Implementation method of brain-eye-flash interface system based on SSVEP and acoustic-optical acquisition

Abstract

The present invention discloses a method for implementing a brain-electric handheld interface system based on SSVEP and acoustic, optical and electrical acquisition, which relates to the technical field of brain-computer interfaces. The method includes S1, collecting and synchronously processing acoustic, optical and electrical 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 electroencephalographic response signals, S3, analyzing the time domain and frequency domain correlation between the electroencephalographic response signal and the composite synchronous stimulation sequence, identifying and optimizing matching features, and S4, based on the optimized matching features, dynamically adjusting the time and intensity parameters of the acoustic, optical and electrical stimulation signals to maintain optimal synchronization; the method for implementing a brain-electric handheld interface system based on SSVEP and acoustic, optical and electrical acquisition regulates the synchronization of the acoustic, optical and electrical integrated system to solve the problem of mismatch between the stimulation signal and the electroencephalographic response.

Description

Technical Field

[0001] The present invention relates to the field of brain-computer interface technology, and in particular to a method for implementing a brain-electrical hand-eye interface system based on SSVEP and acoustic-optical acquisition. Background Art

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

[0003] In the existing technology, software timing, external trigger signals, or additional synchronization hardware are often used to achieve synchronous control of acoustic, optical, and electrical 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 relies on the accuracy of the computer's clock and is easily affected by the operating system's scheduling, resulting in microsecond-level time errors. Although external trigger signals can improve synchronization, they require additional hardware interfaces, increasing system complexity. Therefore, how to improve the synchronization control accuracy of the acoustic, optical, and electrical stimulation system without adding additional hardware burdens and ensure the matching of stimulation signals with EEG responses is a problem that the current SSVEP brain-computer interface system needs to solve. Summary of the Invention

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

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for implementing an EEG-handheld interface system based on SSVEP and acoustic-optical acquisition, the method comprising:

[0006] S1, collect and synchronize the sound, light, and electrical stimulation signals to generate a composite synchronous stimulation sequence;

[0007] 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 EEG response signal feedback, and making adjustments. The specific formula is: Q′=Q+a×(Q target-Q);

[0008] Where Q′ represents the adjusted stimulus signal intensity, Q represents the current stimulus signal intensity, a represents the adjustment coefficient, and Q target represents the target stimulus signal intensity;

[0009] The signal is updated in real time based on the calculated new stimulus intensity;

[0010] S3. Analyze the time and frequency domain correlation between the EEG response signal and the composite synchronous stimulation sequence, identify and optimize matching features, including measuring the periods of the 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;

[0011] Where 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;

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

[0013] Preferably, 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 differences of different signals, and updating the trigger time of each stimulation signal. The specific formula is: B′=B+b×Δt;

[0014] 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.

[0015] Preferably, the step 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, and the specific formula is:

[0016] Among them, t green Represents the time of the adjusted sound, light, and electrical stimulation signals, t cycle represents the basic period of the stimulation signal, X represents the current EEG response signal load level of the brain, and Y represents the brain's maximum adaptability to external stimulation.

[0017] Preferably, the calculation formula for the intensity Q of the current stimulation 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 EEG response feedback adjustment factor, and E represents the EEG response feedback error.

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

[0021] 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.

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

[0023] Among them, B represents the trigger time of the current sound, light and electrical stimulation signal, T s Indicates the set trigger time of the stimulus signal, T d Indicates the device response delay.

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

[0025] 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.

[0026] Preferably, the theoretical ideal signal triggering time B in S1 is ideal The calculation formula is: ideal =1 / f;

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

[0028] Preferably, the S3 further includes:

[0029] Adjust the time parameters of the sound, light, and electrical stimulation signals based on the differences in the correlation between the time domain and the frequency domain, and calculate the time parameters based on the real-time collected EEG responses;

[0030] A new composite synchronous stimulation sequence is generated by synchronously acquiring data, and the spectrum is calculated using Fourier transform to improve recognition accuracy.

