CN115105078B - Method and system for identifying brain cognitive states of pilot - Google Patents
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Abstract
The invention discloses a method and a system for identifying brain cognitive states of pilots, wherein the method comprises the following steps: acquiring a target cognitive task from a behavior scene database, wherein a behavior paradigm of the target cognitive task comprises a plurality of rest states and a plurality of task states; acquiring the response time, the response accuracy and the brain electricity difference value when the target pilot executes the target cognitive task, wherein the brain electricity difference value is the difference value between first data and second data, the first data is brain electricity data of the target pilot in a resting state of a behavior pattern, and the second data is brain electricity data of the target pilot in the task state of the behavior pattern; and estimating the brain cognitive state of the target pilot according to the response time, the response accuracy and the brain electricity difference value. The brain cognitive state of the pilot is identified based on the brain electricity and the behavior pattern, and brain resources and brain resource potential mobilized by the pilot for executing the cognitive task can be obtained, so that the brain cognitive state of the pilot can be comprehensively identified.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a system for identifying brain cognitive states of pilots.
Background
Cognition is the processing of information by individuals for sensory signal reception, detection, conversion, conciseness, synthesis, encoding, storage, extraction, reconstruction, concept formation, judgment, and problem resolution; in psychology, cognition refers to a process of acquiring knowledge by forming mental activities such as concept, perception, judgment, or imagination. Because of the occupational specificity, the pilot processes information contained in a work environment while in that work environment. The cognitive ability of the pilot determines the strength of the information processing cognition, and the cognitive style of the pilot is a personalized and consistent cognitive preference of the pilot in the cognitive process.
In the prior art, the brain cognitive state of a pilot is identified from the dimensionalities of speech capacity, mathematical capacity, logical reasoning, spatial capacity and the like in a scale and question making mode, only the score on an external form can be obtained, and the brain cognitive state of the pilot cannot be comprehensively estimated.
Disclosure of Invention
The invention provides a method and a system for identifying the brain cognitive state of a pilot, which are used for solving the defect that the brain cognitive state of the pilot cannot be comprehensively estimated in the prior art.
The invention provides a method for identifying the brain cognitive states of pilots, which comprises the following steps:
acquiring a target cognitive task from a behavior scene database, wherein a behavior paradigm of the target cognitive task comprises a plurality of rest states and a plurality of task states;
acquiring the response time, the response accuracy and the brain electricity difference value of a target pilot when the target pilot executes the target cognitive task, wherein the brain electricity difference value is a difference value between first data and second data, the first data is brain electricity data of the target pilot in a resting state of the behavior pattern, and the second data is brain electricity data of the target pilot in a task state of the behavior pattern;
and estimating the brain cognitive state of the target pilot according to the response time, the response accuracy and the brain electricity difference value.
The invention also provides a system for identifying a brain cognitive state of a pilot, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a target cognitive task from a behavior scene database, and a behavior pattern of the target cognitive task comprises a plurality of rest states and a plurality of task states;
the second acquisition module is used for acquiring the response time, the response accuracy and the brain electricity difference value when the target pilot executes the target cognitive task, wherein the brain electricity difference value is a difference value between first data and second data, the first data is brain electricity data of the target pilot in a resting state of the behavior pattern, and the second data is brain electricity data of the target pilot in the task state of the behavior pattern;
and the evaluation module is used for evaluating the brain cognitive state of the target pilot according to the response time, the response accuracy and the brain electricity difference value.
The brain cognitive state of the pilot is identified based on the brain electricity and the behavior pattern, and brain resources and brain resource potential mobilized by the pilot for executing the cognitive task can be obtained, so that the brain cognitive state of the pilot can be comprehensively identified.
Drawings
FIG. 1 is a flow chart of a method of identifying a brain-cognitive state of a pilot in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a system for recognizing a brain-cognitive state of a pilot, in accordance with an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides a method for identifying the brain cognitive states of pilots, which is shown in fig. 1 and comprises the following steps:
step 101, obtaining a target cognitive task from a behavior scene database, wherein a behavior paradigm of the target cognitive task comprises a plurality of rest states and a plurality of task states.
