阅读说明：本技术 结合eeg数据质量的运动想象评估方法 (Motor imagery evaluation method combined with EEG data quality ) 是由 李鹏 朱威灵 寿梦婕 傅向向 于 2021-07-19 设计创作，主要内容包括：本发明涉及一种结合EEG数据质量的运动想象评估方法。本发明适用于脑电技术领域。本发明的技术方案为一种结合EEG数据质量的运动想象评估方法,其特征在于：获取经预处理的脑电数据；基于脑电数据的幅值计算得到脑电信号质量评分Score1；基于脑电数据中Theta频段和Beta频段的幅值分布分别计算得到Theta频段质量评分Score2和Beta频段质量评分Score3；基于Score1、Score2和Score3计算得到脑电数据质量评分ScoreSQ；基于脑电数据中Alpha频段数据计算得到运动想象评分ScoreMI；基于ScoreMI和ScoreSQ计算得到作为运动想象评估结果的联合评分Score。(The invention relates to a motor imagery evaluation method combining EEG data quality. The invention is suitable for the technical field of electroencephalogram. The technical scheme of the invention is a motor imagery evaluation method combined with EEG data quality, which is characterized by comprising the following steps: acquiring preprocessed electroencephalogram data; calculating to obtain an electroencephalogram signal quality Score1 based on the amplitude value of the electroencephalogram data; respectively calculating to obtain a Theta frequency band quality Score Score2 and a Beta frequency band quality Score Score3 based on the amplitude distribution of the Theta frequency band and the Beta frequency band in the electroencephalogram data; calculating an electroencephalogram data quality Score ScoreSQ based on Score1, Score2 and Score 3; calculating to obtain a motor imagery score ScoreMI based on Alpha frequency band data in the electroencephalogram data; a joint Score was calculated as a result of motor imagery evaluation based on ScoreMI and ScoreSQ.)

1. A motor imagery assessment method incorporating EEG data quality, characterized by:

acquiring preprocessed electroencephalogram data;

electroencephalogram signal quality Score calculated based on amplitude value of electroencephalogram data₁；

Respectively calculating to obtain a Theta frequency band quality Score based on amplitude distribution of Theta frequency band and Beta frequency band in electroencephalogram data₂And Beta band quality Score₃；

Based on the Score₁、Score₂And Score₃ComputingObtaining the quality Score of the brain electrical data_SQ；

Motor imagery Score calculated based on Alpha frequency band data in electroencephalogram data_MI；

Based on the Score_MIAnd Score_SQA joint Score is calculated as a result of the motor imagery assessment.

2. The method for motor imagery assessment in combination with EEG data quality according to claim 1, wherein said obtaining pre-processed brain electrical data comprises:

acquiring electroencephalogram data in a training process of the trial motor imagery;

removing 50Hz power frequency interference by using a notch filter to obtain electroencephalogram data;

and removing the artifacts in the electroencephalogram data in real time by adopting an ASR algorithm to obtain the electroencephalogram data with the artifacts removed.

3. The method for motor imagery assessment incorporating EEG data quality according to claim 1, wherein said EEG signal quality Score is calculated based on magnitude of EEG data₁The method comprises the following steps:

wherein n is the number of cells divided by the width W in the normal electroencephalogram amplitude range, m is the number of cells divided by the width W in the electroencephalogram amplitude range, j is the jth cell in the normal electroencephalogram amplitude range, k is the kth cell and bin in the electroencephalogram amplitude range_jRepresenting the number of amplitude values in the jth small interval in the normal brain wave amplitude value range, bin_kRepresenting the number of amplitude values in the kth small interval in the electroencephalogram amplitude value range;

wherein, a-a is the range of normal electroencephalogram amplitude, b-b is the range of electroencephalogram amplitude, and P (i) is the probability of the amplitude i appearing in the corresponding electroencephalogram amplitude interval;

4. the EEG data quality-combined motor imagery assessment method according to claim 1, wherein said Theta band quality Score is calculated based on amplitude distributions of Theta band and Beta band in the EEG data₂And Beta band quality Score₃The method comprises the following steps:

taking absolute value abs of the Theta frequency band or Beta frequency band electroencephalogram data, counting the number of abs (data) with amplitude smaller than index, and calculating the percentage of the absolute value abs and the data length L;

wherein index is the maximum value of the absolute value of the amplitude of the electroencephalogram data in the Theta frequency band or the Beta frequency band.

