Implementation method for acquiring electroencephalogram flashlight interface system based on SSVEP and sound-light-electricity

Abstract

The invention discloses an implementation method of an electroencephalogram flashlight interface system based on SSVEP (Steady-State Visual Evoked Potential) and acousto-optic-electric acquisition, and relates to the technical field of brain-computer interfaces, the method comprises the following steps: S1, collecting and synchronously processing acoustic, optical and electric stimulation signals to generate a composite synchronous stimulation sequence, S2, using the composite synchronous stimulation sequence to stimulate a subject in real time, and synchronously collecting electroencephalogram response signals, s3, analyzing time domain and frequency domain correlation degrees between the electroencephalogram reaction signals and the composite synchronous stimulation sequence, and identifying and optimizing matching features, and S4, dynamically adjusting time and intensity parameters of sound, light and electric stimulation signals to maintain optimal synchronism based on the optimized matching features; according to the implementation method of the electroencephalogram flashlight interface system based on SSVEP and acousto-optic-electric acquisition, the synchronism of an acousto-optic-electric integrated system is regulated and controlled so as to solve the problem that a stimulation signal is not matched with an electroencephalogram reaction.

Description

Technical Field

[0001] The present invention relates to the technical field of brain-computer interfaces, and particularly to a method for implementing a brain-computer flashlight interface system based on SSVEP and acoustic-optic-electricity acquisition. Background Art

[0002] In a brain-computer interface (BCI) system based on steady-state visual evoked potential (SSVEP) and acoustic-optic-electric stimulation, the synchronization of external stimulation signals is crucial for improving the stability of electroencephalogram (EEG) responses and the recognition accuracy of the system. Current multi-modal stimulation systems usually use independent hardware devices to provide visual, auditory, and tactile stimulations respectively, and rely on a computer or a microcontroller for synchronous control. However, due to different signal processing delays of different stimulation devices, the stimulation signals may be temporally mismatched, restricting the brain's ability to integrate multi-modal information and affecting the coherence and classification accuracy of SSVEP signals. In addition, factors such as external environmental interference and clock drift may also cause phase shifts of the stimulation signals, further reducing the synchronous accuracy of the system.

[0003] In the prior art, software timing, external trigger signals, or additional synchronous hardware are often used to achieve synchronous control of acoustic-optic-electric signals. However, these methods often have problems such as complex delay compensation, insufficient real-time performance, or high system implementation costs. For example, software timing depends on the clock accuracy of a computer and is easily affected by the operating system scheduling, resulting in time errors at the microsecond level; although external trigger signals can improve synchronization, they require additional hardware interfaces, increasing the system complexity. Therefore, how to improve the synchronous control accuracy of an acoustic-optic-electric stimulation system without increasing the additional hardware burden and ensure the matching between stimulation signals and EEG responses is a problem that needs to be solved in the current SSVEP brain-computer interface system. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for implementing a brain-computer flashlight interface system based on SSVEP and acoustic-optic-electricity acquisition, and to regulate the synchronization of an acoustic-optic-electric integrated system to solve the problem of mismatch between stimulation signals and EEG responses.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A method for implementing a brain-computer flashlight interface system based on SSVEP and acoustic-optic-electricity acquisition, the method comprising:

[0006] S1. Collect and synchronize acoustic, optical, and electrical stimulation signals to generate a composite synchronous stimulation sequence;

[0007] S2. Use the composite synchronous stimulation sequence to perform real-time stimulation on a subject and synchronously collect EEG response signals, including measuring the amplitude change of the EEG response signals, calculating the deviation of the stimulation signals based on the feedback of the EEG response signals, and making adjustments. The specific formula is: Q′ = Q + a×(Q target-Q);

[0008] Wherein, Q' represents the adjusted stimulus signal intensity, Q represents the intensity of the current stimulus signal, a represents the adjustment coefficient, and Q target represents the target stimulus signal intensity;

[0009] Update the signal in real time according to the calculated new stimulus intensity;

[0010] S3. Analyze the time-domain and frequency-domain correlation between the EEG response signal and the composite synchronous stimulus sequence, identify and optimize the matching features, including measuring the periods of sound, light, and electrical signals, analyzing and determining the dominant period of the subject's EEG signal, and calculating the resonance ratio. The specific formula is: A = T 1 / T 2 ;

[0011] Wherein, A represents the synchronous matching degree between the stimulus signal and the EEG signal, and T 1 represents the period of the sound, light, and electrical signals, and T 2 represents the dominant period of the subject's EEG signal;

[0012] S4. Based on the optimized matching features, dynamically adjust the time and intensity parameters of the sound, light, and electrical stimulus signals to maintain the best synchronization.

