Entrance guard advertisement pushing method for non-inductive interaction based on brain-computer interface

Abstract

According to the non-inductive interaction access control advertisement pushing method based on the brain-computer interface, the psychological state and interest preference of the user can be deeply known by collecting the brain wave signals of the user. In the advertisement pushing link, appropriate advertisement information can be coded into target brain wave signals according to the brain wave characteristics of the user, and the target brain wave signals are pushed to the brain of the user. Compared with an existing unified pushing mode, the method has the advantages that the user can feel that the advertisement better meets the requirements of the user, so that the acceptability and the favor of the user to the advertisement are improved, and the interactivity between the advertisement and the user is enhanced. According to the method, the advertisement information is pushed to the brain of the user in a brain wave signal mode through the brain-computer interface system, and visual or auditory interference cannot be generated like an existing mode. And the method can be suitable for senile cognitive impairment users and the like, realizes brain-computer-based non-inductive interaction, and provides applications such as entrance guard interactive opening and the like for the senile cognitive impairment users.

Description

Technical Field

[0001] This invention relates to the field of access control technology, and in particular to an access control advertising push method, electronic device, and computer-readable storage medium based on brain-computer interface for seamless interaction. Background Technology

[0002] The current methods for pushing access control advertisements are as follows:

[0003] Visual Display: Display screens are installed on access control equipment to showcase advertising content in the form of images, videos, etc. When a user approaches the access control system and swipes their card to open the door, the display screen automatically plays preset advertisements. For example, at the access control point of a residential community, the display screen continuously plays promotional advertisements from nearby businesses.

[0004] Voice broadcast: The voice module of the access control device plays advertising voice messages when a user swipes their card to open the door. For example, at the access control point of an office building, users will hear advertisements for nearby restaurants when they swipe their cards.

[0005] SMS or App Push Notifications: Collect users' mobile phone numbers or guide users to download relevant access control apps, and send advertising SMS messages to users' mobile phones or push advertising messages on the app when users pass through the access control system.

[0006] However, the above-mentioned access control advertising push method has the following application defects:

[0007] Lack of precision: Current push notification methods uniformly push messages to all users passing through access control, without considering individual differences and interests. For example, advertisements for maternity and baby products pushed through community access control are ineffective for residents without children, failing to accurately reach the target customer group and wasting advertising resources.

[0008] Highly disruptive: Visual advertisements may distract users and affect their efficiency in passing through access control; voice-announced advertisements may disturb users in certain situations (such as late at night or in quiet office areas) and cause resentment. For example, playing loud advertising messages at library access control will disrupt the quiet atmosphere of the library.

[0009] Low user acceptance: Advertisements sent via SMS or app push notifications are easily perceived as spam by users, leading many to block or ignore them, resulting in poor ad dissemination. Furthermore, frequent push notifications may cause users to uninstall the app or refuse to provide their phone numbers. This is especially problematic for elderly, paralyzed, or cognitively impaired users, making it impossible to provide services to them. Summary of the Invention

[0010] To address the technical problems existing in the prior art, the present invention provides the following technical solution:

[0011] On the one hand, a method for push-to-access advertising based on brain-computer interface for seamless interaction is provided. This method is implemented by an electronic device and includes:

[0012] S1. Brainwave signal acquisition: The brainwave raw signals of the user are acquired in real time through a brain-computer interface system worn on the user's head. The brain-computer interface system includes EEG electrodes, signal amplification circuit and analog-to-digital conversion module. The EEG electrodes acquire the EEG signals from the user's scalp. After being amplified by the signal amplification circuit, the analog-to-digital conversion module converts them into digital brainwave signals.

[0013] S2. Access control processing: The digital brainwave signal is preprocessed, features are extracted, and commands are recognized to obtain the user's door opening command recognition result;

[0014] S3. Access Control Response: When the door opening command recognition result is a door opening command, the brain-computer interface system sends a door opening trigger signal to the access control controller, and the access control controller controls the access control system to open;

[0015] S4. Ad push processing: Encode the ad information to be pushed into the corresponding target brainwave signal;

[0016] S5. Brainwave push: The target brainwave signal is pushed to the user's brain through the brainwave stimulation module of the brain-computer interface system.

