CN113040785A - Upper limb movement rehabilitation treatment method based on motor imagery - Google Patents
Upper limb movement rehabilitation treatment method based on motor imagery Download PDFInfo
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- CN113040785A CN113040785A CN202110209977.4A CN202110209977A CN113040785A CN 113040785 A CN113040785 A CN 113040785A CN 202110209977 A CN202110209977 A CN 202110209977A CN 113040785 A CN113040785 A CN 113040785A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1113—Local tracking of patients, e.g. in a hospital or private home
- A61B5/1114—Tracking parts of the body
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1121—Determining geometric values, e.g. centre of rotation or angular range of movement
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1126—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb using a particular sensing technique
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N1/00—Electrotherapy; Circuits therefor
- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
- A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
- A61N1/36003—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation of motor muscles, e.g. for walking assistance
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N1/00—Electrotherapy; Circuits therefor
- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
- A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
- A61N1/36014—External stimulators, e.g. with patch electrodes
- A61N1/3603—Control systems
- A61N1/36031—Control systems using physiological parameters for adjustment
Abstract
The invention discloses an upper limb movement rehabilitation treatment method based on motor imagery, which can carry out movement intention detection and motor imagery task completion prediction on a patient in real time, thereby evaluating the brain control ability of the patient. According to the evaluation result, carrying out self-adaptive distribution on the brain control and system auxiliary control weights of the patient; and finally, converting the result of the cooperative control into a control instruction, and synchronously controlling the virtual upper limbs. If the patient completes the motor imagery task within the specified time, the system electrically stimulates the corresponding muscle of the upper limb of the patient by the driving function electrical stimulation equipment to promote the patient to complete the action consistent with the virtual upper limb and keep the action for 4 seconds; otherwise, the system does not apply electrical stimulation to the patient. Therefore, according to the method disclosed by the invention, a feasible method is provided for the cerebral apoplexy patient with unstable brain-computer interaction performance to participate in the brain-computer interface motor rehabilitation treatment.
Description
Technical Field
The invention relates to the field of artificial intelligence and the field of brain-computer interface upper limb rehabilitation application research, in particular to an upper limb movement rehabilitation treatment method based on motor imagery.
Background
Stroke is a disease mainly caused by cerebral ischemia and hemorrhagic injury symptoms, and has extremely high fatality rate and disability rate. Stroke is acute and high in fatality rate, and is one of the most important fatal diseases in the world. According to the latest survey by the world health organization: chinese cerebral apoplexy patients have nearly eight patients with different degrees of dyskinesia.
Currently, the rehabilitation therapy form for stroke patients is still mainly the traditional passive motor rehabilitation training and physical therapy. The passive exercise rehabilitation training refers to a patient performing a large number of repetitive limb exercises with the assistance of a professional therapist. Physical therapy such as massage, acupuncture, low frequency electrical stimulation, etc. The traditional passive rehabilitation training and physical therapy have the defects of low efficiency, slow effect and the like.
The research shows that: the brain-computer interface can decode the motor intention of the patient from the brain signal, strengthen the intervention on the brain damage area, activate the plasticity of the cranial nerves and improve the motor relearning ability of the patient. Compared with the traditional treatment means, the rehabilitation effect of the patient using the brain-computer interface technology is better. However, this presents challenges at present. For example, patients with severe stroke have serious illness and are difficult to obtain ideal brain-computer interaction performance, so that rehabilitation training tasks cannot be completed and are frustrated, and the enthusiasm for participating in treatment is also affected. As a result, a subset of patients may have to forego participation in rehabilitation therapy due to poor performance.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art and provides an upper limb movement rehabilitation treatment method based on motor imagery, which can reduce the influence of brain-computer interaction performance on brain-computer interface movement rehabilitation treatment to a certain extent.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: a motor imagery based upper limb motor rehabilitation treatment method comprises the following steps:
firstly, a stroke patient wears an electrode cap to sit on a wheelchair, and performs 30-trial motor imagery tasks along with virtual upper limb actions displayed on a computer screen. The system will acquire the EEG signal of the patient while performing the imagination task and the EEG signal of the patient at rest between each two tasks. The classifier is trained on the EEG signals of these two states.
