Transcranial alternating current stimulation method and system with automatic adjustment function

Abstract

The invention provides a transcranial alternating current stimulation method and system with an automatic adjustment function, and relates to the technical field of transcranial alternating current stimulation, and the method comprises the steps: obtaining, compressing and sampling a multi-channel electroencephalogram signal, carrying out the multi-channel reconstruction through the physiological priori and the related information of adjacent brain regions, extracting the feature parameters of target brain waves, and carrying out the reconstruction of the target brain waves. Based on the parameters, dual-stage transcranial alternating current signals of prior stimulation and post stimulation are output; by introducing an adjacent matrix or a graph Laplacian operator in a compressed sensing acquisition and reconstruction link, high-fidelity recovery of cross-brain region oscillation can be maintained under the conditions of low sampling rate and noise; performing layered screening and dynamic fine tuning on characteristic frequency bands of different brain regions by combining general and individualized prior; the target brain wave phase is locked in advance in the first stimulation stage, and the stimulation intensity of each brain region is independently or cooperatively adjusted in the later stimulation stage, so that the requirement of coupling regulation and control of multiple brain regions is met, and the closed-loop intervention effect on nerve states such as insomnia and anxiety is improved.

Description

Technical Field

[0001] The present application relates to the technical field of transcranial alternating current stimulation, and more specifically, to a transcranial alternating current stimulation method and system with an automatic adjustment function. Background Art

[0002] With the continuous development of non-invasive neuromodulation technologies, transcranial alternating current stimulation (tACS) has received increasing attention in assisting in improving abnormal brain function states such as insomnia and anxiety. Existing research shows that applying electrical stimulation at appropriate frequencies and timings can interact with the endogenous rhythms of the brain, thereby affecting the activity level or phase characteristics of brain regions. However, typical tACS protocols often involve single-channel or few-channel electroencephalogram (EEG) acquisition and output stimulation in an open-loop mode at a fixed frequency or fixed phase, lacking adaptation to real-time brain wave changes and the cooperative state of multiple brain regions. On the other hand, in order to collect sufficient EEG signals in a high-noise environment, a high sampling rate and a long processing delay are usually required, increasing the system power consumption and data bandwidth pressure. When there are synchronization or coupling abnormalities in multiple brain regions, it is difficult to timely capture the overall abnormal fluctuations across brain regions and reasonably allocate the stimulation frequencies, intensities, and timings for each brain region only by independent channel processing and stimulation means. The above limitations often lead to insufficient extraction of key brain wave features and inflexible and inaccurate closed-loop feedback in practical applications, affecting the intervention effects of transcranial alternating current stimulation in scenarios such as insomnia and anxiety. Summary of the Invention

[0003] In view of the deficiencies of the prior art, the present application provides a transcranial alternating current stimulation method and system with an automatic adjustment function.

[0004] In a first aspect, the present application provides a transcranial alternating current stimulation method with an automatic adjustment function, including:

[0005] Obtaining multi-channel EEG signals arranged on multiple brain regions of a target user, and performing compressive sensing sampling on the multi-channel EEG signals based on the characteristic frequency bands of each brain region to generate compressed data;

[0006] Performing multi-channel EEG reconstruction on the compressed data, and combining the physiological priors of each brain region to extract target brain wave characteristic parameters corresponding to a target state;

[0007] Based on the target brain wave characteristic parameters, respectively determining transcranial alternating current stimulation output parameters for each target brain region, and performing signal output;

[0008] Dividing the transcranial alternating current stimulation output parameters into prior stimulation parameters and subsequent stimulation parameters;

[0009] Among them, the prior stimulation parameters are used to pre-stimulate the target brain region to determine the preliminary phase information of the brain waves in the target brain region; the subsequent stimulation parameters are dynamically adjusted based on the preliminary phase information.

[0010] As an optional implementation manner, the multi-channel electroencephalogram reconstruction of the compressed data includes:

[0011] Based on the layout position of the scalp electrodes or the functional division of the brain regions, an adjacency matrix is constructed to represent the correlation relationship between adjacent brain region channels;

[0012] A penalty term corresponding to the adjacency matrix is introduced into the reconstruction optimization objective, so that when the phase or amplitude difference between the reconstructed signals of each pair of adjacent channels exceeds a preset range, a penalty value is increased, and this penalty value is jointly incorporated into the objective function with the data consistency term and the sparse regularization term for solution.

