A training device based on cortical muscle coupling autonomous control and a control method thereof

Abstract

The application provides a training device based on cortical muscle coupling autonomous motion control, the device comprising: an electroencephalogram signal acquisition unit configured to acquire electroencephalogram signals corresponding to target motions from a subject; an electromyogram signal acquisition unit configured to acquire electromyogram signals corresponding to the target motions from the subject; a control unit configured to calculate cortical muscle coherence (CMC) values and electromyogram (EMG) activation levels under the target motions based on processed electroencephalogram signals and electromyogram signals, to identify autonomous motions from the center to the periphery under the target motions, and to send control instructions to a motion assistance unit based on the identification results to guide and assist the target motions of the subject.

Description

TECHNICAL FIELD

[0001] The present application relates to a training device and method, in particular to a training device based on cortical muscle coupling type autonomous movement control and a control method thereof. BACKGROUND

[0002] The number of stroke patients is increasing year by year, while the medical resources are scarce. Rehabilitation robots have become a substitute for long-term clinical rehabilitation services. In addition to high-intensity repetitive training, the key to effective stroke rehabilitation is to accurately mobilize and strengthen the autonomous movement from the central cerebral cortex to the peripheral target muscle to reshape the cortical-muscle pathway that dominates the target muscle and promote motor function reconstruction. The autonomous control type robot that captures the cortical autonomous movement intention (central intention driven) or peripheral autonomous movement force (peripheral force driven) to mobilize the patient's active participation in training has become the mainstream of rehabilitation equipment design in recent years. However, although the current autonomous control type rehabilitation robot is superior to passive training, its rehabilitation effect is still not ideal, not only cannot effectively mobilize the specified neuromuscular pathway related to the target muscle, suppress compensatory movement, but also introduce additional compensatory neural pathways.

[0003] On the one hand, the central intention driven rehabilitation robot, i.e. brain-computer interface, directly converts the central motor intention into peripheral response and drives the affected limb movement by detecting the neural activity pattern induced by motor imagination during the action preparation stage of the brain, without the need for peripheral muscle to actually perform the action, i.e. without involving the activation of cortical-muscle pathway. Such control usually captures the rhythmic neuron discharge in the cortex by using electroencephalography (EEG) with high temporal resolution, and characterizes the central motor intention as the electroencephalography pattern during the imagined action, such as event-related desynchronization and event-related synchronization after imagination. However, the central intention driven rehabilitation robot has limited effect on stroke patients with residual motor ability. The main reason is that only the representation of central intention is considered in the control design of such robot, ignoring the coupling cooperation of central intention and peripheral target muscle and the activation of corresponding cortical-muscle pathway, and thus introducing compensatory pathways that dominate non-target muscles, so that the neural function remodeling and motor function reconstruction of target muscles after stroke cannot be achieved.

[0004] On the other hand, the peripheral force-driven rehabilitation robot utilizes the peripheral target muscle voluntary force in the actual limb movement execution process to perform external control and assist the target movement. Electromyography (EMG) is the most commonly used physiological representation of peripheral muscle voluntary movement, which can measure the real-time muscle activation level, and has the advantages of high signal strength, being proportional to the real-time muscle output, and not being easily affected by the offset effect of abnormal intermuscular coordination after stroke. However, the non-voluntary EMG component introduced by muscle spasm after stroke will significantly increase the target muscle activation level and cause the robot to be triggered without voluntary movement, so that the training process is close to passive training, such as passive stretching of the agonist in compensatory movement, which cannot activate the corresponding cortical-muscle pathway. At present, there is still no reliable voluntary (i.e. from the central cortex to the peripheral muscle) EMG extraction method for real-time control of rehabilitation robots, which causes the peripheral force-driven robot to be unable to effectively mobilize the target muscle voluntary movement, suppress compensatory movement, and cause excessive activation of the additional compensatory cortical-muscle pathway.

[0005] The main problem of the current autonomous control robot control design is that the coupling of the cortex-muscle under voluntary movement cannot be completely and accurately represented, so that the cortical-muscle pathway that dominates the target muscle cannot be effectively recruited and strengthened, and the excessive activation of the additional compensatory neural-muscle pathway is caused. The coherent coupling signal between the electroencephalogram and the electromyogram, i.e. the corticomuscular coherence (CMC), can be used to represent the neural functional coupling connection (the degree of synchronization of neuron discharges) between the central cortex and the peripheral target muscle under voluntary movement in real time. At the same time, the electroencephalogram-electromyogram coherent coupling signal has a high amplitude and is statistically significant during voluntary movement, and has a low amplitude and is not significant during non-voluntary movement such as muscle spasm, so it can be used to identify the voluntary / non-voluntary movement state of stroke patients. However, the electroencephalogram-electromyogram coherent coupling signal has not been used for real-time control of rehabilitation robots to represent the activation of the neural-muscle pathway under voluntary movement. SUMMARY

[0006] In view of the above background, in order to solve one or more of the above problems, the present application provides a training device based on corticomuscular coupling voluntary movement control and a control method thereof.

[0007] Those skilled in the art will derive other objects of the present application from the following description. Therefore, the above object statements are not exhaustive and are only used to illustrate some of the many purposes of the present application.

[0008] Therefore, one aspect of the present application provides a training device based on cortical muscle coupling autonomous movement control, the device comprising: an electroencephalogram signal acquisition unit configured to acquire electroencephalogram signals corresponding to a target action from a subject; an electromyogram signal acquisition unit configured to acquire electromyogram signals corresponding to the target action from the subject; a control unit configured to calculate a cortical muscle coherence (CMC) value and an electromyogram (EMG) activation level under the target action based on processed electroencephalogram signals and electromyogram signals, to identify autonomous movement from the center to the periphery under the target action, and to send a control instruction to an action assistance unit based on the identification result to guide and assist the target action of the subject.

[0009] In one embodiment, the control unit is further configured to send a trigger instruction to the action assistance unit to make the action assistance unit change the target action in the case that the cortical muscle coherence peak value meets a predetermined condition and the electromyogram activation level is within a predetermined EMG range.

