Rehabilitation training method and device based on reshaping embodied sense to regulate cortical muscle coherence

Abstract

The present invention provides a rehabilitation training method and device based on reshaping the embodied sense to regulate cortical muscle coherence. The method collects EEG signals and electromyographic signals, performs short-time Fourier transform to convert them into frequency domain signals, segments multiple frequency bands and calculates the cortical muscle coherence coefficient of each segment to characterize the coordination between neuronal activity and muscle movement in time and frequency, quantifies the quality of the user's embodied sense reshaping when using virtual reality technology for rehabilitation training, and guides the adjustment of the personal perspective and sensory stimulation items used in rehabilitation training to improve the quality of rehabilitation training.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation information processing, and in particular to a rehabilitation training method and device based on reshaping embodied sense to regulate cortical muscle coherence. Background Art

[0002] Stroke, Parkinson's disease and other spinal cord injury diseases in life can cause motor dysfunction such as limb weakness, uncoordinated movement, hemiplegia, etc. In diseases such as stroke, due to blockage or rupture of blood vessels in the brain, the brain tissue is deprived of oxygen and necrosis, which damages the brain area that controls motor function, or compresses the surrounding brain tissue, causing the corresponding loss of nerve function, thereby affecting motor function. Long-term bed rest may also lead to muscle weakness and inability to perform normal activities.

[0003] Virtual Reality (VR) can break through the mode that conventional rehabilitation needs to be carried out in real scenes, and can provide multi-channel sensory stimulation with interactive, immersive and imaginative characteristics, namely 3I, which has unique advantages. The current mainstream VR sports rehabilitation method (or system) is mainly to provide visual input to strengthen sports training or occupational therapy for specific functional disorders through feedback of results. The positioning of VR is to provide patients with corresponding virtual scenes and environments with traditional sports rehabilitation treatment content as the core, and strengthen the specific scene effectiveness of sports training.

[0004] However, the motor control process depends on the interaction between multiple functionally connected brain areas and the ability of the central nervous system to control muscle endings. Cortical muscle coherence is an indicator that measures the coherence between the brain's motor cortex and related muscles. This coherence reflects the brain's control mechanism over muscle activity. Through neural conduction pathways, the neuronal activity of the cerebral cortex and the motor unit activity of the muscles are coordinated in time and frequency. The existing form of rehabilitation training based on simple virtual reality is difficult to assist users in reshaping their body perception ability, thereby strengthening the central nervous system's control over muscle endings. Therefore, a new solution is urgently needed to improve the effect of rehabilitation training. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a rehabilitation training method and device based on reshaping embodied sense to regulate cortical muscle coherence, so as to eliminate or improve one or more defects existing in the prior art, and solve the problem that the prior art cannot introduce quantitative management combined with the central nervous system's control ability over muscle endings to assist in training the coordination of neuronal activity and muscle movement in time and frequency.

[0006] One aspect of the present invention provides a rehabilitation training method for regulating cortical muscle coherence based on reshaping embodied sense, the method comprising the following steps:

[0007] For rehabilitation training tasks in specific virtual reality scenarios, based on embodied sense-related elements and their combinations, the primary sensory area and primary motor area of ​​the user's brain are activated by reshaping the embodied sense during the execution of the rehabilitation training tasks, and the EEG and EMG signals are recorded by EEG sensors and EMG sensors; the embodied sense-related elements include the type of personal perspective and one or more sensory stimulation items; the sensory stimulation items include vision, hearing, touch, force, vestibular sensation, temperature stimulation, olfactory stimulation items, taste stimulation items and airflow stimulation;

[0008] Collecting the EEG signal of the user when performing the rehabilitation training task based on the EEG sensor at a first set frequency, and collecting the EMG signal of the user when performing the rehabilitation training task based on the EMG sensor at a second set frequency;

[0009] The EEG signal and the EMG signal are preprocessed and segmented according to preset time intervals, the EEG signal in the segment is converted from the time domain to the frequency domain to obtain an EEG frequency domain signal, and the sub-frequency bands are divided according to the set frequency intervals; the EMG signal in the segment is converted from the time domain to the frequency domain to obtain an EMG frequency domain signal, and the sub-frequency bands are divided according to the set frequency intervals; the cortical muscle coherence coefficients of the corresponding EEG frequency domain signals and the EMG frequency domain signals are calculated in each of the sub-frequency bands;

[0010] According to the set rules, it is determined whether the cortical muscle coherence coefficient corresponding to each sub-frequency band meets the requirements. If it does not meet the requirements, the embodied sense related elements are changed, introduced or removed according to the preset combination until a target embodied sense related element combination that meets the requirements is found, and rehabilitation training is performed based on the target embodied sense related element combination.

[0011] In some embodiments, the personal perspective includes a first-person perspective, a third-person perspective, and a non-avatar perspective; the rehabilitation training tasks include limb movement rehabilitation training, balance and coordination training, motor cognitive rehabilitation training, upper limb and finger fine motor rehabilitation training, and lower limb movement rehabilitation training.

[0012] In some embodiments, the method sets embodied sense related elements and combinations thereof, including:

[0013] After excluding unavailable sensory stimulation items that are physically limited by the user, obtaining channels and stimulation modes corresponding to a plurality of candidate sensory stimulation items related to the rehabilitation training task;

[0014] The candidate sensory stimulation items are randomly arranged and combined to obtain a plurality of candidate combinations of embodied sense-related elements.