The behavior scene database comprises the following cognitive tasks: visual space attention, working memory and simulation of flight scenes; wherein, the simulation flight scene includes: take-off scenes, offshore flight scenes, mountain flight scenes, shooting scenes, and return scenes.
Specifically, a target behavior pattern can be determined according to the flight registration, the flight model, the flight time and the test evaluation target of the target pilot, and a target cognitive task conforming to the target behavior pattern is acquired from a behavior scene database.
Step 102, obtaining the response time, the response accuracy and the brain electricity difference value of the target pilot when the target pilot executes the target cognitive task.
The brain electricity difference value is a difference value between first data and second data, the first data is brain electricity data of the target pilot in a resting state of the behavior pattern, and the second data is brain electricity data of the target pilot in a task state of the behavior pattern;
in this embodiment, the electroencephalogram data is a time domain feature or a frequency domain feature of electroencephalogram;
correspondingly, the electroencephalogram difference value is the time domain difference between the time domain feature of the electroencephalogram of the tested object in the resting state of the behavior pattern and the time domain feature of the electroencephalogram of the tested object in the task state of the behavior pattern;
or alternatively, the first and second heat exchangers may be,
and the frequency domain difference between the frequency domain characteristics of the brain electricity of the tested object in the rest state of the behavior pattern and the frequency domain characteristics of the brain electricity of the tested object in the task state of the behavior pattern.
And step 103, estimating the brain cognitive state of the target pilot according to the response time, the response accuracy and the brain electricity difference value.
Specifically, the reaction time may be incorporated into a community database, forming a reaction time normal distribution curve, and calculating the quantile of the reaction time of the target pilot in a pilot community, the pilot community including a plurality of flight personnel, the community database including the reaction time of each of the flight personnel to perform the cognitive tasks;
the reaction accuracy rate is brought into a group database to form a reaction accuracy rate normal distribution curve, and the quantile of the reaction accuracy rate of the target pilot in a pilot group is calculated, wherein the pilot group comprises a plurality of flight personnel, and the group database comprises the reaction accuracy rate of each flight personnel for executing the cognitive tasks;
the time domain difference is incorporated into a community database to form a time domain difference normal distribution curve, and the quantile of the time domain difference of the target pilot in a pilot community is calculated, wherein the pilot community comprises a plurality of flying personnel, and the community database comprises the time domain difference of each flying personnel for executing the cognitive tasks;
and the frequency domain difference is incorporated into a community database to form a frequency domain difference normal distribution curve, and the quantile of the frequency domain difference of the target pilot in a pilot community is calculated, wherein the pilot community comprises a plurality of flying personnel, and the community database comprises the frequency domain difference of each flying personnel for executing the cognitive tasks.
And calculating the brain cognitive state rank of the target pilot in the pilot community according to the quantile of the reaction time of the target pilot in the pilot community, the quantile of the reaction accuracy of the target pilot in the pilot community, the quantile of the time domain difference of the target pilot in the pilot community and the quantile of the frequency domain difference of the target pilot in the pilot community.
The embodiment of the invention can identify the brain cognitive state of the pilot based on the brain electricity and the behavior pattern, and can acquire brain resources and brain resource potential mobilized by the pilot for executing the cognitive task, thereby comprehensively identifying the brain cognitive state of the pilot.
In this embodiment, the brain cognitive state of the pilot may be identified by performing the following steps: building a behavioral scenario database including, but not limited to: (1) visual spatial attention: a variant of the Posner paradigm; (2) working memory: n-back paradigm; (3) Simulating flight scenes such as take-off, offshore flight, mountain flight, shooting, return and the like; (4) By selecting the model and the difficulty, richer scenes can be refined; determining a behavior pattern used for testing according to flight registration, a flight model, flight time and a test evaluation target, wherein each behavior pattern comprises a plurality of rest states and a plurality of task states; starting a test, collecting brain electricity data of a target pilot, recording response time and response error in real time, and counting response accuracy according to the response error; if the reaction accuracy is below the threshold, indicating that the target pilot does not enter a state or is not suitable for the 'subdivision group', returning to retest or ending the test of the target pilot; if the reaction accuracy is not lower than the threshold value, the reaction accuracy is 'included into' a subdivision group database 'according to the grade and the aircraft model' during the reaction, so as to form a new reaction accuracy normal distribution curve and a new reaction time normal distribution curve; calculating the quantiles of the reaction time and the accuracy rate in the subdivision groups according to the reaction time and the reaction accuracy rate of the target pilot, and converting the quantiles into percentages; calculating the time domain characteristics and the frequency domain characteristics of the brain electricity of the target pilot, and counting the time domain characteristics and the frequency domain characteristics of the brain electricity resting state and the time domain characteristics and the frequency domain characteristics of the task state of the tested brain electricity. The time domain features include amplitude, mean, variance, kurtosis, etc. of the EEG waveform; the frequency domain adopts wavelet transformation, and the wavelet coefficients of 0-8Hz,8-16Hz,16-32Hz,32-64Hz and 64-128Hz are obtained through multi-layer decomposition. And calculating a difference value corresponding to each characteristic task state and the rest state.
