CN118001585A - Implantable closed-loop nerve regulation and control equipment and method based on cross-brain-region synchronism - Google Patents

Abstract

Embodiments of the present disclosure relate to an implantable closed-loop neuromodulation device and method based on cross-brain-zone synchronicity, wherein the device comprises: the nerve electrode is used for acquiring the brain electrical signals of the user; the processor is used for performing time-frequency processing on the electroencephalogram signals to obtain frequency domain signals, calculating the synchronicity change value, the phase locking value, the connectivity and the coherence coefficient among different brain areas according to the frequency domain signals, and determining a time-frequency analysis result according to the synchronicity change value, the phase locking value, the connectivity and the coherence coefficient; the closed-loop control system is used for adjusting the stimulation parameters according to the time-frequency analysis result and a preset target, and generating stimulation signals to act on the nerve electrodes according to the adjusted stimulation parameters. By adopting the technical scheme, the synchronicity between different brain regions can be monitored and adjusted in real time, and a more effective and personalized solution is provided for the nerve functional diseases.

Description

An implantable closed-loop neural regulation device and method based on cross-brain region synchronization

Technical Field

The present disclosure relates to the field of data processing technology, and in particular to an implantable closed-loop neural regulation device and method based on cross-brain region synchronization.

Background technique

Neuromodulation technology has developed rapidly in recent years, especially in clinical applications in functional neurological diseases. Deep Brain Stimulation (DBS) and Transcranial Magnetic Stimulation (TMS) have been shown to be effective in treating neurological diseases such as Parkinson's disease and epilepsy. However, although they can regulate neural activity in specific brain regions, they fail to effectively consider and regulate the synchronization between brain regions. This limitation is particularly evident in dynamic abnormal states (such as during epileptic seizures).

In addition, DBS and TMS are open-loop neuromodulatory systems that operate without real-time electrophysiological feedback, so it may not be possible to adjust treatment strategies in a timely manner to cope with dynamic changes in brain state.

Summary of the invention

In order to solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides an implantable closed-loop neural regulation method, device, equipment and medium based on cross-brain region synchronization.

The present disclosure provides an implantable closed-loop neural regulation device based on cross-brain region synchronization, the device comprising:

Neural electrodes, used to obtain the user's brain electrical signals;

A processor, configured to perform time-frequency processing on the EEG signal to obtain a frequency domain signal, calculate a synchronization change value, a phase locking value, a connectivity and a coherence coefficient between different brain regions according to the frequency domain signal, and determine a time-frequency analysis result according to the synchronization change value, the phase locking value, the connectivity and the coherence coefficient;

A closed-loop control system is used to adjust stimulation parameters according to the time-frequency analysis results and preset targets, and to generate stimulation signals according to the adjusted stimulation parameters to act on the neural electrodes.

The disclosed embodiment also provides an implantable closed-loop neural regulation method based on cross-brain region synchronization, the method comprising:

Obtain the user's EEG signal;

Performing time-frequency processing on the EEG signal to obtain a frequency domain signal, and calculating the synchronization change value, phase locking value, connectivity and coherence coefficient between different brain regions according to the frequency domain signal;

Determining a time-frequency analysis result according to the synchronization change value, the phase locking value, the connectivity and the coherence coefficient;

The stimulation parameters are adjusted according to the time-frequency analysis results and the preset targets, and a stimulation signal is generated according to the adjusted stimulation parameters to act on the neural electrodes.

An embodiment of the present disclosure also provides an electronic device, which includes: a processor; a memory for storing executable instructions of the processor; the processor is used to read the executable instructions from the memory and execute the instructions to implement an implantable closed-loop neural regulation method based on cross-brain region synchronization as provided in an embodiment of the present disclosure.

The embodiments of the present disclosure also provide a computer-readable storage medium, which stores a computer program for executing the implantable closed-loop neural regulation method based on cross-brain region synchronization as provided in the embodiments of the present disclosure.

