Microsurgery mechanical arm high-precision hand tremor elimination man-machine cooperative control method integrated with electroencephalogram intention prediction

Abstract

The invention discloses a microsurgery mechanical arm high-precision hand tremor elimination man-machine cooperative control method integrated with electroencephalogram intention prediction, and relates to the technical field of medical robots and surgery auxiliary systems. Electroencephalogram signals and hand action data of doctors are synchronously collected and input into a pre-trained micro-world model, so that the human-machine cooperative control method for eliminating the high-precision hand tremor of the doctors is achieved; a real operation intention vector is generated in a submerged space, and a hand trembling trajectory and an ideal operation trajectory are predicted. The high tremor risk state is judged by calculating emotion and cognitive indexes, in combination with intention deviation and tremor data, efficient elimination of hand tremor is achieved through driving force feedback and a reverse anti-phase compensation mechanism of a mechanical arm controller, and an ideal operation track is executed. The system further comprises an abnormal state security protocol, model federal learning evolution and a data security destruction mechanism. According to the invention, advanced and accurate man-machine cooperative control is realized, and the stability and precision of microsurgery are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of medical robots and surgical assistance systems, specifically to a high-precision human-machine collaborative control method for eliminating hand tremors in a microsurgical robotic arm that integrates EEG intention prediction. Background Technology

[0002] Microsurgery, as an important branch of modern surgical medicine, especially in ophthalmology, neurosurgery, and vascular anastomosis, places extremely high demands on the precision, stability, and dexterity of surgical procedures. The success of a surgery hinges on the surgeon's millimeter-level control. However, inherent limitations in human physiology constitute a barrier that current technology struggles to overcome. The most prominent and widespread problem is physiological tremor, or "shaking hand." This high-frequency, minute tremor, caused by muscle micro-twitches, fatigue, tension, and even subconscious nerve signal interference, may have limited impact in macroscopic surgery, but under a microscope magnified dozens of times, it can lead to serious consequences such as damage to delicate tissue structures, vascular rupture, or failed sutures. Current technologies primarily rely on passive mechanical filtering or active compensation systems based on inertial sensors to suppress shaking hand. The former mechanically smooths all hand movement signals through a low-pass filter, but its drawback is its inability to distinguish between intentional, precise manipulation and unconscious tremors, often resulting in sluggish operation, stiffened tactile feedback, and severely sacrificing surgical dexterity and real-time responsiveness. Although the latter can detect jitter and perform reverse motion compensation through gyroscopes, its compensation is based on the physical motion that has already occurred, and there is an inherent delay in detection and execution. It also cannot predict jitter trends, and its compensation effect is limited for complex, high-frequency tremor patterns. Furthermore, it cannot fundamentally understand the doctor's surgical intentions, and it is a "stopgap" treatment that does not address the root cause.

[0003] On the other hand, while brain-computer interface (BCI) technology has made progress in rehabilitation and control, its application in intraoperative real-time control, which demands stringent reliability and real-time performance, still faces significant challenges. Traditional EEG signal decoding technologies primarily focus on discrete command recognition (such as clicking and selecting), and their accuracy and reliability are severely insufficient for predicting continuous, high-precision motor intentions, especially predicting future trajectories hundreds of milliseconds in advance. Signals are susceptible to interference from the complex electromagnetic environment of the operating room, fluctuations in the physician's cognitive state, and the trade-off between the comfort of the acquisition equipment and signal quality. Existing systems often separate EEG decoding from motor control modules, failing to achieve end-to-end, low-latency collaborative closed-loop from neural intention to instrument movement. Furthermore, the physician's cognitive load and psychological state (such as frustration and stress) are key triggers for exacerbating physiological tremors, but existing systems lack quantitative assessment and dynamic response mechanisms for these hidden risk factors.

