”在本文使用时指特征、要素、步骤或组件的存在，但并不排除一个或更多个其它特征、要素、步骤或组件的存在或附加。It should be emphasized that the term “include/comprises” when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

在此，还需要说明的是，如果没有特殊说明，术语“连接”在本文不仅可以指直接连接，也可以表示存在中间物的间接连接。It should also be noted that, unless otherwise specified, the term “connection” herein may refer not only to a direct connection but also to an indirect connection with an intermediate.

在下文中，将参考附图描述本发明的实施例。在附图中，相同的附图标记代表相同或类似的部件，或者相同或类似的步骤。Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

现有技术在训练者认知训练过程中不能对训练者的认知负荷做出直观准确的判断，这就导致了训练者认知训练效率较低，同时现有技术不能将认知训练结果给予训练者视觉反馈。因此，本发明提供一种认知负荷评估方法和系统，用于对训练者的认知训练负荷进行实时评估，提高训练者的认知训练效果。The prior art cannot make an intuitive and accurate judgment on the trainee's cognitive load during the trainee's cognitive training process, which leads to a low efficiency of the trainee's cognitive training. At the same time, the prior art cannot provide visual feedback of the trainee's cognitive training results. Therefore, the present invention provides a cognitive load assessment method and system for real-time assessment of the trainee's cognitive training load and improving the trainee's cognitive training effect.

一个方面，如图1所示，本发明提供了一种认知负荷评估方法，该方法包括步骤S101～S105：In one aspect, as shown in FIG1 , the present invention provides a cognitive load assessment method, the method comprising steps S101 to S105:

S101：获取针对用户大脑前额叶区域采集的近红外光强信号以及用户的心电信号。S101: Acquire a near-infrared light intensity signal collected from the frontal lobe area of the user's brain and the user's electrocardiogram signal.

S102：根据第一设定算法将所述近红外光强信号转换为氧合血红蛋白浓度信号和脱氧血红蛋白浓度信号，并根据氧合血红蛋白浓度信号和脱氧血红蛋白浓度信号计算脑氧饱和度指标。S102: Converting the near-infrared light intensity signal into an oxyhemoglobin concentration signal and a deoxyhemoglobin concentration signal according to a first set algorithm, and calculating a brain oxygen saturation index according to the oxyhemoglobin concentration signal and the deoxyhemoglobin concentration signal.

S103：使用复数域的莫莱小波对氧合血红蛋白浓度信号进行连续小波变换，以提取其在第一设定频段的脑血氧相位信号；使用复数域的莫莱小波对心电信号进行连续小波变换，以提取其在第二设定频段的心电相位信号；根据第二设定算法计算脑血氧相位信号和心电相位信号之间的心脑耦合强度指标。S103: Performing a continuous wavelet transform on the oxygenated hemoglobin concentration signal using the complex domain Morlet wavelet to extract its cerebral blood oxygen phase signal in a first set frequency band; performing a continuous wavelet transform on the electrocardiogram signal using the complex domain Morlet wavelet to extract its electrocardiogram phase signal in a second set frequency band; and calculating the heart-brain coupling strength index between the cerebral blood oxygen phase signal and the electrocardiogram phase signal according to a second set algorithm.

S104：通过频域分析提取心电信号在第三设定频段的心率变异性指标。S104: extracting a heart rate variability index of the electrocardiogram signal in a third set frequency band through frequency domain analysis.

S105：对脑氧饱和度指标、心脑耦合强度指标和心率变异性指标进行加权求和得到认知负荷指数，以通过认知负荷指数表征用户的认知负荷。S105: performing weighted summation on the brain oxygen saturation index, the heart-brain coupling strength index, and the heart rate variability index to obtain a cognitive load index, so as to characterize the user's cognitive load through the cognitive load index.

在步骤S101中，近红外光强时域信号和心电时域信号中同步标定刺激开始时刻，以保证近红外信号以及心电信号在认知训练开始时，及时采集到血氧以及心电信号的变化，这样可以方便用于比较认知训练时的认知负荷。In step S101, the stimulation start time is synchronously calibrated in the near-infrared light intensity time domain signal and the ECG time domain signal to ensure that the near-infrared signal and the ECG signal can timely collect changes in blood oxygen and ECG signals when cognitive training begins, which can be conveniently used to compare cognitive load during cognitive training.

在一些实施例中，近红外光强信号是通过多个近红外信号采集探头对用户大脑前额叶区域采集获得的，各近红外信号采集探头之间的间距为30mm。In some embodiments, the near-infrared light intensity signal is obtained by collecting the prefrontal lobe area of the user's brain through multiple near-infrared signal collection probes, and the distance between each near-infrared signal collection probe is 30 mm.

在本实施例中，红外信号采集探头的设置方法采用的是国际脑电图10-10系统辅助定位法。In this embodiment, the setting method of the infrared signal acquisition probe adopts the international electroencephalogram 10-10 system auxiliary positioning method.

在另一些实施例中，红外信号采集探头的设置方法采用的是国际脑电图10-5系统辅助定位法。In other embodiments, the infrared signal acquisition probe is set up using the International Electroencephalogram 10-5 System Assisted Positioning Method.

在步骤S102中，脑氧饱和度指标是指大脑氧气供给量与氧气需求量之间的关系，当脑氧饱和度越过低，则表明认知训练负荷越高；当脑氧饱和度越高，则表明认知训练负荷越低。In step S102, the brain oxygen saturation index refers to the relationship between the brain's oxygen supply and oxygen demand. When the brain oxygen saturation is too low, it indicates that the cognitive training load is higher; when the brain oxygen saturation is higher, it indicates that the cognitive training load is lower.