[0031] Preferably, the EEG response calculation in S3 is combined with an adaptive filter to eliminate low-frequency noise in real time.

[0032] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0033] This method for implementing an EEG flashlight interface system based on SSVEP and acoustic, optical, and electrical stimulation acquires and synchronously processes acoustic, optical, and electrical stimulation signals to generate a composite synchronous stimulation sequence. The composite synchronous stimulation sequence is used to stimulate the subject in real time, and the EEG response signal is synchronously acquired. The time domain and frequency domain correlation between the EEG response signal and the composite synchronous stimulation sequence is analyzed, and matching features are identified and optimized. Based on the optimized matching features, the time and intensity parameters of the acoustic, optical, and electrical stimulation signals are dynamically adjusted to maintain optimal synchronization, ensuring that each stimulation channel operates under the same time reference, thereby improving the brain's ability to integrate multimodal information and enhancing the coherence of the SSVEP signal. It ensures the best match between EEG responses and stimulation signals, improves the classification accuracy of the brain-computer interface system, optimizes the time alignment of acoustic, optical and electrical stimulation signals at the software level, reduces the system implementation cost, and improves the applicability and portability of the system. It can maintain high-precision stimulation synchronization, improve the stability and reliability of the BCI system, and is suitable for medical rehabilitation, neuroscience research, brain disease diagnosis, virtual reality, augmented reality interaction and other fields, so that the brain-computer interface system can provide efficient neural interaction capabilities in a wider range of application scenarios, improve user experience, and regulate the synchronization of the acoustic, optical and electrical integrated system to solve the problem of mismatch between stimulation signals and EEG responses. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0036] like Figure 1 As shown, the present invention provides a technical solution: a method for implementing an electroencephalogram (EEG) hand-eye interface system based on SSVEP and acoustic-photoelectric acquisition, the method comprising:

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

[0038] 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 EEG response signal feedback, and making adjustments. The specific formula is: Q′=Q+a×(Qtarget -Q);

[0039] Where Q′ represents the adjusted stimulus signal intensity, Q represents the current stimulus signal intensity, a represents the adjustment coefficient, and Q target represents the target stimulus signal intensity;

[0040] The signal is updated in real time based on the calculated new stimulus intensity;

[0041] S3. Analyze the time and frequency domain correlation between the EEG response signal and the composite synchronous stimulation sequence, identify and optimize matching features, including measuring the periods of the 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;

[0042] Where 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;

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

[0044] The method of the present invention realizes precise brain-computer interface (BCI) interaction by integrating multimodal stimulation of sound, light and electricity. First, high-precision synchronous control technology is used to ensure that the sound, light and electrical signals can be synchronously output in strict accordance with 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, the subject's EEG response signal is monitored in real time, and computer algorithms are used to extract features such as EEG amplitude changes, analyze EEG feedback information, calculate the deviation of the stimulation signal, and use the adjustment formula Q′=Q+a×(Q target-Q) Dynamically adjust the stimulation intensity to achieve closed-loop optimization control. At the same time, based on the characteristic analysis of the EEG signal in the time domain and frequency domain, the resonance ratio A=T1 / T2 between the EEG signal and the composite synchronous stimulation sequence is calculated, and the stimulation signal is further optimized to make it more accurately match the subject's EEG dominant cycle to ensure the maximum neural response. Finally, based on the optimized matching characteristics, the system dynamically adjusts the time and intensity parameters of the sound, light, and electrical stimulation signals to maintain optimal synchronization, ensuring that the entire system can operate stably in different individuals and environments, and improving the accuracy and robustness of EEG signal decoding. The present invention realizes more accurate brain-computer interface signal acquisition and interaction through the synchronous control of multimodal stimulation. Compared with the traditional single-mode stimulation method, it can effectively improve the stability and reliability of the EEG response signal. First, based on the combination of multimodal stimulation, the detectability of the EEG signal is improved, so that the user can still maintain a high interaction accuracy in a complex environment. Secondly, the method adopts a real-time feedback adjustment mechanism, which can dynamically adjust the stimulation signal intensity so that the EEG signal can more effectively adapt to the stimulation frequency and improve the response efficiency and accuracy. Furthermore, the present invention calculates the degree of match between EEG signals and stimulation sequences and dynamically adjusts the stimulation signal period based on this match, ensuring that the brain-computer interface system has adaptive optimization capabilities, thereby maintaining 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 level of system automation, enabling brain-computer interface technology to be more widely applied in fields such as neurorehabilitation, intelligent control, and virtual reality (VR) interaction, providing more advanced technical support for neuroscience research and medical applications.

