Method and device for assessing the separation of implicit cognitive ability and explicit behavioral response

Abstract

The present application discloses a method and device for evaluating the separation of implicit cognitive ability and explicit behavioral response, including: obtaining target basic data of a target subject; determining m movement follow-up tasks according to a first preset brain region, a second preset brain region and the target basic data; guiding the target subject to perform m movement follow-up tasks to obtain m real movement data; obtaining m first EEG data and m second EEG data of the target subject; obtaining m theoretical movement data corresponding to the m movement follow-up tasks; determining m movement difference degree values ​​between the m real movement data and the m theoretical movement data; determining the separation degree of the target subject's implicit cognitive ability and explicit behavioral response according to the m movement difference degree values, the m first EEG data and the m second EEG data. By adopting the embodiments of the present application, the separation between the level of implicit cognitive ability and the explicit behavioral response is detected at a low cost.

Description

Technical Field

[0001] The present application relates to the field of medical technology, and in particular to a method and device for evaluating the separation of implicit cognitive ability and explicit behavioral response. Background Art

[0002] Currently, when detecting the separation between implicit cognitive ability levels and explicit behavioral responses, different neuroimaging data (e.g., structural magnetic resonance imaging, functional magnetic resonance imaging, etc.) are usually integrated and analyzed with behavioral data. This can provide a more comprehensive understanding of the relationship and degree of separation between implicit cognitive abilities and explicit behavioral responses. However, this detection method requires professional equipment and technicians to operate, resulting in a high cost for each test.

[0003] Therefore, how to detect the separation between implicit cognitive ability levels and explicit behavioral responses at low cost has become an urgent problem to be solved. Summary of the invention

[0004] The embodiments of the present application provide a method and device for evaluating the separation between implicit cognitive ability and explicit behavioral response. By collecting the EEG data and movement data of the target subject, the separation between the target subject's implicit cognitive ability level and the explicit behavioral response is evaluated based on these data without the need for expensive equipment and a complex operating environment, which greatly reduces the analysis cost, thereby achieving low-cost detection of the separation between the implicit cognitive ability level and the explicit behavioral response.

[0005] In a first aspect, an embodiment of the present application provides a method for evaluating the separation of implicit cognitive ability and explicit behavioral response, the method comprising:

[0006] Obtain target basic data of target subjects;

[0007] Determining m movement follow-up tasks according to the first preset brain region, the second preset brain region and the target basic data; the m movement follow-up tasks are experimental tasks for studying the relationship between implicit cognitive ability and explicit behavioral response; m is an integer greater than 1;

[0008] guiding the target subject to perform the m motion follow-up tasks to obtain m real motion data; each real motion data corresponds to one motion follow-up task;

[0009] Acquire the EEG data generated by the first preset brain area of ​​the target subject in the m movement follow-up tasks to obtain m first EEG data; each first EEG data corresponds to one movement follow-up task;

[0010] Acquire the electroencephalogram data generated by the second preset brain area of ​​the target subject in the m movement follow-up tasks to obtain m second electroencephalogram data; each second electroencephalogram data corresponds to one movement follow-up task;

[0011] Acquire theoretical motion data corresponding to each of the m motion follow-up tasks to obtain m theoretical motion data;

[0012] Determine the motion difference degree values ​​between the m real motion data and the corresponding theoretical motion data among the m theoretical motion data, to obtain m motion difference degree values;

[0013] The degree of separation between the implicit cognitive ability and the explicit behavioral response of the target subject is determined according to the m movement difference degree values, the m first EEG data, and the m second EEG data.

[0014] In a second aspect, an embodiment of the present application provides a device for evaluating the separation of implicit cognitive ability and explicit behavioral response, the device comprising: an acquisition unit, a control unit, and a separation degree detection unit; wherein:

[0015] The acquisition unit is used to acquire target basic data of the target subject;

[0016] The control unit is used to determine m movement follow-up tasks according to the first preset brain area, the second preset brain area and the target basic data; the m movement follow-up tasks are experimental tasks for studying the relationship between implicit cognitive ability and explicit behavioral response; m is an integer greater than 1; guide the target subject to perform the m movement follow-up tasks to obtain m real movement data; each real movement data corresponds to one movement follow-up task;

[0017] The acquisition unit is further used to acquire the EEG data generated by the first preset brain area of ​​the target subject in the m movement follow-up tasks to obtain m first EEG data; each first EEG data corresponds to a movement follow-up task; acquire the EEG data generated by the second preset brain area of ​​the target subject in the m movement follow-up tasks to obtain m second EEG data; each second EEG data corresponds to a movement follow-up task; acquire theoretical movement data corresponding to each movement follow-up task in the m movement follow-up tasks to obtain m theoretical movement data;

[0018] The separation degree detection unit is used to determine the motion difference degree values ​​between the m real motion data and the corresponding theoretical motion data in the m theoretical motion data, and obtain m motion difference degree values; and determine the separation degree between the implicit cognitive ability and the explicit behavioral response of the target subject according to the m motion difference degree values, the m first EEG data, and the m second EEG data.

[0019] In a third aspect, the present application provides an electronic device comprising: a processor and a memory, wherein the memory is used to store one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the present application.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the present application.

[0021] In a fifth aspect, the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the present application. The computer program product may be a software installation package.

[0022] The implementation of this application has the following beneficial effects:

[0023] It can be seen that the method for evaluating the separation of implicit cognitive ability and explicit behavioral response described in the embodiment of the present application includes: obtaining target basic data of the target subject; determining m movement follow-up tasks according to the first preset brain area, the second preset brain area and the target basic data; guiding the target subject to perform m movement follow-up tasks to obtain m real movement data; obtaining the EEG data generated by the first preset brain area of ​​the target subject in the m movement follow-up tasks to obtain m first EEG data; obtaining the EEG data generated by the second preset brain area of ​​the target subject in the m movement follow-up tasks to obtain m second EEG data; obtaining m theoretical movement data corresponding to each movement follow-up task in the m movement follow-up tasks; determining the movement difference degree value between the m real movement data and the corresponding theoretical movement data in the m theoretical movement data to obtain m movement difference degree values; obtaining the movement difference degree value according to the m movement The difference degree value, m first EEG data, and m second EEG data determine the degree of separation between the target subject's implicit cognitive ability and explicit behavioral response. In this way, by collecting the target subject's EEG data and movement data, the EEG data can reflect the brain's neural activity, that is, the implicit cognitive ability, and the movement data can reflect the subject's explicit behavioral response. By comprehensively analyzing these two types of data, the target subject's cognitive and behavioral state can be fully understood. Compared with traditional neuroimaging methods (such as functional magnetic resonance imaging), the present application scheme does not require expensive equipment and a complex operating environment, but only basic EEG acquisition equipment and movement monitoring equipment to evaluate the separation of the target subject's implicit cognitive ability and explicit behavioral response, which greatly reduces the evaluation cost, thereby achieving low-cost detection of the separation between the implicit cognitive ability level and the explicit behavioral response. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0025] Figure 1 is a flow chart of a method for evaluating the separation of implicit cognitive ability and explicit behavioral response provided in an embodiment of the present application;

[0026] Figure 2 It is an implementation scenario diagram of a method for evaluating the separation of implicit cognitive ability and explicit behavioral response provided in an embodiment of the present application;

[0027] Figure 3 is a schematic diagram of the structure of a device for evaluating the separation of implicit cognitive ability and explicit behavioral response provided in an embodiment of the present application;

[0028] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0031] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0032] The electronic devices described in the embodiments of the present application may include smart phones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, laptops, video matrices, monitoring platforms, mobile Internet devices (mobile internet devices, MID) or wearable devices, etc. The above are only examples and not exhaustive, including but not limited to the above devices. Of course, the above electronic devices can also be servers, for example, cloud servers.