[0013] Preferably, S1 includes statistically determining the trigger time error of the sound, light, and electrical signals, determining the adjustment rate to smoothly compensate for the time difference between different signals, and updating the trigger time of each stimulus signal. The specific formula is: B' = B + b × Δt;

[0014] Wherein, B' represents the trigger time of the sound, light, and electrical stimulus signals after synchronization adjustment, B represents the current trigger time of the sound, light, and electrical stimulus signals, b represents the synchronization adjustment rate factor, and Δt represents the time deviation between different stimulus signals.

[0015] Preferably, S4 includes calculating the optimal stimulus time and intensity according to the load level in the EEG response signal and the tolerance of the brain. The specific formula is:

[0016] Wherein, t green represents the time of the adjusted sound, light, and electrical stimulus signals, t cycle represents the basic period of the stimulus signal, X represents the current EEG response signal load level of the brain, and Y represents the maximum adaptability of the brain to external stimuli.

[0017] Preferably, the calculation formula for the intensity Q of the current stimulus signal in S2 is:

[0018] Q = G × S + K × E;

[0019] Among them, Q represents the intensity of the current stimulation signal, G represents the gain factor of the stimulation signal, S represents the currently set basic stimulation intensity, K represents the electroencephalogram response feedback adjustment factor, and E represents the electroencephalogram response feedback error.

[0020] Preferably, the calculation formula for the adjustment coefficient a in S2 is:

[0021] Among them, a represents the adjustment coefficient, γ represents the basic adjustment ratio, E represents the electroencephalogram response feedback error, β represents the stability factor, and V represents the fitness change rate.

[0022] Preferably, the calculation formula for the trigger time B of the current acoustic, optical, and electrical stimulation signals in S1 is: B = T s +T d ;

[0023] Among them, B represents the trigger time of the current acoustic, optical, and electrical stimulation signals, T s represents the set trigger time of the stimulation signal, and T d represents the device response time delay.

[0024] Preferably, the calculation formula for the time deviation Δt between different stimulation signals in S1 is: Δt = B ideal -B;

[0025] Among them, Δt represents the time deviation between different stimulation signals, B ideal represents the theoretically ideal signal trigger time, and B represents the trigger time of the current acoustic, optical, and electrical stimulation signals.

[0026] Preferably, the calculation formula for the theoretically ideal signal trigger time B ideal is: B ideal = 1 / f;

[0027] Among them, B ideal represents the theoretically ideal signal trigger time, and f represents the target trigger frequency of the signal.

[0028] Preferably, S3 further includes:

[0029] Adjust the time parameters of the acoustic, optical, and electrical stimulation signals based on the differences in time-domain and frequency-domain correlation degrees, and calculate the time parameters according to the real-time collected electroencephalogram response;

[0030] Generate a new composite synchronous stimulation sequence through the synchronously collected data, and calculate the spectrum using Fourier transform to improve the recognition accuracy.

[0031] Preferably, the electroencephalogram response calculation in S3 combines an adaptive filter to eliminate low-frequency noise in real time.

[0032] As can be seen from the above technical solutions, the present invention has the following beneficial effects:

[0033] The implementation method of the EEG flashlight interface system based on SSVEP and sound, light and electricity obtains the EEG flashlight interface system by collecting and synchronizing sound, light and electrical stimulation signals, generating a composite synchronous stimulation sequence, using the composite synchronous stimulation sequence to perform real-time stimulation on the subject, and synchronously collecting EEG response signals, analyzing the time-domain and frequency-domain correlation between the EEG response signals and the composite synchronous stimulation sequence, identifying and optimizing matching features, and based on the optimized matching features, dynamically adjusting the time and intensity parameters of the sound, light and electrical stimulation signals to maintain the best synchronization, ensuring that each stimulation channel works under the same time reference, thereby improving the brain's ability to integrate multimodal information, enhancing the coherence of SSVEP signals, ensuring the best match between EEG responses and stimulation signals, improving the classification accuracy of the brain-computer interface system, optimizing the time alignment of sound, light and electrical stimulation signals at the software level, reducing the system implementation cost, while improving the applicability and portability of the system, being able to maintain high-precision stimulation synchronization, improving the stability and reliability of the BCI system, being applicable to multiple fields such as medical rehabilitation, neuroscience research, brain disease diagnosis, virtual reality, and augmented reality interaction, enabling the brain-computer interface system to provide efficient neural interaction capabilities in a wider range of application scenarios, enhancing the user experience, and regulating the synchronization of the sound, light and electricity integrated system to solve the problem of mismatch between stimulation signals and EEG responses. Brief Description of the Drawings

