Motion rehabilitation brain-body function imaging method and device based on multi-modal signals and storage medium

Abstract

The application is suitable for the medical equipment and edge artificial intelligence technical field, and provides a motor rehabilitation brain-body function imaging method and device based on a multi-modal signal and a storage medium, comprising: acquiring electroencephalogram processing signals of a patient collected through an electroencephalogram amplifier at a first time; acquiring multiple limb movement signals of the patient collected through a multi-sensor at the first time; and performing functional imaging analysis on the electroencephalogram processing signals and the multiple limb movement signals to obtain a functional imaging analysis result. The above method is free from dependence on a host computer, greatly improves the real-time performance, reliability and portability of the device, reduces the deployment complexity of the multi-sensor system, simultaneously collects electroencephalogram and limb movement multi-modal signals at the same time, ensures that all signals have a unified time reference, significantly reduces synchronization error, realizes high-precision synchronization and real-time analysis of multi-modal signals in the movement process, and provides more accurate and reliable data basis for brain-body function imaging.

Description

Technical Field

[0001] This application belongs to the field of medical device and edge artificial intelligence technology, and in particular relates to a method, device and storage medium for brain-body functional imaging of motor rehabilitation based on multimodal signals. Background Technology

[0002] The development of personalized diagnosis and treatment for sports rehabilitation urgently requires the synchronous acquisition and joint analysis of multimodal signals such as electroencephalography (EEG) and electromyography (EMG). However, in current clinical research, most signal acquisition systems operate independently, and data is manually or software aligned later. Furthermore, imaging technology relies on large non-wearable devices, and related patents can only achieve partial modal acquisition and simple processing.

[0003] The existing methods not only lack edge AI computing power and rely entirely on host computers to process signals, but also suffer from poor device portability and reliability, making them difficult to adapt to mobile rehabilitation scenarios. Furthermore, they cannot achieve high-precision synchronous acquisition of all modalities, have insufficient EEG channels, lack a unified hardware clock reference for each device, and have large synchronization errors, making it impossible to support real-time signal analysis during movement. Therefore, there is an urgent need for wearable brain-body functional imaging systems and methods that are adaptable to mobile scenarios, can acquire multimodal signals synchronously with high precision, and have real-time edge analysis capabilities. Summary of the Invention

[0004] This application provides a method, device, and storage medium for brain-body functional imaging of motor rehabilitation based on multimodal signals. The method eliminates the dependence on a host computer, significantly improves the real-time performance, reliability, and portability of the device, and reduces the deployment complexity of multi-sensor systems. At the same time, by synchronously acquiring EEG and limb movement multimodal signals at the same time, it ensures that each signal has a unified time reference, significantly reduces synchronization errors, and achieves high-precision synchronization and real-time analysis of multimodal signals during movement, providing a more accurate and reliable data foundation for brain-body functional imaging.

[0005] In a first aspect, embodiments of this application provide a method for motor rehabilitation brain-body functional imaging based on multimodal signals, wherein the EEG amplifier applied to the multimodal signal-based motor rehabilitation brain-body functional imaging device includes: Acquire the patient's brainwave processing signals at the first moment through the EEG amplifier; Acquire multiple limb movement signals of the patient simultaneously collected by multiple sensors at the first moment; Functional imaging analysis was performed on the EEG processing signals and multiple limb movement signals to obtain the functional imaging analysis results.

[0006] In this embodiment, by simultaneously acquiring EEG signals from an EEG amplifier and multiple limb movement signals from multiple sensors at the same first moment, and then using the EEG amplifier to perform functional imaging analysis on both types of signals to obtain the analysis results, this method directly relies on the edge device where the EEG amplifier is located to achieve signal acquisition and analysis, reducing dependence on the host computer and improving the mobility and stability of the device. Furthermore, the simultaneous acquisition mechanism ensures strict temporal alignment between EEG signals and limb movement signals, guaranteeing the synchronization accuracy of multimodal signals from the source, thereby supporting real-time and accurate functional imaging analysis during movement. Therefore, the above method can eliminate strong dependence on the host computer, improve device portability and reliability, and better adapt to mobile rehabilitation scenarios. Simultaneously, by simultaneously acquiring EEG and limb movement multimodal signals, it ensures that each signal has a unified time reference, significantly reducing synchronization errors and achieving high-precision synchronization and real-time analysis of multimodal signals during movement, providing a more accurate and reliable data foundation for brain-body functional imaging.

[0007] In one possible implementation of the first aspect, the first moment is the moment when the user sends a clock synchronization reset trigger signal to the EEG amplifier and multiple sensors through a multimodal signal-based motor rehabilitation brain-body functional imaging device; The clock synchronization reset trigger signal is used to synchronize the EEG amplifier and multiple sensors at the first moment.

[0008] In this embodiment, by defining a unified time reference for multimodal signal acquisition, the clock synchronization reset trigger signal enables the EEG amplifier and multiple sensors to be synchronously reset at the first moment, realizing high-precision synchronous acquisition of multimodal signals at the hardware level. This lays an accurate and consistent time foundation for subsequent signal alignment and brain-body functional imaging joint analysis, improving the accuracy of data fusion analysis and the reliability of equipment acquisition.

[0009] In one possible implementation of the first aspect, acquiring the patient's electroencephalogram (EEG) signal processed by the EEG amplifier at a first moment includes: Acquire the patient's raw electroencephalogram (EEG) signals at the first moment using an EEG amplifier; The original EEG signal was amplified and filtered to obtain the second EEG signal; The second EEG signal is converted from analog to digital to obtain the processed EEG signal.

[0010] In this embodiment, by clarifying the acquisition process of EEG signals as a continuous process of synchronously acquiring raw EEG signals at the first moment, followed by amplification, filtering, and analog-to-digital conversion, the end-side integration of EEG signal acquisition from raw acquisition to digital processing is realized. This ensures that the acquisition time of EEG signals and limb movement signals is synchronized, and the signal quality is improved through front-end conditioning, providing a high-quality, time-aligned digital EEG signal foundation for subsequent multimodal signal joint analysis.