[0010] In one embodiment, the control unit is further configured to send a maintenance instruction to the action assistance unit to make the action assistance unit repeat the target action in the case that the cortical muscle coherence peak value does not meet the predetermined condition and / or the electromyogram activation level is not within the predetermined EMG range.

[0011] In one embodiment, the electroencephalogram data acquisition unit acquires n-channel electroencephalogram signals, where n is greater than or equal to 1, and the electromyogram data acquisition unit acquires single-channel electromyogram signals.

[0012] In one embodiment, the control unit calculates the cortical muscle coherence (CMC) value based on the coherent coupling signal between the n-channel electroencephalogram signals of the brain sensorimotor area and the single-channel electromyogram signals of the target muscle.

[0013] In one embodiment, the control unit calculates the CMC value based on the electroencephalogram signals and electromyogram signals respectively processed by sliding window segmentation using the following expression:

[0014]

[0015] where S EEG,EEG (f) and S EMG,EMG (f) are the autospectra of the electroencephalogram signals and electromyogram signals, respectively, S EEG,EMG (f) = <X EEG (f) X * EMG (f)>, is the cross-spectrum between the electroencephalogram signals and electromyogram signals, where X EEG (f) and X EMG(f) Fourier transform of the electroencephalogram signal and the electromyogram signal, respectively, * represents conjugate, and < > represents mathematical expectation.

[0016] In one embodiment, the cortical muscle coherence peak is set as the maximum value of CMC in a selected effective frequency band of n EEG channels.

[0017] In one embodiment, the effective frequency band is the maximum value of CMC in the beta frequency band.

[0018] In one embodiment, the predetermined condition is whether the cortical muscle coherence peak is greater than a coherence coupling signal confidence level, which is calculated by the following expression:

[0019]

[0020] where P = 0.05 is the statistical significance level, corresponding to a significant probability a = 95, and N is the number of data segments.

[0021] In one embodiment, the electromyogram activation level is obtained by rectifying the EMG signal and performing a sliding average processing using a window function to obtain a real-time EMG activation level, and averaging the real-time EMG activation levels during the entire target motion execution period to obtain an average EMG activation level, which is the EMG activation level for real-time control.

[0022] In one embodiment, the predetermined EMG range corresponds to a muscle contraction level of the target muscle, which is obtained by normalizing the EMG activation level for real-time control, and wherein the normalization expression is as follows:

[0023]

[0024] where EMG Max and EMG Base are the EMG activation levels of the muscle in the isometric maximum voluntary contraction (iMVC) and resting state, i.e. 100% iMVC and 0% iMVC.

[0025] In one embodiment, the predetermined EMG range is below 50% iMVC, in particular 10% - 30% iMVC.

[0026] In one embodiment, the device further comprises a storage unit configured to store the electroencephalogram signals obtained by the electroencephalogram signal acquisition unit.

[0027] In one embodiment, the device further comprises a processing unit configured to receive the electroencephalogram signals from the electroencephalogram signal acquisition unit and the electromyogram signals from the electromyogram signal acquisition unit, process the electroencephalogram signals and electromyogram signals, and send the processed electroencephalogram signals and electromyogram signals to the processing unit.

[0028] In one embodiment, the processing unit comprises an amplifier configured to amplify and A / D convert the electroencephalogram signals and the electromyogram signals at a sampling rate of 1000 Hz.

[0029] In one embodiment, the processing unit comprises a filter configured to filter the electroencephalogram signals and the electromyogram signals to extract the dominant frequency band information of the electroencephalogram signals and the electromyogram signals and remove power frequency interference.

[0030] In one embodiment, the electroencephalogram signal acquisition unit comprises an electroencephalogram electrode cap, and the electromyogram signal acquisition unit comprises an electromyogram electrode.

[0031] In one embodiment, the movement assistance unit comprises a mechanical arm providing assistance torque support and an electric motor driving the mechanical arm.

[0032] According to another aspect of the present application, there is provided a method of controlling a training device based on cortical muscle coupled autonomous movement control, the method comprising: an electroencephalogram signal acquisition step of acquiring electroencephalogram signals corresponding to a target movement from a subject; an electromyogram signal acquisition step of acquiring electromyogram signals corresponding to the target movement from the subject; an identification step of calculating a cortical muscle coherence (CMC) value and an electromyogram (EMG) activation level under the target movement based on the processed electroencephalogram signals and electromyogram signals to identify autonomous movement from the central to the peripheral under the target movement, and sending a control instruction to a movement assistance unit to guide and assist the target movement of the subject based on the identification result.

[0033] According to another aspect of the present application, there is provided a computer readable storage medium capable of storing instructions to cause a computer to execute a method of controlling a training device based on cortical muscle coupled autonomous movement control, the method comprising: an electroencephalogram signal acquisition step of acquiring electroencephalogram signals corresponding to a target movement from a subject; an electromyogram signal acquisition step of acquiring electromyogram signals corresponding to the target movement from the subject; an identification step of calculating a cortical muscle coherence (CMC) value and an electromyogram (EMG) activation level under the target movement based on the processed electroencephalogram signals and electromyogram signals to identify autonomous movement from the central to the peripheral under the target movement, and sending a control instruction to a movement assistance unit to guide and assist the target movement of the subject based on the identification result.

[0034] According to the scheme of various aspects of the present application, the coupling of the central cerebral cortex and the peripheral target muscle and the synergistic activation of the corresponding designated neural pathway can be promoted, and the problem that the control design of the existing rehabilitation equipment cannot accurately recruit the designated neuromuscular pathway and inhibit compensation movement, and introduces additional compensatory neural pathways, is solved, so as to accurately remodel the motor function of the designated muscle. BRIEF DESCRIPTION OF DRAWINGS

[0035] The foregoing and other features of the present application will become more apparent from the following description of preferred embodiments thereof given, by way of example only, in conjunction with the accompanying drawings in which:

[0036] Figure 1 is a schematic diagram of a functional arrangement of a training device according to some embodiments of the present application.