[0033] The following is an explanation of some professional terms involved in this application:

[0034] Implicit cognitive abilities: refers to those potential cognitive processes and abilities that are difficult to directly observe. These abilities usually work at the subconscious level and are not obviously recognized by the individual, but they affect the individual's thinking, decision-making and behavior. For example, subconscious perception, memory, intuition, etc. all fall into the category of implicit cognitive abilities. In some cases, individuals may process information in the environment without realizing it and make decisions or actions based on these implicit cognitions.

[0035] Overt behavioral responses: refers to the behavioral manifestations that can be directly observed by an individual. These behaviors are the individual's response to external stimuli, are usually conscious, controllable, and can be evaluated through observation and measurement. For example, an individual's verbal expressions, body movements, facial expressions, etc. all fall into the category of overt behavioral responses.

[0036] The degree of separation between implicit cognitive ability and explicit behavioral response: refers to the difference or inconsistency between implicit cognitive ability and explicit behavioral response. This degree of separation can be assessed by comparing an individual's implicit cognitive ability and explicit behavioral response. For example, if there is a large difference between an individual's implicit cognitive ability and explicit behavioral response, it means that the degree of separation between the two is high; if the two are relatively consistent, it means that the degree of separation is low.

[0037] Motor follow-up task: It is an experimental task used to study the relationship between implicit cognitive ability and explicit behavioral responses. Specifically, the subjects need to respond according to the signals or sounds displayed on the display screen, or make motor responses according to specific instructions. For example, graphics of different colors or flashing indicator lights may appear on the display screen, and the subjects need to quickly make corresponding actions based on these signals, such as pressing specific buttons, moving fingers or parts of the body, etc., or through sound prompts, requiring the subjects to make specific motor responses after hearing specific sounds. This task aims to observe the subjects' explicit behavioral responses after receiving external stimuli, and at the same time infer the subjects' implicit cognitive abilities by detecting the strength of connections between brain regions, and further analyze the relationship between implicit cognitive abilities and explicit behavioral responses.

[0038] Connection strength of brain regions: refers to the degree of functional association between different brain regions; in EEG data research, the connection strength can be determined by measuring the synchronization of neural electrical activity or magnetic field activity in different brain regions. For example, if the neural electrical activity of two brain regions is highly synchronized at a specific frequency, it means that the connection strength between them is strong. Common indicators of the connection strength of brain regions include coherence, phase synchronization, etc. Coherence reflects the degree of linear correlation between two signals at different frequencies, and phase synchronization measures the stability of the phase difference between two signals at a specific frequency. When studying the relationship between implicit cognitive ability and explicit behavioral responses, the connection strength of brain regions can be used as an important indicator. If the connection strength between certain brain regions is correlated with specific behavioral responses, it can be inferred that these brain regions play a role in the implicit cognitive process.

[0039] See also Figure 1 , Figure 1 : is a flow chart of a method for evaluating the separation of implicit cognitive ability and explicit behavioral response provided in an embodiment of the present application, the method includes but is not limited to the following steps:

[0040] S101. Obtain target basic data of target subjects.

[0041] In the embodiment of the present application, the target basic data may include at least one of the following: age, educational background data, physical condition data, mental health data, etc., which are not limited here.

[0042] In a specific embodiment, the target basic data may be physical condition data. Specifically, a physical examination may be performed on the target subject, including measuring basic physiological indicators such as height, weight, blood pressure, heart rate, and performing examinations such as vision and hearing, thereby obtaining the target basic data. For example, by measuring blood pressure, it is possible to understand whether the subject suffers from diseases such as hypertension; a vision examination may be performed to understand whether the subject's visual function is normal, which may affect his or her performance in certain cognitive tasks. Alternatively, an application may be made to the target hospital for access to relevant information of the target subject. After obtaining authorization from the target hospital, the target basic data may be obtained from the database of the target hospital, wherein the target hospital is the hospital where the target subject is treated.

[0043] S102. Determine m motor follow-up tasks according to the first preset brain region, the second preset brain region and the target basic data; the m motor follow-up tasks are experimental tasks for studying the relationship between implicit cognitive ability and explicit behavioral response; m is an integer greater than 1.

[0044] In the embodiment of the present application, the first preset brain region and the second preset brain region can be preset or defaulted in advance, and the first preset brain region and the second preset brain region represent brain regions that need to be studied.

[0045] In a specific embodiment, the motion follow-up task can be designed according to the first preset brain area, the second preset brain area and the target basic data, thereby obtaining m motion follow-up tasks.

[0046] Optionally, step S102, determining m movement follow-up tasks according to the first preset brain region, the second preset brain region and the target basic data, may include the following steps:

[0047] S21, determining a first human function parameter corresponding to the first preset brain area;

[0048] S22, determining a second human body function parameter corresponding to the second preset brain area;

[0049] S23, determining the target disease corresponding to the target basic data;

[0050] S24, selecting a motion follow-up task corresponding to the target disease from a preset motion follow-up task library to obtain a plurality of reference motion follow-up tasks;

[0051] S25. Determine the motion follow-up tasks corresponding to the first human body function parameter and the second human body function parameter in the multiple reference motion follow-up tasks to obtain the m motion follow-up tasks.

[0052] In an embodiment of the present application, a preset movement follow-up task library can be preset or defaulted in advance. The preset movement follow-up task library is a pre-set set of movement tasks that contains a variety of movement tasks for studying the relationship between implicit cognitive abilities and explicit behavioral responses; the functional parameters may include one of the following: motor function parameters, sensory function parameters, cognitive function parameters, etc., which are not limited here.

[0053] It needs to be explained that motor function parameters mainly involve the body's motor ability, including muscle strength, endurance, coordination, speed, flexibility, etc.; sensory function parameters cover the abilities of various human senses, including visual acuity, auditory discrimination, tactile sensitivity, smell and taste sensitivity, etc.; cognitive function parameters are related to the brain's cognitive activities, including memory, attention, thinking ability, decision-making ability, etc.

[0054] In a specific embodiment, a first human function parameter corresponding to a first preset brain area can be determined first. Specifically, since different brain areas correspond to different human function parameters, a mapping relationship between a preset brain area and a human function parameter can be pre-stored, and a target stimulation coil type corresponding to the first preset brain area can be determined based on the mapping relationship. Similarly, a second human function parameter corresponding to a second preset brain area can also be determined based on the mapping relationship. Then, a corresponding target disease can be determined based on the target basic data. Specifically, the target basic data can be physical condition data. Various indicator data in the target basic data (for example, blood sugar, Beck Depression Inventory score) can be obtained, and it can be determined whether these indicator data are within a normal range, thereby determining the target disease. For example, assuming that the target subject's Beck Depression Inventory score is 45 points and the full score is 63 points, 45 points is significantly higher than the normal range, then it can be preliminarily determined that the target disease is depression. Furthermore, a professional doctor can also diagnose the target subject, thereby obtaining the target disease.

