Multi-mode physical stimulation regulation and control system based on vital interface

Abstract

The invention provides a multi-mode physical stimulation regulation and control system based on a biological interface, which can be applied to the technical field of nerve regulation and control. The multi-mode physical stimulation regulation and control system can comprise a living interface acquisition unit and a plurality of stimulation modules, the processor is used for obtaining Nth multi-modal electromechanical information of the target object through the electromechanical interface acquisition unit after the (N-1) th multi-modal stimulation is carried out on the target object through the stimulation module; determining Nth multi-mode regulation and control demand information for the target object according to the Nth multi-mode vitality feature mapping model according to the Nth multi-mode vitality feature mapping information; outputting an Nth multi-mode stimulation parameter by using a multi-mode stimulation parameter decision model according to the Nth multi-mode regulation and control demand information; and according to the state information of the target object after the (N-1) th multi-mode stimulation, correcting the Nth multi-mode stimulation parameter, and according to the corrected Nth multi-mode stimulation parameter, controlling the plurality of stimulation modules to perform the Nth multi-mode stimulation on the target object.

Description

Technical Field

[0001] This disclosure relates to the field of neuromodulation technology, and more specifically to a multimodal physical stimulation modulation system based on a bio-interface. Background Technology

[0002] Stimulation modulation systems, used to stimulate nerves to achieve neuromodulation, have become key devices in the fields of neuromodulation and rehabilitation therapy. However, some stimulation modulation systems struggle to deliver stable, accurate, safe, and highly coordinated outputs, affecting their neuromodulation efficacy. Summary of the Invention

[0003] In view of the above problems, this disclosure provides a multimodal physical stimulation modulation system based on a life interface.

[0004] According to a first aspect of this disclosure, a multimodal physical stimulation modulation system based on a biomechanical interface is provided, comprising: a biomechanical interface acquisition unit and multiple stimulation modules; and a processor electrically connected to the biomechanical interface acquisition unit and the multiple stimulation modules, and configured to: after performing N-1th multimodal stimulation on a target object through the stimulation modules, obtain Nth multimodal biomechanical information of the target object through the biomechanical interface acquisition unit; wherein the Nth multimodal biomechanical information includes at least two electrical information selected from Nth electroencephalogram (EEG), Nth electromyogram (EMG), Nth electrocardiogram (ECG), or Nth electrodermal transfer (EDT); N is an integer greater than 1; determine the Nth multimodal modulation requirement information for the target object based on the Nth multimodal biomechanical information using a biomechanical feature mapping model; output the Nth multimodal stimulation parameters based on the Nth multimodal modulation requirement information using a multimodal stimulation parameter decision model; correct the Nth multimodal stimulation parameters based on the state information of the target object after the N-1th multimodal stimulation, and control the multiple stimulation modules to perform Nth multimodal stimulation on the target object according to the corrected Nth multimodal stimulation parameters.

[0005] According to embodiments of this disclosure, a multimodal physical stimulation modulation system is provided. In this system, after performing N-1 multimodal stimulation on a target object through stimulation modules, the target object's Nth multimodal bio-electro-electrical information is processed using a bio-feature mapping model to obtain the target object's Nth multimodal modulation requirement information. Subsequently, a multimodal stimulation parameter decision model is used to process the Nth multimodal modulation requirement information to output the Nth multimodal stimulation parameters. Therefore, based on the target object's multimodal stimulation requirements after N-1 multimodal stimulation, the Nth multimodal stimulation parameters can be accurately determined for that multimodal stimulation requirement. Thus, the Nth multimodal stimulation parameters can be used to control multiple stimulation modules to perform Nth multimodal stimulation on the target object.

[0006] By utilizing a vitality feature mapping model and a multimodal stimulation parameter decision model to output the Nth multimodal stimulation parameters corresponding to the Nth multimodal bio-electroelectric information, the incompatibility problem between the data standards of the vitality interface acquisition unit and the data standards of various stimulation devices is at least partially avoided. This enables the coordinated output of multimodal stimulation and improves the efficiency of motion control of the target object. Furthermore, the multimodal physical stimulation control system of this disclosure can integrate an automatic device identification mechanism to support the dynamic access and coordinated scheduling of stimulation devices from different manufacturers. This solves the compatibility problem between the vitality interface acquisition unit and various stimulation devices at the underlying architecture level, reducing the technical difficulty and engineering implementation complexity of the integrated development of the multimodal physical stimulation control system.

[0007] Furthermore, by modifying the Nth multimodal stimulation parameters based on the state information of the target object after the (N-1)th multimodal stimulation, adaptive calibration of the multimodal stimulation parameters is achieved during the Nth round of multimodal stimulation, which at least partially avoids the safety issues caused by stimulating the target object when it is in an abnormal state.

[0008] Based on this, the multimodal physical stimulation control system of this disclosure realizes closed-loop dynamic adaptive adjustment from "collecting bioelectrical information to multimodal stimulation, and then from multimodal stimulation to new bioelectrical information", which can meet the different physiological intervention needs of the target object. Attached Figure Description

[0009] The above-mentioned contents, other objects, features and advantages of this disclosure will become clearer from the following description of embodiments of this disclosure with reference to the accompanying drawings, which will be described in conjunction with the drawings.

[0010] Figure 1 A schematic diagram of a multimodal physical stimulation modulation system according to an embodiment of the present disclosure is shown.

[0011] Figure 2 A schematic diagram of a life interface acquisition unit according to an embodiment of the present disclosure is shown.

[0012] Figure 3 The diagram illustrates integrated electromyographic information, transcranial magnetic stimulation pulse frequency, and electrocorticography neuronal activation frequency according to embodiments of the present disclosure.

[0013] Figure 4 A schematic diagram of closed-loop control according to an embodiment of the present disclosure is shown.

[0014] Figure 5 A schematic diagram of a multimodal physical stimulation modulation system according to an embodiment of the present disclosure is shown. Detailed Implementation

[0015] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0016] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0017] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0018] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0019] In the technical solution disclosed herein, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with relevant laws, regulations, and standards, and necessary measures have been taken to ensure that they do not violate public order and good morals. Corresponding operation entry points are provided for users to choose to authorize or refuse.