The cognitive resource theory is considered that individual differences exist in the cognitive resource reserves of people, and are reflected in the cognitive ability differences of people. At the individual level, the cognitive ability level may be positively correlated with the brain activation level; and when the cognitive tasks with the same difficulty are completed in a group level, the individuals with lower cognitive abilities can achieve ideal effects only after activating more cognitive resources as compensation. The above-mentioned views constitute the basic idea of activating compensation theory. Specifically, individuals with weaker brain activation or less scope in the face of the same cognitive load task are considered to have a higher reserve of cognitive resources, given the same performance of the task (e.g., the same accuracy and response), because such individuals need to mobilize fewer neural resources to complete the task. And such individuals are able to accomplish intensive tasks by mobilizing more neural resources when faced with increased task load or increased demands on reaction intensity. Conversely, individuals exhibiting a greater degree of brain activation in tasks with a relatively lower cognitive load are considered to have less cognitive resource reserves; and when task difficulty increases, such individuals may have difficulty mobilizing enough neural resources to ensure task performance, thereby causing a phenomenon that task performance decreases. When performing the same cognitive task, a person with a lower degree of brain activation is considered to possess a richer cognitive resource and a greater cognitive potential.
Therefore, the corresponding differences of the resting state time domain and frequency domain characteristics and the task state time domain and frequency domain characteristics are incorporated into the subdivision group database to form a new normal distribution curve of the brain electricity time domain and frequency domain differences. When the community is to a certain scale, the differences of various features are in line with normal distribution. According to the brain electricity time domain and frequency domain difference of the target pilot, calculating the quantile of the target pilot in the subdivision group and converting the quantile into percentage; according to the flight level, the flight model, the flight time and the test evaluation target of the target pilot, selecting different coefficients, and calculating the potential ordering of the brain cognitive states of the target pilot in the subdivision groups; the comprehensive sequencing of the target pilots in different stages is counted, and the change of the brain cognitive state of the target pilots after training can be calculated.
Specifically, based on community test data and the aggregate ranking of the targeted pilots at the community, under certain behaviors: (1) Testing the response accuracy of the tested and ranking of the individual in the group accuracy; (2) Testing the ranking of the tested response and individuals in the population response; (3) Testing tested cognitive resource calling and activating compensation data, and ranking individual cognitive resource calling and activating compensation data in a group; (4) Normalizing the three ranks according to a certain proportion to obtain the comprehensive ranks of the individuals in the group, and evaluating the brain cognitive states of the individuals in the group.
Based on this, the application can be made in the following aspects: (1) providing comprehensive ranking indexes for the pilot; (2) The cognitive state and the cognitive ability of the personnel in the specific training mode are evaluated, so that the training effect is improved; (3) The cognitive evaluation is carried out on personnel before and after the task is executed, so that the maximum potential is exerted on the premise of ensuring the safety.
According to the embodiment of the invention, the brain cognitive state of the pilot is identified based on the brain electricity and the behavior pattern, brain resources and brain resource potential mobilized by the pilot for executing the cognitive task can be obtained, corresponding group data and comprehensive ranking of the pilot in the group are obtained based on the behavior data and the brain cognitive resource call, so that the brain cognitive state of the pilot can be comprehensively identified, the cognitive state and the cognitive ability of the pilot in a specific training mode are evaluated, the training effect of the pilot is improved, and the cognitive evaluation is performed before and after the pilot executes the task, so that the maximum potential of the pilot is exerted on the premise of ensuring safety.