[0055] Next, you can select the exercise follow-up tasks corresponding to the target disease from the preset exercise follow-up task library to obtain multiple reference exercise follow-up tasks. Specifically, you can first determine the target disease type of the target disease, and select multiple reference exercise follow-up tasks from the preset exercise follow-up task library according to the target disease type. This is because different types of diseases have different effects on motor function, so it is necessary to select exercise follow-up tasks in a targeted manner. For example, if the target disease type is a cardiovascular disease type, then the selected exercise follow-up tasks can focus on improving the patient's endurance and cardiopulmonary function, such as brisk walking, cycling, etc. For another example, if the target disease type is a nervous system disease type, then some exercises that can promote the large Tasks for brain neuroplasticity, such as fine motor training of the hands, cognitive games, brain teasers, etc.; then, the motion follow-up tasks corresponding to the first human function parameter and the second human function parameter in multiple reference motion follow-up tasks can be determined to obtain m motion follow-up tasks. For example, assuming that the first human function parameter is a motion function parameter and the second human function parameter is a cognitive function parameter, then, motion follow-up tasks related to sports and motion follow-up tasks related to cognitive ability can be selected from these multiple reference motion follow-up tasks to obtain m motion follow-up tasks. For example, riding a bicycle is a motion follow-up task related to sports, and for another example, cognitive games are motion follow-up tasks related to cognitive ability.

[0056] In this way, by determining the first human function parameter corresponding to the first preset brain area; determining the second human function parameter corresponding to the second preset brain area; determining the target disease corresponding to the target basic data; selecting the movement follow-up task corresponding to the target disease from the preset movement follow-up task library to obtain multiple reference movement follow-up tasks; determining the movement follow-up tasks corresponding to the first human function parameter and the second human function parameter in multiple reference movement follow-up tasks to obtain m movement follow-up tasks, on the one hand, by determining the human function parameters corresponding to the first preset brain area and the second preset brain area, we can have a deeper understanding of the specific role of different brain areas in individual cognition and behavior, which is helpful for personalized evaluation and intervention of the function of specific brain areas. On the other hand, selecting the movement follow-up task corresponding to the target disease from the preset movement follow-up task library can ensure the pertinence and effectiveness of the task. These tasks have been pre-designed and verified, and can better reflect the characteristics of the target disease and the needs of the target subjects, thereby improving the accuracy of the evaluation.

[0057] S103, guiding the target subject to perform the m motion follow-up tasks to obtain m real motion data; each real motion data corresponds to one motion follow-up task.

[0058] In an embodiment of the present application, before starting the above-mentioned m motion follow-up tasks, the specific requirements, goals and operation methods of each task can be explained in detail to the target subject, using simple and clear language to ensure that the target subject fully understands the task content. For example, for a task that requires completing a certain number of push-ups within a specific time, explain clearly how to correctly perform push-ups, what the time limit is, and the number standard for completion. It can also be explained through verbal explanations, demonstrations, or providing graphic descriptions (for example, displaying the detailed requirements of the motion follow-up task on the display screen of an electronic device). Then, let the target subject perform the m motion follow-up tasks in turn, let the target subject perform a motion follow-up task at regular intervals, and reserve sufficient time for the target subject to perform the motion follow-up task. When performing the motion follow-up task, the target motion acquisition device (for example, a camera, a motion sensor, a recorder, etc.) can be used to collect data such as the target subject's movements, expressions, and voices, thereby obtaining m real motion data.

[0059] For an example, see Figure 2 , Figure 2 is an implementation scenario diagram of a method for evaluating the separation of implicit cognitive ability and explicit behavioral response provided in an embodiment of the present application, such as Figure 2 As shown, the exercise follow-up task can be displayed on the display screen in the form of text, pictures, videos, etc., and the target staff can also explain to the target subjects based on the pictures on the display screen so that the target subjects can understand the content of the task. The target subjects can also ask questions and interact with the target staff to deepen the target subjects' understanding of the task. In addition, when the target subjects start the exercise follow-up task, the camera on the display screen can record the target subjects' exercise data, and the recording equipment such as a recorder can also record the target subjects' "reply content".

[0060] S104, obtaining the EEG data generated by the first preset brain area of ​​the target subject in the m movement follow-up tasks, and obtaining m first EEG data; each first EEG data corresponds to one movement follow-up task.

[0061] In an embodiment of the present application, when the target subject performs m movement follow-up tasks, the EEG data generated by the first preset brain area can be collected by the target EEG collection device, thereby obtaining m first EEG data.

[0062] Optionally, step S104, obtaining the EEG data generated by the first preset brain area of ​​the target subject in the m movement follow-up tasks to obtain m first EEG data, may include the following steps:

[0063] S41, determining a target EEG acquisition device corresponding to the target disease; the target EEG acquisition device includes a plurality of acquisition electrodes;

[0064] S42, determining a collection electrode corresponding to the first preset brain area among the multiple collection electrodes, and obtaining a first collection electrode;

[0065] S43, obtaining initial acquisition parameters of the target EEG acquisition device;

[0066] S44, obtaining the target illness duration of the target subject for the target disease;

[0067] S45, obtaining a target impact factor corresponding to the target illness duration;

[0068] S46, adjusting the initial acquisition parameters according to the target influencing factors to obtain target acquisition parameters;

[0069] S47. Collect the EEG data generated by the first preset brain area of ​​the target subject in the m movement follow-up tasks through the first collection electrode in the target EEG collection device with the target collection parameters to obtain the m first EEG data.

[0070] In the embodiments of the present application, the target EEG acquisition device may include one of the following: a wearable EEG device, an electroencephalogram device, a multi-channel EEG monitor, etc., which are not limited here; the acquisition parameters may be at least one of the following: sampling rate, acquisition time, signal amplification factor, etc., which are not limited here.

[0071] In a specific embodiment, the target EEG acquisition device corresponding to the target disease can be determined. Specifically, the disease characteristics of the target disease can be obtained first, and then, a suitable EEG acquisition device can be selected according to the disease characteristics. For example, if the target disease is epilepsy, and the disease characteristics are frequent attacks and a long onset time, then a multi-channel EEG monitor capable of continuously monitoring EEG activity for a long time is required. It can perform continuous recording for 24 hours or even longer to capture the EEG characteristics of the attack period and the inter-attack period; then, the acquisition electrode corresponding to the first preset brain area in the multiple acquisition electrodes can be determined to obtain the first acquisition electrode. Specifically, the technical parameters of the multiple acquisition electrodes can be obtained, such as the manufacturing material and surface area of ​​the electrode. Then, the EEG activity characteristics of the first preset brain area can be obtained, and the acquisition electrode with suitable technical parameters can be selected according to the EEG activity characteristics, that is, the first acquisition electrode. For example, assuming that the EEG activity of the first preset brain area is very weak, then a collection electrode with a small surface area and high collection accuracy can be selected as the first collection electrode.