[0011] In one possible implementation of the first aspect, the multiple sensors include multiple limb electromyography and inertial motion sensors, and two plantar pressure sensors; the limb motion signals include electromyography signals and inertial motion signals acquired by the multiple limb electromyography and inertial motion sensors, and plantar pressure signals acquired by the plantar pressure sensors. Among them, electromyographic signals and inertial motion signals and their corresponding clock timestamps are stored in the data buffer area of ​​the EEG amplifier; plantar pressure signals and their corresponding clock timestamps are stored in the data buffer area of ​​the EEG amplifier.

[0012] In this embodiment, by clearly defining the composition of multiple sensors and the specific types of limb movement signals, and uniformly storing various limb movement signals and corresponding clock timestamps in the data buffer area of ​​the EEG amplifier, centralized storage of multimodal limb signals and binding of time information are achieved. This provides a unified data carrier for subsequent synchronous alignment and joint analysis of multimodal signals, simplifies the data retrieval and processing process, and improves the efficiency and accuracy of brain-body functional imaging analysis.

[0013] In one possible implementation of the first aspect, functional imaging analysis is performed on the electroencephalogram (EEG) signals and multiple limb movement signals to obtain functional imaging analysis results, including: The clock timestamps corresponding to the EEG processing signals are synchronized with the clock timestamps corresponding to the multiple limb movement signals to obtain synchronized EEG processing signals and limb movement signals. Functional imaging analysis was performed on the synchronized EEG signals and limb movement signals to obtain the functional imaging analysis results.

[0014] In this embodiment, by clearly defining the brain-body functional imaging analysis process of first synchronizing EEG processing signals and limb movement signals based on clock timestamps and then conducting functional imaging analysis, the time deviation of signal acquisition and transmission is eliminated through precise calibration in the time dimension, ensuring the matching of multimodal signals in the time dimension. This allows subsequent functional imaging analysis to accurately reflect the real synergistic relationship between brain neural activity and limb movement, effectively improving the accuracy and reliability of brain-body functional imaging analysis results.

[0015] In one possible implementation of the first aspect, control commands are generated based on the results of functional imaging analysis; Control commands are sent to the sports rehabilitation equipment to control the equipment's sports rehabilitation training for the patient.

[0016] In this embodiment, control commands are generated and sent to the sports rehabilitation equipment based on the brain-body functional imaging analysis results to regulate rehabilitation training. This realizes an intelligent closed loop from multimodal signal analysis to rehabilitation training control, allowing rehabilitation training movements to be precisely matched with the patient's brain neural activity and limb movement state, thereby improving the personalization, intelligence and effectiveness of sports rehabilitation training.

[0017] Secondly, embodiments of this application provide a brain-body functional imaging device for motor rehabilitation based on multimodal signals, including: an EEG amplifier, multiple limb electromyography and inertial motion sensors, and two plantar pressure sensors; The EEG amplifier is used to realize the multimodal signal-based brain-body functional imaging method for motor rehabilitation as described in any of the first aspects above; The EEG amplifier is also used to acquire analog EEG signals, amplify and filter the analog EEG signals, and perform analog-to-digital conversion to obtain processed EEG signals; Multiple limb electromyography (EMG) and inertial motion sensors are used to collect the patient's EMG and inertial motion signals; Two plantar pressure sensors are used to collect plantar pressure signals from the patient.

[0018] In one possible implementation of the second aspect, the multimodal signal-based motor rehabilitation brain-body functional imaging device further includes a base box; the base box is equipped with an anti-shake button; Users send clock synchronization reset trigger signals to the EEG amplifier and multiple sensors via the anti-shake button.

[0019] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the brain-body functional imaging method as described in any of the first aspects above.

[0020] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the brain-body functional imaging method as described in any of the first aspects above.

[0021] Fifthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to execute the brain-body functional imaging method of any one of the first aspects described above.

[0022] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the structure of the motor rehabilitation brain-body functional imaging device based on multimodal signals provided in the embodiments of this application; Figure 2 This is a schematic flowchart of the brain-body functional imaging method for motor rehabilitation based on multimodal signals provided in the embodiments of this application; Figure 3 This is a schematic flowchart of the process for acquiring EEG processing signals provided in an embodiment of this application; Figure 4 This is a schematic flowchart of functional imaging analysis of multimodal signals provided in an embodiment of this application; Figure 5 This is a schematic flowchart of the application functional imaging analysis provided in the embodiments of this application; Figure 6 This is a schematic diagram of the overall process of the brain-body functional imaging method for motor rehabilitation based on multimodal signals provided in the embodiments of this application; Figure 7 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. Detailed Implementation

[0025] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0026] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0027] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0028] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0029] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0030] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0031] The development of personalized diagnosis and treatment for sports rehabilitation urgently requires the synchronous acquisition and joint analysis of multimodal signals such as electroencephalography (EEG) and electromyography (EMG). However, in current clinical research, most signal acquisition systems operate independently, and data is manually or software aligned later. Furthermore, imaging technology relies on large non-wearable devices, and related patents can only achieve partial modal acquisition and simple processing.

[0032] The existing methods not only lack edge AI computing power and rely entirely on host computers to process signals, but also suffer from poor device portability and reliability, making them difficult to adapt to mobile rehabilitation scenarios. Furthermore, they cannot achieve high-precision synchronous acquisition of all modalities, have insufficient EEG channels, lack a unified hardware clock reference for each device, and have large synchronization errors, making it impossible to support real-time signal analysis during movement. Therefore, there is an urgent need for wearable brain-body functional imaging systems and methods that are adaptable to mobile scenarios, can acquire multimodal signals synchronously with high precision, and have real-time edge analysis capabilities.