[0017] Preferably, S2. Access control processing: preprocessing, feature extraction, and command recognition of the digital brainwave signal to obtain the user's door opening command recognition result, including:

[0018] S21. Preprocessing: The digital brainwave signal is subjected to discrete wavelet transform denoising processing to obtain a denoised brainwave signal; the discrete wavelet transform denoising processing includes:

[0019] a. Perform j-level discrete wavelet decomposition on the digital brainwave signal to obtain approximation coefficients. and detail coefficient Where j is the decomposition level and k is the translation parameter, the scaling function of the discrete wavelet decomposition is: The wavelet function is ;

[0020] b. Regarding the detail coefficients Thresholding is performed to obtain the detail coefficients (d'j,k) after thresholding. The thresholding process can be either hard thresholding or soft thresholding. , where σ is the noise standard deviation in the digital brainwave signal, and N is the length of the digital brainwave signal;

[0021] c. Perform discrete wavelet inverse transform on the approximation coefficients (aj,k) and the thresholded detail coefficients (d'j,k) to obtain the denoised brainwave signal;

[0022] S22. Feature Extraction: The denoised brainwave signal is processed by frame segmentation. The Welch method is used to calculate the power spectral density (PSD) of each frame, and the power proportion of each brainwave frequency band is extracted as a feature vector. The brainwave frequency bands include delta waves (0.5-4Hz), theta waves (4-8Hz), alpha waves (8-13Hz), beta waves (13-30Hz), and gamma waves (30-100Hz). The formula for calculating the power proportion is as follows:

[0023]

[0024] Where (PSD(f)) is the power spectral density of the denoised brainwave signal at frequency f, and band is the corresponding brainwave frequency band. The sampling rate of the digital brainwave signal is given; where “∫” represents the integral operation of frequency f within the band, “PSD(f)df” represents the power integral within the band, the denominator is the power integral of the entire band (0.5-100Hz), “ / ” represents the division operation, and the whole represents the proportion of the target frequency band power to the total power;

[0025] S23. Command recognition: Input the feature vector into a pre-trained support vector machine (SVM) classifier to obtain the door opening command recognition result;

[0026] The decision function of the SVM classifier is:

[0027]

[0028] in, For the feature vector, For the Lagrange multipliers corresponding to the support vectors, Labels for support vectors ( This represents the instruction to open the door. (Represents a non-door opening command) The feature vectors of the support vectors, The term is the bias term; in the formula, “∑” represents the summation operation from 1 to the total number of support vectors, “K(x,x_i)” represents the kernel function (preferably a linear kernel “x·x_i”, where “·” is the inner product operation), and “+” represents the addition operation.

[0029] Preferably, in step S21, the number of layers j of the discrete wavelet decomposition is 2-5, and the wavelet function is one of db4, sym8 or coif5;

[0030] In step S22, the framing process uses a Hamming window or a Hanning window, with a frame length of 256-1024 points and an overlap length of 50%-75% of the frame length.

[0031] In step S23, the kernel function of the support vector machine classifier is a linear kernel, a polynomial kernel, or a radial basis function (RBF) kernel, and the penalty parameter C ranges from 1 to 100.

[0032] Preferably, S4. Ad push processing: encoding the ad information to be pushed into a corresponding target brainwave signal, including:

[0033] S41. Advertising Information Encoding: Feature extraction is performed on the advertising information to obtain an advertising feature vector; when the advertising information is text, the semantic embedding vector of the text is extracted using the BERT model as the advertising feature vector; when the advertising information is an image, the visual feature vector of the image is extracted using a convolutional neural network as the advertising feature vector.

[0034] S42. Brainwave signal generation: Map the advertising feature vector to the modulation parameters of the brainwave signal to generate the target brainwave signal;

[0035] The calculation formula for the target brainwave signal is as follows:

[0036]

[0037] Wherein, (A(t)) is the amplitude modulation function of the target brainwave signal. , Based on amplitude, Here, (v(t)) is the amplitude modulation coefficient, (v(t)) is the time series of the advertising feature vector after dimensionality reduction, and (f(t)) is the frequency modulation function of the target brainwave signal. , The center frequency is (30-100Hz). These are the frequency modulation coefficients; The phase of the target brainwave signal.

[0038] Preferably, in step S41, the dimensionality reduction of the advertising feature vector is performed using principal component analysis (PCA) or linear discriminant analysis (LDA) to reduce the high-dimensional feature vector to a 1-10 dimensional time series.

[0039] In step S42, the basic amplitude The frequency modulation coefficient is 1-10μV. The amplitude modulation coefficient is 5-20Hz. The value is 1-5 μV.

[0040] Preferably, S5. Brainwave push: The target brainwave signal is pushed to the user's brain through the brainwave stimulation module of the brain-computer interface system, specifically in the following manner:

[0041] The brainwave stimulation module uses transcranial alternating current stimulation (tACS) or transcranial magnetic stimulation (TMS) to convert the target brainwave signal into an electrical or magnetic stimulation signal, which is then applied to the user's head to achieve seamless push of advertising information.

[0042] The stimulation frequency of the brainwave stimulation module is the same as the center frequency of the target brainwave signal. The stimulation intensity is 1-5 mA (electrical stimulation) or 1-2 T (magnetic stimulation), and the stimulation duration is 1-5 seconds.