在一些实施例中，第一设定算法为比尔朗伯特定律，比尔朗伯特定律先将近红外光强信号转化为光密度数据，再将光密度数据转化为氧合血红蛋白浓度信号和脱氧血红蛋白浓度信号。In some embodiments, the first setting algorithm is the Beer-Lambert law, which first converts the near-infrared light intensity signal into optical density data, and then converts the optical density data into an oxygenated hemoglobin concentration signal and a deoxygenated hemoglobin concentration signal.

在本实施例中，红外光在穿过微血管后，光强就会减弱，血红蛋白浓度越高，红外光强减弱越明显。因此在知道红外光入射光强和出射光强的基础上，就可以通过比尔朗伯特定律对血红蛋白的浓度进行计算。In this embodiment, after the infrared light passes through the capillaries, the light intensity will be weakened, and the higher the hemoglobin concentration, the more obvious the weakening of the infrared light intensity. Therefore, based on knowing the incident light intensity and the outgoing light intensity of the infrared light, the hemoglobin concentration can be calculated by the Beer-Lambert law.

在一些实施例中，根据氧合血红蛋白浓度信号和脱氧血红蛋白浓度信号计算脑氧饱和度指标，包括：In some embodiments, calculating the brain oxygen saturation index according to the oxygenated hemoglobin concentration signal and the deoxygenated hemoglobin concentration signal includes:

脑氧饱和度指标计算公式为：The calculation formula of brain oxygen saturation index is:

其中，c为脑氧饱和度指标，a为氧合血红蛋白浓度信号，b为脱氧血红蛋白浓度信号。Among them, c is the cerebral oxygen saturation index, a is the oxygenated hemoglobin concentration signal, and b is the deoxygenated hemoglobin concentration signal.

在步骤S103中，心脑耦合强度指标是心电信号与脑电信号之间的关联性指标，反映出心与脑之间相互关联的状态。心脑耦合强度指标越大，心与脑之间相互激发的强度越强，则认知训练负荷越高；心脑耦合强度指标越小，心与脑之间相互激发的强度越弱，则认知训练负荷越低。In step S103, the heart-brain coupling strength index is a correlation index between the ECG signal and the EEG signal, reflecting the state of the correlation between the heart and the brain. The larger the heart-brain coupling strength index, the stronger the intensity of mutual stimulation between the heart and the brain, and the higher the cognitive training load; the smaller the heart-brain coupling strength index, the weaker the intensity of mutual stimulation between the heart and the brain, and the lower the cognitive training load.

在一些实施例中，第二设定算法的计算步骤包括：In some embodiments, the calculation steps of the second setting algorithm include:

选取所述第一设定频段的脑血氧相位信号和所述第二设定频段的心电相位信号，采用随机微分方程分别构建两个由脑血氧相位信号振荡和心电相位信号振荡组成的耦合函数模型；Selecting the cerebral blood oxygen phase signal of the first set frequency band and the electrocardiogram phase signal of the second set frequency band, and using stochastic differential equations to respectively construct two coupling function models consisting of cerebral blood oxygen phase signal oscillations and electrocardiogram phase signal oscillations;

根据贝叶斯定理建立所述耦合函数模型的二次型负对数形式的似然函数，计算所述似然函数的驻点坐标，得到所述第一设定频段的脑血氧相位信号和所述第二设定频段的心电相位信号之间相位振子模型的耦合系数和耦合矩阵，采用所述二次型负对数形式的似然函数对得到的所述耦合系数和所述耦合矩阵递归计算，直至稳定状态下得到耦合系数矩阵；A likelihood function in the form of a quadratic negative logarithm of the coupling function model is established according to the Bayesian theorem, the stationary point coordinates of the likelihood function are calculated, the coupling coefficient and coupling matrix of the phase oscillator model between the cerebral blood oxygen phase signal of the first set frequency band and the electrocardiogram phase signal of the second set frequency band are obtained, and the coupling coefficient and the coupling matrix obtained are recursively calculated using the likelihood function in the form of a quadratic negative logarithm until a coupling coefficient matrix is obtained in a stable state;

根据所述耦合系数矩阵计算第一设定频段的所述脑血氧相位信号与第二设定频段的心电相位信号的耦合强度指标。The coupling strength index of the cerebral blood oxygen phase signal in the first set frequency band and the electrocardiogram phase signal in the second set frequency band is calculated according to the coupling coefficient matrix.

在本实施例中，似然函数的驻点坐标为似然函数达到稳定点时的取值。In this embodiment, the stationary point coordinates of the likelihood function are the values taken when the likelihood function reaches a stable point.

在一些实施例中，脑血氧相位信号和心电相位信号间的耦合函数模型为：In some embodiments, the coupling function model between the cerebral blood oxygen phase signal and the electrocardiogram phase signal is:

其中，i≠j，i,j＝{1,2}，φ_i表示脑血氧相位信号的相位振子模型，φ_j表示心电相位信号的相位振子模型，t为时变参数，ω(t)为固有频率参数，ξ(t)为高斯白噪声，q(φ_i,φ_j,t)为基函数。Wherein, i≠j, i,j＝{1,2},_φi represents the phase oscillator model of the cerebral blood oxygen phase signal,_φj represents the phase oscillator model of the electrocardiogram phase signal, t is a time-varying parameter, ω(t) is a natural frequency parameter, ξ(t) is Gaussian white noise, and q(_φi ,_φj , t) is a basis function.

基函数用傅里叶级数形式表示为：The basis function is expressed in Fourier series form as:

其中，φ_i表示脑血氧相位信号的相位振子模型，φ_j表示心电相位信号的相位振子模型。Among them, φ_i represents the phase oscillator model of the cerebral blood oxygen phase signal, and φ_j represents the phase oscillator model of the electrocardiogram phase signal.