[0004] From a safety and ethical perspective, existing systems typically lack multi-layered, intelligent safety boundary designs. Failure of the compensation system often directly leads to a sharp increase in surgical risks, while lacking a forceful safety circuit breaker mechanism for extreme situations. Furthermore, when processing sensitive physiological data such as EEG data, existing solutions are weak in data privacy protection, localization, and prevention of data leakage, failing to meet increasingly stringent medical data security regulations and ethical requirements. The systems also have limitations in adaptability, unable to operate independently for extended periods in extreme environments without stable network support (such as battlefields or deep space), and lack mechanisms to assist and promote the long-term skill development of physicians, potentially leading to over-reliance on auxiliary systems. Therefore, developing an intelligent human-machine collaborative control system that deeply integrates high-precision neural intent prediction, prospective tremor modeling, and psychological state perception, while possessing high security, strong adaptability, and privacy protection capabilities, has become an urgent technological need to overcome the bottlenecks in the precision and safety of microsurgery. Summary of the Invention

[0005] The purpose of this invention is to provide a high-precision human-machine collaborative control method for eliminating hand tremors in a microsurgical robotic arm that integrates brain-computer interface for intention prediction. This method predicts the surgeon's intention in real time and identifies hand tremors through a brain-computer interface, driving the robotic arm to collaboratively complete high-precision and stable operations, thereby significantly improving the accuracy, safety and intelligence of the surgery.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a high-precision human-machine collaborative control method for eliminating hand tremors in a microsurgical robotic arm integrating EEG intention prediction, comprising the following steps: (1) The 128-channel dry electrode EEG device and XR glasses, which are integrated with the microsurgical robotic arm system during the operation, synchronously and in real time collect the EEG signals and hand micro-movement data of the surgeon, providing multimodal input for the prediction of intention and tremor hand. (2) Input the EEG signals and hand movement data collected in step (1) into a pre-trained micro-world model specifically designed for microsurgical operation scenarios. This model is based on the Yann LeCun joint embedding prediction architecture, which integrates multimodal information in the model latent space and generates an intent vector that represents the doctor's true surgical intention. This intent prediction has a lead time of 0.12-0.38 seconds. (3) While generating the intention vector, the micro-world model simultaneously predicts the potential physiological tremor trajectory of the doctor's hand within the next 0.3-second time window, as well as the ideal master-level tremor-free operation trajectory in the current surgical situation, which is learned based on a large amount of master-level surgical data. (4) Based on real-time collected EEG and physiological data, calculate the frustration component in the emotional value function used to quantify the feeling of surgical frustration, as well as the cognitive load index reflecting the doctor's current mental exertion level; when the calculated frustration value is greater than the preset threshold of 0.70, or the cognitive load value is greater than the preset threshold of 0.88, the system determines that the doctor is currently in a high-risk state of tremor. (5) Compare the intention vector obtained in step (2) with the master-level trajectory predicted in step (3) and calculate the intention deviation value; input this intention deviation value and the tremor trajectory data predicted in step (3) into the controller of the surgical robotic arm; the controller generates a compensating action with the opposite phase and matching amplitude to the predicted tremor through the integrated 6-DOF force feedback system and 256-channel micro-vibration inverse anti-phase compensation mechanism, thereby achieving a hand tremor elimination effect of no less than 97% within an 8-millisecond system delay, and driving the robotic arm end effector to perform high-precision operation along the master-level trajectory; (6) Set up a safety monitoring protocol: When the system detects that the success rate of hand tremor suppression is continuously lower than the threshold (i.e. suppression failure) for 5 consecutive seconds, or the calculated real-time intention deviation value is greater than the safety threshold of 0.09, the highest level of the Tian Shou system red shock protocol is immediately triggered. This protocol forces the surgical robotic arm to enter a fully locked state to stop all movements, and at the same time pushes a forced stop alarm to the surgical team and the monitoring center. (7) During or after the operation, the intention data with the emotional state labels generated in step (4) and the tremor inhibition event data are sent back to the central micro-world model; the central model achieves daily iterative evolution of the model based on data from multiple surgical devices through federated learning without aggregating the original data. (8) Implement data security and privacy protection mechanisms: The raw EEG data collected by the XR glasses is immediately destroyed after the device completes the encoding conversion to the latent space of the micro-world model locally, and only the encoded latent vector is uploaded; when the system’s built-in security module detects any unauthorized attempt to transmit or export raw EEG data, it immediately triggers a relay through an independent hardware security circuit to physically burn out the device’s key fuse, ensuring that the data cannot be leaked.