[0020] In scenarios involving automated decision-making using personal information, the methods, devices, and systems provided in this disclosure all offer users corresponding entry points for choosing to agree to or reject the automated decision-making results. If the user chooses to reject, the process proceeds to the expert decision-making stage. Here, "automated decision-making" refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests, or economic, health, and credit status through computer programs, and then making a decision. Here, "expert decision-making" refers to the activity of making decisions by personnel who specialize in a particular field, possess specialized experience, knowledge, and skills, and have reached a certain level of professional expertise.

[0021] Vital interfaces, as a core carrier connecting biological tissues and external machine systems, have become a key technology in the fields of neuromodulation and rehabilitation therapy. However, in some solutions, when applying vital interfaces to multimodal stimulation scenarios, there are some problems that urgently need to be solved.

[0022] First, the utilization mode of bio-interface signals is singular, lacking a closed-loop control mechanism. For example, in some solutions, bio-interfaces mostly follow a unidirectional working mode of "biosignal acquisition - machine command output." For instance, there are prosthetic motion control systems based on electroencephalography (EEG). Such systems fail to establish an effective linkage mechanism between machine-side stimulus feedback and the physiological state at the biological end. Specifically, when the prosthesis performs grasping movements, such systems struggle to dynamically adjust the patient's muscle tension based on real-time feedback, resulting in low motion control accuracy (e.g., errors exceeding 10 mm) and a tendency to induce adverse physiological reactions such as muscle fatigue or spasms.

[0023] Secondly, the compatibility between multimodal stimulation and the bio-interface is poor, resulting in insufficient synergistic regulatory efficacy. In some multimodal stimulation systems (such as systems combining electrical and magnetic stimulation), the stimulation modules used to apply multiple modalities are independent of each other and lack standardized data interaction protocols with the bio-interface. This leads to two problems: first, stimulation parameters are difficult to dynamically optimize based on real-time biosignals acquired by the bio-interface; second, a single type of stimulation method cannot simultaneously meet the dual needs of deep neural pathway activation (such as functional reconstruction of the corticospinal tract after spinal cord injury) and superficial muscle synergistic control (such as fine motor skills of the hand's grasping muscles). Based on these factors, the regulatory efficacy of multimodal stimulation systems is generally below 45%.

[0024] Furthermore, in some solutions, the biosensor interface exhibits poor compatibility, making it difficult to achieve coordinated multimodal stimulation systems. The lack of a unified data interface standard between biosensors from different manufacturers and multimodal stimulation systems leads to significant signal transmission delays (exceeding 300 ms) and prominent data format incompatibility issues. This hinders the realization of real-time closed-loop control of "biosignal acquisition - stimulation strategy decision-stimulation signal output," and also significantly increases the complexity of clinical operations (e.g., requiring manual configuration and switching of parameters for multimodal stimulation modules).

[0025] Furthermore, some multimodal stimulation systems lack safety controls and exhibit poor individual adaptability. For example, such systems do not establish individualized safety thresholds based on signals from the life-support interface, easily triggering adverse reactions such as epileptic seizures and skin burns. Moreover, the stimulation parameters adopt a "one-size-fits-all" approach, ignoring differences in patient conditions (such as acute or recovery-stage stroke) and the degree of neurological damage, resulting in large fluctuations in the modulation effect.

[0026] Based on this, some solutions suffer from four major pain points: "one-way interaction of the life interface, poor adaptability of multi-modal stimulation, weak equipment compatibility, and lack of safety control." There is an urgent need to build an integrated system that integrates "closed-loop feedback of the life interface, dynamic regulation of multi-modal stimulation, and synergistic compatibility of multi-stimulation modules" to improve the accuracy, safety, and universality of neuromodulation and rehabilitation therapy.

[0027] In view of this, the present disclosure provides a closed-loop control system based on "bio-interface signal acquisition - feature analysis - stimulus decision-making - multi-mode output - signal feedback correction" to achieve real-time dynamic interaction between bioelectrical signals and machine physical stimuli. Based on this, the system effectively improves the accuracy and response speed of human-computer interaction by establishing a two-way feedback mechanism.

[0028] Figure 1 A schematic diagram of a multimodal physical stimulation modulation system according to an embodiment of the present disclosure is shown.

[0029] like Figure 1 As shown, the multimodal physical stimulation modulation system of this embodiment includes a vitality interface acquisition unit, multiple stimulation modules, and a processor.

[0030] The biomechanical interface acquisition unit can be worn on the head and limbs of a target object and can acquire biomechanical signals of the target object in multiple modalities. For example, the multiple modalities of biomechanical signals may include at least two of the following: electroencephalogram (EEG), electromyogram (EMG), electrocardiogram (ECG), or electrodermal signal.

[0031] The processor can be an industrial-grade embedded computer, which can be equipped with a high-performance central processing unit (CPU) and graphics processing unit (GPU), with memory such as 32 GB, a real-time operating system, and a response latency of less than 20 ms.

[0032] The processor can be electrically connected to the bio-electro-optical (BEO) acquisition unit and receive bio-electro-optical signals of multiple modalities from the BEO interface acquisition unit. Subsequently, the processor can perform feature extraction on the bio-electro-optical signals of each modality to obtain corresponding bio-electro-optical information. In this way, multi-modal bio-electro-optical information corresponding to multiple modalities of bio-electro-optical signals can be obtained. For example, the multi-modal bio-electro-optical information may include at least two of the following electrical information: electroencephalogram (EEG), electromyography (EMG), electrocardiogram (ECG), or electrodermal conductance (EDC).

[0033] Furthermore, the processor can pre-deploy a biomechanical feature mapping model. This model is constructed based on the biomechanical feature mapping relationship between multimodal bioelectroelectric information and the multimodal modulation requirements of the target object. Thus, the processor can utilize the biomechanical feature mapping model to process the aforementioned multimodal bioelectroelectric information to determine the multimodal modulation requirements of the target object. This multimodal modulation requirement information can be used to characterize the multimodal stimulation requirements of the target object. For example, the multimodal modulation requirement information can indicate that the target object has a stimulation requirement for at least two of the following: electrical stimulation, transcranial magnetic stimulation, mechanical vibration stimulation, or acoustic stimulation.

