CN113729733B - Motor imagery nerve response ability detection device based on behavior test - Google Patents
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Abstract
The invention discloses a motor imagery nerve response capability detection device based on behavior test, which comprises: the test module is used for detecting motor imagery nerve response capability and combining motor behavior test and cognitive behavior test; and the multiple linear regression analysis module is used for calculating the predicted value of the motor imagery neural response characteristic intensity-relative ERD energy facing the alpha and beta typical frequency bands so as to realize the implementation target of rapid detection. In order to verify the effect of the invention, a motor imagery electroencephalogram comparison experiment is implemented, the relative ERD energy actual value of the hand/foot motor imagery is calculated, and the implementation effect of the invention is illustrated through the high fitting of the predicted value and the actual value; the method provided by the invention can be used for rapidly screening qualified users of the MI-BCI from large-scale potential users, so that the defects of high economy, manpower and time cost of the traditional electroencephalogram screening are avoided, and technical guarantee is provided for popularization and application of the MI-BCI.
Description
Technical Field
The invention relates to the field of motor imagery neural response, in particular to a motor imagery neural response capability detection device based on a behavior test.
Background
Brain-computer interface (Brain Computer Interface, BCI) constructs a direct communication channel between the brain and external devices [1] . Compared to other human output paths, e.g. gestures, speech [2] And the like, which is a more efficient man-machine interaction mode. The BCI enables monitoring of the expression or physiological state of brain intent by detecting and analyzing neural activity information. Based on the sensing mode, the sensor can be classified into fMRI [3] 、NIRS [4] 、EEG [5] Etc., and the advantages and applicable scenarios thereof are different. Wherein EEG-BCI based on brain waves is expected to be used for active control of external equipment in daily operation due to the advantages of high time resolution, portability of equipment and the like [6] 。
The technical principle of EEG-BCI is characterized by electroencephalogram data containing neural response information, and the mode identification and the control instruction output are realized by adopting algorithms such as machine learning and the like. Commonly used brain electrical features such as sensorimotor rhythms (motor imagery), evoked potentials (SSVEP, P300), slow cortical rhythms, etc [7] . However, the problem of "BCI illiterate" severely limits the wide spread of EEG-BCI, which means that some users cannot induce corresponding neural responses during the use of brain-computer interfaces, resulting in difficulty in correctly recognizing instructions by the BCI system [8] . Currently, a great deal of research is actively attempting to promote the applicability of BCI, however, it has not yet emerged that one BCI can be applied to all individuals. Research has proved that [9] About 10-30% of BCI users are illiterate and are closely related to the type of BCI. That is, a user may be illiterate of a certain type of BCI, but other types of BCI may be normally used. This specificity presents a great difficulty for users screening for a certain type of BCI.
The motor imagery brain-computer interface (Motor imagery brain computer interface, MI-BCI) has extremely strong application potential in various fields such as aerospace, military, medical treatment and the like as an active BCI without external stimulus. The control signal source of the system is the energy attenuation/rise characteristic of the cerebral cortex in alpha and beta frequency bands, namely Event-related desynchronization/synchronization (Event-related desynchronization, ERD/ERS) phenomenon, of a user in imagining limb movement. Undoubtedly, illiterate phenomena are also present in MI-BCI. Studies have shown that only the 2-instruction MI-BCI system has about 6.7% of the tested performance to be poor and the recognition accuracy to be less than 59% (50% of the random level).
At present, only if a user actually completes an MI electroencephalogram experiment, whether the user can induce ERD/ERS characteristics can be checked. The method has extremely high cost and relates to three aspects of economy, manpower and time. Generally, electroencephalography requires the joint support of hardware devices and software systems. The hardware device comprises: multi-channel electrode cap, brain electric amplifier, computer for data acquisition and analysis, etc. The software system is mainly acquisition and data analysis software. The price of the whole system is usually more than twenty thousand yuan. In terms of manpower cost, the whole experiment needs at least one master pilot to conduct in a whole-course guidance mode, and the whole experiment must have rich electroencephalogram acquisition experience, data processing knowledge and engineering practice capability. Experiments also require significant time costs, including: the main test debugging equipment and software, the scalp or hair to be tested are cleaned before and after the test, the electrode cap is worn before the test, the conductive paste is coated, and the like, and the preparation time is usually more than 1.5 hours for one complete electroencephalogram test. Obviously, this conventional approach is not suitable for rapid screening of qualified users among a large number of potential users. With the increasing maturity of MI-BCI technology, the continuous increase of application scenarios, how to screen users in huge population with high efficiency, standardization and low cost is a significant challenge, and is a technical problem that must be solved before MI-BCI enters into large-scale application.