[0013] As an optional implementation manner, when performing the multi-channel electroencephalogram reconstruction, the reconstruction difference between any two channels marked as adjacent according to the adjacency matrix is detected;

[0014] When the reconstruction difference exceeds a preset reconstruction difference threshold, the measurement matrix is adjusted to obtain new compressed data in subsequent iterations, and the multi-channel electroencephalogram reconstruction is continued based on the new compressed data.

[0015] As an optional implementation manner, when performing the multi-channel electroencephalogram reconstruction on the compressed data, it includes:

[0016] Based on the adjacency matrix, the graph Laplacian operator is calculated, and the graph Laplacian operator is incorporated into the reconstruction optimization objective;

[0017] During the iterative solution process, the differences of each channel are constrained by the graph Laplacian operator, so that the reconstructed signals of adjacent channels are locally consistent in phase or amplitude.

[0018] As an optional implementation manner, when performing the multi-channel electroencephalogram reconstruction with graph Laplacian operator constraint, the local difference between the channels marked as adjacent according to the adjacency matrix is detected;

[0019] In response to determining that the local difference corresponds to the true brain region feature rather than noise interference, the constraint weight of the graph Laplacian operator is reduced to allow the corresponding channel to maintain a reconstruction result different from that of the adjacent channels;

[0020] In response to determining that the local difference belongs to noise interference, the constraint weight of the graph Laplacian operator is maintained or increased to reduce the reconstruction deviation between the interfered channel and the adjacent channels.

[0021] As an alternative implementation, extracting the target brain wave feature parameters corresponding to the target state by combining the physiological priors of each brain region includes:

[0022] Based on the preset functional priors of brain regions, set corresponding target frequency bands for the frontal lobe, parietal lobe, and occipital lobe respectively;

[0023] After the multi-channel EEG reconstruction is completed, perform the first round of frequency band screening on the reconstructed signals of each brain region according to the functional priors;

[0024] Combined with the individual priors established by the user during baseline acquisition or historical records, locate the typical peak frequency or phase of the user within the target frequency band obtained in the first round of screening;

[0025] In response to the difference between the individual priors and the functional priors, locally expand the frequency band range of the corresponding brain region.

[0026] As an alternative implementation, in response to detecting strong peaks or phases that cannot be covered by the individual priors and the functional priors, mark the corresponding frequency band as a suspicious interval and increase the sampling density or iteration weight in the next reconstruction cycle;

[0027] In response to repeatedly detecting that the strong peaks or phases persist and are not related to noise interference, update the peak range corresponding to the corresponding brain region in the individual priors.

[0028] As an alternative implementation, when it is detected that target brain wave abnormalities exist in multiple brain regions and the correlation weights between any two brain regions in the adjacency matrix are greater than a preset threshold, generate a coupled abnormal brain region group;

[0029] According to the target brain wave characteristics of each brain region in the coupled abnormal brain region group, respectively determine and store the corresponding initial parameters of the prior stimulation, including the stimulation frequency, initial phase, and pre-stimulation duration;

[0030] Simultaneously apply the prior stimulation to the coupled abnormal brain region group and generate a phase locking index. If the phase locking index is greater than or equal to the first threshold after the prior stimulation ends, output identification information to indicate entering the post-stimulation stage;

[0031] In the post-stimulation stage, calculate and store the personalized stimulation intensity or frequency parameters for each brain region respectively, and perform closed-loop stimulation output for each brain region based on the frequency parameters.

[0032] As an alternative implementation, in the post-stimulation stage, real-time obtain the phase difference or energy difference of each brain region in the coupled abnormal brain region group, and generate and update the coupling degree measurement result;

[0033] When it is detected that the target brain wave amplitude difference or phase deviation degree of any brain region is lower than the second threshold, the stimulation intensity or frequency of this brain region is downshifted based on the current coupling degree measurement result, and the downshift parameters are stored for subsequent iteration use;

[0034] When it is detected that the phase or energy shift occurs again in the overall coupled abnormal brain region group, the downshift parameters and the coupling degree measurement result are used to determine whether to regenerate the initial parameters of the prior stimulation.