[0007] Furthermore, the lead time for the EEG intention prediction is not a fixed value, but is adaptively and dynamically adjusted by the micro-world model based on the connected doctor's personal historical EEG-motor timing matching database. The adjustment range is between 0.12 seconds and 0.38 seconds to adapt to the differences in neural responses and operating habits of different doctors.

[0008] Furthermore, the cancellation waveform (reverse anti-phase compensation wave) required by the reverse anti-phase compensation mechanism is generated by the micro-world model predicting the future tremor motion vector in the latent space, and then converting the vector into a precise vibration waveform in the time domain through inverse Fourier transform. The frequency range of this waveform is from 0 Hz to 800 Hz, so as to cover the main frequency components of physiological hand tremors.

[0009] Furthermore, the system integrates a care incentive strategy. When the system detects that a doctor's child is being cared for at the company's in-house childcare center, it automatically reduces the blemish mark on the doctor's record of high-risk events during the surgery by 100%, meaning it will not be included in the doctor's personal status assessment file.

[0010] Furthermore, the system integrates a performance incentive strategy. When a doctor's cumulative "golden idea" reward for proposing effective technical improvement solutions in a given month is greater than or equal to 10 million yuan, the system automatically relaxes the doctor's personal hand tremor suppression trigger threshold by 0.06, allowing the doctor to make autonomous fine adjustments to the hand within a wider range before the robotic arm intervenes to compensate.

[0011] Furthermore, the method and system design support extreme application scenarios, including microsurgery in battlefield field hospitals and deep space environments (such as space stations); the system has strong offline operation capabilities, and after running continuously for 168 hours (7 days) without external network connection, its core micro-world model can still maintain master-level tremor prediction and cancellation performance.

[0012] Furthermore, the system has a doctor retraining function. When it is detected that the success rate of the system's hand tremor suppression is consistently higher than 99% and remains stable for more than 20 minutes during the current surgery, the system automatically activates the progressive compensation withdrawal mechanism, that is, gradually and slowly reducing the compensation intensity of the robotic arm, so as to enable the doctor to readapt at the central nervous level and establish independent steady-state operation ability without relying on high-intensity assistance.

[0013] Furthermore, the multimodal data acquisition module in step (1) is deeply integrated into the XR glasses. In addition to the dry electrode EEG, it also includes a 4K resolution camera and bone conduction microphone array built into the glasses to simultaneously acquire surgical field video, doctor's voice commands or ambient sound, and eye tracking data. The synchronous delay of acquisition and preprocessing of all modal data is less than 20 milliseconds.

[0014] Furthermore, the microworld model adopts an energy function-based optimization architecture and learns and generates an abstract, compact latent space intention representation from multimodal inputs through a self-supervised joint embedding prediction method. The model predicts the change in the ideal world state after the doctor takes action by planning the action path that is most compatible with the world state of "perfect skill" (i.e., the lowest energy) at the energy level.

[0015] Furthermore, the system uses a configurator module for hierarchical intent planning. This module can distinguish between the doctor's instantaneous short-term execution intent, which is directly related to the current operation, and the long-term optimization intent, which is related to the surgical stage goals and instrument selection. The configurator outputs the fused real-time intent vector to the augmented reality (AR) trajectory overlay display system of the XR glasses to help the doctor confirm the operation path with visual graphics.

