Brain-computer interface-based motor imagery difficulty regulation and control method and device and storage medium

Abstract

The invention discloses a motor imagery difficulty regulation and control method and device based on a brain-computer interface and a storage medium. Electroencephalogram signals of a plurality of brain areas of a user are collected, and an average power spectral density curve of the electroencephalogram signals of each brain area is extracted; extracting relative intensity of alpha rhythm energy and beta rhythm energy, an energy peak value in an alpha rhythm frequency range and an energy peak value in a beta rhythm frequency range according to the average power spectral density curve; according to the relative intensity of the alpha rhythm energy and the beta rhythm energy and each energy peak value, utilizing the classification model obtained by training to predict and obtain the imagination accuracy of the user executing the motor imagination task; a motor imagery difficulty level is determined based on the imagery accuracy. Through refined feature extraction, the accuracy and stability of the classification model can be improved; and the extracted features are input into the trained classification model, so that the accuracy of imagination can be quickly and accurately obtained, and the difficulty level of motor imagination is further determined.

Description

Technical Field

[0001] The present application belongs to the field of signal processing technology, and specifically relates to a method, device and storage medium for controlling the difficulty of motor imagery based on a brain-computer interface. Background Art

[0002] Stroke causes brain cell death or brain tissue damage, and has a high incidence rate. Its sequelae lead to severe disability, placing a heavy burden on patients and their families. In recent years, the incidence of stroke has gradually increased, and the age of onset has gradually decreased, increasing the medical burden on the entire society and causing irreversible physical, psychological and spiritual damage to patients and their families.

[0003] Motor imagery-based brain-computer interface for stroke treatment is a rehabilitation technology that assists stroke patients in reshaping their motor nerves by training the connection between the brain's motor cortex and muscle movement. Motor imagery therapy usually asks patients to imagine themselves doing a certain sport, thereby stimulating neurons in the cerebral cortex and accelerating the directional recovery process. However, due to individual differences and the varying degrees of damage to the brain caused by stroke, patients have different degrees of motor imagery mastery, resulting in poor treatment results. Existing equipment adopts a treatment paradigm of uniform difficulty for all patients, which makes the learning curve of some patients too high, resulting in the inability to continue to concentrate on treatment. Summary of the invention

[0004] The present application aims to address the above technical problems existing in the prior art and to provide a method, device and storage medium for regulating the difficulty of motor imagery based on a brain-computer interface, so as to achieve regulation of the difficulty of motor imagery according to the patient's performance so that it is within a suitable range, thereby helping the patient to recover better.

[0005] In order to achieve the above technical objectives, this application adopts the following technical solutions.

[0006] In a first aspect, an embodiment of the present application provides a method for controlling the difficulty of motor imagery based on a brain-computer interface, comprising:

[0007] Collect EEG signals from multiple brain regions of the user;

[0008] respectively extracting the average power spectral density curve of the electroencephalogram signal of each brain region;

[0009] Extracting the relative intensities of alpha rhythm energy and beta rhythm energy, the energy peak in the alpha rhythm frequency range, and the energy peak in the beta rhythm frequency range according to the average power spectrum density curve;

[0010] According to the relative strength of the alpha rhythm energy and the beta rhythm energy and each of the energy peaks, using the classification model obtained through training, predicting the imagination accuracy of the user performing the motor imagery task;

[0011] The motor imagery difficulty level is determined according to the imagery accuracy rate.

[0012] In some embodiments, determining the difficulty level of motor imagery according to the imagery accuracy rate includes:

[0013] Determining the category of the user's motor imagination difficulty according to the imagination accuracy rate;

[0014] Based on the imagination accuracy and the category, the motor imagination difficulty level is determined, and the expression is as follows:

[0015]

[0016] Wherein, MI is the difficulty level of motor imagery, L n is the center value of the motor imagery difficulty level interval corresponding to the category, S n is the standard deviation of imagination accuracy within the category, P pre is the imagination accuracy, M n is the central value of the imagination accuracy within the category.

