CN114469641A - Functional electrical stimulation dyskinesia mirror image training method based on myoelectric recognition - Google Patents
Functional electrical stimulation dyskinesia mirror image training method based on myoelectric recognition Download PDFInfo
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Abstract
The invention provides a functional electrical stimulation cerebral apoplexy dyskinesia mirror image training method based on myoelectric recognition, which specifically comprises the following steps: the affected side of the testee is fixed on the exoskeleton robot, and the affected side arm can be driven by the exoskeleton robot to complete corresponding actions. The myoelectricity collecting device is arranged on the side-healthy arm. During training, the subject imagines that both hands do the same action at the same time and the healthy arms actually complete the action. Corresponding gesture actions are recognized by collecting surface electromyographic signals of the healthy side, and the exoskeleton robot drives the affected side to perform the corresponding gesture actions. Meanwhile, functional electrical stimulation is applied to muscles corresponding to the action of the affected side. By selecting a subject to perform an experiment, acquiring electroencephalogram data before and after training by the method, and calculating an evaluation index E for comparative analysis with the electroencephalogram data before and after traditional mirror image training, the effect of the method is better than that of the traditional method.
Description
Technical Field
The invention belongs to the field of pattern recognition, relates to a myoelectricity recognition and functional electrical stimulation technology, and particularly relates to a training method for driving an affected side limb to perform healthy and affected bilateral synchronous actions by an exoskeleton robot through healthy side gesture myoelectricity recognition. And recognizing gesture actions according to the healthy lateral myoelectricity, and performing functional electrical stimulation mirror training on the corresponding muscle of the affected side. The absolute value E of the difference between desynchronization characteristic values related to the electroencephalogram signal mu rhythm time domain events in the left and right movement control areas of the cerebral cortex is calculated and used as an evaluation index of the remodeling effect of the sensory movement cortex, and whether the damaged movement control area of the patient is improved or not is indicated. Compared with the traditional exoskeleton robot mirror image training method based on myoelectric recognition, the method of mirror image training and functional electric stimulation is verified to have better effect.
Background
Mirror image Therapy (MT), a commonly used rehabilitation Therapy, is to place a Mirror between the arm or the legs of the patient to urge the patient to move through the healthy limb, the Mirror image generating the illusion of normal movement of the affected limb, or to perform the same movement of the affected limb as the healthy limb under external mechanical assistance. By this method, remodeling of the cerebral motor sensory cortex of a patient can be stimulated. A commonly used and well-effective rehabilitation training method for dyskinesia is a Functional Electrical Stimulation (FES) technology, which externally stimulates limb muscles of dyskinesia of a patient through a preset current pulse sequence, can not only assist the movement of an affected limb, but also improve the conduction of muscle spindle and tendon spindle of a corresponding action muscle group to a nerve center to promote the remodeling of a sensory motor cortex. The motor imagery can activate the motor-related cerebral cortex like actual movement, promotes the reorganization or reconstruction of sensory motor cortex functions, and is used for the active rehabilitation training treatment of patients with cerebral injury and acroparalysis. With the miniaturization of Electromyography sensors, gesture recognition technology based on surface Electromyography (sEMG) becomes one of research hotspots in the field of gesture recognition due to its natural interaction mode. With the development of acquisition technology and processing technology, the gesture recognition accuracy can reach more than 90%. Electroencephalograms (EEG) are the combined effect of electrical activity of groups of neurons in the cerebral cortex, and are the result of the co-activity of many neurons. Event-Related desynchronization (ERD) and Event-Related Synchronization (ERS) phenomena are the result of the resonance of a large number of neuron activities on physiological electrical signals, which indicates the interaction between neurons and corresponding neurons corresponding to EEG of a certain frequency band, and the ERD value of which can reflect the activity level of cortical region nerves. Therefore, the action of the healthy side is recognized through the surface myoelectric signals of the gesture, the exoskeleton robot on the affected side is controlled according to the recognition result to complete the action same as the healthy side, and simultaneously, FES is applied to the corresponding muscle on the affected side, so that the feasibility is provided for further promoting the rehabilitation of dyskinesia, and the rehabilitation effect can be verified through the ERD characteristics of the cortex electroencephalogram before and after training.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a functional electrical stimulation dyskinesia mirror image training method based on electromyographic recognition.
A functional electrical stimulation dyskinesia mirror image training method based on electromyographic recognition comprises the following steps:
step 1, dividing testees of different sexes into A, B groups with equal number of people according to a single variable principle, wherein the group A adopts healthy side sEMG identification and finishes mirror image training by an exoskeleton robot mechanical assistance affected side; the B group is identified through the healthy side sEMG and mechanically assists the affected side by the exoskeleton robot, and simultaneously FES stimulation is given to corresponding muscles of the moving limb of the affected side; the healthy side sEMG acquisition point position and the B group FES stimulation positions are correspondingly selected according to different limb actions.