[0037] Figure 2 is a schematic diagram of the principle of the present application for action training of a user according to some embodiments of the present application.

[0038] Figure 3 is a schematic diagram of the construction of an example of a training device according to some embodiments of the present application.

[0039] Figure 4 is an example of a flowchart of a method of controlling a training device according to some embodiments of the present application. DETAILED DESCRIPTION

[0040] In the claims and the preceding description of the application, the word "comprising" or variations such as "comprise" or "comprises" is used in the inclusive, open-ended sense, that is, meaning "including but not limited to", to the extent that such terms are so used in the patent law of any country. Nothing in this detailed description is to be taken to mean that the present application will not include other features or steps.

[0041] It should be understood that if any prior art publication is referred to herein, such reference does not constitute an admission that the publication forms part of the common general knowledge in the art, in any country.

[0042] The functional arrangement of a training device according to some embodiments of the present application will be described below in conjunction with Figure 1

[0043] The training device based on the cortical muscle coupling type autonomous movement control comprises an electroencephalogram signal acquisition unit 110, an electromyogram signal acquisition unit 120, a processing unit 130, a control unit 140 and an auxiliary unit 150.

[0044] ​The EEG signal acquisition unit 110 is used to acquire EEG signals corresponding to a target action from an object. The object can be a subject undergoing training. The target action can be a specific action performed by the object, such as the action performed by the wrist joint of the object's upper limb, as specifically described below. Specific examples of the EEG signal acquisition unit 110 include, for example, the EEG electrode cap 106, which is described in detail below.

[0045] The electromyography (EMG) signal acquisition unit 120 is used to acquire EMG signals corresponding to the target action from the object. Examples of the EMG signal acquisition unit 120 include, for example, the EMG electrode 107, which is described in detail below.

[0046] Control unit 130 is used to calculate cortical muscle coherence (CMC) values ​​and electromyographic (EMG) activation levels under target actions based on processed electroencephalogram (EEG) and electromyographic (EMG) signals to identify voluntary movements from the central to the peripheral nervous system under target actions, and to send control commands to the motion assistance unit based on the identification results to guide and assist the target actions of the subject. Examples of control unit 130 include, for example, the computer control platform 103 described in detail below.

[0047] Processing unit 140 is used to process electroencephalogram (EEG) and electromyogram (EMG) signals, such as amplification, filtering, etc. Examples of processing unit 140 include amplifier 102 and filters (not shown), which are described in detail below.

[0048] The assist unit 150 is used to assist the user's movements, such as wrist flexion and extension movements as described in detail below. The assist unit 150 may include, for example, the robot component 105 as described in detail below.

[0049] The following will be combined with the appendix Figure 2 This will briefly explain the principle of the present invention based on cortical-muscle coupling voluntary motor control. (Appendix) Figure 2 The diagram illustrates the present invention for training wrist flexion and extension movements in users, such as stroke patients, which can promote the collaboration between the central cortex and peripheral wrist flexion / extension muscles and the activation of corresponding neural pathways, thereby reshaping wrist joint flexion and extension function.

[0050] Wherein, first, the sensory motor area electroencephalogram signal and the target muscle electromyogram signal are acquired, and then the platform is controlled to perform: feature extraction, including calculating CMC and EMG activation level, to represent the autonomous movement of the central to the peripheral target muscle; and feature conversion, including generating a control command for the peripheral device based on the action recognition result, to generate an instruction for the action feedback device. Meanwhile, information about the target muscle and the target action, such as the target muscle real-time contraction level, the target muscle required contraction range, the target action (joint extension / flexion), and information about the state of the action feedback device, etc. are displayed on the action instruction interface, so as to facilitate the user / operator to perform corresponding operation.

[0051] The application is not limited to the following description of the peripheral wrist joint flexor / extensor muscle. Figure 2 The application can also be related to the training of the following aspects:

[0052] The coupling of the brain cortex and the flexor and extensor muscles of the wrist joint and the corresponding muscle activation can be utilized to promote the activation of the neural pathway of the central cortex and the peripheral wrist joint flexor / extensor muscle, and to reshape the flexion and extension function of the wrist joint.

[0053] The coupling of the brain cortex and the flexor and extensor muscles of the elbow joint and the corresponding muscle activation can be utilized to promote the activation of the neural pathway of the central cortex and the peripheral elbow joint flexor / extensor muscle, and to reshape the flexion and extension function of the elbow joint.

[0054] The coupling of the brain cortex and the flexor and extensor muscles of the shoulder joint and the corresponding muscle activation can be utilized to promote the activation of the neural pathway of the central cortex and the peripheral shoulder joint flexor / extensor muscle, and to reshape the flexion and extension function of the shoulder joint.

[0055] The coupling of the brain cortex and the adductor and abductor muscles of the shoulder joint and the corresponding muscle activation can be utilized to promote the activation of the neural pathway of the central cortex and the peripheral shoulder joint adductor / abductor muscle, and to reshape the adduction / abduction function of the shoulder joint.

[0056] The coupling of the brain cortex and the flexor and extensor muscles of the ankle joint and the corresponding muscle activation can be utilized to promote the activation of the neural pathway of the central cortex and the peripheral ankle joint flexor / extensor muscle, and to reshape the flexion and extension function of the ankle joint.

[0057] The coupling of the brain cortex and the flexor and extensor muscles of the knee joint and the corresponding muscle activation can be utilized to promote the activation of the neural pathway of the central cortex and the peripheral knee joint flexor / extensor muscle, and to reshape the flexion and extension function of the knee joint.

[0058] The coupling of the brain cortex and the flexor and extensor muscles of the hand toe and the corresponding muscle activation can be utilized to promote the activation of the neural pathway of the central cortex and the peripheral finger flexor / extensor muscle, and to reshape the flexion and extension function of the finger joint.

[0059] By utilizing the coupling between the cerebral cortex and the wrist pronation / supination muscles and the activation of the corresponding muscles, the neural pathways between the central cortex and the peripheral wrist pronation / supination muscles can be activated, thereby reshaping wrist pronation / supination function.