[0034] Furthermore, the processor can also be equipped with a multimodal stimulation parameter decision model. This model can characterize the relationship between the target object's multimodal modulation demand information and the multimodal stimulation parameters. Thus, the processor can use the multimodal stimulation parameter decision model to process the multimodal modulation demand information and output the multimodal stimulation parameters. These parameters can be used to control the multimodal stimulation module. Based on this, the processor can be electrically connected to the multimodal stimulation module and control it according to the multimodal stimulation parameters to provide multimodal stimulation to the target object.

[0035] Based on this, the multimodal physical stimulation modulation system of this disclosure embodiment can perform N rounds of multimodal stimulation on the target object. N is an integer greater than 1. Specifically, after the (N-1)th multimodal stimulation is performed on the target object through the stimulation module, the processor can obtain the Nth multimodal bio-electro-electrical information of the target object through the bio-interface acquisition unit. Subsequently, the processor can use the bio-feature mapping model to determine the Nth multimodal modulation requirement information for the target object based on the Nth multimodal bio-electro-electrical information. Then, the processor can use the multimodal stimulation parameter decision model to output the Nth multimodal stimulation parameters based on the Nth multimodal modulation requirement information. Based on this, the processor can determine the state information of the target object after the (N-1)th multimodal stimulation and correct the Nth multimodal stimulation parameters based on the state information. In this way, multiple stimulation modules can be controlled to perform Nth multimodal stimulation on the target object according to the corrected Nth multimodal stimulation parameters.

[0036] Based on this, in this embodiment, after the target object is subjected to the (N-1)th multimodal stimulation through the stimulation module, the target object's Nth multimodal bio-electro-mechanical information is processed using the vitality feature mapping model to obtain the target object's Nth multimodal regulation requirement information. Subsequently, the Nth multimodal regulation requirement information is processed using the multimodal stimulation parameter decision model to output the Nth multimodal stimulation parameter. Therefore, the Nth multimodal stimulation parameter can be accurately determined according to the target object's multimodal stimulation requirement after the (N-1)th multimodal stimulation. Thus, multiple stimulation modules can be controlled to perform Nth multimodal stimulation on the target object through the Nth multimodal stimulation parameter. Since the Nth multimodal stimulation parameter corresponding to the Nth multimodal bio-electro-mechanical information is output using the vitality feature mapping model and the multimodal stimulation parameter decision model, the problem of incompatibility between the data standard of the vitality interface acquisition unit and the data standards of multiple stimulation devices is at least partially avoided, realizing the coordinated output of multimodal stimulation and improving the efficiency of motion regulation of the target object. Based on this, the multimodal physical stimulation control system of this disclosure can integrate an automatic device identification mechanism to support the dynamic access and collaborative scheduling of stimulation devices from different manufacturers. It solves the compatibility problem between the vitality interface acquisition unit and multiple stimulation devices from the underlying architecture level, reducing the technical difficulty and engineering implementation complexity of the integrated development of the multimodal physical stimulation control system.

[0037] Furthermore, by modifying the Nth multimodal stimulation parameters based on the state information of the target object after the (N-1)th multimodal stimulation, adaptive calibration of the multimodal stimulation parameters is achieved during the Nth round of multimodal stimulation, which at least partially avoids the safety issues caused by stimulating the target object when it is in an abnormal state.

[0038] Based on this, the multimodal physical stimulation control system of this disclosure realizes closed-loop dynamic adaptive adjustment from "collecting bioelectrical information to multimodal stimulation, and then from multimodal stimulation to new bioelectrical information", which can meet the different physiological intervention needs of the target object.

[0039] In this embodiment, the biomechanical interface acquisition unit can acquire biomechanical signals of multiple modalities in N rounds. Furthermore, it can send these biomechanical signals of multiple modalities to the processor. For example, the biomechanical interface acquisition unit can acquire N electroencephalogram (EEG) signals, N electromyographic (EMG) signals, N electrocardiogram (ECG) signals, and N electrodermal (ED) signals, and send the corresponding EEG, EMG, ECG, and EED signals in N rounds respectively. Subsequently, the processor can perform feature extraction on the N EEG, N EMG, N ECG, and N EED signals respectively to obtain the Nth EEG information, the Nth EMG information, the Nth ECG information, and the Nth EED information.

[0040] Figure 2 A schematic diagram of a life interface acquisition unit according to an embodiment of the present disclosure is shown.

[0041] like Figure 2 As shown, the life interface acquisition unit may include non-invasive acquisition sub-units and invasive acquisition sub-units, etc.

[0042] The non-invasive acquisition subunit may include an EEG acquisition cap and an electromyography (EMG) sensor (e.g., with a common-mode rejection ratio greater than 80 dB, preferably greater than 110 dB). For example, the EEG acquisition cap may be a 64-channel EEG cap (e.g., with 24-bit precision), and the EMG sensor may be an 8-channel surface EMG sensor.

[0043] Specifically, the EEG acquisition cap can collect EEG signals (e.g., electroencephalograms) from regions such as the motor cortex (specifically, the C3 and C4 locations) and prefrontal cortex (specifically, the F3 and F4 locations) of the target subject based on a sampling frequency of 2000 Hz, focusing on capturing the primary EEG signal related to movement. For example, this EEG signal may include beta waves (13-30 Hz) related to motor intention and μ waves (8-13 Hz) related to motor execution. The C3, C4, F3, and F4 locations are standardized electrode locations for EEG acquisition.

[0044] An EMG sensor (e.g., with a sampling frequency of 2000 Hz and a bandwidth of 20–450 Hz) can be attached to a target muscle group (e.g., the biceps brachii and flexor carpi radialis) and acquire the temporal characteristics of muscle activation as an electromyographic signal. It should be understood that the electromyographic signal can be an electromyogram (EMG).