5. The method for motor imagery assessment in combination with EEG data quality according to claim 1, wherein said Score based₁、Score₂And Score₃Score for obtaining quality Score of electroencephalogram data through calculation_SQThe method comprises the following steps:

6. according to claimThe method for evaluating motor imagery in combination with EEG data quality of claim 1, wherein the motor imagery Score is calculated based on Alpha frequency band data in the EEG data_MIThe method comprises the following steps:

performing down-sampling on the electroencephalogram data of the Alpha frequency band;

calculating the electroencephalogram data energy of the Alpha frequency band after the down-sampling, wherein the electroencephalogram data energy of the Alpha frequency band is calculated by adopting the following formula:

the reddata is data after down-sampling, and N is the length of the data after down-sampling;

extracting the maximum electroencephalogram data energy of each channel to represent the ERS effect of the tested object;

ERS according to the set minimum ERS effect_minAnd highest ERS effect ERS_maxCalculating the ERS Effect Score with the training levels LV of the different training phases_ERS，Score_ERSCalculated using the following formula:

adopts ERS effect to distinguish whether the tested object carries out motor imagery or actual movement, and the Score of the motor imagery_MIScore with ERS Effect Score_ERSThe same is true.

7. The method for motor imagery assessment in combination with EEG data quality according to claim 1, wherein said Score based_MIAnd Score_SQCalculating a joint Score as a result of the motor imagery evaluation, comprising:

Score＝0.4*Score_SQ+0.6*Score_ERS。

8. a motor imagery assessment apparatus incorporating EEG data quality, comprising:

the data acquisition module is used for acquiring the preprocessed electroencephalogram data;

a Score calculating module I for calculating the amplitude value based on the electroencephalogram data to obtain the quality Score of the electroencephalogram signal₁；

A Score calculating module II for respectively calculating the Thea frequency band quality Score based on the amplitude distribution of the Thea frequency band and the Beta frequency band in the EEG data₂And Beta band quality Score₃；

Score calculation module III for Score based₁、Score₂And Score₃Score for obtaining quality Score of electroencephalogram data through calculation_SQ；

A motor imagery scoring module used for calculating and obtaining a motor imagery Score based on Alpha frequency band data in the electroencephalogram data_MI；

A joint Score calculation module for Score-based_MIAnd Score_SQA joint Score is calculated as a result of the motor imagery assessment.

9. A storage medium having stored thereon a computer program executable by a processor, the computer program comprising: the computer program when executed implements the steps of a method of motor imagery assessment in combination with EEG data quality according to any one of claims 1 to 7.

10. A motor imagery training system, comprising:

the electroencephalogram acquisition equipment is used for acquiring electroencephalogram data in the process of trial motor imagery training;

a data processing device having a memory and a processor, wherein the memory has stored thereon a computer program executable by the processor, the computer program when executed implementing the steps of the motor imagery assessment method in combination with EEG data quality of any one of claims 1 to 7;

and training a feedback device to perform feedback based on the joint Score in the motor imagery evaluation method combined with the EEG data quality.

Technical Field

The invention relates to a motor imagery evaluation method combining EEG data quality. Is applicable to the technical field of electroencephalogram.

Background

The brain-computer interface is a technology for achieving man-machine interaction by analyzing and processing electroencephalogram signals, and is widely applied to rehabilitation training and treatment of patients with brain injury. The rehabilitation training mode commonly used in the brain-computer interface at present comprises: motor Imagery (MI) and P300, wherein the basic principle of Motor Imagery is: when the unilateral hand motor imagery or exercise is tried to be performed, the brain energy of the alpha frequency band of the motor function brain area on the same side of the unilateral hand is inhibited (ERD), and the energy of the alpha frequency band of the motor function brain area on the opposite side of the unilateral hand is Enhanced (ERS). When the subject continues to train the motor imagery many times, the effect of ERD/ERS will be enhanced accordingly. Because of the relevance of motor function brain region ERD/ERS phenomena to motor imagery, brain-computer interface motor imagery is often applied in motor function deficient patient rehabilitation training.