[0060] By leveraging the coupling between the cerebral cortex and the hip adductor / abductor muscles, and the activation of corresponding muscles, the neural pathways between the central cortex and the peripheral hip adductor / abductor muscles can be activated, thereby reshaping hip adduction / abductor function; and

[0061] By utilizing the coupling between the cerebral cortex and the internal / external rotator muscles of the shoulder joint, and the activation of the corresponding muscles, the neural pathways between the central cortex and the peripheral internal / external rotator muscles of the shoulder joint can be activated, thereby reshaping the internal / external rotation function of the shoulder joint.

[0062] According to the above-described cortical-muscle coupling-based voluntary motor control of the present invention, the coupling of central electroencephalogram (EEG) signals and peripheral electromyographic (EMG) signals can be used to identify brain-derived voluntary EMG signals. Simultaneously, real-time EMG signals are combined to determine the magnitude of peripheral force exertion, and these are simultaneously introduced into the real-time control of the training robot to characterize the degree of voluntary movement and force exertion from the central nervous system to the peripheral target muscles. This control design can mobilize and enhance the coupling and cooperation between the central cerebral cortex and the peripheral target muscles, as well as the activation of corresponding cortical-muscle neural pathways, thereby solving the problem that existing voluntary control rehabilitation devices cannot accurately recruit designated cortical-muscle pathways and introduce additional compensatory cortical-muscle pathways.

[0063] The following will be combined with the appendix Figure 3 The construction of an example of a training device according to some embodiments of the present invention will be described below. A detailed description will be given using an implementation example of applying the present invention to wrist flexion and extension training after stroke.

[0064] The training device in this example includes: a brain-muscle signal amplifier (hereinafter referred to as amplifier) ​​102, a computer control platform 103, a human-computer interaction interface 104, a robot component 105, an EEG electrode cap 106, and electromyography electrodes 107.

[0065] The EEG electrode cap 106 and the electromyography (EMG) electrode 107 are used for acquiring EEG and EMG signals. The EEG electrode cap (106) is placed on the head of the user (101) according to the international 10-20 standard system to acquire 15 channels of EEG data (i.e., CZ, C1, C2, C3, C4, FCZ, FC1, FC2, FC3, FC4, CPZ, CP1, CP2, CP3, and CP4) of the sensorimotor areas of the brain. The EMG electrode (107) is attached to the extensor carpi ulnaris (ECU) and flexor carpi radialis muscles on the affected side to simultaneously acquire EMG data of the antagonist muscles of the wrist joint.

[0066] The processing components, such as the amplifier 102, are used to process the collected brain and muscle electrical signals. The brain and muscle electrical signals collected by the EEG electrode cap 106 and the EMG electrode 107 are A / D converted at a sampling rate of 1000 Hz and sent to the amplifier 102, which amplifies the signals by a certain factor (e.g., EEG: 10000 times, EMG: 1000 times) and transmits the signals to the computer control platform 103 via a computer USB interface.

[0067] The computer control platform 103 is capable of performing functions such as computation processing and system control, and is equipped with a LabVIEW control platform developed by software such as LabVIEW. The computer control platform 103 is capable of synchronously acquiring and processing brain and muscle electrical signals, guiding target movements (e.g., wrist extension and wrist flexion) in real time, and assisting target movements. The computer control platform 103 can also store the collected raw brain and muscle electrical signal data in its own memory or in an external memory, which can be used to evaluate the training effect of wrist movement function. In the present embodiment, the computer control platform 103 receives the processed brain and muscle electrical signals to calculate the CMC and EMG activation levels in real time to control the guidance and assistance of target movements.

[0068] The human-machine interaction interface 104, for example, can be directly embedded in and controlled by the LabVIEW control platform and presented on the display of a computer (e.g., a desktop computer, a tablet computer, a mobile phone, etc., but not limited thereto), and can also be presented in the form of a touch screen. The human-machine interaction interface 104 can display information, such as information about brain and muscle electrical signals (e.g., CMC and EMG activation levels), information about auxiliary components (e.g., whether the robot components are triggered), and the like, and can also display user information, results of analysis based on previously stored data, and the like. The human-machine interaction interface can also guide the user to perform target movements (e.g., joint flexion, extension, and rest). In one embodiment, the human-machine interaction interface 104 displays target movements (e.g., wrist extension and wrist flexion), real-time contraction levels of target muscles, and target contraction ranges, for example, using a dial with two fixed pointers and one movable pointer to indicate the target contraction range and real-time contraction level of target muscles, respectively.

[0069] The robot components 105 receive movement instructions from the computer control platform 103 to provide movement assistance to the user, such as assisting a specific joint to perform target movements such as flexion / extension. The robot components 105, for example, can include a mechanical arm 108 that provides auxiliary torque support and an electric motor (109) that drives the mechanical arm, which receives movement instructions from the control platform via a computer USB interface and provides extension and flexion movement assistance to the user's wrist joint at a constant angular velocity.

[0070] The CMC-EMG driven control is described below in connection with the above training device of the present application. The computer control platform 103 synchronously acquires the brain and muscle electrical signals from the electroencephalogram cap 106 and the electromyogram electrodes 107, and calculates the CMC and EMG activation levels in real time to represent the autonomous movement from the central to the peripheral target muscle under the target action for system control. Once the computer control platform 103 determines that the CMC peak value is significant and the EMG activation level is within the target range, for example, below 50% iMVC, more specifically, within the range of 10%-30% iMVC, with respect to the target muscle (wrist extensor / wrist flexor), the robotic component 105 will be triggered to assist the target wrist joint extension / flexion action, and the instruction action displayed on the human-machine interaction interface 104 will also be updated from wrist joint extension (or flexion) to wrist joint flexion (or extension), at which time it is "triggered successfully". Conversely, if the CMC peak value is not significant or the EMG activation level does not reach the target range, the robot (105) will not provide action assistance and will repeatedly display the current target action on the human-machine interaction interface 104, at which time it is "triggered unsuccessfully".

