Brain-computer interface control system and method based on multi-mode fusion

Abstract

The invention discloses a brain-computer interface control system and method based on multi-mode fusion, and relates to the technical field of brain-computer interface control. Comprising a microelectrode array management module which is responsible for arranging a microelectrode array, collecting initial signals and monitoring electroencephalogram signal characteristics in real time; the parameter optimization module is used for dynamically adjusting sampling parameters of the microelectrode array so as to optimize signal quality; the multi-channel signal processing module is used for carrying out weighted fusion and noise suppression on a plurality of electrode channel signals; the self-calibration feedback module is used for optimizing acquisition parameters of the microelectrode array according to feedback self-calibration electrode parameters; the control instruction generation module is used for generating an instruction; and the output adjusting module is used for dynamically adjusting output. According to the invention, accurate acquisition and processing of the electroencephalogram signals are realized, so that the flexibility and accuracy of the system in real-time application are improved.

Description

Technical Field

[0001] The present invention relates to the field of brain-computer interface control technology, and in particular to a brain-computer interface control system and method based on multi-mode fusion. Background Art

[0002] As a cutting-edge technology, brain-computer interface (BCI) has achieved rapid development in recent years. Traditional brain-computer interface technology mainly relies on one-way information transmission between the brain and external devices. By collecting electroencephalogram (EEG) signals and converting them into control instructions, BCI technology can control computers, robotic arms, wheelchairs and other equipment. The development of these technologies began with early EEG signal detection and neuronal activity research, and gradually evolved into real-time control using more complex algorithms and signal processing methods.

[0003] Although the existing brain-computer interface technology can already control external devices by collecting EEG signals, there are still considerable challenges in signal processing. The existing microelectrode array signal acquisition technology has deficiencies in automatically adjusting and optimizing sampling parameters, and cannot make adaptive adjustments based on signal characteristics in real time, resulting in large fluctuations in signal quality. In addition, the existing technology also has room for improvement in the fusion and noise suppression of EEG signals. The signal fusion algorithm for multiple channels is relatively simple, making it difficult to effectively suppress noise and enhance signal reliability. Summary of the invention

[0004] In view of the problems existing in the existing brain-computer interface technology, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to solve the signal acquisition stability, real-time performance and signal processing problems.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, an embodiment of the present invention provides a brain-computer interface control system based on multi-mode fusion, which includes a microelectrode array management module, which is responsible for the arrangement and initial signal acquisition of the microelectrode array, and monitors the characteristics of the EEG signal in real time; a parameter optimization module, which is used to dynamically adjust the sampling parameters of the microelectrode array to optimize the signal quality; a multi-channel signal processing module, which performs weighted fusion and noise suppression on multiple electrode channel signals; a self-calibration feedback module, which optimizes the acquisition parameters of the microelectrode array according to the feedback self-calibration electrode parameters; a control instruction generation module, which is used to generate instructions; an output adjustment module, which is used to dynamically adjust the output; the microelectrode array management module is connected to the parameter optimization module, and the parameter optimization module is connected to the multi-channel signal processing module; the multi-channel signal processing module is connected to the self-calibration feedback module; the self-calibration feedback module is connected to the output adjustment module; and the output adjustment module is connected to the output adjustment module.

[0007] In the second aspect, an embodiment of the present invention provides a brain-computer interface control method based on multi-mode fusion, which includes: arranging a microelectrode array in the target area, initializing the microelectrode array and performing initial microelectrode array signal acquisition, and real-time monitoring and collecting EEG signal characteristics; automatically adjusting the microelectrode array signal acquisition parameters according to the signal characteristics to optimize the electrode channel signal quality; processing multiple electrode channel signals to enhance signal reliability and output stability; performing self-calibration based on a feedback mechanism to optimize microelectrode array signal acquisition parameters; generating control instructions, and dynamically adjusting the output according to actual scenario needs.