基函数通过傅里叶级数形式表示后，耦合函数模型可解构为：After the basis function is expressed in Fourier series form, the coupling function model can be deconstructed as:

其中，Φ_i,k(φ_i,φ_j,t)为基函数，φ_i表示脑血氧相位信号的相位振子模型，φ_j表示心电相位信号的相位振子模型，i＝0时，

即固有频率；

和Φ_i,k为傅里叶分量成分，K为傅里叶级数的最高阶数。Wherein, Φ_i,k (φ_i ,φ_j ,t) is the basis function, φ_i represents the phase oscillator model of the cerebral blood oxygen phase signal, φ_j represents the phase oscillator model of the electrocardiogram phase signal, and when i=0,

That is, the natural frequency;

and Φ_i,k are the Fourier component components, and K is the highest order of the Fourier series.

似然函数表达式为：The likelihood function expression is:

其中，n＝1,2,...,N，N为相位信息序列点数，h为采样步长，E为噪声矩阵，c_k为耦合系数矩阵，Φ_k为傅里叶分量成分，φ为所求耦合关系的通道，φ_l中的l代表i或者j，φ_i表示脑血氧相位信号的相位振子模型，φ_j表所述心电相位信号的相位振子模型。Wherein, n=1,2,...,N, N is the number of phase information sequence points, h is the sampling step, E is the noise matrix, c_k is the coupling coefficient matrix, Φ_k is the Fourier component, φ is the channel of the desired coupling relationship, l in φ_l represents i or j, φ_i represents the phase oscillator model of the cerebral blood oxygen phase signal, and φ_j represents the phase oscillator model of the electrocardiogram phase signal.

耦合系数矩阵表达式为：The coupling coefficient matrix expression is:

c_k＝Ξ^-1r；c_k =Ξ^-1 r;

其中，Ξ为密度矩阵，r为中间矩阵变量。Among them, Ξ is the density matrix and r is the intermediate matrix variable.

根据耦合系数矩阵计算第一设定频段的脑血氧相位信号与第二设定频段的心电相位信号的耦合强度，计算式为：The coupling strength between the cerebral blood oxygen phase signal of the first set frequency band and the electrocardiogram phase signal of the second set frequency band is calculated according to the coupling coefficient matrix. The calculation formula is:

其中，CS_i,j表示第一设定频段的脑血氧相位信号与第二设定频段的心电相位信号的耦合强度，C_k^(i：j)表示脑血氧相位信号的相位振子φ_i与心电相位信号的相位振子φ_j间的耦合系数，K为傅里叶级数的最高阶数。Among them, CS_i,j represents the coupling strength between the cerebral blood oxygen phase signal of the first set frequency band and the electrocardiogram phase signal of the second set frequency band, C_k^(i:j) represents the coupling coefficient between the phase oscillator φ_i of the cerebral blood oxygen phase signal and the phase oscillator φ_j of the electrocardiogram phase signal, and K is the highest order of the Fourier series.

进一步的，噪声矩阵表达式为：Furthermore, the noise matrix expression is:

其中，n＝1,2,...,N，N为相位信息序列点数，h为采样步长，c_k为耦合系数矩阵，φ.为所求耦合关系的通道，φ_i表示脑血氧相位信号的相位振子模型，φ_j表示心电相位信号的相位振子模型。Wherein, n=1,2,...,N, N is the number of phase information sequence points, h is the sampling step, c_k is the coupling coefficient matrix, φ. is the channel of the desired coupling relationship, φ_i represents the phase oscillator model of the cerebral blood oxygen phase signal, and φ_j represents the phase oscillator model of the electrocardiogram phase signal.

密度矩阵表达式为：The density matrix expression is:

其中，h为采样步长，E为噪声矩阵。Among them, h is the sampling step size and E is the noise matrix.

中间矩阵变量表达式为：The intermediate matrix variable expression is:

其中，Ξ为密度矩阵，E为噪声矩阵，φ_i表示脑血氧相位信号的相位振子模型，φ_j表示心电相位信号的相位振子模型，h为采样步长。Wherein, Ξ is the density matrix, E is the noise matrix, φ_i represents the phase oscillator model of the cerebral blood oxygen phase signal, φ_j represents the phase oscillator model of the electrocardiogram phase signal, and h is the sampling step size.

在一些实施例中，第一设定频段为0.02～0.07Hz频段；第二设定频段为0.6～2Hz频段。In some embodiments, the first set frequency band is a frequency band of 0.02-0.07 Hz; the second set frequency band is a frequency band of 0.6-2 Hz.

在本实施例中，第一设定频段的自发振荡主要与自发皮层神经活动引起的血流动力学波动有关。第一设定频段为分析脑血氧相位信号的常用频段。第二设定频段的自发振荡与心脏活动有关，第二设定频段为分析心电相位信号的常用频段。In this embodiment, the spontaneous oscillation of the first set frequency band is mainly related to the hemodynamic fluctuation caused by spontaneous cortical nerve activity. The first set frequency band is a common frequency band for analyzing cerebral blood oxygen phase signals. The spontaneous oscillation of the second set frequency band is related to cardiac activity, and the second set frequency band is a common frequency band for analyzing electrocardiogram phase signals.

在步骤S104中，心率变异性指标指逐次心跳周期差异性变化情况，是反映交感和副交感神经张力及其平衡，以及神经、体液对心率的调节情况的重要参考指标。训练者在认知训练过程中由于认知负荷的不同心跳频率也会不同，若心率变异性指标数值大于正常值，则认知训练负荷较高；若心率变异性指标数值小于正常值，则认知训练负荷较低。In step S104, the heart rate variability index refers to the difference in the heartbeat cycle, which is an important reference index reflecting the tension and balance of the sympathetic and parasympathetic nerves, as well as the regulation of the heart rate by nerves and body fluids. During the cognitive training process, the heart rate of the trainee will be different due to different cognitive loads. If the heart rate variability index value is greater than the normal value, the cognitive training load is high; if the heart rate variability index value is less than the normal value, the cognitive training load is low.

