CN113729733A - Behavior test-based motor imagery neural response capability detection device - Google Patents
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Abstract
The invention discloses a motor imagery neural response capability detection device based on behavior test, which comprises: the testing module is used for detecting the response capability of motor imagery nerves and combining a motor behavior test with a cognitive behavior test; and the multivariate linear regression analysis module calculates the predicted value of the relative ERD energy, which is the motor imagery neural response characteristic intensity facing 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 contrast experiment is implemented, the relative ERD energy actual value of the hand/foot motor imagery is obtained through calculation, and the implementation effect of the invention is demonstrated through the high fitting of the predicted value and the actual value; the method rapidly screens qualified MI-BCI users from large-scale potential users, avoids the defects of high economy, labor and time cost of the traditional electroencephalogram screening, and provides technical support for popularization and application of 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 behavior test.
Background
Brain-Computer Interface (BCI) constructs a direct communication channel between the Brain and external devices[1]. Compared with other human output paths, e.g. gestures, speech[2]And the like, which is a more efficient man-machine interaction mode. BCI enables monitoring of the expression of brain intent or physiological state by detecting and analyzing neural activity information. It can be classified as fMRI based, depending on the sensing mode[3]、NIRS[4]、EEG[5]And the advantages and applicable scenes of various types of BCIs are different. The EEG-BCI based on the electroencephalogram has the advantages of high time resolution, portability of equipment and the like, and is expected to be used for actively controlling external equipment in daily work[6]。
The technical principle of EEG-BCI is characterized by electroencephalogram data containing nerve response information, and the algorithms such as machine learning and the like are adopted to realize mode recognition and output control instructions. Commonly used electroencephalogram features such as sensorimotor rhythm (motor imagery), evoked potential (SSVEP, P300), and bradycortical rhythm[7]. However, the problem of BCI illiterate severely limits the wide popularization of EEG-BCI, which means that part of users cannot induce corresponding neural response in the process of using a brain-computer interface, so that the BCI system is difficult to correctly recognize instructions[8]. At present, a great deal of research is actively attempting to improve the applicability of BCI, however, it has not appeared that one BCI can be applied to all individuals. Research proves that[9]About 10-30% of BCI users have illiterate phenomena and are closely related to the type of BCI. That is, one user may be illiterate with a certain type of BCI, but may normally use other types of BCI. This specificity presents great difficulties for users screening for a certain type of BCI.
The Motor imagery brain-computer interface (MI-BCI) is used as an active BCI without external stimulation, and has strong application potential in various fields such as aerospace, military, medical treatment and the like. The control signal source of the system is the energy attenuation/elevation characteristic of the cerebral motor cortex in alpha and beta frequency bands during the process of imagination of limb movement of a user, namely an Event-related desynchronization/synchronization (ERD/ERS) phenomenon. The illiterate phenomenon is, of course, also present in MI-BCI. Studies have shown that the 2-instruction only MI-BCI system has about 6.7% of the tested performance is poor, with recognition accuracy below 59% (at a random level of 50%).
Currently, only when 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, electroencephalogram experiments require common support of hardware devices and software systems. The hardware device includes: a multi-channel electrode cap, an electroencephalogram amplifier, a computer for data acquisition and analysis and the like. The software system is mainly acquisition and data analysis software. The price of the whole system is usually more than twenty-ten thousand yuan. In the aspect of labor cost, the whole experiment needs at least one main test person to guide the implementation in the whole process, and the experiment must have rich electroencephalogram acquisition experience, data processing knowledge and engineering practice capability. Experiments also require significant time costs, including: the preparation time of main test debugging equipment and software, scalp or hair cleaning before and after an experiment, electrode cap wearing before the experiment, conductive paste smearing and the like is short, and one complete electroencephalogram experiment usually takes more than 1.5 hours. Obviously, this conventional approach is not suitable for quickly screening qualified users among a large number of potential users. With the increasing maturity of MI-BCI technology and the continuous increase of application scenes, how to efficiently, normatively and inexpensively screen users in a large population is a great challenge and a technical problem which must be solved before MI-BCI enters large-scale application.
In recent years, along with the increasing maturity of MI-BCI technology, its utility in various application scenarios such as military affairs and medical treatment is gradually increasing. However, BCI illiteration makes MI-BCI inapplicable to all people. The potential users are screened by the traditional electroencephalogram experiment method, and the time and the labor are consumed.