[0035] In a second aspect, the present application provides a transcranial alternating current stimulation system with an automatic adjustment function, including:

[0036] An acquisition module, configured to acquire multi-channel electroencephalogram signals arranged in multiple brain regions of a target user, and perform compressive sensing sampling on the multi-channel electroencephalogram signals based on the characteristic frequency bands of each brain region to generate compressed data;

[0037] A processing module, configured to perform multi-channel electroencephalogram reconstruction on the compressed data, and combine the physiological priors of each brain region to extract target brain wave characteristic parameters corresponding to the target state;

[0038] An output module, based on the target brain wave characteristic parameters, respectively determines the transcranial alternating current stimulation output parameters for each target brain region, and performs signal output;

[0039] The output module is further configured to divide the transcranial alternating current stimulation output parameters into prior stimulation parameters and subsequent stimulation parameters;

[0040] Among them, the prior stimulation parameters are used to perform pre-stimulation on the target brain region to determine the preliminary phase information of the brain wave of the target brain region; the subsequent stimulation parameters are dynamically adjusted based on the preliminary phase information.

[0041] Compared with the prior art, the present application proposes an electroencephalogram reconstruction method that takes into account both multi-channel compressive sensing sampling and physiological prior fusion, and models the association relationship between adjacent brain regions based on the adjacency matrix. On the one hand, by adding a sparse regularization or graph Laplacian operator in a low sampling rate environment, stable electroencephalogram reconstruction under high noise is realized; on the other hand, by combining general functional priors and individual priors to hierarchically screen the target frequency bands of each brain region, the abnormal brain wave peak value and phase can be accurately extracted. On this basis, a two-stage output scheme of prior stimulation and subsequent stimulation is adopted, which helps to lock in the phase of the target brain region first, and then strengthen or maintain individual brain regions. Especially when there are coupling fluctuations between multiple brain regions, the overall synchronization degree of adjacent brain regions can be enhanced through unified initial stimulation and dynamic adjustment means. Compared with the prior art, the present application reduces both sampling and calculation overheads, improves the flexibility of reconstruction and closed-loop regulation, and meets the multi-brain region collaborative regulation requirements in scenarios such as insomnia and anxiety. Description of the Drawings

[0042] Figure 1 Flow chart of the transcranial alternating current stimulation method with automatic adjustment function provided by the embodiment of the present application;

[0043] Figure 2 Schematic diagram of a priori information provided by the embodiment of the present application;

[0044] Figure 3 Flow chart for constructing an adjacency matrix provided by the embodiment of the present application

[0045] Figure 4 Schematic diagram of the transcranial alternating current stimulation system with automatic adjustment function provided by the embodiment of the present application.

[0046] Reference signs: 10, acquisition module; 20, processing module; 30, output module. Detailed implementation manners

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0048] Refer to Figure 1 As shown, it is a flow chart of the transcranial alternating current stimulation method with automatic adjustment function provided by the embodiment of the present application. The transcranial alternating current stimulation method with automatic adjustment function includes S101 to S104, where:

[0049] S101: Obtain multi-channel electroencephalogram signals arranged in multiple brain regions of a target user, and perform compressive sensing sampling on the multi-channel electroencephalogram signals based on the characteristic frequency bands of each brain region to generate compressed data;

[0050] S102: Perform multi-channel electroencephalogram reconstruction on the compressed data, and combine the physiological a priori of each brain region to extract target brain wave characteristic parameters corresponding to the target state;

[0051] S103: Based on the target brain wave characteristic parameters, respectively determine the transcranial alternating current stimulation output parameters for each target brain region, and perform signal output;

[0052] S104: Divide the transcranial alternating current stimulation output parameters into prior stimulation parameters and posterior stimulation parameters; wherein, the prior stimulation parameters are used to perform pre-stimulation on the target brain region to determine the preliminary phase information of the brain waves of the target brain region; the posterior stimulation parameters are dynamically adjusted based on the preliminary phase information.

[0053] In specific implementation, the present application respectively arranges electrodes in multiple target brain regions of the user's head to form a multi-channel electroencephalogram acquisition path.