[0017] In some embodiments, determining the category of the user's motor imagination difficulty according to the imagination accuracy rate includes: clustering the training data set according to the motor imagination accuracy rates in advance into various categories;

[0018] Determine the central value of the imagination accuracy rate within each category based on the clustering results;

[0019] The distance between the imagination accuracy and each central value is calculated, and the user's movement imagination difficulty is classified into the category with the closest distance.

[0020] In some embodiments, the method also includes: determining the average of n consecutive imagination accuracy rates; comparing the average with a threshold range, if the average is greater than the threshold range, increasing the motor imagination difficulty level by one level to increase the motor imagination difficulty; if the average is less than the threshold range, reducing the motor imagination difficulty level by one level to reduce the motor imagination difficulty.

[0021] In some embodiments, the fitting formula of the power spectrum density curve is as follows:

[0022]

[0023] Wherein, P(x) is the fitting model of the power spectrum density curve, is the energy peak model of the alpha rhythm frequency range, is the energy peak model of the β rhythm frequency range, θ(x|a3,b3) is the noise fitting model; a1,a2,a3,b1,b2,b3,k1,k2,k3 are the fitting parameters.

[0024] In some embodiments, the expression of the noise fitting model θ(x|a,b) is as follows:

[0025]

[0026] Among them, x is a variable, and its value range is the same as the passband frequency; a is the first parameter, and b is the second parameter.

[0027] In some embodiments, according to the relative strength of the alpha rhythm energy and the beta rhythm energy and each of the energy peaks, using the classification model obtained through training, predicting the imagination accuracy of the user performing the motor imagery task includes:

[0028] The brain regions include a first brain region, a second brain region, and a third brain region;

[0029] extracting the relative intensities of alpha rhythm energy and beta rhythm energy based on the average power spectral density curve of the first brain region;

[0030] extracting the energy peak in the alpha rhythm frequency range and the energy peak in the beta rhythm frequency range based on the average power spectral density curves of the second brain region and the third brain region, respectively;

[0031] Determine the total energy peak values ​​E of the second brain region and the third brain region respectively according to the energy peak values ​​in the alpha rhythm frequency range and the energy peak values ​​in the beta rhythm frequency range;

[0032] The calculation formula of the total energy peak value E is as follows:

[0033]

[0034] in, is the energy peak model of the alpha rhythm frequency range, is the energy peak model of the β rhythm frequency range, θ(x|α3,b3) is the noise fitting model; a1,a2,a3,b1,b2,b3,k1,k2,k3 are the fitting parameters.

[0035] In some embodiments, the method further includes: determining the age and gender of the user, and selecting the corresponding classification model according to the age and gender.

[0036] In a second aspect, the embodiment of the present application also includes a motor imagery difficulty control device based on a brain-computer interface, including:

[0037] An EEG signal acquisition module is configured to acquire EEG signals from multiple brain regions of a user;

[0038] A feature extraction module is configured to extract the average power spectrum density curve of the EEG signal of each brain region respectively; based on the average power spectrum density curve, extract the relative intensity of α rhythm energy and β rhythm energy, the energy peak in the α rhythm frequency range, and the energy peak in the β rhythm frequency range;

[0039] The difficulty level identification module is configured to predict the imagination accuracy of the user performing the motor imagination task based on the relative intensity of the alpha rhythm energy and the beta rhythm energy and each energy peak value using the classification model obtained through training; and determine the motor imagination difficulty level based on the imagination accuracy.

[0040] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes a method for controlling the difficulty of motor imagery based on a brain-computer interface as provided in any possible embodiment of the first aspect.