[0043] Preferably, it further includes:

[0044] Step S6. Feedback Adjustment: Adjust the advertising push strategy based on the user's brainwave response signal. The brainwave response signal is the brainwave signal collected by the brain-computer interface system after the user receives the advertisement. By analyzing the emotional characteristics of the brainwave response signal, adjust the content or push frequency of the advertising information.

[0045] On the other hand, a brain-computer interface-based access control advertising push system for seamless interaction is provided. This system implements the aforementioned brain-computer interface-based access control advertising push method. The system includes:

[0046] A brain-computer interface system is used to collect raw brainwave signals from users in real time. The brain-computer interface system includes EEG electrodes, a signal amplification circuit, and an analog-to-digital conversion module. The EEG electrodes collect brainwave signals from the user's scalp, which are amplified by the signal amplification circuit and then converted into digital brainwave signals by the analog-to-digital conversion module.

[0047] The access control module processes the digital brainwave signal, performs preprocessing, feature extraction, and command recognition to obtain the user's door opening command recognition result; when the door opening command recognition result is a door opening command, the brain-computer interface system sends a door opening trigger signal to the access controller, and the access controller controls the access control system to open.

[0048] The advertising push module is used to encode the advertising information to be pushed into the corresponding target brainwave signal; and push the target brainwave signal to the user's brain through the brainwave stimulation module of the brain-computer interface system.

[0049] The interactive control module coordinates the access control and advertising processes and adjusts advertising strategies based on user feedback.

[0050] On the other hand, an electronic device is provided, comprising: a processor; and a memory storing computer-readable instructions, wherein when the computer-readable instructions are executed by the processor, any one of the above-described methods for push-to-access-advertisements-based-brain-computer-interface-based-seamless-interaction-based-interaction-based-interface-based-advertisement-methods is implemented.

[0051] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, the at least one instruction being loaded and executed by a processor to implement any of the above-described access control advertising push methods based on brain-computer interface for seamless interaction.

[0052] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following:

[0053] This invention, by collecting users' brainwave signals, can gain a deeper understanding of their psychological state and interests. In the advertising delivery process, appropriate advertising information can be encoded into target brainwave signals based on the user's brainwave characteristics and pushed to the user's brain. For example, if a user shows a positive brainwave response to sports-related topics, advertisements for sports brands can be pushed, greatly improving the accuracy of advertising and enhancing advertising effectiveness.

[0054] This invention utilizes the unique brainwave signals of each user, enabling personalized advertising content tailored to each individual. Compared to existing uniform push methods, users will perceive the ads as more aligned with their needs, thereby increasing user acceptance and positive perception of the ads and enhancing the interactivity between the ads and the users.

[0055] This method uses a brain-computer interface system to push advertising information to the user's brain in the form of brainwave signals, avoiding visual or auditory interference like existing methods. Users can receive advertising information without being disturbed, ensuring efficient access control without impacting the surrounding environment and improving the user experience. It is also suitable for elderly users with cognitive impairments, enabling seamless brain-computer interaction and providing applications such as interactive access control. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a flowchart of an access control advertising push method based on brain-computer interface for seamless interaction, provided by an embodiment of the present invention.

[0058] Figure 2 This is a block diagram of an access control advertising push system based on brain-computer interface for seamless interaction, provided by an embodiment of the present invention.

[0059] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0060] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0061] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0062] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0063] In this embodiment of the invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0064] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0065] This invention provides a method for push-to-advertisement access control based on brain-computer interface for seamless interaction. This method can be implemented by an electronic device, which can be a terminal or a server. Figure 1 The flowchart shown is for a brain-computer interface-based method for seamless interaction in access control advertising. The processing flow of this method may include the following steps:

[0066] S1. Brainwave signal acquisition: The brainwave raw signals of the user are acquired in real time through a brain-computer interface system worn on the user's head. The brain-computer interface system includes EEG electrodes, signal amplification circuit and analog-to-digital conversion module. The EEG electrodes acquire the EEG signals from the user's scalp. After being amplified by the signal amplification circuit, the analog-to-digital conversion module converts them into digital brainwave signals.

[0067] S2. Access control processing: The digital brainwave signal is preprocessed, features are extracted, and commands are recognized to obtain the user's door opening command recognition result;

[0068] S3. Access Control Response: When the door opening command recognition result is a door opening command, the brain-computer interface system sends a door opening trigger signal to the access control controller, and the access control controller controls the access control system to open;

[0069] S4. Ad push processing: Encode the ad information to be pushed into the corresponding target brainwave signal;

[0070] S5. Brainwave push: The target brainwave signal is pushed to the user's brain through the brainwave stimulation module of the brain-computer interface system.

[0071] This invention utilizes a wearable brain-computer interface to output and analyze the user's brainwave signals, recognizing and responding to the user's door-opening commands, thus achieving contactless access control. Simultaneously, it can push advertisements, converting the advertising information into corresponding brainwave signals and pushing them to the user's brain via the brain-computer interface.