[0015] In some embodiments, the first set frequency and the second set frequency are 1-2 kHz.

[0016] After the EEG signal and the EMG signal are preprocessed and segmented according to the preset time interval, the method further includes: using low frequency 0.1~4Hz and high frequency 15-250Hz for bandpass filtering; performing power frequency notching, requiring that the number of sampling points of the power frequency notch within the preset time interval is at least 512. In some embodiments, the method uses short-time Fourier transform to convert the time domain signal to the frequency domain, and the calculation formula is:

[0017] ;

[0018] in, represents the original input signal, is the window function; is the time parameter, indicating the center position of the time segment; represents angular frequency; t represents time; is a negative exponential function; represents the result of the short-time Fourier transform;

[0019] The method further includes: introducing overlap into the short-time Fourier transform when segmenting to improve the time-frequency resolution, and when framing, each frame of data overlaps with the previous frame by 10-50%.

[0020] In some embodiments, sub-frequency bands are planned according to set intervals within a set frequency band, and the cortical muscle coherence coefficients of the corresponding EEG frequency domain signals and the EMG frequency domain signals are calculated in each of the sub-frequency bands, including:

[0021] The low frequency of the analysis frequency band is greater than or equal to 4 Hz, and the high frequency is less than or equal to 250 Hz. The interval between the low frequency and the high frequency is at least 4 Hz, and each 1 Hz is divided into 1 segment. The calculation formula of the cortical muscle coherence coefficient is:

[0022] ;

[0023] in, is the square value of coherence, Represents the EEG frequency domain signal and the EMG frequency domain signal The cross power spectral density of Represents the EEG frequency domain signal The auto-power spectral density of Represents the myoelectric frequency domain signal The autopower spectral density of .

[0024] In some embodiments, judging whether the cortical muscle coherence coefficient corresponding to each sub-frequency band meets the requirements according to the set rules includes:

[0025] A coordinate system is constructed with frequency as the horizontal axis and the cortical muscle coherence coefficient as the vertical axis, the cortical muscle coherence coefficient generated during the rehabilitation training is marked, and a curve fitting is performed to obtain a test curve;

[0026] The closed area between the test curve and the standard curve is calculated. If the area is greater than a set threshold, it means that the requirement is not met; otherwise, the requirement is met.

[0027] In some embodiments, a rehabilitation training device for regulating cortical muscle coherence based on reshaping embodied sense comprises:

[0028] Virtual reality equipment, used to provide rehabilitation training tasks in preset virtual reality scenes;

[0029] The embodied sense reconstruction module includes the personal perspective selection module and the multi-sensory stimulation module;

[0030] The personal perspective selection module is used to provide personal perspective selection, and the multi-sensory stimulation module is used to provide multiple sensory stimulation items;

[0031] The embodied sense reshaping module is used to initialize the embodied sense associated element combination of the virtual character and introduce the virtual reality scene, so as to activate the primary sensory area of ​​the user's brain by reshaping the embodied sense during the execution of the rehabilitation training task;

[0032] A cortical muscle coherence feedback module, including an EEG sensor, an EMG sensor, and a data analysis module;

[0033] The electroencephalogram sensor is used to collect electroencephalogram signals of the user when performing the rehabilitation training task according to a first set frequency, and the electromyography sensor is used to collect electromyography signals of the user when performing the rehabilitation training task according to a second set frequency;

[0034] The data analysis module is used to pre-process and segment the EEG signal and the EMG signal according to a preset time interval, convert the EEG signal in the segment from the time domain to the frequency domain to obtain the EEG frequency domain signal, and divide the sub-frequency bands according to the set frequency interval; convert the EMG signal in the segment from the time domain to the frequency domain to obtain the EMG frequency domain signal, and divide the sub-frequency bands according to the set frequency interval; calculate the cortical muscle coherence coefficient of the corresponding EEG frequency domain signal and the EMG frequency domain signal in each of the sub-frequency bands; determine whether the cortical muscle coherence coefficient corresponding to each sub-frequency band meets the requirements according to the set rules, and if it does not meet the requirements, change, introduce or remove the embodied sense related elements in a preset order until a target embodied sense related element combination that meets the requirements is found, and perform rehabilitation training based on the target embodied sense related element combination;

[0035] Among them, planning sub-frequency bands according to set intervals within a set frequency band, and calculating the cortical muscle coherence coefficient of the corresponding EEG frequency domain signal and the EMG frequency domain signal in each sub-frequency band, including:

[0036] The analysis frequency band is 8-30 Hz, and each 1 Hz is divided into 1 segment. The calculation formula of the cortical muscle coherence coefficient is:

[0037] ;

[0038] in, is the square value of coherence, Represents the EEG frequency domain signal and the EMG frequency domain signal The cross power spectral density of Represents the EEG frequency domain signal The auto-power spectral density of Represents the myoelectric frequency domain signal The autopower spectral density of .

[0039] In some embodiments, the virtual reality device is a VR display device; the corticomuscular coherence feedback module also includes a display module for displaying the corticomuscular coherence coefficient.

[0040] On the other hand, the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon, which implements the steps of the above method when the computer program / instruction is executed by a processor.