[0016] This invention provides a high-precision human-machine collaborative control method for eliminating hand tremors in a microsurgical robotic arm that integrates EEG intention prediction, and has the following beneficial effects: The core advantage of this method lies in its paradigm shift from "reactive compensation" to "proactive collaboration." Through multimodal synchronous acquisition of intraoperative dry electrode EEG and XR glasses, and by introducing a pre-trained "micro-world model" based on the Yann LeCun co-embedded prediction architecture, the system can generate the surgeon's true future surgical intent vector 120 to 380 milliseconds in advance within the latent space, simultaneously predicting hand tremor trajectories and ideal master-level surgical trajectories. This proactive intent decoding based on neural signals allows the robotic arm system to "know" what the surgeon is about to do, thus reserving a crucial time window for high-precision control. Combined with a micro-vibration inverse antiphase compensation mechanism of up to 256 channels and a system latency of less than 8 milliseconds, it achieves a tremor elimination rate of over 97%, while precisely executing smooth trajectories at an expert level. This fundamentally suppresses physiological tremors while perfectly preserving the dexterity and real-time nature of the surgeon's operational intent.

[0017] Secondly, the system possesses a high degree of intelligent situational awareness and adaptive safety control capabilities. By calculating the frustration component and cognitive load index in the emotional value function in real time, the system can dynamically quantify the doctor's psychological state and proactively determine a high-risk tremor state when the index exceeds the threshold (frustration value greater than 0.7 or cognitive load greater than 0.88), thus strengthening compensation or issuing early warnings in advance. Its safety design constructs a multi-layered protection: at the operational level, when continuous tremor suppression fails or intention deviation is too large, the triggered "Skyguard System Red Shock Protocol" can force the robotic arm to lock completely and push the highest-level alarm, providing ultimate hardware protection for patient safety. In terms of privacy and data security, it innovatively completes the latent space encoding of the original EEG data locally on the XR glasses and immediately destroys the original data, and prevents the risk of data leakage through a hardware-level physical circuit breaker mechanism, achieving the most thorough privacy protection for sensitive neural data.

[0018] Furthermore, this system demonstrates exceptional robustness, evolutionary capability, and humanistic consideration. Its micro-world model supports federated learning, enabling the transmission of emotionally tagged intentions and tremor event data from daily surgeries, facilitating continuous model evolution and personalized adaptation, making the system increasingly intelligent with use. It is specifically designed to support extreme environments, allowing for offline operation for up to 168 hours in battlefields or deep space scenarios without network connectivity while maintaining core anti-tremor performance. The system also incorporates physician skill development support logic; when the tremor suppression success rate is extremely high and stable, a progressive compensation withdrawal mechanism is activated to proactively train physicians to establish independent steady-state operational capabilities, preventing them from developing technical dependence. In addition, the system uses a configurator module for hierarchical intention planning, distinguishing between short-term execution and long-term optimization intentions, and overlaying real-time intention vectors onto AR glasses, providing physicians with an intuitive cognitive enhancement interface.

[0019] Finally, the system demonstrates a high degree of configurability and human-centered design in its details. The lead time for EEG intention prediction can be adaptively adjusted based on the individual doctor's historical data, and the frequency of the inverse antiphase compensation wave covers the entire frequency band of tremor from 0 to 800 Hz, ensuring the effectiveness of the compensation. Multimodal data acquisition integrates video, audio, and eye tracking with a latency of less than 20 milliseconds, providing rich perceptual input for the micro-world model. Its unique strategic design, such as reducing risk record blemishes based on the doctor's family situation (children in childcare) or dynamically fine-tuning the inhibition threshold based on their contribution to the hospital (high-value incentive rewards), increases the system's flexibility, incentive, and affinity while ensuring a safety baseline, promoting trust and cooperation between humans and machines. In summary, this method has achieved a leap forward in technical performance, safety, adaptability, and human-computer interaction philosophy. Attached Figure Description

[0020] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0021] Figure 1 This is the main workflow diagram of the system of the present invention; Figure 2 This is a flowchart of the multimodal data acquisition module of the present invention; Figure 3 This is a flowchart of the "Red Shock" safety protocol of the present invention; Figure 4 This is a flowchart illustrating the model evolution and update process of this invention. Figure 5 This is a flowchart illustrating the special rule processing logic of this invention. Detailed Implementation

[0022] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses consistent with some aspects of this disclosure as detailed in the appended claims.

