Clinical motion control device, method and system, electronic equipment and medium

Abstract

The invention provides a clinical motion control device, method and system, electronic equipment and a medium, and the device comprises an ultra-micro array implantation probe which is configured to be implanted into a brain motor cortex of a patient and collect a neuroelectric signal of the brain motor cortex; the integrated data processing module is electrically connected with the ultra-micro array implanted probe and is configured to convert the neuroelectric signals into digital signals and perform neural decoding on the digital signals to obtain motion control signals containing the motion intention of the patient; and the data transmission module is in communication connection with the integrated data processing module and is configured to transmit the motion control signal to external rehabilitation equipment through a wired interface or a wireless communication mode, so that the external rehabilitation equipment drives the limbs of the patient to execute corresponding rehabilitation motions. According to the invention, large-scale, high-precision and synchronous monitoring can be carried out on brain motor cortex neural activities, the signal acquisition quality is improved through an integrated structure design, and the signal decoding precision is improved at the same time.

Description

Technical Field

[0001] This application belongs to the field of brain-computer interface technology and relates to a clinical motor control device, method and system, electronic device and medium. Background Technology

[0002] With the increasing trend of population aging and the rising incidence of neurological diseases such as stroke, spinal cord injury, and amyotrophic lateral sclerosis (ALS), motor dysfunction caused by damage to the central or peripheral nervous system has become a major challenge that urgently needs to be addressed in the field of clinical rehabilitation medicine. Many patients face varying degrees of limb paralysis or loss of motor control during the sequelae period of these diseases, severely impacting their quality of life and social participation.

[0003] In recent years, intelligent rehabilitation assistive devices based on brain-computer interfaces (BCI), such as intelligent prostheses and exoskeletons, have shown great potential in the replacement and reconstruction of motor function. These devices achieve direct control of external actuators by analyzing neural electrical signals related to the patient's intentions, thereby helping patients regain their voluntary motor abilities. Among these, high-quality acquisition and real-time accurate analysis of neural electrical signals, as the core biological information source reflecting the brain's motor intentions, are key prerequisites for achieving efficient brain-controlled movement.

[0004] To meet clinical needs, neural electrical signal acquisition technologies have been continuously developed. Currently, commonly used methods mainly include surface electromyography (sEMG) and scalp electroencephalography (EEG). These technologies can capture motor-related neural activity to a certain extent and provide raw signals for motor decoding. However, existing technologies still face bottlenecks and limitations in practical clinical applications, such as the following: (1) Insufficient signal accuracy: Existing technologies are limited by low spatial resolution and severe signal attenuation, making it difficult to obtain neural activity information with high spatiotemporal resolution; in addition, there are also obvious limitations in signal bandwidth, dynamic range and signal-to-noise ratio, making it difficult to support high-precision decoding of complex and fine movements. (2) Severe noise interference: The neural signal acquisition process is highly susceptible to environmental electromagnetic interference and bioelectric noise such as electromyography and electrocardiography, which leads to a decrease in signal reliability; (3) Significant transmission delay: Existing wireless transmission schemes are limited by the balance between bandwidth and power consumption, which often introduces a non-negligible time delay, making it difficult to meet the strict requirements of real-time brain control systems for low latency; (4) Highly invasive: High-precision signal acquisition relies heavily on implantable electrodes, some of which have problems such as surgical risks, tissue rejection and poor long-term stability. (5) Complex operation: System configuration, parameter calibration and daily maintenance usually require the intervention of professional technicians, which limits its widespread use in home or community scenarios; (6) Poor portability: Most existing acquisition systems are bulky and heavy, and require multiple cables to be connected, which makes them inconvenient to wear, results in a poor user experience, and leads to low patient compliance with long-term use, making it difficult to achieve daily and continuous neurological monitoring and intervention. (7) Low integration: It is easy to cause high overall power consumption, serious heat generation and poor communication reliability of the system, which seriously restricts the practicality and stability of brain-computer interface system in clinical environment. Summary of the Invention

[0005] This application provides a clinical motor control device, method, system, electronic device, and medium to solve the technical problems of poor quality of neural electrical signal acquisition and insufficient decoding accuracy in the prior art.

[0006] In a first aspect, a clinical motor control device based on neural electrical signals includes: An ultra-micro array implantation probe is configured to be implanted into the motor cortex of a patient's brain and to collect neural electrical signals from the motor cortex; the neural electrical signals are analog signals, including single neuron action potential signals and local field potential signals; An integrated data processing module, electrically connected to the ultra-micro array implanted probe, is configured to convert the neural electrical signals into digital signals and perform neural decoding on the digital signals to obtain motion control signals containing the patient's motion intentions; The data transmission module, which is communicatively connected to the integrated data processing module, is configured to transmit the motion control signal to an external rehabilitation device via a wired interface or wireless communication, so that the external rehabilitation device can drive the patient's limbs to perform corresponding rehabilitation movements.

[0007] In one implementation of the first aspect, the ultra-micro array implantation probe includes: The microfilament electrode array includes multiple microfilament electrodes arranged according to a preset rule, each of which is made by twisting and winding multiple ultra-micro alloy electrode wires. A flexible substrate circuit board is configured to provide mechanical support and structural protection for the microfilament electrode array; the microfilament electrode array is attached to the flexible substrate circuit board and electrically connected to the flexible substrate circuit board.

[0008] In one implementation of the first aspect, the integrated data processing module includes: The signal input module is configured to receive the neural electrical signals acquired by the ultra-micro array implanted probe; An analog-to-digital converter module, electrically connected to the signal input module, is configured to perform gain amplification, filtering, and digitization processing on the neural electrical signal to obtain the digitized signal. The field-programmable gate array control unit, electrically connected to the analog-to-digital converter module, is configured to perform data preprocessing, data quality monitoring, effective waveform extraction, feature extraction, and real-time decoding on the digitized signal to obtain the motion control signal.