[0008] As a preferred solution of the brain-computer interface control method based on multi-mode fusion described in the present invention, the automatic adjustment of the microelectrode array signal acquisition parameters according to the signal characteristics includes the following steps: dynamically optimizing the sampling frequency of the microelectrode array according to the monitored and collected signal characteristics; automatically adjusting the gain and impedance matching of the microelectrode array according to different neural signal frequency bands and modes.

[0009] As a preferred solution of the brain-computer interface control method based on multi-mode fusion described in the present invention, the dynamic optimization of the sampling parameters of the microelectrode array includes: the dynamic adjustment of the sampling frequency is adaptively optimized according to the frequency characteristics of the signal, and the formula is designed based on the Nyquist criterion combined with the adaptive adjustment mechanism, which is as follows: ; in, is the sampling frequency at the current time t, is the signal frequency detected at the current time t, It is the frequency compensation amount dynamically adjusted according to the signal frequency band and noise level at time t. It is the set minimum sampling frequency; for low-frequency signals, the sampling frequency is lowered to reduce data redundancy; for high-frequency signals, the sampling frequency is increased to capture rapidly changing signals.

[0010] As a preferred solution of the brain-computer interface control method based on multi-mode fusion described in the present invention, the automatic adjustment of the gain and impedance matching of the microelectrode array includes the following contents: the adjustment of the gain of the microelectrode array is adaptively optimized according to the amplitude characteristics of the signal, and the adjustment process is as follows: ; in, is the gain at time t, is the initial gain value, is the ideal amplitude of the target signal, is the signal amplitude monitored in real time, is the adjustment factor; the optimization of the impedance matching of the microelectrode array is achieved by real-time monitoring of the impedance between the electrode and the skin or brain tissue and dynamically adjusting the contact conditions. The formula is as follows: ; in, is the effective impedance of the current electrode contact, is the initial impedance value, is the target contact resistance, is the contact resistance value measured in real time, is the impedance matching adjustment factor.

[0011] As a preferred solution of the brain-computer interface control method based on multi-mode fusion described in the present invention, the self-calibration based on the feedback mechanism and the optimization of the microelectrode array signal acquisition parameters include: evaluating the acquisition results after each signal adjustment, evaluating the signal quality under the new parameters, and comparing with the initial settings: if the signal quality continues to improve, the system will automatically maintain the current microelectrode array signal acquisition parameters and save the current state as the new benchmark; if the signal quality decreases or fluctuates, it will be restored to the previous stable parameter state through the rollback mechanism; the microelectrode array signal acquisition parameters are gradually optimized according to the trend of signal changes, and the optimal microelectrode array signal acquisition conditions are found through continuous small adjustments; the results of each self-calibration adjustment will be fed back to the signal quality evaluation to form a closed-loop feedback loop, and the optimal microelectrode array signal acquisition parameter settings are found through multiple rounds of feedback and self-calibration adjustments.

[0012] As a preferred solution of the brain-computer interface control method based on multi-mode fusion described in the present invention, the calculation formula for the signal quality evaluation is as follows: ; in, ~ are signal quality weight factors respectively; is the signal strength, is the frequency response, is the signal-to-noise ratio.

[0013] As a preferred solution of the brain-computer interface control method based on multi-mode fusion described in the present invention, wherein: the processing of multiple electrode channel signals includes weighted fusion and noise suppression.

[0014] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the brain-computer interface control method based on multi-mode fusion as described in the second aspect of the present invention are implemented.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the brain-computer interface control method based on multi-mode fusion as described in the second aspect of the present invention are implemented.

[0016] The beneficial effects of the present invention are as follows: the present invention monitors the characteristics of EEG signals in real time by arranging microelectrode arrays on the cerebral cortex or scalp surface, and realizes adaptive adjustment of signal acquisition by adopting dynamically optimized sampling parameters, thereby ensuring accurate capture of different EEG signal frequency bands. At the same time, through weighted fusion and noise suppression technology, the reliability and output stability of the signal are enhanced, and the problems of unstable signal acquisition, inflexible parameter adjustment, and poor noise suppression effect in the existing brain-computer interface technology are solved, and the accurate acquisition and processing of EEG signals are realized, thereby improving the flexibility and accuracy of the system in real-time applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 This is a structural diagram of the brain-computer interface control system based on multi-mode fusion.