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 and
A 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 realizes prediction of neural characteristic intensity induced in a process of executing motor imagery by a user by implementing a comprehensive test scheme comprising motor behaviors and cognitive behaviors and matching a multiple regression model, so that qualified users of MI-BCI can be quickly screened from large-scale potential users, the defects of high economic, manpower and time costs of traditional electroencephalogram screening are avoided, technical support is provided for popularization and application of MI-BCI, and considerable social benefits and economic benefits are expected to be obtained, and detailed description is given below:
a motor imagery neural response capability detection apparatus based on behavioral testing, the apparatus comprising:
the testing module is used for detecting the response capability of motor imagery nerves and combining a motor behavior test with a cognitive behavior test;
and the multivariate linear regression analysis module is used for calculating the motor imagery neural response characteristic strength predicted value facing alpha and beta typical frequency bands to realize the implementation target of rapid detection.
Wherein the test module comprises:
the exercise behavior testing unit is used for testing the fine exercise of the ordinary nail plate, a user completes the test of two single hands of the left hand and the right hand, the rest interval is fixed to be 30s, and the time consumed by a group of complete tests is 90 s.
Further, the test module further comprises:
the imagination action task testing unit is used for completing the tests by a tester by using kinesthetic imagery and visual imagery, wherein the visual imagery does not distinguish a first person visual imagery from a third person visual imagery, and the tester selects one of the first person visual imagery and the third person visual imagery according to imagination habits to complete the tests.
In one embodiment, the visual imagery includes:
the knee and leg lifting action at the maximum angle; taking off at the original position; parallel movement of the non-sharp arm requires horizontal movement from the side of the body to the front of the body; the standing body is moved forward to touch the toes with the fingertips.
Preferably, MI is tested in 8 groups, and the test sequence is changed from a single KL-J-AM-WB to:
setting the test duration of each group of actions to be 15s, and finishing not less than 2 times of imagination actions by a tester in the period; the interclass interval was set to 15s and the total duration was 225 s.
Preferably, the motor imagery neural response characteristic calculation module relates to main brain electrical channels of FC3, C5, C3, C1, CP3, FC4, C2, C4, C6 and CP 4.
Wherein, the linear regression equation of the multiple linear regression analysis module is as follows:
Y1＝1.2715-0.0012×X1-0.0657×X2-0.0266×X4
Y2＝-0.1476-0.0105×X1+0.019×X3-0.013×X4
wherein, Y is adopted1And Y2Representing the predicted value rERD of the region of interest in a typical frequency band under the motor imagery taskalphaAnd rERDbetaValue, adopt X1,X2,X3,X4Represents the mean value of four test scores of PPT-RH, PPT-LH, V-MIQ and K-MIQ.
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, and the scores obtained by the test are substituted into a corresponding regression analysis model to obtain a relative ERD value capable of representing the motor imagery neural response strength, and whether a user is a qualified MI-BCI user is judged and screened according to the value;
2. the method is based on short-time low-cost behavior tests, and obtains the neural characteristic value capable of representing the motor imagery response intensity by substituting the test scoring result into the multiple regression model, overcomes the defect of high cost of BCI illiterate screening in the traditional electroencephalogram experiment, is expected to realize the rapid screening of MI-BCI qualified users in large-scale crowds, and lays a foundation for the large-scale technical application of the users.
Drawings
FIG. 1 is a diagram of a behavioral testing protocol for detecting motor imagery neural response capability;
FIG. 2 is a pictorial view of a pop nail plate;
FIG. 3 is a diagram of a notional action score scale test scheme;
wherein, (a) is a schematic diagram of 4 exercise test tasks; (b) is a schematic diagram of a test flow; (c) is a schematic diagram of the test scoring criteria.
FIG. 4 is a diagram of an experimental design of an imaginary action brain wave;
wherein, (a) is a single motor imagery test flow chart; (b) the dorsiflexion movement of the wrist joint and the ankle joint is schematically shown.
Fig. 5 is a diagram of the international 10-20 system 64 channel electroencephalogram distribution.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below.
In order to solve the problems in the background art, the embodiment of the invention designs a novel behavior testing scheme and a detection analysis model, aiming at screening MI-BCI users efficiently, normatively and at low cost.