And secondly, after the training of the classifier is finished, the patient needs to execute 60 trial upper limb motor imagery rehabilitation treatment tasks according to system prompts. The rehabilitation task of upper limb motor imagery is divided into two phases. In the former stage, the patient needs to complete the motor imagery task within 10 seconds. During the course of a patient's motor imagery task, the system performs motor intent detection and brain control ability assessment on the patient's EEG signals every 500 milliseconds. According to the evaluation result, carrying out self-adaptive distribution on the brain-computer control weight; and finally, converting the result of the cooperative control into a control instruction, and synchronously controlling the virtual upper limbs in real time.
Thirdly, in the later stage, if the patient completes the motor imagery task within the appointed time, the system electrically stimulates the corresponding muscles of the upper limbs of the patient by the driving function electrical stimulation equipment to promote the patient to complete the action consistent with the virtual upper limbs and keep the action for 4 seconds; otherwise, no electrical stimulation is applied to the patient. And (5) the patient takes a rest for 5 seconds after finishing the current treatment task, and the second step and the third step are repeated continuously after the rest is finished, and the same treatment task is carried out for the next time until all treatment tasks are finished.
The requirement of the patient to perform the motor imagery task in steps one, two and three is not limited to a fixed imagery task, and the specific content of the task and the state of the completed task can be generally defined according to the diseased condition of the patient. For example, in case of a right upper limb wrist motor dysfunction of a stroke patient, the therapist may set the motor imagery task for the patient by raising the wrist of the right side (initial state of the task) lying on the table to an angle of 60 degrees with respect to the horizontal plane of the table (state at the completion of the task). To help the patient understand the task, the system not only voice prompts, but also visually presents a three-dimensional virtual upper limb movement corresponding to the task at the same time as the prompt.
In step two, the brain control ability assessment mainly comprises two steps:
A. sequentially preprocessing, characteristic extraction and classification recognition are carried out on the EEG signals of the first 2 seconds at the current moment, and finally the output value of a classifier is used as the intensity of the motor imagery of the patient at the current moment;
B. and predicting the task completion degree at the current moment according to the 4 motor imagery intensities included by the patient at the current moment and the minimum motor imagery intensity required for completing the treatment task of the real. Higher completion of the task indicates greater brain control ability of the patient, and vice versa;
and step two, self-adaptive distribution of brain-computer control weight specifically comprises the following steps:
A. calculating the probability of failure of the patient to complete the task at the current moment;
B. calculating the degree of assistance the system applies to the patient in completing the task;
C. optimal weights assigned to patient control and system-assisted control are calculated according to an optimization method with the goal of simultaneously minimizing the likelihood of task failure and the degree of applied assistance.
The system driving function electrical stimulation equipment in the step three electrically stimulates muscles corresponding to the upper limbs of the patient to promote the muscles to finish the actions consistent with the virtual upper limbs, and the method specifically comprises the following steps:
A. calculating the relative angles of main joints of the virtual upper limb, such as shoulder joints, elbow joints, wrist joints, metacarpophalangeal joints and the like according to the three-dimensional model of the virtual upper limb;
B. respectively applying electric stimulation with corresponding intensity to the electric stimulation electrode patches related to the joint muscle movement;
C. sensing the relative angle of each joint in real time through an angle sensor fixed on each joint of the upper limb; and calculating the angle difference between the sensed relative angle and the relative angle of the model, and using the angle difference as the input of the PID controller, wherein the PID controller adjusts the intensity of the electric stimulation in real time according to a PID control algorithm, so that the action consistent with the virtual upper limb is quickly obtained.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention evaluates the brain control ability of the patient by detecting the motor intention of the patient and predicting the completion degree of the current rehabilitation treatment task. The evaluation of the brain control ability provides reference basis for applying proper control assistance to the patient in the system, thereby avoiding the adverse effect of improper assistance degree on rehabilitation treatment.