And 2, before training of the two groups of subjects in the step 1 begins, collecting EEG signals of a left central C3 area and a right central C4 area when the two groups of subjects perform specific motion motor imagery simultaneously by hands.
And 3, carrying out mirror image training on the subjects classified in the step 1.
And 4, after a training period is carried out in the step 3, EEG signals of a left central C3 area and a right central C4 area are collected when two groups of subjects simultaneously perform specific motion motor imagery.
And 5, preprocessing the EEG data acquired in the step 2 and the step 4 by band-pass filtering at a frequency band of 8-12 Hz, and calculating a sensory-motor cortex movement control capability evaluation index E.
Step 6: and E values obtained by calculating before and after two groups of training methods are compared, and the improvement effect of the two training methods on the sensory-motor cortex movement control capability is analyzed.
Preferably, the EEG data acquisition of the steps 2 and 4 comprises the specific steps;
acquisition of EEG data. The subject makes motor imagery according to video guidance and acquires C3, C4 lead EEG signals. The method specifically comprises the following steps: the motor imagery takes 10s of rest and 10 groups of data are recorded.
Preferably, step 3 mirrors the specific steps of training;
the affected side is fixed on the exoskeleton robot, and the affected side arm is driven by the exoskeleton robot to complete corresponding actions; the myoelectricity collecting device is arranged on the side-healthy arm. During training, the A group of testees act with both hands simultaneously, the healthy side arms actually execute the action, and the affected side does not execute the action in place due to the motor dysfunction. At the moment, corresponding gesture actions are recognized by collecting sEMG signals of the healthy side, and the exoskeleton robot is controlled to drive the affected side to perform corresponding gesture actions. Group B subjects applied functional electrical stimulation to the affected side "acting muscles simultaneously on a group a basis. Subjects were all at a fixed time of day such as 9 a.m.: mirror image training was performed at 00 fixed times, each time lasting 20min, 1 time per day, 5 days per week, for 4 weeks.
Preferably, the calculation and analysis method of the evaluation index E is as follows:
calculating the mean value of all EEG sampling values after filtering, and calculating the mean value of the difference squares;
x in the formula (1)ijThe value of the j sampling for the i experiment, N the number of experiments, AjIs the average of the squares of the j-th sampled differences,
expressed as the average of the j-th samples of all groups;
calculating an energy change rate (ERD);
in the formula (2), [ R, s ] is a rest interval, and R is the mean value of the difference squares of all sampling values in the rest interval; in the formula (3), [ m, n ] is a motor imagery interval.
Calculating the difference absolute value E between the ERDs of the C3 and the C4;
Ei＝|ERDc3i-ERDc4i| (4)
in the formula (4), ERDc3iEnergy Change Rate, ERD, for the ith sample of the C3 leadc4iThe energy change rate of the i-th sample for the C4 lead; eiI.e., an assessment of the improvement in sensorimotor cortical control of each group.
Preferably, the method further comprises the step of performing differential significance analysis by using a t test according to an evaluation standard E calculated before and after training of each group of subjects;
the method specifically comprises the following steps: calculating the P value between two groups of mirror images before training if P is>0.05, the two groups of data have no statistical difference and can be regarded as the same group. E calculated from two sets of mirror images before and after trainingiAnd E, performing comparison before and after the group training and comparison between groups respectively. If E is decreased before and after the mirror image training and has statistical difference, P<0.05, it indicates that the training method has an effect on improving the sensory-motor cortex control ability. When comparing between groups, if after training, the evaluation index E of group B is obviously less than that of group A, and two groups of data have statistical difference, P<0.05, it indicates that the B training method is more effective in improving sensorimotor cortex control than the A training method.
Compared with the prior art, the invention has the advantages that: compared with the existing training method, the training method has the advantages that the activation effect on the brain movement sensory cortex activeness is better, and the control capability of the sensory movement cortex can be more effectively improved. Meanwhile, the training effect is evaluated from the aspect of neural plasticity through EEG analysis, and the method is a convenient and effective mode.
Drawings
FIG. 1 is a functional block diagram of an implementation of the present invention;
FIG. 2 is a diagram of a position distribution of an electroencephalogram signal collected according to an embodiment of the present invention;
FIG. 3 is an experimental paradigm of an example of the invention;
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings in which: the embodiment is implemented on the premise of the technical scheme of the invention, and a detailed implementation mode and a specific operation process are given.