[0054] Specifically, in order to reduce the hardware resource occupancy and transmission bandwidth, a measurement matrix can be designed according to the common target brain wave frequency bands of each brain region. The coefficients of the measurement matrix can be implemented in hardware circuits or software, so as to perform a linear projection operation on the original electroencephalogram (EEG) signals in the analog or digital stage and output the compressed data. By sparsifying or structuring the measurement matrix, the main information related to the frequency bands of the target brain regions can be obtained at a lower sampling rate, and the sensitivity to key oscillation components can be improved. If an increase in noise or a decrease in reconstruction quality is detected at a certain stage, the measurement matrix can be temporarily switched to a backup one or the sampling frequency can be increased to ensure the accuracy of subsequent reconstruction.

[0055] Exemplarily, the ADS1299 acquisition chip can be selected and combined with the STM32 series microcontroller or the Field-Programmable Gate Array (FPGA) to perform the linear projection and data compression of the measurement matrix; in the Verilog or C language environment, the electroencephalogram (EEG) signals are preprocessed through a sparse filtering unit with fixed coefficients. Subsequently, the compressed data is transmitted to the host computer or the cloud via Wi-Fi or Bluetooth, and the key oscillation band information is restored by using sparse reconstruction algorithms such as Orthogonal Matching Pursuit (OMP) and Iterative Hard Thresholding (IHT) in the Python or MATrix LABoratory (MATLAB) environment. When an increase in the noise level or a decrease in the reconstruction quality is detected, the measurement matrix can be dynamically switched to a backup one or the sampling rate can be increased in the firmware to effectively control the bandwidth and power consumption while ensuring the quality of key signals.

[0056] After obtaining the compressed data, the present application transmits it to the processing unit to perform multi-channel EEG reconstruction. The reconstruction process can be based on cross-channel covariance analysis or an optimization model based on sparse priors, which is used to distinguish effective brain waves and noise components. To make the system stable in a noisy environment, a robust estimator sub-module for Gaussian noise, electromyogram interference or sporadic pulse noise can be introduced in the reconstruction, and stable brain wave components are extracted through sparse regularization or low-rank decomposition. In the reconstructed multi-channel EEG waveforms, the physiological priors of each brain region can be used to strengthen the attention to specific frequency bands. For example, when monitoring insomniac users, the slow wave components in the range of 0.5 - 1 Hz are mainly retained, and when monitoring anxious users, the α wave energy and phase in the range of 8 - 12 Hz are concerned.

[0057] During the process of extracting the target brain wave characteristic parameters described in this application, the typical peak frequency refers to the frequency corresponding to the main power peak presented by the reconstructed signal in a certain brain region within the target frequency band. Specifically, after performing spectral analysis on multi-channel electroencephalogram, the power spectrum or amplitude distribution can be calculated within a specified frequency band (such as 0.5 - 1 Hz, 8 - 12 Hz, etc.), and the frequency point with the highest energy is searched as the "typical peak frequency" of this brain region in the current state. This means that if the maximum power point of a brain region within the 8 - 12 Hz interval appears at 10 Hz, then 10 Hz can be regarded as the typical peak frequency of this brain region.

[0058] In specific implementation, the system will combine the user's individualized prior data, such as the actual peak distribution of the α wave measured by the user during the baseline phase. If multiple observations show that most of its maximum power is concentrated in the range of 9.3 Hz - 9.7 Hz, then the algorithm will regard this frequency band as the user's typical peak range and focus on the power characteristics within this interval during the current reconstruction analysis. If the actually detected power peak slightly deviates from the previous range, the system can determine whether it is necessary to expand the search bandwidth or perform a higher-resolution reconstruction to confirm whether the deviation is transient noise or a new typical peak. In this way, the "typical peak frequency" is used in the present invention to quantitatively describe the main energy concentration characteristics of each brain region within the target frequency band, and provide an accurate phase and frequency matching reference for subsequent pre- / post-stimulus.

[0059] It should be noted that the target state is not limited to anxiety, and the solution disclosed in this application is equally applicable to insomnia, depression, etc.

[0060] When extracting the target brain wave characteristic parameters, the energy, phase, and peak frequency of the brain wave in the target frequency band can be calculated for each brain region respectively. If the difference between the detected individualized baseline level and the current measurement exceeds a preset threshold, the corresponding brain region can be marked as needing key regulation and given a higher priority in the subsequent stimulus output link. For the marked brain regions, the system will generate transcranial alternating current stimulation output parameters, including parameters such as stimulation frequency, phase, and intensity.