[0009] In one implementation of the first aspect, a cranial fixation device is further included; the cranial fixation device comprises: A fixation frame, disposed on the surface of the patient's skull, is configured to support and position the integrated data processing module at a designated location on the surface of the patient's skull; the fixation frame is provided with mounting holes that are aligned with pre-set holes on the patient's skull; An extension pad, located between the fixation frame and the patient's skull and surrounding the periphery of the mounting hole, is configured to increase the contact area between the fixation frame and the patient's skull. The connector passes sequentially through the mounting holes of the fixation frame and the pre-set holes on the patient's skull to achieve a rigid connection between the fixation frame and the patient's skull; A membrane connection assembly, comprising a biocompatible adhesive material configured to securely attach the fixation device to the patient's skull.

[0010] In one implementation of the first aspect, the data transmission module includes: The signal receiving module is configured to receive the motion control signal via the SPI data transmission protocol; The Bluetooth transmitter module is configured to transmit the motion control signal to the external rehabilitation device in real time via Bluetooth Low Energy technology.

[0011] In one implementation of the first aspect, a power management module is further included, which is electrically connected to the integrated data processing module and the data transmission module respectively, and is configured to provide operating power to the integrated data processing module and the data transmission module.

[0012] Secondly, this application provides a clinical motor control method based on neural electrical signals, comprising: An ultra-micro array implantable probe is implanted into the motor cortex of the patient's brain, and neural electrical signals from the motor cortex are collected through the ultra-micro array implantable probe; the neural electrical signals are analog signals, including single neuron action potential signals and local field potential signals; The neural electrical signals are converted into digital signals by an integrated data processing module, and the digital signals are neurally decoded to obtain motor control signals containing the patient's motor intentions. The motion control signal is transmitted to an external rehabilitation device via a wired interface or wireless communication through a data transmission module, so that the external rehabilitation device can drive the patient's limbs to perform corresponding rehabilitation exercises.

[0013] Thirdly, this application provides a clinical motion control system based on neural electrical signals, comprising: Clinical motor control devices based on neural electrical signals as described in any of the above; An external rehabilitation device, communicatively connected to the clinical motion control device, is configured to receive motion control signals sent by the clinical motion control device and, in response to the motion control signals, drive the patient's limbs to perform corresponding rehabilitation exercises.

[0014] Fourthly, this application provides an electronic device, comprising: A memory configured to store computer programs; A processor configured to execute a computer program stored in the memory to cause the electronic device to perform the clinical motor control method based on neural electrical signals described above.

[0015] Fifthly, this application provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the aforementioned clinical motor control method based on neural electrical signals.

[0016] As described above, the clinical motion control device, method, system, electronic device, and medium described in this application have the following beneficial effects: (1) The use of an ultra-micro array implantation probe can realize large-scale, high-precision, and synchronized monitoring of the neural activity of the cerebral cortex; at the same time, it can effectively reduce implantation risks, reduce surgical trauma and tissue damage, and has good biocompatibility and long-term stability, making it suitable for long-term implantation and continuous use. (2) Through integrated structural design, the device is miniaturized and lightweight; at the same time, the signal transmission path is shortened significantly, effectively suppressing noise coupling and signal attenuation during transmission, significantly improving the signal-to-noise ratio and signal fidelity, thereby comprehensively improving signal quality; (3) By constructing a high-efficiency, low-latency and high-reliability data transmission channel, the real-time and accurate transmission of neural signals between the acquisition end and the execution end is effectively guaranteed, providing key communication support for the realization of mind-driven external rehabilitation equipment control, and significantly improving the response speed, stability and clinical applicability of the brain control system. Attached Figure Description

[0017] Figure 1The diagram shown is a schematic representation of a clinical motor control system based on neural electrical signals according to an embodiment of this application.

[0018] Figure 2 The diagram shows an application scenario of a clinical motion control device based on neural electrical signals, according to an embodiment of this application.

[0019] Figure 3 The diagram shown is a schematic representation of a clinical motor control device based on neural electrical signals according to an embodiment of this application.

[0020] Figure 4 The diagram shown is a structural schematic of an ultra-micro array implantation probe according to an embodiment of this application.

[0021] Figure 5 The diagram shown is a structural schematic of an ultra-micro array implantation probe according to another embodiment of this application.

[0022] Figure 6 The diagram shown is a structural schematic of an integrated data processing module according to an embodiment of this application.

[0023] Figure 7 The diagram shown is a structural schematic of a data transmission module according to an embodiment of this application.

[0024] Figure 8 The diagram shown is a schematic representation of a clinical motor control device based on neural electrical signals, according to another embodiment of this application.

[0025] Figure 9 The diagram shown is a product structure schematic of a clinical motion control device based on neural electrical signals according to an embodiment of this application.

[0026] Figure 10 The diagram shown is a product structure schematic of a clinical motion control device based on neural electrical signals, which is another embodiment of this application.

[0027] Figure 11 The diagram shown is an exploded view of an integrated module according to an embodiment of this application.

[0028] Figure 12 The flowchart shown is a clinical motor control method based on neural electrical signals according to an embodiment of this application.

[0029] Figure 13 The diagram shown is a schematic representation of a clinical motor control system based on neural electrical signals, according to another embodiment of this application.

[0030] Figure 14 The diagram shown is a structural schematic of an electronic device according to an embodiment of this application. Detailed Implementation

[0031] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0032] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0033] The following embodiments of this application provide a clinical motor control device, method, system, electronic device, and medium, which can be applied to neurophysiological scientific research, the development and optimization of brain-computer interface systems, and clinical rehabilitation treatment of patients with motor dysfunction. This technical solution enables large-scale, high-precision, and synchronized monitoring of cerebral cortex neural activity. Through integrated structural design, it improves signal acquisition quality and signal decoding accuracy.

[0034] Please see Figure 1 The image shown is a schematic diagram of a clinical motor control system based on neural electrical signals according to an embodiment of this application.

[0035] like Figure 1 As shown, this application provides a clinical motor control system based on neural electrical signals, the system including a clinical motor control device based on neural electrical signals and an external rehabilitation device.

[0036] Specifically, the external rehabilitation device is communicatively connected to the clinical motion control device and is configured to receive motion control signals sent by the clinical motion control device, and in response to the motion control signals, drive the patient's limbs to perform corresponding rehabilitation exercises.

[0037] Please see Figure 2 The image shown is an application scenario diagram of a clinical motion control device based on neural electrical signals according to an embodiment of this application.