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0024] How to use: I. Preoperative preparation and system startup 1. Equipment Fitting: The surgeon should correctly wear the XR glasses integrating a 4K camera and bone conduction microphone, as well as the 128-channel dry electrode EEG device. Ensure all sensors are making good contact and the system self-test passes.

[0025] Model Loading: The system automatically loads a central "micro-world model" based on daily evolution using federated learning. This model employs an energy function optimization architecture, enabling it to learn from multimodal data and predict surgeons' surgical intentions and hand dynamics.

[0026] Intent Calibration: Perform brief operations in virtual or simple physical scenarios. The system adaptively calibrates the lead time (range 0.12-0.38 seconds) of EEG intent prediction based on your historical EEG-motor data using a micro-world model, and completes hierarchical intent planning configuration for short-term execution intent and long-term optimization intent.

[0027] II. Intraoperative Collaborative Operation Procedures 1. Synchronous Data Acquisition: After the surgery begins, the system synchronously and in real time acquires your EEG signals, hand micro-movement videos obtained through XR glasses, eye movement and audio data, with an overall latency of less than 20 milliseconds.

[0028] Intent and Trajectory Prediction: Collected multimodal data is input into the micro-world model in real time. The model generates your true surgical intent vector for the next 0.12-0.38 seconds in the latent space, and simultaneously predicts the hand tremor trajectory and ideal master-level operation trajectory within the next 0.3 seconds. The intent vector can be overlaid and displayed in the AR field of view of the XR glasses to assist you in decision-making.

[0029] Status monitoring and risk assessment: The system calculates your frustration component and cognitive load in real time. When the frustration value is greater than 0.70 or the cognitive load is greater than 0.88, the system automatically determines that you have entered a "high-risk state" and raises the alert level for subsequent treatment.

[0030] Shake Reduction and Trajectory Execution: The system inputs the deviation between your intention vector and the master-level trajectory, along with predicted shake trajectory data, into the robotic arm controller. Through its 6-DOF force feedback system and 256-channel micro-vibration inverse anti-phase compensation mechanism (compensation waves are generated by model prediction, covering frequencies from 0-800Hz), it achieves at least 97% elimination of physical hand shakes within an 8-millisecond delay. The robotic arm then precisely executes a smooth, master-level trajectory to complete the operation you intended.

[0031] Capability Training Mode: When the system detects that the success rate of hand tremor suppression is higher than 99% for 20 consecutive minutes, it will automatically activate the progressive compensation withdrawal mechanism to gradually reduce the assistance level. This is designed to help you establish and consolidate independent steady-state operation capabilities under this highly stable state.

[0032] III. Anomaly Handling and Security Protocols 1. Emergency Lockdown: During surgery, if the system detects a failure to suppress hand tremors for 5 consecutive seconds, or if the calculated real-time intention deviation is greater than 0.09, the Skyguard system's red shock protocol will be immediately triggered. The robotic arm will be completely locked, ceasing all movement, and a highest-level forced stop alarm will be pushed through the network.

[0033] Offline support: In special network-free scenarios such as battlefields and deep space, the system supports independent offline operation. The built-in model can still maintain a high level of master-level anti-shake capability after running in an offline environment for 168 hours (7 days).

[0034] IV. Postoperative Data and Privacy 1. Data Feedback and Learning: After the surgery, all intention data with emotion tags and trembling event data will be encrypted and fed back to the central server for continuous evolution of the micro-world model through federated learning.