[0072] Please refer to the appendix below. Figure 2 The system architecture shown will provide a detailed understanding of the implementation principles of this invention.

[0073] like Figure 2 As shown, the system mainly consists of a brain-computer interface system, an access control module, an advertising push module, and an interactive control module.

[0074] Brain-computer interface system: includes an EEG acquisition module (such as the TGAM module from NeuroConn Technology, which includes EEG electrodes, signal amplification circuits, and analog-to-digital conversion modules) and a brainwave stimulation module (such as the NeuroConnDC-STIMULATORPLUS from Germany, which supports transcranial alternating current stimulation (tACS) / transcranial magnetic stimulation (TMS), responsible for acquiring user brainwave signals and pushing advertising brainwave signals.

[0075] Access control module: For example, the DK8200 / D-NT model controller is used, which includes a preprocessing unit, a feature extraction unit, and an instruction recognition unit. It is responsible for processing brainwave signals to recognize door opening instructions and triggering the access control to open.

[0076] The ad push module includes an ad encoding unit and a brainwave generation unit, responsible for encoding ad information into brainwave signals. Push notifications can be sent from the backend to the app or to ad screens, etc.

[0077] Interactive control module: coordinates the access control and advertising processes (such as pushing ads after the door is opened) and adjusts the advertising strategy based on user feedback.

[0078] The interaction flow is as follows:

[0079] Brainwave Acquisition: When a user approaches the access control system, the brain-computer interface is automatically activated to acquire raw brainwave signals;

[0080] Access control: The access control module processes brainwave signals and triggers the access control to open upon recognizing an opening command;

[0081] Ad push: The interactive control module instructs the ad push module to encode pre-stored ad information into brainwave signals and push them to the user's brain via tACS;

[0082] Feedback Adjustment: Collect brainwave signals of user response after receiving advertisements (such as the proportion of gamma wave power reflecting excitement level) and adjust the advertisement content (such as increasing the frequency of coffee advertisement push if users are interested in coffee advertisements).

[0083] The process of brainwave signal analysis, recognition, and command output is as follows:

[0084] 1. Access Control: Brainwave Command Recognition Algorithm

[0085] Objective: To identify the user's door-opening command from the collected brainwave signals (such as the enhancement of β waves (13-30Hz) when imagining "opening the door").

[0086] (1) Preprocessing: Discrete wavelet transform (DWT) denoising

[0087] The raw brainwave signal contains power frequency noise (50Hz), electromyographic noise (>30Hz), etc., which need to be denoised by DWT.

[0088] DWT decomposition: The digital brainwave signal x(n) is decomposed into (j) levels to obtain approximate coefficients. (Low-frequency components) and detail coefficient (High-frequency noise):

[0089]

[0090] The algorithm's operating logic is as follows: First, determine the number of decomposition layers j (2-5 layers), select the db4 wavelet as the scaling function and wavelet function, perform multi-layer decomposition on the input brainwave signal x(n), and separate the approximate coefficients reflecting the main features of the signal and the detail coefficients containing noise through downsampling and translation operations, laying the foundation for subsequent denoising processing.

[0091] in, For scaling functions (such as db4 wavelets). Let be the wavelet function, (j) be the number of decomposition levels (2-5), and (k) be the translation parameter; in the formula, “∑” represents the summation operation over k from -∞ to +∞, “2” represents the summation operation over k from -∞ to +∞. j "x" is the scale factor. "" indicates that the original signal x(n) is subjected to downsampling and translation operations. The scaling function of the discrete wavelet decomposition is The wavelet function is Here, the expression for the function is used, with n replaced by t. In actual calculations, the specific value of n will be used.

[0092] Thresholding: for detail coefficients Perform soft thresholding (preserve valid signals, suppress noise):

[0093]

[0094] The algorithm operates as follows: a soft thresholding function is applied to the high-frequency detail coefficients obtained from the decomposition. Coefficients with absolute values ​​less than the threshold are set to zero, while coefficients with absolute values ​​greater than the threshold are retained and the threshold is subtracted. This process suppresses noise while preserving the edge information of the effective signal, thus improving the denoising effect.

[0095] threshold Using Donoho adaptive thresholding:

[0096]

[0097] The algorithm operates as follows: the noise standard deviation σ is estimated using the first 100 samples, and an adaptive threshold is calculated in combination with the signal length N. This ensures that the threshold is dynamically adjusted according to the noise level and signal length, thereby achieving robust denoising for signals with different signal-to-noise ratios.

[0098] in, Let N be the noise standard deviation (estimated from the first 100 samples), and (N) be the signal length (e.g., 1024 points); in the formula, “√” indicates square root operation, and “2logN” is the threshold adjustment factor. "" indicates the product of the noise standard deviation and the adjustment factor.