In recent years, along with the increasing maturity of MI-BCI technology, the practicability of the technology in various application scenes such as military, medical treatment and the like is gradually improved. But BCI illiterate phenomenon makes MI-BCI unsuitable for all people. The screening of potential users by traditional electroencephalogram experiments is time-consuming and labor-consuming.
Reference to the literature
[1]Yin E,Zhou Z,Jiang J,ChenF,Liu Y and Hu D 2013A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm J.Neural Eng.10
[2]Karpov A A and Yusupov R M 2018Multimodal Interfaces of Human–Computer Interaction Her.Russ.Acad.Sci.88 67–74
[3]Sokunbi M O,Linden D E J,Habes I,Johnston S and Ihssen N 2014Real-time fMRI brain-computer interface:Development of a‘motivational feedback‘subsystem for the regulation of visual cue reactivity Front.Behav.Neurosci.8 1–10
[4]Naseer N and Hong K S 2015fNIRS-based brain-computer interfaces:Areview Front.Hum.Neurosci.9 1–15
[5]Abiri R,Borhani S,Sellers E W,Jiang Y and Zhao X 2019A comprehensive review of EEG-based brain-computer interface paradigms J.Neural Eng.16 1–43
[6]Luu T P,Nakagome S,He Y and Contreras-Vidal J L 2017Real-time EEG-based brain-computer interface to a virtual avatar enhances cortical involvement in human treadmill walking Sci.Rep.7 1–12
[7]McFarland D J and Wolpaw J R 2011Brain-computer interfaces for communication and control Commun.ACM 54 60–6
[8]Volosyak I,Valbuena D,Lüth T,Malechka T andA 2011BCI demographics II:How many(and What Kinds of)people can use a high-frequency SSVEP BCIIEEE Trans.Neural Syst.Rehabil.Eng.19 232–9
[9]Ahn M,Cho H,Ahn S and Jun S C 2013High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery PLoS One 8
[10]Gu B,Xu M,Xu L,Chen L,Ke Y,Wang K,Tang J and Ming D 2021Optimization of Task Allocation for Collaborative Brain–Computer Interface Based on Motor Imagery Front.Neurosci.15 1–11
Disclosure of Invention
The invention provides a motor imagery neural response capability detection device based on behavior test, which is used for realizing prediction of nerve characteristic intensity induced in the process of executing motor imagery by a user by implementing a comprehensive test scheme comprising motor behavior and cognitive behavior and matching a multiple regression model, further rapidly screening qualified users of MI-BCI from large-scale potential users, avoiding the defects of high economy, manpower and time cost of traditional electroencephalogram screening, providing technical support for popularization and application of MI-BCI, and hopefully obtaining considerable social benefit and economic benefit, and being described in detail below:
a motor imagery neural response capability detection apparatus based on behavioral testing, the apparatus comprising:
the test module is used for detecting motor imagery nerve response capability and combining motor behavior test and cognitive behavior test;
and the multiple linear regression analysis module is used for calculating motor imagery neural response characteristic intensity predicted values facing the alpha and beta typical frequency bands and realizing the implementation target of rapid detection.
Wherein, the test module includes:
the exercise behavior testing unit is used for the fine exercise test of the common nail plate, a user finishes the two single-hand tests of the left hand and the right hand, the rest interval is fixed to be 30s, and the time for a group of complete tests is 90s.
Further, the test module further includes:
the imagination action task testing unit is used for a tester to finish the test by using kinescope imagination and visual imagination modes, wherein the visual imagination does not distinguish the first person visual imagination and the third person visual imagination, and the tester selects one of the first person visual imagination and the third person visual imagination to finish the test according to imagination habits.
In one embodiment, the visual illusion comprises:
knee and leg lifting actions with the maximum angle; the in-situ station takes off; parallel movement is not beneficial to arm movement, and horizontal movement from the body side to the front of the body is required; the stance forward flexion requires the toe to be touched with the fingertip.