As shown in fig. 1, the present embodiment includes the following steps:
step 1, before training, collecting EEG values of C3 and C4 leads when two groups of subjects perform elbow flexion motor imagery on both sides simultaneously. The C3, C4 lead acquisition positions are shown in fig. 2. The specific process is as follows:
when the test chair is tried to be seated in the middle of the armchair, the video of the elbow bending action is played by the display screen to guide the test hands to do the elbow bending motion imagination at the same time, and C3 and C4 lead EEG signals are acquired. The method specifically comprises the following steps: after 10s of rest after the motor imagery, 10 groups of data are recorded as two groups of original data before training.
Step 2, dividing the testees into A, B groups according to a single variable principle and different sexes randomly and evenly, so that the number of people with the same sex in each group is equal, and the group A adopts healthy side sEMG identification and finishes training by assisting the affected side with the exoskeleton robot; the group B is the affected side mechanically assisted by the exoskeleton robot through healthy side sEMG recognition, and simultaneously FES stimulation is given to corresponding muscles of the moving limb of the affected side. The specific period and training method are as follows:
the affected side of the testee is fixed on the exoskeleton robot, and the affected side arm is driven by the exoskeleton robot to complete corresponding actions. The myoelectricity collecting device is arranged on the side-healthy arm. During training, the subject performs elbow bending movements with both hands, the healthy arm actually performs the movements, and the movement of the affected side is not performed sufficiently due to the motor dysfunction. At the moment, corresponding gesture actions are recognized by collecting sEMG signals of the healthy side, and the exoskeleton robot is controlled to drive the affected side to perform corresponding gesture actions. Group B applied functional electrical stimulation to the muscles of the affected side of the "elbow flexion" motion simultaneously.
Group a was tested for mechanical assistance mirror training, group B was tested for mechanical assistance + FES stimulation mirror training, A, B two groups were tested at a fixed time per day such as 9 am: 00 mirror image training is carried out, each time lasts for 20min, 1 time per day, 5 days per week and 4 weeks continuously.
And 3, after training is finished, synchronizing step 1, and collecting EEG values of C3 and C4 leads under the condition that two hands do elbow bending motion imagination after two groups of subjects are subjected to mirror image training. The overall experimental scheme is shown in figure 3.
Step 4, processing the data collected before and after two groups of mirror image training, calculating to obtain a control ability evaluation index E as shown in table 1, and comparing and analyzing the result to verify that the functional electrical stimulation dyskinesia mirror image training method based on electromyographic recognition is effective;
TABLE 1
The specific analysis is as follows:
according to the results of Table 1 below,
a, B are the average values of E before and after training. A. Comparing before and after training images in group B, and testing the tested micro rhythm in group A before training
After the mirror image training of the peripheral mechanical assistance
Group B under the mu rhythm of the subject
After the mirror image training of peripheral mechanical assistance and FES stimulation
Obviously, the group A mechanical assistance mirror image training method and the group B mechanical assistance and FES stimulation mirror image training method have an effect on remodeling of the sensory and motor cortex of the affected side. A. B, comparing the two groups before and after training, wherein A, B the evaluation index E before the two groups are trained is found by difference analysis through t test, and the two groups before the training have no statistical difference (P)>0.05) can be considered as the same group. After a period of different training, A, B shows improved evaluation index E in both groups, while group B shows the best sensory-motor cortex remodeling in patients with unilateral motor dysfunction, as shown in Table 2. Based on the method, the functional electrical stimulation dyskinesia mirror image training method based on myoelectric recognition is effective for unilateral dyskinesia patients, and has better effect than the mirror image training method based on myoelectric recognition mechanical assistance;
TABLE 2
Claims (5)
1. A functional electrical stimulation dyskinesia mirror image training method based on myoelectric recognition is characterized in that: the method comprises the following steps:
step 1, dividing testees of different sexes into A, B groups with equal number of people according to a single variable principle, wherein the group A adopts healthy side sEMG identification and finishes mirror image training by an exoskeleton robot mechanical assistance affected side; the group B is identified through healthy side sEMG, mechanically assists the affected side by the exoskeleton robot, and simultaneously gives FES stimulation to corresponding muscles of the motion part of the affected side; the healthy side sEMG acquisition point position and the B group FES stimulation positions are correspondingly selected according to different limb actions;
step 2, collecting EEG signals of a left central C3 area and a right central C4 area when two hands of the two groups of subjects do specific motion motor imagery simultaneously before training of the two groups of subjects in the step 1 starts;
step 3, respectively carrying out two mirror image trainings on the two groups of subjects divided into A, B in the step 1;
step 4, after a training period is carried out in the step 3, EEG signals of a left central C3 area and a right central C4 area are collected when two groups of subjects simultaneously perform specific motion motor imagery;
step 5, preprocessing the EEG data acquired in the step 2 and the step 4 by band-pass filtering at a frequency band of 8-12 Hz, and calculating a sensory-motor cortex movement control capability evaluation index E;
step 6: and E values obtained by calculating before and after two groups of training methods are compared, and the improvement effect of the two training methods on the sensory-motor cortex movement control capability is analyzed.