[0041] The beneficial effects of the present invention are at least:

[0042] The rehabilitation training method and device based on reshaping the sense of embodiment and regulating cortical muscle coherence described in the present invention collect EEG signals and electromyographic signals, convert them into frequency domain signals through short-time Fourier transform, divide multiple frequency bands and calculate the cortical muscle coherence coefficient of each segment to characterize the coordination between neuronal activity and muscle movement in time and frequency, quantify the quality of the user's sense of embodiment reshaping when using virtual reality technology for rehabilitation training, and guide the adjustment of the personal perspective and sensory stimulation items used in rehabilitation training to improve the quality of rehabilitation training.

[0043] Additional advantages, purposes, and features of the present invention will be described in part in the following description, and will become apparent to those skilled in the art after studying the following, or may be learned from the practice of the present invention. The purposes and other advantages of the present invention may be achieved and obtained by the structures specifically indicated in the specification and the accompanying drawings.

[0044] Those skilled in the art will appreciate that the objectives and advantages that can be achieved with the present invention are not limited to the above specific description, and the above and other objectives that can be achieved by the present invention will be more clearly understood from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of the present application, and do not constitute a limitation of the present invention. In the drawings:

[0046] Figure 1 Schematic diagram of a process of a rehabilitation training method for regulating cortical-muscle coherence based on reshaping embodied sense according to an embodiment of the present invention.

[0047] Figure 2 This is a schematic diagram of the structure of a rehabilitation training device for regulating cortical-muscle coherence based on reshaping embodied sense according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0049] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0050] It should be emphasized that the term “include / comprises” when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0051] Sense of embodiment (SOE) is a feeling that a virtual body experiences in an immersive virtual environment, which is the same as a biological body. Some scholars believe that the sense of embodiment is composed of the sense of location, agency, and ownership, which are usually described as affective, motor, and spatial. The reconstruction of the sense of embodiment is the experience of a replaced illusion of the virtual avatar in the visual, motor, and proprioceptive levels in the virtual reality environment, and the experience of the external body, body parts, or objects being perceived as one's own. Humans constantly receive information streams from the visual, tactile, proprioceptive, vestibular, and interoceptive systems to gain a sense of their own body. People can expand the boundaries of their bodies, incorporate different prostheses, such as rubber hands or virtual hands, into their body schema, and reshape the sense of embodiment of the prosthesis. Virtual reality technology creates an interactive environment for users through various devices such as head-mounted displays (HMDs), head tracking, real-time motion capture, tactile feedback, and audio. Multisensory joint stimulation can be used to induce a sense of embodiment in a virtual avatar, and synchronized visual-tactile joint stimulation increases the sense of embodiment. In order to study the sense of embodiment in virtual reality, some researchers have used the full body illusion of the avatar, while others have used the rubber hand illusion and the virtual hand illusion. In the experiment of the virtual hand illusion, questionnaires and proprioceptive drift showed that participants had a sense of ownership of the virtual hand. In the process of rehabilitation training, the reshaping of the sense of embodiment can assist in training the central nervous system's ability to control muscle endings.

[0052] The present invention introduces the CMC index (ortico-muscular coherence), which is the cortical muscle coherence to measure the quality of embodied reconstruction. CMC is an index to measure the coherence between the motor cortex of the brain and the relevant muscles. The calculation of coherence is usually based on spectrum analysis and cross-spectrum normalization, and the formula is as follows:

[0053] ;

[0054] in, Indicates the frequency The coherence on represents the cross power spectral density of signal S1 and signal S2, represents the power spectral density of signal S1, represents the power spectral density of signal S2. The value of coherence is normalized to satisfy the range of 0 to 1, where 1 indicates that the two signals are completely coherent and 0 indicates incoherence.

[0055] Corticomuscular coherence is a method used to understand how cortical activity controls muscle movement and detects functional coupling between the brain's motor cortex and related muscles. The uplink and downlink corticomuscular pathways are two different directions, both of which can produce coherence, but the downlink pathway is clearer and more certain than the uplink pathway. Therefore, the general definition of CMC indicates the corticomuscular coherence under the downlink pathway. With the diversification of signal acquisition techniques, corticomuscular coherence shows a wide research space, and different types of signals from different methods can be analyzed. According to recent studies, the most common techniques for collecting brain activity signals are scalp electroencephalography (EEG), magnetoencephalography (MEG), and electrocorticography (ECoG), while muscle activity signals include surface electromyography (sEMG) and ultrasound. In the study of corticomuscular coherence, EEG-EMG, MEG-EMG, and ECoG-EMG are the three most commonly used methods to analyze the functional coupling between the brain cortex and muscle activity, and these three sets of signals are used to calculate coherence parameters. In addition to EEG, signals from the nerve center can also be detected by means such as MEG, near-infrared fNIRS, etc., which can detect the cortex. In addition to EMG, muscle signals can also be detected by means such as AMG, which can detect muscles.

[0056] In addition to traditional coherence analysis, Fourier coherence and wavelet coherence have also been reported in some studies. Comparing Fourier coherence and wavelet coherence, both methods can study non-stationary signals such as sEMG and EEG. However, the window size of wavelet analysis is variable and more adaptable to the frequency of oscillatory signals. Therefore, wavelet coherence has more accurate results than Fourier coherence. In addition, wavelet coherence can show the CMC amplitude in the entire task time series, and strong coherence under specific movements can be observed, so it is easier to find factors affecting CMC.