[0041] Compared with the prior art, the method for regulating the difficulty of motor imagery based on brain-computer interface provided in the embodiment of the present application can capture the subtle changes in the user's brain activity when performing motor imagery tasks of different difficulty levels by collecting EEG signals from multiple brain regions of the user and extracting the average power spectrum density curve, making the assessment of the difficulty of motor imagery more accurate; through refined feature extraction, the accuracy and stability of the classification model can be improved; the extracted features are input into the trained classification model to predict the accuracy of motor imagery, and the motor imagery difficulty level can be determined based on the accuracy, so as to achieve the regulation of the difficulty of motor imagery according to the patient's performance, so that it is in a suitable range, thereby helping the patient to recover better. This step not only realizes the automated assessment of the difficulty of motor imagery, but also provides strong support for subsequent difficulty regulation.

[0042] Compared with the prior art, the motor imagery difficulty control device based on brain-computer interface provided in the embodiment of the present application has the same beneficial technical effects as the above-mentioned motor imagery difficulty control method based on brain-computer interface, which will not be elaborated on. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings described herein are for explanation purposes only and are not intended to limit the scope of the present application in any way. In addition, the shapes and proportional dimensions of the components in the drawings are only for illustration purposes and are used to help understand the present application. They do not specifically limit the shapes and proportional dimensions of the components of the present application. Under the guidance of the present application, those skilled in the art can select various possible shapes and proportional dimensions to implement the present application according to specific circumstances. In the drawings:

[0044] Figure 1A schematic diagram of a flow chart of a method for controlling the difficulty of motor imagery based on a brain-computer interface provided in an embodiment;

[0045] Figure 2 A schematic flow chart of a method for controlling the difficulty of motor imagery based on a brain-computer interface provided in another embodiment;

[0046] Figure 3 A schematic block diagram of the structure of a motor imagery difficulty control device based on a brain-computer interface provided in an embodiment. DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions 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 should fall within the scope of protection of this application.

[0048] This embodiment provides a method for controlling the difficulty of motor imagery based on a brain-computer interface. Figure 1 , comprising: step S1: collecting EEG signals of multiple brain regions of the user;

[0049] Step S2: extracting the average power spectrum density curve of the EEG signal of each brain region respectively;

[0050] Step S3: extracting the relative intensities of α rhythm energy and β rhythm energy, the energy peak in the α rhythm frequency range, and the energy peak in the β rhythm frequency range according to the average power spectrum density curve;

[0051] Step S4: According to the relative intensities of α rhythm energy and β rhythm energy and the peak values ​​of each energy, the classification model obtained through training is used to predict the imagination accuracy of the user performing the motor imagery task;

[0052] Step S5: Determine the motor imagery difficulty level according to the imagery accuracy rate.

[0053] In the specific implementation, in step S1, the user can wear an electrode cap to distribute multiple electrodes on the user's head to record the EEG activity of the cerebral cortex. The electrode cap can be a dry electrode, a wet electrode, a gel electrode, etc. In the electrical signal acquisition stage, the user wears the EEG acquisition device to collect the user's EEG signal and record the EEG activity of the user's cerebral cortex.

[0054] As an example, the brain areas for collecting EEG signals may include the frontal lobe area (Fz or nearby leads) and the left and right motor areas (usually C3, C4 or nearby leads). In the EEG recording, Fz represents the electrode position located in the center of the forehead, and the leads near Fz refer to other electrode positions around Fz, which are also commonly used to record EEG activity in the frontal lobe area. C3 and C4 represent the electrode positions located in the central areas of the left and right hemispheres of the brain, respectively. Leads near C3 and C4 can also record EEG activity in the motor area.

[0055] In the specific implementation, it may also include processing the collected EEG signals, such as filtering: first perform a 50Hz notch on the overall data to filter out the power frequency interference, and then perform a bandpass filter on the EEG data, usually with a bandpass of 3Hz-25Hz, using a fourth-order zero-phase Butterworth filter for filtering. Artifact removal: remove physiological artifacts such as electrooculography, electromyography, and electrocardiogram from the filtered EEG data. Segmentation: divide the entire EEG signal data into time windows. Preprocessing the EEG signal can effectively remove noise.

[0056] In a specific embodiment, the collected EEG signals include static EEG signals and motor imagery EEG signals when the user performs a motor imagery task.