[0071] In the above CMC-EMG driven control of the present application, the electroencephalogram and electromyogram signals during the execution of the target action are respectively subjected to filtering processing to extract the main frequency band information of the two signals and remove power frequency interference, for calculating the CMC and EMG activation levels. In the CMC-EMG driven control, the CMC value is estimated based on the coherent coupling signal between the n-channel electroencephalogram of the brain sensorimotor area and the single-channel electromyogram of the target muscle, for identifying the brain-derived autonomous electromyogram signal, i.e., distinguishing the autonomous movement / non-autonomous movement state of the target muscle of the user, and the calculation expression is as follows:

[0072]

[0073] wherein S EEG,EEG (f) and S EMG,EMG (f) are the autospectrum of the electroencephalogram signal and the electromyogram signal, respectively, S EEG,EMG (f) = <X EEG (f) X * EMG (f)>, is the cross-spectrum between the electroencephalogram signal and the electromyogram signal, wherein X EEG (f) and X EMG (f) are the Fourier transform of the electroencephalogram signal and the electromyogram signal, respectively, * represents conjugate, and <> represents mathematical expectation.

[0074] wherein the peak CMC can be set as the maximum value of the CMC within the selected effective frequency band, for example, the beta frequency band (15-30 Hz), of the n EEG channels. The CMC peak value significance judgment refers to judging whether the peak CMC is greater than the coherent coupling signal confidence level, and the calculation expression is as follows:

[0075]

[0076] wherein P=0.05 is the statistical significant level, corresponding to the significant probability a=95, and N is the number of data segments.

[0077] The EMG activation level in the CMC-EMG driven control described above is calculated based on single-channel EMG on the target muscle during action execution, and can represent the peripheral muscle strength level of the user. The calculation process is as follows: first, rectify the EMG and use a window function to perform sliding average processing to obtain the real-time EMG activation level; then, average the real-time EMG activation level during the entire action execution to obtain the average EMG activation level, which is the EMG activation level used for real-time control. Finally, the muscle contraction level of the target muscle can be obtained by normalizing the EMG activation level in real-time control to determine whether the EMG activation level used for real-time control is within the target range, and the normalization expression is as follows

[0078]

[0079] wherein EMG Max and EMG Base are the EMG activation levels of the muscle in the maximum isometric voluntary contraction (iMVC) and resting state, i.e. 100% iMVC and 0% iMVC.

[0080] According to the training device described above, the brain-derived autonomic electromyographic signal of the user can be distinguished through the brain-muscle electrical coherence coupling signal, and the peripheral muscle strength output can be represented by the electromyographic activation level, which solves the problem that the traditional active control type training robot cannot accurately mobilize the autonomic movement of the user such as a stroke patient from the center to the target muscle of the periphery, and is beneficial to realize the reconstruction of the specific joint movement function of the user.

[0081] The above-mentioned method flow of the cortical muscle coupling type autonomic movement control in the present application will be described below in conjunction with the accompanying Figure 4 drawings.

[0082] In this embodiment, 15-channel electroencephalogram data of the brain sensory motor area is obtained for the electroencephalogram signal, and two channels, i.e. ECU-ED and FCR-FD, are included for the electromyographic signal. Among them, ECU is used for wrist extension, and FCR is used for wrist flexion.

[0083] In the flowchart shown in the accompanying Figure 4 , steps S112-S118, i.e. the steps for the electromyographic signal, can be performed in parallel with steps S122-S126, i.e. the steps for the electroencephalogram signal, or not in parallel.

[0084] For the EMG signal, the following steps S112-S118 are performed.

[0085] In step S112, channel selection is performed, for example, ECU-ED is selected for wrist extension or FCR-FD is selected for wrist flexion.

[0086] In step S114, the EMG signal of the user during the execution of the target action is obtained, and the EMG signal of the target muscle is band-pass filtered at 10-500 Hz to extract the main frequency band information, and the power frequency interference is removed by 50 Hz notch, which is used to calculate the EMG activation level.

[0087] In step S116, the EMG is rectified and a window function sliding average processing with a window length of 100 milliseconds is performed to obtain the real-time EMG activation level, and the real-time EMG activation level during the 10-second action execution is averaged to obtain the average EMG activation level for CMC-EMG driving.

[0088] In step S118, the muscle contraction evaluation is calculated, the average EMG activation level is normalized by formula (3) to obtain the muscle contraction level of the target muscle, which is used for subsequent judgment whether the target muscle contraction level of the user is within the target range.

[0089] For the EEG signal, the following steps S122-S126 are performed.

[0090] In step S122, the EEG signal of the user during the execution of the target action is obtained, and the EEG signal is band-pass filtered at 5-80 Hz to extract the main frequency band information, and the power frequency interference is removed by 50 Hz notch, which is used to calculate the CMC activation level.

[0091] In step S124, the CMC between the 15-channel EEG signal and the single-channel EMG signal is calculated. According to formula (1), the CMC is calculated, wherein the EEG signal and the EMG signal are segmented by a sliding window with a window length of 1024 points and an overlap of 50%.

[0092] In step S126, the maximum value of the CMC in the beta band (15-30 Hz) among the 15 EEG channels is selected, i.e. the peak CMC.

[0093] After obtaining the muscle contraction level of the target muscle and the peak CMC, the following steps S130-S150 are performed.

[0094] In step S130, it is determined whether the following conditions are met simultaneously: peak CMC > significance level and whether the muscle contraction level is within a predetermined range. Wherein, the coherent coupling signal confidence level is calculated according to formula (2) and is used for the significance determination of peak CMC, that is, whether peak CMC is greater than the coherent coupling signal confidence level, in this example, the number of data segments N = 22, and the corresponding coherent coupling signal confidence level CL (α%) = 0.133. Wherein, the target range is, for example, 50% iMVC or less, especially 10%-30% iMVC.