Referring to fig. 2, a schematic structural diagram of a system for identifying a brain cognitive state of a pilot according to an embodiment of the present invention includes:
the first obtaining module 210 is configured to obtain a target cognitive task from a behavior scenario database, where a behavior paradigm of the target cognitive task includes a plurality of rest states and a plurality of task states.
The behavior scene database comprises the following cognitive tasks: visual space attention, working memory and simulation of flight scenes; wherein, the simulation flight scene includes: take-off scenes, offshore flight scenes, mountain flight scenes, shooting scenes, and return scenes.
Specifically, the first obtaining module 210 is specifically configured to determine a target behavior pattern according to the flight registration, the aircraft model, the flight time and the test evaluation target of the target pilot, and obtain a target cognitive task conforming to the target behavior pattern from a behavior scene database.
The second obtaining module 220 is configured to obtain a reaction time, a reaction accuracy and an electroencephalogram difference value when the target pilot performs the target cognitive task, where the electroencephalogram difference value is a difference value between first data and second data, the first data is electroencephalogram data of the target pilot in a rest state of the behavior pattern, and the second data is electroencephalogram data of the target pilot in a task state of the behavior pattern.
The electroencephalogram data is a time domain feature or a frequency domain feature of electroencephalogram.
Correspondingly, the electroencephalogram difference value is the time domain difference between the time domain feature of the electroencephalogram of the tested object in the resting state of the behavior pattern and the time domain feature of the electroencephalogram of the tested object in the task state of the behavior pattern;
or alternatively, the first and second heat exchangers may be,
and the frequency domain difference between the frequency domain characteristics of the brain electricity of the tested object in the rest state of the behavior pattern and the frequency domain characteristics of the brain electricity of the tested object in the task state of the behavior pattern.
An evaluation module 230, configured to evaluate a brain cognitive state of the target pilot according to the response time, the response accuracy, and the brain electrical difference.
Specifically, the evaluation module 230 is specifically configured to incorporate the reaction time into a group database, form a normal distribution curve of reaction times, and calculate a quantile of the reaction time of the target pilot in a pilot group, where the pilot group includes a plurality of flight personnel, and the group database includes the reaction time of each of the flight personnel to perform the cognitive task;
the reaction accuracy rate is brought into a group database to form a reaction accuracy rate normal distribution curve, and the quantile of the reaction accuracy rate of the target pilot in a pilot group is calculated, wherein the pilot group comprises a plurality of flight personnel, and the group database comprises the reaction accuracy rate of each flight personnel for executing the cognitive tasks;
the time domain difference is incorporated into a community database to form a time domain difference normal distribution curve, and the quantile of the time domain difference of the target pilot in a pilot community is calculated, wherein the pilot community comprises a plurality of flying personnel, and the community database comprises the time domain difference of each flying personnel for executing the cognitive tasks;
and the frequency domain difference is incorporated into a community database to form a frequency domain difference normal distribution curve, and the quantile of the frequency domain difference of the target pilot in a pilot community is calculated, wherein the pilot community comprises a plurality of flying personnel, and the community database comprises the frequency domain difference of each flying personnel for executing the cognitive tasks.
And calculating the brain cognitive state rank of the target pilot in the pilot community according to the quantile of the reaction time of the target pilot in the pilot community, the quantile of the reaction accuracy of the target pilot in the pilot community, the quantile of the time domain difference of the target pilot in the pilot community and the quantile of the frequency domain difference of the target pilot in the pilot community.
The embodiment of the invention can identify the brain cognitive state of the pilot based on the brain electricity and the behavior pattern, and can acquire brain resources and brain resource potential mobilized by the pilot for executing the cognitive task, thereby comprehensively identifying the brain cognitive state of the pilot.