The electroencephalogram signal is generated by the discharge of cerebral neurons, is extremely weak, and is easily interfered by various noises in the acquisition process. The noise of EEG signal quality mainly comes from two parts, namely the noise is generated by the activities of other organs except the brain in a tested person (such as myoelectricity and electrode slippage artifacts generated by the violent shaking of the body), and the noise is generated by external environment interference (such as power frequency interference generated by other electrical equipment). EEG data quality is a key factor in determining the final performance of the BCI algorithm.

At present, the core of a brain-computer interface motor imagery scheme is based on an ERD/ERS phenomenon, and the content of the scheme mainly comprises three parts, namely data preprocessing, feature extraction and classification. Algorithms widely applied in the EEG data preprocessing process comprise a filter, an ICA, an ASR and the like, algorithms most widely applied in the feature extraction process comprise a PSD, a PCA, a CPS, an FBCSP and the like, and classifiers such as an SVM, an LDA, a Bayesian and the like are mainly applied in the classification process. However, the current solutions have several disadvantages: the aspect of feature extraction is too redundant, so that ERD/ERS imagination generated by motor imagination cannot be effectively expressed, and the calculation burden of a program is increased; the process that the ERD/ERS effect of the tested object is not attached to the classification feedback aspect is enhanced along with the training, so that the training feedback experience is poor and the training effect is slow due to part of the imagination of the tested object; although the electroencephalogram artifacts of the abnormal activity parameters to be tested are removed as much as possible in the data preprocessing process in the current scheme, partial artifacts still exist, so that the accuracy of classification feedback is reduced, and the influence of the continuous abnormal activity to be tested on the motor imagery feedback training is not reflected in the current scheme.

Disclosure of Invention

The technical problem to be solved by the invention is as follows: in view of the existing problems, a motor imagery assessment method combining EEG data quality is provided.

The technical scheme adopted by the invention is as follows: a motor imagery assessment method incorporating EEG data quality, characterized by:

acquiring preprocessed electroencephalogram data;

electroencephalogram signal quality Score calculated based on amplitude value of electroencephalogram data₁；

Respectively calculating to obtain a Theta frequency band quality Score based on amplitude distribution of Theta frequency band and Beta frequency band in electroencephalogram data₂And Beta band quality Score₃；

Based on the Score₁、Score₂And Score₃Score for obtaining quality Score of electroencephalogram data through calculation_SQ；

Motor imagery Score calculated based on Alpha frequency band data in electroencephalogram data_MI；

Based on the Score_MIAnd Score_SQA joint Score is calculated as a result of the motor imagery assessment.

The acquiring of the preprocessed electroencephalogram data comprises:

acquiring electroencephalogram data in a training process of the trial motor imagery;

removing 50Hz power frequency interference by using a notch filter to obtain electroencephalogram data;

and removing the artifacts in the electroencephalogram data in real time by adopting an ASR algorithm to obtain the electroencephalogram data with the artifacts removed.

The EEG signal quality Score is obtained by amplitude calculation based on the EEG data₁The method comprises the following steps:

wherein n is the normal brain electrical amplitude range according to the widthW is the number of the divided cells, m is the number of the cells of the electroencephalogram amplitude range divided according to the width W, j is the jth cell in the normal electroencephalogram amplitude range, k is the kth cell and bin in the electroencephalogram amplitude range_jRepresenting the number of amplitude values in the jth small interval in the normal brain wave amplitude value range, bin_kRepresenting the number of amplitude values in the kth small interval in the electroencephalogram amplitude value range;

wherein, a-a is the range of normal electroencephalogram amplitude, b-b is the range of electroencephalogram amplitude, and P (i) is the probability of the amplitude i appearing in the corresponding electroencephalogram amplitude interval;

respectively calculating to obtain a Theta frequency band quality Score based on amplitude distribution of Theta frequency band and Beta frequency band in the electroencephalogram data₂And Beta band quality Score₃The method comprises the following steps:

taking absolute value abs of the Theta frequency band or Beta frequency band electroencephalogram data, counting the number of abs (data) with amplitude smaller than index, and calculating the percentage of the absolute value abs and the data length L;

wherein index is the maximum value of the absolute value of the amplitude of the electroencephalogram data in the Theta frequency band or the Beta frequency band.