[0057] As an example, during the static EEG signal collection, the user needs to open both eyes and look at the display screen. When a moving cursor slowly appears in the central area of ​​the display screen, the user begins to focus on the cursor and collects the set time (e.g., 1 minute). In one embodiment, the time window of the resting task data is 5 seconds, and the sliding interval is 2 seconds.

[0058] The motor imagery EEG signals of the user when performing the motor imagery task are collected. The motor imagery task can be a traditional arrow-guided motor imagery task or a motor imagery task accompanied by a regular rhythm. The user can imagine the left hand moving, or the right hand moving according to the prompt. As an example, each motor imagery task includes 20 groups of motor imagery, each group includes 1 time for the left and right hands. Each motor imagery includes resting for 3 seconds and imagining for 5 seconds, for a total of 8 seconds. Optionally, the data of the first 3 seconds and the last 5 seconds of each motor imagery are collected when collecting the motor imagery EEG signals.

[0059] Step S2 and step S3 implement the feature extraction stage.

[0060] Step S2 can calculate the average power spectral density (PSD) curve of the EEG signals collected by each lead of the prefrontal brain area, the left motor brain area, and the right motor brain area for each time window, and then average the power spectral density curves of the same task state (left-hand motor imagery or right-hand motor imagery), that is, the average prefrontal brain area PSD data, the average left motor brain area PSD data, and the average right motor brain area PSD data. If a certain brain area has only a single electrode, there is no need for averaging processing to form a second feature set.

[0061] In some embodiments, the averaged power spectral density curve can be smoothed. After the processing is completed, each sample of the second feature set includes PSD data of three brain regions (prefrontal brain region, left motor brain region and right motor brain region), namely, PSD data of the prefrontal brain region, PSD data of the left motor brain region and PSD data of the right motor region.

[0062] In some embodiments, baseline correction of motor imagery EEG signals is also included, and the specific method includes: subtracting the PSD data of the previous 3 seconds from the PSD data of the 5 seconds after each imagery to obtain the corrected PSD data.

[0063] In some embodiments, step S3 extracts features from the second feature set. For each sample, the specific steps are as follows: for the PSD data of the prefrontal brain region, the α rhythm energy and the β rhythm energy are calculated. For each time window, the spectrum data of the prefrontal brain region, the energy area under 8Hz-13Hz is calculated as the α energy value, and the energy area under 14Hz-32Hz is calculated as the β energy value; the α / β value is calculated as the relative intensity feature of the α rhythm energy and the β rhythm energy.

[0064] In some embodiments, the corrected left motor brain area PSD data and the corrected right motor area PSD data are further processed to calculate the energy peak value, and the steps are as follows: by means of nonlinear fitting, the PSD curves of the left motor brain area PSD data and the right motor area PSD data are respectively modeled, and the energy peak value of the PSD curve in the α rhythm frequency range and the energy peak value in the β rhythm frequency range are calculated. The modeling fitting formula of the power spectrum density is as follows:

[0065]

[0066] Among them, P(x) is the fitting model of the power spectrum density curve, is the energy peak model of the alpha rhythm frequency range, is the energy peak model of the β rhythm frequency range, θ(x|a3,b3) is the noise fitting model, a1,a2,a3,b1,b2,b3,k1,k2,k3 are fitting parameters. To ensure that the fitting effect is as close to the real as possible, a1,a2,a3,k1,k2,k3 are greater than 0.

[0067] As an example, the b1 value range is 8-12Hz to ensure that the peak is within the α rhythm frequency range; the b2 value range is 12-20Hz to ensure that the peak is within the β rhythm frequency range; the b3 value is related to the lower limit of the bandpass filter and the filter order. In experience, the lower limit is plus or minus 2. For example, when the data bandpass filter range is 3Hz-40Hz, the b2 value range is ln(1) to ln(5).