[0019] Figure 2 This is a flow chart of the brain-computer interface control method based on multi-mode fusion. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments. Example

[0023] Reference Figure 1~Figure 2 , which is the first embodiment of the present invention, and provides a brain-computer interface control system based on multi-mode fusion, including: The microelectrode array management module is responsible for the layout of the microelectrode array and the initial signal acquisition, and monitors the characteristics of the EEG signal in real time; the parameter optimization module is used to dynamically adjust the microelectrode array signal acquisition to optimize the signal quality; the multi-channel signal processing module is used to enhance the reliability and stability of the electrode channel signal; the self-calibration feedback module is used to self-calibrate the electrode parameters according to the feedback; the control instruction generation module is used to generate instructions; the output adjustment module is used to dynamically adjust the output.

[0024] Furthermore, this embodiment also provides a brain-computer interface control method based on multi-mode fusion, including: S1: Arrange the microelectrode array in the target area (cerebral cortex or scalp surface), initialize the microelectrode array and perform the initial microelectrode array signal acquisition, and monitor the EEG signal characteristics in real time.

[0025] Specifically, standard initial microelectrode array signal parameters, such as sampling frequency, gain, and electrode contact quality, are used to start the acquisition system and record the neuronal activity in the target brain region. During the acquisition process, the system records the EEG signals in real time to generate initial EEG signal data.

[0026] Furthermore, the collected signals are analyzed in real time to detect signal characteristics (such as amplitude, frequency and noise level), and the main frequency bands in the EEG signals (such as α waves, β waves, etc.) are identified through frequency domain analysis techniques (such as FFT). The change trend of the signal is observed using time domain analysis methods (such as sliding window method), and the signal stability and noise interference are detected, including signal strength, signal-to-noise ratio (SNR), etc.

[0027] S2: Automatically adjust the sampling parameters of the microelectrode array according to the signal characteristics to optimize the signal quality.

[0028] S2.1: Dynamically optimize the sampling frequency of the microelectrode array based on the characteristics of the monitored signals.

[0029] Preferably, based on the collected signal characteristics, it is analyzed whether the current collection parameters need to be optimized, wherein the signal quality is analyzed through a feedback mechanism to identify the deviation between the collection parameters and the signal characteristics; according to the dynamic changes of the signal, it is determined whether the parameters of the collection equipment, such as sampling frequency, gain, and impedance, need to be adjusted in real time.

[0030] Specifically, the dynamic adjustment of the sampling frequency is adaptively optimized according to the frequency characteristics of the signal (such as α wave, β wave, etc.), and the formula is designed based on the Nyquist criterion combined with the adaptive adjustment mechanism, as follows: ; in, is the sampling frequency at the current time t (dynamically changes with time), is the signal frequency detected at the current time t (such as the frequency range of the α wave), It is the frequency compensation amount dynamically adjusted according to the signal frequency band and noise level at time t. is the minimum sampling frequency.

[0031] According to the EEG signals in different frequency bands (such as α waves, β waves, etc.), the sampling frequency of the microelectrode array is dynamically adjusted to ensure accurate signal acquisition: for low-frequency signals (such as δ waves), the sampling frequency can be lowered to reduce data redundancy; for high-frequency signals (such as β waves), the sampling frequency is increased to capture rapidly changing signals. For example, for β waves (13-30Hz), the sampling frequency should be set to above 500Hz, while for δ waves (0.5-4Hz), it can be reduced to about 250Hz.

[0032] S2.2: Automatically adjust the sampling gain, impedance matching and other parameters of the microelectrode array according to different neural signal frequency bands and modes to ensure the sensitivity and accuracy of signal acquisition.