[0061] Exemplarily, after completing the reconstruction of multi-channel EEG and obtaining the brain waves in the target frequency band, the energy, phase, and peak frequency of each brain region can be calculated one by one through Python or MATLAB, and these parameters can be compared with the individual baseline level; if the difference in a certain brain region exceeds the set threshold, the system will mark it as a key area to be regulated. In the next regulation session, combined with the existing ADS1299 and FPGA architectures, the system will generate corresponding transcranial alternating current stimulation (tACS) output parameters according to the marked brain regions, including stimulation frequency, phase, and intensity, etc., to preferentially act on these key brain regions to help correct or strengthen the desired brain wave oscillations.

[0062] To avoid potential discomfort or lack of phase synchronization caused by large-scale stimulation during the initial regulation, the prior stimulation parameters can be applied first, for a relatively short time and with a relatively low current amplitude to observe the brain region response. If complete phase information is extracted during this process and the user does not experience excessive discomfort, the system can automatically enter the post-stimulation stage, appropriately increasing or adjusting the stimulation intensity and frequency to keep them synchronized with the EEG phase monitored in real time.

[0063] In actual operation, the system first enters the prior stimulation stage: for example, for the pre-marked frontal lobe or parietal lobe, a tACS signal of about 0.8 mA is applied first, and the frequency is set according to the previously measured peak of the α wave (such as 10 Hz). At this time, the FPGA will combine with the microcontroller to obtain the real-time EEG and evaluate the phase response of the target brain region through phase detection (which can be achieved using the Hilbert transform); during the 5-second prior stimulation process, if both the EEG phase stability (for example, the phase locking value PLV is greater than 0.6) and the user comfort level (such as the subjective evaluation score is less than or equal to 2) meet the thresholds, the system determines that the stimulation parameters can be further increased. Then, the system automatically enters the post-stimulation stage, strengthening the regulation effect by increasing the tACS intensity from 0.8 mA to 1.5 mA or fine-tuning the frequency (such as searching for the best synchronization frequency in the range of 9.5 - 10.5 Hz); if the phase synchronization degree detected by the EEG drops significantly (PLV is less than 0.4) or the user experiences obvious discomfort (the subjective evaluation score is greater than or equal to 3) at this time, the system will return to the prior stimulation parameters or stop the stimulation to ensure safety and acceptability.

[0064] In addition, a time-sliced scheduling mechanism can be established within the processing unit to determine the end time of the prior stimulation and the start time of the subsequent stimulation by polling the EEG reconstruction results of each brain region. If the processing unit detects that the prior stimulation fails to achieve phase synchronization or the user experiences a high-intensity discomfort reaction, it will automatically reduce the stimulation amplitude or pause the pre-stimulation, and monitor the noise level and EEG characteristics of the brain region again. If it is detected during the subsequent stimulation stage that the target waveform in the brain region has been steadily enhanced, the maintenance time of the current parameters can be extended or the stimulation intensity can be gradually reduced. Conversely, if a brain wave decline or phase drift is detected during the subsequent stimulation stage, the processing unit will dynamically adjust the stimulation output parameters to restore the output phase alignment and ensure that the power does not exceed the safety limit. The above process continuously cycles in the system, thereby achieving automated closed-loop regulation for insomniac and anxious users while meeting safety constraints.

[0065] In some wearable implementations, the above acquisition and reconstruction functions can be integrated on the same hardware platform, and the key feature parameters after reconstruction are sent to a peripheral device or the cloud via a low-power wireless module for further analysis. The switching timing between the prior stimulation and the subsequent stimulation can be fine-tuned according to historical statistical laws to accommodate the personalized needs of different users. For users with significantly low slow-wave or α-wave bases, the duration of the prior stimulation can be extended to ensure sufficient initial phase locking. For sensitive populations, the current amplitude and increase rate can be strictly restricted during the prior stimulation stage.

[0066] In this way, using this differential strategy, targeted management of sleep and mood can be achieved in clinical or home scenarios, and adverse reactions that may occur during the stimulation process can be reduced. The entire method is supported by clear hardware and algorithms at each step, and can better adapt to the low-power and high-precision requirements in practical applications.