[0057] Basic principles of the present invention:

[0058] The cerebral cortex controls muscle contraction and movement through descending neural pathways. In this process, neural electrical signals are transmitted from the cerebral cortex to the spinal cord, and then from the spinal cord to the muscles, causing muscle excitement and contraction. At the same time, muscle contraction also generates feedback signals, which are transmitted back to the cerebral cortex through ascending neural pathways, forming a closed-loop neuromuscular control system. Electroencephalogram (EEG) signals reflect the group activity of brain neurons and can provide information about the functional state of the cerebral cortex. Electromyogram (EMG) signals reflect the contraction state of muscles and can provide information about muscle activity.

[0059] The brain area that controls movement includes the primary sensory area, etc., and the remodeling of the sense of embodiment will activate the primary sensory area, thereby contributing to the recovery of the motor control function. The present invention remodels the sense of embodiment in virtual reality patients through multi-sensory joint stimulation, remodels the sense of embodiment of the virtual avatar in the virtual environment, and thus better activates the cerebral cortex in the motor area.

[0060] Specifically, one aspect of the present invention provides a rehabilitation training method based on reshaping the sense of embodiment to regulate cortical muscle coherence, such as Figure 1 As shown, the method includes the following steps S101-S104:

[0061] Step S101: for a rehabilitation training task in a specific virtual reality scene, based on embodied sense-related elements and their combinations, the primary sensory area and primary motor area of ​​the user's brain are activated by reshaping the embodied sense during the execution of the rehabilitation training task, and the EEG and EMG signals are recorded through EEG sensors and EMG sensors; the embodied sense-related elements include the type of personal perspective and one or more sensory stimulation items; the sensory stimulation items include vision, hearing, touch, force, vestibular sensation, temperature stimulation, olfactory stimulation items, taste stimulation items and airflow stimulation.

[0062] Step S102: collecting EEG signals of the user when performing the rehabilitation training task based on the EEG sensor at a first set frequency, and collecting EMG signals of the user when performing the rehabilitation training task based on the EMG sensor at a second set frequency.

[0063] Step S103: Pre-process and segment the EEG signals and EMG signals according to preset time intervals, convert the EEG signals in the segments from the time domain to the frequency domain to obtain EEG frequency domain signals, and divide them into sub-frequency bands according to the set frequency intervals; convert the EMG signals in the segments from the time domain to the frequency domain to obtain EMG frequency domain signals, and divide them into sub-frequency bands according to the set frequency intervals; calculate the cortical muscle coherence coefficients of the corresponding EEG frequency domain signals and EMG frequency domain signals in each of the sub-frequency bands.

[0064] Step S104: Determine whether the cortical muscle coherence coefficient corresponding to each sub-frequency band meets the requirements according to the set rules. If it does not meet the requirements, change, introduce or remove the embodied sense related elements according to the preset combination until a target embodied sense related element combination that meets the requirements is found, and perform rehabilitation training based on the target embodied sense related element combination.

[0065] In step S101, providing rehabilitation training tasks through virtual reality (VR) technology can create an adaptive rehabilitation experience for patients through an immersive, interactive and personalized virtual environment. VR rehabilitation training can design different virtual scenes according to the specific needs of patients (such as limb function recovery after stroke, sports injury rehabilitation or cognitive function improvement), such as hand-eye coordination training, simulated gait exercises, virtual object grasping, etc. These training projects record the patient's motion data in real time through precise motion capture devices and sensors, helping patients to repeat specific movements in a safe and controllable virtual environment, reducing fatigue and monotony in traditional rehabilitation training. At the same time, VR technology can stimulate patients' interest in participation and training motivation through instant feedback and gamification design, such as prompting action errors or rewarding behaviors that complete goals through audio-visual feedback. In addition, VR can also record training progress and generate data reports, so that rehabilitation therapists can optimize training plans based on patients' performance. For cognitive function training, VR can simulate real-life situations (such as shopping in a supermarket, crossing the road), allowing patients to practice daily behaviors in a virtual world.

[0066] Rehabilitation training tasks include limb movement rehabilitation training, balance and coordination training, motor cognitive rehabilitation training, upper limb and finger fine motor rehabilitation training, and lower limb movement rehabilitation training.

[0067] First, limb movement rehabilitation training is mainly used to restore patients' limb movement disorders caused by stroke, fractures or nerve damage. Through active or passive movement training, patients can gradually restore muscle strength, joint flexibility and motor function. Secondly, balance and coordination training aims to improve patients' decreased balance and coordination problems caused by central nervous system damage or aging. The training content includes standing balance, single-leg standing and gait exercises to reduce the risk of falling and improve mobility.