[0033] Specifically, the gain parameter is automatically adjusted according to the signal amplitude to ensure that the collected signal amplitude is within the range that the device can handle. If the signal strength is weak, the gain will be automatically increased (such as from 10,000 times to 50,000 times) to enhance the signal strength and prevent the small signal from being masked by noise; if the signal strength is too large, the gain will be automatically reduced to avoid signal overload or device saturation. The gain adjustment is adaptively optimized according to the amplitude characteristics of the signal to avoid signal overload or small signals being masked by noise. The adjustment process is as follows: ; in, is the gain at time t, is the initial gain value, is the ideal amplitude of the target signal, is the signal amplitude monitored in real time, It is an adjustment factor used to control the dynamic response speed of the gain (generally the value is 0.5-1.5, and the specific adjustment is based on different signals).

[0034] Check the impedance between the electrode and the skin or brain tissue to ensure the stability of signal conduction. If the electrode impedance is high (such as greater than 10kΩ), the system will prompt or automatically adjust the electrode contact quality or use an impedance matching circuit to reduce the impedance; automatically optimize the impedance matching parameters of the electrode to avoid signal distortion or increased noise due to excessive impedance.

[0035] Impedance matching can be optimized by monitoring the impedance between the electrode and the skin or brain tissue in real time and dynamically adjusting the contact conditions. The formula is as follows: ; in, is the effective impedance of the current electrode contact, is the initial impedance value, is the target contact resistance (such as the ideal electrode-skin contact resistance value), is the contact resistance value measured in real time, It is the adjustment factor of impedance matching, which controls the speed and sensitivity of adjustment.

[0036] It can be seen that adaptive optimization of signal acquisition is achieved by automatically adjusting the gain parameters according to the signal amplitude. Current brain-computer interface technology usually relies on static settings. This method may cause signal quality to deteriorate when facing environmental changes. The present invention overcomes this shortcoming and adopts a dynamic adjustment mechanism to ensure sensitivity and accuracy during signal acquisition. Dynamic gain adjustment can effectively respond to the processing requirements of signals of different strengths, avoiding signals that are too small to be masked by noise, or distortion caused by signal overload. For example, when the signal strength is detected to be weak, the automatic increase in gain ensures that valid signals can be captured even in high-noise environments.

[0037] S3: Perform weighted fusion and noise suppression on multiple electrode channel signals to enhance signal reliability and output stability.

[0038] For each electrode channel in the microelectrode array, EEG signals are collected to obtain independent signal data of multiple channels. The signal captured by each channel may contain different noise and distortion, so subsequent processing is required to enhance the signal quality.

[0039] A weighted fusion algorithm is applied to the signals of multiple electrode channels. Different weight coefficients are set for the signals of each channel. Through weighted average processing, multiple signals can be effectively fused and the noise and interference that may exist in some channel signals can be reduced.

[0040] Specifically, the formula of the weighted fusion algorithm is as follows: ; in, is the fused signal, is the weight of electrode channel i, is the collected signal of electrode channel i, and N is the total number of electrode channels.

[0041] Furthermore, during the signal fusion process, noise suppression is performed on the signal of each electrode channel, and an adaptive filter (such as an LMS filter or a Kalman filter) is used to reduce the impact of high-frequency noise or environmental interference. The stability of the output signal is ensured by weighted averaging or noise suppression of the signals from multiple electrodes.

[0042] S4: Self-calibration based on the feedback mechanism gradually optimizes the acquisition parameters of the microelectrode array.

[0043] The acquisition results after each signal adjustment are evaluated, the signal quality under the new parameters is assessed, and compared with the initial settings: if the signal quality is found to continue to improve (for example, SNR and signal strength are better than the previous settings), the system will automatically maintain the current microelectrode array acquisition parameters and save the current state as the new baseline; if the signal quality decreases or fluctuates, the rollback mechanism will be used to restore to the previous stable parameter state to avoid long-term signal degradation. This mechanism allows the system to make tentative adjustments in a short period of time and quickly roll back to a safe state when an abnormality occurs.

[0044] Among them, the calculation formula for evaluating the signal quality under the new parameters is as follows: ; in, ~ are signal quality weight factors respectively; is the signal strength, is the frequency response, is the signal-to-noise ratio.