The technical scheme of the implementation of the invention is divided into two parts, which are used for behavior test for detecting motor imagery neural response capability and neural response characteristic value detection based on behavior test results, and the design of the behavior test scheme is shown as figure 1 and mainly comprises the following steps: test instructions for explaining the test flow and the notes; 3 groups of the general nail plate fine movement tests and 3 groups of the imagination action evaluation scale tests are carried out alternately, and the respective use time is respectively 90s and 225 s; the rest time of the test break is divided into two, long rest (long break, denoted · in the figure) and short rest (short break, denoted · in the figure), and the time taken for the overall test scenario is 1530s (25.5 min). And then calculating the average value of three groups of scores in the two types of tests, and respectively substituting the average value into a multi-element linear regression equation of two typical frequency bands of alpha and beta to obtain the motor imagery neural response characteristic strength, thereby realizing the implementation target of rapid detection.
The following description focuses on the detailed procedures of the two types of tests, calculates the predicted value of the motor imagery neural response intensity based on the 20 tested results, and illustrates the technical advantages of the invention by analyzing the difference between the predicted value and the actual value in the corresponding electroencephalogram experiment.
First, the fine movement test of the ordinary nail plate
The fine movement test of the staple board used in the embodiment of the invention is a simplification of the test of the staple board. The Purdue peg board test (PPT) is a physical test used to measure the flexibility of one/both hand fingers. The test is invented by university of Pushu in 1948, and the design is originally designed for carrying out skill evaluation on assembly line work, so that proper workers are screened to complete the work, and the test is also widely used for neuropsychological evaluation, auxiliary positioning of brain injury and the like. The bund nail plate test comprises four sub-items, right hand test (30s), left hand test (30s), both hands test (30s) and assembly test (60 s).
The embodiment of the invention only requires a test user to finish two single-hand tests of a left hand and a right hand in the test of the common nail plate, the rest interval is fixed to be 30s, and a group of complete tests takes 90 s. The nail plate used in the embodiment of the invention is a Lafayetteq Model 32020A, as shown in FIG. 3. The nail plate includes two parallel rows of 25 nail holes. Mating nails, washers, and collars were placed in four grooves above the pegboard, with 25 identical nails placed in each of the leftmost and rightmost grooves, and 20 collars and 40 washers in the middle two grooves. One-handed testing requires that the nail be removed from the groove on the same side and inserted into the nail hole in a sequence from top to bottom as much as possible within a defined time. The nails were counted and scored according to the number of nails inserted by one hand, and the final scoring results were obtained by averaging 3 sets of test scores in the protocol and recorded as PPT-RH (right hand test score) and PPT-LH (left hand test score).
Second, imagination action evaluation scale test
The imagination action scoring scale test is a self-evaluation Questionnaire of imagination action ability after improvement of motion image query-3 (MIQ-3). Different from the original test, the embodiment of the invention requires a tester to respectively use a kinesthetic imagining (K-MI) mode and a Visual imagining (V-MI) mode to complete imagining action tasks. For V-MI, the first person visual imagery and the third person visual imagery are not distinguished, a tester only needs to select one of the visual imagery to complete the test according to imagination habits, and the test of the embodiment of the invention shows stronger adaptability. The test tasks of the four types of imaginary movements are consistent with the MIQ-3, and as shown in fig. 4(a), the test tasks are respectively (1) the knee lifting and leg lifting movement with the largest angle; (2) taking off at the original position; (3) parallel movement of the non-sharp arm requires horizontal movement from the side of the body to the front of the body; (4) the standing body is required to be forward bent to touch the toes with the fingertips.
The number, sequence and test requirements of the test tasks in a single test are optimized on the basis of the original MIQ-3. The single test flow is shown in FIG. 4(b), where MI test was changed from the original 12 sets to 8 sets. 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 requirements for each M test task, setting the test duration of each group of actions to be 15s, and requiring the tester to complete not less than 2 rounds of imagination actions during the period. To ensure test continuity, the interclass interval was also set to 15s for a total duration of 225s (15 x 15). The subjects are scored according to imagination difficulty degree, and the scoring criterion is shown in figure 4(c), and the subjects are divided into two types of visual imagination and kinesthetic imagination, wherein the scores are graded from low to high and are 7 in total. After scores of four actions under two types of imagination modes are superposed and averaged, corresponding score results of the group of tests can be obtained. The final scoring results were obtained by averaging 3 sets of test scores in the protocol and recorded as V-MIQ (visual imagery score) and K-MIQ (kinesthetic score).
Third, motor imagery electroencephalogram verification experiment
The signal sources of existing MI-BCI systems typically perform motor imagery induced response features for the hands or feet. Therefore, two motor imagination of the Right-Hand Left foot (Right Hand & Left Feet, RHLF) and the Left-Hand Right foot (Left Hand & Right Feet, LHRF) are selected as the motor tasks of the electroencephalogram experiment. The experiment comprises 6 blocks in total, each block comprises 28 test times, the two types of motion tasks are performed in a crossed mode, namely after the experiment is finished, electroencephalogram data of 84 test times can be obtained through each type of motion.