[0068] In some embodiments, the perspective includes the first-person perspective, the third-person perspective, and the non-avatar perspective; the preferences for the first-person vision and the third-person perspective vary in different sports tasks and different age groups. The first-person perspective helps improve the accuracy of sports performance: in some fine movement tasks, such as when athletes shoot and gymnasts complete balance beam movements, they can imagine the details of the movements from their own perspective and accurately grasp the position of the limbs and the point of force. For example, shooters can accurately adjust the coordination of breathing, aiming, and pulling the trigger according to their own feelings, and gymnasts can better perceive the slight changes in body posture to maintain balance and complete difficult movements, reducing the error rate. The third-person perspective has obvious advantages in learning new sports tasks and improving execution speed: when learning new skills of complex ball sports, athletes observe their own movements from the perspective of a bystander, and can clearly see the overall trajectory of the movement, the order and rhythm of the coordination of each limb, and quickly grasp the framework and key links of the movement process as if a coach is guiding them from the side, accelerating the learning process; and when performing tasks, this perspective helps athletes break through the limitations of their own perspective, macro-control the speed and rhythm of the movement, optimize the movement strategy, and improve the overall speed. For example, basketball players can quickly judge the timing of running and passing and adjust the advancement speed from an external perspective during fast breaks. In addition, the third-person perspective can observe the movement of the lower limbs more clearly, and will not bend over to observe the movement and affect normal movement. Other studies have shown that patients over 64 years old use the third-person perspective more often than younger patients.

[0069] In virtual reality (VR) scenes, various sensory stimuli can be provided in real time through specialized equipment and technical means to create a highly immersive multi-sensory experience for users. First, visual stimulation is provided by high-resolution VR head-mounted display devices, combined with 3D modeling, real-time rendering and dynamic light and shadow technology to present realistic scenes and objects. Auditory stimulation is achieved by spatial audio technology, providing a sound experience of directionality, distance and environmental details through headphones or speakers. Tactile stimulation is usually generated by tactile gloves or force feedback devices, which allow users to feel the reality of touch or operation through vibration, pressure or object surface simulation. Force stimulation can be achieved through exoskeleton devices or force feedback joysticks to simulate the weight, resistance or reaction force of objects and enhance the sense of interaction. Vestibular sensory stimulation is provided by dynamic seats or motion platforms to simulate the body's rotation, acceleration or weightlessness, enhancing the perception of virtual movement. Temperature stimulation is provided by wearable temperature regulation devices, such as heating or cooling modules on gloves or clothing, to simulate the hot and cold changes in the environment. Olfactory stimulation is provided by micro-odor release devices, which trigger specific odors according to the scene, such as floral, food or smoke. Taste stimulation is still under development, and some studies have achieved this by electrically stimulating the taste buds of the tongue or releasing trace amounts of edible substances. Airflow stimulation uses fans or directional airflow devices to simulate the airflow effects caused by wind or object movement. These sensory stimuli are integrated and synchronized to enable users to gain a multi-sensory real experience in virtual scenes. It should be noted that these sensory stimulation projects are only a partial list. In actual application scenarios, other types of sensory stimulation projects can also be configured and deployed based on demand and equipment capabilities.

[0070] In some embodiments, the method sets embodied sense related elements and combinations thereof, including steps S1011-S1012:

[0071] Step S1011: after excluding unavailable sensory stimulation items due to physiological limitations of the user, a plurality of candidate sensory stimulation items related to the rehabilitation training task are obtained.

[0072] Step S1012: Randomly arrange and combine the candidate sensory stimulation items to obtain multiple candidate combinations of embodied sense-related elements.

[0073] In step S102, electroencephalogram (EEG) signals are collected by attaching an electrode cap or electrode patches to the scalp, converting the electrical activities of cerebral cortical neurons into electrical signals. The acquisition device includes a high-precision amplifier and a filter, which are used to amplify weak EEG signals and remove interference (such as power frequency noise or electromyogram artifacts). During the acquisition process, conductive gel needs to be applied between the electrodes and the skin or dry electrodes are used to ensure signal quality. Electromyogram (EMG) signals are collected by attaching surface electrodes or inserting needle electrodes onto the target muscle to record the electrical signals generated during muscle contraction. Surface EMG signal acquisition is more common because it is non-invasive and easy to operate; needle electrodes are suitable for specific deep muscle analysis. The acquisition device also requires a high-sensitivity amplifier, a filter, and an analog-to-digital converter to capture and process EMG signals. The key to EEG and EMG signal acquisition is to ensure good grounding of the device, good skin contact, and reduction of environmental electromagnetic interference, so as to obtain high-quality bioelectrical data for subsequent analysis and applications.

[0074] In some embodiments, the first set frequency and the second set frequency are 1 - 2 kHz.

[0075] After preprocessing and segmenting the EEG signals and EMG signals at a preset time interval, the following steps are further included: performing band-pass filtering with a low frequency of 0.1 - 4 Hz and a high frequency of 15 - 250 Hz; performing power frequency notch filtering, and requiring that the number of sampling points within the preset time interval is at least 512.

[0076] Specifically, band-pass filtering is performed with a - b Hz (0.1 < a < 4, 15 < b < 250), and the specific frequency band is determined according to the type of movement task to be analyzed. For example, the cortical muscle coherence value in the β band (13 - 30 Hz) is related to static output, and the coherence in the γ band (31 - 45 Hz) is related to dynamic force output; power frequency notch filtering is determined according to the device and the common 220v 50Hz electricity; the time interval is y seconds, and it is required that the number of sampling points x * y > 512 because sufficient data in each segment is needed for power spectral density calculation.

[0077] Preprocessing and segmenting is to divide the continuously recorded long-time signal into multiple smaller time window segments at a preset time interval. The segmented signal is more suitable for time-domain or frequency-domain analysis because within a smaller time range, the signal can generally be considered stationary, and processing segmented signals is more efficient than processing the entire long-time signal.