在一些实施例中，第三设定频段为0.04～0.15Hz频段。In some embodiments, the third set frequency band is a frequency band of 0.04-0.15 Hz.

在本实施例中，第三设定频段的自发振荡反映交感和迷走神经的双重调节，第三设定频段为分析心率变异性的常用频段。In this embodiment, the spontaneous oscillation in the third set frequency band reflects the dual regulation of the sympathetic and vagus nerves, and the third set frequency band is a commonly used frequency band for analyzing heart rate variability.

在步骤S105中，认知负荷指数计算公式为：In step S105, the cognitive load index calculation formula is:

CI＝c1*TOI+c2*HRV+c3*CS；CI = c1*TOI+c2*HRV+c3*CS;

其中，c1、c2、c3为权重系数，CI为认知负荷指数，TOI为脑氧饱和度指标，HRV为心率变异性指标，CS为心脑耦合强度指标。Among them, c1, c2, and c3 are weight coefficients, CI is the cognitive load index, TOI is the brain oxygen saturation index, HRV is the heart rate variability index, and CS is the heart-brain coupling intensity index.

另一方面，如图2所示，本发明提供了一种认知负荷评估系统，所述系统包括：On the other hand, as shown in FIG2 , the present invention provides a cognitive load assessment system, the system comprising:

信息采集模块100，包括近红外光强信号采集模块110和心电信号采集模块120，近红外光强信号采集模块110采集用户大脑前额叶区域的近红外光强信号，心电信号采集模块120采集所述用户的心电信号。信息采集模块100根据所需采集的信号种类分别设置于用户身体的各部位；Theinformation collection module 100 includes a near-infrared light intensity signal collection module 110 and an electrocardiogramsignal collection module 120. The near-infrared light intensity signal collection module 110 collects near-infrared light intensity signals from the frontal lobe region of the user's brain, and the electrocardiogramsignal collection module 120 collects the electrocardiogram signals of the user. Theinformation collection modules 100 are respectively arranged at various parts of the user's body according to the types of signals to be collected;

信息处理分析模块200，连接信息采集模块100，信息处理模块根据第一设定算法将近红外光强信号转换为氧合血红蛋白浓度信号和脱氧血红蛋白浓度信号，并根据氧合血红蛋白浓度信号和脱氧血红蛋白浓度信号计算脑氧饱和度指标；The information processing and analysis module 200 is connected to theinformation acquisition module 100, and the information processing module converts the near-infrared light intensity signal into an oxygenated hemoglobin concentration signal and a deoxygenated hemoglobin concentration signal according to a first set algorithm, and calculates the brain oxygen saturation index according to the oxygenated hemoglobin concentration signal and the deoxygenated hemoglobin concentration signal;

使用复数域的莫莱小波对氧合血红蛋白浓度信号进行连续小波变换，以提取其在第一设定频段的脑血氧相位信号；使用复数域的莫莱小波对心电信号进行连续小波变换，以提取其在第二设定频段的心电相位信号；根据第二设定算法计算脑血氧相位信号和心电相位信号之间的心脑耦合强度指标；Performing continuous wavelet transform on the oxygenated hemoglobin concentration signal using Morlet wavelet in the complex domain to extract its cerebral blood oxygen phase signal in the first set frequency band; performing continuous wavelet transform on the electrocardiogram signal using Morlet wavelet in the complex domain to extract its electrocardiogram phase signal in the second set frequency band; calculating the heart-brain coupling strength index between the cerebral blood oxygen phase signal and the electrocardiogram phase signal according to the second set algorithm;

通过频域分析提取心电信号在第三设定频段的心率变异性指标，将得到的心脑耦合强度指标；Extract the heart rate variability index of the ECG signal in the third set frequency band through frequency domain analysis, and obtain the heart-brain coupling strength index;

指标融合模块300，连接信息处理分析模块200，指标融合模块300对信息处理分析模块200得到的脑氧饱和度指标、心脑耦合强度指标和心率变异性指标进行加权求和得到认知负荷指数；Theindex fusion module 300 is connected to the information processing and analysis module 200, and theindex fusion module 300 performs weighted summation on the brain oxygen saturation index, the heart-brain coupling strength index and the heart rate variability index obtained by the information processing and analysis module 200 to obtain the cognitive load index;

认知负荷评级模块400，连接指标融合模块300，认知负荷评级模块400根据用户认知能力对指标融合模块300传递来的认知负荷指数进行认知负荷评级，同时根据评级结果对用户认知训练过程中的参数进行实时调整；The cognitiveload rating module 400 is connected to theindex fusion module 300. The cognitiveload rating module 400 rates the cognitive load index transmitted by theindex fusion module 300 according to the user's cognitive ability, and adjusts the parameters of the user's cognitive training process in real time according to the rating result.

交互模块500，连接认知负荷评级模块400，交互模块500用于显示并存储认知负荷评级结果、用户认知训练记录、各阶段测评结果以及评估用户认知负荷等级时使用的参数。Theinteraction module 500 is connected to the cognitiveload rating module 400 and is used to display and store cognitive load rating results, user cognitive training records, evaluation results of each stage, and parameters used in evaluating the user's cognitive load level.