The experimental process of a single trial is as shown in fig. 5(a), firstly, a cross arrow lasting for 1s is presented in the center of the LCD screen to prompt that the subject is about to start the motor imagery task; subsequently, arrow symbols for prompting motor imagery tasks are displayed, ↗ for RHLF and ↖ for LHRF, this phase lasting 4s, and the subject performs the corresponding hand wrist and foot ankle dorsiflexion MI, as in fig. 5 (b); finally, a "REST" character is prompted to REST for 2 s.
Before the experiment begins, informing the testee of the whole experiment process and related cautions, requiring all experiment related personnel to close the mobile phone, arranging the testee to sit on the experiment chair, adjusting the position of the display and the sight level of the testee, enabling the head of the testee to be about 45cm away from the display, enabling the forearms of the testee to be relaxed and arranged on handrails at two sides of the chair, and enabling the head to be connected with corresponding equipment by wearing an electroencephalogram electrode cap. In the experimental process, the examinees are required to watch the center of the screen with eyes and concentrate on the attention, all experiments are completed according to the experimental requirements, and the limb actions irrelevant to tasks are reduced as much as possible in the period.
The experiment acquires 20 synchronous electroencephalogram experimental data of two types of motor imagery to be tested, and the acquisition device is a Neuroscan synomps 264 electroencephalogram amplifier and matched Scan4.5 acquisition software. The sampling rate is set to 1000Hz, the impedance is kept below 15k omega, and the filter is subjected to band-pass filtering at 0.5-80 Hz and notch filtering at 50 Hz. The data preprocessing is mainly to convert the original ". cnt" data into ". mat" format through Matlab EEGLAB toolbox, and to reduce to 200Hz by adopting a down-sampling algorithm. And carrying out 5-33 Hz filtering processing on the data after the down sampling by adopting four-order Butterworth band-pass filtering so as to remove extremely high/low frequency interference in the electroencephalogram signals.
Fourthly, calculating motor imagery neural response characteristics
Event-related desynchronization (ERD) is a main electroencephalogram physiological characteristic induced by motor imagery, usually expressed as energy attenuation of sensory motor cortex in alpha (8-13Hz) and beta (14-28Hz) frequency bands, and is a core characteristic for decoding the movement intention of limbs at present. The method comprises the steps of firstly calculating event-related spectral perturbations (ERSP) of two typical frequency bands, obtaining absolute energy values of an MI preparation period (single test time of 0-1 s) and an MI task period (single test time of 1-5 s), and calculating relative ERD energy values based on the absolute energy values to serve as key characteristics for representing motor imagery neural responses.
Event-related Spectral Perturbation (ERSP) is a classic time-frequency analysis method based on Short-time Fourier Transform (STFT), and is widely applied to observing energy changes of electroencephalogram signals related to events in a frequency domain. After data preprocessing, the electroencephalogram data can be segmented according to motor imagery events, and calculation of ERSP can be carried out, wherein the formula is as follows:
where n is the total number of event-related trials, Fk(f, t) represents the energy spectrum estimate of the kth trial (trial) at frequency f and time t. The interested time and frequency band are selected for superposition, and the absolute energy value P in a certain time period of a certain frequency band can be obtained.
Subsequently, the absolute ERD energy (P) is determined from the multiple trial runs (n-trial)n) Calculating relative ERD energy (rERD), defining the formula as follows:
in the formula, TrelaxAnd TtaskAre respectively experimentsNumber of EEG samples in the mid-preparation period and task period corresponding to average energy PrelaxAnd Ptask。
As mentioned above, the phenomenon of ERD of motor imagery is mainly distributed in bilateral sensory motor cortex of brain, and according to the international 10-20 standard lead arrangement, as shown in FIG. 6, the main brain electrical channels involved in bilateral sensory motor are found to be FC3, C5, C3, C1, CP3, FC4, C2, C4, C6 and CP4, the ten leads are selected as the region of interest, and the average relative ERD value in two typical frequency bands, namely rERD, is calculatedalphaAnd rERDbeta。
Five, multiple linear regression analysis
One of the main innovation points of the embodiment of the invention is to provide a relative ERD value multiple regression prediction equation based on the two types of test scores through a large number of early research and analysis. By Y1And Y2Representing the relative ERD value rERD of the region of interest in a typical frequency band under the motor imagery taskalphaAnd rERDbeta. By X1,X2,X3,X4Represents the mean value of four test scores of PPT-RH, PPT-LH, V-MIQ and K-MIQ.