The technical solution provided by the embodiment of the present disclosure has the following advantages over the prior art: the implantable closed-loop neural regulation solution based on cross-brain region synchronization provided by the embodiment of the present disclosure obtains the user's EEG signal; performs time-frequency processing on the EEG signal to obtain a frequency domain signal, and calculates the synchronization change value, phase locking value, connectivity and coherence coefficient between different brain regions based on the frequency domain signal; determines the time-frequency analysis result based on the synchronization change value, phase locking value, connectivity and coherence coefficient; adjusts the stimulation parameters based on the time-frequency analysis results and preset goals, and generates a stimulation signal based on the adjusted stimulation parameters to act on the neural electrodes. The above technical solution can monitor and adjust the synchronization between different brain regions in real time, providing a more effective and personalized solution for neurofunctional diseases. In addition, through the closed-loop feedback mechanism, the device can more effectively provide real-time feedback stimulation for the dynamic changes in the states between different brain regions, thereby improving the accuracy and effect of treatment.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

FIG1 is a schematic diagram of the structure of an implantable closed-loop neural regulation device based on cross-brain region synchronization provided by an embodiment of the present disclosure;

FIG2 is a structural example diagram of an implantable closed-loop neural regulation device based on cross-brain region synchronization provided by an embodiment of the present disclosure;

FIG3 is a flow chart of an implantable closed-loop neural regulation method based on cross-brain region synchronization provided in an embodiment of the present disclosure.

Detailed ways

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and/or in parallel. In addition, the method embodiments may include additional steps and/or omit the steps shown. The scope of the present disclosure is not limited in this respect.

The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

Taking traditional transcranial electrical stimulation as an example, it fails to take into account the phase difference between the stimulation waveform and the endogenous oscillation of the brain, resulting in large variability in the stimulation effect. Non-invasive electrical stimulation cannot effectively synchronize the activity of neuronal groups like implanted devices, which limits its effectiveness in neuroelectrophysiological activities. In addition, most of the existing invasive and non-invasive neuromodulatory devices are open-loop devices, which lack the ability to capture and regulate brain functional networks in real time, and cannot effectively track changes in brain state, limiting the treatment effect on functional brain diseases. Inter-brain region synchronization refers to the temporal coordination of neuronal activity between different brain regions. In neurofunctional diseases such as epilepsy, this synchronization is often destroyed. Current neuromodulatory technologies generally lack the ability to monitor and adjust synchronization between brain regions in real time.

It is understandable that compared with the traditional open-loop system, the closed-loop control system adjusts the stimulation parameters according to the real-time feedback of brain electrophysiological signals, and can provide more personalized and precise treatment. The current closed-loop control system also focuses on the regulation of a single brain area.

In general, synchronization between brain regions plays a key role in the coordination of neuronal activity, information transmission, and higher-level brain functions. In functional neurosurgical diseases such as Parkinson's disease and epilepsy, normal synchronization is disrupted, leading to the occurrence of various symptoms. Restoring or regulating this synchronization may improve symptoms. Real-time monitoring using EEG (Electroencephalogram) or related technologies can provide detailed information about the state of the brain, especially in terms of synchronization between brain regions. In addition, advanced data analysis and machine learning algorithms can better parse these data, thereby helping to identify abnormal synchronization and provide therapeutic evidence for feedback neuromodulation.

In related technologies, DBS implants electrodes into specific areas of the brain, usually brain nuclei or brain pathways associated with specific diseases. These electrodes generate electrical pulses through an externally controllable generator, thereby directly stimulating specific areas of the brain. DBS can regulate abnormal neural activity, thereby improving disease symptoms, and is mainly used to treat movement disorders such as Parkinson's disease, essential tremor, and refractory epilepsy. It is also used to treat certain mental illnesses, such as depression and obsessive-compulsive disorder.

TMS uses brief, focused pulses of magnetic fields delivered through the scalp and skull to stimulate specific areas of the brain. These magnetic field pulses can induce electrical activity in neurons beneath the cerebral cortex, thereby changing the function of neural networks. Unlike DBS, TMS is a non-invasive technique that does not require surgery or implanted devices. TMS is primarily used to treat major depressive disorder, especially in patients who have not responded well to traditional drug treatments. It is also being studied for the treatment of other psychiatric and neurological disorders, such as anxiety disorders, schizophrenia, and Parkinson's disease.