The Score-based₁、Score₂And Score₃Score for obtaining quality Score of electroencephalogram data through calculation_SQThe method comprises the following steps:

the motor imagery Score is obtained by calculation based on Alpha frequency band data in the electroencephalogram data_MIThe method comprises the following steps:

performing down-sampling on the electroencephalogram data of the Alpha frequency band;

calculating the electroencephalogram data energy of the Alpha frequency band after the down-sampling, wherein the electroencephalogram data energy of the Alpha frequency band is calculated by adopting the following formula:

the reddata is data after down-sampling, and N is the length of the data after down-sampling;

extracting the maximum electroencephalogram data energy of each channel to represent the ERS effect of the tested object;

ERS according to the set minimum ERS effect_minAnd highest ERS effect ERS_maxCalculating the ERS Effect Score with the training levels LV of the different training phases_ERS，Score_ERSCalculated using the following formula:

adopts ERS effect to distinguish whether the tested object carries out motor imagery or actual movement, and the Score of the motor imagery_MIScore with ERS Effect Score_ERSThe same is true.

The Score-based_MIAnd Score_SQCalculating a joint Score as a result of the motor imagery evaluation, comprising:

Score＝0.4*Score_SQ+0.6*Score_ERS。

a motor imagery assessment apparatus incorporating EEG data quality, comprising:

the data acquisition module is used for acquiring the preprocessed electroencephalogram data;

a Score calculating module I for calculating the amplitude value based on the electroencephalogram data to obtain the quality Score of the electroencephalogram signal₁；

A Score calculating module II for respectively calculating the Thea frequency band quality Score based on the amplitude distribution of the Thea frequency band and the Beta frequency band in the EEG data₂And Beta band quality Score₃；

Score calculation module III for Score based₁、Score₂And Score₃Score for obtaining quality Score of electroencephalogram data through calculation_SQ；

A motor imagery scoring module used for calculating and obtaining a motor imagery Score based on Alpha frequency band data in the electroencephalogram data_MI；

A joint Score calculation module for Score-based_MIAnd Score_SQA joint Score is calculated as a result of the motor imagery assessment.

A storage medium having stored thereon a computer program executable by a processor, the computer program comprising: the computer program when executed implements the steps of the motor imagery assessment method in combination with EEG data quality.

A motor imagery training system, comprising:

the electroencephalogram acquisition equipment is used for acquiring electroencephalogram data in the process of trial motor imagery training;

a data processing device having a memory and a processor, wherein the memory has stored thereon a computer program executable by the processor, said computer program when executed implementing the steps of the motor imagery assessment method in combination with EEG data quality;

and training a feedback device to perform feedback based on the joint Score in the motor imagery evaluation method combined with the EEG data quality.

The invention has the beneficial effects that: according to the method, the electroencephalogram data quality evaluation is incorporated into the motor imagery calculation score, and the final algorithm score is obtained by integrating the real-time electroencephalogram data quality score and the motor imagery classification score, so that the accuracy of the algorithm and the training effectiveness can be improved by feeding back, and the tested person can be more concentrated on the motor imagery training.

The invention can analyze the quality of the electroencephalogram data in real time and feed back the electroencephalogram data, remind the tested person to adjust in time, reduce invalid training and improve training efficiency. When the quality of the electroencephalogram data is continuously poor, the motor imagery training stops, a warning of signal quality abnormity is sent out, and when the signal quality meets the requirement, the training continues.