[0099] Inverse transform reconstruction: using approximation coefficients and the processed detail coefficient Perform inverse DWT to obtain the denoised brainwave signal (x'(n)).

[0100] (2) Feature extraction: Power spectral density (PSD) and frequency band power ratio

[0101] Extracting frequency domain features of brainwaves (power percentage of each frequency band) reflects the user's thought state:

[0102] Framing and Windowing: The denoised (x'(n)) is framed (frame length (N=256), overlap (L=128), Hamming window (w(n))) to obtain the frame signal. ((m) is the frame index).

[0103] PSD calculation: The PSD of each frame of signal is calculated using the Welch method.

[0104]

[0105] The algorithm operates as follows: the denoised brainwave signal is divided into frames and a Hamming window is added. The power spectrum is obtained by performing a Fourier transform on each frame. The variance of the spectrum estimation is reduced by averaging multiple frames, and finally a smooth power spectral density curve is obtained, which provides a basis for calculating the power ratio of the frequency band.

[0106] Where (M) is the total number of frames, and (f) is the frequency ( , (where is the sampling rate, such as 250Hz); where “1 / M” represents averaging over M frames of signal, “|·|²” represents taking the square of the modulus of the Fourier transform result, “W(f)” is the Fourier transform of the window function, and “Δf=fs / N” represents the frequency resolution.

[0107] Frequency band power distribution: Calculate the power distribution of delta waves (0.5-4Hz), theta waves (4-8Hz), alpha waves (8-13Hz), beta waves (13-30Hz), and gamma waves (30-100Hz) as a feature vector. :

[0108]

[0109] The algorithm operates as follows: within the 0.5-100Hz full frequency band, the total power of each target frequency band (δ, θ, α, β, γ) is calculated by integration, and then divided by the total power of the full frequency band to obtain the proportion, forming a feature vector that can characterize the user's mental state.

[0110] Where (band) is the target frequency band (e.g., 13-30Hz for β wave); in the formula, “∫” represents the integral operation of frequency f within the band, “PSD(f)df” represents the power integral within the band, the denominator is the power integral of the entire band (0.5-100Hz), and the whole represents the proportion of the target frequency band power to the total power.

[0111] (3) Instruction recognition: Support Vector Machine (SVM) classification

[0112] The feature vectors are classified using SVM to identify opening instructions (label (y=1)) and non-opening instructions ((y=-1)):

[0113] Decision function:

[0114]

[0115] The algorithm operates as follows: it optimizes the training samples to obtain support vectors and Lagrange multipliers, maps the feature vectors to a high-dimensional space through a kernel function, constructs a linear classification hyperplane, and realizes binary classification decision for opening instructions and non-opening instructions.

[0116] in, Support vectors (key samples in the training samples). For Lagrange multipliers ( (C) is the penalty parameter, such as (C=10). Here, ∑ represents the bias term, (S) represents the number of support vectors; in the formula, “∑” represents the summation operation of i from 1 to S, “y_i” is the label of the support vector (1 or -1), and “K(x,x_i)” represents the kernel function (such as the linear kernel “x·x_i”).

[0117] Classification logic: If If the command is selected, it is recognized as an open door command; otherwise, it is considered a non-open door command.

[0118] (4) Examples of operating mechanisms

[0119] Input: Raw brainwave signals collected by the brain-computer interface when the user imagines "opening the door" ( (N=1024)).

[0120] Preprocessing: Perform 3-level DWT decomposition using db4 wavelet and calculate the threshold. ( (where the noise standard deviation is used), soft thresholding is applied to the detail coefficients to reconstruct the denoised signal.

[0121] Feature extraction: Frame segmentation (256 points, 128 overlap), PSD calculation, and β-wave power ratio obtained. (Much higher than the normal 0.2), eigenvector .

[0122] Command recognition: Input SVM (linear kernel, (C=10)), decision function output The system recognizes the command as an open command and triggers the access control system to open.

[0123] 2. Ad Push: Brainwave Signal Generation Algorithm

[0124] Objective: To encode advertising information (text / images) into brainwave signals and push them to the user's brain via a brain-computer interface.

[0125] (1) Advertising information encoding: semantic / visual feature extraction

[0126] Text advertising: Extracting semantic embedding vectors from text using the BERT model (Such as the semantic features of "XX Coffee is half price today").

[0127] Image advertising: Extracting visual feature vectors from images using a VGG16 convolutional neural network. (e.g., the edges and color features of a coffee cup image).

[0128] Dimensionality reduction: PCA is used to reduce the high-dimensional feature vector to a 1-dimensional time series (v(t)) (range ([-1,1])) to facilitate subsequent modulation.