Therefore, DBS and TMS are open-loop neuromodulatory systems that operate without real-time electrophysiological feedback, and therefore may not be able to adjust treatment strategies in a timely manner to respond to dynamic changes in brain state. Responsive neurostimulation is an implantable closed-loop brain neuromodulator, which means that it is able to automatically adjust stimulation based on changes in real-time EEG activity. This ability to monitor and respond in real time makes reactive neurostimulation particularly effective in preventing and reducing the frequency of epileptic seizures. Responsive neurostimulation not only stimulates the brain, but also monitors EEG activity in real time. When abnormal neural activity is detected (such as a specific EEG pattern before an epileptic seizure), reactive neurostimulation automatically provides electrical stimulation, and the parameters of the electrical stimulation (such as intensity and frequency) can be personalized according to the specific needs of the patient.

Specifically, the neural excitation-inhibition balance within and across brain regions is also called neural oscillation. Existing neuromodulation technologies have many limitations. For example, they do not take into account the impact of the phase difference between the stimulation waveform and endogenous brain oscillation on the effect of electrical stimulation. The variability of the effect is very large, so usually only specific brain regions can be stimulated, rather than the entire brain network. For non-invasive neuromodulation devices such as TMS, their accuracy is low, the stimulation intensity and efficacy are limited, and they are limited in stimulating deep brain regions and precisely regulating the synchronization between brain regions. Excessive excitation at the level of single neurons does not necessarily increase the overall level of neural activity, because the "asynchrony" between neuronal groups hinders the coordinated participation in cognitive processing.

At present, various electrical stimulator products are generally unable to generate stimulation signals in a closed loop according to the changing state of EEG signals. The implantable closed-loop electrical stimulation system of the embodiment of the present disclosure can monitor brain activity in real time through EEG, and then apply an electrical stimulation waveform with a specified phase difference from the endogenous oscillation. When abnormal synchronization between brain regions is detected, the device can automatically adjust the stimulation parameters to restore normal synchronization. The embodiment of the present disclosure will apply cutting-edge data analysis methods and machine learning algorithms to parse the EEG data collected from the brain. These algorithms can identify specific pathological EEG patterns and determine the best stimulation strategy to restore normal synchronization of neural activity. The designed implantable device can automatically adjust the stimulation parameters (such as intensity, frequency, and timing) based on the real-time data analysis results to optimize the treatment effect. This closed-loop control mechanism can adapt to real-time changes in the patient's brain activity, thereby providing more personalized and efficient treatment.

The present invention combines a variety of advanced stimulation generation, recording and analysis technologies, which are suitable for different clinical and research scenarios, and can identify and monitor brain function in various neuropsychiatric diseases. At the same time, by controlling and regulating the discharge of neurons, the function and abnormality of neural networks can also be studied, which is helpful for the development of potential treatments.

FIG1 is a schematic diagram of the structure of an implantable closed-loop neural regulation device based on cross-brain region synchronization provided by an embodiment of the present disclosure. The device can be implemented by software and/or hardware and can generally be integrated into an electronic device. As shown in FIG1 , the device includes:

The neural electrode 101 is used to obtain the user's brain electrical signal.

Processor 102 is used to perform time-frequency processing on the EEG signal to obtain a frequency domain signal, calculate the synchronization change value, phase locking value, connectivity and coherence coefficient between different brain regions based on the frequency domain signal, and determine the time-frequency analysis result based on the synchronization change value, the phase locking value, the connectivity and the coherence coefficient.

The closed-loop control system 103 is used to adjust the stimulation parameters according to the time-frequency analysis results and the preset targets, and generate stimulation signals according to the adjusted stimulation parameters to act on the neural electrodes.

In one embodiment of the present disclosure, the processor 102 is specifically configured to: obtain each instantaneous phase corresponding to different brain regions in the frequency domain signal; and calculate the phase locking value between different brain regions according to the phase difference between the each instantaneous phase.