[0034] Figure 1 It is a flowchart of the method of the present invention. Detailed Embodiments

[0035] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0036] As Figure 1 shown, the present invention provides a technical solution: an implementation method of an EEG flashlight interface system based on SSVEP and sound, light and electricity, the method includes:

[0037] S1. Collect and synchronize sound, light and electrical stimulation signals to generate a composite synchronous stimulation sequence;

[0038] S2. Use the composite synchronous stimulation sequence to perform real-time stimulation on the subject, and synchronously collect EEG response signals, including measuring the amplitude change of the EEG response signals, calculating the deviation of the stimulation signals according to the feedback of the EEG response signals, and making adjustments. The specific formula is: Q′ = Q + a×(Qtarget -Q);

[0039] where Q' represents the adjusted intensity of the stimulation signal, Q represents the intensity of the current stimulation signal, a represents the adjustment coefficient, and Q target represents the target stimulation signal intensity;

[0040] Update the signal in real time according to the calculated new stimulation intensity;

[0041] S3. Analyze the time-domain and frequency-domain correlation between the electroencephalogram (EEG) response signal and the composite synchronous stimulation sequence, identify and optimize the matching features, including measuring the periods of sound, light, and electrical signals, analyzing and determining the dominant period of the subject's EEG signal, and calculating the resonance ratio. The specific formula is: A = T 1 / T 2 ;

[0042] where A represents the synchronous matching degree between the stimulation signal and the EEG signal, T 1 represents the periods of sound, light, and electrical signals, and T 2 represents the dominant period of the subject's EEG signal;

[0043] S4. Based on the optimized matching features, dynamically adjust the time and intensity parameters of the sound, light, and electrical stimulation signals to maintain the best synchronization.

[0044] The method of the present invention realizes precise brain-computer interface (BCI) interaction by integrating multi-modal stimulations of sound, light, and electricity. First, using high-precision synchronous control technology, ensure that the sound, light, and electrical signals can be synchronously output strictly according to the preset time sequence to form a composite synchronous stimulation sequence, thereby minimizing the stimulation deviation and improving the signal consistency. During the stimulation process, monitor the subject's EEG response signal in real time, extract features such as EEG amplitude changes using computer algorithms, analyze the EEG feedback information, calculate the deviation of the stimulation signal, and use the adjustment formula Q' = Q + a×(Q target -Q) to dynamically adjust the stimulation intensity to achieve closed-loop optimization control. At the same time, based on the feature analysis of the time domain and frequency domain of the EEG signal, calculate the resonance ratio A = T 1 / T 2, further optimize the stimulation signal to make it more precisely match the dominant EEG cycle of the subject, ensuring the maximum neural response. Finally, based on the optimized matching features, the system dynamically adjusts the time and intensity parameters of the acoustic, optical, and electrical stimulation signals to maintain the best synchronization, ensuring that the entire system can operate stably under different individuals and environments, and improving the accuracy and robustness of EEG signal decoding. Through the synchronous control of multi-modal stimulation, the present invention realizes more precise acquisition and interaction of brain-computer interface signals. Compared with the traditional single-mode stimulation method, it can effectively improve the stability and reliability of EEG response signals. First, based on the combination of multi-modal stimulation, the detectability of EEG signals is improved, enabling users to maintain a high interaction accuracy in complex environments. Second, this method adopts a real-time feedback adjustment mechanism, which can dynamically adjust the intensity of the stimulation signal, enabling the EEG signal to more effectively adapt to the stimulation frequency and improving the response efficiency and accuracy. In addition, the present invention calculates the matching degree between the EEG signal and the stimulation sequence, and dynamically adjusts the stimulation signal period based on this, ensuring that the brain-computer interface system has the ability of adaptive optimization, so as to maintain high signal transmission stability and robustness under different individuals and usage conditions. At the same time, this method reduces the need for human intervention and improves the automation level of the system, enabling the brain-computer interface technology to be more widely applied in fields such as neurorehabilitation, intelligent control, virtual reality (VR) interaction, etc., providing more advanced technical support for neuroscience research and medical applications.