[0129] (2) Generation of brainwave signals: frequency and amplitude modulation

[0130] The advertising features (v(t)) are mapped to the frequency (gamma wave, 30-100Hz) and amplitude parameters of brain waves to generate the target brain wave signal:

[0131]

[0132] The algorithm operates as follows: taking the instantaneous amplitude A(t) and instantaneous frequency f(t) obtained by modulating the advertising feature time series v(t) as input, the frequency integral result is superimposed with the phase through a sine function as the phase term, and then multiplied with the amplitude to generate the target brainwave signal that changes with time, thereby realizing the brainwave encoding of advertising information.

[0133] Where: s(t) is the target brainwave signal, A(t) is the instantaneous amplitude, f(t) is the instantaneous frequency, t is the time variable, φ is the initial phase, · represents the multiplication operation, and sin() is the sine function.

[0134] Amplitude modulation: , (Typical brainwave amplitude, such as 5μV). (Modulation coefficient, controlling the amplitude variation range, such as 1μV); where “A(t)” is the instantaneous amplitude, “A0” is the basic amplitude, “kA” is the amplitude modulation coefficient, “v(t)” is the advertising characteristic time series, “+” indicates addition operation, and “·” indicates multiplication operation.

[0135] The algorithm operates as follows: using the basic amplitude A0 as a reference, the advertising feature v(t) is mapped to the amplitude offset through the modulation coefficient kA, and the signal amplitude is dynamically adjusted so that the amplitude change is positively correlated with the intensity of the advertising semantic / visual features, thereby enhancing the recognizability of the information carried by the signal.

[0136] Frequency modulation: , (The center frequency of the gamma wave reflects the state of excitation, such as 40 Hz). (Modulation coefficient, controlling the frequency variation range, such as 10Hz); where “f(t)” is the instantaneous frequency, “f0” is the center frequency, “kf” is the frequency modulation coefficient, “v(t)” is the advertising characteristic time series, “+” indicates addition operation, and “·” indicates multiplication operation.

[0137] The algorithm operates as follows: using the center frequency f0 of the γ wave as a reference, the advertising feature v(t) is converted into a frequency offset through the modulation coefficient kf, so that the instantaneous frequency changes with v(t) within the γ wave range of 30-100Hz, ensuring that the signal is in the frequency band that reflects the state of excitement.

[0138] Phase: (Constant, simplified design, such as 0); where “φ” is the initial phase, in radians (rad).

[0139] The algorithm operates as follows: the initial phase is fixed at 0 radians to avoid introducing additional complexity due to phase changes, simplifying the generation and synthesis process of the target brainwave signal and ensuring signal stability.

[0140] (3) Brainwave push: transcranial stimulation

[0141] The generated (s(t)) is input into the tACS module of the brain-computer interface, converted into an electrical stimulation signal (current intensity 1-5mA), and applied to the user's scalp (such as the prefrontal cortex) through electrodes, so that the brain receives the corresponding brainwave signal, thereby realizing the seamless push of advertising information.

[0142] Example of operating mechanism:

[0143] Advertising encoding: A 768-dimensional semantic vector was extracted from the text "XX Coffee Half Price Today" using BERT, and then dimensionality was reduced using PCA. (Duration 2 seconds, sampling rate 250Hz).

[0144] Brainwave generation: frequency modulation (like At that time, (f(t)=68Hz); amplitude modulation (like hour, ); Generate brainwave signals .

[0145] Brainwave push: (s(t)) is converted into tACS electrical stimulation signal (current 3mA) and applied to the user's prefrontal cortex. The user perceives the advertising content through brain neural decoding (such as "hearing" the advertising voice or "seeing" the advertising image).

[0146] On the other hand, a brain-computer interface-based access control advertising push system for seamless interaction is provided. This system implements the aforementioned brain-computer interface-based access control advertising push method. The system includes:

[0147] A brain-computer interface system is used to collect raw brainwave signals from users in real time. The brain-computer interface system includes EEG electrodes, a signal amplification circuit, and an analog-to-digital conversion module. The EEG electrodes collect brainwave signals from the user's scalp, which are amplified by the signal amplification circuit and then converted into digital brainwave signals by the analog-to-digital conversion module.

[0148] The access control module processes the digital brainwave signal, performs preprocessing, feature extraction, and command recognition to obtain the user's door opening command recognition result; when the door opening command recognition result is a door opening command, the brain-computer interface system sends a door opening trigger signal to the access controller, and the access controller controls the access control system to open.

[0149] The advertising push module is used to encode the advertising information to be pushed into the corresponding target brainwave signal; and push the target brainwave signal to the user's brain through the brainwave stimulation module of the brain-computer interface system.

[0150] The interactive control module coordinates the access control and advertising processes and adjusts advertising strategies based on user feedback.

[0151] Please implement system interactions in accordance with the principles outlined above.