Preferably, the MI test is 8 groups, the test order is changed from single KL-J-AM-WB:
KL-J-AM-WB-J-KL-WB-AM, setting the test duration of each group of actions to 15s, and enabling a tester to complete imagination actions of not less than 2 rounds during the test duration; the inter-group interval was set to 15s and the total duration was 225s.
Preferably, the main brain electrical channels related to the motor imagery neural response characteristic calculation module are FC3, C5, C3, C1, CP3, FC4, C2, C4, C6, CP4.
The linear regression equation of the multiple linear regression analysis module is as follows:
Y 1 ＝1.2715-0.0012×X 1 -0.0657×X 2 -0.0266×X 4
Y 2 ＝-0.1476-0.0105×X 1 +0.019×X 3 -0.013×X 4
wherein Y is adopted 1 And Y 2 Predictive value rERD representing region of interest in a typical frequency band under motor imagery alpha And rERD beta Value of X 1 ,X 2 ,X 3 ,X 4 The mean of the four test scores PPT-RH, PPT-LH, V-MIQ, K-MIQ is shown.
The technical scheme provided by the invention has the beneficial effects that:
1. the invention designs a behavior test scheme for detecting motor imagery neural response characteristics, substitutes scores obtained by the test into a corresponding regression analysis model to obtain a relative ERD value capable of representing motor imagery neural response intensity, and judges and screens whether a user is a qualified MI-BCI user according to the value;
2. the invention is based on a short-time low-cost behavioral test, and the neural characteristic value capable of representing motor imagery response intensity is obtained by substituting the test scoring result into a multiple regression model, so that the defect of high cost of screening BCI illiterate by a traditional electroencephalogram experiment is overcome, and the invention is hopeful to realize quick screening of MI-BCI qualified users in large-scale crowds and lays a foundation for entering a large-scale technical application.
Drawings
FIG. 1 is a diagram of a behavioral testing protocol for detecting motor imagery neural response capabilities;
FIG. 2 is a physical diagram of a common nail plate;
FIG. 3 is a diagram of a notional action score scale test plan;
wherein, (a) is a schematic diagram of 4 motion test tasks; (b) a schematic diagram of a test flow; (c) is a schematic diagram of test scoring criteria.
FIG. 4 is a diagram of an experimental design of brain electrical in imagination;
wherein, (a) is a single motor imagery test flow chart; (b) is a schematic view of dorsiflexion movements of the wrist and ankle.
FIG. 5 is a graph showing the distribution of 64-channel brain electrodes of the International 10-20 System.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in further detail below.
In order to solve the problems in the background technology, the embodiment of the invention designs a novel behavior test scheme and a detection analysis model, and aims to screen MI-BCI users efficiently, normally and at low cost.
The technical scheme of the invention is divided into two parts, namely a behavior test for detecting motor imagery neural response capability and a neural response characteristic value detection based on a behavior test result, wherein the design of the behavior test scheme is shown in figure 1 and mainly comprises the following steps: test instructions for explaining the test flow and notes; the fine movement test of the 3 groups of common nail plates and the test of the 3 groups of imagination action evaluation scales are carried out alternately, and the time for each test is respectively 90s and 225s; the rest time of the test interval is divided into a long rest (long rest, indicated by o in the figure) and a short rest (short rest, indicated by x in the figure), and the total test time is 1530s (25.5 min). And then calculating the average value of three groups of scores in the two types of tests, and substituting the average value into a multiple linear regression equation of two typical frequency bands of alpha and beta respectively to obtain the characteristic intensity of motor imagery neural response, thereby realizing the implementation target of rapid detection.
The detailed flow of the two types of tests will be described in detail, and the motor imagery neural response intensity predicted value based on 20 tested test results is calculated, and the technical advantages of the invention are illustrated by analyzing the difference between the motor imagery neural response intensity predicted value and the actual value in the corresponding electroencephalogram experiment.
1. Fine motion test for common nail plate
The fine motion test of the common nail plate used in the embodiment of the invention is the simplification of the test of the common nail plate. The universal nailer plate test (Purdue pegboard test, PPT) is a physical test used to measure one-hand/two-hand finger dexterity. The test was invented by the university of Probex 1948, and the design is initially to carry out skill assessment on the working of the pipeline work, so that proper workers are screened to finish the work, and the test is also widely used for neuropsychological assessment, auxiliary positioning of brain injury and the like. The common nail plate test includes four sub-items, namely a right hand test (30 s), a left hand test (30 s), a two hand test (30 s) and an assembly test (60 s).