In one embodiment of the present disclosure, the processor 102 is specifically used to: obtain a first autopower spectral density, a second autopower spectral density, and a cross-power spectral density corresponding to two EEG signals; and calculate a coherence coefficient between the two EEG signals based on the first autopower spectral density, the second autopower spectral density, and the cross-power spectral density.

In one embodiment of the present disclosure, the processor 102 is specifically used to: obtain weight information corresponding to the synchronization change value, the phase locking value, the connectivity and the coherence coefficient; perform weighted sum calculation on the synchronization change value, the phase locking value, the connectivity and the coherence coefficient according to the weight information to obtain the time-frequency analysis result.

In one embodiment of the present disclosure, the closed-loop control system 103 is specifically used to: determine an error value based on the time-frequency analysis result and a preset target; determine a first proportional control coefficient, a second proportional control coefficient and a third proportional control coefficient based on the error value; and generate the stimulation signal based on the adjusted first proportional control coefficient, second proportional control coefficient and third proportional control coefficient and the error value.

In one embodiment of the present disclosure, the closed-loop control system 103 is further used to: amplify, filter and shape-adjust the stimulation signal to generate a target stimulation waveform; and convert the stimulation signal into an analog signal.

In one embodiment of the present disclosure, the closed-loop control system 103 is also used to: detect stimulation parameters in real time, and control the neural electrodes to stop working when abnormal information is detected.

In the disclosed embodiment, as shown in FIG2 , the processor 102 may be a central processing unit, such as a central processing unit with high-speed computing capability, low power consumption, and suitable for complex data processing and real-time control tasks, and integrated with data encryption and security functions to ensure the security of data transmission.

In the disclosed embodiment, the neural electrode 101 can be made of a biocompatible titanium alloy to reduce tissue reaction, and a conductive polymer is coated on the surface to enhance the signal receiving ability. The tiny size reduces damage to brain tissue.

In the disclosed embodiment, the closed-loop control system 103 may be composed of an analog/digital signal converter, a microcontroller, and a stimulation module. An integrated algorithm is used to process input signals and determine optimal stimulation parameters. It has the ability to adjust the frequency, intensity, and duration of electrical stimulation to adapt to the neural activity monitored in real time. The stimulation module is composed of a signal conditioning unit (amplifier, filter, oscillator), a signal conditioning circuit, a digital-to-analog converter (DAC), an output control circuit, and a safety and monitoring circuit. It is equipped with a real-time feedback mechanism to automatically adjust the stimulation parameters according to the EEG activity and treatment response.

In the disclosed embodiment, the implantable closed-loop neuromodulation device based on cross-brain region synchronization may also include a wireless data communication module, which may use, for example, Bluetooth 5.0 for wireless communication. An integrated security protocol protects the transmitted data from unauthorized access and supports remote communication with a smartphone, tablet or medical device.

In the disclosed embodiment, the battery of the implantable closed-loop neuromodulation device based on cross-brain region synchronization can use a high-density lithium polymer battery with a miniaturized design to adapt to the space limitations of the implantable device, provide long-term continuous operation capability, and reduce the charging frequency.

In the disclosed embodiment, a biocompatible shell, waterproof and dustproof bioactive ceramic materials are used to adapt to the human body environment, and the user interaction interface can be simple and easy to operate, and can be controlled by a touch screen or physical buttons. Provide real-time feedback information, such as battery power, connection status, device operation status, etc. Support multiple languages to meet the needs of different users.

Specifically, cross-brain region synchronization analysis is highly integrated in the central processing unit, and specifically includes the following contents: time-frequency analysis, using short-time Fourier transform (STFT) to perform time-frequency analysis on neural oscillations, the algorithm formula is: STFT(x(t))＝∫x(τ)ω(t-τ)e ^-j2πfτ dτ, where x(t) is the EEG signal and w(t) is the window function; STFT analysis can identify changes in synchronization within a specific frequency range, thereby obtaining the value of synchronization changes.