[0068] In some embodiments, the expression of the energy peak model is as follows:

[0069]

[0070] Where x is a random variable, a is the variance of the first distribution, and b is the parameter of the second distribution.

[0071] As an example, the expression of the noise fitting model θ(x|a,b) is as follows:

[0072]

[0073] Among them, x is a random variable, a is the first parameter, and b is the second parameter.

[0074] In some embodiments, after fitting is completed, the total energy peak value E of the entire brain area is calculated, and the calculation formula is:

[0075]

[0076] As an example, the random variable x takes values ​​in the range of 8 Hz to 20 Hz.

[0077] It can be understood that the energy value may change with the change of the random variable x, and the maximum energy value is taken as the energy peak value.

[0078] After the above feature extraction process, each piece of data in the second feature set obtains three feature values, which can be respectively:

[0079] 1) The α / β value of the prefrontal brain area;

[0080] 2) Total energy peak E1 in the left motor brain area;

[0081] 3) Total energy peak E2 of the right motor brain area.

[0082] Then, the resting-state EEG signals and the motor imagery EEG signals when the user performs the motor imagery task are normalized separately and then averaged to obtain a total of 6 feature values ​​corresponding to the two tasks for each user, 2 average values ​​of α / β in the prefrontal brain area, and 4 average values ​​of motor-related potential peaks in the motor area. They are also normalized separately to obtain the third feature set.

[0083] In some embodiments, step S4 inputs the features of the third feature set into a classification model to predict the imagination accuracy of the user when performing a motor imagery task.

[0084] In an embodiment, the classification model is used to detect the imagination accuracy of the user when performing the current motor imagination task, and predict the imagination accuracy of the corresponding action to be obtained, so as to predict the initial difficulty of the motor imagination that the user can bear.

[0085] In some embodiments, a large amount of user motor imagery EEG and corresponding imagery accuracy data may be collected, and a third feature set and corresponding imagery accuracy may be extracted using each set of EEG signals, and the obtained regression model may be trained as a classification model.

[0086] Optionally, users are divided into 6 groups (male / female; young / middle-aged / elderly) according to their age and gender, and each group includes an independent motor imagery difficulty level category and the imagination accuracy rate of the motor imagery difficulty level category.

[0087] Optionally, the corresponding classifier is selected according to the user's age and gender (male / female; young / middle-aged / elderly). The model input feature number is 6, and the output result is the imagination accuracy rate P of the motor imagery task. pre ,According to the imagination accuracy, the imagination accuracy of the user performing the motor imagination task is predicted.

[0088] In some embodiments, determining the difficulty level of motor imagery according to the imagination accuracy rate includes: pre Represents the imagination accuracy rate achieved by the user when performing the motor imagination task at the default difficulty level, as the first classification output; determines the category of the user's motor imagination difficulty based on the imagination accuracy rate; calculates the motor imagination difficulty level MI based on the imagination accuracy rate and the category, as the second classification output, the formula is as follows:

[0089]

[0090] Among them, MI is the difficulty level of motor imagery, L n S is the center value of the motor imagery difficulty level interval corresponding to the category mapping. n is the standard deviation of imagination accuracy within the category, P pre is the imagined accuracy rate obtained by the classification model prediction, M n is the central value of the imagination accuracy within the category.

[0091] As an example, if the imagination accuracy range obtained by the classification model prediction of the first category is 30%-56%, the imagination accuracy center value of its category is 40%. Through the calculation formula of the motor imagination difficulty level MI, the imagination accuracy can be mapped to the level range of 0-2.5, and the imagination accuracy center value of 40% corresponds to the motor imagination difficulty level 1.25.

[0092] In some embodiments, the category to which the user's motion imagination difficulty belongs is determined based on the imagination accuracy, including: clustering the motion imagination accuracy of the training data set in advance into various categories; determining the central value of the imagination accuracy within each category based on the clustering results; calculating the distance between the imagination accuracy and each central value, and classifying the user's motion imagination difficulty into the category with the closest distance.