2. The traditional brain-computer interaction rehabilitation treatment is completely determined by the brain-computer interaction performance, and the brain-computer interaction performance can directly influence whether a patient can be in a scheduled rehabilitation training task on time. However, the severe stroke patient has a serious illness and an extremely unstable physical condition, and is difficult to obtain a good brain-computer interaction performance. The invention adaptively allocates the weights of the brain control of the patient and the system auxiliary control according to the brain control capability of the patient. When the brain control ability of the patient is low, the system provides assistance to a certain degree, helps the patient to complete the rehabilitation task on the premise of self-effort, and improves the completion degree, so that the patient is prevented from being contused; when the brain control ability of the patient is high and the patient can complete the rehabilitation training task, the system can not provide auxiliary control for the patient. Therefore, the invention provides a feasible method for the cerebral apoplexy patient with unstable brain-computer interaction performance to participate in the brain-computer interface motor rehabilitation treatment.
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FIG. 1 is a system framework diagram of the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described below with reference to examples; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.
The motor imagery-based upper limb motor rehabilitation treatment method provided by the embodiment can be used for detecting motor intentions and predicting the completion degree of rehabilitation treatment tasks of a patient in real time, and further evaluating the brain control ability of the patient. According to the evaluation result, carrying out self-adaptive distribution on the brain control and system auxiliary control weights of the patient; and finally, converting the result of the cooperative control into a control instruction, and performing synchronous control on the virtual upper limbs. If the patient completes the treatment task within the specified time, the system drives the functional electrostimulation device to perform electrostimulation on the corresponding muscle of the upper limb of the patient, so as to promote the patient to complete the action consistent with the virtual upper limb.
The rehabilitation treatment method for upper limb movement based on motor imagery comprises the following steps:
firstly, a stroke patient wears an electrode cap to sit on a wheelchair, and performs 30-trial motor imagery tasks along with virtual upper limb actions displayed on a computer screen. The system will acquire the EEG signal of the patient while performing the imagination task and the EEG signal of the patient at rest between each two tasks. The classifier is trained on the EEG signals of these two states.
And secondly, after the training of the classifier is finished, the patient needs to execute 60 trial upper limb motor imagery rehabilitation treatment tasks according to system prompts. The rehabilitation task of upper limb motor imagery is divided into two phases. In the former stage, the patient needs to complete the motor imagery task within 10 seconds. During the course of a patient's motor imagery task, the system performs motor intent detection and brain control ability assessment on the patient's EEG signals every 500 milliseconds. According to the evaluation result, carrying out self-adaptive distribution on the brain-computer control weight; and finally, converting the result of the cooperative control into a control instruction, and synchronously controlling the virtual upper limbs in real time.
Thirdly, in the later stage, if the patient completes the motor imagery task within the appointed time, the system electrically stimulates the corresponding muscles of the upper limbs of the patient by the driving function electrical stimulation equipment to promote the patient to complete the action consistent with the virtual upper limbs and keep the action for 4 seconds; otherwise, no electrical stimulation is applied to the patient. And (5) the patient takes a rest for 5 seconds after finishing the current treatment task, and the second step and the third step are repeated continuously after the rest is finished, and the same treatment task is carried out for the next time until all treatment tasks are finished.
In the first step and the second step, the requirement of the patient to perform the motor imagery task is not limited to a fixed imagery task, and the specific content of the task and the state of the completed task can be generally defined according to the diseased condition of the patient. For example, in case of a right upper limb wrist motor dysfunction of a stroke patient, the therapist may set the motor imagery task for the patient by raising the wrist of the right side (initial state of the task) lying on the table to an angle of 60 degrees with respect to the horizontal plane of the table (state at the completion of the task). To help the patient understand the task, the system not only voice prompts, but also visually presents a three-dimensional virtual upper limb movement corresponding to the task at the same time as the prompt.
In the second step, the evaluation of the brain control ability of the patient is specifically realized by the following two steps:
A. sequentially preprocessing, characteristic extraction and classification recognition are carried out on the EEG signals of the first 2 seconds at the current moment, and finally the output value of a classifier is used as the intensity of the motor imagery of the patient at the current moment;
B. and predicting the task completion degree at the current moment according to the 4 motor imagery intensities included by the patient at the current moment and the minimum motor imagery intensity required for completing the treatment task of the real. Higher completion of the task indicates greater brain control ability of the patient, and vice versa;
the brain-computer control weight self-adaptive distribution in the step two is realized by the following steps:
A. calculating the probability of failure of the patient to complete the task at the current moment;
B. calculating the degree of assistance the system applies to the patient in completing the task;
C. optimal weights assigned to patient control and system-assisted control are calculated according to an optimization method with the goal of simultaneously minimizing the likelihood of task failure and the degree of applied assistance.