[0067] Exemplarily, four electrodes can be respectively configured at the frontal and parietal positions of the user's head to form a total of eight-channel EEG acquisition array. The hardware part can use an analog front-end module with a multi-channel programmable gain amplifier. The front-end limits the acquired EEG signals within a range of approximately 0.1 Hz to 40 Hz through band-pass filtering. The analog output of each channel is then sequentially sent to an analog-to-digital converter via a multiplexer controlled by a single-chip microcomputer. The conversion accuracy can be selected as 12 bits or 16 bits, depending on the specific power consumption budget. To reduce the overall data transmission volume and achieve low-power operation, a sparse or structured measurement matrix can be pre-loaded at the embedded end, and the sampled EEG signals are linearly projected in a random or structured sparse manner to obtain compressed data with a lower dimension. For insomnia intervention, the frontal and parietal lobes are usually related to slow-wave sleep, so the frequency domain information in the range of 0.5 Hz to 1 Hz can be emphasized and retained as much as possible in the measurement matrix.

[0068] In the software part, to accelerate the processing speed, the compressive sensing reconstruction algorithm can be run on a digital signal processor with floating-point operation capabilities or a low-power FPGA. The reconstruction process can be divided into two main stages: after the processor starts, load the prior of the cross-channel covariance matrix, and construct an optimization equation combined with the sparse assumption of the slow-wave component, and then restore the EEG data of each channel by iterative solution. After multi-channel reconstruction is completed, the software estimates the power density and phase in the range of 0.5 Hz to 1 Hz based on the timing waveform of each channel, and compares it with the user's baseline data to judge the current deep sleep tendency.

[0069] After obtaining the characteristic parameters, the system calculates the corresponding transcranial alternating current stimulation output parameters for each brain region, including the stimulation frequency and phase alignment method. In the initial stage, the output parameters can be set to the prior stimulation mode, and an alternating current amplitude of 0.3 mA to 0.5 mA and a frequency range close to the slow wave can be selected. The prior stimulation time can be set to about dozens of seconds to two minutes, depending on the user's tolerance to the stimulation and the EEG response speed. If during this period the system monitors that the phase of the slow-wave signal is basically locked with the external stimulation and the user does not report obvious discomfort, the software will automatically switch the stimulation of this brain region to the post-stimulation mode, and the amplitude can be slightly increased to 0.6 mA to 0.8 mA, or more flexible phase fine-tuning can be performed within the same frequency range. If it is monitored that the slow-wave intensity has increased significantly and remains stable, the system will extend the interval of the post-stimulation or reduce the amplitude to save energy and prevent over-stimulation.

[0070] Furthermore, in conventional multi-channel reconstruction, the system often treats each channel as independent or only constrained by the global sparse prior. In this way, when adjacent electrode regions are affected by similar noise disturbances, local reconstruction instability or cross-brain region phase incoherence is likely to occur.

[0071] Therefore, as an alternative implementation, a spatial or functional correlation model of adjacent brain regions can be introduced into the reconstruction algorithm, so as to mathematically achieve synchronous correction or joint sparse representation of adjacent signals.

[0072] In a specific implementation, in the multi-channel EEG acquisition link, an adjacency matrix can be constructed according to the layout of the electrodes on the scalp to represent the spatial or functional adjacent relationship between each channel. The adjacency matrix can be obtained based on the geometric distance of the scalp electrodes or the known functional division of brain regions in relevant references. For electrode pairs that are close to each other or have a coupling relationship in function, the corresponding elements in the adjacency matrix can be set to larger weights, otherwise to smaller or zero weights. When performing multi-channel EEG reconstruction, on the basis of the original sparse regularization or low-rank constraint, an additional regular term based on the adjacency matrix can be added. Exemplarily, it can adopt the following form:

[0073] ;

[0074] Among them, represents the EEG signal representation or coefficient vector to be solved (which can also be regarded as a matrix depending on the situation). When a specific transform domain (such as the wavelet domain, discrete cosine domain, or discrete Fourier domain) is used in the sampling and reconstruction processes, can be regarded as the set of coefficients in that transform domain; if directly processed in the time domain, can also be regarded as the discrete sampling point sequence of the multi-channel EEG signal. represents the measurement matrix, which is used to implement compressive sensing sampling. At the hardware or algorithm level, projects the original EEG signal onto a lower-dimensional measurement space. It is usually designed according to random sparsity or structured sparsity and can be implemented in embedded hardware in the form of sparse multiplication. represents the compressed measurement data (observed data) actually sampled. Due to the use of the compressive sensing method, its dimension will be lower than that of the original EEG signal with full sampling, but it can recover a relatively high-fidelity EEG waveform during reconstruction.