[0045] The system will gradually optimize the microelectrode array signal acquisition according to the trend of signal changes, and find the optimal microelectrode array signal acquisition conditions through continuous small adjustments. In order to avoid signal fluctuations caused by frequent adjustments, the system sets limits on the adjustment frequency and range to ensure that the microelectrode array signal acquisition remains stable during long-term operation; the results of each self-calibration adjustment will be fed back to the system's signal evaluation to form a closed-loop feedback loop. Through multiple rounds of feedback and self-calibration adjustments, the system gradually finds the optimal microelectrode array acquisition parameter settings.

[0046] Among them, the feedback loop update formula is: ; in, is the updated set of electrode acquisition parameters, is the collection parameter set of the previous round, is the learning rate, which is used to control the step size of parameter adjustment. is the gradient of signal quality to acquisition parameters, and P is the set of acquisition parameters.

[0047] Through the self-calibration mechanism, the system can continuously adjust the acquisition parameters of the microelectrode array according to the quality of the collected signal, ensuring the stability and accuracy of signal acquisition and avoiding signal degradation during long-term operation.

[0048] S5: Generate control instructions and dynamically adjust outputs according to actual scenario needs to ensure real-time response.

[0049] The processed high-quality EEG signals are transmitted to the control command generation module, which receives signals from multiple electrode channels and generates output commands according to the predetermined control model. The control model will identify the corresponding signal pattern based on the characteristics of the collected EEG signals and convert them into specific operation commands, such as physical operations such as moving a robotic arm, robot or exoskeleton, or virtual controls such as manipulating objects, characters in a virtual environment or performing computer tasks.

[0050] In order to adapt to the changing needs of actual scenarios, the system will dynamically adjust the output of control instructions based on scenario feedback. For example, when the frequency of EEG signal changes is high (such as in fast motion control scenarios), the system needs to increase the output frequency to ensure timely response to EEG instructions; on the contrary, in stable or slow tasks, the frequency can be appropriately reduced to save computing resources; in scenarios requiring fine control (such as precision operations, medical instrument control), the system will improve the output accuracy of instructions; and in scenarios with lower task accuracy requirements (such as rough operations or navigation tasks), the accuracy can be reduced to improve the system response speed.

[0051] This embodiment also provides a computer device, which is suitable for the case of a brain-computer interface control method based on multi-mode fusion, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the brain-computer interface control method based on multi-mode fusion proposed in the above embodiment.

[0052] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0053] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the brain-computer interface control method based on multi-mode fusion as proposed in the above embodiment is implemented.

[0054] In summary, the present invention arranges a microelectrode array on the cerebral cortex or scalp surface to monitor the characteristics of EEG signals in real time, and uses dynamically optimized sampling parameters to achieve adaptive adjustment of signal acquisition, ensuring accurate capture of different EEG signal frequency bands. At the same time, through weighted fusion and noise suppression technology, the reliability and output stability of the signal are enhanced, and the problems of unstable signal acquisition, inflexible parameter adjustment, and poor noise suppression effect in the existing brain-computer interface technology are solved, and accurate acquisition and processing of EEG signals are achieved, thereby improving the flexibility and accuracy of the system in real-time applications. Example

[0055] Referring to Table 1, which is the second embodiment of the present invention, this embodiment provides a brain-computer interface control method based on multi-mode fusion. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0056] The experiment was conducted in a standardized laboratory environment, and the participants were professionally trained volunteers. The experimental equipment included a high-precision microelectrode array, a signal acquisition system, a real-time signal analysis module, and a control instruction generation module. First, the microelectrode array was precisely arranged in the target area (cerebral cortex) to ensure good contact between the electrode and the brain tissue. After the arrangement was completed, the microelectrode array was initialized, the initial sampling frequency was set to 500Hz, the gain was set to 20,000 times, and the impedance matching parameters were adjusted to the ideal value. The acquisition system was started, the initial signal acquisition began, and the activity signals of brain neurons were continuously recorded.