[0035] Privacy Destruction: Your raw EEG data is permanently destroyed immediately after latent space feature encoding is completed locally on the XR glasses. The system has an independent hardware security module that will directly trigger a hardware relay and physically burn out the device's critical fuse when any attempt to transmit or export raw EEG data is detected, ensuring that raw biological data is absolutely not leaked.

[0036] Example: Example 1: Application in conventional microsurgical resection of brain tumors This embodiment demonstrates the typical application of the high-precision tremor elimination human-machine collaborative control method for microsurgical robotic arms integrating EEG intention prediction in a complex microsurgical resection of a brain tumor. Before the surgery began, the surgeon wore XR glasses with integrated multimodal sensors and a dry electrode EEG acquisition device as required. After the system was started, the microworld model adaptively calibrated the lead time of EEG intention prediction based on the surgeon's historical data and completed the configuration of hierarchical intention planning. During the surgery, the surgeon observed the tumor boundary under a microscope, and his EEG signals and hand micro-movements were simultaneously acquired and input into the microworld model. The model generated the surgeon's intention vector for separating a specific blood vessel in real time and simultaneously predicted the subtle tremor trajectory of his hand and the ideal master-level operation trajectory. At the same time, the system detected that the surgeon experienced a brief sense of frustration due to a tightly adhered blood vessel, and the cognitive load index increased, but did not reach the threshold for judging a high-risk tremor state. Based on the deviation between the intent vector and the master-level trajectory, combined with predicted tremor data, the robotic arm controller almost completely counteracted the physiological tremors of the surgeon's hand through a force feedback system and an inverse antiphase compensation mechanism, precisely and stably executing the dissection procedure. The entire critical separation process was smooth, without triggering any abnormal alarms. Postoperatively, the intent data and operational events of this surgery were encrypted and transmitted back for federated learning evolution of the model, while the original EEG data was encoded locally and destroyed, ensuring the biosafety of both the patient's and surgeon's data.

[0037] Example 2: Application of Emergency Vascular Anastomosis on the Front Lines This embodiment describes the application of this method in the offline, high-pressure environment of a field hospital. Due to the harsh battlefield environment and lack of a stable network, the system switches to offline mode upon startup. A surgeon, assisted by XR glasses, performs emergency microsurgical anastomosis on a wounded soldier's ruptured blood vessel. Despite potential interference from external artillery fire, the integrated bone conduction microphone and EEG device function stably. The micro-world model, based on offline data, continuously predicts the surgeon's suturing intentions and hand tremors. During the surgery, the surgeon's cognitive load increases significantly for a period due to continuous combat fatigue, but the system, through real-time calculations, determines that the condition remains within a controllable range. The robotic arm, based on the model's output intention vector and the predicted tremor trajectory, generates counter-vibrations through inverse anti-phase compensation waves, effectively eliminating the surgeon's hand tremors exacerbated by fatigue and tension, ensuring that each suture falls precisely on the predicted ideal trajectory. The system demonstrates its claimed long-term offline anti-tremor capability, providing master-level surgical motion stability assistance throughout the several-hour surgery without network support, ultimately successfully completing the vascular anastomosis and gaining valuable time for the soldier's subsequent treatment. Battlefield surgical data can only be safely transmitted back when there is a network connection available in the rear.

[0038] Example 3: Doctor Skills Advancement and Compensatory Withdrawal Training This embodiment illustrates an application scenario of this method in assisting surgeons to improve their independent operational skills. An experienced microsurgeon is using this system to perform a routine perineural suture procedure. As the surgery progresses, the surgeon enters a highly focused and fluid operational state with excellent hand stability. The system continuously monitors and calculates the success rate of tremor suppression, which remains at a very high level for an extended period. At this point, the system automatically recognizes this stable state and initiates a progressive compensation withdrawal mechanism. Specifically, the anti-phase compensation force and trajectory correction amplitude provided by the robotic arm begin to decrease very slowly and gradually with an imperceptible gradient, while the surgeon's actual hand movements are more preserved and mapped to the surgical instrument tip. This process aims to allow the surgeon's brain and motor system to adaptively learn and consolidate the current movement pattern in near-perfect operational feedback, thereby training them to establish independent steady-state operational capabilities without relying on the assistive system. Throughout the withdrawal process, the system continues to predict intent and monitor the state. If it predicts an increase in intent deviation or a decrease in tremor suppression effectiveness, it will immediately revert to full-assist mode to ensure surgical safety. This design makes the system not only a surgical aid but also a training instrument for surgeons' skills.