[0033] To address the aforementioned technical challenges, this application provides a brain-body functional imaging method for motor rehabilitation based on multimodal signals. This method addresses the pain points of existing technologies, such as reliance on host computers, lack of edge computing power, incomplete multimodal acquisition, and large synchronization errors. It designs a wearable device consisting of a base box, a 64-channel high-density EEG system, 24 electromyographic inertial sensors, and 2 plantar pressure sensors. The base box enables hardware-level clock synchronization, charging, and wireless communication. Utilizing an EEG amplifier with edge artificial intelligence (AI) computing power, it achieves high-precision synchronous acquisition and edge-side integrated analysis of multimodal signals, eliminating reliance on host computers and improving the device's portability, reliability, and real-time performance. This method is suitable for various scenarios, including motor function assessment and neurorehabilitation training.

[0034] See Figure 1 This is a schematic diagram of the structure of the motor rehabilitation brain-body functional imaging device based on multimodal signals provided in this application embodiment, as shown below. Figure 1 As shown, the device clearly demonstrates the physical form, key structural designations, interface layout, and component relationships of its four core components. The designations, corresponding structures, and functions of each component are as follows. The overall design exhibits modular, wearable, and integrated features, specifically: I. Base Box (Core Control / Charging / Synchronization / Communication Unit) The base box is a covered enclosure structure, serving as the clock synchronization, charging, and wireless communication base station for all devices. It consists of two main parts: the enclosure (101) and the cover (102). The remaining components are integrated into the upper / side / interior of the enclosure. Specifically: EEG storage base (103): The specially designed base on the upper part of the box is used to store the EEG amplifier. It has built-in matching contacts to achieve precise placement and connection of the EEG amplifier. EEG base charging and synchronization contact (104): Located inside the EEG storage base (103), it is adapted to the corresponding contact of the EEG amplifier. When connected, it charges the EEG amplifier and transmits the clock synchronization reset trigger signal. Limb sensor storage base (105): There are 26 of these bases on the upper part of the box, which are used to store 24 limb electromyography and inertial motion sensors and 2 foot pressure sensors, providing a dedicated placement space for each limb sensor; Limb base charging and synchronization contacts (106): located in each limb sensor storage base (105), matching the corresponding contacts of the limb sensor and foot pressure sensor to realize charging and clock synchronization signal transmission; Wireless communication antenna base (107): The base structure on the upper part of the box can be folded and stored to fix the wireless communication antenna. It can be opened when in use and stored when not in use, improving the portability of the device; Antenna (108): Mounted on antenna base (107), it provides high-speed wireless local area network communication for the device and enables data transmission between various sensors and EEG amplifier and base box; Anti-shake button (109): A manual operation button on the top of the enclosure, used to manually issue a clock synchronization reset trigger signal. After pressing, the base enclosure sends a synchronization command to all devices to achieve hardware-level clock unification. Storage space (110): A dedicated storage area at the top of the box, used to store equipment accessories such as EEG caps, adhesive tapes, and data cables, achieving integrated storage of accessories; 12V DC power supply / charging interface (111): Located on the side of the enclosure, it charges the built-in battery of the base enclosure and provides 12V DC power to the base enclosure and connected equipment. 8-bit wired trigger parallel communication interface (112): Located on the side of the enclosure, it provides a wired trigger parallel communication channel for the device, and is suitable for wired synchronization and data transmission scenarios of multiple devices; RJ45 network communication interface (113) for host computer: Located on the side of the enclosure, it enables wired network connection between the base enclosure and the host computer, and can transmit analysis results and multimodal synchronization data back to the host computer, adapting to scenarios requiring host computer assistance; (Note: The base enclosure also integrates a 12V built-in battery, power board, microcontroller control board, and wireless router board, providing power supply, clock synchronization control, and wireless communication support for the above structures, forming the core internal circuitry.) Figure 1 (No external labels are provided).

[0035] II. EEG Amplifier (201, Edge AI Computing Power / Signal Processing Core) The EEG amplifier is the core of the device for signal acquisition, preprocessing, and fusion analysis, and it has built-in edge AI computing power. Figure 1 The numbers in the original text all refer to its external core structure, specifically: EEG amplifier (201): The main device, integrating core modules such as analog front-end circuit, analog-to-digital conversion module, edge computing chip, and data buffer window, to realize EEG signal processing and multimodal data fusion analysis; Independent Type-C charging port (202): Located above the EEG amplifier, it provides an independent charging channel, allowing it to be charged without relying on the base box, thus improving the flexibility of use; EEG cap interface (203): Located above the EEG amplifier, it connects to the 64-channel high-density EEG cap to realize the acquisition and transmission of EEG signals; Charging and synchronization contacts (204): Located on the side of the EEG amplifier, matching the base box 104 contacts, and connected when stored in the 103 base to realize charging and receive clock synchronization reset trigger signals; Heat dissipation holes (205): Located on the side of the EEG amplifier, they dissipate heat from the internal computing chip and circuit modules, ensuring the stability of the device during long-term operation; Universal input / output interface (206): Located on the front of the EEG amplifier, it can output custom control command signals to the outside based on the brain-body functional imaging analysis results, and is compatible with brain-computer interface and rehabilitation training equipment linkage scenarios. Display (207): Located on the front of the EEG amplifier, it displays the device's working status, collected parameters, analysis results and other information in real time, enabling human-computer interaction; Operation buttons (208-213) 0: Located on the front of the EEG amplifier (6 in total), these are manual operation buttons that can realize functions such as powering on / off the device, configuring parameters, starting / stopping data acquisition, and saving data.

[0036] III. Limb electromyography and inertial motion sensors (301, limb signal acquisition unit, 24 in total) These are small, adhesive sensors, 24 of which are evenly distributed and attached to the muscles of the patient's limbs to collect electromyographic and inertial motion signals. Figure 1 Core labeling and structure: Limb electromyography and inertial motion sensor (301): The sensor body has a built-in electromyography acquisition module and an IMU inertial measurement module to realize the synchronous acquisition of 1-channel electromyography signal and 3-axis acceleration + angle inertial signal; Electromyographic electrodes (302, 303): These are bipolar electromyographic electrodes located on the surface of the sensor, directly contacting the muscles and skin of the limb to collect raw electromyographic signals; Indicator light (304): Located on the top of the sensor, it indicates the working status of the device in real time (such as charging, data acquisition, synchronization successful, fault, etc.) for easy manual inspection; Charging and synchronization contact (305): Located on the side of the sensor, it matches the base box 106 contact and is connected when stored in the 105 base to realize charging and receive clock synchronization reset trigger signal; Serial number (306): Marked on the surface of the sensor. Each sensor has a unique serial number to distinguish the 24 acquisition channels, avoid signal confusion, and facilitate data traceability and analysis.