The steps of a method described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (2)
1. A method of identifying a brain-cognitive state of a pilot, comprising the steps of:
acquiring a target cognitive task from a behavior scene database, wherein a behavior paradigm of the target cognitive task comprises a plurality of rest states and a plurality of task states;
acquiring the response time, the response accuracy and the brain electricity difference value of a target pilot when the target pilot executes the target cognitive task, wherein the brain electricity difference value is a difference value between first data and second data, the first data is brain electricity data of the target pilot in a resting state of the behavior pattern, and the second data is brain electricity data of the target pilot in a task state of the behavior pattern;
according to the response time, the response accuracy and the brain electricity difference value, estimating the brain cognitive state of the target pilot;
the behavior scene database comprises the following cognitive tasks: visual space attention, working memory and simulation of flight scenes;
wherein, the simulation flight scene includes: a take-off scene, an offshore flight scene, a mountain flight scene, a shooting scene and a return scene;
the obtaining the target cognitive task from the behavior scene database specifically comprises the following steps:
determining a target behavior pattern according to the flight registration, the flight model, the flight time and the test evaluation target of the target pilot, and acquiring a target cognitive task conforming to the target behavior pattern from a behavior scene database;
the electroencephalogram data is a time domain feature or a frequency domain feature of electroencephalogram;
the electroencephalogram difference value is the time domain difference between the time domain characteristics of the electroencephalogram of the tested object in the resting state of the behavior pattern and the time domain characteristics of the electroencephalogram of the tested object in the task state of the behavior pattern;
or alternatively, the first and second heat exchangers may be,
a frequency domain difference between a frequency domain feature of the brain electricity of the tested object in a resting state of the behavior pattern and a frequency domain feature of the brain electricity of the tested object in a task state of the behavior pattern;
and evaluating the brain cognitive state of the target pilot according to the response time, the response accuracy and the brain electricity difference value, wherein the method specifically comprises the following steps of:
bringing the reaction time into a group database to form a reaction time normal distribution curve, and calculating the quantile of the reaction time of the target pilot in a pilot group, wherein the pilot group comprises a plurality of flying personnel, and the group database comprises the reaction time of each flying personnel for executing the cognitive task;
the reaction accuracy rate is brought into a group database to form a reaction accuracy rate normal distribution curve, and the quantile of the reaction accuracy rate of the target pilot in a pilot group is calculated, wherein the pilot group comprises a plurality of flight personnel, and the group database comprises the reaction accuracy rate of each flight personnel for executing the cognitive tasks;
the time domain difference is incorporated into a community database to form a time domain difference normal distribution curve, and the quantile of the time domain difference of the target pilot in a pilot community is calculated, wherein the pilot community comprises a plurality of flying personnel, and the community database comprises the time domain difference of each flying personnel for executing the cognitive tasks;
the frequency domain difference is brought into a group database to form a frequency domain difference normal distribution curve, and the quantile of the frequency domain difference of the target pilot in a pilot group is calculated, wherein the pilot group comprises a plurality of flying personnel, and the group database comprises the frequency domain difference of each flying personnel for executing the cognitive tasks;
calculating brain cognitive state ordering of the target pilot in the pilot community according to the quantile of the reaction time of the target pilot in the pilot community, the quantile of the reaction accuracy of the target pilot in the pilot community, the quantile of the time domain difference of the target pilot in the pilot community and the quantile of the frequency domain difference of the target pilot in the pilot community;
when recognizing the brain cognitive state of a pilot, a behavior scene database is first constructed, comprising (1) visual space attention: a variant of the Posner paradigm; (2) working memory: n-back paradigm; (3) Simulated flight scenes of take-off, offshore flight, mountain flight, shooting and return; (4) refining the scene by selecting the model and the difficulty;
calculating time domain features and frequency domain features of brain electricity of a target pilot, and counting time domain and frequency domain features of a tested brain electricity resting state and time domain and frequency domain features of a task state; calculating a difference value corresponding to each characteristic task state and a rest state; according to the brain electricity time domain and frequency domain difference of the target pilot, calculating the quantile of the target pilot in the subdivision group and converting the quantile into percentage; according to the flight level, the flight model, the flight time and the test evaluation target of the target pilot, selecting different coefficients, and calculating the potential ordering of the brain cognitive states of the target pilot in the subdivision groups; and (3) counting comprehensive sequences of the target pilot in different stages, and calculating the change of the brain cognitive state of the target pilot after training.