The invention gives feedback to the tested person through the flexible ERS effect evaluation standard, is suitable for the process of increasing the ERS effect gradually along with the training, and can keep the tested person concentrated in the training process by appropriate accuracy feedback, thereby improving the training effect and the tested person experience.

The invention sets the lowest ERS effect, the highest ERS effect standard and the training levels in different training stages in the algorithm, can adapt to the training requirements of different testees or the same testee in different training stages, and the testee can automatically adjust the difficulty level according to the system prompt or the training experience.

Drawings

FIG. 1 is a flow chart of an embodiment.

Detailed Description

The embodiment is a motor imagery evaluation method combining EEG data quality, which comprises the following steps:

and S1, acquiring the preprocessed electroencephalogram data.

S11, collecting EEG data X (t) of C3, CZ and C4 channels in real time during the training process of the trial motor imagery_real。

S12, removing 50Hz power frequency interference by using notch filter to obtain EEG data X (t)_notch。

S13, sampling ASR algorithm real-time removal of X (t)_notchThe asr (artifact subspaces recovery) algorithm is a real-time automatic artifact removal method, which can be mainly used to remove transient or large amplitude EEG signal artifacts.

The ASR algorithm core is similar to the principal component analysis algorithm, reconstructing the EEG signal by removing the components with the larger variance and inverse transforming the remaining components. The ASR algorithm correlation formula is as follows:

X_t＝M_rS_t (1)

(X_t)_clean＝M_r(S_t)_clean (4)

X_trepresented as EEG data over time t, which may be decomposed into components S_tAnd a mixing matrix M_rAs in equation 1. Y is_tIs represented by X_tThe principal component space of (a) as in equation 2. Can find out cleaner (S) according to formula 3_t)_cleanWherein the matrix X⁺Represented as the pseudo-inverse of matrix X. The artifact-removed EEG data X (t) can be obtained according to equation 4_clean。

S2, dividing EEG data into Theta, Alpha, and Beta bands in real time using a butterworth bandpass filter. The butterworth filter is described in the following equation, where n is the filter order, H is the filter coefficient of length n +1, and vectors a and b are the transfer function coefficients of the filter.

S3, calculating the amplitude value based on the electroencephalogram data to obtain an electroencephalogram signal quality Score₁The core is to generate histogram bins of EEG data, which may be considered to be of poor quality if the ratio of bins of greater or lesser magnitude to overall bins is greater, or the difference between bins of adjacent magnitude is greater.

Score₁It is mainly calculated by two values, Ratiobin and Ratiocount. The Ratiobin is calculated as follows: a general reaction scheme of X (t)_cleanThe amplitude is divided into several cells with a width of 5uv, each cell having an amplitude of-200 uv to 200uv, the normal brain wave amplitude range is-100 uv to 100uv, if the sum of the normal amplitude number in the interval is less, the data quality is relatively poor. The calculation formula of Ratiobin is as follows (5):

j is the jth small interval in the normal electroencephalogram amplitude range (the value range is-90 uv, the range is divided according to the width of 5uv, (90- (-90))/5 ═ 36, and the value of j is 1-36); k is the kth small interval in the electroencephalogram amplitude range (the value range is-120 uv, the k is divided according to the width of 5uv, (120- (-120))/5 ═ 48, and the value of k is 1-48); bin (n)_jRepresenting the number of amplitude values in the jth cell in the normal electroencephalogram amplitude value range, finding the amplitude values in the corresponding cells through an algorithm, and counting the number; bin (n)_kRepresenting the number of amplitude values in the kth small interval in the amplitude value range of the brain electricity.