[0095] In the case where the determination result in step S130 is "yes", step S140 is performed, that is, a trigger instruction is sent to the robot component 150 so that the robot component 150 is triggered, and a change target action instruction is sent to the robot component, for example, wrist joint flexion / extension → extension / wrist joint flexion.

[0096] In the case where the determination result in step S130 is "no", step S150 is performed, that is, a maintenance instruction is sent to the auxiliary unit, at this time, the robot component fails to trigger, and the current target action is repeated.

[0097] The above control method and device for a rehabilitation training system of the present application have the following characteristics: the real-time control method of cortical muscle coupling is used to mobilize and strengthen the specified neuromuscular pathway of the target muscle; the coupling of central brain electrical signals and peripheral electromyographic signals is used to identify brain-derived autonomous electromyographic signals; the cortical muscle coherent coupling signal and the peripheral electromyographic signal are combined to represent the degree of autonomous movement and force of the target muscle from the center to the periphery; and the real-time feedback of the target action is provided by the action assisting device to optimize the autonomous movement of the target muscle from the center to the periphery.

[0098] According to the above control method and device for a rehabilitation training system of the present application, the specified neuromuscular pathway that dominates the target muscle can be effectively mobilized and strengthened, compensatory movement is inhibited, and additional compensatory neural pathways are avoided.

[0099] The present application is described in detail above in conjunction with the drawings, but it should be regarded as illustrative rather than limiting, and it should be understood that only exemplary embodiments are shown and described, and the present application is not limited in any way. It can be understood that any feature described herein can be used in any embodiment. The illustrative embodiments do not exclude each other or other embodiments not listed herein. Therefore, the present application also provides a combination including one or more of the above-described exemplary embodiments. Modifications and variations of the present application can be made without departing from the spirit and scope of the present application, and therefore, such limitations should only be made as indicated by the appended claims.

[0100] The following lists each item claimed by the present application.

[0101] 1. A training device based on cortical muscle coupling autonomous movement control, the device comprising:

[0102] a brain electrical signal acquisition unit configured to acquire, from a subject, a brain electrical signal corresponding to a target action;

[0103] a muscle electrical signal acquisition unit configured to acquire, from the subject, a muscle electrical signal corresponding to the target action;

[0104] a control unit configured to calculate, based on the processed brain electrical signal and muscle electrical signal, a cortical muscle coherence (CMC) value and an electromyogram (EMG) activation level under the target action, to identify autonomous movement from the central to the peripheral under the target action, and to send a control instruction to an action assisting unit based on the identification result to guide and assist the target action of the subject.

[0105] 2. The device according to item 1, wherein the control unit is further configured to, in a case where the CMC peak value meets a predetermined condition and the EMG activation level is within a predetermined EMG range, send a trigger instruction to the action assisting unit to make the action assisting unit change the target action.

[0106] 3. The device according to item 2, wherein the control unit is further configured to, in a case where the CMC peak value does not meet the predetermined condition and / or the EMG activation level is not within the predetermined EMG range, send a maintenance instruction to the action assisting unit to make the action assisting unit repeat the target action.

[0107] 4. The device according to item 2 or 3, wherein the brain electrical data acquisition unit acquires n-channel brain electrical signals, where n is greater than or equal to 1, and the muscle electrical data acquisition unit acquires single-channel muscle electrical signals.

[0108] 5. The device according to item 4, wherein the control unit calculates the CMC value based on a coherent coupling signal between n-channel brain electrical signals of a brain sensorimotor area and single-channel muscle electrical signals of a target muscle.

[0109] 6. The device according to item 5, wherein the control unit calculates the CMC value based on the brain electrical signal and the muscle electrical signal respectively processed by a sliding window segmentation using the following expression:

[0110]

[0111] wherein S EEG,EEG (f) and S EMG,EMG(f) the auto-spectrum of the electroencephalogram signal and the electromyogram signal, respectively, S EEG,EMG (f) = <X EEG (f) X * EMG (f) >, the cross-spectrum between the electroencephalogram signal and the electromyogram signal, where X EEG (f) and X EMG (f) the Fourier transform of the electroencephalogram signal and the electromyogram signal, respectively, * represents the conjugate, and < > represents the mathematical expectation.

[0112] 7. The apparatus according to item 4, wherein the cortical muscle coherence peak is set to the maximum value of CMC in a selected effective frequency band in n EEG channels.

[0113] 8. The apparatus according to item 7, wherein the effective frequency band is the maximum value of CMC in the beta frequency band.

[0114] 9. The apparatus according to item 2 or 3, wherein the predetermined condition is whether the cortical muscle coherence peak is greater than a coherence coupling signal confidence level, which is calculated by the following expression:

[0115]

[0116] where P = 0.05 is the statistical significance level, corresponding to a significant probability a = 95, and N is the number of data segments.

[0117] 10. The apparatus according to item 2 or 3, wherein the electromyogram activation level is obtained by:

[0118] rectifying the EMG signal and performing a sliding average processing with a window function to obtain a real-time EMG activation level;

[0119] averaging the real-time EMG activation level during the entire target motion execution period to obtain an average EMG activation level, which is the EMG activation level for real-time control.

[0120] 11. The apparatus according to item 2 or 3, wherein the predetermined EMG range corresponds to a muscle contraction level of the target muscle, which is obtained by normalizing the EMG activation level for real-time control, and wherein the normalization expression is as follows:

[0121]

[0122] where EMG Max and EMG BaseEMG activation levels of the muscle at isometric maximum voluntary contraction (iMVC) and at rest, i.e. 100% iMVC and 0% iMVC, respectively.

[0123] 12. The apparatus according to item 11, wherein the predetermined EMG range is 50% iMVC or below.

[0124] 13. The apparatus according to item 12, wherein the predetermined EMG range is 10-30% iMVC.

[0125] 14. The apparatus according to item 1, wherein the apparatus further comprises a storage unit configured to store the electroencephalogram signals acquired by the electroencephalogram signal acquisition unit.