[0093] As an example, the first category of imagination accuracy is mapped to the first interval of the motor imagination difficulty level (such as 0-2.5), and the central value of the imagination accuracy in the first category is mapped to the first central value of the motor imagination difficulty level (such as 1.25), indicating that the user's motor imagination is the most difficult; the second category of imagination accuracy is mapped to the second interval (such as 2.6-5), and the central value of the imagination accuracy in the second category is mapped to the second central value of the motor imagination difficulty level (such as 3.75), indicating that the user's motor imagination is slightly difficult; the third category of imagination accuracy is mapped to the third interval of the motor imagination difficulty level (such as 5-7.5), and the central value of the imagination accuracy in the third category is mapped to the third central value of the motor imagination difficulty level (such as 6.75), indicating that the user's motor imagination difficulty is appropriate; the fourth category of imagination accuracy is mapped to the fourth interval of the motor imagination difficulty level (7.6-10), and the central value of the imagination accuracy in the fourth category is mapped to the fourth central value of the motor imagination difficulty level (such as 8.75), indicating that the user's motor imagination is relatively easy.

[0094] Optionally, the calculated motor imagery difficulty level MI range cannot exceed the mapped category boundary value, and retains 1 decimal place. If the user's imagination accuracy is classified as the third category, the motor imagery difficulty level interval center value L corresponding to the third category n If the calculated value of MI is less than the first boundary (such as 5) or greater than the second boundary (such as 7.5), the boundary value can be taken.

[0095] In some embodiments, the central imagination accuracy of the third category of users is the standard imagination accuracy P0, and the standard deviation of the imagination accuracy of the third category of users is S0.

[0096] The motor imagery difficulty level MI calculation formula is used to calculate the imagination accuracy P preConverted into motor imagery difficulty level MI, the scoring range is 0-10, 0 represents the lowest difficulty of the system setting, and 10 represents the highest difficulty of the system setting, ensuring that the accuracy of various types of users is close to the standard motor imagery accuracy when actually performing motor imagery tasks.

[0097] The method for regulating the difficulty of motor imagery based on a brain-computer interface provided in the embodiment of the present application can formulate a personalized rehabilitation treatment plan according to the patient's brain activity characteristics and improve the treatment effect.

[0098] Based on the above embodiments, some embodiments provide a method for controlling the difficulty of motor imagery based on a brain-computer interface, such as Figure 2 As shown, it also includes online difficulty assessment: during the treatment process, the user performs the motor imagery task according to the prompts, collects the user's EEG signals in real time, and outputs whether the user's imagination is correct according to the difficulty strategy. At the same time, the difficulty is adjusted in real time according to the user's performance. The user's current score is evaluated based on the user's real-time performance, and the difficulty is adjusted according to the evaluation results.

[0099] Optionally, after obtaining the initial motor imagery ability level of each user, targeted rehabilitation training is performed on each user according to the motor imagery difficulty grading table.

[0100] Collect the user's EEG data in real time, determine the average of the imagination accuracy of n consecutive times, and obtain the average imagination accuracy PM. The calculation formula is as follows:

[0101]

[0102] Among them, PM refers to the most recent n motor imagery tasks of each user, and P i Refers to the imagination accuracy of a motor imagery task. Optionally, the motor imagery accuracy P i is 1, error P i is 0.

[0103] The value of n can be 10, which is an experience value. After the nth motor imagery task, an evaluation is performed after each motor imagery task, and PM is output to adjust the difficulty of motor imagery.

[0104] Taking P0 as the difficulty baseline, the normal range is P0-S0 to P0+P0. If the average imagination accuracy PM falls within the normal range, no difficulty intervention is performed; if PM is greater than the normal range, MI increases by 1. If PM is less than the normal range, MI decreases by 1.