在本实施例中，近红外光强信号采集模块110采用功能近红外光谱仪采集近红外光强信号，心电信号采集模块120采用心电仪采集训练者的心电信号。交互模块500设有显示器510和存储器520，显示器510用于显示训练者的认知训练参数，同时便于训练者在认知训练过程中修改训练参数以调整认知训练强度；存储器520用于存储训练者的过往认知训练结果。In this embodiment, the near-infrared light intensity signal acquisition module 110 uses a functional near-infrared spectrometer to acquire near-infrared light intensity signals, and the electrocardiogramsignal acquisition module 120 uses an electrocardiogram to acquire the electrocardiogram signals of the trainee. Theinteractive module 500 is provided with adisplay 510 and amemory 520. Thedisplay 510 is used to display the trainee's cognitive training parameters, and at the same time facilitates the trainee to modify the training parameters during the cognitive training process to adjust the cognitive training intensity; thememory 520 is used to store the trainee's past cognitive training results.

进一步的，信息处理分析模块200对收集到的近红外光强信号以及心电信号进行滤波以去除噪声，除去噪声信号提高了脑血氧信号和心电信号分析时的准确性。Furthermore, the information processing and analysis module 200 filters the collected near-infrared light intensity signal and electrocardiogram signal to remove noise. Removing the noise signal improves the accuracy of analyzing the cerebral blood oxygen signal and the electrocardiogram signal.

进一步的，交互模块500的显示器510上还显示训练者认知训练过程中的血压变化情况。Furthermore, thedisplay 510 of theinteractive module 500 also displays the changes in the trainee's blood pressure during the cognitive training process.

在一些实施例中，认知负荷评估系统包括控制模块，控制模块根据设定好的参数控制训练者的认知训练强度以及认知训练时长。In some embodiments, the cognitive load assessment system includes a control module, which controls the cognitive training intensity and duration of the trainee according to set parameters.

在另一些实施例中，认知负荷评估系统还包括训练模块，训练模块能够提供注意力训练、记忆训练、定向力训练等认知功能训练，同时训练模块的认知训练内容分为1、2、3等多个认知负荷等级，可供训练者选择。In other embodiments, the cognitive load assessment system also includes a training module, which can provide cognitive function training such as attention training, memory training, and orientation training. At the same time, the cognitive training content of the training module is divided into multiple cognitive load levels such as 1, 2, and 3 for trainees to choose.

下面结合具体实施例对本发明进行说明：The present invention will be described below in conjunction with specific embodiments:

一种认知负荷评估方法，包括以下步骤：A cognitive load assessment method comprises the following steps:

1)参与者接受认知训练，佩戴近红外设备以及心电测量设备，其中近红外设备的光源探头模板覆盖双侧额叶区域；1) Participants received cognitive training and wore near-infrared devices and electrocardiogram measurement devices, where the light source probe template of the near-infrared device covered the bilateral frontal lobe areas;

2)步骤1)执行之后，按照预先设定的认知训练参数进行认知训练；2) After step 1) is executed, cognitive training is performed according to pre-set cognitive training parameters;

3)实时处理分析反馈脑氧饱和度(TOI)、心脑耦合强度(CS)以及心率变异性(HRV)并通过显示设备实时呈现结果；其中，TOI表征脑血氧的饱和度指标参数，CS表征心电-脑氧效应连接强度指标参数，HRV表征心率变异性指标参数；3) Real-time processing and analysis of feedback of brain oxygen saturation (TOI), heart-brain coupling strength (CS) and heart rate variability (HRV) and presenting the results in real time through a display device; among them, TOI represents the saturation index parameter of brain blood oxygen, CS represents the index parameter of ECG-brain oxygen effect connection strength, and HRV represents the index parameter of heart rate variability;

4)指标融合模块400将接收来的脑氧饱和度、心脑耦合强度以及心率变异性这三个指标进行指标融合，提取出认知负荷指数；4) Theindex fusion module 400 fuses the three received indexes of brain oxygen saturation, heart-brain coupling intensity and heart rate variability to extract the cognitive load index;

5)认知负荷评级模块300通过判断认知负荷指数是否超出正常范围，对认知功能训练参数进行实时的自适应调整，并在显示设备上反馈给受试者。5) The cognitiveload rating module 300 makes real-time adaptive adjustments to cognitive function training parameters by determining whether the cognitive load index exceeds a normal range, and feeds back the information to the subject on a display device.

6)一次认知训练结束时，显示设备显示当次训练的参数，包括但不限于脑氧饱和度、心脑耦合参数、心率变异性以及认知训练结果；6) At the end of a cognitive training session, the display device displays the parameters of the training session, including but not limited to brain oxygen saturation, heart-brain coupling parameters, heart rate variability, and cognitive training results;

7)多次认知负荷评估按照步骤1)～6)进行，训练结束时显示设备显示多次训练参数，包括但不限于血氧饱和度、心脑耦合参数、心率变异性以及认知负荷评估结果，对比评价康复训练效果。7) Multiple cognitive load assessments are performed according to steps 1) to 6). At the end of the training, the display device displays multiple training parameters, including but not limited to blood oxygen saturation, heart-brain coupling parameters, heart rate variability, and cognitive load assessment results, to compare and evaluate the rehabilitation training effect.

一种知负荷评估系统包括：信息采集模块100、信息处理分析模块200、指标融合模块300、认知负荷评级模块400、交互模块500。A cognitive load assessment system includes: aninformation collection module 100, an information processing and analysis module 200, anindicator fusion module 300, a cognitiveload rating module 400, and aninteraction module 500.