The Ratiocount is calculated as follows: calculating the probability of the amplitude i in the range from-100 uv to 100uv of the normal brain electrical amplitude, if the probability is more than 0, counting_iRecording as 1, otherwise recording as 0, and obtaining count in normal interval_iIs the number of 1; calculating the probability of the occurrence of the amplitude value i in the interval from-200 uv to 200uv, and if the probability is greater than 0, counting_iRecording as 1, otherwise recording as 0, and calculating count_iIs the number of 1; passing count in normal amplitude interval_iAnd the count in the interval from-200 uv to 200uv_iThe ratio of (d) calculates Ratiocount. The calculation formula is as (6) (7)

Wherein, P (i) is the probability of the amplitude i appearing in the corresponding electroencephalogram amplitude interval, the data point of the amplitude i in the corresponding electroencephalogram amplitude interval is found through algorithm traversal, and if the data point of the amplitude i exists, P (i) is more than 0; if there are no data points of magnitude i, then p (i) is 0;

score is obtained by the arithmetic mean of ratio count and ratio bin₁The calculation formula is as (8):

s4, respectively calculating to obtain a Theta frequency band quality Score based on amplitude distribution of the Theta frequency band and the Beta frequency band in the electroencephalogram data₂And Beta band quality Score₃。

Score₂And Score₃The amplitude distribution of the Theta frequency band and the Beta frequency band is calculated, and the calculation formula is as shown in 9. The Theta band ranges between 4 and 7Hz, and the absolute value of the EEG magnitude in this band should not exceed 30uv (index in equation 9). The Beta band ranges between 14 and 30Hz, and the absolute value of the EEG magnitude should not exceed 20uv (index in equation 9).

The calculation process takes Theta frequency band as an example: taking absolute value abs of Theta frequency range EEG data, counting the number of abs (data) with amplitude smaller than index, and calculating the percentage of the absolute value abs and the data length L.

S5 Score-based₁、Score₂And Score₃Score for obtaining quality Score of electroencephalogram data through calculation_SQ，Score_SQAccording to Score₁、Score₂And Score₃The arithmetic mean of (a) yields, as in equation (10):

s6, calculating to obtain motor imagery Score based on Alpha frequency band data in electroencephalogram data_MI。

S61, performing down-sampling on the electroencephalogram data of the alpha frequency band in the sampling window, and improving the overall calculation efficiency of the algorithm through the down-sampling.

And S62, respectively calculating the average value of EEG data energy of Alpha frequency bands after C3, C4 and CZ channel downsampling, wherein the specific calculation formula is shown as (11), wherein reddata is data after downsampling, and N is the length of data after downsampling.

S63, when the subject performs motor imagery or actual movement, event-related desynchronization (ERD) and event-related synchronization (ERS) effects occur in the left and right brain area Alpha frequency band electroencephalogram data. Of the ERD and ERS effects, the ERS effect appears more pronounced on EEG data energy, and is therefore used in this embodiment to distinguish whether a subject is undergoing motor imagery or actual movement. In this example, the maximum EEG data energy of three channels C3, C4 and CZ is extracted to represent the RES effect of the test, and the specific calculation formula is as follows:

ERS＝max(power_c3,pwoer_cz,power_c4)

s64, ERS according to the set minimum ERS effect_minAnd highest ERS effect ERS_maxCalculating the ERS Effect Score with the training levels LV of the different training phases_ERS，Score_ERSUsing equation (12) below, ERS effect was scored for Score in this example_ERSScore as motor imagery Score_MI。

S7 Score-based_MIAnd Score_SQA joint Score is calculated as a result of the motor imagery assessment. EEG data is extremely susceptible to interference from abnormal activity of the subject during motor imagery training, and even if ASR is used in the algorithm to remove most of the EEG data artifacts, there are still artifacts left in the EEG data, resulting in accurate determination of ERS effects by equation (12)And (4) sex.

During motor imagery training, the trial of a large number of abnormal activities not only results in reduced EEG data quality (and Score)_SQThe value becomes lower) and also to some extent shows that the subject is not paying attention to the motor imagery.

The feedback score ultimately given to the test is therefore obtained by a weighted average of the ERS effect score and the data quality score. The tested person can adjust and adapt to different training stages of the tested person through the formula (13), so that the optimal motor imagery training feedback is achieved.

Score＝0.4*Score_SQ+0.6*Score_ERS (13)

And S8, displaying the tested EEG data quality score in real time, and stopping motor imagery training if the EEG data quality score is lower than 70 within 5 seconds, giving feedback prompts to the tested EEG, and continuing the motor imagery training after the signal quality is recovered.