[0105] The embodiment of the present application analyzes the collected electroencephalography (EEG) signals to predict the difficulty of motor imagery that the patient can bear. The initial difficulty is set according to the prediction result, and the motor imagery training is started. During the training process, the difficulty of motor imagery is adjusted in real time according to the patient's performance. The difficulty of motor imagery can be adjusted according to the evaluation results of the user's motor imagery ability. If the user's motor imagery ability is weak (the total energy peak E value is low), the user shows a low degree of action completion or obvious fatigue, and the difficulty of motor imagery can be appropriately reduced; if the user's motor imagery ability is strong (the total energy peak E value is high), the user shows a high degree of action completion, and the difficulty of motor imagery can be appropriately increased.

[0106] The embodiment of the present application can adjust the difficulty of treatment in real time according to the individual differences and rehabilitation status of the patient, thereby improving the treatment effect. At the same time, the feedback module can help the patient understand his or her own rehabilitation status and encourage the patient to adhere to the treatment.

[0107] Based on the same inventive concept as above, an embodiment of the present application also provides a motor imagery difficulty control device based on a brain-computer interface, including: an EEG signal acquisition module, a feature extraction module and a difficulty level recognition module.

[0108] The EEG signal acquisition module is configured to acquire EEG signals from multiple brain regions of the user;

[0109] The feature extraction module is configured to extract the average power spectrum density curve of the EEG signal of each brain region respectively; based on the average power spectrum density curve, extract the relative intensity of α rhythm energy and β rhythm energy, the energy peak in the α rhythm frequency range, and the energy peak in the β rhythm frequency range;

[0110] The difficulty level identification module is configured to predict the imagination accuracy of the user performing the motor imagination task based on the relative intensity of the alpha rhythm energy and the beta rhythm energy and the energy peaks using the classification model obtained through training; and determine the motor imagination difficulty level based on the imagination accuracy.

[0111] In some embodiments, such as Figure 3 As shown, the motor imagery difficulty control device based on the brain-computer interface also includes a difficulty evaluation module and a difficulty adjustment module; the difficulty evaluation module is configured to determine the mean of the accuracy of n consecutive imaginations; and compare the mean with the threshold range; the difficulty adjustment module is configured to increase the motor imagery difficulty level by one level if the mean is greater than the threshold range, so that the motor imagery difficulty increases; if the mean is less than the threshold range, the motor imagery difficulty level is reduced by one level, so that the motor imagery difficulty decreases. The embodiment of the present application can adjust the difficulty at any time according to the patient's performance to ensure that the treatment is always within the range acceptable to the patient.

[0112] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the above-mentioned method for regulating the difficulty of motor imagery based on brain-computer interface.

[0113] Each embodiment in this specification focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referenced to each other.

[0114] The above is a detailed introduction to the method, device and storage medium for controlling the difficulty of motor imagination based on brain-computer interface provided by the present application. 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 concept of the present application and should not be understood as limiting the scope of protection of the present application.

Claims

1. A method for controlling the difficulty of motor imagery based on brain-computer interface, characterized in that: include: Collect EEG signals from multiple brain regions of the user; respectively extracting the average power spectral density curve of the electroencephalogram signal of each brain region; Extracting the relative intensities of alpha rhythm energy and beta rhythm energy, the energy peak in the alpha rhythm frequency range, and the energy peak in the beta rhythm frequency range according to the average power spectrum density curve; According to the relative strength of the alpha rhythm energy and the beta rhythm energy and each of the energy peaks, using the classification model obtained through training, predicting the imagination accuracy of the user performing the motor imagery task; The motor imagery difficulty level is determined according to the imagery accuracy rate.

2. The method for controlling the difficulty of motor imagery based on brain-computer interface according to claim 1, characterized in that: The motor imagery difficulty level is determined based on the imagery accuracy, including: Determining the category of the user's motor imagination difficulty according to the imagination accuracy rate; Based on the imagination accuracy and the category, the motor imagination difficulty level is determined, and the expression is as follows: Wherein, MI is the difficulty level of motor imagery, L n is the center value of the motor imagery difficulty level interval corresponding to the category, S n is the standard deviation of the imagination accuracy within the category, P pre M is the predicted accuracy of the imagination obtained. n is the center value of the imagination accuracy within the category.