[0045] The invasive acquisition subunit is suitable for scenarios involving severe neurological injury. The invasive acquisition subunit may include electrocorticography (ECoG) electrodes and signal conditioners (e.g., 1000× gain). For example, the ECoG electrodes may be a 32-channel ECoG electrode array (e.g., a sampling frequency of 5000 Hz and a resolution of 16 bits).

[0046] ECoG electrodes can be implanted into the M1 region of the motor cortex of the target subject to collect cortical neuronal cluster firing signals as a second EEG signal.

[0047] In addition, the life interface acquisition unit may also include an auxiliary acquisition subunit. The auxiliary acquisition subunit may include an integrated three-lead electrocardiogram (ECG) module (e.g., with a sampling frequency of 500Hz) and a fingertip electrodermal signal (EDA) sensor, etc.

[0048] A three-lead ECG module can be used to acquire electrocardiogram (ECG) signals. For example, ECG signals may include heart rate variability (HRV) signals. A fingertip EDA sensor (e.g., sampling frequency 100 Hz) can be used to acquire the target subject's electrodermal (EDA) signals to determine the target subject's stress state based on the EDA signals.

[0049] Based on this, the aforementioned sub-units enable the synchronous acquisition of multiple types of bioelectrical signals. Furthermore, the bioelectrical interface acquisition unit supports the IEEE 1451.4 protocol, with a data transmission rate greater than or equal to 1 Gbps and a local buffer capacity greater than or equal to 2 TB. For example, the bioelectrical interface acquisition unit may also include a standardized synchronization trigger module conforming to the IEEE 1451.4 bioelectrical interface protocol to achieve time synchronization between the bioelectrical signals and the stimulation module, with a synchronization accuracy of ±0.3ms, ensuring the timing matching of signals and stimulation.

[0050] In this embodiment of the disclosure, the Nth EEG information includes beta waves and action potential firing frequency. The processor can obtain beta waves and Nth EMG information through a non-invasive acquisition subunit, and obtain the action potential firing frequency through an invasive acquisition subunit.

[0051] For example, the processor may be equipped with a signal processing library. This library may integrate algorithms such as wavelet transform, independent component analysis (ICA), and wavelet packet decomposition to preprocess the received bioelectrical signals.

[0052] For example, for the first and second EEG signals described above, the 50 Hz power frequency interference and motion artifacts can be filtered out using db8 wavelet transform, and the electrooculogram (EOG) and electrocardiogram artifacts can be separated by ICA. Subsequently, wavelet packet decomposition can be used to extract the β-wave power from the first EEG signal and the action potential firing frequency from the second EEG signal, and the β-wave power and action potential firing frequency can be used as EEG information.

[0053] In addition, for the electromyographic signals described above, a 10Hz high-pass filter can be used to remove baseline drift, and after full-wave rectification, a 50ms moving average can be processed to calculate integrated electromyographic information (iEMG) and average power frequency (MPF) as electromyographic information.

[0054] For the ECG signal described above, adaptive filtering can be applied to eliminate noise. Based on the denoised HRV signal, the standard deviation of normal sinus interval (SDNN), high-frequency component (HF), and low-frequency component (LF) can be determined, and the ratio of the high-frequency component to the low-frequency component (i.e., HF / LF) can be calculated. In this way, SDNN, high-frequency component, low-frequency component, and component ratio can be used as ECG information.

[0055] For the electrodermal signal described above, adaptive filtering can also be performed to eliminate noise, and information such as the peak value of the electrodermal conductance can be determined based on the denoised electrodermal signal. The peak value of the electrodermal conductance and other information can be used as electrodermal information to determine the stress state of the target object.

[0056] Based on this, the embodiments disclosed herein can realize the acquisition, preprocessing and feature analysis of vital interface signals, stimulus decision-making, multimodal stimulus output and closed-loop feedback correction, thereby achieving closed-loop dynamic adaptive adjustment, which can meet the different physiological intervention needs of target objects.

[0057] Furthermore, the processor may also be equipped with a feature mapping library. This feature mapping library may contain a vitality feature mapping model trained on a large amount of data. For example, the vitality feature mapping model may be constructed based on the vitality feature mapping relationship between multimodal bioelectrical information and the multimodal regulatory needs of the target object's neurons. For example, the vitality feature mapping model may be implemented based on a trained neural network, which will not be elaborated here.

[0058] Based on this, the Nth multimodal modulation demand information includes the first modulation demand information. The Nth EEG information includes beta wave power. When the beta wave power indicates that the target object has a motor intention, and the target object's electromyographic information is below a predetermined muscle activation threshold, the first modulation demand information can indicate that the target object's muscle nerves have an activation demand.

[0059] For example, if the increase in beta wave power is greater than or equal to a predetermined power threshold, it can be determined that the target subject has a motor intention. If the integrated electromyography (EMG) information is less than a predetermined EMG threshold, it can be determined that the target subject's muscle activation level is low. Thus, it can be determined that the target subject's muscle nerves have an activation need. For example, if the increase in beta wave power is >25% and iEMG is <0.2 mV (i.e., motor intention is clear but muscle activation is insufficient), it can be determined that the target subject has a need for "enhanced muscle-nerve synergistic stimulation," and vice versa, which will not be elaborated further.

[0060] In addition, the Nth EEG information may also include the frequency of action potential firing. The Nth multimodal modulation demand information also includes second and third modulation demand information.

[0061] Based on this, when the action potential firing frequency is below a predetermined cortical activation threshold, the second regulatory demand information is used to characterize that the target subject's cortical nerves have an activation demand. For example, when the action potential firing frequency is below the predetermined cortical activation threshold, it can be determined that the target subject's cortical nerve activity is low. Thus, it can be determined that the target subject's cortical nerves have an activation demand. For example, when the action potential firing frequency is <5Hz, it can be determined that the target subject has a need for "deep magnetic stimulation to activate the cortex," and vice versa, which will not be elaborated further.