According to the embodiment of the invention, only a test user is required to complete two single-hand tests of the left hand and the right hand in the common nail plate test, the rest interval is fixed to be 30s, and the time for a group of complete tests is 90s. The nail plate used in the examples of the present invention was Lafayetteq Model 32020A, as shown in FIG. 2. The pegboard includes two parallel rows of 25 pegboard rows. The matched nails, washers, and collars are placed in four grooves above the pegboard, 25 identical nails are placed in the leftmost and rightmost grooves, respectively, and 20 collars and 40 washers are placed in the middle two grooves. One-hand testing requires that the test take nails out of the recess on the same side and insert the nail holes, in a top-down order, as much as possible in a defined time. Counting and scoring are carried out according to the number of nails inserted by one hand, and 3 groups of test scores in the scheme are averaged to obtain final scoring results, and the final scoring results are recorded as PPT-RH (right-hand test score) and PPT-LH (left-hand test score).
2. Imagination action evaluation scale test
The imagination action scoring scale test is a imagination action ability self-evaluation questionnaire modified from Movement Imagery Questionnaire-3 (MIQ-3). Unlike the original test, the embodiment of the invention requires the tester to complete the task of imagination action by respectively using a kinescope imagination (Kinaesthetic imagery, K-MI) mode and a Visual imagination (V-MI) mode. For V-MI, the first person vision imagination and the third person vision imagination are not distinguished, and a tester selects one of the vision imagination and the third person vision imagination to finish the test according to imagination habits. The test tasks of the four types of imagination motions are consistent with MIQ-3, and as shown in the figure 3 (a), the four types of imagination motions are respectively (1) the knee lifting and leg lifting motions with the maximum angles; (2) in-situ station take-off action; (3) Parallel movement is not beneficial to arm movement, and horizontal movement from the body side to the front of the body is required; (4) Stance anteversion action requires effort to touch the toes with the fingertips.
The number, order and test requirements of the test tasks in a single test are optimized pertinently on the basis of the original MIQ-3. The single test flow is shown in fig. 3 (b), and the MI test is changed from the original 12 groups to 8 groups. The test sequence is changed from single KL-J-AM-WB to KL-J-AM-WB-J-KL-WB-AM, so that the balance and stability among the test actions are stronger. In addition, the test also makes detailed demands on each M test task, sets the test duration of each group of actions to be 15s, and requires the tester to complete imagination actions of not less than 2 rounds in the period. To ensure test consistency, the inter-group spacing was also set to 15s, with a total duration of 225s (15 x 15). The subjects scored according to difficulty level of imagination, scoring criteria see fig. 3 (c), and were classified into two types of visual imagination and kinesthesia imagination, from low to high, for a total of 7 score levels. And obtaining the corresponding scoring results of the group of tests by carrying out superposition averaging on the scoring of the four actions under two imagination modes. The final scoring results were averaged over 3 sets of test scores in the protocol and recorded as V-MIQ (visual imagination score) and K-MIQ (kinesthetic imagination score).
3. Motor imagery electroencephalogram verification experiment
The signal source of existing MI-BCI systems typically performs motor imagery-induced response characteristics for the hands or feet. Therefore, two motor imaginations, i.e., right Hand Left Foot (RHLF) and Left Hand Right Foot (LHRF), are selected as the motor tasks of the electroencephalogram. The experiment comprises 6 blocks, each block comprises 28 trials, and two types of exercise tasks are performed in an intersecting manner, namely, after the experiment is finished, each type of exercise can acquire electroencephalogram data of 84 trials.
The experimental flow of single test is shown in fig. 4 (a), firstly, a cross arrow lasting for 1s is presented in the center of an LCD screen to prompt that the tested motor imagery task is about to start; subsequently, arrow symbols for prompting motor imagery tasks are displayed, ↗ represents RHLF, ↖ represents LHRF, this period lasts for 4s altogether, and the subject performs corresponding hand wrist joint and foot ankle joint dorsiflexion MI, as shown in fig. 4 (b); finally, the REST character of 2s is prompted.