[0078] For EEG and EMG signals, 0.5-100 Hz is a common effective frequency band. Bandpass filtering is used to retain components within a specific frequency range (0.5-100 Hz) in the signal, while removing low-frequency signals below 0.5 Hz (such as baseline drift) and high-frequency noise above 100 Hz (such as electromagnetic interference or system noise). Low-frequency drift may come from unstable electrode contact, motion artifacts, or electrochemical reactions between the electrode and the skin. Bandpass filtering can effectively reduce these interferences. High-frequency noise usually comes from the electronic system of the device or environmental electromagnetic interference. Bandpass filtering can enhance the signal-to-noise ratio of the signal.

[0079] In step S103, wavelet transform or short-time Fourier transform can be used to perform time-frequency domain conversion. In some embodiments, the method uses short-time Fourier transform to convert the time domain signal to the frequency domain, and the calculation formula is:

[0080] ;

[0081] in, represents the original input signal, is the window function; is the time parameter, indicating the center position of the time segment; represents angular frequency; t represents time; is a negative exponential function; Represents the result of short-time Fourier transform.

[0082] The method further includes: introducing overlap when segmenting the short-time Fourier transform to improve the time-frequency resolution, and when framing, each frame of data overlaps with the previous frame by 10-50%.

[0083] In some embodiments, sub-frequency bands are planned according to set intervals within a set frequency band, and the cortical muscle coherence coefficients of the corresponding EEG frequency domain signals and EMG frequency domain signals are calculated within each sub-frequency band, including:

[0084] The low frequency of the analysis frequency band is greater than or equal to 4Hz, the high frequency is less than or equal to 250Hz, the interval between the low frequency and the high frequency is at least 4Hz, and each 1Hz is divided into 1 segment. The calculation formula of the cortical muscle coherence coefficient is:

[0085] ;

[0086] in, is the square value of coherence, Represents EEG frequency domain signal and EMG frequency domain signals The cross power spectral density of Represents EEG frequency domain signal The auto-power spectral density of Represents the myoelectric frequency domain signal The autopower spectral density of .

[0087] In step S104, a standard value can be set for the cortical muscle coherence coefficient corresponding to each sub-band. If the standard value is reached, it means that the coordination ability between the cerebral cortex and muscle activity has reached the requirement during the embodied sense remodeling process. If the standard value is not reached, such as lower than 0.01, it is necessary to further modify the embodied sense related elements, including the personal perspective and the sensory stimulation items. In the specific implementation process, it can be directly selected from the candidate combination of embodied sense related elements obtained by permutation and combination during initialization.

[0088] In some embodiments, judging whether the cortical muscle coherence coefficient corresponding to each sub-frequency band meets the requirements according to the set rules includes steps S1041-S1042:

[0089] Step S1041: construct a coordinate system with frequency as the horizontal axis and cortical muscle coherence coefficient as the vertical axis, mark the cortical muscle coherence coefficient generated during the rehabilitation training, and perform curve fitting to obtain a test curve.

[0090] Step S1042: Calculate the closed area between the test curve and the standard curve. If the closed area is larger than a set threshold, it means that the requirement is not met; otherwise, the requirement is met.

[0091] In other embodiments, the point-to-point Euclidean distance, average vertical deviation, dynamic time planning distance or curve fitting error between the test curve and the standard curve can also be calculated as parameters to measure the deviation. Based on the deviation, it is judged whether the introduced perspective and sensory stimulation items in the rehabilitation training process can ensure that the user can effectively reshape the sense of embodiment and achieve coordination between the motor cortex and related muscles.

[0092] like Figure 2 As shown, the present invention provides a rehabilitation training device for regulating cortical muscle coherence based on reshaping embodied sense, and the device includes: virtual reality equipment, embodied sense reshaping module and cortical muscle coherence feedback module.

[0093] Virtual reality equipment is used to provide rehabilitation training tasks in preset virtual reality scenes.

[0094] The embodied sense remodeling module includes a personal perspective selection module and a multi-sensory stimulation module; the personal perspective selection module is used to provide personal perspective selection, and the multi-sensory stimulation module is used to provide multiple sensory stimulation items; the embodied sense remodeling module is used to initialize the combination of embodied sense-related elements of the virtual character and introduce a virtual reality scene, so as to activate the primary sensory area of ​​the user's brain by remodeling the embodied sense during the execution of the rehabilitation training task. In some embodiments, the multi-sensory stimulation module is configured by combining three types of sensory stimulation items: tactile stimulation, visual stimulation, and auditory stimulation.

[0095] The cortical muscle coherence feedback module includes an EEG sensor, an EMG sensor and a data analysis module; the EEG sensor is used to collect EEG signals of the user when performing rehabilitation training tasks according to a first set frequency, and the EMG sensor is used to collect EMG signals of the user when performing rehabilitation training tasks according to a second set frequency.