Thus, the multiple linear regression analysis equation for relative ERD values can be expressed as:
Y1＝1.2715-0.0012×X1-0.0657×X2-0.0266×X4 (5)
Y2＝-0.1476-0.0105×X1+0.019×X3-0.013×X4 (6)
the predicted estimated value can be calculated by the equation
And
and solving a key index-goodness of fit R for evaluating the prediction effect of the model2It is defined as:
wherein, yiIndicates the actual value of the i-th rrerd,
indicates the ith prediction value of rERD,
represents the mean of the actual values of rERD.
In addition, for the relationship between rERD and each test score in the evidence regression equation, the pearson correlation between the rERD and each test score is calculated respectively, and the correlation coefficient and the significance level are obtained, and the results are shown in Table 1.
TABLE 1 Pearson 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 the correlation between the relative ERD values of the different frequency bands and the test scores, rERDalphaMainly has high correlation with PPT-LH, PPT-RH and K-MIQ, rERDbetaThere is a significant correlation with PPT-LH, V-MIQ, K-MIQ. In addition, since rERD represents energy attenuation and takes a negative value, only rERD can be found by combining correlation coefficientsbetaThe V-MIQ is in negative correlation, and the others are in positive correlation, and the relevance preliminarily proves the rationality of the multiple regression model. Finally, the goodness of fit R is determined from the regression analysis equation in the table2The method has the advantages that the predicted fitting effect is good, the difference between the predicted value and the true value is small, and the motor imagery neural response characteristic intensity is effectively detected.
In conclusion, the technical scheme provided by the invention can be used for rapidly detecting the neural response characteristic intensity of the hand and foot limb motor imagery by depending on single-person behavior test (the test duration is less than 30min) without any high-price equipment and combining a multiple linear regression prediction model, and provides a feasible technical method for efficiently screening the user suitable for the motor imagery brain-computer interface at low cost.
The invention designs a motor imagery neural response capability rapid detection device based on behavior test. The invention can expand the application adaptability of the motor imagery brain-computer interface in the fields of disabled people rehabilitation, electronic entertainment, industrial control and the like, saves a large amount of economic, manpower and time costs, and is expected to obtain considerable social and economic benefits.
In the embodiment of the present invention, except for the specific description of the model of each device, the model of other devices is not limited, as long as the device can perform the above functions.
Those skilled in the art will appreciate that the drawings are only schematic illustrations of preferred embodiments, and the above-described embodiments of the present invention are merely provided for description and do not represent the merits of the embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. A motor imagery neural response capability detection apparatus based on behavioral testing, the apparatus comprising:
the testing module is used for detecting the response capability of motor imagery nerves and combining a motor behavior test with a cognitive behavior test;
and the multivariate linear regression analysis module is used for calculating the motor imagery neural response characteristic strength predicted value facing alpha and beta typical frequency bands to realize the implementation target of rapid detection.
2. The motor imagery neural response capacity detection device based on behavioral testing of claim 1, wherein the testing module comprises:
the exercise behavior testing unit is used for testing the fine exercise of the ordinary nail plate, a user completes the test of two single hands of the left hand and the right hand, the rest interval is fixed to be 30s, and the time consumed by a group of complete tests is 90 s.
3. The motor imagery neural response capacity detection device based on behavioral testing of claim 1, wherein the testing module further comprises:
the imagination action task testing unit is used for completing the tests by a tester by using kinesthetic imagery and visual imagery, wherein the visual imagery does not distinguish a first person visual imagery from a third person visual imagery, and the tester selects one of the first person visual imagery and the third person visual imagery according to imagination habits to complete the tests.
4. A behavioral testing-based motor imagery neural response capability detection apparatus according to claim 3, wherein the kinesthetic imagery and visual imagery include:
the knee and leg lifting action at the maximum angle; taking off at the original position; parallel movement of the non-sharp arm requires horizontal movement from the side of the body to the front of the body; the standing body is moved forward to touch the toes with the fingertips.
5. A motor imagery neural response capability detection apparatus based on behavioral testing, according to claim 3, wherein MI tests are 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 15s, and the tester completes no less than 2 rounds of imagination actions during the period; the interclass interval was set to 15s and the total duration was 225 s.