2. A system for identifying a brain-cognitive state of a pilot, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a target cognitive task from a behavior scene database, and a behavior pattern of the target cognitive task comprises a plurality of rest states and a plurality of task states;
the second acquisition module is used for acquiring the response time, the response accuracy and the brain electricity difference value when the target pilot executes the target cognitive task, wherein the brain electricity difference value is a difference value between first data and second data, the first data is brain electricity data of the target pilot in a resting state of the behavior pattern, and the second data is brain electricity data of the target pilot in the task state of the behavior pattern;
the evaluation module is used for evaluating the brain cognitive state of the target pilot according to the response time, the response accuracy and the brain electricity difference value;
the behavior scene database comprises the following cognitive tasks: visual space attention, working memory and simulation of flight scenes;
wherein, the simulation flight scene includes: a take-off scene, an offshore flight scene, a mountain flight scene, a shooting scene and a return scene;
the first acquisition module is specifically configured to determine a target behavior pattern according to a flight registration, a flight model, a flight time and a test evaluation target of the target pilot, and acquire a target cognitive task conforming to the target behavior pattern from a behavior scene database;
the electroencephalogram data is a time domain feature or a frequency domain feature of electroencephalogram;
the electroencephalogram difference value is the time domain difference between the time domain characteristics of the electroencephalogram of the tested object in the resting state of the behavior pattern and the time domain characteristics of the electroencephalogram of the tested object in the task state of the behavior pattern;
or alternatively, the first and second heat exchangers may be,
a frequency domain difference between a frequency domain feature of the brain electricity of the tested object in a resting state of the behavior pattern and a frequency domain feature of the brain electricity of the tested object in a task state of the behavior pattern;
the evaluation module is specifically configured to incorporate the reaction time into a group database to form a normal reaction time distribution curve, and calculate the quantile of the reaction time of the target pilot in a pilot group, where the pilot group includes a plurality of flight personnel, and the group database includes the reaction time of each of the flight personnel to perform the cognitive task;
the reaction accuracy rate is brought into a group database to form a reaction accuracy rate normal distribution curve, and the quantile of the reaction accuracy rate of the target pilot in a pilot group is calculated, wherein the pilot group comprises a plurality of flight personnel, and the group database comprises the reaction accuracy rate of each flight personnel for executing the cognitive tasks;
the time domain difference is incorporated into a community database to form a time domain difference normal distribution curve, and the quantile of the time domain difference of the target pilot in a pilot community is calculated, wherein the pilot community comprises a plurality of flying personnel, and the community database comprises the time domain difference of each flying personnel for executing the cognitive tasks;
the frequency domain difference is brought into a group database to form a frequency domain difference normal distribution curve, and the quantile of the frequency domain difference of the target pilot in a pilot group is calculated, wherein the pilot group comprises a plurality of flying personnel, and the group database comprises the frequency domain difference of each flying personnel for executing the cognitive tasks;
calculating brain cognitive state ordering of the target pilot in the pilot community according to the quantile of the reaction time of the target pilot in the pilot community, the quantile of the reaction accuracy of the target pilot in the pilot community, the quantile of the time domain difference of the target pilot in the pilot community and the quantile of the frequency domain difference of the target pilot in the pilot community;
when recognizing the brain cognitive state of a pilot, a behavior scene database is first constructed, comprising (1) visual space attention: a variant of the Posner paradigm; (2) working memory: n-back paradigm; (3) Simulated flight scenes of take-off, offshore flight, mountain flight, shooting and return; (4) refining the scene by selecting the model and the difficulty;
calculating time domain features and frequency domain features of brain electricity of a target pilot, and counting time domain and frequency domain features of a tested brain electricity resting state and time domain and frequency domain features of a task state; calculating a difference value corresponding to each characteristic task state and a rest state; according to the brain electricity time domain and frequency domain difference of the target pilot, calculating the quantile of the target pilot in the subdivision group and converting the quantile into percentage; according to the flight level, the flight model, the flight time and the test evaluation target of the target pilot, selecting different coefficients, and calculating the potential ordering of the brain cognitive states of the target pilot in the subdivision groups; and (3) counting comprehensive sequences of the target pilot in different stages, and calculating the change of the brain cognitive state of the target pilot after training.
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