[0062] When the electrocardiogram (ECG) and electrodermal conductance (EDA) data of the target subject indicate that the autonomic nervous system is in a state of tension, the third regulatory demand information is used to characterize that the autonomic nervous system of the target subject has an inhibitory demand. For example, if the proportion of the above components is lower than a predetermined proportion threshold and the above EDA is greater than a predetermined activity threshold, it is determined that the autonomic nervous system of the target subject is in a state of tension. Thus, it can be determined that the autonomic nervous system of the target subject has an inhibitory demand. For example, if HRV HF / LF < 0.8 and EDA > 5 μS, it can be determined that the target subject has a demand to "reduce the stimulation intensity and trigger a safety warning," and vice versa, which will not be elaborated further.

[0063] In addition, the processor may also be equipped with a stimulus decision library. This stimulus decision library may encompass feature-parameter matching databases and multimodal collaborative logic algorithms, among other things. For example, the stimulus decision library may include multimodal stimulus parameter decision models. These multimodal stimulus parameter decision models may be implemented based on trained neural networks, which will not be elaborated upon here.

[0064] For example, various stimulation modules include at least one of the following: electrical stimulation module, transcranial magnetic stimulation (TMS) module, mechanical vibration module, or acoustic stimulation module.

[0065] For example, an electrical stimulation module may include a constant current source and a constant current type electrical stimulator (e.g., output range 0.1–8 mA, pulse width 50–1000 μs, 8 channels), with a response delay ≤50 ms. A TMS module based on a repetitive transcranial magnetic stimulation (TMS) device (e.g., pulse intensity 0.5–2.5 T, repetition frequency 1–30 Hz, figure-8 coil) can control the pulse intensity via capacitor charging and discharging, with a trigger delay ≤5100 ms. A mechanical vibration module may include a multi-channel servo vibrator (e.g., amplitude 0.1–2.0 mm, frequency 10–200 Hz, 4 channels). This channel servo vibrator can be based on a servo motor and an eccentric wheel driven by the servo motor, with amplitude adjustment accuracy of ±0.05 mm. An acoustic stimulation module may utilize a high-fidelity acoustic stimulator (e.g., frequency 200–5000 Hz, sound pressure level 40–90 dB, 2 channels), with a frequency adjustment step of 10 Hz. For example, a high-fidelity sound stimulator can be a high-fidelity Class D amplifier driving a loudspeaker, etc.

[0066] The bio-interface acquisition unit associates a timestamp with each signal acquired at each time step. In this way, the processor can simultaneously trigger multimodal stimulation based on the timestamps (such as the time of the β-wave peak, the start time of EMG activation, etc.), ensuring that the stimulation is synchronized with the biological rhythm, so that the timing error is ≤50 ms.

[0067] Furthermore, the Nth multimodal stimulation parameters include stimulation parameters of at least two of the following: electrical stimulation, transcranial magnetic stimulation, mechanical vibration stimulation, or acoustic stimulation.

[0068] For example, the processor can send parameter instructions carrying corresponding stimulation parameters to various stimulation modules via a standardized bus (such as USB 4.0). Each stimulation module's internal distributor parses the parameter instructions into drive signals for stimulating the target object. The distributor can support parallel output of up to eight stimulation signals and is compatible with the communication protocols of the respective stimulation modules. Based on this, multiple stimulation modules can synchronously output multi-mode stimulation based on the parameter instructions. Furthermore, the processor can independently control the start and stop of each stimulation module and adjust its stimulation parameters in real time, which will not be elaborated further here.

[0069] Based on this, the embodiments of this disclosure can establish a standardized multimodal acquisition and adaptation system. For example, by designing a universal biomechanical interface data interaction protocol and defining a unified data format and communication standard, plug-and-play adaptation can be achieved between various modal biomechanical signals such as EEG, EMG, and eye movement acquired by the biomechanical interface acquisition unit and physical output stimulation modules such as electrical stimulation, optical stimulation, and mechanical vibration.

[0070] After the stimulus output, the biomechanical interface acquisition unit acquires biomechanical signals in real time at a sampling interval of 20 ms. Furthermore, the processor may also include a safety control library. This library may contain modules such as EEG abnormal discharge detection and HRV / EDA safety threshold judgment. Based on this, the processor can determine the target object's state information according to the acquired biomechanical signals, enabling accurate multimodal stimulation based on this information. This will be explained in detail below.

[0071] For example, the status information may include first status information and second status information.

[0072] The processor can determine the first state information from at least two types of electrical information based on the stimulation type of the Nth multimodal stimulation parameter. For example, in the case of stimulation types of transcranial magnetic stimulation and electrical stimulation, the EEG information can be determined as the first state information from at least two types of electrical information.

[0073] When the first state information indicates that the target object is in an abnormal state, the processor can modify the Nth multimodal stimulus parameter according to the first predetermined parameter value to reduce the stimulus intensity of the Nth multimodal stimulus.

[0074] For example, in cases where EEG information represents abnormal brain discharges (such as spikes) in the target object, TMS can be stopped, the intensity of electrical stimulation can be reduced by 50%, and an audible and visual alarm can be triggered.

[0075] The processor can determine the differences between the bioelectromechanical information of the Nth multimodal stimulus and the corresponding bioelectromechanical information of the (N-1)th multimodal stimulus (e.g., at least one of iEMG boost magnitude, β-wave power variation, or HRV stability). Based on this, the processor can determine the differences as second state information. If the second state information is below a predetermined change threshold, the processor can adjust the Nth multimodal stimulus parameters according to second predetermined parameters to increase the stimulus intensity of the Nth multimodal stimulus.

[0076] For example, if the iEMG increase is less than 15%, it can be determined that the muscle nerve activation level has not reached the activation target. Therefore, without exceeding the predetermined electrical stimulation threshold, the electrical stimulation intensity can be increased by 0.3 mA, and the TMS frequency can be increased by 2 Hz.