[0126] 15. The apparatus according to item 1, wherein the apparatus further comprises a processing unit configured to receive the electroencephalogram signals from the electroencephalogram signal acquisition unit and the electromyogram signals from the electromyogram signal acquisition unit, process the electroencephalogram signals and electromyogram signals, and send the processed electroencephalogram signals and electromyogram signals to the processing unit.

[0127] 16. The apparatus according to item 15, wherein the processing unit comprises an amplifier configured to A / D convert and amplify the electroencephalogram signals and the electromyogram signals at a sampling rate of 1000 Hz.

[0128] 17. The apparatus according to item 15, wherein the processing unit comprises a filter configured to filter the electroencephalogram signals and the electromyogram signals to extract dominant frequency band information of the electroencephalogram signals and the electromyogram signals and remove power frequency interference.

[0129] 18. The apparatus according to item 1, wherein the electroencephalogram signal acquisition unit comprises an electroencephalogram electrode cap and the electromyogram signal acquisition unit comprises electromyogram electrodes.

[0130] 19. The apparatus according to item 1, wherein the motion assistance unit comprises a mechanical arm providing assistance torque support and an electric motor driving the mechanical arm.

[0131] 20. A method of controlling a training apparatus, the method being based on cortical muscle-coupled autonomous motor control, the method comprising:

[0132] an electroencephalogram signal acquisition step of acquiring electroencephalogram signals corresponding to a target motion from a subject;

[0133] an electromyogram signal acquisition step of acquiring electromyogram signals corresponding to the target motion from the subject;

[0134] The identification step calculates a cortical muscle coherence (CMC) value and an electromyogram (EMG) activation level under the target action based on the processed electroencephalogram signal and the processed electromyogram signal, to identify an autonomous movement from the center to the periphery under the target action, and sends a control instruction to the motion assistance unit based on the identification result to guide and assist the target action of the subject.

[0135] 21. The method of item 20, wherein in the identification step, in a case where the cortical muscle coherence peak value satisfies a predetermined condition and the electromyogram activation level is within a predetermined EMG range, a trigger instruction is sent to the motion assistance unit to make the motion assistance unit change the target action.

[0136] 22. The method of item 21, wherein in the identification step, in a case where the cortical muscle coherence peak value does not satisfy the predetermined condition and / or the electromyogram activation level is not within the predetermined EMG range, a maintenance instruction is sent to the motion assistance unit to make the motion assistance unit repeat the target action.

[0137] 23. The method of item 21 or 22, wherein in the electroencephalogram signal acquisition step, n-channel electroencephalogram signals are acquired, where n is greater than or equal to 1, and in the electromyogram signal acquisition step, single-channel electromyogram signals are acquired.

[0138] 24. The method of item 23, wherein in the identification step, the cortical muscle coherence (CMC) value is calculated based on a coherence coupling signal between n-channel electroencephalogram signals of a brain sensorimotor area and single-channel electromyogram signals of a target muscle.

[0139] 25. The method of item 24, wherein based on the electroencephalogram signal and the electromyogram signal respectively processed by a sliding window segmentation, the CMC value is calculated using the following expression:

[0140]

[0141] wherein S EEG,EEG (f) and S EMG,EMG (f) are the autospectrum of the electroencephalogram signal and the electromyogram signal respectively, S EEG,EMG (f) = <X EEG (f) X * EMG (f)>, is the cross-spectrum between the electroencephalogram signal and the electromyogram signal, wherein X EEG (f) and X EMG (f) are the Fourier transform of the electroencephalogram signal and the electromyogram signal respectively, * represents conjugate, and <> represents mathematical expectation.

[0142] 26. The method of item 24, wherein the cortical muscle coherence peak is set as the maximum value of CMC in a selected effective frequency band of n EEG channels.

[0143] 27. The method of item 26, wherein the selected effective frequency band is the maximum value of CMC in a beta frequency band.

[0144] 28. The method of item 21 or 22, wherein the predetermined condition is whether the cortical muscle coherence peak is greater than a coherence coupling signal confidence level, which is calculated by the following expression:

[0145]

[0146] where P = 0.05 is the statistical significance level corresponding to a significant probability a = 95, and N is the number of data segments.

[0147] 29. The method of item 21 or 22, wherein the EMG activation level is obtained by:

[0148] rectifying the EMG signal and performing a sliding average with a window function to obtain a real-time EMG activation level;

[0149] averaging the real-time EMG activation level over the entire duration of the target movement to obtain an average EMG activation level, which is the EMG activation level for real-time control.

[0150] 30. The method of item 21 or 22, wherein the predetermined EMG range corresponds to a muscle contraction level of the target muscle, which is obtained by normalizing the EMG activation level for real-time control, and wherein the normalization expression is as follows:

[0151]

[0152] where EMG Max and EM Base are the EMG activation levels of the muscle at isometric maximum voluntary contraction (iMVC) and at rest, i.e., 100% iMVC and 0% iMVC, respectively.

[0153] 31. The method of item 30, wherein the predetermined EMG range is 50% iMVC or less.

[0154] 32. The method of item 31, wherein the predetermined EMG range is 10%-30% iMVC.

[0155] 33. The method of item 20, further comprising a storing step of storing the obtained electroencephalogram signals in a memory.

[0156] 34. The method of item 20, wherein the electroencephalogram signals and the electromyogram signals are A / D converted and amplified at a sampling rate of 1000 Hz.

[0157] 35. The method of item 20, wherein the electroencephalogram signals and the electromyogram signals are filtered to extract dominant band information of the electroencephalogram signals and the electromyogram signals and remove power frequency interference.

[0158] 36. A computer-readable storage medium capable of storing instructions to cause a computer to execute a method of controlling a training device, the method being based on cortical muscle coupled autonomous motor control, comprising:

[0159] an electroencephalogram signal obtaining step of obtaining electroencephalogram signals corresponding to a target action from a subject;

[0160] an electromyogram signal obtaining step of obtaining electromyogram signals corresponding to the target action from the subject;

[0161] an identifying step of calculating a cortical muscle coherence (CMC) value and an electromyogram (EMG) activation level under the target action based on the processed electroencephalogram signals and electromyogram signals to identify autonomous movement from the center to the periphery under the target action, and sending a control instruction to a movement assisting unit to guide and assist the target action of the subject based on the identification result.