[0037] IV. Plantar pressure sensor (401, plantar signal acquisition unit, 2 in total, one for the left foot and one for the right foot). These are insole-type sensors, one for each foot, used to collect pressure signals from the soles of both feet. Figure 1 Core labeling and structure: Foot pressure sensor (401): The sensor body has a built-in pressure signal acquisition module, which is integrated with the insole to adapt to the shape of the human foot and realize the accurate acquisition of foot pressure signals. Insole (402): Connected to sensor (401), it is a flexible insole structure that fits the sole of the foot and serves as the carrier for pressure detection points; Pressure sensors (403): There are 8 in total, distributed on the forefoot and heel of the insole (402), directly contacting the sole of the foot to collect the sole pressure signal of different parts of the foot; Indicator light (404): Located on the side of the sensor, it has the same function as 304, indicating the working status of the device such as charging, data acquisition, and synchronization; Charging and synchronization contact (405): Located on the side of the sensor, it matches the base box 106 contact and is connected when stored in the 105 base to realize charging and receive clock synchronization reset trigger signal; Serial number (406): Marked on the surface of the sensor. The left and right foot sensors have unique serial numbers to distinguish the left and right foot acquisition channels, which facilitates gait pressure analysis.

[0038] Overall, Figure 1 The labeling clearly presents the core external structure, interface / contact matching relationships, and functional positioning of each component of the device, intuitively demonstrating the device design logic of "centralized control of the base box + distributed acquisition of data from various sensors + edge analysis by the EEG amplifier," as well as its core design features of wearable, portable, and high-precision synchronization. Utilizing... Figure 1 The equipment was used to perform brain-body functional imaging analysis on patients undergoing sports rehabilitation, as detailed below: First, using Figure 1 Before using the equipment, an initialization process is required. When the user opens the base box, the system power automatically starts, and the base box, along with the EEG amplifier, limb electromyography and inertial motion sensors, and plantar pressure sensors, all enter the initialization process simultaneously. This completes preliminary preparations such as device self-test, circuit wake-up, communication link establishment, and pre-configuration of acquisition parameters, laying the foundation for subsequent clock synchronization and multimodal signal acquisition.

[0039] See Figure 2 This is a flowchart illustrating the brain-body functional imaging method for motor rehabilitation based on multimodal signals provided in this application embodiment. It is applied to the EEG amplifier of a brain-body functional imaging device for motor rehabilitation based on multimodal signals. As an example and not a limitation, the method may include the following steps: S101, acquire the brainwave processing signal of the patient collected by the EEG amplifier at the first moment.

[0040] In this embodiment, at the first unified moment of the system (the acquisition start node when the clock synchronization reset trigger signal is issued and all device clocks are reset uniformly), the EEG amplifier completes the acquisition and preprocessing of the EEG signal on the patient's scalp surface, and directly obtains the EEG processing signal that can be used for subsequent analysis.

[0041] In one embodiment, the first moment is the moment when the user sends a clock synchronization reset trigger signal to the EEG amplifier and multiple sensors through a multimodal signal-based motion rehabilitation brain-body functional imaging device; The clock synchronization reset trigger signal is used to synchronize the EEG amplifier and multiple sensors at the first moment.

[0042] In this embodiment, the moment when the user issues the clock synchronization reset trigger signal is designated as the first moment. This trigger signal serves as a unified instruction, enabling the EEG amplifier and all limb and foot multi-sensors to complete the internal clock reset at the same moment. This establishes an absolutely unified time reference at the hardware level for the subsequent synchronous acquisition of multimodal signals such as EEG, EMG, inertial motion, and foot pressure, completely eliminating the time deviation caused by the independent operation of each device and ensuring the high accuracy of subsequent signal acquisition, alignment, and analysis.

[0043] For example, when a user opens the base box of a multimodal signal-based brain-body functional imaging device for motor rehabilitation, after the system is powered on, the base box and multiple sensors, including the EEG amplifier, limb electromyography and inertial motion sensors, and plantar pressure sensors, complete initialization. Once the device is ready to acquire data, the user presses the anti-shake button on the base box. The base box then sends a clock synchronization reset trigger signal to the EEG amplifier and all multiple sensors. The moment this signal is sent is the first moment defined in the method. This trigger signal is transmitted through the high-speed wireless local area network of the base box and the charging synchronization contacts between devices to the clock modules inside the EEG amplifier and each multiple sensor. This triggers the internal high-precision clocks of all devices to synchronously complete a zero reset at the first moment, ensuring that the clock counts of the EEG amplifier and each multiple sensor proceed synchronously from this moment onwards, laying a unified hardware timing foundation for the subsequent synchronous acquisition of multimodal signals.

[0044] In the above method, by defining a unified time reference for multimodal signal acquisition, the clock synchronization reset trigger signal enables the EEG amplifier and multiple sensors to be synchronously reset at the first moment, realizing high-precision synchronous acquisition of multimodal signals at the hardware level. This lays an accurate and consistent time foundation for subsequent signal alignment and brain-body functional imaging joint analysis, and improves the accuracy of data fusion analysis and the reliability of equipment acquisition.

[0045] In one embodiment, see Figure 3 This is a schematic flowchart of the process for acquiring EEG processing signals provided in an embodiment of this application, as shown below. Figure 3 As shown, step S101 includes: S201, acquire the patient's raw EEG signal at the first moment through the EEG amplifier.