Before the experiment starts, the whole experimental flow and related notice are informed to the subject, all relevant experiment personnel are required to close the mobile phone, the subject is arranged to sit on the experiment chair, the position of the display and the sight level of the subject are adjusted, the head of the subject is about 45cm away from the display, the forearms of the hands of the subject are loosened and placed on armrests on two sides of the chair, and the head wears an brain electrode cap which is connected with corresponding equipment. In the experimental process, the eyes of the subjects are required to watch the center of the screen, the attention is focused, all experiments are completed according to the experimental requirements, and limb actions irrelevant to tasks are reduced as much as possible during the experimental process.
The experiment totally collects 20 synchronous electroencephalogram experimental data of two motor imagination to be tested, and the collecting device is a Neuroscan Synamps2 64 conductive electroencephalogram amplifier and matched Scan4.5 collecting software. The sampling rate is set to 1000Hz, the impedance is kept below 15kΩ, the band-pass filtering is performed at 0.5-80 Hz, and the notch filtering is performed at 50 Hz. The data preprocessing mainly comprises the steps of converting original 'cnt' data into 'mat' format through a Matlab EEGLAB tool box, and reducing the data to 200Hz by adopting a downsampling algorithm. And performing 5-33 Hz filtering treatment on the downsampled data by adopting fourth-order Butterworth band-pass filtering, so as to remove the extremely high/low frequency interference in the electroencephalogram signals.
4. Motor imagery neural response feature computation
Event-related desynchronization (ERD) is a major motor imagery-induced electrophysiological feature, usually manifested as energy attenuation of the sensory motor cortex in the alpha (8-13 Hz) and beta (14-28 Hz) bands, a central feature that currently decodes limb motor intent. The event-related spectral perturbation (ERSP) of two typical bands is first calculated to obtain absolute energy values for MI preparation period (single test time 0-1 s) and MI task period (single test time 1-5 s), and based on this, the relative ERD energy values are calculated as key features characterizing motor imagery neural responses.
Event-related spectral perturbation (Event-related Spectral Perturbation, ERSP) is a classical time-frequency analysis method based on Short-time fourier transform (Short-time Fourier Transform, STFT), and is widely used for observing energy changes of electroencephalogram signals related to events in the frequency domain. After data preprocessing, the electroencephalogram data can be segmented according to motor imagery events, and ERSP calculation can be performed according to the following formula:
where n is the total number of event related trials, F k (f, t) represents the energy spectrum estimate of the kth test time (real) at frequency f and time t. And selecting the interested time and frequency band to be overlapped, and obtaining the absolute energy value P in a certain period of a certain frequency band.
Subsequently, the absolute ERD energy (P) in terms of multiple trials (n real) n ) The relative ERD energy (relative ERD power, ererd) is calculated and the formula is defined as follows:
wherein T is relax And T task The number of EEG samples in the preparation period and the task period in the experiment respectively, and the corresponding average energy is P relax And P task 。
As described above, the ERD phenomenon of motor imagery is mainly distributed in the bilateral sensorimotor cortex of the brain, and according to the international 10-20 standard lead arrangement, as shown in FIG. 5, it can be found that the major electroencephalogram channels involved in bilateral sensorimotor are FC3, C5, C3, C1, CP3, FC4, C2, C4, C6 and CP4, the ten leads are selected as the interested regions, and the average relative E under two typical frequency bands is calculatedRD value, i.e. rERD alpha And rERD beta 。
5. Multiple linear regression analysis
One main innovation point of the embodiment of the invention is that a relative ERD value multiple regression prediction equation based on the two types of test scores is provided through a large amount of early-stage research analysis. By Y 1 And Y 2 Representing the relative ERD value rlerd of the region of interest in a typical band under motor imagery tasks alpha And rERD beta . By X 1 ,X 2 ,X 3 ,X 4 The mean of the four test scores PPT-RH, PPT-LH, V-MIQ, K-MIQ is shown.
Thus, the multiple linear regression analysis equation for the relative ERD values can be expressed as:
Y 1 ＝1.2715-0.0012×X 1 -0.0657×X 2 -0.0266×X 4 (5)
Y 2 ＝-0.1476-0.0105×X 1 +0.019×X 3 -0.013×X 4 (6)
the predictive estimate can be calculated by the above equationAnd->And obtaining a key index-fitting goodness R for evaluating the model prediction effect 2 It is defined as:
wherein y is i Representing the i-th real value of ererd,represents the ith rERD predictive value, < ->Representing the mean of the actual value of ererd.