[0072] Next, the initial acquisition parameters of the target EEG acquisition device can be obtained. Specifically, the mapping relationship between the preset EEG acquisition device and the acquisition parameters can be pre-stored, and the initial acquisition parameters corresponding to the target EEG acquisition device can be determined based on the mapping relationship; then, the target illness duration of the target subject for the target disease can be obtained. Specifically, the target subject can be directly asked to obtain the target illness duration, or the target illness duration can be obtained from the database of the above-mentioned target hospital; then, the target impact factor corresponding to the target illness duration can be obtained. Specifically, the mapping relationship between the preset illness duration and the impact factor can be pre-stored, and the target impact factor corresponding to the target illness duration can be determined based on the mapping relationship; then, the initial acquisition parameters can be adjusted according to the target impact factor, and the specific calculation formula is as follows:

[0073] Target acquisition parameters = initial acquisition parameters × (1 + target impact factor);

[0074] The target acquisition parameters can be obtained according to the above formula; then, the EEG data generated by the first preset brain area of ​​the target subject in the m motor follow-up tasks can be collected by the first acquisition electrode in the target EEG acquisition device with the target acquisition parameters to obtain m first EEG data. Specifically, according to the first preset brain area and the preset EEG electrode positioning standard (for example, the international 10-20 system), the first placement position of the first acquisition electrode on the scalp can be accurately found, and the first acquisition electrode can be set at the first placement position on the scalp of the target subject so that the first acquisition electrode accurately corresponds to the first preset brain area. When the target subject performs m motor follow-up tasks, EEG data are collected to obtain m first EEG data.

[0075] S105, obtaining the EEG data generated by the second preset brain area of ​​the target subject in the m movement follow-up tasks, and obtaining m second EEG data; each second EEG data corresponds to one movement follow-up task.

[0076] In an embodiment of the present application, EEG data generated by the second preset brain area of ​​the target subject in m motor follow-up tasks are obtained to obtain m second EEG data. Specifically, the method of obtaining the m second EEG data can be the same as the method of obtaining the m first EEG data described above.

[0077] S106. Obtain theoretical motion data corresponding to each of the m motion follow-up tasks to obtain m theoretical motion data.

[0078] In the embodiments of the present application, theoretical motion data refers to the idealized motion performance data expected or simulated in a motion follow-up task. For example, suppose a certain motion follow-up task is to play 5 numbers on a mobile phone, and ask the target subject to memorize these 5 numbers and say the recorded numbers 1 minute after the playback is completed to obtain 5 actual numbers. In theory, these 5 numbers should be accurately remembered and repeated. This is the theoretical motion data.

[0079] In a specific embodiment, a mapping relationship between preset theoretical motion data of motion follow-up tasks may be pre-stored, and m theoretical motion data corresponding to m motion follow-up tasks may be determined based on the mapping relationship.

[0080] S107 , determining motion difference degree values ​​between the m real motion data and corresponding theoretical motion data among the m theoretical motion data, to obtain m motion difference degree values.

[0081] In the embodiment of the present application, the motion difference degree value is used to quantify the numerical value representing the difference between the actual motion data and the theoretical motion data.

[0082] In a specific embodiment, the motion difference degree values ​​of the m real motion data and the m theoretical motion data may be calculated in sequence in time order to obtain m motion difference degree values.

[0083] Optionally, step S107, determining the motion difference degree values ​​between the m real motion data and the corresponding theoretical motion data in the m theoretical motion data to obtain m motion difference degree values, may include the following steps:

[0084] A1. Acquire target real motion data and target theoretical motion data; the target real motion data is any one of the m real motion data; the target theoretical motion data is the theoretical motion data corresponding to the target real motion data among the m theoretical motion data;

[0085] A2. Decomposing the target real motion data into actions to obtain multiple real actions;

[0086] A3, performing motion decomposition on the target theoretical motion data to obtain multiple theoretical motions;

[0087] A4. Matching the multiple real actions with corresponding theoretical actions in the multiple theoretical movements to obtain multiple matching degrees;

[0088] A5. Obtain a target weight set corresponding to the multiple real actions, wherein the target weight set includes multiple weights, and each weight corresponds to a real action;

[0089] A6. Performing calculations based on the target weight set and the multiple matching degrees to obtain a target motion matching degree value;

[0090] A7. Determine a motion difference degree value corresponding to the target real motion data and the target theoretical motion data according to the target motion matching degree value.

[0091] In the embodiment of the present application, the target motion matching degree value is a numerical value used to quantify the degree of proximity between the actual motion data and the theoretical motion data, and the target motion matching degree value ranges from 0 to 100%.

[0092] In a specific embodiment, the target real motion data and the target theoretical motion data can be obtained from the above-mentioned m real motion data and m theoretical motion data; then, the target real motion data can be subjected to action decomposition to obtain multiple real actions. Specifically, the target real motion data can be a motion video, which can be judged according to the start and end of the action, and the motion video can be divided into multiple segments containing a single real action, ensuring that each segment contains only one complete action, thereby obtaining multiple real actions; then, the target theoretical motion data can be subjected to action decomposition to obtain multiple theoretical movements. Specifically, the method for obtaining multiple theoretical actions can be the same as the above-mentioned method for obtaining multiple real actions.

[0093] Then, multiple real actions can be matched with corresponding theoretical actions in multiple theoretical actions to obtain multiple matching degrees. Specifically, the Euclidean distances between multiple real actions and multiple theoretical actions can be calculated according to a preset matching degree calculation method (for example, the Euclidean distance method) to obtain multiple Euclidean distances. Then, a mapping relationship between preset Euclidean distances and matching degrees can be pre-stored, and multiple matching degrees corresponding to multiple Euclidean distances can be determined based on the mapping relationship. Then, a target weight set corresponding to the multiple real actions can be obtained. Then, a weighted operation is performed according to the target weight set and the multiple matching degrees to obtain a target motion matching degree value. Finally, the motion difference degree value corresponding to the target real motion data and the target theoretical motion data can be determined according to the target motion matching degree value, as follows:

[0094] Motion difference degree value i=1-target motion matching degree value;

[0095] The motion difference degree value i in the above formula is the motion difference degree value corresponding to the target real motion data and the target theoretical motion data.

[0096] In this way, by obtaining the target's real motion data and the target's theoretical motion data; performing action decomposition on the target's real motion data to obtain multiple real actions; performing action decomposition on the target's theoretical motion data to obtain multiple theoretical motions; matching multiple real actions with corresponding theoretical actions in multiple theoretical motions to obtain multiple matching degrees; obtaining target weight sets corresponding to multiple real actions; performing calculations based on the target weight sets and multiple matching degrees to obtain target motion matching degree values; determining motion difference degree values ​​corresponding to the target's real motion data and the target's theoretical motion data based on the target motion matching degree values, and obtaining target weight sets corresponding to multiple real actions, this process takes into account the importance differences of different actions in the entire motion task, and can assign higher weights to actions with high importance, thereby making the calculated target motion matching degree values ​​more accurate, and thereby improving the accuracy of the motion difference degree values.

[0097] Optionally, step A5, obtaining target weight sets corresponding to the multiple real actions, may include the following steps:

[0098] B1. Obtaining the duration of each real action in the multiple real actions to obtain multiple durations;

[0099] B2. Determine a weight corresponding to each of the multiple durations to obtain the multiple weights; the longer the duration, the greater the weight.

[0100] In an embodiment of the present application, the duration of each real action among multiple real actions can be obtained to obtain multiple durations. Specifically, the target real motion data can be a motion video. The start moment and the end moment of each real action among the multiple real actions in the motion video can be observed to obtain multiple start moments and multiple end moments. Then, the corresponding start moment among the multiple start moments is subtracted from the multiple end moments to obtain multiple durations; the weight corresponding to each duration in the multiple durations is determined to obtain multiple weights. Specifically, the mapping relationship between the preset duration and the weight can be pre-stored, and the multiple weights corresponding to the multiple durations, that is, the target weight set, are determined based on the mapping relationship.

[0101] S108. Determine the degree of separation between the target subject's implicit cognitive ability and explicit behavioral response according to the m movement difference degree values, the m first EEG data, and the m second EEG data.