[0045] S1 includes statistically determining the triggering time error of the acoustic, optical, and electrical signals, determining the adjustment rate to smoothly compensate for the time difference between different signals, and updating the triggering time of each stimulation signal. The specific formula is: B′ = B + b × Δt;

[0046] Wherein, B′ represents the triggering time of the synchronized acoustic, optical, and electrical stimulation signals, B represents the current triggering time of the acoustic, optical, and electrical stimulation signals, b represents the rate factor of synchronous adjustment, and Δt represents the time deviation between different stimulation signals.

[0047] The method of the present invention statistically analyzes the triggering time error of the acoustic, optical, and electrical stimulation signals, calculates the time deviation between each signal, and adjusts the triggering time accordingly to ensure the precise synchronization of the multi-modal stimulation signals. First, in step S1, the system uses a high-precision time measurement unit to statistically determine the triggering time error of the acoustic, optical, and electrical signals and establish a time feature database for each stimulation signal. Then, according to the measured time deviation Δt, the adjustment rate b is calculated, and the triggering time of each stimulation signal is corrected using the time smoothing compensation algorithm. The adjustment process follows the following formula:

[0048] B′ = B + b × Δt;

[0049] Among them, B′ represents the triggering time of the synchronized audio, optical, and electrical stimulation signals, B represents the current triggering time, bbb represents the synchronization adjustment rate factor, and Δt represents the time deviation between different stimulation signals. Through this calculation method, the system can establish an accurate time correction mechanism between multi-modal stimulation signals, thereby reducing the error caused by asynchronous stimulation signals. Finally, the adjusted audio, optical, and electrical stimulation signals can act on the subject in an optimized synchronization manner, improving the stability and consistency of the electroencephalogram response signal. By statistically analyzing and dynamically adjusting the time error of the stimulation signal, the present invention greatly improves the synchronization accuracy of the brain-computer interface system. First, a time compensation strategy is adopted to make the triggering times of different stimulation signals more consistent, reducing the phase shift problem of the electroencephalogram response signal caused by time error, thereby improving the detectability and accuracy of the electroencephalogram signal. Second, this method can adapt to the neural response characteristics of different individuals by adaptively adjusting the synchronization rate, realizing personalized synchronization optimization. In addition, the introduction of the time correction mechanism significantly reduces the system's dependence on high-precision external synchronization devices, improves the practicability and universality of the brain-computer interface system, and makes it easier to be popularized in application scenarios such as neurorehabilitation, intelligent control, and virtual reality (VR).

[0050] S4 includes calculating the optimal stimulation time and intensity according to the load level in the electroencephalogram response signal and the tolerance of the brain. The specific formula is as follows:

[0051] Where t green represents the time of the adjusted audio, optical, and electrical stimulation signals, t cycle represents the basic period of the stimulation signal, X represents the load level of the current electroencephalogram response signal of the brain, and Y represents the maximum adaptability of the brain to external stimuli.

[0052] Based on the adaptability of the brain to external stimuli and combined with the load level of the electroencephalogram signal, the present invention dynamically adjusts the time parameters of the stimulation signal. In the process of S4, the system first obtains the current electroencephalogram response signal and calculates its load level X through an algorithm. At the same time, the system presets the maximum adaptability Y of the brain to external stimuli to set the threshold of the stimulation intensity. Through the calculation formula:

[0053] The system can determine the optimal stimulation time, so that the stimulation signal neither over-stimulates the brain nor reduces the response effect due to insufficient stimulation. This adjustment strategy ensures the dynamic adaptation of the stimulation signal and improves the intelligence level of the BCI (brain-computer interface) system.

[0054] By monitoring the EEG load level in real time and adaptively adjusting the stimulation time, the present invention enables the brain-computer interface system to maintain a stable and efficient working state under different users and environmental conditions. Compared with the fixed-cycle stimulation method, this method can effectively reduce stimulation fatigue, improve the persistence of EEG responses, and avoid signal attenuation caused by excessive stimulation. Since this method can dynamically adjust stimulation parameters, it is particularly suitable for individualized brain-computer interaction requirements, such as applications in neurorehabilitation, EEG control systems, and attention enhancement training. In addition, this method reduces the need for human intervention and improves the automation level of the brain-computer interface, enabling the system to maintain optimal performance during long-term use.

[0055] In S2, the calculation formula for the intensity Q of the current stimulation signal is: Q = G × S + K × E;

[0056] Where, Q represents the intensity of the current stimulation signal, G represents the gain factor of the stimulation signal, S represents the currently set basic stimulation intensity, K represents the EEG response feedback adjustment factor, and E represents the EEG response feedback error.