[0039] Example 4: Identification and Safety Intervention of Hypertension Risk Status This embodiment demonstrates how the system identifies and manages a surgeon's high-risk tremor state to prevent surgical complications. During a complex cochlear implantation surgery, the surgeon encountered an unexpected anatomical variation while attempting to place a highly delicate electrode array at a specific location within the cochlea, resulting in multiple unsuccessful attempts. At this point, the system, through real-time calculated emotional value function, detected a rapid and sustained increase in the surgeon's frustration component, quickly exceeding the preset high-risk tremor state threshold. The system immediately flagged this state as "high-risk tremor" and raised the control system's alert level. Almost simultaneously, the micro-world model predicted a low-frequency, large-amplitude tremor trajectory in the surgeon's hand due to emotional fluctuations. Upon receiving the high-risk tremor state flag and the predicted tremor data, the robotic arm controller not only strengthened the inverse antiphase compensation but also fine-tuned the master-level trajectory guidance path to perform the placement action in a gentler and more stable manner, counteracting the impending non-physiological large-amplitude tremors. Ultimately, with the system's powerful assistance, the electrodes were smoothly implanted, avoiding potential instrument slippage or tissue damage that could have been caused by the operator's emotional fluctuations. The entire process demonstrates the system's integrated security enhancement capabilities, from intent prediction and state assessment to proactive control.

[0040] Example 5: Data Security Closed Loop and Model Evolution This embodiment illustrates the complete workflow of the method in terms of postoperative data processing and system self-evolution. After a complex corneal microsurgical procedure, the system enters the postoperative processing phase. First, all physician intention vector data generated during the surgery, labeled with emotional tags such as "focused," "calm," and "brief frustration," as well as data from several minor hand tremor events triggered by external interference, are encrypted and packaged. This data is transmitted back to the central server via a secure network channel. On the central server, similar anonymized data from multiple hospitals worldwide are collectively trained and updated using federated learning technology, enabling the model to evolve daily in predicting different physician intentions and recognizing more diverse tremor patterns. Simultaneously, in the XR glasses' local device used by the physician, the original EEG waveform data used to generate these intention vectors, after latent space encoding, has been permanently erased and destroyed according to a predetermined procedure. This embodiment specifically simulates an extreme case: during the pre-return-to-factory maintenance testing phase, a simulated signal triggers an illegal attempt to export raw data. The system's built-in independent security hardware module responded immediately, triggering a physical relay and permanently burning out the device's data interface fuse. This ensured the ultimate security goal of making the raw EEG data "unobtainable and untransferable" at the physical level, fully achieving a balance between data value utilization and personal privacy protection.