[0102] In the embodiment of the present application, the target subject can be analyzed based on m movement difference degree values, m first EEG data, and m second EEG data to obtain the degree of separation between the target subject's implicit cognitive ability and explicit behavioral response.

[0103] Optionally, step S108, determining the degree of separation between the target subject's implicit cognitive ability and explicit behavioral response according to the m movement difference degree values, the m first EEG data, and the m second EEG data, may include the following steps:

[0104] C1. Determine the reference cognitive parameters of the target subject in the m movement follow-up tasks according to the m real movement data and the m theoretical movement data, and obtain m reference cognitive parameters; each reference cognitive parameter includes one of the following: accuracy, average reaction time, and error frequency;

[0105] C2. Determine a target adjustment parameter corresponding to each of the m motion difference degree values ​​to obtain m target adjustment parameters;

[0106] C3. adjusting corresponding reference cognitive parameters among the m reference cognitive parameters according to the m target adjustment parameters to obtain m actual cognitive parameters;

[0107] C4. Calculating the connection strength between the first preset brain region and the second preset brain region according to the m first EEG data and the m second EEG data to obtain m connection strengths;

[0108] C5. Determine a theoretical cognitive parameter corresponding to each of the m connection strengths to obtain m theoretical cognitive parameters;

[0109] C6. Calculating the deviations between the m actual cognitive parameters and corresponding theoretical cognitive parameters among the m theoretical cognitive parameters to obtain m deviations;

[0110] C7. Determine the target mean values ​​corresponding to the m deviations;

[0111] C8. When the target mean is greater than or equal to the preset mean, determining that the degree of separation between the target subject's implicit cognitive ability and explicit behavioral response is in a strong separation state;

[0112] C9. When the target mean is less than the preset mean, it is determined that the degree of separation between the target subject's implicit cognitive ability and explicit behavioral response is in a weak separation state.

[0113] In the embodiment of the present application, the preset mean value can be preset in advance or defaulted.

[0114] In a specific embodiment, the reference cognitive parameters of the target subject in the m movement follow-up tasks can be determined according to the m real movement data and the m theoretical movement data to obtain m reference cognitive parameters. Specifically, the reference cognitive parameters can be the accuracy rate. In the m real movement data, the real correct times when the target subject makes the correct result are counted to obtain m real correct times, and the theoretical correct times in the m theoretical movement data are obtained to obtain m theoretical correct times. The m real correct times are divided by the corresponding theoretical correct times in the m theoretical correct times to obtain m accuracy rates, that is, m reference cognitive parameters. For example, in a certain number repetition memory task, assuming that 6 out of 10 numbers repeated by the subject are correct, and 8 numbers should be repeated correctly in the theoretical movement data, then the accuracy rate = (6 / 8) × 100% = 75%.

[0115] Next, the target adjustment parameter corresponding to each of the m motion difference degree values ​​can be determined to obtain m target adjustment parameters. Specifically, a mapping relationship between preset motion difference degree values ​​and target adjustment parameters can be pre-stored, and the m target adjustment parameters corresponding to the m motion difference degree values ​​can be determined based on the mapping relationship. The value range of the target adjustment parameters can be -0.3~0.3; then, the corresponding reference cognitive parameters in the m reference cognitive parameters can be adjusted according to the m target adjustment parameters. The specific calculation formula is as follows:

[0116] Actual cognitive parameter a = reference cognitive parameter a × (1 + target adjustment parameter a);

[0117] Among them, the reference cognitive parameter a is any reference cognitive parameter among the m reference cognitive parameters, and the target adjustment parameter a is the target adjustment parameter corresponding to the reference cognitive parameter a. According to the above formula, m actual cognitive parameters can be obtained by calculating m times; then, the connection strength between the first preset brain area and the second preset brain area can be calculated according to the m first EEG data and the m second EEG data to obtain m connection strengths; further, the theoretical cognitive parameter corresponding to each connection strength among the m connection strengths can be determined to obtain m theoretical cognitive parameters. Specifically, the mapping relationship between the preset connection strengths and the theoretical cognitive parameters can be pre-stored, and the m theoretical cognitive parameters corresponding to the m connection strengths can be determined based on the mapping relationship; then, the deviation between the m actual cognitive parameters and the corresponding theoretical cognitive parameters among the m theoretical cognitive parameters can be calculated, and the specific calculation formula is as follows:

[0118] Deviation b = | (actual cognitive parameter b - theoretical cognitive parameter b) / theoretical cognitive parameter b | × 100%;

[0119] Among them, the actual cognitive parameter b is any reference cognitive parameter among the m actual cognitive parameters, and the theoretical cognitive parameter b is the theoretical cognitive parameter corresponding to the actual cognitive parameter b. According to the above formula, m deviations can be obtained by calculating m times; the average deviation of the above m deviations is calculated according to the average value calculation formula, that is, the target mean; when the target mean is greater than or equal to the preset mean, it indicates that the deviation between the actual cognitive parameter and the theoretical cognitive parameter is large, and this large deviation means that there is a large difference between the implicit cognitive ability and the explicit behavioral response in the target subject, that is, the correlation between the target subject's internal cognitive ability and its external behavior is weak, that is, the degree of separation between the target subject's implicit cognitive ability and the explicit behavioral response is in a strong separation state; when the target mean is less than the preset mean, it indicates that the deviation between the actual cognitive parameter and the theoretical cognitive parameter is small, and the correlation between the target subject's internal cognitive ability and its external behavior is strong, that is, the degree of separation between the target subject's implicit cognitive ability and the explicit behavioral response is in a weak separation state.

[0120] In this way, by combining multi-source data such as real motion data, theoretical motion data, and EEG data, the target subjects can be comprehensively evaluated. The real motion data reflects the actual behavioral performance of the target subjects, the theoretical motion data provides an expected standard, and the EEG data provides information about the activity and connection of different brain regions from a neurophysiological level. The comprehensive use of this multi-dimensional data can provide a deeper understanding of the state of the target subjects, thereby improving the accuracy of the assessment of the degree of separation between implicit cognitive ability and explicit behavioral responses.

[0121] Optionally, step C4, calculating the connection strength between the first preset brain region and the second preset brain region according to the m first EEG data and the m second EEG data to obtain m connection strengths, may include the following steps:

[0122] D1. Acquire reference first EEG data and reference second EEG data; the reference first EEG data is any first EEG data among the m first EEG data; the reference second EEG data is the second EEG data corresponding to the reference first EEG data among the m second EEG data;

[0123] D2. Filter the reference first EEG data and the reference second EEG data according to a preset filtering algorithm to obtain target first EEG data and target second EEG data;

[0124] D3, calculating the target coherence coefficient between the target first EEG data and the target second EEG data;

[0125] D4, determining a reference connection strength corresponding to the target coherence coefficient;

[0126] D5. Obtaining the equipment accuracy parameters of the target EEG acquisition equipment;

[0127] D6. Determine the target optimization factor corresponding to the equipment accuracy parameter;

[0128] D7. Optimize the reference connection strength according to the target optimization factor to obtain the connection strength between the reference first EEG data and the reference second EEG data.

[0129] In the embodiment of the present application, the preset filtering algorithm can be preset or defaulted in advance; the device accuracy parameters may include at least one of the following: resolution, common mode rejection ratio, time synchronization accuracy, etc., which are not limited here.