[0046] In this embodiment, at the first moment after the system hardware-level clock is synchronized (the time node when all devices start collecting data uniformly), the EEG amplifier directly collects the raw EEG potential signal from the patient's scalp surface without any conditioning or conversion through the matching high-density EEG cap. This provides raw data for subsequent EEG signal preprocessing and multimodal joint analysis, while ensuring that the raw EEG signal is strictly synchronized with the acquisition time of limb movement signals. This is a fundamental step in achieving high-precision multimodal acquisition.

[0047] Specifically, after the base box, EEG amplifier, limb sensor, and foot pressure sensor complete system initialization, and the base box sends a clock synchronization reset trigger signal to reset the internal clocks of all devices and synchronously start the multimodal acquisition process, at the preset first moment, the EEG amplifier connects to the 64-channel high-density EEG cap through the EEG cap interface on its top. The electrodes on the EEG cap directly contact the corresponding cortical position on the patient's scalp to capture the scalp surface potential signal generated by the activity of neuronal groups in the cerebral cortex. This potential signal, which has not undergone any amplification, filtering, or other conditioning, is the raw EEG signal. Subsequently, through the connection link between the EEG cap and the amplifier, the raw EEG signal acquired at the first moment is transmitted in real time to the EEG amplifier, providing raw data for subsequent signal preprocessing.

[0048] S202 amplifies and filters the original EEG signal to obtain the second EEG signal.

[0049] In the embodiments of this application, the first round of signal conditioning operations performed on the acquired raw EEG signals involves amplification and filtering to remove interference from the raw EEG signals, extract effective signals, and enhance signal strength. This process transforms the originally weak and noisy raw EEG signals into clearer and more suitable second EEG signals for subsequent analysis, laying a high-quality signal foundation for subsequent analog-to-digital conversion and multimodal joint analysis.

[0050] For example, the EEG amplifier retrieves the original EEG signal from the patient's scalp surface, which was acquired at the first moment and stored internally. The signal is then transmitted to the amplifier's built-in analog front-end circuit. First, the weak original EEG potential signal is precisely amplified by the precision amplification module in the circuit to increase the signal amplitude to meet the signal strength requirements for subsequent processing. Then, the circuit's dedicated filtering module filters out invalid interference signals such as power frequency interference, environmental noise, and motion artifacts mixed in with the original signal, retaining the effective EEG signal characteristics generated by brain neural activity. After the above amplification and filtering are synergistically conditioned, a second EEG signal with acceptable signal quality is obtained, which can be used for subsequent analog-to-digital conversion and other operations.

[0051] S203 performs analog-to-digital conversion on the second EEG signal to obtain the processed EEG signal.

[0052] In the embodiment of the present application, it is a process of converting the amplified and filtered analog second electroencephalogram (EEG) signal into a digital EEG processing signal, which realizes the conversion of the EEG signal from the analog domain to the digital domain, enables the signal to be recognized, stored and subjected to subsequent multi-modal joint analysis by the edge computing power module of the EEG amplifier, and is the core link connecting signal analog conditioning and digital analysis.

[0053] Exemplarily, the EEG amplifier transmits the second EEG signal after amplification and filtering to the built-in analog-to-digital conversion module. This module performs discrete sampling and quantization processing on the continuous analog second EEG signal according to the preset sampling accuracy and rate, converts the original analog electrical signal into a signal in digital coding form, and at the same time binds a high-precision clock timestamp unified with the original EEG signal to the converted digital signal, finally forming an EEG processing signal that can be directly stored in the data buffer area of the EEG amplifier and can be parsed by the edge AI computing power module for subsequent brain-body functional imaging analysis.

[0054] In the above method, by clarifying the unified time reference for multi-modal signal acquisition, the clock synchronization reset trigger signal enables the EEG amplifier and multi-sensors to be synchronously reset at the first moment, achieving high-precision synchronous acquisition of multi-modal signals at the hardware level, laying an accurate and consistent time foundation for subsequent signal alignment and brain-body functional imaging joint analysis, and improving the accuracy of data fusion analysis and the reliability of device acquisition.

[0055] S102, obtain multiple limb movement signals of the patient synchronously collected by multi-sensors at the first moment.

[0056] In the embodiment of the present application, at the first moment (the moment when the clock synchronization reset trigger signal is issued and all device clocks are reset uniformly) calibrated at the system hardware level, through multiple types of limb sensors supporting the device, multiple limb movement-related signals such as the patient's electromyogram (EMG), inertial movement, and plantar pressure are synchronously collected. This step realizes the complete consistency of the acquisition time starting point of the limb movement signal and the EEG signal, solves the problem of asynchronous and large-error multi-modal signal acquisition in the prior art, provides a limb movement data basis with unified time and comprehensive dimensions for subsequent synchronous alignment, fusion analysis of EEG and limb movement signals, and brain-body functional imaging, and is an important data support for analyzing the brain-body movement interaction mechanism.

[0057] In one embodiment, the multi-sensors include multiple limb EMG and inertial movement sensors, and two plantar pressure sensors; the limb movement signals include EMG signals and inertial movement signals collected by multiple limb EMG and inertial movement sensors, and plantar pressure signals collected by the plantar pressure sensors; Among them, electromyographic signals and inertial motion signals and their corresponding clock timestamps are stored in the data buffer area of ​​the EEG amplifier; plantar pressure signals and their corresponding clock timestamps are stored in the data buffer area of ​​the EEG amplifier.

[0058] In this embodiment, at the first moment when the base box sends the clock synchronization reset trigger signal, the EEG amplifier completes the internal clock reset, and the 24 limb electromyography and inertial motion sensors and 2 plantar pressure sensors equipped with the device also simultaneously complete the clock reset and start the acquisition process.

[0059] Twenty-four sensors, attached to the muscles of the patient's limbs, collect electromyographic signals generated by the nerve drive of the limb muscles through bipolar electromyographic electrodes. At the same time, the built-in inertial measurement unit (IMU) module collects the inertial motion signals of the limb's three-axis acceleration and angle. Two insole-type plantar pressure sensors collect the pressure distribution and change signals of the patient's sole through eight pressure detection points distributed on the forefoot and heel. After the various sensors start collecting data at the first moment, they capture the corresponding raw limb motion signals in real time. This enables multiple sensors to simultaneously collect multiple limb motion signals such as electromyography, inertial motion, and plantar pressure from the same starting point at the same time. All collected signals are bound to a clock timestamp with the same reference as the EEG signal, preparing for subsequent data transmission and fusion analysis.