In the third step, the system drives the functional electrostimulation device to perform electrostimulation on the corresponding muscle of the upper limb of the patient, so as to promote the patient to complete the action consistent with the virtual upper limb, and the method specifically comprises the following steps:
A. calculating the relative angles of main joints of the virtual upper limb, such as shoulder joints, elbow joints, wrist joints, metacarpophalangeal joints and the like according to the three-dimensional model of the virtual upper limb;
B. respectively applying electric stimulation with corresponding intensity to the electric stimulation electrode patches related to the joint muscle movement;
C. sensing the relative angle of each joint in real time through an angle sensor fixed on each joint of the upper limb; and calculating the angle difference between the sensed relative angle and the relative angle of the model to be used as the input of the PID controller, and adjusting the intensity of the electric stimulation in real time by the PID controller according to a PID control algorithm so as to quickly obtain the action consistent with the virtual upper limb.
Detecting the movement intention in the step two, specifically comprising the following steps:
A. extracting 2-second EEG signals forwards at the current moment, and sequentially carrying out Common Average Reference (CAR) filtering and 8-30 Hz band-pass filtering;
B. projecting the filtered EEG signal in Common Spatial Pattern (CSP) to obtain feature vector;
C. the obtained feature vectors are input to a classifier (Bayes, SLda, or SVM) to obtain predicted classes and corresponding output values. If the predicted category is consistent with the category of the imagination task, taking the output value of the classifier as the intensity of the motor imagination; if the categories are not consistent, then the patient is currently without motor intent.
Fig. 1 is a flow chart of the rehabilitation therapy method for upper limb movement based on motor imagery. When the patient carries out the upper limb movement rehabilitation treatment, 60 trial treatment tasks are required to be executed according to system prompts. In each treatment task, the time required for the patient to perform the imagination task was 10 seconds. Within a defined 10 seconds, the system operates as follows in sequence every 500 milliseconds:
firstly, carrying out one-time movement intention detection (with the motor imagery intensity S) on the EEG signal of a patientr) And task completion prediction;
secondly, the system evaluates the current brain control ability of the patient according to the detection and prediction results;
thirdly, the system calculates the optimal weight k allocated to the patient control and the system control by utilizing an optimization method according to the evaluation result and aiming at simultaneously reducing the possibility of task failure and the auxiliary degreeuAnd km；
Fourthly, the result S of the cooperative controlfAs a control command, the virtual upper limbs are synchronously controlled.
If the patient completes the motor imagery task within the specified 10 seconds (for example, the wrist is lifted from a flat position to an included angle of 60 degrees with the horizontal plane of the desktop), the system drives the functional electrical stimulation device to electrically stimulate the corresponding muscle of the upper limb of the patient, so that the patient is prompted to complete the action consistent with the virtual upper limb, and the action is kept for 4 seconds; otherwise, no electrical stimulation will be applied to the patient.
The patient takes a rest for 5 seconds after finishing the current treatment task, and continues to carry out the next same treatment task after the rest is finished until all treatment tasks are finished.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention; various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. An upper limb motor rehabilitation treatment method based on motor imagery is characterized by comprising the following steps:
firstly, a stroke patient wears an electrode cap to sit on a wheelchair, and performs 30-trial motor imagery tasks along with virtual upper limb actions displayed on a computer screen. The system will acquire the EEG signal of the patient while performing the imagination task and the EEG signal of the patient at rest between each two tasks. The classifier is trained on the EEG signals of these two states.
And secondly, after the training of the classifier is finished, the patient needs to execute 60 trial upper limb motor imagery rehabilitation treatment tasks according to system prompts. The rehabilitation task of upper limb motor imagery is divided into two phases. In the former stage, the patient needs to complete the motor imagery task within 10 seconds. During the course of a patient's motor imagery task, the system performs motor intent detection and brain control ability assessment on the patient's EEG signals every 500 milliseconds. According to the evaluation result, carrying out self-adaptive distribution on the brain-computer control weight; and finally, converting the result of the cooperative control into a control instruction, and synchronously controlling the virtual upper limbs in real time.