[0130] In a specific embodiment, reference first EEG data and reference second EEG data can be first obtained from m first EEG data and m second EEG data; then, the reference first EEG data and the reference second EEG data can be filtered according to a preset filtering algorithm (for example, a low-pass filtering algorithm) to obtain target first EEG data and target second EEG data; then, the target coherence coefficient between the target first EEG data and the target second EEG data can be calculated. Specifically, data of a preset time length (for example, 10 seconds, 30 seconds) can be respectively intercepted from the target first EEG data and the target second EEG data to obtain part of the target first EEG data and part of the target second EEG data, ensuring that the time lengths of the two are equal. Then, the target coherence coefficient can be calculated according to the coherence coefficient calculation formula. The specific calculation formula is as follows:

[0131]

[0132] in, is the auto-power spectral density of the first EEG data of some targets, is the auto-power spectral density of the second EEG data of the partial target, is the cross power spectrum density of the partial target first EEG data and the partial target second EEG data; is the coherence coefficient at frequency point k; the calculation methods of autopower spectral density and cross-power spectral density are common methods and are not limited here; then, the coherence coefficients of different frequency points can be calculated according to the above formula, and the coherence coefficients at preset frequency points can be used as the target coherence coefficient, and the preset frequency points can be preset or defaulted in advance, or, the coherence coefficients in a specific frequency band (such as α band: 8-13Hz, β band: 13-30Hz, etc.) can be calculated according to the above formula to obtain multiple coherence coefficients, and the average value of the multiple coherence coefficients is calculated, and the average value is used as the target coherence coefficient.

[0133] Next, the reference connection strength corresponding to the target coherence coefficient can be determined. Specifically, a mapping relationship between a preset coherence coefficient and the connection strength can be pre-stored, and the reference connection strength corresponding to the target coherence coefficient can be determined based on the mapping relationship. Then, the device accuracy parameter of the target EEG acquisition device can be obtained. Specifically, the device accuracy parameter can be a resolution, and the device accuracy parameter can be obtained from the instruction manual or technical manual of the target EEG acquisition device. Alternatively, the target device model of the target EEG acquisition device can be obtained, and the device accuracy parameter can be determined according to the target device model. The mapping relationship between the preset device model and the accuracy parameter can be pre-stored, and the device accuracy parameter corresponding to the target device model can be determined based on the mapping relationship. Next, the target optimization factor corresponding to the device accuracy parameter can be determined. Specifically, a mapping relationship between a preset accuracy parameter and an optimization factor can be pre-stored, and the target optimization factor corresponding to the device accuracy parameter can be determined based on the mapping relationship. The value range of the target optimization factor can be -0.12~0.12. Finally, the reference connection strength can be optimized according to the target optimization factor to obtain the connection strength between the reference first EEG data and the reference second EEG data. The specific calculation formula is as follows:

[0134] Connection strength c = reference connection strength × (1 + target optimization factor);

[0135] The connection strength c is the connection strength between the reference first EEG data and the reference second EEG data.

[0136] The implementation of this application has the following beneficial effects:

[0137] It can be seen that the method for evaluating the separation of implicit cognitive ability and explicit behavioral response described in the embodiment of the present application includes: obtaining target basic data of the target subject; determining m movement follow-up tasks according to the first preset brain area, the second preset brain area and the target basic data; guiding the target subject to perform m movement follow-up tasks to obtain m real movement data; obtaining the EEG data generated by the first preset brain area of ​​the target subject in the m movement follow-up tasks to obtain m first EEG data; obtaining the EEG data generated by the second preset brain area of ​​the target subject in the m movement follow-up tasks to obtain m second EEG data; obtaining m theoretical movement data corresponding to each movement follow-up task in the m movement follow-up tasks; determining the movement difference degree value between the m real movement data and the corresponding theoretical movement data in the m theoretical movement data to obtain m movement difference degree values; obtaining the movement difference degree value according to the m movement The difference degree value, m first EEG data, and m second EEG data determine the degree of separation between the target subject's implicit cognitive ability and explicit behavioral response. In this way, by collecting the target subject's EEG data and movement data, the EEG data can reflect the brain's neural activity, that is, the implicit cognitive ability, and the movement data can reflect the subject's explicit behavioral response. By comprehensively analyzing these two types of data, the target subject's cognitive and behavioral state can be fully understood. Compared with traditional neuroimaging methods (such as functional magnetic resonance imaging), the present application scheme does not require expensive equipment and a complex operating environment, but only basic EEG acquisition equipment and movement monitoring equipment to evaluate the separation of the target subject's implicit cognitive ability and explicit behavioral response, which greatly reduces the evaluation cost, thereby achieving low-cost detection of the separation between the implicit cognitive ability level and the explicit behavioral response.

[0138] See also Figure 3 , Figure 3 300 is a schematic diagram of a structure of a device 300 for evaluating the separation of implicit cognitive ability and explicit behavioral response provided in an embodiment of the present application. The device 300 for evaluating the separation of implicit cognitive ability and explicit behavioral response includes: an acquisition unit 301, a control unit 302, and a separation degree detection unit 303; wherein:

[0139] The acquisition unit 301 is used to acquire target basic data of the target subject;

[0140] The control unit 302 is used to determine m movement follow-up tasks according to the first preset brain area, the second preset brain area and the target basic data; the m movement follow-up tasks are experimental tasks for studying the relationship between implicit cognitive ability and explicit behavioral response; m is an integer greater than 1; guide the target subject to perform the m movement follow-up tasks to obtain m real movement data; each real movement data corresponds to one movement follow-up task;

[0141] The acquisition unit 301 is further used to acquire the EEG data generated by the first preset brain area of ​​the target subject in the m movement follow-up tasks to obtain m first EEG data; each first EEG data corresponds to a movement follow-up task; acquire the EEG data generated by the second preset brain area of ​​the target subject in the m movement follow-up tasks to obtain m second EEG data; each second EEG data corresponds to a movement follow-up task; acquire the theoretical movement data corresponding to each movement follow-up task in the m movement follow-up tasks to obtain m theoretical movement data;

[0142] The separation degree detection unit 303 is used to determine the motion difference degree values ​​between the m real motion data and the corresponding theoretical motion data in the m theoretical motion data, and obtain m motion difference degree values; determine the separation degree between the implicit cognitive ability and the explicit behavioral response of the target subject according to the m motion difference degree values, the m first EEG data, and the m second EEG data.

[0143] Optionally, in determining m movement follow-up tasks according to the first preset brain region, the second preset brain region and the target basic data, the control unit 302 is specifically configured to:

[0144] Determining a first human function parameter corresponding to the first preset brain area;

[0145] Determining a second human body function parameter corresponding to the second preset brain area;

[0146] Determining a target disease corresponding to the target basic data;

[0147] Selecting a motion follow-up task corresponding to the target disease from a preset motion follow-up task library to obtain a plurality of reference motion follow-up tasks;

[0148] Determine the motion follow-up tasks corresponding to the first human body function parameter and the second human body function parameter in the multiple reference motion follow-up tasks to obtain the m motion follow-up tasks.