[0057] During the signal acquisition process, the system monitors the characteristics of the EEG signal in real time, including amplitude, frequency and noise level. Fast Fourier transform (FFT) is used to analyze the signal in the frequency domain to identify the main EEG frequency bands, such as alpha waves (8-12Hz), beta waves (13-30Hz), etc. At the same time, the sliding window method is used to observe the change trend of the signal in the time domain to evaluate the stability of the signal and the noise interference. Based on the collected signal characteristics, the system automatically adjusts the sampling parameters.

[0058] A weighted fusion algorithm is used to perform weighted averaging processing on the signals from different electrodes. At the same time, an adaptive filter (such as LMS filter) is applied to perform noise suppression processing on the fused signal to further improve the reliability and stability of the signal.

[0059] The processed high-quality EEG signals are transmitted to the control command generation module. Based on a predetermined control model, this module converts the EEG signal characteristics into specific operation commands, such as controlling the movement of a robotic arm or manipulating objects in a virtual environment. In order to meet the needs of different scenarios, the system dynamically adjusts the frequency and accuracy of output commands based on real-time feedback. The following table records the experimental data comparison between the method of the present invention and the traditional brain-computer interface method under different parameters: Table 1 Comparison table of EEG signal processing experiments

[0060] It can be clearly seen from the above experimental data table that the multi-mode fusion brain-computer interface control method based on the present invention shows significant advantages in multiple key parameters. First, in terms of sampling frequency, traditional methods generally use a fixed sampling frequency of 500Hz, while the method of the present invention dynamically adjusts the sampling frequency according to different EEG signal frequency bands. For example, when processing high-frequency β waves, Method 1 and Method 2 of the present invention increase the sampling frequency to 1000Hz and 750Hz, ensuring the accurate capture of fast signals; and when processing low-frequency δ waves, the sampling frequency is appropriately reduced to 250Hz, effectively reducing data redundancy. This dynamic adjustment mechanism not only optimizes the data acquisition efficiency, but also improves the response speed of the system, significantly reducing the signal distortion and data processing burden caused by the fixed sampling frequency in the traditional method.

[0061] Signal-to-noise ratio (SNR) is an important indicator for measuring signal quality. The SNRs of traditional methods 1 and 2 are 15dB and 16dB respectively, while the SNR values ​​of the method of the present invention under different configurations are significantly improved, up to 25dB. This improvement is mainly due to the multi-mode fusion and adaptive parameter adjustment mechanism introduced by the present invention in the process of signal acquisition and processing, which effectively suppresses noise interference and enhances the strength of useful signals. In addition, through weighted fusion algorithm and adaptive filtering technology, the method of the present invention further improves the reliability and stability of the signal, so that the system can decode EEG signals more accurately in practical applications, and improve the accuracy and response efficiency of control instructions.

[0062] In summary, the brain-computer interface control method based on multi-mode fusion shows obvious innovation and superiority in multiple key parameters such as sampling frequency, signal-to-noise ratio, gain adjustment, impedance matching, signal stability and response time. Compared with traditional methods, the present invention significantly improves the efficiency and quality of signal acquisition and processing through dynamic optimization and adaptive adjustment, enhances the reliability and real-time performance of the system, and solves the problems of signal distortion, noise interference and slow response in the prior art.

[0063] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A brain-computer interface control system based on multi-mode fusion, characterized in that: include, The microelectrode array management module is responsible for the layout of the microelectrode array and the initial signal acquisition, and monitors the characteristics of the EEG signals in real time; A parameter optimization module, which is used to dynamically adjust the sampling parameters of the microelectrode array to optimize the signal quality; Multi-channel signal processing module, which performs weighted fusion and noise suppression on signals from multiple electrode channels; A self-calibration feedback module optimizes the acquisition parameters of the microelectrode array according to the feedback of the self-calibration electrode parameters; A control instruction generation module, used for generating instructions; Output regulation module, used to dynamically adjust output; The microelectrode array management module is connected to the parameter optimization module, and the parameter optimization module is connected to the multi-channel signal processing module; the multi-channel signal processing module is connected to the self-calibration feedback module; The self-calibration feedback module is connected to the output regulation module; and the output regulation module is connected to the output regulation module.