3. The method for controlling the difficulty of motor imagery based on brain-computer interface according to claim 2, characterized in that: Determining the category of the user's motor imagination difficulty according to the imagination accuracy rate includes: Clustering is performed in advance based on the motor imagery accuracy of the training data set to obtain each category; Determine the central value of the imagination accuracy rate within each category based on the clustering results; The distances between the imagination accuracy and each center value are calculated, and the user's movement imagination difficulty is classified into the category with the closest distance.

4. The method for controlling the difficulty of motor imagery based on brain-computer interface according to claim 1, characterized in that: The method further comprises: Determine the mean of the accuracy of n consecutive imaginations; Compare the mean with the threshold range. If the mean is greater than the threshold range, increase the motor imagination difficulty level by one level to increase the motor imagination difficulty; if the mean is less than the threshold range, reduce the motor imagination difficulty level by one level to reduce the motor imagination difficulty.

5. The method for controlling the difficulty of motor imagery based on brain-computer interface according to claim 1, characterized in that: The fitting formula of the power spectral density curve is as follows: Wherein, P(x) is the fitting model of the power spectrum density curve, is the energy peak model of the alpha rhythm frequency range, is the energy peak model of the β rhythm frequency range, θ(x|a3,b3) is the noise fitting model; a1,a2,a3,b1,b2,b3,k1,k2,k3 are the fitting parameters.

6. The method for controlling the difficulty of motor imagery based on brain-computer interface according to claim 5, characterized in that: The expression of the noise fitting model θ(x|a,b) is as follows: Among them, x is a variable, and its value range is the same as the passband frequency; a is the first parameter, and b is the second parameter.

7. The method for controlling the difficulty of motor imagery based on brain-computer interface according to claim 1, characterized in that: According to the relative strength of the alpha rhythm energy and the beta rhythm energy and each of the energy peaks, using the classification model obtained through training, predicting the imagination accuracy of the user performing the motor imagery task, including: The brain regions include a first brain region, a second brain region, and a third brain region; extracting the relative intensities of alpha rhythm energy and beta rhythm energy based on the average power spectral density curve of the first brain region; extracting the energy peak in the alpha rhythm frequency range and the energy peak in the beta rhythm frequency range based on the average power spectral density curves of the second brain region and the third brain region, respectively; Determine the total energy peak values ​​E of the second brain region and the third brain region respectively according to the energy peak values ​​in the alpha rhythm frequency range and the energy peak values ​​in the beta rhythm frequency range; The calculation formula of total energy peak E is as follows: in, is the energy peak model of the alpha rhythm frequency range, is the energy peak model of the β rhythm frequency range, θ(x|a3,b3) is the noise fitting model; a1,a2,a3,b1,b2,b3,k1,k2,k3 are the fitting parameters.

8. The method for controlling the difficulty of motor imagery based on brain-computer interface according to claim 1, characterized in that: The method further comprises: Determine the age and gender of the user, and select the corresponding classification model according to the age and gender.

9. A motor imagery difficulty control device based on a brain-computer interface, characterized in that: include: An EEG signal acquisition module is configured to acquire EEG signals from multiple brain regions of a user; The feature extraction module is configured to extract the average power spectrum density curve of the EEG signal of each brain region respectively; according to the average power spectrum density curve, extract the relative intensity of α rhythm energy and β rhythm energy, the energy peak in the α rhythm frequency range, and the energy peak in the β rhythm frequency range; The difficulty level identification module is configured to predict the imagination accuracy of the user performing the motor imagination task based on the relative intensity of the alpha rhythm energy and the beta rhythm energy and each energy peak value using the classification model obtained through training; and determine the motor imagination difficulty level based on the imagination accuracy.

10. A computer-readable storage medium, characterized in that: The computer storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the processor executes the method for controlling the difficulty of motor imagery based on a brain-computer interface as described in any one of claims 1 to 8.

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