[0152] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... Figure 3 As shown, electronic device 410 may include a first processor 2001.

[0153] Optionally, the electronic device 410 may also include a memory 2002 and a transceiver 2003.

[0154] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0155] The following is combined with Figure 3 A detailed description of each component of electronic device 410 is provided below:

[0156] The first processor 2001 is the control center of the electronic device 410. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0157] Optionally, the first processor 2001 can perform various functions of the electronic device 410 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0158] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 3 CPU0 and CPU1 are shown in the diagram.

[0159] In a specific implementation, as one example, the electronic device 410 may also include multiple processors, for example... Figure 3 The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor or a multi-core processor. Here, a processor can refer to one or more devices, circuits, and / or processing cores used to process data (such as computer program instructions).

[0160] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0161] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently and be connected via the interface circuit of the electronic device 410. Figure 3 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0162] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0163] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 3 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0164] Optionally, the transceiver 2003 can be integrated with the first processor 2001, or it can exist independently and be connected via the interface circuit of the electronic device 410. Figure 3 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0165] It should be noted that, Figure 3 The structure of the electronic device 410 shown does not constitute a limitation on the router. Actual knowledge structure identification devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0166] Furthermore, the technical effect of the electronic device 410 can be referred to the technical effect of the access control advertising push method based on brain-computer interface for seamless interaction described in the above method embodiments, and will not be repeated here.

[0167] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0168] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0169] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0170] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0171] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0172] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0173] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0174] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, systems, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0175] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0176] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0177] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0178] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0179] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for push-to-access advertising in a brain-computer interface for seamless interaction, characterized in that, The method includes: S1. Brainwave signal acquisition: The brainwave raw signals of the user are acquired in real time through a brain-computer interface system worn on the user's head. The brain-computer interface system includes EEG electrodes, signal amplification circuit and analog-to-digital conversion module. The EEG electrodes acquire the EEG signals from the user's scalp. After being amplified by the signal amplification circuit, the analog-to-digital conversion module converts them into digital brainwave signals. S2. Access control processing: The digital brainwave signal is preprocessed, features are extracted, and commands are recognized to obtain the user's door opening command recognition result; S3. Access Control Response: When the door opening command recognition result is a door opening command, the brain-computer interface system sends a door opening trigger signal to the access control controller, and the access control controller controls the access control system to open; S4. Ad push processing: Encode the ad information to be pushed into the corresponding target brainwave signal; S5. Brainwave push: The target brainwave signal is pushed to the user's brain through the brainwave stimulation module of the brain-computer interface system.

2. The access control advertising push method based on brain-computer interface for seamless interaction according to claim 1, characterized in that, The S2. Access control processing: preprocessing, feature extraction, and command recognition of the digital brainwave signal to obtain the user's door opening command recognition result, including: S21. Preprocessing: The digital brainwave signal is subjected to discrete wavelet transform denoising processing to obtain a denoised brainwave signal; the discrete wavelet transform denoising processing includes: a. Perform j-level discrete wavelet decomposition on the digital brainwave signal to obtain approximate coefficients. and detail coefficient Where j is the decomposition level and k is the translation parameter, the scaling function of the discrete wavelet decomposition is: The wavelet function is ; b. Regarding the detail coefficients Thresholding is performed to obtain the detail coefficients (d'j,k) after thresholding. The thresholding process can be either hard thresholding or soft thresholding. , where σ is the noise standard deviation in the digital brainwave signal, and N is the length of the digital brainwave signal; c. Perform discrete wavelet inverse transform on the approximation coefficients (aj,k) and the thresholded detail coefficients (d'j,k) to obtain the denoised brainwave signal; S22. Feature Extraction: The denoised brainwave signal is processed by frame segmentation. The Welch method is used to calculate the power spectral density (PSD) of each frame, and the power proportion of each brainwave frequency band is extracted as a feature vector. The brainwave frequency bands include delta waves (0.5-4Hz), theta waves (4-8Hz), alpha waves (8-13Hz), beta waves (13-30Hz), and gamma waves (30-100Hz). The formula for calculating the power proportion is as follows: ， Where (PSD(f)) is the power spectral density of the denoised brainwave signal at frequency f, and band is the corresponding brainwave frequency band. The sampling rate of the digital brainwave signal is given; where "∫" represents the integral operation of frequency f in the band, "PSD(f)df" represents the power integral in the band, the denominator is the power integral of the entire band (0.5-100Hz), " / " represents the division operation, and the whole represents the proportion of the target frequency band power to the total power. S23. Command recognition: Input the feature vector into a pre-trained support vector machine (SVM) classifier to obtain the door opening command recognition result; The decision function of the SVM classifier is: ， in, For the feature vector, For the Lagrange multipliers corresponding to the support vectors, Labels for support vectors ( This represents the instruction to open the door. (Represents a non-door opening command) The feature vectors of the support vectors, The term is the bias term; in the formula, "∑" represents the summation operation from 1 to the total number of support vectors, "K(x,x_i)" represents the kernel function (preferably a linear kernel "x·x_i", where "·" is the inner product operation), and "+" represents the addition operation.