[0057] Based on the adaptive control mechanism of the brain-computer interface (BCI) system, the present invention optimizes the intensity of the stimulation signal in real time through EEG feedback. During the process of S2, the system first generates an initial stimulation signal according to the preset basic stimulation intensity SSS, and at the same time combines the gain factor G to adjust the signal amplitude. During the stimulation process, the system synchronously collects the EEG response signal and calculates the feedback error E, which reflects the deviation between the current stimulation signal and the target EEG response. The feedback adjustment factor K is used to dynamically adjust the stimulation signal to make it more accurately match the subject's neural state. Finally, through the calculation formula:

[0058] Q = G × S + K × E;

[0059] The intensity of the stimulation signal is adjusted so that it is always within the optimal range, thereby improving the stability and response accuracy of BCI interaction.

[0060] By introducing an EEG feedback error correction mechanism, the present invention enables the stimulation signal to adapt to the individual's EEG response state in real time, improving the flexibility and intelligence level of the BCI system. Compared with the traditional fixed-intensity stimulation method, this method can dynamically adjust stimulation parameters, reduce interaction failures caused by over-strong or over-weak stimulation, and improve the adaptability of the system. In addition, by adjusting the gain factor G and the feedback adjustment factor K, the stimulation intensity can be optimized for different individuals or application scenarios, thereby enhancing the versatility and robustness of the system. This method is particularly suitable for personalized neuromodulation, rehabilitation training, and high-precision brain-computer interaction applications, such as medical rehabilitation, intelligent control, and brain-computer collaborative working environments.

[0061] In S2, the calculation formula for the adjustment coefficient a is:

[0062] Among them, a represents the adjustment coefficient, γ represents the basic adjustment ratio, E represents the electroencephalogram (EEG) response feedback error, β represents the stability factor, and V represents the fitness change rate.

[0063] In the S2 process of the present invention, the adaptability of the stimulation signal is dynamically optimized by calculating the adjustment coefficient a. First, the system collects the EEG response signal and calculates the feedback error E, which is used to measure the deviation between the current stimulation signal and the expected EEG response. Then, in combination with the stability factor β and the fitness change rate V, the magnitude of the adjustment coefficient a is determined. The adjustment calculation formula is as follows: Among them, the basic adjustment ratio γ sets the basic adjustment intensity of the system, the fitness change rate V reflects the fluctuation degree of the EEG signal within a certain time range, and the stability factor β prevents over-adjustment or system oscillation by regulating the fitness change rate. This method ensures that the stimulation signal can be finely adjusted according to the real-time EEG feedback, enabling the system to still operate efficiently in a dynamic environment. By introducing a calculation method for the adjustment coefficient based on the feedback error EEE, the present invention enables the stimulation signal to more flexibly adapt to the EEG fluctuations of individuals, improving the adaptive ability of the brain-computer interface (BCI). Compared with the traditional fixed adjustment method, this method can effectively reduce the problems of over-response or adjustment lag of the system, and enhance the precise control ability of the stimulation signal. In addition, by introducing the stability factor β, the system can maintain stability in the face of severe EEG fluctuations, preventing instability caused by environmental changes or individual state changes. At the same time, this method can dynamically optimize the individualized stimulation parameters, which is applicable to multiple application scenarios such as neurorehabilitation, intelligent control, and human-computer interaction, making the BCI system more intelligent and efficient.

[0064] The calculation formula for the trigger time B of the current acoustic, optical, and electrical stimulation signals in S1 is: B = T s + T d ;

[0065] Among them, B represents the trigger time of the current acoustic, optical, and electrical stimulation signals, T s represents the set trigger time of the stimulation signal, and T d represents the device response delay.

[0066] In the S1 process of the present invention, the system first sets the basic trigger time T s of the stimulation signal according to the experimental design, and then combines the actual response delay T d of the device to calculate the final trigger time B. Since different types of stimulation signals (such as acoustic, optical, and electrical) may have different processing delays at the hardware level, it is necessary to take into account the response characteristics of the device to ensure the synchronization of all stimulation signals. The calculation formula is as follows: B = T s + T d; where, T s is preset by experimental parameters to ensure that the stimulation signals are executed according to a predefined time series, and T d reflects the inherent delay of the device during signal output, which is affected by factors such as the device's hardware performance and signal transmission rate. Through this calculation formula, the system can effectively compensate for the device delay, ensuring that all stimulation signals reach precise synchronization at the execution moment, improving the controllability and accuracy of the electroencephalogram response. The present invention improves the time accuracy of the stimulation signals and ensures strict synchronization between different stimulation modes (sound, light, electricity) by introducing a device response time delay compensation mechanism during the stimulation signal triggering process. Compared with the traditional fixed-time triggering method, this method can effectively reduce the synchronization deviation caused by the device response delay and improve the precise control of the electroencephalogram signals by the brain-computer interface (BCI) system. In addition, this method is applicable to different types of hardware devices, enabling the BCI system to flexibly adapt to various stimulation devices, thereby improving the compatibility and applicability of the system. The present invention is particularly suitable for BCI research and applications with high time accuracy requirements, such as neurorehabilitation, brain-controlled devices, attention training, and augmented reality (AR) and other fields.