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

[0046] 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.

[0047] The method of the present invention performs statistical analysis on the trigger time errors of the acoustic, optical, and electrical stimulation signals, calculates the time deviation between each signal, and adjusts the trigger time accordingly to ensure the precise synchronization of the multimodal stimulation signals. First, in step S1, the system uses a high-precision time measurement unit to count the trigger time errors of the acoustic, optical, and electrical signals and establishes a time feature database for each stimulation signal. Then, based on the measured time deviation Δt, the adjustment rate b is calculated, and the time smoothing compensation algorithm is used to correct the trigger time of each stimulation signal. The adjustment process follows the following formula:

[0048] B′=B+b×Δt;

[0049] Here, B′ represents the trigger time of the acoustic, optical, and electrical stimulation signals after synchronization adjustment, B represents the current trigger 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 a precise time correction mechanism between multimodal stimulation signals, thereby reducing the errors caused by asynchrony of the stimulation signals. Ultimately, the adjusted acoustic, optical, and electrical stimulation signals can be applied to the subject in an optimized synchronization manner, improving the stability and consistency of the EEG response signals. This present invention significantly improves the synchronization accuracy of the brain-computer interface system by statistically analyzing and dynamically adjusting the time errors of the stimulation signals. First, a time compensation strategy is adopted to ensure more consistent trigger times for different stimulation signals, reducing the phase shift of the EEG response signals caused by time errors, thereby improving the detectability and accuracy of the EEG signals. Second, by adaptively adjusting the synchronization rate, this method can adapt to the neural response characteristics of different individuals and achieve personalized synchronization optimization. Furthermore, the introduction of the time correction mechanism significantly reduces the system's reliance on high-precision external synchronization equipment, improving the practicality and universality of the brain-computer interface system, making it easier to promote in application scenarios such as neurorehabilitation, intelligent control, and virtual reality (VR).

[0050] S4 includes calculating the optimal stimulation time and intensity based on the load level in the EEG response signal and the brain's tolerance. The specific formula is:

[0051] Among them, t green Represents the time of the adjusted sound, light, and electrical stimulation signals, t cycle represents the basic period of the stimulation signal, X represents the current EEG response signal load level of the brain, and Y represents the brain's maximum adaptability to external stimulation.

[0052] The present invention dynamically adjusts the time parameters of the stimulation signal based on the brain's adaptability to external stimulation and the EEG signal load level. In the S4 process, the system first obtains the current EEG response signal and calculates its load level X through an algorithm. At the same time, the system presets the brain's maximum adaptability to external stimulation, Y, which is used to set the threshold of the stimulation intensity. The calculation formula is:

[0053] The system can determine the optimal stimulation time, ensuring that the stimulation signal neither overstimulates the brain nor understimulates it, resulting in a reduced response. This adjustment strategy ensures dynamic adaptation of the stimulation signal and improves the intelligence level of the BCI (brain-computer interface) system.

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

[0055] The calculation formula of the intensity Q of the current stimulus signal in S2 is: Q = G × S + K × E;

[0056] 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.