[0038] In this embodiment, the clinical motor control device is used to acquire and capture neural electrical activity signals from the patient's motor cortex in real time and with high throughput, while simultaneously processing the signals in real time. The device supports wireless or wired transmission modes and can transmit effective motor control signals to an external receiver.

[0039] The following will describe in detail the principle and implementation of a clinical motor control device based on neural electrical signals according to this embodiment, so that those skilled in the art can understand this clinical motor control device based on neural electrical signals without creative effort.

[0040] Please see Figure 3 The image shown is a schematic diagram of the structure of a clinical motor control device based on neural electrical signals according to an embodiment of this application.

[0041] like Figure 3 As shown in the figure, this application provides a clinical motion control device based on neural electrical signals. The device includes an ultra-micro array implanted probe, an integrated data processing module, and a data transmission module.

[0042] In one embodiment of this application, the ultra-micro array implantation probe is configured to be implanted into the motor cortex of a patient's brain and to collect the neural electrical signals of the motor cortex.

[0043] The aforementioned microarray implanted probe is a neural signal sensor used to monitor electrical activity at the level of single neurons in the brain. The microarray implanted probe can be positioned in key brain regions closely related to the generation of limb movement intentions, such as the primary motor cortex (M1) or supplementary motor area (SMA), to provide high-quality raw signal input for subsequent processing.

[0044] In one embodiment of this application, the neural electrical signal is an analog signal type, including single neuron action potential (AP) signals and local field potential (LFP) signals. The single neuron action potential signal refers to the action potential generated by a single neuron, which mainly reflects the discrete random firing events of a single neuron. The local field potential signal is a low-frequency electrophysiological signal generated by multiple neighboring neuron groups during synaptic activity, which can reflect the rhythmic activity and functional state changes at the neural network level.

[0045] Please see Figure 4 The image shown is a schematic diagram of the structure of an ultra-micro array implantation probe according to an embodiment of this application.

[0046] like Figure 4 As shown, the ultra-micro array implantation probe includes a microfilament electrode array and a flexible substrate circuit board.

[0047] In this embodiment, the microfilament electrode array includes multiple microfilament electrodes arranged according to a preset rule.

[0048] Please see Figure 5The image shown is a schematic diagram of the structure of an ultra-micro array implantation probe according to another embodiment of this application.

[0049] like Figure 5 As shown, the microfilament electrodes in the microfilament electrode array are arranged at a fixed interval to form a two-dimensional planar array structure. Preferably, the center-to-center distance between adjacent microfilament electrodes is 300 μm. This spacing is set based on a comprehensive consideration of the physiological characteristics of neural tissue and the spatial sampling coverage of the electrodes: the effective signal acquisition radius of a single microfilament electrode is approximately 150 μm, within which the action potentials and local field potentials of a single neuron can be effectively captured. When the distance between adjacent electrodes is 300 μm, their acquisition ranges achieve seamless connection or slight overlap in space, thereby significantly reducing the signal acquisition blind zone and ensuring continuous, high-coverage monitoring of the neuronal population activity in the target brain region.

[0050] It should be noted that the spacing between the microwire electrodes can be flexibly adjusted according to the structural characteristics of the target brain region, the density of neuronal distribution, and specific application requirements. For example, in brain regions with densely packed neurons, a smaller electrode spacing (such as 100 μm or 200 μm) can be used to increase spatial sampling density; while in areas with sparsely distributed neurons or where a large range of projection pathways needs to be covered, a larger spacing (such as 400 μm to 1000 μm) can be used to expand the monitoring range and reduce tissue damage.

[0051] In this embodiment, each of the microwire electrodes is made by twisting and winding multiple ultra-micro alloy electrode wires together.

[0052] For example, four ultra-micro alloy electrode wires with a diameter of 10 μm can be uniformly twisted and wound in a spiral manner to form a single microwire electrode. After twisting, the outer diameter of the microwire electrode is approximately 40 μm, and the length can be customized according to actual application requirements, with a typical length of 10 cm.

[0053] This implementation method achieves extremely small electrode diameter and overall volume while ensuring mechanical strength and conductivity, which is conducive to minimally invasive implantation into brain tissue, significantly reduces invasive damage to brain tissue, and improves implantation safety and biocompatibility.

[0054] In this embodiment, the ultra-micro array implanted probe adopts a multi-channel design to support high-density neural signal acquisition.

[0055] Specifically, the ultra-micro array implantation probe can integrate signal recording channels of different sizes, such as 256 channels, 512 channels, 1024 channels or 1600 channels. The number of channels can be flexibly configured according to the actual application scenario, and typical values ​​include but are not limited to the above range.

[0056] The total number of channels in the microarray implantation probe corresponds to the number of microalloy electrode wires. For example, when using 256 independent microalloy electrode wires, 64 microelectrodes can be integrated by forming one microelectrode with every four electrode wires.

[0057] In this embodiment, the ultra-micro array implantation probe is made of biocompatible materials, which can effectively reduce the immune rejection response of brain tissue after implantation and significantly improve the safety of long-term implantation and the stability of neural signal acquisition.

[0058] Specifically, the ultra-micro alloy electrode wire uses platinum-iridium alloy wire as the conductive core material, with an alloy ratio of 80% platinum and 20% iridium. Platinum-iridium alloy is an approved biosafety material for brain implantation, and its superior biocompatibility, flexibility, corrosion resistance, and conductivity make it a key conductive material in brain-computer interface medical devices and neural electrodes.

[0059] To improve the biocompatibility of the microfilament electrode and prevent leakage, a medical-grade polyimide (PI) insulating coating with a thickness of approximately 2 μm can be applied to the outer periphery of the ultra-micro alloy electrode wire. Polyimide, as a high-performance medical polymer material, possesses excellent biocompatibility, is non-toxic and non-irritating to human tissue, and can effectively reduce immune rejection reactions caused by implantation. Simultaneously, this material exhibits excellent tensile strength and mechanical toughness, enabling it to withstand the complex physiological stress environment within the body, ensuring that the microfilament electrode maintains structural integrity and electrical insulation over a long period in dynamic brain tissue.