In addition, to verify the relationship between the re d and each test score in the regression equation, pearson correlation was calculated for each of the two, and the correlation coefficient and the significance level were obtained, and the above results are shown in table 1.
TABLE 1 Person correlation and multiple regression analysis
Wherein p <0.05, p <0.01, p <0.005
As can be seen from the table, there is a difference in correlation between the relative ERD values of the different frequency bands and the test scores, rERD alpha Is highly correlated with the existence of PPT-LH, PPT-RH, K-MIQ, rERD beta There is a significant correlation with PPT-LH, V-MIQ, K-MIQ. In addition, because the rERD characterizes the energy attenuation, taking a negative value, in combination with the correlation coefficient, only rERD can be found beta The correlation with V-MIQ is negative, and the other correlations are positive, and the correlation preliminarily proves the rationality of the multiple regression model. Finally, according to the goodness of fit R of the regression analysis equation in the table 2 The fitting effect of prediction is good, the difference between the predicted value and the true value is small, and effective detection of motor imagery neural response characteristic intensity is realized.
In summary, the technical scheme provided by the invention relies on single person behavior test (the test duration is less than 30 min), does not need any high-price equipment, can be combined with a multiple linear regression prediction model to rapidly detect the nerve response characteristic intensity of motor imagery of hands and feet, and provides a practical technical method for efficiently and low-cost screening users suitable for motor imagery brain-computer interfaces.
The invention designs a motor imagery nerve response capability rapid detection device based on a behavior test. The invention can expand the application adaptability of the motor imagery brain-computer interface in the fields of disabled person rehabilitation, electronic entertainment, industrial control and the like, saves a great deal of economic, manpower and time costs and is expected to obtain considerable social and economic benefits.
The embodiment of the invention does not limit the types of other devices except the types of the devices, so long as the devices can complete the functions.
Those skilled in the art will appreciate that the drawings are schematic representations of only one preferred embodiment, and that the above-described embodiment numbers are merely for illustration purposes and do not represent advantages or disadvantages of the embodiments.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (1)
1. Motor imagery neural response capability detection device based on behavioral testing, characterized in that it comprises:
the test module is used for detecting motor imagery nerve response capability and combining motor behavior test and cognitive behavior test;
the multiple linear regression analysis module calculates motor imagery neural response characteristic intensity prediction values facing the alpha and beta typical frequency bands, and achieves the implementation target of rapid detection;
wherein, the test module includes:
the exercise behavior testing unit is used for performing fine exercise testing on the common nail plate, a user finishes two single-hand testing of left hand and right hand, the rest interval is fixed to be 30s, and a group of complete testing takes 90s;
the test module further comprises:
the imagination action task testing unit is used for a tester to finish the test by using kinescope imagination and visual imagination modes, wherein the visual imagination does not distinguish the first person visual imagination and the third person visual imagination, and the tester selects one of the first person visual imagination and the third person visual imagination to finish the test according to imagination habits;
the kinesthetic and visual imaginations include:
knee and leg lifting actions with the maximum angle; the in-situ station takes off; parallel movement is not beneficial to arm movement, and horizontal movement from the body side to the front of the body is required; standing the body forward bending action, and requiring the toe to be touched by a fingertip;
the MI test is 8 groups, the test sequence is changed from single KL-J-AM-WB to KL-J-AM-WB-J-KL-WB-AM, the test duration of each group of actions is set to be 15s, and the tester completes imagination actions of not less than 2 rounds in the period; the inter-group interval is set to 15s and the total duration is 225s;
the linear regression equation of the multiple linear regression analysis module is as follows:
Y 1 ＝1.2715-0.0012×X 1 -0.0657×X 2 -0.0266×X 4
Y 2 ＝-0.1476-0.0105×X 1 +0.019×X 3 -0.013×X 4
wherein Y is adopted 1 And Y 2 Predictive value rERD representing region of interest in a typical frequency band under motor imagery alpha And rERD beta By X 1 ，X 2 ，X 3 ，X 4 The mean value of the four test scores, namely a right hand test score PPT-RH, a left hand test score PPT-LH, a visual imagination score V-MIQ and a kinesthesia imagination score K-MIQ, is expressed.
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