2. The mirror image training method for functional electrical stimulation dyskinesia based on electromyographic recognition of claim 1, wherein: step 2, step 4, EEG data acquisition;
2-1. collection of EEG data; the subject makes motor imagery according to video guidance and acquires C3, C4 lead EEG signals.
3. The mirror image training method for functional electrical stimulation dyskinesia based on electromyographic recognition of claim 1, wherein: step 3, carrying out mirror image training;
the affected side is fixed on the exoskeleton robot, and the affected side arm is driven by the exoskeleton robot to complete corresponding actions; a myoelectricity acquisition device is arranged on the side-healthy arm; during training, the A group of testees do actions with both hands simultaneously, the healthy side arms actually execute the actions, and the action execution of the affected side is not in place due to the movement dysfunction; at the moment, corresponding gesture actions are recognized by collecting sEMG signals of the healthy side, and the exoskeleton robot is controlled to drive the affected side to perform corresponding gesture actions; group B subjects applied functional electrical stimulation to the affected side "acting muscles simultaneously on a group a basis.
4. The mirror image training method for functional electrical stimulation dyskinesia based on electromyographic recognition of claim 1, wherein: the calculation analysis method of the evaluation index E is as follows:
calculating the mean value of all EEG sampling values after filtering, and calculating the mean value of the difference squares;
x in the formula (1)ijThe value of the j sampling for the i experiment, N the number of experiments, AjIs the average of the squares of the j-th sampled differences,
expressed as the average of the j-th samples of all groups;
calculating an energy change rate (ERD);
in the formula (2), [ R, s ] is a rest interval, and R is the mean value of the difference squares of all sampling values in the rest interval; in the formula (3), [ m, n ] is a motor imagery interval;
calculating the difference absolute value E between the ERDs of the C3 and the C4;
Ei＝|ERDc3i-ERDc4i| (4)
in the formula (4), ERDc3iEnergy Change Rate, ERD, for the ith sample of the C3 leadc4iFor the ith acquisition of C4 leadThe rate of change of energy of the sample; eiI.e., an assessment of the improvement in sensorimotor cortical control of each group.
5. The mirror image training method for functional electrical stimulation dyskinesia based on electromyographic recognition of claim 1, wherein: the method also comprises the step of performing difference significance analysis by using a t test according to an evaluation standard E calculated before and after training of each group of testees;
the method specifically comprises the following steps: calculating the P value between two groups of mirror images before training if P is>0.05, the two groups of data have no statistical difference and can be regarded as the same group; e calculated from two sets of mirror images before and after trainingiAverage value E, carrying out comparison before and after the group training and comparison between groups respectively; if E is decreased before and after the mirror image training and has statistical difference, P<0.05, the training method has an effect on improving the sensory-motor cortex control ability; when comparing between groups, if after training, the evaluation index E of the B group is obviously smaller than that of the A group, and the two groups of data have statistical difference, P<0.05, indicating that the training method in group B is more effective in improving sensorimotor cortex control than the training method in group A.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115054250A (en) * | 2022-06-10 | 2022-09-16 | 北京航空航天大学 | Image overflow motion detection analysis method and system |
| CN116831598A (en) * | 2023-06-14 | 2023-10-03 | 中国医学科学院生物医学工程研究所 | Brain muscle signal evaluation method and device |
| CN117442400A (en) * | 2023-12-21 | 2024-01-26 | 深圳市心流科技有限公司 | Correction method, device, equipment and storage medium of intelligent artificial limb |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115054250A (en) * | 2022-06-10 | 2022-09-16 | 北京航空航天大学 | Image overflow motion detection analysis method and system |
| CN116831598A (en) * | 2023-06-14 | 2023-10-03 | 中国医学科学院生物医学工程研究所 | Brain muscle signal evaluation method and device |
| CN116831598B (en) * | 2023-06-14 | 2024-06-04 | 中国医学科学院生物医学工程研究所 | Brain muscle signal evaluation method and device |
| CN117442400A (en) * | 2023-12-21 | 2024-01-26 | 深圳市心流科技有限公司 | Correction method, device, equipment and storage medium of intelligent artificial limb |
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