[0060] To further enhance the biocompatibility and long-term neural signal acquisition stability of ultra-micro alloy electrode wires, a layer of spider silk protein can be coated onto the polyimide coating surface. Spider silk protein, as a natural multifunctional biomaterial, possesses excellent biocompatibility, controllable biodegradability, outstanding mechanical strength, and stable physicochemical properties. Applying it to the insulating layer surface of implantable neural electrodes can not only further reduce immune responses and glial scar formation but also promote the integration and repair of local neural tissue, thereby significantly improving the stability and signal-to-noise ratio of long-term signal recording after implantation.

[0061] Spider silk protein coatings can be achieved through a specific solution coating process, ensuring uniform coverage of the outer surface of the microfilament electrode. After coating, curing and cross-linking treatments are performed to form a dense and firmly bonded composite coating, effectively enhancing its chemical stability and mechanical durability in physiological environments, preventing coating peeling or excessive degradation, and ensuring the long-term functional reliability of the electrode in vivo.

[0062] In one embodiment of this application, the flexible substrate circuit board is configured to provide mechanical support and structural protection for the microfilament electrode array.

[0063] In this embodiment, the microfilament electrode array is attached to the flexible substrate circuit board and electrically connected to the flexible substrate circuit board.

[0064] Specifically, multiple microfilament electrodes are integrated onto the flexible substrate circuit board in a predetermined arrangement to form a three-dimensional high-density electrode array. This three-dimensional structure enables multi-layer, multi-point neural signal acquisition within a limited brain region, significantly increasing the channel density per unit volume. This design allows the high-throughput microfilament electrode array to simultaneously record the electrical activity of multiple neuronal nodes from the same neural network within the same target brain region, thereby acquiring spatially highly correlated and temporally precisely aligned neural signal data, providing a richer and more comprehensive information foundation for achieving high-precision neural decoding.

[0065] In this embodiment, the flexible substrate circuit board not only serves as a support and interconnection carrier for the microfilament electrodes, but also plays a crucial role in terms of electrical performance, biocompatibility, and chemical stability, specifically in the following four aspects: (1) Electrical insulation function: The flexible substrate uses high-performance polyimide (PI) and other high-quality insulating materials, which can effectively realize electrical isolation between each electrode channel, suppress crosstalk and current leakage, and ensure high-fidelity transmission and acquisition accuracy of neural signals; (2) Mechanical protection function: The substrate structure provides physical support and protection for the microwire electrode, resisting mechanical stress, tissue movement or external disturbance that may be encountered during implantation and long-term service in the body, preventing electrode breakage or connection failure, and improving the robustness of the overall structure. (3) Biocompatibility assurance: The selected materials have excellent biocompatibility. Long-term placement in the brain tissue environment is not likely to trigger significant immune response or tissue rejection. At the same time, it is conducive to the integration and repair of neural tissue at the interface and promotes the stable coexistence of the implant and the host tissue. (4) Chemical stability support: The substrate material has good corrosion resistance and can resist the long-term erosion of cerebrospinal fluid, enzymes and other bioactive substances, ensuring that the probe maintains structural integrity and functional stability in complex physiological environments, and meeting the stringent requirements of clinical implantable devices for long-term reliability.

[0066] In this embodiment, the flexible substrate circuit board is made of a polymer material.

[0067] For example, the polymer material can be polyvinyl alcohol (PVA). PVA is a water-soluble polymer with excellent film-forming properties, good chemical stability, and outstanding flexibility. It can form transparent, uniform, and mechanically strong thin film structures, is easy to process, and is compatible with micro- and nano-manufacturing processes, thus having a broad application basis in the biomedical field. Based on the excellent physical and chemical properties of PVA, as a substrate material for ultra-micro array implantation probes, it can not only provide stable structural support and electrical insulation platform for multi-channel microfilament electrodes, but also significantly improve the overall biocompatibility of the device. In addition, the PVA surface is conducive to cell adhesion and growth, which can promote the integration of neural tissue and the implantation interface, accelerate the healing and regeneration of local tissues, thereby improving the host response after long-term implantation and enhancing the stability and reliability of neural signal acquisition.

[0068] In this embodiment, the microfilament electrode array is electrically connected to the flexible substrate circuit board.

[0069] Specifically, this application employs a multi-point feedthrough connection technology to precisely connect each micro-electrode wire to a corresponding conductive line in the substrate circuit board. This connection method not only ensures efficient conduction of high-density, multi-channel signals, meeting the needs of high-throughput neural signal acquisition, but also significantly improves the mechanical stability and long-term reliability of the electrical connection by optimizing the contact interface and connection structure, effectively reducing the risk of signal failure due to loosening, breakage, or poor contact.

[0070] In this implementation, an ultra-micro array implantation probe is used, which can achieve large-scale, high-precision, and synchronized monitoring of neural activity in the cerebral cortex. At the same time, it effectively reduces implantation risks, reduces surgical trauma and tissue damage, and has good biocompatibility and long-term stability, making it suitable for long-term implantation and continuous use.

[0071] In one embodiment of this application, the integrated data processing module is electrically connected to the ultra-micro array implanted probe and is configured to convert the neural electrical signal into a digital signal and perform neural decoding on the digital signal to obtain a motion control signal containing the patient's motion intention.

[0072] Please see Figure 6 The diagram shown is a structural schematic of an integrated data processing module according to an embodiment of this application.

[0073] like Figure 6 As shown, the integrated data processing module includes a signal input module, an analog-to-digital conversion module, and a field-programmable gate array (FPGA) control unit.

[0074] Specifically, the signal input module is configured to receive the neural electrical signals acquired by the ultra-micro array implanted probe.

[0075] The signal input module is equipped with a dedicated reference channel for inputting a reference signal. This reference signal is used to perform differential operations with the neural electrical signals acquired by each channel in the ultra-micro array implanted probe to effectively eliminate common-mode interference such as environmental noise, power fluctuations, and biological background noise, significantly improve the signal-to-noise ratio, and ensure the high fidelity of the original neural signal.

[0076] In this embodiment, the analog-to-digital conversion module is electrically connected to the signal input module and is configured to perform gain amplification, filtering, and digitization processing on the neural electrical signal to obtain the digitized signal.

[0077] Specifically, this application can amplify the neural electrical signal using a low-noise operational amplifier to effectively increase the signal amplitude, improve the system signal-to-noise ratio, avoid quantization resolution loss due to insufficient signal size, or clipping distortion caused by excessive signal size, and ensure signal integrity.