[0057] The present invention is based on the adaptive control mechanism of the brain-computer interface (BCI) system, and optimizes the intensity of the stimulation signal in real time through EEG feedback. In the S2 process, the system first generates an initial stimulation signal based on the preset basic stimulation intensity SSS, and 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 so that it can more accurately match the subject's neural state. Finally, through the calculation formula:

[0058] Q = G × S + K × E;

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

[0060] The present invention introduces an EEG feedback error correction mechanism, which enables the stimulation signal to adapt to the individual's EEG response state in real time, thereby improving the flexibility and intelligence level of the BCI system. Compared with the traditional fixed-intensity stimulation method, this method can dynamically adjust the stimulation parameters, reduce the interaction failure caused by excessive or 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 improving the versatility and robustness of the system. This method is particularly suitable for personalized neural regulation, rehabilitation training, and high-precision brain-computer interaction applications, such as medical rehabilitation, intelligent control, and brain-computer collaborative working environments.

[0061] The calculation formula of the adjustment coefficient a in S2 is:

[0062] 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.

[0063] During S2, the present invention dynamically optimizes the adaptability of the stimulation signal 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, the adjustment coefficient a is determined by combining the stability factor β and the fitness change rate V. The adjustment calculation formula is as follows: Among them, the basic adjustment ratio γ sets the basic adjustment strength of the system, the fitness change rate V reflects the degree of fluctuation 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 real-time EEG feedback, allowing the system to maintain efficient operation in a dynamic environment. By introducing an adjustment coefficient calculation method based on the feedback error EEE, the present invention enables the stimulation signal to more flexibly adapt to individual EEG fluctuations, thereby improving the adaptive capability of the brain-computer interface (BCI). Compared with traditional fixed adjustment methods, this method can effectively reduce the problems of system over-response or adjustment lag, and enhance the ability to accurately control 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 changes in individual state. At the same time, this method can dynamically optimize individual stimulation parameters and is suitable for 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 sound, light, and electrical stimulation signal in S1 is: B = T s +T d ;

[0065] Among them, B represents the trigger time of the current sound, light and electrical stimulation signal, T s Indicates the set trigger time of the stimulus signal, T d Indicates the device response delay.

[0066] In the S1 process of the present invention, the system first sets the basic trigger time T of the stimulation signal according to the experimental design. s , and then combined with the actual response delay T of the device d Calculate the final trigger time B. Since different types of stimulus signals (such as sound, light, and electricity) may have different processing delays at the hardware level, the response characteristics of the device need to be taken into account to ensure the synchronization of all stimulus signals. The calculation formula is as follows: B = T s +T d; Among them, T s The experimental parameters are preset to ensure that the stimulation signal is executed according to the established time sequence, and T d It reflects the inherent delay of the device in the signal output process, which is affected by factors such as the device hardware performance and the signal transmission rate. Through this calculation formula, the system can effectively compensate for the device delay, ensure that all stimulation signals are precisely synchronized at the execution moment, and improve the controllability and accuracy of the EEG response. The present invention improves the time accuracy of the stimulation signal by introducing a device response delay compensation mechanism in the stimulation signal triggering process, and ensures strict synchronization between different stimulation modes (sound, light, electricity). Compared with the traditional fixed time triggering method, the present method can effectively reduce the synchronization deviation caused by the device response delay and improve the precise control of the brain-computer interface (BCI) system over the EEG signal. In addition, the method is applicable to different types of hardware devices, so that the BCI system can be flexibly adapted 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 control equipment, attention training and augmented reality (AR) and other fields.

[0067] The calculation formula of 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, B ideal represents the theoretical ideal signal trigger time, and B represents the trigger time of the current sound, light, and electrical stimulation signals.