[0077] In this embodiment of the present disclosure, the processor can modify the stimulation parameters of transcranial magnetic stimulation based on the action potential firing frequency and integrated electromyography information, so as to control the pulse frequency of transcranial magnetic stimulation. Figure 3 This illustration schematically depicts integrated electromyographic information, transcranial magnetic stimulation pulse frequency, and electrocorticography neuronal activation frequency according to embodiments of the present disclosure. Reference Figure 3It can be seen that by controlling the pulse frequency of transcranial magnetic stimulation in this way, the action potential firing frequency, integrated electromyographic information, and the pulse frequency of transcranial magnetic stimulation can be gradually stabilized, achieving safe and stable transcranial magnetic stimulation. It should be noted that... Figure 3 In the diagram, the horizontal axis could be, for example, the time step. The vertical axis could be, for example, an index value, which corresponds to the normalized integrated electromyography information, the normalized transcranial magnetic stimulation pulse frequency, and the normalized cortical electroencephalogram neuronal activation frequency, which will not be elaborated upon here.

[0078] For example, using the EMG data of the muscle groups of the target limb and the limb grasping error fed back by a position sensor installed on the target limb as third state information, if the third state information is greater than a predetermined error threshold, the stimulation parameters of the mechanical vibration stimulation can be corrected according to a third predetermined parameter to increase the stimulation intensity. For example, if the limb grasping error is >5 mm, the mechanical vibration frequency can be adjusted to 100 Hz to enhance the tactile feedback effect.

[0079] It should be understood that the embodiments disclosed herein are not limited thereto, and Table 1 below schematically illustrates more specific parameter ranges. The range of multimodal stimulation parameters output by the multimodal stimulation parameter decision module and the corresponding safety thresholds are shown in Table 1 below. It can be understood that the safety thresholds are the aforementioned predetermined change thresholds, predetermined electrical stimulation thresholds, and predetermined error thresholds, etc.

[0080] Table 1

[0081]

[0082] Based on this, embodiments of this disclosure construct a personalized safety threshold and parameter adaptation model based on the multimodal biomechanical information of the target object. This model optimizes stimulation parameters through machine learning algorithms, effectively reducing the risk of adverse reactions while ensuring therapeutic efficacy, and significantly improving the individual adaptability of the system. Furthermore, the multimodal physical stimulation modulation system of embodiments of this disclosure can achieve synergistic output of multimodal stimulation by taking into account both deep neural modulation and surface functional activation, thereby meeting different physiological intervention needs.

[0083] In this embodiment of the disclosure, the processor is further configured to: determine a primary stimulus and an auxiliary stimulus from at least two stimuli based on the Nth multimodal modulation demand information. The primary stimulus is used to stimulate the nerves of the target object. The auxiliary stimulus is used to enhance the effect of the primary stimulus on the target object. Multiple stimulation modules can apply the primary and auxiliary stimuli to the target object under the control of the processor.

[0084] For example, based on different multimodal modulation demand information, the stimulation scenario required by the target object at present can be determined, so that the accurate main stimulus and auxiliary stimulus can be selected according to the different stimulation scenarios.

[0085] Specifically, for the scenario of "motor function reconstruction": EMG signal-triggered electrical stimulation (e.g., stimulation frequency of 30 Hz, stimulation current intensity of 2 mA, pulse width of 300 μs) is used as the main stimulus to activate muscles, and TMS (e.g., peak magnetic field strength of 1.8 T, stimulation frequency of 15 Hz) is applied to the motor cortex to enhance neural drive, while mechanical vibration (e.g., amplitude of 0.8 mm, vibration frequency of 80 Hz) is used to relax antagonistic muscles.

[0086] For the "prosthetic control" scenario: EEG motion intention signals (e.g., β wave peak) trigger electrical stimulation (e.g., stimulation frequency of 50 Hz, stimulation current intensity of 1 mA, pulse width of 200 μs) to provide feedback on prosthetic contact force, and simultaneously supplemented by acoustic stimulation (e.g., stimulation frequency of 2000 Hz, sound pressure level of 60 dB) to provide cues for action completion.

[0087] For the "insomnia treatment" scenario: the main stimulation is TMS, and the auxiliary stimulation includes electrical stimulation and sound stimulation.

[0088] Figure 4 A schematic diagram of closed-loop control according to an embodiment of the present disclosure is shown.

[0089] like Figure 4 As shown in this embodiment, bioelectrical signals of multiple modalities of the target object can be collected. The collected bioelectrical signals can then be preprocessed and analyzed (e.g., through wavelet transform) to obtain bioelectrical information. Next, multimodal stimulation parameters can be determined based on the bioelectrical information. Furthermore, a safety threshold can be determined for the bioelectrical information, and abnormal discharge detection can be performed on the target object based on the bioelectrical information. Thus, the multimodal stimulation parameters can be corrected based on the safety threshold and the abnormal state of the target object, and then multimodal stimulation can be output based on the corrected multimodal stimulation parameters. Subsequently, new bioelectrical signals of the target object can be collected. In this way, multi-round closed-loop motion control of the target object is achieved, improving the efficiency and accuracy of stimulation.

[0090] Figure 5 A schematic diagram of a multimodal physical stimulation modulation system according to an embodiment of the present disclosure is shown.

[0091] like Figure 5As shown, the multimodal physical stimulation control system of this embodiment includes a biomechanical interface acquisition unit, a processor, and multiple stimulation modules, and may also include a human-computer interaction device. The human-computer interaction device can be electrically connected to the biomechanical interface acquisition unit, the processor, and the multiple stimulation modules. Furthermore, the human-computer interaction device can receive and display biomechanical signals from the biomechanical interface acquisition unit, biomechanical information from the processor, stimulation parameters from the multiple stimulation modules, and other information. For example, the processor can also control the multiple stimulation modules to output multimodal stimulation based on a parameter instruction set, and the human-computer interaction unit can receive and display the parameter instruction set from the multiple stimulation modules.

[0092] For example, this human-computer interaction device can be configured with a 19-inch touch screen (2560×1440 resolution) and an audible and visual alarm, and provides a variety of external device interfaces such as USB4.0 interface ×4, EtherCAT interface ×2, and HDMI interface ×2.

[0093] In addition, human-computer interaction devices may also include a visual interface. This visual interface can display the waveforms of vital interface signals (such as beta waves, iEMG), stimulation parameters (type / intensity / frequency), and regulatory status (normal / warning / abnormal) in real time.