[0060] In addition, after the system completes clock synchronization reset and starts multimodal acquisition at the first moment, 24 limb electromyography and inertial motion sensors attached to the patient's limb muscles simultaneously acquire electromyography and inertial motion signals, and 2 insole-type plantar pressure sensors simultaneously acquire plantar pressure signals. During the acquisition process, all three types of signals are bound in real time to a high-precision clock timestamp that is of the same origin as the EEG signal. Subsequently, these electromyography and inertial motion signals carrying a unified time reference are transmitted to the EEG amplifier via a high-speed wireless local area network. The plantar pressure signal is also transmitted to the EEG amplifier via a high-speed wireless local area network.

[0061] The EEG amplifier stores all received electromyographic signals, inertial motion signals and their corresponding clock timestamps, and plantar pressure signals and their corresponding clock timestamps in its built-in data buffer area. This enables centralized and timestamp-correlated storage of multiple types of limb motion signals, preparing data for subsequent multimodal signal synchronization and brain-body functional imaging analysis.

[0062] S103 performs functional imaging analysis on EEG processing signals and multiple limb movement signals to obtain functional imaging analysis results.

[0063] In this embodiment, after the acquisition, preprocessing and unified storage of EEG and limb movement multimodal signals are completed, the edge computing power of the EEG amplifier is used to conduct joint analysis of the time-consistent EEG processed signals and limb movement signals such as electromyography, inertial motion and plantar pressure. Finally, functional imaging analysis results that can reflect the interaction between brain neural activity and limb movement are generated. This is a key step to realize real-time edge analysis and provide objective evidence for sports rehabilitation.

[0064] In the above method, EEG signals collected by an EEG amplifier and multiple limb movement signals collected by multiple sensors are acquired simultaneously at the same first moment. The EEG amplifier is then used to perform functional imaging analysis on both types of signals to obtain the analysis results. This method directly relies on the edge device where the EEG amplifier is located to achieve signal acquisition and analysis, reducing dependence on a host computer and improving device mobility and stability. Furthermore, the simultaneous acquisition mechanism ensures strict temporal alignment between EEG signals and limb movement signals, guaranteeing the synchronization accuracy of multimodal signals from the source, thereby supporting real-time and accurate functional imaging analysis during movement. Therefore, this method can eliminate strong dependence on a host computer, improve device portability and reliability, and better adapt to mobile rehabilitation scenarios. Simultaneously, by simultaneously acquiring multimodal EEG and limb movement signals, a unified time reference is ensured for each signal, significantly reducing synchronization errors and achieving high-precision synchronization and real-time analysis of multimodal signals during movement, providing a more accurate and reliable data foundation for brain-body functional imaging.

[0065] In one embodiment, see Figure 4 This is a schematic flowchart of functional imaging analysis of multimodal signals provided in an embodiment of this application, such as... Figure 4 As shown, step S103 includes: S301, synchronize and align the clock timestamps corresponding to the EEG processing signals and the clock timestamps corresponding to the multiple limb movement signals to obtain synchronized and aligned EEG processing signals and limb movement signals.

[0066] In this embodiment, the clock timestamps of the same hardware reference bound to the EEG processing signals and various limb movement signals are used to calibrate and match all signals in the time dimension, eliminating the slight time deviations generated during signal acquisition and transmission, so that the EEG signals and limb movement signals are completely synchronized on the time axis, laying a time-unified signal foundation for subsequent accurate brain-body functional imaging joint analysis.

[0067] For example, the EEG amplifier retrieves the stored EEG processing signals and their corresponding clock timestamps from its own data buffer area, as well as all limb movement signals such as electromyography, inertial motion, and plantar pressure, and their respective bound clock timestamps. Relying on edge computing power, it starts a time synchronization alignment algorithm, taking the first moment of the system (the moment the clock synchronization reset trigger signal is sent) as the unified time origin. It calibrates the clock timestamps of all signals with this time origin, corrects the time axis offset of signals with slight transmission and acquisition delays, so that the EEG processing signals and each limb movement signal form a one-to-one corresponding signal data group at the same time node. Finally, it obtains synchronized and aligned EEG processing signals and limb movement signals that are completely matched in time dimension and can be directly used for joint analysis.

[0068] S302, perform functional imaging analysis on the synchronized EEG processing signal and limb movement signal to obtain the functional imaging analysis results.

[0069] In this embodiment of the application, time-aligned EEG signals and limb movement signals are combined for joint analysis. The correspondence between brain activity and limb movement is calculated by an algorithm, and finally functional imaging analysis results that can be used for rehabilitation assessment are obtained.

[0070] For example, at the EEG amplifier end, the EEG signals, electromyography signals, inertial motion signals and plantar pressure signals that have been synchronized and aligned according to clock timestamps are first processed jointly using built-in edge AI computing power and imaging algorithms. The activation state of the brain's motor cortex, limb muscle activity, limb movement posture and plantar pressure distribution characteristics are analyzed respectively. The temporal and functional correlation between EEG and various limb signals is also analyzed. Finally, the brain-body functional imaging calculation is completed to obtain functional imaging analysis results that reflect the coordinated movement state of the brain and limbs.

[0071] The above method clarifies the brain-body functional imaging analysis process by first synchronizing EEG processing signals and limb movement signals based on clock timestamps, and then conducting functional imaging analysis. The precise calibration in the time dimension eliminates the time deviation of signal acquisition and transmission, ensuring the matching of multimodal signals in the time dimension. This allows the subsequent functional imaging analysis to accurately reflect the real synergistic relationship between brain neural activity and limb movement, effectively improving the accuracy and reliability of brain-body functional imaging analysis results.