[0069] In the process of S1, the present invention optimizes and adjusts the triggering time of different stimulation signals by calculating the time deviation Δ to ensure strict synchronization between them. First, the system presets the theoretical ideal signal triggering time B ideal This time is determined based on the experimental design or EEG response characteristics and represents the optimal triggering moment for each stimulus signal under ideal conditions. Subsequently, the system measures the current actual triggering time B and calculates its deviation from the theoretical time Δt, which is calculated as follows: Δt = B ideal -B;

[0070] When Δt≠0, the system dynamically optimizes the triggering time of the stimulus 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 to compensate; if B is later than B ideal , you can optimize the signal processing path or increase the hardware response speed to reduce time deviation.

[0071] The present invention ensures that the triggering time of all stimulation signals is strictly synchronized by calculating and compensating for the time deviation between different stimulation signals, thereby improving the time accuracy 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 the problem of signal asynchrony caused by device delays or environmental interference. In addition, this method can be applied to various multimodal stimulation systems, such as EEG signal control, neurorehabilitation training, virtual reality (VR) and augmented reality (AR) systems, to improve the accuracy of human-computer interaction and user experience.

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

[0073] Among them, B ideal represents the theoretical ideal signal trigger time, and f represents the target trigger frequency of the signal.

[0074] In the process of S1, the present invention uses the target trigger frequency f to determine the theoretical ideal signal trigger time B ideal , thus providing a time base for the system to ensure the precise synchronization of multimodal stimulation 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 inverse 1 / f. In actual applications, the system will calculate the ideal trigger time according to the set target frequency, and use it as a reference to adjust the trigger moment B of the actual stimulation signal. The system can calculate the time deviation Δt and adjust the trigger strategy according to the 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. The present invention calculates the ideal trigger time B ideal , providing a stable time reference for the brain-computer interface system, enabling precise synchronization of multimodal stimulation 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 system synchronization. In addition, this method is suitable for a variety of 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 the sound, light, and electrical stimulation signals based on the differences in the correlation between the time domain and the frequency domain, and calculating the time parameters based on the EEG responses collected in real time;

[0076] A new composite synchronous stimulation sequence is generated by synchronously acquiring data, and the spectrum is calculated using Fourier transform to improve recognition accuracy.

[0077] During the S3 process, the present invention optimizes the timing parameters of stimulation signals by analyzing the time and frequency domain characteristics of EEG signals to improve the recognition accuracy and stability of the brain-computer interface (BCI) system. First, the system collects EEG response signals in real time and calculates their time and frequency domain characteristics, analyzing the impact of different stimulation signals on the EEG response. Based on the temporal correlation between different stimulation signals, the timing parameters of the acoustic, optical, and electrical stimulation signals are adjusted to better match the subject's neural activity. Subsequently, based on the synchronously collected data, the system generates a new composite synchronous stimulation sequence and uses Fourier transform to calculate the spectral characteristics of the EEG signals and extract the main frequency components. The Fourier transform can convert time domain signals into frequency domain signals, thereby analyzing the response strength of the subject to different stimulation signals, optimizing the synchronization of stimulation signals, and improving the recognition accuracy of the target signal. Finally, based on the analysis results, the system dynamically adjusts the parameters of the stimulation signals to make them more consistent with the individual's neural characteristics, thereby improving the decoding accuracy and system robustness of the BCI. By combining time and frequency domain analysis techniques, the present invention achieves more precise stimulation signal adjustment and improves the recognition rate of EEG signals. Compared to traditional fixed-parameter stimulation methods, this method can optimize the stimulation signal in real time, adapting it to the individual's neural state, thereby improving the applicability of the BCI system. Furthermore, the introduction of the Fourier transform enables the system to more accurately analyze the spectral characteristics of EEG signals, reduce noise interference, and improve the ability to extract the target signal. This method is particularly suitable for applications requiring high-precision brain-computer interaction, such as brain-controlled devices, neurorehabilitation training, and augmented reality (AR), improving user experience and system response speed.