[0094] Furthermore, this human-computer interaction device is also compatible with third-party life interface devices and stimulation modules, and has the function of automatically identifying device models and loading drivers.

[0095] In addition, this human-computer interaction device can store "multimodal bioelectrical information - stimulation parameters - stimulation effect data" for 30 days, supports export of reports in multiple formats, and can interact with the hospital's system data.

[0096] Based on this, the multimodal physical stimulation control system of this disclosure dynamically matches stimulation parameters with biosignals from the bio-interface, achieving a control accuracy of over 94% (35% higher than some fixed parameter methods), and controlling the signal-stimulation delay within 150 ms (60% lower than existing bio-interface systems), enabling real-time linkage between movement intention and stimulation output.

[0097] Furthermore, the multimodal physical stimulation modulation system of this disclosure can achieve at least four-mode synergy of electrical, magnetic, acoustic and mechanical vibration, taking into account "deep neural activation (TMS), superficial muscle modulation (electrical stimulation), tactile feedback (mechanical vibration), and sensory guidance (acoustic stimulation)", and is suitable for multiple scenarios such as motor function reconstruction, prosthetic control and nerve injury treatment, with a clinical effectiveness rate of over 88%.

[0098] Furthermore, the multimodal physical stimulation modulation system of this disclosure supports the IEEE 1451.4 biomechanical interface protocol and access to devices (acquisition / stimulation) from multiple vendors, eliminating the need for manual data format adaptation and improving clinical operation efficiency by 50%. It also supports switching between non-invasive and invasive biomechanical interfaces, making it suitable for different groups such as healthy individuals, stroke patients, or spinal cord injury patients.

[0099] Furthermore, the multimodal physical stimulation modulation system of this disclosure establishes a real-time safety threshold based on vital interface signals (abnormal EEG discharge, HRV stress characteristics, etc.), reducing the adverse reaction rate (such as muscle spasm, skin irritation) to below 3% (85% lower than some systems); and supports individualized parameter customization to meet the needs of patients at different stages of their condition.

[0100] The effects of the multimodal physical stimulation modulation system of this disclosure will be described below with reference to specific embodiments.

[0101] For example, the multimodal physical stimulation modulation system of this embodiment can be applied to some stroke rehabilitation patients (assuming these patients are 3-8 months post-stroke, Brunnstrom motor function stage II-IV, with 20 cases using non-invasive acquisition subunits and 20 cases using invasive acquisition subunits). It should be noted that all such patients (i.e., the target subjects) have signed informed consent forms and the system has been approved by the medical ethics committee. The application goal can be: through the modulation of this system, to achieve the reconstruction of the patient's upper limb grasping function, specifically, an improvement of 1-2 levels in Brunnstrom stage, a 30% increase in grasping force, and a prosthetic grasping error of <5mm.

[0102] For example, the parameters of the non-invasive acquisition subunit can be: a 64-channel EEG acquisition cap (e.g., sampling rate 2000 Hz) to acquire β waves in leads C3 and C4, and an 8-channel EMG sensor to acquire (e.g., sampling rate 2000 Hz) signals from the biceps brachii and flexor carpi radialis muscles.

[0103] The parameters of the invasive acquisition subunit can be: a 32-channel ECoG electrode (e.g., sampling rate of 5000 Hz) is implanted in the M1 region of the left motor cortex to extract the action potential firing frequency.

[0104] In the initial stimulation protocol, primary and secondary stimuli can be set based on baseline biomechanical interface characteristics (e.g., including low beta wave power, iEMG < 0.15 mV, ECoG firing rate < 8 Hz). For example, the primary stimulus can be electrical stimulation (e.g., stimulation frequency of 30 Hz, stimulation current intensity of 2 mA, pulse width of 300 μs) acting on the grasping muscles. The secondary stimuli can be TMS (e.g., peak magnetic field strength of 1.8 T, stimulation frequency of 15 Hz) and mechanical vibration (e.g., amplitude of 0.8 mm, vibration frequency of 80 Hz), with TMS acting on the left motor cortex and mechanical vibration acting on the wrist joint antagonist muscles.

[0105] In the experimental procedure, baseline data can be collected first, followed by closed-loop control.

[0106] For example, patients wear bio-interface devices and sit quietly for 10 minutes to collect bioelectrical signals, establishing individual baseline characteristics (e.g., β-wave baseline power and iEMG baseline value).

[0107] Subsequently, closed-loop control can be implemented.

[0108] Minutes 1-10: Output the initial stimulation protocol, collect bioelectrical signals every 20ms, and monitor iEMG to increase to 0.25mV (reaching 167% of baseline), then maintain the parameters;

[0109] At 12 minutes: In the non-invasive group, one patient's EEG showed spikes (abnormal discharges). The system immediately paused TMS, the electrical stimulation intensity was reduced to 1 mA, and the spikes disappeared after 10 seconds. TMS was then resumed (intensity reduced to 1.5 T).

[0110] At 20 minutes: In the invasive group, the ECoG action potential firing rate increased to 18 Hz (enhanced neural activity). The system reduced the TMS frequency to 10 Hz, increased the electrical stimulation frequency to 40 Hz, and simultaneously stimulated the grasping action with acoustic stimulation (e.g., stimulation frequency of 2000 Hz and sound pressure level of 60 dB).

[0111] Subsequently, efficacy assessment can be performed. For example, 40 minutes of daily adjustment for 8 consecutive weeks. The results are as follows: 38 patients showed an improvement of 1-2 grades in Brunnstrom stage (effectiveness rate 95%), and the average grip strength increased from 1.1 kg to 1.8 kg (improvement 63.6%); the grip error in the prosthesis control group decreased from 12 mm to 4.2 mm (error reduction 65%); only 1 patient experienced mild skin redness (adverse reaction rate 2.5%).