2. A brain-computer interface control method based on multi-mode fusion, based on the brain-computer interface control system based on multi-mode fusion according to claim 1, characterized in that: Also includes, Arranging a microelectrode array in a target area, initializing the microelectrode array and performing initial microelectrode array signal acquisition, and monitoring and acquiring EEG signal characteristics in real time; Automatically adjust microelectrode array signal acquisition parameters according to the signal characteristics to optimize electrode channel signal quality; Process multiple electrode channel signals to enhance signal reliability and output stability; Self-calibration based on feedback mechanism to optimize microelectrode array signal acquisition parameters; Generate control instructions and dynamically adjust output according to actual scenario needs.

3. The brain-computer interface control method based on multi-mode fusion as claimed in claim 2, characterized in that: The automatic adjustment of the microelectrode array signal acquisition parameters according to the signal characteristics comprises the following steps: Dynamically optimize the sampling frequency of the microelectrode array according to the characteristics of the monitored and collected signals; Automatically adjust the gain and impedance matching of the microelectrode array according to different neural signal frequency bands and modes.

4. The brain-computer interface control method based on multi-mode fusion as claimed in claim 3, characterized in that: The sampling parameters of the dynamic optimization microelectrode array include: The dynamic adjustment of the sampling frequency is adaptively optimized according to the frequency characteristics of the signal. The formula is designed based on the Nyquist criterion combined with the adaptive adjustment mechanism, as follows: ； in, is the sampling frequency at the current time t, is the signal frequency detected at the current time t, It is the frequency compensation amount dynamically adjusted according to the signal frequency band and noise level at time t. is the minimum sampling frequency set; For low-frequency signals, the sampling frequency is lowered to reduce data redundancy; for high-frequency signals, the sampling frequency is increased to capture rapidly changing signals.

5. The brain-computer interface control method based on multi-mode fusion as claimed in claim 4, characterized in that: The automatic adjustment of the gain and impedance matching of the microelectrode array includes the following: The gain of the microelectrode array is adjusted by adaptive optimization according to the amplitude characteristics of the signal. The adjustment process is as follows: ； in, is the gain at time t, is the initial gain value, is the ideal amplitude of the target signal, is the signal amplitude monitored in real time, is the adjustment factor; The impedance matching of the microelectrode array is optimized by monitoring the impedance between the electrode and the skin or brain tissue in real time and dynamically adjusting the contact conditions. The formula is as follows: ； in, is the effective impedance of the current electrode contact, is the initial impedance value, is the target contact resistance, is the contact resistance value measured in real time, is the impedance matching adjustment factor.

6. The brain-computer interface control method based on multi-mode fusion as claimed in claim 5, characterized in that: The self-calibration based on the feedback mechanism to optimize the microelectrode array signal acquisition parameters includes: Evaluate the acquisition results after each signal adjustment, evaluate the signal quality under the new parameters, and compare with the initial settings: if the signal quality continues to improve, the system will automatically maintain the current microelectrode array signal acquisition parameters and save the current state as the new baseline; if the signal quality decreases or fluctuates, the rollback mechanism will be used to restore to the previous stable parameter state; According to the trend of signal changes, the microelectrode array signal acquisition parameters are gradually optimized, and the optimal microelectrode array signal acquisition conditions are found through continuous small adjustments; The result of each self-calibration adjustment will be fed back to the signal quality evaluation to form a closed feedback loop. Through multiple rounds of feedback and self-calibration adjustment, the optimal microelectrode array signal acquisition parameter setting is found.

7. The brain-computer interface control method based on multi-mode fusion according to claim 6, characterized in that: The calculation formula for the signal quality assessment is as follows: ； in, ~ are signal quality weight factors respectively; is the signal strength, is the frequency response, is the signal-to-noise ratio.

8. The brain-computer interface control method based on multi-mode fusion as claimed in claim 7, characterized in that: The processing of multiple electrode channel signals includes weighted fusion and noise suppression.

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