Thirdly, in the later stage, if the patient completes the motor imagery task within the appointed time, the system electrically stimulates the corresponding muscles of the upper limbs of the patient by the driving function electrical stimulation equipment to promote the patient to complete the action consistent with the virtual upper limbs and keep the action for 4 seconds; otherwise, no electrical stimulation is applied to the patient. And (4) the patient takes a rest for 5 seconds after finishing the current treatment task, and the second step and the third step are repeated to carry out the same treatment task for the next time after finishing the rest until all treatment tasks are finished.
2. The motor imagery based upper limb movement rehabilitation method of claim 1, wherein the patient's need to perform motor imagery tasks in steps one, two and three are not limited to a fixed imagery task, and the specific content of the task and the status of the completed task can be generally defined according to the patient's illness. For example, in case of a right upper limb wrist motor dysfunction of a stroke patient, the therapist may set the motor imagery task for the patient by raising the wrist of the right side (initial state of the task) lying on the table to an angle of 60 degrees with respect to the horizontal plane of the table (state at the completion of the task). To help the patient understand the task, the system not only voice prompts, but also visually presents a three-dimensional virtual upper limb movement corresponding to the task at the same time as the prompt.
3. The motor imagery based upper limb motor rehabilitation method of claim 1, wherein the brain control ability of the patient in step two is evaluated by the following two steps:
A. sequentially preprocessing, characteristic extraction and classification recognition are carried out on the EEG signals of the first 2 seconds at the current moment, and finally the output value of a classifier is used as the intensity of the motor imagery of the patient at the current moment;
B. and predicting the task completion degree at the current moment according to the 4 motor imagery intensities included by the patient at the current moment and the minimum motor imagery intensity required for completing the treatment task of the real. Higher completion of the task indicates greater brain control of the patient and vice versa.
4. The motor imagery based upper limb motor rehabilitation method of claim 1, wherein the brain-computer controlled weight adaptive distribution in the second step is specifically performed by the following steps:
A. calculating the probability of failure of the patient to complete the task at the current moment;
B. calculating the degree of assistance the system applies to the patient in completing the task;
C. optimal weights assigned to patient control and system-assisted control are calculated according to an optimization method with the goal of simultaneously minimizing the likelihood of task failure and the degree of applied assistance.
5. The motor imagery based upper limb motor rehabilitation method of claim 1, wherein the system driving functional electrical stimulation device in step three electrically stimulates corresponding muscles of the upper limb of the patient to enable the muscles to perform actions consistent with the virtual upper limb, and the method comprises the following steps:
A. calculating the relative angles of main joints of the virtual upper limb, such as shoulder joints, elbow joints, wrist joints, metacarpophalangeal joints and the like according to the three-dimensional model of the virtual upper limb;
B. respectively applying electric stimulation with corresponding intensity to the electric stimulation electrode patches related to the joint muscle movement;
C. sensing the relative angle of each joint in real time through an angle sensor fixed on each joint of the upper limb; and calculating the angle difference between the sensed relative angle and the relative angle of the model, and using the angle difference as the input of the PID controller, wherein the PID controller adjusts the intensity of the electric stimulation in real time according to a PID control algorithm, so that the action consistent with the virtual upper limb is quickly obtained.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113995956A (en) * | 2021-11-30 | 2022-02-01 | 天津大学 | Stroke electrical stimulation training intention recognition method based on myoelectric expected posture adjustment |
| TWI769069B (en) * | 2021-08-27 | 2022-06-21 | 財團法人亞洲大學 | Multi-stimulation neurorehabilitation assistance system |
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2021
- 2021-02-24 CN CN202110209977.4A patent/CN113040785A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI769069B (en) * | 2021-08-27 | 2022-06-21 | 財團法人亞洲大學 | Multi-stimulation neurorehabilitation assistance system |
| CN113995956A (en) * | 2021-11-30 | 2022-02-01 | 天津大学 | Stroke electrical stimulation training intention recognition method based on myoelectric expected posture adjustment |
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