[0067] The calculation formula for the time deviation Δt between different stimulation signals in S1 is: Δt = B ideal - B;

[0068] where, Δt represents the time deviation between different stimulation signals, and B ideal represents the theoretically ideal signal triggering time, and B represents the triggering time of the current sound, light, and electricity stimulation signals.

[0069] In the process of S1 of the present invention, the method of calculating the time deviation Δ is used to optimize and adjust the triggering times of different stimulation signals to ensure strict synchronization between them. First, the system presets the theoretically ideal signal triggering time B ideal , which is determined based on experimental design or electroencephalogram response characteristics and represents the optimal triggering moment of each stimulation signal under ideal conditions. Subsequently, the system measures the current actual triggering time B, calculates the deviation Δt between it and the theoretical time, and its calculation formula is as follows: Δt = B ideal - B;

[0070] When Δt ≠ 0, the system dynamically optimizes the triggering time of the stimulation signal by adjusting the control strategy to compensate for the synchronization error between different signals. For example, in some cases, if B is earlier than B ideal , the system can introduce a short-time delay mechanism for compensation; if B is later than B ideal , the signal processing path can be optimized or the hardware response speed can be increased to reduce the time deviation.

[0071] By calculating and compensating for the time deviation between different stimulus signals, the present invention ensures that the trigger times of all stimulus signals are strictly synchronized, improving the time precision and response consistency of the brain-computer interface (BCI) system. Compared with the traditional fixed-time triggering method, this method can dynamically adapt to the neural response characteristics of different devices and individuals, reducing signal asynchronization problems caused by device delays or environmental interference. In addition, this method can be applied to various multimodal stimulus systems, such as electroencephalogram (EEG) signal control, neurorehabilitation training, virtual reality (VR), and augmented reality (AR) systems, improving the accuracy of human-computer interaction and the user experience.

[0072] The theoretically ideal signal trigger time B in S1 ideal The calculation formula is: B ideal = 1 / f;

[0073] Where, B ideal represents the theoretically ideal signal trigger time, and f represents the target trigger frequency of the signal.

[0074] In the process of S1 of the present invention, the theoretically ideal signal trigger time B is determined using the target trigger frequency f ideal , thereby providing a time reference for the system to ensure the precise synchronization of multimodal stimulus signals. The theoretical calculation formula is as follows: B ideal = 1 / f; The core idea of this formula is based on the periodicity of the signal, that is, for the target trigger frequency f, the corresponding theoretical trigger time should be its reciprocal 1 / f. In practical applications, the system calculates the ideal trigger time according to the set target frequency and uses it as a reference to adjust the trigger moment B of the actual stimulus signal. The system can calculate the time deviation Δt and adjust the trigger strategy according to this deviation to ensure that the signal is strictly executed according to the target frequency, thereby improving the time synchronization accuracy of the BCI (brain-computer interface) system. By calculating the ideal trigger time B ideal , a stable time reference is provided for the brain-computer interface system, enabling precise synchronization of multimodal stimulus signals and improving the stability and consistency of signal control. Compared with the traditional fixed-interval triggering method, this method can dynamically adjust the trigger time according to the target frequency, reducing the impact of external environmental changes or device clock drift on the system synchronization. In addition, this method is applicable to various brain-computer interface application scenarios, including steady-state visual evoked potential (SSVEP) control, neurorehabilitation training, and brain-controlled interaction in virtual reality (VR) environments, improving the adaptability and robustness of the system.

[0075] S3 also includes adjusting the time parameters of acoustic, optical, and electrical stimulus signals based on the differences in time-domain and frequency-domain correlation degrees, and calculating the time parameters according to the real-time collected electroencephalogram responses;

[0076] Generate a new composite synchronous stimulation sequence from the synchronously collected data, and calculate the spectrum using Fourier transform to improve the recognition accuracy.