[0149] Optionally, in the aspect of obtaining the EEG data generated by the first preset brain area of ​​the target subject in the m movement follow-up tasks to obtain the m first EEG data, the obtaining unit 301 is specifically used for:

[0150] Determine a target EEG acquisition device corresponding to the target disease; the target EEG acquisition device includes a plurality of acquisition electrodes;

[0151] Determine a collection electrode corresponding to the first preset brain area among the multiple collection electrodes to obtain a first collection electrode;

[0152] Obtaining initial acquisition parameters of the target EEG acquisition device;

[0153] Obtaining a target illness duration of the target subject for the target disease;

[0154] Obtain a target impact factor corresponding to the target illness duration;

[0155] Adjusting the initial acquisition parameters according to the target impact factor to obtain target acquisition parameters;

[0156] The EEG data generated by the first preset brain area of ​​the target subject in the m movement follow-up tasks are collected by the first collection electrode in the target EEG collection device with the target collection parameters to obtain the m first EEG data.

[0157] Optionally, in determining the motion difference degree values ​​of the m real motion data and the corresponding theoretical motion data in the m theoretical motion data to obtain the m motion difference degree values, the separation degree detection unit 303 is specifically used to:

[0158] Acquire target real motion data and target theoretical motion data; the target real motion data is any one of the m real motion data; the target theoretical motion data is the theoretical motion data corresponding to the target real motion data in the m theoretical motion data;

[0159] Performing action decomposition on the target real motion data to obtain multiple real actions;

[0160] Performing motion decomposition on the target theoretical motion data to obtain a plurality of theoretical motions;

[0161] Matching the multiple real actions with corresponding theoretical actions in the multiple theoretical movements to obtain multiple matching degrees;

[0162] Obtaining a target weight set corresponding to the multiple real actions, the target weight set including multiple weights, each weight corresponding to a real action;

[0163] Performing calculations based on the target weight set and the multiple matching degrees to obtain a target motion matching degree value;

[0164] The motion difference degree value corresponding to the target real motion data and the target theoretical motion data is determined according to the target motion matching degree value.

[0165] Optionally, in the aspect of acquiring the target weight sets corresponding to the multiple real actions, the separation degree detection unit 303 is specifically used to:

[0166] Obtaining the duration of each real action in the multiple real actions to obtain multiple durations;

[0167] Determine a weight corresponding to each of the multiple durations to obtain the multiple weights; the longer the duration, the greater the weight.

[0168] Optionally, in determining the degree of separation between the implicit cognitive ability and the explicit behavioral response of the target subject according to the m motion difference degree values, the m first EEG data, and the m second EEG data, the degree of separation detection unit 303 is specifically used to:

[0169] Determine reference cognitive parameters of the target subject in the m movement follow-up tasks according to the m real movement data and the m theoretical movement data, and obtain m reference cognitive parameters; each reference cognitive parameter includes one of the following: accuracy, average reaction time, and error frequency;

[0170] Determine a target adjustment parameter corresponding to each of the m motion difference degree values ​​to obtain m target adjustment parameters;

[0171] Adjust corresponding reference cognitive parameters among the m reference cognitive parameters according to the m target adjustment parameters to obtain m actual cognitive parameters;

[0172] Calculating the connection strength between the first preset brain region and the second preset brain region according to the m first EEG data and the m second EEG data to obtain m connection strengths;

[0173] Determine a theoretical cognitive parameter corresponding to each of the m connection strengths to obtain m theoretical cognitive parameters;

[0174] Calculating the deviations between the m actual cognitive parameters and corresponding theoretical cognitive parameters among the m theoretical cognitive parameters to obtain m deviations;

[0175] Determine the target mean values ​​corresponding to the m deviations;

[0176] When the target mean is greater than or equal to the preset mean, it is determined that the degree of separation between the implicit cognitive ability and the explicit behavioral response of the target subject is in a strong separation state;

[0177] When the target mean is less than the preset mean, it is determined that the degree of separation between the implicit cognitive ability and the explicit behavioral response of the target subject is in a weak separation state.

[0178] Optionally, in the aspect of calculating the connection strength between the first preset brain region and the second preset brain region according to the m first EEG data and the m second EEG data to obtain m connection strengths, the separation degree detection unit 303 is specifically used to:

[0179] Acquire reference first EEG data and reference second EEG data; the reference first EEG data is any first EEG data among the m first EEG data; the reference second EEG data is the second EEG data corresponding to the reference first EEG data among the m second EEG data;

[0180] Filter the reference first EEG data and the reference second EEG data according to a preset filtering algorithm to obtain target first EEG data and target second EEG data;

[0181] Calculating a target coherence coefficient between the target first EEG data and the target second EEG data;

[0182] Determining a reference connection strength corresponding to the target coherence coefficient;

[0183] Obtaining device accuracy parameters of the target EEG acquisition device;

[0184] Determine the target optimization factor corresponding to the equipment accuracy parameter;

[0185] The reference connection strength is optimized according to the target optimization factor to obtain the connection strength between the reference first EEG data and the reference second EEG data.

[0186] In a specific implementation, the device 300 for assessing the separation of implicit cognitive ability and explicit behavioral response described in the embodiment of the present invention can also execute other implementations described in the method for assessing the separation of implicit cognitive ability and explicit behavioral response provided in the above embodiment of the present invention, which will not be repeated here.

[0187] See also Figure 4 , Figure 4 It is a structural diagram of an electronic device provided in an embodiment of the present application, wherein the electronic device includes a processor, a memory, a communication interface, and one or more programs, wherein the processor, the memory, and the communication interface are interconnected via a bus, wherein the one or more programs are stored in the memory and are configured to be executed by the processor, wherein the one or more programs include instructions for executing other implementations described in the method for assessing the separation of implicit cognitive ability and explicit behavioral response provided in the above embodiment of the present invention, which will not be described in detail herein.

[0188] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method recorded in the above method embodiments, and the above computer includes an electronic device.

[0189] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0190] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0191] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0192] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0193] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0194] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0195] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0196] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for assessing the separation of implicit cognitive abilities from explicit behavioral responses, characterized in that: The method comprises: Obtain target basic data of target subjects; Determining m movement follow-up tasks according to the first preset brain region, the second preset brain region and the target basic data; the m movement follow-up tasks are experimental tasks for studying the relationship between implicit cognitive ability and explicit behavioral response; m is an integer greater than 1; guiding the target subject to perform the m motion follow-up tasks to obtain m real motion data; each real motion data corresponds to one motion follow-up task; Acquire the EEG data generated by the first preset brain area of ​​the target subject in the m movement follow-up tasks to obtain m first EEG data; each first EEG data corresponds to one movement follow-up task; Acquire the electroencephalogram (EEG) data generated by the second preset brain area of ​​the target subject in the m movement follow-up tasks to obtain m second EEG data; each second EEG data corresponds to one movement follow-up task; Acquire theoretical motion data corresponding to each of the m motion follow-up tasks to obtain m theoretical motion data; Determine the motion difference degree values ​​between the m real motion data and the corresponding theoretical motion data among the m theoretical motion data, to obtain m motion difference degree values; Determining the degree of separation between the implicit cognitive ability and the explicit behavioral response of the target subject according to the m movement difference degree values, the m first EEG data, and the m second EEG data; Wherein, determining the degree of separation between the implicit cognitive ability and the explicit behavioral response of the target subject according to the m movement difference degree values, the m first EEG data, and the m second EEG data includes: Determine reference cognitive parameters of the target subject in the m movement follow-up tasks according to the m real movement data and the m theoretical movement data, and obtain m reference cognitive parameters; each reference cognitive parameter includes one of the following: accuracy, average reaction time, and error frequency; Determine a target adjustment parameter corresponding to each of the m motion difference degree values ​​to obtain m target adjustment parameters; Adjust corresponding reference cognitive parameters among the m reference cognitive parameters according to the m target adjustment parameters to obtain m actual cognitive parameters; Calculating the connection strength between the first preset brain region and the second preset brain region according to the m first EEG data and the m second EEG data to obtain m connection strengths; Determine a theoretical cognitive parameter corresponding to each of the m connection strengths to obtain m theoretical cognitive parameters; Calculating the deviations between the m actual cognitive parameters and corresponding theoretical cognitive parameters among the m theoretical cognitive parameters to obtain m deviations; Determine the target mean values ​​corresponding to the m deviations; When the target mean is greater than or equal to the preset mean, it is determined that the degree of separation between the implicit cognitive ability and the explicit behavioral response of the target subject is in a strong separation state; When the target mean is less than the preset mean, it is determined that the degree of separation between the implicit cognitive ability and the explicit behavioral response of the target subject is in a weak separation state.