Claims

1. A training device based on cortical-muscle coupling voluntary motor control, the device comprising: The EEG signal acquisition unit is configured to acquire EEG signals corresponding to the target action from the object; An electromyography (EMG) signal acquisition unit is configured to acquire an EMG signal corresponding to the target action from the object. The control unit is configured to calculate the cortical muscle coherence (CMC) value and electromyographic (EMG) activation level under the target action based on the processed electroencephalogram (EEG) and electromyogram (EMG) signals, in order to identify voluntary movements from the central to the peripheral nervous system under the target action, and to send control commands to the motion assisting unit based on the identified results to guide and assist the target action of the subject.

2. The apparatus according to claim 1, wherein, The control unit is further configured to send a trigger command to the motion assist unit to cause the motion assist unit to change the target motion when the cortical muscle coherence peak meets a predetermined condition and the electromyography activation level is within a predetermined EMG range.

3. The apparatus according to claim 2, wherein, The control unit is further configured to send a maintenance command to the motion assist unit to cause the motion assist unit to repeat the target motion when the cortical muscle coherence peak does not meet the predetermined condition and / or the electromyography activation level is not within the predetermined EMG range.

4. The apparatus according to claim 2 or 3, wherein, The EEG signal acquisition unit acquires n channels of EEG signals, where n is greater than or equal to 1, and the EMG signal acquisition unit acquires a single channel of EMG signals.

5. The apparatus according to claim 4, wherein, The control unit calculates the cortical-muscle coherence (CMC) value based on the coherent coupling signal between the n-channel EEG signal of the brain's sensorimotor area and the single-channel EMG signal of the target muscle.

6. The apparatus according to claim 5, wherein, The control unit calculates the CMC value based on the EEG and EMG signals obtained through sliding window segmentation, respectively, using the following expression: Among them, S EEG,EEG (f) and S EMG,EMG (f) are the autospectral spectra of EEG and EMG signals, respectively. EEG,EMG (f)= <X EEG (f)X * EMG (f)> represents the cross spectrum between electroencephalogram (EEG) and electromyogram (EMG) signals, where X EEG (f) and X EMG (f) represents the Fourier transform of the EEG and EMG signals, respectively. * represents conjugate, and <> represents mathematical expectation.

7. The apparatus according to claim 4, wherein, The cortical-muscle coherence peak is set to an effective frequency band selected from the n EEG channels.

8. The apparatus according to claim 7, wherein, The effective frequency band is the maximum value of CMC within the beta frequency band.

9. The apparatus according to claim 2 or 3, wherein, The predetermined condition is that the peak value of the cortical-muscle coherence is greater than the confidence level of the coherent coupling signal, and the confidence level of the coherent coupling signal is calculated by the following expression: Where P = 0.05 is the statistical significance level, corresponding to a significance probability α = 95, and N is the number of data segments.

10. The device according to claim 2 or 3, wherein the electromyographic activation level is obtained by: The EMG signal is rectified and processed by a window function to obtain the real-time EMG activation level. The average EMG activation level is obtained by averaging the real-time EMG activation levels throughout the entire target action execution period, and this average EMG activation level is the EMG activation level used for real-time control.

11. The apparatus according to claim 2 or 3, wherein, The predetermined EMG range corresponds to the muscle contraction level of the target muscle, which is obtained by normalizing the EMG activation level used for real-time control, wherein the normalization expression is as follows: EMG Max and EMG Base The values ​​represent the EMG activation levels of the muscle under isometric maximum voluntary contraction (iMVC) and resting state, respectively, i.e., 100% iMVC and 0% iMVC.

12. The apparatus according to claim 11, wherein, The predetermined EMG range is below 50% iMVC.

13. The apparatus according to claim 12, wherein, The predetermined EMG range is 10%-30% iMVC.

14. The apparatus according to claim 1, wherein, The device further includes a storage unit configured to store the electroencephalogram (EEG) signals acquired by the EEG signal acquisition unit.

15. The apparatus according to claim 1, wherein, The device further includes a processing unit configured to receive electroencephalogram (EEG) signals from the EEG signal acquisition unit and electromyogram (EMG) signals from the EMG signal acquisition unit, process the EEG signals and EMG signals, and send the processed EEG signals and EMG signals to the control unit.

16. The apparatus according to claim 15, wherein, The processing unit includes an amplifier that performs A / D conversion and amplifies the electroencephalogram (EEG) signal and the electromyogram (EMG) signal at a sampling rate of 1000 Hz.

17. The apparatus of claim 15, wherein the processing unit includes a filter that filters the electroencephalogram (EEG) signal and the electromyogram (EMG) signal to extract the main frequency band information of the EEG signal and the EMG signal and remove power frequency interference.

18. The apparatus according to claim 1, wherein, The electroencephalogram (EEG) signal acquisition unit includes an EEG electrode cap, and the electromyogram (EMG) signal acquisition unit includes EMG electrodes.

19. The apparatus according to claim 1, wherein, The motion assistance unit includes a robotic arm that provides auxiliary torque support and an electric motor that drives the robotic arm.

20. A computer-readable storage medium capable of storing instructions to cause a method for controlling a training device when the computer executes the instructions, the method being based on corticomuscular coupled voluntary motor control, comprising: The steps for acquiring electroencephalogram (EEG) signals are as follows: acquire EEG signals corresponding to the target action from the object. The electromyographic signal acquisition step involves acquiring electromyographic signals corresponding to the target action from the object. The identification step involves calculating the cortical muscle coherence (CMC) value and electromyographic (EMG) activation level under the target action based on the processed electroencephalogram (EEG) and electromyographic (EMG) signals to identify voluntary movements from the central nervous system to the periphery under the target action, and sending control commands to the motion assistance unit based on the identification results to guide and assist the target action of the subject.

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