[0077] In the S3 process of the present invention, by analyzing the time-domain and frequency-domain characteristics of the electroencephalogram (EEG) signals, the time parameters of the stimulation signals are optimized to improve the recognition accuracy and stability of the brain-computer interface (BCI) system. First, the system collects EEG response signals in real time, calculates their time-domain and frequency-domain characteristics, and analyzes the effects of different stimulation signals on the EEG response. Regarding the time correlation between different stimulation signals, the time parameters of the acoustic, optical, and electrical stimulation signals are adjusted to make them more compatible with the neural activities of the subject. Subsequently, based on the synchronously collected data, the system generates a new composite synchronous stimulation sequence, and calculates the spectral characteristics of the EEG signals using Fourier transform to extract the main frequency components. Fourier transform can convert the time-domain signal into a frequency-domain signal, thereby analyzing the response intensity of the subject to different stimulation signals, optimizing the synchrony of the stimulation signals, and improving the recognition accuracy of the target signal. Finally, according to the analysis results, the system dynamically adjusts the parameters of the stimulation signals to make them more in line with the individual neural characteristics, thereby improving the decoding accuracy of the BCI and the system robustness. The present invention realizes more precise adjustment of the stimulation signals by combining time-domain and frequency-domain analysis techniques, and improves the recognition rate of the EEG signals. Compared with the traditional fixed-parameter stimulation method, this method can optimize the stimulation signals in real time to make them adapt to the individual neural state, thereby improving the applicability of the BCI system. In addition, the introduction of Fourier transform enables the system to more accurately analyze the spectral characteristics of the EEG signals, reduce noise interference, and improve the ability to extract target signals. This method is particularly suitable for applications that require high-precision brain-computer interaction, such as brain-controlled devices, neurorehabilitation training, and augmented reality (AR) and other fields, improving the user experience and the system response speed.

[0078] In S3, electroencephalogram (EEG) response calculation is combined with an adaptive filter to eliminate low-frequency noise in real time. In the process of this invention in S3, by introducing an adaptive filter to optimize EEG signal processing, low-frequency noise is eliminated in real time, improving the reliability and accuracy of the signal. During the EEG signal acquisition process, low-frequency noise mainly comes from environmental interference, poor electrode contact, and physiological artifacts (such as eye movement and electromyogram interference). To effectively reduce these interferences, this method uses an adaptive filter to adjust the filtering parameters in real time to optimize the signal-to-noise ratio of the EEG signal. The basic working mechanism of adaptive filtering is to compare a reference signal (such as an EEG low-frequency noise model) with the target signal and dynamically adjust the weight coefficients of the filter to minimize the impact of noise. Common adaptive filtering algorithms include least mean square error and recursive least squares. In this invention, the system continuously updates the filter parameters based on the real-time collected data to ensure that it can adapt to the noise characteristics of different individuals and suppress the impact of low-frequency noise on the target signal in the shortest time. During the process of calculating the EEG signal spectrum by Fourier transform (FT), combining with an adaptive filter can further improve the signal quality, making the target SSVEP frequency components clearer, reducing the interference of non-target frequencies, and enhancing the system's ability to identify the target signal. This invention realizes real-time low-frequency noise suppression by combining an adaptive filter to optimize EEG signal processing, improving the detectability of the SSVEP target signal. Compared with traditional fixed filters (such as band-pass filters or Kalman filters), adaptive filtering can be dynamically adjusted according to the real-time changes in the noise environment, enabling the system to maintain stable signal quality under different environmental conditions. In addition, this method reduces misjudgments caused by low-frequency noise interference, improves the decoding accuracy of the brain-computer interface (BCI) system, and makes it more suitable for application scenarios such as brain-controlled devices, neurorehabilitation training, and intelligent human-computer interaction.

[0079] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for implementing an EEG handheld interface system based on SSVEP and acoustic-optical acquisition, characterized in that: The method comprises: S1, collect and synchronously process sound, light and electrical stimulation signals to generate a composite synchronous stimulation sequence; S2. Use a composite synchronous stimulation sequence to stimulate the subject in real time and synchronously collect EEG response signals, including measuring the amplitude change of the EEG response signal, calculating the deviation of the stimulation signal based on the feedback of the EEG response signal, and making adjustments. The specific formula is: Q′=Q+a×(Q target -Q); Where Q′ represents the adjusted stimulus signal strength, Q represents the current stimulus signal strength, a represents the adjustment coefficient, and Q target Indicates the target stimulus signal intensity; The signal is updated in real time based on the calculated new stimulus intensity; S3, analyzing the time domain and frequency domain correlation between the EEG response signal and the composite synchronous stimulation sequence, identifying and optimizing the matching features, including measuring the periods of sound, light, and electrical signals, analyzing and determining the dominant period of the subject's EEG signal, and calculating the resonance ratio. The specific formula is: A = T1 / T2; Among them, A represents the synchronization matching degree between the stimulation signal and the EEG signal, T1 represents the period of the sound, light and electrical signals, and T2 represents the dominant period of the subject's EEG signal; S4. Based on the optimized matching features, dynamically adjust the time and intensity parameters of the sound, light, and electrical stimulation signals to maintain optimal synchronization.