It is understandable that EEG signals are continuous and non-periodic and need to be processed using short-time Fourier. The result of time-frequency analysis here is the frequency domain expression of the time domain EEG signal. Whether it is synchronization analysis or correlation analysis, the signal must first be converted from the time domain to the frequency domain before analysis can be performed. This is the basis of signal analysis and processing.

Specifically, the phase lock value (PLV) analysis is done by the formula PLV is used to measure the phase consistency between different brain regions, where ∆φ is the phase difference and N is the number of samples. PLV helps to assess the synchronization between brain regions.

Among them, the PLV value ranges from 0 to 1, and the closer it is to 1, the more locked or synchronized the phase of the signal is. When the signals of different brain regions or frequency bands show a higher PLV value, it indicates that the phase relationship between them is more stable and synchronized. The phase difference is inherent between each brain region, and is obtained by extracting the instantaneous phase (Hilbert transform) of the collected and preprocessed EEG signal and calculating the difference between them.

Specifically, EEG data is used to evaluate the statistical dependence between different brain regions. Correlation or covariance analysis is used to quantify functional connectivity, which can be processed based on the preprocessed EEG signal (discrete sampling value). The correlation output is the Pearson correlation coefficient, and the covariance output is the covariance matrix (symmetric matrix, the elements on the diagonal are the variance of each signal, and the elements on the off-diagonal represent the covariance between different signals).

Specifically, the synchronization between different brain regions is evaluated by quantifying the linear correlation between two signals. The formula is: Where _Pxy is the cross power spectral density of signals x and y, _Pxx is the power spectral density of signal x, and _Pyy is the power spectral density of signal y.

It is understandable that the results of the synchronization analysis of brain regions are not judged from a single quantitative indicator, but are obtained by combining the above four processes. This combination refers to the weighted calculation of each indicator.

Specifically, the closed-loop control system implements the basic principle of closed loop. The closed-loop control system automatically adjusts the parameters of the stimulation module based on the real-time neural oscillation analysis results. The feedback control algorithm (PID control) is used to adjust the stimulation intensity, frequency and timing according to real-time data and preset goals. The control algorithm is: Where e(t) is the error term, and _Kp , _Ki , and _Kd are the parameters of the PID controller.

Specifically, the real-time neural oscillation analysis result is the aforementioned time-frequency analysis result. Parameter adjustment is the adjustment of _Kp , _Ki , and _Kd . The specific adjustment depends on the actual response. For example, for the proportional module _Kp ·e(t), if the current system's response speed to the error is too fast, the error proportional parameter _Kp is adjusted down, otherwise it is adjusted up. The same is true for _Ki and _kd , which are used to adjust the system's response speed to the error, but from three levels, namely, proportional multiplication, integration, and differentiation. How to match the adjustment parameters can be analyzed and set according to the specific situation.

Therefore, it is possible to automatically adjust according to the response u(t), evaluate the control effect of the system according to real-time data, system stability, error changes, overshoot and other indicators, and gradually adjust the parameters of the PID controller according to the observed system response, increase or decrease the proportion of integral and differential terms, so that the system error signal e(t) converges to the expected target value faster and ensures the stability of the system. Among them, e(t) represents the error signal in the closed-loop control system. This error signal usually refers to the difference between the real-time neural oscillation data measured by the system and the expected target. More specifically, e(t) is a measure that describes the difference between the actual neural signal characteristics and the expected neural signal characteristics at the current time t.

Specifically, the operation of the stimulation module in the closed-loop control system, signal generation and adjustment. According to the analysis results of the central processing unit, the signal conditioning unit generates a specific stimulation signal. The signal is amplified, filtered and shaped by the signal conditioning circuit to generate the required stimulation waveform. Signal conversion and output, DAC converts the digital signal into an analog signal (the waveform is an analog signal, and the analog-to-digital conversion before signal processing is because the computer can only process binary digital signals, and then converts it back to an analog signal when it is visualized). The output control circuit further adjusts the current and voltage of the signal to ensure that the stimulation parameters meet the treatment requirements. Safety monitoring. The safety and monitoring circuit detects the working parameters of the stimulation module in real time to prevent any possible overload or abnormal conditions. Once an abnormality is detected, the stimulation is adjusted or interrupted immediately to protect the patient's safety.