[0078] This application utilizes active or passive filtering circuits to filter the amplified signal, effectively suppressing power frequency interference, electromagnetic noise, and non-physiologically relevant high-frequency components. Filtering not only further improves the signal-to-noise ratio but also smooths the signal waveform, reduces the disturbance of high-frequency noise to the sampling process, and improves conversion accuracy.

[0079] This application enables the digitization of a signal that has undergone gain amplification and filtering via an analog-to-digital converter (ADC). The ADC converts a continuous-time, continuous-amplitude analog signal into a discrete-time, discrete-amplitude digital signal through three steps: sampling, quantization, and encoding.

[0080] In this embodiment, the field-programmable gate array control unit is electrically connected to the analog-to-digital converter module and is configured to perform data preprocessing, data quality monitoring, effective waveform extraction, feature extraction, and real-time decoding on the digitized signal to obtain the motion control signal.

[0081] FPGAs offer flexibility and parallel processing capabilities, enabling them to meet the real-time processing requirements of high-throughput neural signals. They support low-latency data reception, synchronous processing, and control command output, satisfying the system's stringent requirements for real-time performance and reliability.

[0082] Specifically, the digitized signal output by the ADC is transmitted to the FPGA via a parallel or serial data bus. This signal contains three key data types: data bits, a clock signal, and a valid signal. The data bits represent the digitized neural signal after ADC conversion, typically output in two's complement format, and their bit width determines the quantization accuracy. The clock signal is used to synchronize data transmission between the ADC and the FPGA, ensuring that the FPGA accurately captures data at the correct clock edge and preventing sampling misalignment. The valid signal indicates whether the data output on the current data bus is valid, enabling the FPGA to determine the data readiness status and achieve precise synchronous reading and buffer management.

[0083] The FPGA performs data preprocessing on the digitized signal, including denoising the digitized signal using a built-in digital filter. The digital filter can be flexibly configured as a low-pass, high-pass, or band-pass filter according to application requirements, effectively suppressing residual high-frequency noise, baseline drift, and power frequency interference, thereby improving the signal-to-noise ratio and fidelity.

[0084] The FPGA performs data quality monitoring on the digitized signal, including checking data validity, valid data range, data integrity, and outlier detection. This monitoring mechanism not only significantly improves the reliability and trustworthiness of neural signal data but also promptly detects potential problems such as poor electrode contact, hardware failure, or environmental interference, triggering alarms or adaptive compensation mechanisms to ensure the long-term stability and security of the system.

[0085] The FPGA performs effective waveform extraction on the digital signal by employing peak detection, zero-crossing detection, and envelope extraction algorithms to quickly and accurately identify and extract physiologically significant effective action potential waveform signals from the digital signal.

[0086] The FPGA performs feature extraction on the digital signal, including extracting basic statistical features, frequency domain features, time domain features, and higher-order statistical features. The basic statistical features include mean, variance, and standard deviation.

[0087] The real-time decoding of the digital signal by the FPGA includes: performing online classification or regression operations on the feature vectors extracted from the digital signal using a lightweight machine learning decoding model deployed in the FPGA, and outputting the corresponding motion control signal.

[0088] In this embodiment, the shell structure design of the integrated data processing module takes into account factors such as biocompatibility, mechanical stability, electrical isolation, protective performance, and the comfort of the subject after implantation.

[0089] Specifically, the outer shell is made of medical-grade titanium alloy to ensure biocompatibility under long-term implantation conditions. Its inner wall is covered with a thermal insulation material to effectively isolate the internal electronic components from the effects of body temperature fluctuations and the humid intracranial environment. The module features a compact design with an overall diameter of only 2 cm, facilitating neurosurgical implantation and minimizing pressure on intracranial tissues. Six titanium alloy ring structures are integrated at the bottom of the shell for precise matching with the skull, ensuring a tight and reliable fit between the device and the skull surface. The internal structure adopts a modular layout, with functional modules such as the analog-to-digital converter and the field-programmable gate array control unit arranged in separate zones, improving assembly efficiency and ease of maintenance. Simultaneously, a built-in heat conduction channel is designed to effectively conduct and dissipate heat, ensuring the stability of electronic components during long-term operation. Furthermore, the shell joints utilize highly sealing materials and processes to effectively prevent the infiltration of cerebrospinal fluid and other bodily fluids, ensuring long-term reliable system operation.

[0090] In this implementation, the device is miniaturized and lightweight through integrated structural design; at the same time, the analog signal transmission path is significantly shortened, noise coupling and signal attenuation during transmission are effectively suppressed, and the signal-to-noise ratio and signal fidelity are significantly improved, thereby comprehensively improving signal quality.

[0091] In one embodiment of this application, the clinical motor control device based on neural electrical signals further includes a cranial fixation device. The cranial fixation device includes a fixation frame, an expansion pad, a connector, and a membrane connection assembly.

[0092] In this embodiment, the fixation frame is disposed on the surface of the patient's skull and is configured to support and position the integrated data processing module at a designated position on the surface of the patient's skull; the fixation frame is provided with mounting holes, which are aligned with preset holes on the patient's skull.

[0093] Specifically, the fixation frame is made of medical-grade titanium alloy, which can effectively reduce foreign body reactions, avoid magnetic resonance imaging artifacts, and support long-term in-body placement after surgery.

[0094] The expansion pad is located between the fixation frame and the patient's skull and is arranged around the periphery of the mounting hole. It is configured to increase the contact area between the fixation frame and the patient's skull, while also cushioning the pressure of the fixation frame on the patient's skull, reducing the risk of local stress concentration, and preventing bone resorption or skin breakdown. It is especially suitable for patients with thin skulls or weak postoperative healing ability.

[0095] The connector passes sequentially through the mounting holes of the fixation frame and the pre-set holes on the patient's skull, thereby achieving a rigid connection between the fixation frame and the patient's skull.

[0096] Specifically, the connector is a medical-grade fastening element, such as a craniotomy screw. With the screw initially screwed in but not fully tightened, the position and tilt angle of the integrated data processing module carried by the fixation frame can be finely adjusted to accommodate differences in skull curvature and scalp thickness among different patients, thereby ensuring device fit and implantation safety.

[0097] The membrane connection assembly includes a biocompatible adhesive material configured to securely attach the fixation device to the patient's skull.