2. The method for implementing the electroencephalogram-handheld interface system based on SSVEP and acoustic-optical acquisition according to claim 1 is characterized in that: The S1 includes statistically determining the trigger time errors of the sound, light and electrical signals, determining the adjustment rate to smoothly compensate for the time difference of different signals, and updating the trigger time of each stimulation signal. The specific formula is: B′=B+b×Δt; Among them, B′ represents the trigger time of the sound, light and electrical stimulation signals after synchronous adjustment, B represents the trigger time of the current sound, light and electrical stimulation signals, b represents the rate factor of synchronous adjustment, and Δt represents the time deviation between different stimulation signals.

3. The method for implementing the electroencephalogram-handheld interface system based on SSVEP and acoustic-optical acquisition according to claim 1 is characterized in that: S4 includes calculating the optimal stimulation time and intensity according to the load level in the EEG response signal and the tolerance of the brain. The specific formula is: Among them, t green represents the time of the adjusted sound, light, and electrical stimulation signals, t cycle represents the basic cycle of the stimulus signal, X represents the current EEG response signal load level of the brain, and Y represents the brain's maximum adaptability to external stimuli.

4. The method for implementing the electroencephalogram-handpiece interface system based on SSVEP and acoustic-optical acquisition according to claim 1 is characterized in that: The calculation formula of the intensity Q of the current stimulation signal in S2 is: Q = G × S + K × E; Among them, Q represents the intensity of the current stimulation signal, G represents the gain factor of the stimulation signal, S represents the currently set basic stimulation intensity, K represents the EEG response feedback adjustment factor, and E represents the EEG response feedback error.

5. The method for implementing the electroencephalogram-hand-eye interface system based on SSVEP and acoustic-optical acquisition according to claim 1 is characterized in that: The calculation formula of the adjustment coefficient a in S2 is: Among them, a represents the adjustment coefficient, γ represents the basic adjustment ratio, E represents the EEG response feedback error, β represents the stability factor, and V represents the fitness change rate.

6. The method for implementing the electroencephalogram-hand-eye interface system based on SSVEP and acoustic-optical acquisition according to claim 1 is characterized in that: The calculation formula of the trigger time B of the current sound, light and electrical stimulation signal in S1 is: B=T s +T d ; Among them, B represents the trigger time of the current sound, light, and electrical stimulation signals, T s Indicates the set trigger time of the stimulus signal, T d Indicates the device response delay.

7. The method for implementing the electroencephalogram-hand-eye interface system based on SSVEP and acoustic-optical acquisition according to claim 2 is characterized in that: The calculation formula of the time deviation Δt between different stimulation signals in S1 is: Δt = B ideal -B; Where Δt represents the time deviation between different stimulation signals, B ideal represents the theoretical ideal signal trigger time, and B represents the trigger time of the current sound, light, and electrical stimulation signals.

8. The method for implementing the electroencephalogram-hand-eye interface system based on SSVEP and acoustic-optical-electric acquisition according to claim 7 is characterized in that: The theoretical ideal signal trigger time B in S1 ideal The calculation formula is: ideal =1 / f; Among them, B ideal represents the theoretical ideal signal trigger time, and f represents the target trigger frequency of the signal.

9. The method for implementing the electroencephalogram-hand-eye interface system based on SSVEP and acoustic-optical acquisition according to claim 1 is characterized in that: The S3 further includes: Adjust the time parameters of the sound, light and electrical stimulation signals based on the difference in the correlation between the time domain and the frequency domain, and calculate the time parameters based on the EEG response collected in real time; A new composite synchronous stimulation sequence is generated by synchronously acquiring data, and the spectrum is calculated using Fourier transform to improve recognition accuracy.

10. The method for implementing the electroencephalogram-hand-eye interface system based on SSVEP and acoustic-optical acquisition according to claim 9 is characterized in that: The S3 EEG response calculation is combined with an adaptive filter to remove low-frequency noise in real time.

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