6. The motor imagery neural response capacity detection device based on behavioral testing of claim 1, wherein the linear regression equation of the multiple linear regression analysis module is:
Y1＝1.2715-0.0012×X1-0.0657×X2-0.0266×X4
Y2＝-0.1476-0.0105×X1+0.019×X3-0.013×X4
wherein, Y is adopted1And Y2Indicating interest in motor imagery tasksPredicted value rERD of region in typical frequency bandalphaAnd rERDbetaBy using X1,X2,X3,X4Represents the mean value of four test scores of PPT-RH, PPT-LH, V-MIQ and K-MIQ.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102613972A (en) * | 2012-03-28 | 2012-08-01 | 西安电子科技大学 | Extraction method of characteristics of electroencephalogram signals based on motor imagery |
| CN102985002A (en) * | 2010-03-31 | 2013-03-20 | 新加坡科技研究局 | Brain-computer interface system and method |
| CN105597281A (en) * | 2015-12-18 | 2016-05-25 | 山东大学 | Method and device for evaluating and training fine motion function of fingers |
| CN106264520A (en) * | 2016-07-27 | 2017-01-04 | 深圳先进技术研究院 | A kind of neural feedback athletic training system and method |
| CN110969108A (en) * | 2019-11-25 | 2020-04-07 | 杭州电子科技大学 | Limb action recognition method based on autonomic motor imagery electroencephalogram |
| CN111067517A (en) * | 2019-12-20 | 2020-04-28 | 天津大学 | Motor imagery response capability screening method based on resting state electroencephalogram characteristics |
| US20200337645A1 (en) * | 2018-01-08 | 2020-10-29 | Mayo Foundation For Medical Education And Research | System and Method for Classifying and Modulating Brain Behavioral States |
| US20200401938A1 (en) * | 2019-05-29 | 2020-12-24 | The Board Of Trustees Of The Leland Stanford Junior University | Machine learning based generation of ontology for structural and functional mapping |
| US20210251555A1 (en) * | 2018-08-03 | 2021-08-19 | Rehabswift Pty Ltd | Stroke Rehabilitation Method and System Using a Brain-Computer Interface (BCI) |
-
2021
- 2021-09-10 CN CN202111063502.5A patent/CN113729733B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102985002A (en) * | 2010-03-31 | 2013-03-20 | 新加坡科技研究局 | Brain-computer interface system and method |
| CN102613972A (en) * | 2012-03-28 | 2012-08-01 | 西安电子科技大学 | Extraction method of characteristics of electroencephalogram signals based on motor imagery |
| CN105597281A (en) * | 2015-12-18 | 2016-05-25 | 山东大学 | Method and device for evaluating and training fine motion function of fingers |
| CN106264520A (en) * | 2016-07-27 | 2017-01-04 | 深圳先进技术研究院 | A kind of neural feedback athletic training system and method |
| US20200337645A1 (en) * | 2018-01-08 | 2020-10-29 | Mayo Foundation For Medical Education And Research | System and Method for Classifying and Modulating Brain Behavioral States |
| US20210251555A1 (en) * | 2018-08-03 | 2021-08-19 | Rehabswift Pty Ltd | Stroke Rehabilitation Method and System Using a Brain-Computer Interface (BCI) |
| US20200401938A1 (en) * | 2019-05-29 | 2020-12-24 | The Board Of Trustees Of The Leland Stanford Junior University | Machine learning based generation of ontology for structural and functional mapping |
| CN110969108A (en) * | 2019-11-25 | 2020-04-07 | 杭州电子科技大学 | Limb action recognition method based on autonomic motor imagery electroencephalogram |
| CN111067517A (en) * | 2019-12-20 | 2020-04-28 | 天津大学 | Motor imagery response capability screening method based on resting state electroencephalogram characteristics |
Non-Patent Citations (2)
| Title |
|---|
| ZHONGPENG WANG, LONG CHEN, WEIBO YI, BIN GU, SHUANG LIU, XINGWEI AN, MINPENG XU, HONGZHI QI, FENG HE, BAIKUN WAN, DONG MING: "Enhancement of cortical activation for motor imagery during BCI-FES training", 《ANNU INT CONF IEEE ENG MED BIOL SOC》 * |
| 赵欣，武海霞， 陈龙2， 王仲朋，顾斌，明东: "运动想象结合作业疗法在脑卒中患者康复训练中的研究进展", 《中国生物医学工程学报》, vol. 40, no. 2 * |
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