[0098] Specifically, the biocompatible adhesive material can be a medical-grade adhesive or removable tape, possessing good adhesion properties and causing no tissue irritation or trauma, making it suitable for long-term or short-term implantation scenarios. Furthermore, the component integrates biocompatible fiber materials, which work synergistically with the fixation frame to enhance the overall fixation strength and long-term stability of the integrated data processing module, preventing displacement caused by micro-movements or external forces.

[0099] In one embodiment of this application, the data transmission module is communicatively connected to the integrated data processing module and is configured to transmit the motion control signal to an external rehabilitation device via a wired interface or wireless communication, so that the external rehabilitation device can drive the patient's limbs to perform corresponding rehabilitation exercises.

[0100] Please see Figure 7 The diagram shown is a structural schematic of a data transmission module according to an embodiment of this application.

[0101] like Figure 7 As shown, the data transmission module includes a signal receiving module and a Bluetooth transmitting module.

[0102] Specifically, the signal receiving module is configured to receive the motion control signal via the SPI data transmission protocol. The received motion control signal is temporarily stored in the buffer memory of the receiving end for subsequent wireless transmission.

[0103] It should be noted that reliable establishment of SPI communication depends on the correct clock polarity and phase configuration, and requires accurate hardware connection and pin assignment of signal lines such as Master-Out-Slave-In (MOSI), Master-In-Slave-Out (MISO), Serial Clock (SCK), and Chip Select (SS).

[0104] In this embodiment, the Bluetooth transmitting module is configured to transmit the motion control signal to the external rehabilitation device in real time via Bluetooth Low Energy (BLE) technology.

[0105] Specifically, the Bluetooth transmitting module integrates a high-performance BLE communication module, supports Bluetooth 5.0 or higher communication standards, significantly improves data throughput per unit time, and effectively meets the needs of high-channel neural signal acquisition systems for high-bandwidth, low-latency data transmission.

[0106] In this embodiment, the external rehabilitation device includes wearable exoskeleton walkers and smart prostheses, etc.

[0107] In other embodiments, the motion control signal may not directly drive the rehabilitation device, but may be transmitted to an external receiving terminal via a wireless or wired communication link for further analysis, storage, or remote monitoring.

[0108] Specifically, the external receiving terminals include dedicated signal receivers (such as PandaControl receivers), local servers, personal computers (PCs), mobile terminals (such as tablets or smartphones), and cloud data platforms.

[0109] This implementation method effectively ensures the real-time and accurate transmission of neural signals between the acquisition end and the execution end by constructing a high-efficiency, low-latency, and high-reliability data transmission channel. It provides key communication support for realizing mind-driven control of external rehabilitation equipment and significantly improves the response speed, stability, and clinical applicability of the brain control system.

[0110] Please see Figure 8 The image shown is a schematic diagram of a clinical motor control device based on neural electrical signals according to another embodiment of this application.

[0111] like Figure 8 As shown, the clinical motion control device based on neural electrical signals described in this application also includes a power management module.

[0112] In this embodiment, the power management module is electrically connected to both the integrated data processing module and the data transmission module, and is configured to provide operating power to both the integrated data processing module and the data transmission module.

[0113] Specifically, the power management module uses wireless power supply technology to replace traditional rechargeable batteries or disposable batteries that need to be replaced periodically, avoiding the risk of secondary surgery due to battery life limitations and significantly improving clinical applicability.

[0114] The wireless power supply technology is based on the principle of magnetic resonance coupling (MRC), enabling efficient energy transfer, power conversion, and power consumption management in long-term implantation applications. The magnetic resonance coupling includes the following key aspects:

[0115] (1) Fully utilize the zero-resistance characteristics of superconducting materials to significantly improve the efficiency and stability of wireless power transmission. Specifically, the power management module of this application selects barium titanium niobate as the superconducting material; uses low-loss piezoelectric ceramic as the insulating layer to effectively reduce dielectric loss during energy coupling; and uses an extremely thin copper film as the conductor structure to minimize conductive loss while taking into account the mechanical strength and structural reliability of the material.

[0116] (2) A resonant circuit design is adopted. By precisely tuning the resonant frequency, the energy transfer efficiency and system operation stability are maximized, thereby effectively supporting efficient energy collection and conversion. Specifically, multi-layer copper material is used to construct the induction coil to reduce the loss caused by the skin effect and improve the quality factor. At the same time, a variable capacitor and an inductor regulator are integrated in the tuning circuit to achieve dynamic matching and adaptive adjustment of the frequency.

[0117] (3) A high-voltage supercapacitor is constructed using a high specific surface area composite material based on graphene and nanofiber to achieve rapid storage and instantaneous buffering of wireless received energy; a lithium polymer battery is cascaded after the supercapacitor to provide continuous and stable long-term power supply by utilizing its high energy density characteristics.

[0118] (4) A liquid nitrogen circulation cooling system is adopted to provide a stable and efficient low-temperature working environment for high-temperature superconducting components, thereby maintaining the high-performance operation of the system; the external packaging is made of biocompatible materials to ensure that it can resist the erosion and damage of the internal environment.

[0119] (5) Adopt medical-grade standardized connectors to ensure a safe, reliable and plug-and-play physical connection between the power management module and other functional modules, thereby improving the system’s scalability and ease of maintenance. At the same time, integrate a wireless communication module that supports medical communication protocols (such as BLE for Medical) to enable secure data transmission, remote parameter configuration and status monitoring with external monitoring equipment or medical networks, thereby ensuring the system’s interoperability and compliance in the clinical environment.

[0120] In the embodiments of this application, the clinical motion control device based on neural electrical signals can have various product forms and modular architecture designs according to actual application scenarios, manufacturing processes and clinical needs.

[0121] Please see Figure 9 The image shown is a schematic diagram of the product structure of a clinical motion control device based on neural electrical signals according to an embodiment of this application. Figure 9 As shown, the ultra-micro array implanted probe and the integrated data processing module constitute a separate dual-module structure. The two modules are electrically connected and communicate with each other via flexible multi-channel leads or micro connectors.

[0122] Please see Figure 10 The image shown is a schematic diagram of the product structure of a clinical motion control device based on neural electrical signals, according to another embodiment of this application. Figure 10 As shown, the ultra-micro array implanted probe and the integrated data processing module are integrated into the same physical package to form an integrated module.