[0072] In one embodiment, see Figure 5 This is a flowchart illustrating the application of functional imaging analysis provided in an embodiment of this application, such as... Figure 5 As shown, it includes: S401 generates control commands based on the results of functional imaging analysis.

[0073] In this embodiment of the application, after obtaining the brain-body functional imaging analysis results, corresponding control commands are automatically generated based on the analyzed brain and limb movement states to drive external rehabilitation equipment or provide rehabilitation prompts, thereby realizing the transformation from "analysis results" to "actual control".

[0074] For example, based on the previously obtained functional imaging analysis results, combined with the preset motor rehabilitation strategy and control logic, the EEG amplifier comprehensively judges the patient's brain movement intention, actual limb movement status and brain-body coordination, and generates corresponding control commands according to the set rules.

[0075] S402 sends control commands to the sports rehabilitation equipment to control the sports rehabilitation equipment to perform sports rehabilitation training on the patient.

[0076] In this embodiment, the control commands generated earlier are transmitted to the corresponding rehabilitation training device, so that the device can cooperate with the patient to perform rehabilitation training according to the commands, thereby realizing a complete closed loop from brain-body signal analysis to rehabilitation control.

[0077] For example, the EEG amplifier generates control commands and sends them to the connected motor rehabilitation equipment through its own general input / output interface or wireless communication. After receiving the control commands, the rehabilitation equipment drives the corresponding training mechanism to perform actions according to the commands, assisting, guiding or providing feedback to the patient to complete the corresponding limb motor rehabilitation training, thereby realizing intelligent rehabilitation control based on brain-body functional imaging results.

[0078] In addition, after the EEG amplifier completes the functional imaging analysis of the synchronized and aligned signals, generates complete brain-body functional imaging analysis results, and its data buffer area has organized the synchronized and aligned multimodal data (including EEG processing signals, electromyography signals, inertial motion signals, plantar pressure signals, etc., and their corresponding unified clock timestamps), the EEG amplifier establishes a high-speed wireless communication connection with the base box through its built-in wireless communication module. Then, according to the preset communication protocol and data encapsulation format, it packages the functional imaging analysis results and synchronized multimodal data and sends them to the base box. The base box receives and parses the data packet, stores the functional imaging analysis results and multimodal data in its internal storage unit, and completes the entire process from edge-side intelligent analysis to central node data aggregation, providing data support for subsequent remote display, data review, or system linkage.

[0079] In the above method, control commands are generated and sent to the sports rehabilitation equipment based on the brain-body functional imaging analysis results to regulate rehabilitation training. This realizes an intelligent closed loop from multimodal signal analysis to rehabilitation training control, which allows rehabilitation training movements to be accurately matched with the patient's brain neural activity and limb movement state, thereby improving the personalization, intelligence and effectiveness of sports rehabilitation training.

[0080] This application provides a brain-body functional imaging device for motor rehabilitation based on multimodal signals, including an EEG amplifier, multiple limb electromyography and inertial motion sensors, and two plantar pressure sensors; The EEG amplifier is used to realize the multimodal signal-based brain-body functional imaging method for motor rehabilitation as described in any of the steps above; The EEG amplifier is also used to acquire analog EEG signals, amplify and filter the analog EEG signals, and perform analog-to-digital conversion to obtain processed EEG signals; Multiple limb electromyography (EMG) and inertial motion sensors are used to collect the patient's EMG and inertial motion signals; Two plantar pressure sensors are used to collect plantar pressure signals from the patient.

[0081] In the embodiments of this application, such as Figure 1 The multimodal signal-based brain-body functional imaging device for motor rehabilitation shown mainly consists of an EEG amplifier, multiple limb electromyography and inertial motion sensors, and two plantar pressure sensors. The EEG amplifier is responsible for executing the entire imaging method and completing the acquisition, amplification, and analog-to-digital conversion of EEG signals to obtain processed EEG signals. The limb sensors are responsible for acquiring electromyography and inertial motion signals, and the plantar pressure sensors are responsible for acquiring plantar pressure signals. The three work together to achieve multimodal signal acquisition and brain-body functional imaging analysis.

[0082] In one embodiment, the motion rehabilitation brain-body functional imaging device based on multimodal signals further includes a base box; the base box is equipped with an anti-shake button; Users send clock synchronization reset trigger signals to the EEG amplifier and multiple sensors via the anti-shake button.

[0083] In this embodiment, the motor rehabilitation brain-body functional imaging device of this application also includes a base box, on which an anti-shake button is provided; when the user presses the anti-shake button, a clock synchronization reset trigger signal is uniformly sent to the EEG amplifier and various multi-sensors, so that all devices complete the clock reset at the same time, ensuring that the subsequent multimodal signal acquisition time is strictly synchronized.

[0084] In one embodiment, see Figure 6 This is a schematic diagram of the overall process of the brain-body functional imaging method for motor rehabilitation based on multimodal signals provided in the embodiments of this application, as follows: Figure 6 As shown, it includes: S501: System initialization. The user turns on the power to start the base box. The base box, EEG amplifier, limb electromyography and inertial motion sensors, and plantar pressure sensors complete the initialization operation of the entire device.

[0085] S502: Clock synchronization reset. When the user presses the anti-shake button on the base box, the base box sends a clock synchronization reset trigger signal to the EEG amplifier and all limb sensors. The internal clocks of each device are reset uniformly and the acquisition process is started synchronously.

[0086] S503: EEG signal acquisition. The EEG amplifier acquires simulated EEG signals from the patient's scalp surface through EEG cap electrodes.

[0087] S504: Acquisition of limb electromyography and inertial motion signals. 24 limb electromyography and inertial motion sensors simultaneously acquire the patient's electromyography and inertial motion signals.

[0088] S505: Plantar pressure signal acquisition, two plantar pressure sensors simultaneously acquire the patient's plantar pressure signal.

[0089] S506: Wireless transmission of limb signals. After pairing the limb movement signals collected by S504 and S505 with the clock timestamps of each sampling point, the signals are transmitted to the EEG amplifier through the high-speed wireless local area network of the base box.