信息采集模块100用于同步采集认知功能训练时近红外光信号以及心电信号。近红外光强信号采集模块110，用于采集受试者的前额叶脑区的脑血氧信号；心电信号采集模块120，用于采集受试者的心电信号。在本文中，在认知任务正式开始时，在近红外光强信号采集模块110以及心电信号采集模块120所采集的时域信号中同步标定刺激开始时刻，以保证在近红外信号以及心电信号在认知训练开始时，即时采集到血氧以及心电信号的变化，这样可以方便用于比较认知训练时的认知负荷。Theinformation acquisition module 100 is used to synchronously acquire near-infrared light signals and electrocardiogram signals during cognitive function training. The near-infrared light intensity signal acquisition module 110 is used to acquire the cerebral blood oxygen signal of the prefrontal lobe brain area of the subject; the electrocardiogramsignal acquisition module 120 is used to acquire the electrocardiogram signal of the subject. In this article, when the cognitive task officially begins, the stimulation start time is synchronously calibrated in the time domain signals acquired by the near-infrared light intensity signal acquisition module 110 and the electrocardiogramsignal acquisition module 120 to ensure that the changes in blood oxygen and electrocardiogram signals are immediately acquired when the cognitive training begins. This can be conveniently used to compare the cognitive load during cognitive training.

近红外光谱仪的近红外探头布置：采用国际脑电图10-10系统辅助定位法，标准探头间距为30mm，近近红外信号覆盖大脑前额叶区域的近红外光源探头模板。Layout of near-infrared probes of near-infrared spectrometer: Adopting the international EEG 10-10 system assisted positioning method, the standard probe spacing is 30 mm, and the near-infrared signal covers the near-infrared light source probe template of the frontal lobe area of the brain.

信息处理分析模块200用于处理和分析从信息采集模块100同步采集到的脑血氧信号以及心电信号，并将处理和分析后所得的信号数据传输至指标融合模块300。The information processing and analysis module 200 is used to process and analyze the cerebral blood oxygen signal and the electrocardiogram signal synchronously collected from theinformation collection module 100, and transmit the signal data obtained after processing and analysis to theindicator fusion module 300.

对近红外光谱信号施加的处理包括：The processing applied to the NIR spectral signal includes:

使用比尔朗伯特定律，将光强度信号转换为氧合血红蛋白浓度信号以及脱氧血红蛋白浓度信号，提取脑氧饱和度指标。脑氧饱和度指标为氧合血红蛋白浓度信号与总血红蛋白浓度(氧合血红蛋白浓度与脱氧血红蛋白浓度之和)的比值。然后使用复数域的莫莱小波对每个通道的氧合血红蛋白浓度信号进行连续小波变换，提取出氧合血红蛋白浓度在0.02～0.07Hz频段的相位信息。The light intensity signal is converted into oxygenated hemoglobin concentration signal and deoxygenated hemoglobin concentration signal using the Beer-Lambert law to extract the brain oxygen saturation index. The brain oxygen saturation index is the ratio of the oxygenated hemoglobin concentration signal to the total hemoglobin concentration (the sum of the oxygenated hemoglobin concentration and the deoxygenated hemoglobin concentration). Then, the complex domain Morley wavelet is used to perform a continuous wavelet transform on the oxygenated hemoglobin concentration signal of each channel to extract the phase information of the oxygenated hemoglobin concentration in the 0.02-0.07 Hz frequency band.

使用复数域的莫莱小波对心电信号进行连续小波变化，提取出心电信号在0.6～2Hz频段的相位信息，并同步计算心电和脑血氧相位信号之间的心脑耦合强度。The complex domain Morlet wavelet is used to perform continuous wavelet transformation on the ECG signal, extract the phase information of the ECG signal in the frequency band of 0.6-2 Hz, and synchronously calculate the heart-brain coupling strength between the ECG and cerebral blood oxygen phase signals.

使用频域分析，提取心电信号在0.04～0.15Hz频段的心率变异性。Frequency domain analysis was used to extract the heart rate variability of the ECG signal in the frequency range of 0.04 to 0.15 Hz.

认知负荷评级模块400来判断认知训练过程中认知负荷是否超出正常范围，并将结果实时显示到显示其中。The cognitiveload rating module 400 determines whether the cognitive load exceeds the normal range during cognitive training and displays the result in real time.

显示器510用于呈现认知训练的模式、难度等信息，并实时呈现脑氧饱和度、心脑耦合状态以及心率变异性的结果。Thedisplay 510 is used to present information such as the mode and difficulty of cognitive training, and to present the results of brain oxygen saturation, heart-brain coupling status, and heart rate variability in real time.

认知训练系统还可以包括存储模块，用于存储多次训练的参数，包括但不限于所述心脑耦合强度CS、脑氧饱和度TOI、心率变异性HRV等，便于评价长周期认知训练的效果。The cognitive training system may also include a storage module for storing parameters of multiple trainings, including but not limited to the heart-brain coupling intensity CS, brain oxygen saturation TOI, heart rate variability HRV, etc., to facilitate the evaluation of the effect of long-term cognitive training.

综上所述，本发明所述认知负荷评估方法及系统，将认知训练与脑血氧信号以及心电信号相结合，通过系统设定算法将脑血氧信号以及心电信号转化为认知负荷指数，得到的认知负荷指数能够直观的反映训练者认知训练过程中的认知负荷高低。训练者根据认知负荷评估结果调整认知训练强度，确保了训练过程中的认知负荷强度不会过高或过低，提高了训练者的认知训练效率。In summary, the cognitive load assessment method and system of the present invention combines cognitive training with brain blood oxygen signals and electrocardiogram signals, and converts brain blood oxygen signals and electrocardiogram signals into cognitive load indexes through system setting algorithms. The obtained cognitive load index can intuitively reflect the cognitive load level of the trainee during cognitive training. The trainee adjusts the intensity of cognitive training according to the cognitive load assessment results, ensuring that the cognitive load intensity during the training process is not too high or too low, thereby improving the trainee's cognitive training efficiency.