The implantable closed-loop neural regulation device based on cross-brain region synchronization in the disclosed embodiment utilizes advanced neural signal analysis technology and a closed-loop control system to precisely regulate neural activity through a stimulation module to achieve the purpose of treating neurological functional diseases.

Specifically, FIG3 is a flow chart of an implantable closed-loop neural control method based on cross-brain region synchronization provided by an embodiment of the present disclosure, which can be performed by an implantable closed-loop neural control device based on cross-brain region synchronization, wherein the device can be implemented by software and/or hardware, and can generally be integrated into an electronic device. As shown in FIG3, the method includes:

Step 201: Obtain the user's EEG signal.

Step 202: Perform time-frequency processing on the EEG signal to obtain a frequency domain signal, and calculate the synchronization change value, phase locking value, connectivity and coherence coefficient between different brain regions based on the frequency domain signal.

Step 203: Determine the time-frequency analysis result according to the synchronization change value, the phase locking value, the connectivity and the coherence coefficient.

Step 204: adjust stimulation parameters according to the time-frequency analysis results and preset targets, and generate stimulation signals according to the adjusted stimulation parameters to act on the neural electrodes.

Specifically, the device operates in the following ways: data acquisition, where the implanted neural electrodes monitor the patient's EEG activity in real time and transmit the data to the central processor; data analysis, where the central processor analyzes the EEG data and uses time-frequency analysis, PLV analysis and other methods to evaluate the synchronization between brain regions. Based on the analysis results, the processor determines whether the stimulation parameters need to be adjusted; stimulation execution, where once it is determined that adjustment is required, the closed-loop control system directs the stimulation module to provide electrical stimulation according to the set parameters. The stimulation module generates precise electrical stimulation signals, which are applied to specific brain regions through implanted electrodes; feedback adjustment, where the system continuously monitors EEG activity and stimulation effects, and automatically adjusts stimulation parameters based on feedback results. This ensures the continuity and personalization of treatment, while improving the accuracy and safety of treatment effects.

As an example of a scenario, take the user (patient) operation process as an example: device startup, such as the user turns on the device through an external wireless controller or smartphone application, and the visual interface provides device status information, such as battery power, connection status, etc.; personalized settings, the user selects or adjusts a specific stimulation scheme according to the doctor's guidance, such as stimulation frequency, intensity, etc. These settings can be adjusted according to the user's comfort and treatment response.

As an example of a scenario, taking the doctor's operation process as an example, for initial configuration and implantation, the doctor makes initial settings based on the patient's basic condition and EEG data, including stimulation parameters and electrode positions. The doctor performs surgery to implant the electrodes and the device body into the patient's body; the device is adjusted and monitored, and the device operating status and patient response are regularly checked, and the treatment plan is adjusted as needed. Detailed settings are made through the corresponding software, including adjustments to the stimulation intensity, frequency, and timing; data analysis and adjustment, regular downloading and analysis of the patient's EEG data to evaluate the treatment effect, and necessary parameter adjustments and optimizations are made based on the analysis results.

The implantable closed-loop neural regulation method based on cross-brain region synchronization provided by the embodiments of the present disclosure can execute the implantable closed-loop neural regulation device based on cross-brain region synchronization provided by any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

The embodiments of the present disclosure also provide a computer program product, including a computer program/instruction, which, when executed by a processor, implements the implantable closed-loop neural regulation method based on cross-brain region synchronization provided by any embodiment of the present disclosure.

According to one or more embodiments of the present disclosure, the present disclosure provides an electronic device, including:

processor;

a memory for storing instructions executable by the processor;

The processor is used to read the executable instructions from the memory and execute the instructions to implement any implantable closed-loop neural regulation method based on cross-brain region synchronization as provided in the present disclosure.