[0096] The data analysis module is used to pre-process and segment the EEG signals and EMG signals according to preset time intervals, convert the EEG signals in the segments from the time domain to the frequency domain to obtain EEG frequency domain signals, and divide them into sub-frequency bands according to the set frequency intervals; convert the EMG signals in the segments from the time domain to the frequency domain to obtain EMG frequency domain signals, and divide them into sub-frequency bands according to the set frequency intervals; calculate the cortical muscle coherence coefficients of the corresponding EEG frequency domain signals and EMG frequency domain signals in each sub-frequency band; determine whether the cortical muscle coherence coefficients corresponding to each sub-frequency band meet the requirements according to the set rules, and if they do not meet the requirements, change, introduce or remove the embodied sense related elements in a preset order until a target embodied sense related element combination that meets the requirements is found, and perform rehabilitation training based on the target embodied sense related element combination.

[0097] Among them, planning sub-frequency bands according to set intervals within the set frequency band, and calculating the cortical muscle coherence coefficients of the corresponding EEG frequency domain signals and EMG frequency domain signals in each sub-frequency band, including:

[0098] The analysis frequency band is 8-30 Hz, with each 1 Hz being divided into 1 segment. The calculation formula for the cortical muscle coherence coefficient is:

[0099] ;

[0100] in, is the square value of coherence, Represents EEG frequency domain signal and EMG frequency domain signals The cross power spectral density of Represents EEG frequency domain signal The auto-power spectral density of Represents the myoelectric frequency domain signal The autopower spectral density of .

[0101] In some embodiments, the virtual reality device is a VR display device; the cortical muscle coherence feedback module also includes a display module for displaying the cortical muscle coherence coefficient.

[0102] On the other hand, the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon, which implements the steps of the above method when the computer program / instruction is executed by a processor.

[0103] Corresponding to the above method, the present invention also provides an apparatus / system, which includes a computer device, the computer device includes a processor and a memory, the memory stores computer instructions, the processor is used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the apparatus / system implements the steps of the method described above.

[0104] The embodiment of the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the aforementioned edge computing server deployment method are implemented. The computer-readable storage medium can be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0105] In summary, the rehabilitation training method and device for regulating cortical muscle coherence based on reshaping the sense of embodiment described in the present invention collects EEG and EMG signals and converts them into frequency domain signals using short-time Fourier transform technology. Then, multiple frequency intervals are divided, and the cortical muscle coherence coefficient of each interval is calculated to characterize the synchronization between neuronal activity and muscle movement in the time and frequency dimensions. This process aims to quantify the degree of embodied reshaping of users in virtual reality rehabilitation training. Based on these quantitative results, it is possible to guide the adjustment of the personal perspective and sensory stimulation items in rehabilitation training, aiming to further optimize and enhance the effect of rehabilitation training.

[0106] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0107] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0108] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with features of other embodiments or replace features of other embodiments.

[0109] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A rehabilitation training method based on reshaping embodied sense to regulate cortical muscle coherence, characterized in that: The method comprises the following steps: For rehabilitation training tasks in specific virtual reality scenarios, based on embodied sense-related elements and their combinations, the primary sensory area and primary motor area of ​​the user's brain are activated by reshaping the embodied sense during the execution of the rehabilitation training tasks, and the EEG and EMG signals are recorded by EEG sensors and EMG sensors; the embodied sense-related elements include the type of personal perspective and one or more sensory stimulation items; the sensory stimulation items include vision, hearing, touch, force, vestibular sensation, temperature stimulation, olfactory stimulation items, taste stimulation items and airflow stimulation; Collecting the EEG signal of the user when performing the rehabilitation training task based on the EEG sensor at a first set frequency, and collecting the EMG signal of the user when performing the rehabilitation training task based on the EMG sensor at a second set frequency; The EEG signal and the EMG signal are preprocessed and segmented according to preset time intervals, the EEG signal in the segment is converted from the time domain to the frequency domain to obtain an EEG frequency domain signal, and the sub-frequency bands are divided according to the set frequency intervals; the EMG signal in the segment is converted from the time domain to the frequency domain to obtain an EMG frequency domain signal, and the sub-frequency bands are divided according to the set frequency intervals; the cortical muscle coherence coefficients of the corresponding EEG frequency domain signals and the EMG frequency domain signals are calculated in each of the sub-frequency bands; According to the set rules, it is determined whether the cortical muscle coherence coefficient corresponding to each sub-frequency band meets the requirements. If it does not meet the requirements, the embodied sense associated elements are changed, introduced or removed according to the preset combination until a target embodied sense associated element combination that meets the requirements is found, and rehabilitation training is performed based on the target embodied sense associated element combination; Among them, planning sub-frequency bands according to set intervals within a set frequency band, and calculating the cortical muscle coherence coefficient of the corresponding EEG frequency domain signal and the EMG frequency domain signal in each sub-frequency band, including: The low frequency of the analysis frequency band is greater than or equal to 4 Hz, and the high frequency is less than or equal to 250 Hz. The interval between the low frequency and the high frequency is at least 4 Hz, and each 1 Hz is divided into 1 segment. The calculation formula of the cortical muscle coherence coefficient is: ； in, is the square value of coherence, Represents the EEG frequency domain signal and the EMG frequency domain signal The cross power spectral density of Represents the EEG frequency domain signal The auto-power spectral density of Represents the myoelectric frequency domain signal The autopower spectral density of .

2. The rehabilitation training method based on reshaping the sense of embodiment and regulating cortical muscle coherence according to claim 1, characterized in that: The personal perspective includes the first-person perspective, the third-person perspective and the perspective without virtual avatar; the rehabilitation training tasks include limb movement rehabilitation training, balance and coordination training, motor cognitive rehabilitation training, upper limb and finger fine motor rehabilitation training and lower limb movement rehabilitation training.