[0123] Please see Figure 11 The diagram shown is an exploded view of an integrated module according to an embodiment of this application. Figure 11 The composition and assembly relationship of the ultra-micro array implanted probe and the integrated data processing module are demonstrated.

[0124] It should be noted that the above two product forms are merely illustrative examples, and the technical solutions protected by this application cover all reasonable variations in function implementation, module layout, and integration level.

[0125] Please see Figure 12 The above is a flowchart of a clinical motor control method based on neural electrical signals according to an embodiment of this application.

[0126] like Figure 12 As shown in the figure, this application provides a clinical motor control method based on neural electrical signals, the method including the following steps S100 to S300.

[0127] In step S100, an ultra-micro array implantation probe is implanted into the motor cortex of the patient's brain, and neural electrical signals from the motor cortex are collected through the ultra-micro array implantation probe.

[0128] Specifically, the neural electrical signals are analog signals, including single neuron electrical activity signals and local field potential signals.

[0129] In step S200, the neural electrical signal is converted into a digital signal by an integrated data processing module, and the digital signal is neurally decoded to obtain a motor control signal containing the patient's motor intention.

[0130] In step S300, the motion control signal is transmitted to an external rehabilitation device via a wired interface or wireless communication through a data transmission module, so that the external rehabilitation device can drive the patient's limbs to perform corresponding rehabilitation exercises.

[0131] It should be noted that the specific implementation of the clinical motor control method based on neural electrical signals described in this embodiment has been described in detail in the aforementioned related embodiments, so it will not be repeated here.

[0132] The scope of protection of the clinical motor control method based on neural electrical signals described in this application is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this application is included within the scope of protection of this application.

[0133] Please see Figure 13 The diagram shown is a schematic representation of a clinical motor control system based on neural electrical signals, according to another embodiment of this application.

[0134] like Figure 13 As shown, this application provides a clinical motion control system based on neural electrical signals, including a clinical motion control device based on neural electrical signals and an external rehabilitation device as described in any of the above claims.

[0135] In this embodiment, the structural design and working principle of the core components of the clinical motion control device, such as the ultra-micro array implanted probe, the integrated data processing module, and the data transmission module, as well as the configuration and control mechanism of the external rehabilitation equipment, have been described in detail in the foregoing embodiments and will not be repeated here.

[0136] This application utilizes minimally invasive neurosurgical techniques for probe implantation. This minimally invasive implantation method precisely places the probe into the target nerve region through a tiny surgical window, minimizing mechanical damage and physiological disturbance to surrounding tissues. This technology not only significantly reduces the risk of intraoperative trauma and postoperative complications but also helps shorten the patient's recovery period, improving the safety and tolerability of the surgery. Furthermore, the integration of high-precision navigation and image-guided technology further ensures the accuracy and long-term stability of probe implantation.

[0137] Specifically, the minimally invasive implantation method includes the following steps:

[0138] (1) Preoperative planning. Specifically, high-resolution imaging technology (such as MRI, CT or ultrasound) is used to reconstruct and accurately assess the patient's neuroanatomical structure in three dimensions, clarify the location, morphology and adjacent relationships of the target nerve region; combined with individualized physiological and pathological characteristics, a customized implantation path plan is formulated to determine the implantation site, needle angle, insertion depth and spatial orientation of the probe, to ensure accurate targeting and provide a reliable basis for the accuracy and safety of subsequent surgery.

[0139] (2) Establishing a surgical window. Local anesthesia is administered to the patient to effectively reduce intraoperative discomfort and improve surgical tolerance. Strict aseptic techniques are followed, and the surgical area is thoroughly disinfected and draped to minimize the risk of infection. Under image-guided guidance, a small bone window or puncture channel, approximately 1 cm in diameter, is drilled at the target location on the scalp using precision minimally invasive surgical instruments (such as puncture needles and micro-cutters). This tiny incision significantly reduces trauma to superficial tissues, effectively controls intraoperative bleeding, and promotes rapid postoperative healing, shortening the recovery period and fully demonstrating the advantages of minimally invasive surgery.

[0140] (3) Probe Implantation. Specifically, through the established surgical window, the probe is gently and controllably introduced into the target neural region using a dedicated catheter or guide. The probe can be advanced manually with precision or mechanically with robot assistance to ensure stability and controllability during insertion and avoid mechanical damage to surrounding brain tissue or neural structures. During this process, real-time image-guided technology (such as ultrasound, CT navigation, or MRI navigation) is used to dynamically monitor the probe's path, spatial position, and implantation angle. The navigation system also provides real-time feedback on the probe's three-dimensional coordinates and attitude information to ensure it accurately reaches the predetermined depth and direction, achieving high-precision targeted implantation and guaranteeing the effectiveness and long-term stability of signal acquisition.

[0141] (4) Fixing the probe. Once the probe has accurately reached the target position, the external part of the probe is firmly fixed using medical-grade biocompatible materials (such as medical bio-adhesive, absorbable sutures, or miniature fixation nails) to ensure its stability during the patient's subsequent daily activities or rehabilitation training, and to avoid affecting the signal acquisition quality or causing tissue damage due to minor displacement. After fixation, the implantation channel of the probe is carefully sutured, and the scalp incision is aseptically bandaged to complete the surgical procedure.

[0142] (5) Postoperative monitoring. Close monitoring of the patient is required after surgery, with a focus on observing the stability of vital signs and the healing of the surgical area, and preventing complications such as infection. At the same time, the function of the implanted probe needs to be initially verified through a dedicated software system to confirm that it can stably and reliably collect electrical signals from the target nerve area and establish a normal communication connection with external rehabilitation equipment, in order to prepare for subsequent clinical applications.

[0143] In the brain-computer interface laboratory, the technical solution of this application has been validated on various animal models, including mice, rats, rabbits, large animals, and non-human primates, comprehensively evaluating its performance under different physiological conditions. Furthermore, in a hospital setting, this application has been applied to clinical trials in specific populations, including volunteers with high-level paraplegia and patients with specific neurological diseases, directly verifying its safety and effectiveness in real-world clinical scenarios.