进一步的，通过对训练者的心电信号和脑电信号进行监测，增加了对训练者认知负荷评估的准确性。Furthermore, by monitoring the trainees' electrocardiogram (ECG) and electroencephalogram (EEG) signals, the accuracy of the trainees' cognitive load assessment is increased.

进一步的，交互模块通过显示器将训练过程中的认知负荷评级结果反馈给训练者，使得训练者可以对训练强度进行自适应性调整。Furthermore, the interactive module feeds back the cognitive load rating results during the training process to the trainer through a display, so that the trainer can adaptively adjust the training intensity.

与上述方法相应地，本发明还提供了一种装置/系统，该装置/系统包括计算机设备，所述计算机设备包括处理器和存储器，所述存储器中存储有计算机指令，所述处理器用于执行所述存储器中存储的计算机指令，当所述计算机指令被处理器执行时该装置/系统实现如前所述方法的步骤。Corresponding to the above method, the present invention also provides an apparatus/system, which includes a computer device, the computer device includes a processor and a memory, the memory stores computer instructions, the processor is used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the apparatus/system implements the steps of the method described above.

本发明实施例还提供一种计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时以实现前述边缘计算服务器部署方法的步骤。该计算机可读存储介质可以是有形存储介质，诸如随机存储器(RAM)、内存、只读存储器(ROM)、电可编程ROM、电可擦除可编程ROM、寄存器、软盘、硬盘、可移动存储盘、CD-ROM、或技术领域内所公知的任意其它形式的存储介质。The embodiment of the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the aforementioned edge computing server deployment method are implemented. The computer-readable storage medium can be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field.

本领域普通技术人员应该可以明白，结合本文中所公开的实施方式描述的各示例性的组成部分、系统和方法，能够以硬件、软件或者二者的结合来实现。具体究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。当以硬件方式实现时，其可以例如是电子电路、专用集成电路(ASIC)、适当的固件、插件、功能卡等等。当以软件方式实现时，本发明的元素是被用于执行所需任务的程序或者代码段。程序或者代码段可以存储在机器可读介质中，或者通过载波中携带的数据信号在传输介质或者通信链路上传送。It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is specifically performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

需要明确的是，本发明并不局限于上文所描述并在图中示出的特定配置和处理。为了简明起见，这里省略了对已知方法的详细描述。在上述实施例中，描述和示出了若干具体的步骤作为示例。但是，本发明的方法过程并不限于所描述和示出的具体步骤，本领域的技术人员可以在领会本发明的精神后，作出各种改变、修改和添加，或者改变步骤之间的顺序。It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

本发明中，针对一个实施方式描述和/或例示的特征，可以在一个或更多个其它实施方式中以相同方式或以类似方式使用，和/或与其他实施方式的特征相结合或代替其他实施方式的特征。In the present invention, features described and/or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and/or combined with features of other embodiments or replace features of other embodiments.

以上所述仅为本发明的优选实施例，并不用于限制本发明，对于本领域的技术人员来说，本发明实施例可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims (8)

1. A method of cognitive load assessment, the method comprising the steps of:

acquiring a near infrared light intensity signal acquired for a forehead lobe area of a brain of a user and an electrocardiosignal of the user;

converting the near infrared light intensity signal into an oxyhemoglobin concentration signal and a deoxyhemoglobin concentration signal according to a first setting algorithm, and calculating a brain oxygen saturation index according to the oxyhemoglobin concentration signal and the deoxyhemoglobin concentration signal; the first setting algorithm is the Bill Law;

performing continuous wavelet transformation on the oxyhemoglobin concentration signal by using Mo Laixiao waves of a complex domain so as to extract a cerebral blood oxygen phase signal of the oxyhemoglobin concentration signal in a first set frequency band; using Mo Laixiao wave of complex domain to carry out continuous wavelet transformation on the electrocardiosignal so as to extract electrocardiosignal phase signals of the electrocardiosignal in a second set frequency band; calculating a heart-brain coupling strength index between the brain blood oxygen phase signal and the electrocardio phase signal according to a second setting algorithm; the calculating step of the second setting algorithm comprises the following steps:

selecting a cerebral blood oxygen phase signal of the first set frequency band and an electrocardio phase signal of the second set frequency band, and respectively constructing a coupling function model between the cerebral blood oxygen phase signal and the electrocardio phase signal by a random differential equation;

establishing a quadratic negative logarithmic form likelihood function of the coupling function model according to a Bayes theorem, calculating standing point coordinates of the likelihood function, obtaining a coupling coefficient and a coupling matrix of a phase oscillator model between a cerebral blood oxygen phase signal of the first set frequency band and an electrocardio phase signal of the second set frequency band, and recursively calculating the obtained coupling coefficient and the coupling matrix by adopting the quadratic negative logarithmic form likelihood function until a coupling coefficient matrix is obtained in a stable state;

calculating the coupling strength of the cerebral blood oxygen phase signal of the first set frequency band and the electrocardio phase signal of the second set frequency band according to the coupling coefficient matrix;

extracting heart rate variability indexes of the electrocardiosignals in a third set frequency band through frequency domain analysis; wherein the first set frequency band is a frequency band of 0.02-0.07 Hz; the second set frequency band is a frequency band of 0.6-2 Hz; the third set frequency band is a frequency band of 0.04-0.15 Hz;

carrying out weighted summation on the brain oxygen saturation index, the heart-brain coupling strength index and the heart rate variability index to obtain a cognitive load index so as to represent the cognitive load of a user through the cognitive load index; the cognitive load index calculation formula is as follows:

CI＝c1*TOI+c2*HRV+c3*CS；

wherein c1, c2 and c3 are weight coefficients, CI is the cognitive load index, TOI is the brain oxygen saturation index, HRV is the heart rate variability index, and CS is the heart brain coupling strength index.