[0112] Based on this, the multimodal physical stimulation modulation system of this disclosure focuses on the interdisciplinary field of biomedical engineering, neuromodulation, and bio-machine interface (BMI), proposing a multimodal bio-machine interface modulation system driven by bio-electroelectric signals such as electroencephalography (EEG), electrocorticography (ECoG), and electromyography (EMG). This system innovatively integrates multimodal physical stimulation methods such as electrical stimulation, magnetic stimulation, acoustic stimulation, and mechanical vibration, constructing a systematic modulation method and supporting devices. Its application scenarios cover cutting-edge medical fields such as the reconstruction of motor function neuroplasticity in stroke patients, synergistic control strategies for neural prostheses and exoskeletons, closed-loop intervention for neurological dysfunctions such as Parkinson's disease tremor, and activation and repair of neural conduction pathways after spinal cord injury.

[0113] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0114] Those skilled in the art will understand that the features described in the various embodiments of this disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments of this disclosure can be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0115] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A multimodal physical stimulation modulation system based on a life-organizing interface, characterized in that, include: Vitality interface acquisition unit and multiple stimulation modules; as well as The processor, electrically connected to the vitality interface acquisition unit and the multiple stimulation modules, is used for: After performing the (N-1)th multimodal stimulation on the target object through the stimulation module, the Nth multimodal biomechanical information of the target object is obtained through the biomechanical interface acquisition unit; wherein, the Nth multimodal biomechanical information includes at least two electrical information selected from the Nth electroencephalogram (EEG), Nth electromyogram (EMG), Nth electrocardiogram (ECG), or Nth electrodermal conductance (EDC); N is an integer greater than 1; Based on the biomechanical information of the Nth mode, the biomechanical information of the target object is determined using the biomechanical feature mapping model. The multimodal stimulation parameter decision model is used to output the Nth multimodal stimulation parameter based on the Nth multimodal regulation demand information; Based on the state information of the target object after the (N-1)th multimodal stimulus, the Nth multimodal stimulus parameters are corrected, and the multiple stimulation modules are controlled to perform the Nth multimodal stimulus on the target object according to the corrected Nth multimodal stimulus parameters.

2. The multimodal physical stimulation modulation system according to claim 1, characterized in that, The state information includes first state information; the Nth multimodal stimulation parameters include stimulation parameters of at least two of the following: electrical stimulation, transcranial magnetic stimulation, mechanical vibration stimulation, or acoustic stimulation. The processor is also used for: Based on the stimulation type of the Nth multimodal stimulation parameter, the first state information is determined from the at least two electrical information types; When the first state information indicates that the target object is in an abnormal state, the Nth multimodal stimulation parameter is corrected according to the first predetermined parameter value to reduce the stimulation intensity of the Nth multimodal stimulus.

3. The multimodal physical stimulation modulation system according to claim 2, characterized in that, The status information also includes second status information; The processor is also used for: Determine the differences between the Nth multimode biomechanical information and the same type of electrical information of the N-1th multimode biomechanical information corresponding to the N-1th multimode stimulus; The difference information is determined as the second state information.

4. The multimodal physical stimulation modulation system according to claim 3, characterized in that, The processor is also used for: If the second state information is lower than a predetermined change threshold, the Nth multimodal stimulation parameters are corrected according to the second predetermined parameters to increase the stimulation intensity of the Nth multimodal stimulus.

5. The multimodal physical stimulation modulation system according to any one of claims 2 to 4, characterized in that, The processor is further configured to: determine a primary stimulus and an auxiliary stimulus from the at least two stimuli based on the Nth multimodal modulation demand information; the primary stimulus is used to stimulate the nerves of the target object, and the auxiliary stimulus is used to enhance the effect of the primary stimulus on the target object; The multiple stimulation modules are also used to: apply the main stimulus and the auxiliary stimulus to the target object under the control of the processor.

6. The multimodal physical stimulation modulation system according to any one of claims 1 to 4, characterized in that, The vitality feature mapping model is constructed based on the vitality feature mapping relationship between multimodal bio-electro-information and the multimodal regulatory needs of the target object's nervous system; The Nth multimodal modulation demand information includes the first modulation demand information; the Nth EEG information includes β-wave power; wherein... When the β-wave power indicates that the target object has a movement intention and the electromyographic information of the target object is below a predetermined muscle activation threshold, the first modulation demand information is used to indicate that the target object's muscle nerves have an activation demand.

7. The multimodal physical stimulation modulation system according to claim 6, characterized in that, The Nth EEG information also includes the action potential firing frequency; the Nth multimodal modulation demand information also includes second modulation demand information and third modulation demand information; wherein... When the action potential firing frequency is lower than a predetermined cortical activation threshold, the second modulation demand information is used to characterize that the cortical nerves of the target object have an activation demand; When the electrocardiogram and electrodermal data of the target object indicate that the target object's autonomic nervous system is in a state of tension, the third regulatory demand information is used to indicate that the target object's autonomic nervous system has an inhibitory demand.

8. The multimodal physical stimulation modulation system according to claim 7, characterized in that, The vitality interface acquisition unit is used to acquire N electroencephalogram (EEG) signals, N electromyogram (EMG) signals, N electrocardiogram (ECG) signals, and N electrodermal signals. The processor is further configured to: extract features from the N electroencephalogram (EEG) signals, the N electromyogram (EMG) signals, the N electrocardiogram (ECG) signals, and the N electrodermal (ED) signals respectively, to obtain the Nth EEG information, the Nth EMG information, the Nth ECG information, and the Nth EED information.

9. The multimodal physical stimulation modulation system according to any one of claims 1 to 4, characterized in that, The Nth EEG information includes beta waves and action potential firing frequency; The vitality interface acquisition unit includes a non-invasive acquisition subunit and an invasive acquisition subunit; The processor is also used to obtain the β wave and the Nth electromyographic information through the non-invasive acquisition subunit, and to obtain the action potential firing frequency through the invasive acquisition subunit.

10. The multimodal physical stimulation modulation system according to any one of claims 1 to 4, characterized in that, The multiple stimulation modules include at least one of the following: an electrical stimulation module, a transcranial magnetic stimulation module, a mechanical vibration module, or an acoustic stimulation module.

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