3. The rehabilitation training method based on reshaping embodied sense and regulating cortical muscle coherence according to claim 2, characterized in that: The method for setting embodied sense related elements and their combinations includes: After excluding unavailable sensory stimulation items that are physically limited by the user, obtaining channels and stimulation modes corresponding to a plurality of candidate sensory stimulation items related to the rehabilitation training task; The channels and stimulation modes corresponding to the candidate sensory stimulation items are randomly arranged and combined to obtain a plurality of candidate combinations of embodied sense-related elements.

4. The rehabilitation training method based on reshaping embodied sense and regulating cortical muscle coherence according to claim 3, characterized in that: The first set frequency and the second set frequency are 1-2 kHz; After the EEG signal and the EMG signal are preprocessed and segmented according to the preset time interval, the method further includes: performing band-pass filtering using a low frequency of 0.1-4 Hz and a high frequency of 15-250 Hz; The power frequency notch is executed, requiring that the number of sampling points of the power frequency notch within the preset time interval is at least 512.

5. The rehabilitation training method based on reshaping the sense of embodiment and regulating cortical muscle coherence according to claim 4, characterized in that: The method uses short-time Fourier transform to convert the time domain signal to the frequency domain, and the calculation formula is: ； in, represents the original input signal, is the window function; is the time parameter, indicating the center position of the time segment; represents angular frequency; t represents time; is a negative exponential function; represents the result of the short-time Fourier transform; The method further includes: introducing overlap into the short-time Fourier transform when segmenting to improve the time-frequency resolution, and when framing, each frame of data overlaps with the previous frame by 10-50%.

6. The rehabilitation training method based on reshaping the sense of embodiment and regulating cortical muscle coherence according to claim 1, characterized in that: According to the set rules, determine whether the cortical muscle coherence coefficient corresponding to each sub-frequency band meets the requirements, including: A coordinate system is constructed with frequency as the horizontal axis and the cortical muscle coherence coefficient as the vertical axis, the cortical muscle coherence coefficient generated during the rehabilitation training is marked, and a curve fitting is performed to obtain a test curve; The closed area between the test curve and the standard curve is calculated. If the area is greater than a set threshold, it means that the requirement is not met; otherwise, the requirement is met.

7. A rehabilitation training device based on reshaping embodied sense to regulate cortical muscle coherence, characterized in that: The device comprises: Virtual reality equipment, used to provide rehabilitation training tasks in preset virtual reality scenes; The embodied sense reconstruction module includes the personal perspective selection module and the multi-sensory stimulation module; The personal perspective selection module is used to provide personal perspective selection, and the multi-sensory stimulation module is used to provide multiple sensory stimulation items; The embodied sense reshaping module is used to initialize the embodied sense associated element combination of the virtual character and introduce the virtual reality scene, so as to activate the primary sensory area and the primary motor area of ​​the user's brain by reshaping the embodied sense during the execution of the rehabilitation training task; A cortical muscle coherence feedback module, including an EEG sensor, an EMG sensor, and a data analysis module; The electroencephalogram sensor is used to collect electroencephalogram signals of the user when performing the rehabilitation training task according to a first set frequency, and the electromyography sensor is used to collect electromyography signals of the user when performing the rehabilitation training task according to a second set frequency; The data analysis module is used to pre-process and segment the EEG signal and the EMG signal according to a preset time interval, convert the EEG signal in the segment from the time domain to the frequency domain to obtain the EEG frequency domain signal, and divide the sub-frequency bands according to the set frequency interval; convert the EMG signal in the segment from the time domain to the frequency domain to obtain the EMG frequency domain signal, and divide the sub-frequency bands according to the set frequency interval; calculate the cortical muscle coherence coefficient of the corresponding EEG frequency domain signal and the EMG frequency domain signal in each of the sub-frequency bands; determine whether the cortical muscle coherence coefficient corresponding to each sub-frequency band meets the requirements according to the set rules, and if it does not meet the requirements, change, introduce or remove the embodied sense related elements in a preset order until a target embodied sense related element combination that meets the requirements is found, and perform rehabilitation training based on the target embodied sense related element combination; Among them, planning sub-frequency bands according to set intervals within a set frequency band, and calculating the cortical muscle coherence coefficient of the corresponding EEG frequency domain signal and the EMG frequency domain signal in each sub-frequency band, including: The analysis frequency band is 8-30 Hz, and each 1 Hz is divided into 1 segment. The calculation formula of the cortical muscle coherence coefficient is: ； in, is the square value of coherence, Represents the EEG frequency domain signal and the EMG frequency domain signal The cross power spectral density of Represents the EEG frequency domain signal The auto-power spectral density of Represents the myoelectric frequency domain signal The autopower spectral density of .

8. The rehabilitation training device for regulating cortical muscle coherence based on reshaping embodied sense according to claim 7, characterized in that: The virtual reality device is a VR display device; the cortical muscle coherence feedback module also includes a display module for displaying the cortical muscle coherence coefficient; the embodied sense reshaping module includes stimulation devices for various sensory channels.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method as described in any one of claims 1 to 6 are implemented.

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