2. The cognitive load assessment method according to claim 1, wherein the near infrared light intensity signals are acquired by a plurality of near infrared signal acquisition probes for a prefrontal region of the brain of the user, and a distance between the near infrared signal acquisition probes is 30mm.

3. The cognitive load assessment method according to claim 1, wherein the first set algorithm is a beer's law, which converts the near infrared light intensity signal into optical density data first, and then converts the optical density data into the oxyhemoglobin concentration signal and the deoxyhemoglobin concentration signal.

4. The cognitive load assessment method according to claim 1, wherein calculating a brain oxygen saturation indicator from the oxyhemoglobin concentration signal and the deoxyhemoglobin concentration signal comprises:

the brain oxygen saturation index calculation formula is as follows:

wherein c is the brain oxygen saturation index, a is the oxyhemoglobin concentration signal, and b is the deoxyhemoglobin concentration signal.

5. The cognitive load assessment method according to claim 1, wherein a coupling function model between the cerebral blood oxygen phase signal and the electrocardiographic phase signal is:

where i+.j, i, j= {1,2}, φ_i A phase oscillator model phi representing the cerebral blood oxygen phase signal_j A phase oscillator model representing the electrocardio phase signal, t being a time-varying parameter, ω (t) being a natural frequency parameter, ζ (t) being white gaussian noise, q (phi)_i ,φ_j T) is a basis function;

after the basis function is expressed in a Fourier series form, the coupling function model is deconstructed into:

wherein phi is_i A phase oscillator model phi representing the cerebral blood oxygen phase signal_j A phase oscillator model representing the electrocardiographic phase signal, i=0,

i.e. natural frequency>

And phi_i,k K is the highest order of the Fourier series as the Fourier component;

the likelihood function expression is:

where n=1, 2,..n, N is the phase signalThe number of points in the information sequence, h is the sampling step length, E is the noise matrix, c_k For coupling coefficient matrix phi_k As a component of the Fourier components, phi_· For the channel of the required coupling relation phi_l In (1) represents i or j, phi_i A phase oscillator model phi representing the cerebral blood oxygen phase signal_j A phase vibrator model representing the electrocardiographic phase signal;

the coupling coefficient matrix expression is:

c_k ＝Ξ^-1 r；

wherein, xi is density matrix, r is intermediate matrix variable, E is noise matrix, phi_i Phase oscillator model phi representing cerebral blood oxygen phase signals_j A phase oscillator model for representing an electrocardio phase signal, wherein h is a sampling step length;

according to the coupling coefficient matrix, the coupling strength of the cerebral blood oxygen phase signal of the first set frequency band and the electrocardio phase signal of the second set frequency band is calculated, and the calculation formula is as follows:

wherein CS is_i,j Representation ofCoupling strength of the cerebral blood oxygen phase signal of the first set frequency band and the electrocardio phase signal of the second set frequency band, C_k^(i：j) Phase vibrator phi representing cerebral blood oxygen phase signal_i Phase vibrator phi of electrocardio phase signal_j The coupling coefficient between the two is K, which is the highest order of the Fourier series.

6. A cognitive load assessment system, the system comprising:

the information acquisition module comprises a near-infrared cerebral blood oxygen acquisition module and an electrocardio acquisition module, wherein the infrared cerebral blood oxygen is used for acquiring near-infrared light intensity signals of a forehead lobe area of a user's brain, and the electrocardio acquisition module is used for acquiring electrocardio signals of the user;

the information processing and analyzing module is connected with the information acquisition module, converts the near infrared light intensity signal into an oxygenated hemoglobin concentration signal and a deoxygenated hemoglobin concentration signal according to a first setting algorithm, and calculates a brain oxygen saturation index according to the oxygenated hemoglobin concentration signal and the deoxygenated hemoglobin concentration signal; performing continuous wavelet transformation on the oxyhemoglobin concentration signal by using Mo Laixiao waves of a complex domain so as to extract a cerebral blood oxygen phase signal of the oxyhemoglobin concentration signal in a first set frequency band; using Mo Laixiao wave of complex domain to carry out continuous wavelet transformation on the electrocardiosignal so as to extract electrocardiosignal phase signals of the electrocardiosignal in a second set frequency band; calculating a heart-brain coupling strength index between the brain blood oxygen phase signal and the electrocardio phase signal according to a second setting algorithm; extracting heart rate variability indexes of the electrocardiosignals in a third set frequency band through frequency domain analysis;

the index fusion module is connected with the information processing and analyzing module, and performs weighted summation on the brain oxygen saturation index, the heart-brain coupling strength index and the heart rate variability index which are obtained by the information processing and analyzing module to obtain a cognitive load index; the cognitive load index calculation formula is as follows:

CI＝c1*TOI+c2*HRV+c3*CS；

wherein c1, c2 and c3 are weight coefficients, CI is the cognitive load index, TOI is the brain oxygen saturation index, HRV is the heart rate variability index, and CS is the heart-brain coupling strength index;

the cognitive load rating module is connected with the index fusion module, and is used for rating the cognitive load of the cognitive load index transmitted by the index fusion module according to the cognitive ability of the user and simultaneously adjusting parameters in the cognitive training process of the user in real time according to the rating result;

and the interaction module is connected with the cognitive load rating module and is used for displaying and storing the cognitive load rating result.

7. The cognitive load assessment system of claim 6, wherein the near infrared light intensity signals are acquired by a plurality of near infrared signal acquisition probes for a prefrontal region of the user's brain, the distance between the near infrared signal acquisition probes being 30mm.

8. The cognitive load assessment system of claim 6, wherein the first set algorithm is a beer's law that converts the near infrared light intensity signal to optical density data prior to converting the optical density data to the oxygenated and deoxygenated hemoglobin concentration signals.

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