The embodiment also provides a motor imagery evaluation device combined with the EEG data quality, which comprises a data acquisition module, a score calculation module I, a score calculation module II, a score calculation module III, a motor imagery score module and a combined score calculation module.

The data acquisition module is used for acquiring the preprocessed electroencephalogram data; the Score calculation module I is used for calculating the amplitude value based on the electroencephalogram data to obtain an electroencephalogram signal quality Score₁(ii) a The Score calculating module II is used for respectively calculating the Theta frequency band quality Score based on the amplitude distribution of the Theta frequency band and the Beta frequency band in the electroencephalogram data₂And Beta band quality Score₃(ii) a The Score calculation module III is used for Score-based analysis₁、Score₂And Score₃Score for obtaining quality Score of electroencephalogram data through calculation_SQ(ii) a The motor imagery scoring module is used for calculating to obtain a motor imagery Score based on Alpha frequency band data in the electroencephalogram data_MI(ii) a The joint Score calculation module is used for Score-based_MIAnd Score_SQA joint Score is calculated as a result of the motor imagery assessment.

The present embodiment also provides a storage medium having stored thereon a computer program executable by a processor, the computer program when executed implementing the steps of the motor imagery assessment method of the present embodiment in combination with EEG data quality.

The embodiment also provides a motor imagery training system, which is provided with electroencephalogram acquisition equipment, data processing equipment and training feedback equipment, wherein the electroencephalogram acquisition equipment is used for acquiring electroencephalogram data in the process of trial motor imagery training; the data processing device has a memory and a processor, wherein the memory has stored thereon a computer program executable by the processor, the computer program when executed implementing the steps of the motor imagery assessment method in this example in combination with EEG data quality; and training a feedback device to feed back based on the joint Score in the motor imagery evaluation method combined with the EEG data quality.

The use method of the motor imagery training system in this example is as follows:

training preparation: the examinee correctly wears the electroencephalogram cap before the motor imagery training begins, and is familiar with the whole process of the motor imagery training. The sampling rate of the electroencephalogram acquisition equipment is 250Hz, and the acquisition electrode is C3/C4/CZ.

Acquiring electroencephalogram data: the method comprises the steps that 1 minute or so of clean electroencephalogram data in a resting state is collected before training is started to be used for ASR model training, then an initial grade value is input into a subject, a training starting button is clicked, a control object and a target object appear on a computer screen of a training feedback device, the target object can randomly appear on the left side and the right side above the control object, the subject needs to drive the control object to move towards the target object through left and right motor imagery, and meanwhile electroencephalogram data start to be collected and processed in real time.

The data processing equipment carries out real-time notch filtering processing on the acquired electroencephalogram data, and removes power frequency interference.

The EEG data without power frequency interference is processed by adopting a sliding window with the time of 1s and the moving step length of 1, and then a signal quality Score is calculated₁。

And performing real-time artifact removal processing on the EEG data without power frequency interference by using ASR.

And performing band-pass filtering on the data subjected to artifact removal processing, extracting data of Alpha, Beta and Theta frequency band frequency bands required by an algorithm, and processing a sliding window with the use time of the data of the 3 frequency bands being 1s and the moving step length being 1.

Calculating a signal quality Score based on Beta and Theta frequency band data₂And Score₃；

By Score₁、Score₂And Score₃Calculating to obtain a signal overall quality Score_SQ；

The electroencephalogram data of Alpha frequency bands are down-sampled to 125Hz, and then a motor imagery Score is calculated_MI；

Score scoring by motor imagery_MIScore with overall signal quality Score_SQCalculating a combined Score;

score for signal quality Score_SQJudging, if the signal quality for a long time is below 70 minutes, the system will show a warning prompt, and when the signal quality is recovered, the warning disappears and the training is continued;

the control command is generated through the joint Score, that is, when the joint Score is greater than 60, the control object can actively move towards the target object, and the moving speed of the control object becomes faster as the joint Score increases. The system can judge whether the current grade value needs to be increased or decreased according to your combined Score, and give a prompt, and the user can adjust the grade value in real time according to the user's feeling.