2. The method according to claim 1, characterized in that The determining m movement follow-up tasks according to the first preset brain region, the second preset brain region and the target basic data includes: Determining a first human function parameter corresponding to the first preset brain area; Determining a second human body function parameter corresponding to the second preset brain area; Determining a target disease corresponding to the target basic data; Selecting a motion follow-up task corresponding to the target disease from a preset motion follow-up task library to obtain a plurality of reference motion follow-up tasks; Determine the motion follow-up tasks corresponding to the first human body function parameter and the second human body function parameter in the multiple reference motion follow-up tasks to obtain the m motion follow-up tasks.

3. The method according to claim 2, characterized in that The step of obtaining the EEG data generated by the first preset brain area of ​​the target subject in the m movement follow-up tasks to obtain m first EEG data includes: Determine a target EEG acquisition device corresponding to the target disease; the target EEG acquisition device includes a plurality of acquisition electrodes; Determine a collection electrode corresponding to the first preset brain area among the multiple collection electrodes to obtain a first collection electrode; Obtaining initial acquisition parameters of the target EEG acquisition device; Obtaining a target illness duration of the target subject for the target disease; Obtain a target impact factor corresponding to the target illness duration; Adjusting the initial acquisition parameters according to the target impact factor to obtain target acquisition parameters; The EEG data generated by the first preset brain area of ​​the target subject in the m movement follow-up tasks are collected by the first collection electrode in the target EEG collection device with the target collection parameters to obtain the m first EEG data.

4. The method according to any one of claims 1 to 3, characterized in that: The step of determining the motion difference degree values ​​between the m real motion data and the corresponding theoretical motion data in the m theoretical motion data to obtain m motion difference degree values ​​comprises: Acquire target real motion data and target theoretical motion data; the target real motion data is any one of the m real motion data; the target theoretical motion data is the theoretical motion data corresponding to the target real motion data in the m theoretical motion data; Performing action decomposition on the target real motion data to obtain multiple real actions; Performing motion decomposition on the target theoretical motion data to obtain a plurality of theoretical motions; Matching the multiple real actions with corresponding theoretical actions in the multiple theoretical movements to obtain multiple matching degrees; Obtaining a target weight set corresponding to the multiple real actions, the target weight set including multiple weights, each weight corresponding to a real action; Performing calculations based on the target weight set and the multiple matching degrees to obtain a target motion matching degree value; The motion difference degree value corresponding to the target real motion data and the target theoretical motion data is determined according to the target motion matching degree value.

5. The method according to claim 4, characterized in that The obtaining of target weight sets corresponding to the multiple real actions includes: Obtaining the duration of each real action in the multiple real actions to obtain multiple durations; Determine a weight corresponding to each of the multiple durations to obtain the multiple weights; the longer the duration, the greater the weight.

6. The method according to claim 3, characterized in that The calculating the connection strength between the first preset brain region and the second preset brain region according to the m first EEG data and the m second EEG data to obtain m connection strengths includes: Acquire reference first EEG data and reference second EEG data; the reference first EEG data is any first EEG data among the m first EEG data; the reference second EEG data is the second EEG data corresponding to the reference first EEG data among the m second EEG data; Filter the reference first EEG data and the reference second EEG data according to a preset filtering algorithm to obtain target first EEG data and target second EEG data; Calculating a target coherence coefficient between the target first EEG data and the target second EEG data; Determining a reference connection strength corresponding to the target coherence coefficient; Obtaining device accuracy parameters of the target EEG acquisition device; Determine the target optimization factor corresponding to the equipment accuracy parameter; The reference connection strength is optimized according to the target optimization factor to obtain the connection strength between the reference first EEG data and the reference second EEG data.

7. A device for assessing the separation of implicit cognitive ability and explicit behavioral response, characterized in that: The device comprises: an acquisition unit, a control unit, and a separation degree detection unit; wherein: The acquisition unit is used to acquire target basic data of the target subject; The control unit is used to determine m movement follow-up tasks according to the first preset brain area, the second preset brain area and the target basic data; the m movement follow-up tasks are experimental tasks for studying the relationship between implicit cognitive ability and explicit behavioral response; m is an integer greater than 1; guide the target subject to perform the m movement follow-up tasks to obtain m real movement data; each real movement data corresponds to one movement follow-up task; The acquisition unit is further used to acquire the EEG data generated by the first preset brain area of ​​the target subject in the m movement follow-up tasks to obtain m first EEG data; each first EEG data corresponds to a movement follow-up task; acquire the EEG data generated by the second preset brain area of ​​the target subject in the m movement follow-up tasks to obtain m second EEG data; each second EEG data corresponds to a movement follow-up task; acquire theoretical movement data corresponding to each movement follow-up task in the m movement follow-up tasks to obtain m theoretical movement data; The separation degree detection unit is used to determine the movement difference degree values ​​between the m real movement data and the corresponding theoretical movement data in the m theoretical movement data, and obtain m movement difference degree values; determine the separation degree between the implicit cognitive ability and the explicit behavioral response of the target subject according to the m movement difference degree values, the m first EEG data, and the m second EEG data; Wherein, in determining the degree of separation between the implicit cognitive ability and the explicit behavioral response of the target subject according to the m motion difference degree values, the m first EEG data, and the m second EEG data, the degree of separation detection unit is specifically used for: Determine reference cognitive parameters of the target subject in the m movement follow-up tasks according to the m real movement data and the m theoretical movement data, and obtain m reference cognitive parameters; each reference cognitive parameter includes one of the following: accuracy, average reaction time, and error frequency; Determine a target adjustment parameter corresponding to each of the m motion difference degree values ​​to obtain m target adjustment parameters; Adjust corresponding reference cognitive parameters among the m reference cognitive parameters according to the m target adjustment parameters to obtain m actual cognitive parameters; Calculating the connection strength between the first preset brain region and the second preset brain region according to the m first EEG data and the m second EEG data to obtain m connection strengths; Determine a theoretical cognitive parameter corresponding to each of the m connection strengths to obtain m theoretical cognitive parameters; Calculating the deviations between the m actual cognitive parameters and corresponding theoretical cognitive parameters among the m theoretical cognitive parameters to obtain m deviations; Determine the target mean values ​​corresponding to the m deviations; When the target mean is greater than or equal to the preset mean, it is determined that the degree of separation between the implicit cognitive ability and the explicit behavioral response of the target subject is in a strong separation state; When the target mean is less than the preset mean, it is determined that the degree of separation between the implicit cognitive ability and the explicit behavioral response of the target subject is in a weak separation state.

8. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store one or more programs and is configured to be executed by the processor, wherein the program includes instructions for executing the steps in the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 6.

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