[0078] In S3, the EEG response calculation is combined with an adaptive filter to eliminate low-frequency noise in real time. In the S3 process, the present invention optimizes EEG signal processing by introducing an adaptive filter to eliminate low-frequency noise in real time and improve 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 electromyography interference). In order to effectively reduce these interferences, the present method uses an adaptive filter to adjust the filter 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 the reference signal (such as the EEG low-frequency noise model) with the target signal and dynamically adjust the weight coefficient of the filter to minimize the impact of noise. Common adaptive filtering algorithms include minimum mean square error and recursive least squares. In the present invention, the system will continuously update the filter parameters based on the data collected in real time 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. In the process of calculating the EEG signal spectrum by Fourier transform (FT), combining an adaptive filter can further improve the signal quality, make the target SSVEP frequency component clearer, reduce the interference of non-target frequencies, and enhance the system's ability to identify the target signal. The present invention optimizes EEG signal processing by combining an adaptive filter, achieves real-time low-frequency noise suppression, and improves the detectability of the SSVEP target signal. Compared with traditional fixed filters (such as bandpass filters or Kalman filters), adaptive filtering can be dynamically adjusted according to real-time changes in the noise environment, so that the system can 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 control equipment, neurorehabilitation training, and intelligent human-computer interaction.

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

Claims

1. A method for implementing an EEG flashlight interface system based on SSVEP and acoustic-optical acquisition, characterized in that: The method comprises: S1, collect and synchronize the 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 simultaneously collect EEG response signals, including measuring the amplitude change of the EEG response signal, calculating the deviation of the stimulation signal based on the EEG response signal feedback, and making adjustments. The specific formula is: ; in, represents the adjusted stimulus signal intensity, Indicates the intensity of the current stimulus signal, a indicates the adjustment coefficient, represents the target stimulus signal intensity; The signal is updated in real time based on the calculated new stimulus intensity; S3. Analyze the time and frequency domain correlation between the EEG response signal and the composite synchronous stimulation sequence, identify and optimize matching features, including measuring the periods of the 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; Where 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 characteristics, dynamically adjust the time and intensity parameters of the sound, light, and electrical stimulation signals to maintain optimal synchronization; The 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: ; in, Indicates the time of the adjusted sound, light and electrical stimulation signals, represents the basic period of the stimulation signal, X represents the current EEG response signal load level of the brain, and Y represents the brain's maximum adaptability to external stimulation.

2. The method for implementing the EEG-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 differences of different signals, and updating the trigger time of each stimulation signal. The specific formula is: ; in, represents the trigger time of the sound, light, and electrical stimulation signals after synchronous adjustment, B represents the current trigger time of the 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: The calculation formula of the intensity Q of the current stimulation signal in S2 is: Q=G×S+K×E; in, Indicates the intensity of the current stimulation signal, G indicates the gain factor of the stimulation signal, S indicates the currently set basic stimulation intensity, K indicates the EEG response feedback adjustment factor, and E indicates the EEG response feedback error.

4. 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 calculation formula of the adjustment coefficient a in S2 is: a=γ× ; 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.

5. The method for implementing the electroencephalogram-handheld interface system based on SSVEP and acoustic-optical acquisition according to claim 2 is characterized in that: The calculation formula for the trigger time B of the current sound, light and electrical stimulation signal in S1 is: B= + ; Among them, B represents the trigger time of the current sound, light and electrical stimulation signals, Indicates the set trigger time of the stimulus signal, Indicates the device response delay.

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

7. The method for implementing the electroencephalogram-handheld interface system based on SSVEP and acoustic-optical acquisition according to claim 6 is characterized in that: The theoretical ideal signal trigger time in S1 The calculation formula is: =1 / f; in, represents the theoretical ideal signal trigger time, and f represents the target trigger frequency of the signal.

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

9. The method for implementing the electroencephalogram-handheld interface system based on SSVEP and acoustic-optical acquisition according to claim 8, characterized in that: The S3 EEG response calculation is combined with an adaptive filter to eliminate low-frequency noise in real time.

Images