[0090] S507: Multimodal signal storage. The EEG amplifier amplifies, filters, and converts the EEG signals collected by S503 into digital EEG processing signals. After pairing with the sampling point clock timestamps, it stores the signals along with the limb movement signals transmitted by S506 into its own internal data buffer window.

[0091] S508: Multimodal signal analysis. The EEG amplifier synchronizes and aligns the EEG processing signals, limb movement signals, and corresponding unified clock timestamps within the data buffer window, and then performs brain-body functional imaging analysis.

[0092] S509: Generates and outputs control commands. Based on the brain-body functional imaging analysis results of S508, the EEG amplifier generates control commands according to user-defined programming rules and outputs them to the outside through its own general input / output interface.

[0093] S510: Data feedback base box. The EEG amplifier sends the functional imaging analysis results of S508, as well as the synchronized and aligned multimodal data, to the base box to complete data aggregation and storage.

[0094] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0095] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0096] Figure 7 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. For example... Figure 7 As shown, the terminal device 7 of this embodiment includes: at least one processor 70 ( Figure 7 (Only one is shown in the image) a processor, a memory 71, and a computer program 72 stored in the memory 71 and capable of running on at least one processor 70, wherein the processor 70 executes the computer program 72 to implement the steps in any of the above-described functional imaging analysis method embodiments.

[0097] The terminal device can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 7 The example of terminal device 7 is merely an illustration and does not constitute a limitation on terminal device 7. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0098] The processor 70 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, embedded graphics processing units (Embedded GPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0099] In some embodiments, memory 71 may be an internal storage unit of terminal device 7, such as a hard disk or memory of terminal device 7. In other embodiments, memory 71 may be an external storage device of terminal device 7, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on terminal device 7. Furthermore, memory 71 may include both internal storage units and external storage devices of terminal device 7. Memory 71 is used to store operating system, application programs, bootloader, data, and other programs, such as program code of computer programs. Memory 71 can also be used to temporarily store data that has been output or will be output.

[0100] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the steps in the above-described method embodiments.

[0101] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0102] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / terminal equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0103] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0104] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0105] In the embodiments provided in this application, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0106] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0107] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A brain-body functional imaging method for motor rehabilitation based on multimodal signals, characterized in that, An electroencephalogram (EEG) amplifier applied to a multimodal signal-based brain-body functional imaging device for motor rehabilitation; the method includes: Acquire the patient's brainwave processing signals at the first moment through the EEG amplifier; Acquire multiple limb movement signals of the patient simultaneously collected by multiple sensors at the first moment; Functional imaging analysis was performed on the electroencephalogram (EEG) signals and multiple limb movement signals to obtain functional imaging analysis results.

2. The brain-body functional imaging method for motor rehabilitation based on multimodal signals as described in claim 1, characterized in that, The first moment is the moment when the user sends a clock synchronization reset trigger signal to the EEG amplifier and the multi-sensor through the multimodal signal-based motor rehabilitation brain-body functional imaging device; The clock synchronization reset trigger signal is used to synchronize the EEG amplifier and the multiple sensors at the first moment.

3. The brain-body functional imaging method for motor rehabilitation based on multimodal signals as described in claim 2, characterized in that, The acquisition of the patient's electroencephalogram (EEG) signals processed by the EEG amplifier at the first moment includes: Acquire the patient's raw electroencephalogram (EEG) signals at the first moment using an EEG amplifier; The original EEG signal is amplified and filtered to obtain a second EEG signal; The second EEG signal is converted from analog to digital to obtain the processed EEG signal.

4. The brain-body functional imaging method for motor rehabilitation based on multimodal signals as described in claim 2, characterized in that, The multi-sensor includes multiple limb electromyography and inertial motion sensors, as well as two plantar pressure sensors; the limb motion signal includes electromyography signals and inertial motion signals collected by the multiple limb electromyography and inertial motion sensors, and plantar pressure signals collected by the plantar pressure sensors. The electromyographic signals and inertial motion signals, along with their corresponding clock timestamps, are stored in the data buffer area of ​​the EEG amplifier; the plantar pressure signals and their corresponding clock timestamps are also stored in the data buffer area of ​​the EEG amplifier.

5. The brain-body functional imaging method for motor rehabilitation based on multimodal signals as described in claim 4, characterized in that, The functional imaging analysis of the electroencephalogram (EEG) signals and multiple limb movement signals yields functional imaging analysis results, including: The clock timestamps corresponding to the EEG processing signals and the clock timestamps corresponding to the multiple limb movement signals are synchronized and aligned to obtain synchronized EEG processing signals and limb movement signals. Functional imaging analysis was performed on the synchronized EEG signals and limb movement signals to obtain the functional imaging analysis results.

6. The brain-body functional imaging method for motor rehabilitation based on multimodal signals as described in claim 1, characterized in that, The method further includes: Control commands are generated based on the functional imaging analysis results; The control commands are sent to the sports rehabilitation equipment to control the sports rehabilitation equipment to perform sports rehabilitation training on the patient.

7. A brain-body functional imaging device for motor rehabilitation based on multimodal signals, characterized in that, It includes an EEG amplifier, multiple limb electromyography and inertial motion sensors, and two plantar pressure sensors; The EEG amplifier is used to implement the multimodal signal-based brain-body functional imaging method for motor rehabilitation as described in any one of claims 1 to 6 above; The EEG amplifier is also used to acquire analog EEG signals, amplify and filter the analog EEG signals, and perform analog-to-digital conversion to obtain processed EEG signals. Multiple limb electromyography and inertial motion sensors are used to acquire the patient's electromyography and inertial motion signals; The two plantar pressure sensors are used to acquire plantar pressure signals from the patient.

8. The motor rehabilitation brain-body functional imaging device based on multimodal signals as described in claim 7, characterized in that, It also includes a base box; the base box is equipped with an anti-shake button; The user sends a clock synchronization reset trigger signal to the EEG amplifier and the multi-sensor via the anti-shake button.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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