3. The access control advertising push method based on brain-computer interface for seamless interaction according to claim 1, characterized in that, In step S21, the number of discrete wavelet decomposition layers j is 2-5 layers, and the wavelet function is one of db4, sym8 or coif5. In step S22, the framing process uses a Hamming window or a Hanning window, with a frame length of 256-1024 points and an overlap length of 50%-75% of the frame length. In step S23, the kernel function of the support vector machine classifier is a linear kernel, a polynomial kernel, or a radial basis function (RBF) kernel, and the penalty parameter C ranges from 1 to 100.

4. The access control advertising push method based on brain-computer interface for seamless interaction according to claim 1, characterized in that, S4. Ad push processing: Encoding the ad information to be pushed into the corresponding target brainwave signal, including: S41. Advertising Information Encoding: Feature extraction is performed on the advertising information to obtain an advertising feature vector; when the advertising information is text, the semantic embedding vector of the text is extracted using the BERT model as the advertising feature vector; when the advertising information is an image, the visual feature vector of the image is extracted using a convolutional neural network as the advertising feature vector. S42. Brainwave signal generation: Map the advertising feature vector to the modulation parameters of the brainwave signal to generate the target brainwave signal; The calculation formula for the target brainwave signal is as follows: ， Wherein, (A(t)) is the amplitude modulation function of the target brainwave signal. , Based on amplitude, Here, (v(t)) is the amplitude modulation coefficient, (v(t)) is the time series of the advertising feature vector after dimensionality reduction, and (f(t)) is the frequency modulation function of the target brainwave signal. , The center frequency is (30-100Hz). These are the frequency modulation coefficients; The phase of the target brainwave signal.

5. The access control advertising push method based on brain-computer interface for seamless interaction according to claim 4, characterized in that, In step S41, the dimensionality reduction of the advertising feature vector is performed using principal component analysis (PCA) or linear discriminant analysis (LDA) to reduce the high-dimensional feature vector to a 1-10 dimensional time series. In step S42, the basic amplitude The frequency modulation coefficient is 1-10μV. The amplitude modulation coefficient is 5-20Hz. The value is 1-5 μV.

6. The access control advertising push method based on brain-computer interface for seamless interaction according to claim 1, characterized in that, S5. Brainwave Push: The target brainwave signal is pushed to the user's brain via the brainwave stimulation module of the brain-computer interface system, specifically as follows: The brainwave stimulation module uses transcranial alternating current stimulation (tACS) or transcranial magnetic stimulation (TMS) to convert the target brainwave signal into an electrical or magnetic stimulation signal, which is then applied to the user's head to achieve seamless push of advertising information. The stimulation frequency of the brainwave stimulation module is the same as the center frequency of the target brainwave signal. The stimulation intensity is 1-5 mA (electrical stimulation) or 1-2 T (magnetic stimulation), and the stimulation duration is 1-5 seconds.

7. The access control advertising push method based on brain-computer interface for seamless interaction according to claim 1, characterized in that, Also includes: Step S6. Feedback Adjustment: Adjust the advertising push strategy based on the user's brainwave response signal. The brainwave response signal is the brainwave signal collected by the brain-computer interface system after the user receives the advertisement. By analyzing the emotional characteristics of the brainwave response signal, adjust the content or push frequency of the advertising information.

8. A brain-computer interface-based access control advertising push system for seamless interaction, wherein the brain-computer interface-based access control advertising push system is used to implement the brain-computer interface-based access control advertising push method as described in any one of claims 1-7, characterized in that, The system includes: A brain-computer interface system is used to collect raw brainwave signals from users in real time. The brain-computer interface system includes EEG electrodes, a signal amplification circuit, and an analog-to-digital conversion module. The EEG electrodes collect brainwave signals from the user's scalp, which are amplified by the signal amplification circuit and then converted into digital brainwave signals by the analog-to-digital conversion module. The access control module processes the digital brainwave signal, performs preprocessing, feature extraction, and command recognition to obtain the user's door opening command recognition result; when the door opening command recognition result is a door opening command, the brain-computer interface system sends a door opening trigger signal to the access controller, and the access controller controls the access control system to open. The advertising push module is used to encode the advertising information to be pushed into the corresponding target brainwave signal; and push the target brainwave signal to the user's brain through the brainwave stimulation module of the brain-computer interface system. The interactive control module coordinates the access control and advertising processes and adjusts advertising strategies based on user feedback.

9. An electronic device, characterized in that, The electronic device includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 7.

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