[0144] Experimental data show that the implantation success rate of this application exceeds 95%, the effective acquisition rate of neural electrical signals exceeds 70%, and it can achieve stable, long-term signal acquisition for more than one year, fully demonstrating its reliability and durability. Regarding wireless data transmission, even in complex environments with multiple obstacles, the system latency of this application remains consistently below 50 milliseconds, fully meeting the stringent real-time requirements of brain-computer interface applications. Compared with other existing technologies, this application has achieved excellent experimental verification results in key indicators such as signal clarity, transmission stability, implantation success rate, acquisition efficiency, and real-time performance, demonstrating significant technical advantages and clinical application potential.

[0145] The clinical motor control devices, methods, systems, electronic devices, and media described in this application not only help improve patients' treatment outcomes and quality of life but also directly promote the overall optimization of the social healthcare system. By achieving more efficient treatment, hospitalization and rehabilitation cycles are significantly shortened, and long-term medical and nursing costs are reduced. Simultaneously, this technology drives innovation in the fields of neuroscience and bioengineering, accelerates the commercialization of related technologies, promotes industrial chain upgrades, and creates new employment opportunities and market demand, resulting in significant social and economic benefits. This application provides crucial support for the development of implantable high-precision brain-controlled technology, powerfully promotes in-depth neuroscience research, accelerates the innovation and upgrading of related medical devices, and significantly improves the clinical effectiveness and implementation efficiency of neuromodulation therapy.

[0146] Please see Figure 14 The image shown is a schematic diagram of the structure of an electronic device according to an embodiment of this application.

[0147] like Figure 14 As shown in the figure, an embodiment of this application provides an electronic device, including a memory and a processor.

[0148] Specifically, the memory is configured to store a computer program; the processor is configured to execute the computer program stored in the memory to cause the electronic device to perform the clinical motor control method based on neural electrical signals described above.

[0149] In some embodiments, the processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0150] In some embodiments, the memory may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0151] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units 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 of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0152] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0153] Those skilled in the art will further 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, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. 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.

[0154] This application also provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the aforementioned clinical motor control method based on neural electrical signals. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The aforementioned storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0155] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0156] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A clinical motion control device based on neural electrical signals, characterized in that, include: An ultra-micro array implantation probe is configured to be implanted into the motor cortex of a patient's brain and to collect neural electrical signals from the motor cortex; the neural electrical signals are analog signals, including single neuron action potential signals and local field potential signals; An integrated data processing module, electrically connected to the ultra-micro array implanted probe, is configured to convert the neural electrical signals into digital signals and perform neural decoding on the digital signals to obtain motion control signals containing the patient's motion intentions; The data transmission module is communicatively connected to the integrated data processing module and is configured to transmit the motion control signal to an external rehabilitation device via a wired interface or wireless communication, so that the external rehabilitation device can drive the patient's limbs to perform corresponding rehabilitation exercises. The aforementioned ultra-micro array implantation probe includes: The microfilament electrode array includes multiple microfilament electrodes arranged according to a preset rule, each of which is made by twisting and winding multiple ultra-micro alloy electrode wires. A flexible substrate circuit board is configured to provide mechanical support and structural protection for the microfilament electrode array; the microfilament electrode array is attached to the flexible substrate circuit board and electrically connected to the flexible substrate circuit board.

2. The apparatus according to claim 1, characterized in that, The integrated data processing module includes: The signal input module is configured to receive the neural electrical signals acquired by the ultra-micro array implanted probe; An analog-to-digital converter module, electrically connected to the signal input module, is configured to perform gain amplification, filtering, and digitization processing on the neural electrical signal to obtain the digitized signal. The field-programmable gate array control unit, electrically connected to the analog-to-digital converter module, is configured to perform data preprocessing, data quality monitoring, effective waveform extraction, feature extraction, and real-time decoding on the digitized signal to obtain the motion control signal.

3. The apparatus according to claim 1, characterized in that, It also includes a cranial fixation device; the cranial fixation device includes: A fixation frame, disposed on the surface of the patient's skull, is configured to support and position the integrated data processing module at a designated location on the surface of the patient's skull; the fixation frame is provided with mounting holes that are aligned with pre-set holes on the patient's skull; An extension pad, located between the fixation frame and the patient's skull and surrounding the periphery of the mounting hole, is configured to increase the contact area between the fixation frame and the patient's skull. The connector passes sequentially through the mounting holes of the fixation frame and the pre-set holes on the patient's skull to achieve a rigid connection between the fixation frame and the patient's skull; A membrane connection assembly, comprising a biocompatible adhesive material configured to securely attach the fixation device to the patient's skull.

4. The apparatus according to claim 1, characterized in that, The data transmission module includes: The signal receiving module is configured to receive the motion control signal via the SPI data transmission protocol; The Bluetooth transmitter module is configured to transmit the motion control signal to the external rehabilitation device in real time via Bluetooth Low Energy technology.

5. The apparatus according to claim 1, characterized in that, It also includes a power management module, which is electrically connected to the integrated data processing module and the data transmission module respectively, and is configured to provide operating power to the integrated data processing module and the data transmission module.

6. A clinical motor control method based on neural electrical signals, characterized in that, include: The patient's motor cortex was acquired by implanting a probe using an ultra-micro array; the neural electrical signals were analog signals, including single neuron action potential signals and local field potential signals. The neural electrical signals are converted into digital signals by an integrated data processing module, and the digital signals are neurally decoded to obtain motor control signals containing the patient's motor intentions. The motion control signal is transmitted to an external rehabilitation device via a wired interface or wireless communication through a data transmission module, so that the external rehabilitation device can drive the patient's limbs to perform corresponding rehabilitation exercises.

7. A clinical motion control system based on neural electrical signals, characterized in that, include: A clinical motion control device based on neural electrical signals as described in any one of claims 1 to 5; An external rehabilitation device, communicatively connected to the clinical motion control device, is configured to receive motion control signals sent by the clinical motion control device and, in response to the motion control signals, drive the patient's limbs to perform corresponding rehabilitation exercises.

8. An electronic device, characterized in that, include: A memory configured to store computer programs; A processor configured to execute a computer program stored in the memory to cause the electronic device to perform the clinical motor control method based on neural electrical signals as described in claim 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the clinical motor control method based on neural electrical signals as described in claim 6.

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