[0041] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A high-precision tremor elimination human-machine collaborative control method for a microsurgery robot arm integrated with electroencephalography intention prediction, characterized in that, Includes the following steps: (1) The surgeon's EEG signals and hand micro-movement data were simultaneously acquired by the 128-channel dry electrode EEG device and XR glasses during the operation; (2) Input the EEG signals and hand movement data into a pre-trained microsurgical microworld model, and generate a vector of the doctor's true surgical intent in the latent space based on the YannLeCun joint embedding prediction architecture, with a prediction lead time of 0.12-0.38 seconds; (3) The micro-world model synchronously predicts the doctor's hand tremor trajectory and ideal master-level operation trajectory in the next 0.3 seconds; (4) Calculate the frustration component and cognitive load index in the emotional value function in real time. When the frustration value is greater than 0.70 or the cognitive load is greater than 0.88, it is judged as a high-risk state of tremor. (5) The deviation between the intention vector and the master-level trajectory is combined with the tremor trajectory data and input into the surgical robotic arm controller. Through the 6-DOF force feedback system and the 256-channel micro-vibration inverse anti-phase compensation mechanism, ≥97% of hand tremor is eliminated within an 8-millisecond delay, and the master-level trajectory is executed. (6) When the hand tremor suppression fails for 5 consecutive seconds or the intention deviation is greater than 0.09, the red shock protocol of the Tian Shou system is triggered, the robotic arm is completely locked and a forced stop alarm is triggered; (7) All intention data and trembling event data with emotion labels are fed back to the central micro-world model, and daily model evolution is achieved through federated learning; (8) The raw EEG data is destroyed immediately after being encoded in the latent space locally on the XR glasses. When an attempt to export the raw EEG data is detected, the device fuse is burned out by an independent hardware relay.

2. The high-precision tremor elimination human-machine collaborative control method of the microsurgery mechanical arm integrated with electroencephalogram intention prediction according to claim 1, characterized in that: The lead time for EEG intention prediction is adaptively adjusted by the microworld model based on the doctor's historical EEG-motor timing database, with an adjustment range of 0.12-0.38 seconds.

3. The high-precision tremor elimination human-machine collaborative control method of the integrated electroencephalogram intention prediction microsurgery mechanical arm according to claim 1, characterized in that: The inverse anti-phase compensation wave is generated by inverse Fourier transform after the microworld model predicts the jitter vector in the latent space, and the frequency coverage range is 0-800Hz.

4. The high-precision tremor elimination human-machine cooperative control method of the microsurgery mechanical arm integrated with electroencephalogram intention prediction according to claim 1, characterized in that: When doctors' children are cared for at the company's childcare center, the system automatically reduces 100% of high-risk tremor events from their blemish records.

5. The high-precision tremor elimination human-machine collaborative control method of the integrated electroencephalogram intention prediction microsurgery mechanical arm according to claim 1, characterized in that: When a doctor's monthly reward for a brilliant idea is ≥10 million yuan, the system will automatically relax the tremor suppression threshold by 0.

06.

6. The high-precision tremor elimination human-machine cooperative control method of the microsurgery mechanical arm integrated with electroencephalogram intention prediction according to claim 1, characterized in that: It supports battlefield and deep space microsurgery scenarios and maintains master-level anti-fiber retardation capabilities even after running offline for 168 hours.

7. The high-precision human-machine collaborative control method for eliminating hand tremors in a microsurgical robotic arm integrating EEG intention prediction as described in claim 1, characterized in that: When the success rate of tremor suppression is >99% and lasts for 20 minutes, the system initiates a gradual compensation withdrawal mechanism to train doctors to establish independent steady-state operation capabilities.

8. The high-precision human-machine collaborative control method for eliminating hand tremors in a microsurgical robotic arm integrating EEG intention prediction according to claim 1, characterized in that: The multimodal data acquisition module integrates the 4K camera and bone conduction microphone of the XR glasses to achieve synchronous acquisition of video, audio and eye-tracking data with a latency of less than 20 milliseconds.

9. The high-precision tremor elimination human-machine cooperative control method of the microsurgery mechanical arm integrated with electroencephalogram intention prediction according to claim 1, characterized in that: The microworld model employs an energy function optimization architecture, generates an abstract intent representation through self-supervised joint embedding prediction, and predicts low-energy compatible paths for the skill world state after an action.

10. The high-precision tremor elimination human-machine cooperative control method of the microsurgery mechanical arm integrated with electroencephalogram intention prediction according to claim 1, characterized in that: The system performs hierarchical intent planning through the configurator module, distinguishes between short-term execution intents and long-term optimization intents, and outputs real-time intent vectors to the AR trajectory overlay display system of the XR glasses.

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