According to one or more embodiments of the present disclosure, the present disclosure provides a computer-readable storage medium, wherein the storage medium stores a computer program, wherein the computer program is used to execute any implantable closed-loop neural regulation method based on cross-brain region synchronization as provided in the present disclosure.

The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other.

In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

Although the subject matter has been described in language specific to structural features and/or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims (10)

1. An implantable closed-loop neuromodulation device based on cross-brain-region synchronicity, comprising:

the nerve electrode is used for acquiring the brain electrical signals of the user;

the processor is used for performing time-frequency processing on the electroencephalogram signals to obtain frequency domain signals, calculating a synchronicity change value, a phase locking value, a connectivity and a coherence coefficient between different brain areas according to the frequency domain signals, and determining a time-frequency analysis result according to the synchronicity change value, the phase locking value, the connectivity and the coherence coefficient;

And the closed-loop control system is used for adjusting the stimulation parameters according to the time-frequency analysis result and a preset target, and generating a stimulation signal to act on the nerve electrode according to the adjusted stimulation parameters.

2. The implantable closed-loop neuromodulation device as in claim 1, wherein the processor is configured to:

Acquiring each instantaneous phase corresponding to different brain regions in the frequency domain signal;

And calculating phase locking values between different brain areas according to the phase differences between the instantaneous phases.

3. The implantable closed-loop neuromodulation device as in claim 1, wherein the processor is configured to:

acquiring a first self-power spectral density, a second self-power spectral density and a cross-power spectral density corresponding to two electroencephalogram signals;

And calculating a coherence coefficient between the two electroencephalogram signals according to the first self-power spectral density, the second self-power spectral density and the cross-power spectral density.

4. The implantable closed-loop neuromodulation device as in claim 1, wherein the processor is configured to:

Acquiring weight information corresponding to the synchronicity change value, the phase locking value, the connectivity and the coherence coefficient;

And carrying out weighted summation calculation on the synchronicity change value, the phase locking value, the connectivity and the coherence coefficient according to the weight information to obtain the time-frequency analysis result.

5. The implantable closed-loop neuromodulation device as in claim 1, wherein the closed-loop control system is specifically configured to:

Determining an error value according to the time-frequency analysis result and a preset target;

determining a first proportional control coefficient, a second proportional control coefficient and a third proportional control coefficient according to the error value;

and generating the stimulation signal according to the adjusted first proportional control coefficient, the second proportional control coefficient, the third proportional control coefficient and the error value.

6. The implantable closed-loop neuromodulation device as in claim 1, wherein the closed-loop control system is further configured to:

Amplifying, filtering and shape adjusting the stimulation signals to generate target stimulation waveforms;

The stimulus signal is converted to an analog signal.

7. The implantable closed-loop neuromodulation device based on cross-brain-zone synchronicity of claim 1, the closed-loop control system further for:

and detecting the stimulation parameters in real time, and controlling the neural electrode to stop working when abnormal information is detected.

8. An implantable closed-loop nerve regulation and control method based on cross-brain-region synchronism is characterized by comprising the following steps:

acquiring an electroencephalogram signal of a user;

Performing time-frequency processing on the electroencephalogram signals to obtain frequency domain signals, and calculating a synchronicity change value, a phase locking value, a connectivity and a coherence coefficient between different brain areas according to the frequency domain signals;

Determining a time-frequency analysis result according to the synchronicity change value, the phase locking value, the connectivity and the coherence coefficient;

and adjusting the stimulation parameters according to the time-frequency analysis result and a preset target, and generating a stimulation signal to act on the nerve electrode according to the adjusted stimulation parameters.

9. An electronic device, the electronic device comprising:

A processor;

A memory for storing the processor-executable instructions;

the processor is configured to read the executable instructions from the memory and execute the instructions to implement the implantable closed-loop neuromodulation method of claim 8 based on cross-brain-region synchronicity.

10. A computer readable storage medium, wherein the storage medium stores a computer program for performing the method of cross-brain-region synchrony-based implanted closed-loop neuromodulation as claimed in claim 8.