[0075] is called the data fidelity term or data consistency term, which is used to measure the deviation between the current solution in the measurement space and the actual observation . The squared two-norm ( ) is commonly used to measure the deviation. If this term is smaller, it means that when is mapped back to the measurement space through , it is closer to the true sampling value.

[0076] and are the trade-off coefficients respectively, which are used to adjust the importance of different regularization terms in the optimization objective. If the value is larger, it means that the algorithm attaches more importance to the -represented prior assumption; if the value is larger, the joint constraint on adjacent brain regions will be stronger.

[0077] is used to represent the conventional prior constraints on the EEG signal or its coefficients, such as:

[0078] Sparsity prior (such as norm, total variation, etc.), which is used to encourage the signal or coefficients to remain sparse in a certain domain, thereby suppressing noise; low-rank prior. If the multi-channel EEG is regarded as a matrix, the nuclear norm or other low-rank norms can be used to measure its rank, encouraging the existence of a correlation structure between different leads; other common denoising or smoothing constraints, such as Intensity suppression under the norm or threshold processing based on the wavelet domain.

[0079] This part is used to globally suppress noise and emphasize the compressibility or smoothness of the signal in a given domain.

[0080] It represents a regularization term related to the correlation between adjacent brain regions. A is the adjacency matrix (or weighted adjacency matrix), which is used to characterize the adjacent or functional correlation relationship between each channel. The construction and form of the adjacency matrix A can be found at the end of this article and will not be elaborated here.

[0081] The design of the regularization term can be carried out in various ways:

[0082] As an alternative implementation, adjacent smoothing constraints can be adopted to impose certain smoothness or phase consistency requirements on the reconstruction results of adjacent brain region channels. For example, when the phase or amplitude difference between adjacent channels at the same moment is too large, a penalty term is increased.

[0083] As an alternative implementation, graph Laplacian penalty can be adopted. The EEG signal is regarded as a distribution on a graph structure, and a certain smoothness or low-frequency characteristic between adjacent nodes (channels) is encouraged through graph signal processing methods (such as the Laplacian operator).

[0084] As an alternative implementation, coupled sparsity can be adopted. When it is known that adjacent brain regions have synchronous or coherent oscillations in the pathological states of insomnia or anxiety, synchronous non-zero coefficients at the same frequency points can be encouraged for both, avoiding meaningless independent activations or noise spikes.

[0085] Through this constraint, the solution of each channel can be adaptively adjusted in the optimization, reducing local unreasonable oscillations or noise interference, while better retaining the possible synchronous rhythms across brain regions and improving the characterization of real physiological activities.

[0086] It means to find the optimal X to minimize the objective function. Generally, iterative methods such as the split Bregman method, the alternating direction method of multipliers (ADMM), and the iterative soft thresholding algorithm (ISTA) can be used to solve this problem. The key lies in choosing a suitable algorithm that can not only maintain low computational overhead but also converge to a high-fidelity solution under sparse priors and adjacency constraints.

[0087] The above calculation formula aims to balance data fidelity and various prior constraints, enabling high-quality reconstruction of multi-channel EEG signals in the actual environment of low sampling rate (downsampling due to compressive sensing) and noise interference to solve the following technical problems:

[0088] Cross-brain waveform segmentation: If adjacent brain regions should have good synchronization or similarity in principle, but have extreme differences after reconstruction due to noise or insufficient sampling, adjacency constraints can be used to allow them to maintain reasonable similarity;

[0089] Local noise: Adjacent brain regions are often disturbed by similar environments. By combining constraints, the noise can be prevented from being over-amplified in a certain lead.

[0090] Pathological resonance detection: If users with insomnia or anxiety have common characteristic fluctuations in certain adjacent brain regions, this formula can help highlight such common patterns in reconstruction and more accurately identify brain wave features associated with pathological conditions.

[0091] In specific implementation, a penalty can be set for the phase difference or amplitude difference between adjacent channels in the frequency domain to reduce unreasonable cross-region mutations and ensure that the phase of slow waves or alpha waves in adjacent brain regions is smoother;

[0092] As an optional implementation, the idea of graph signal processing can be adopted, each channel is regarded as a graph node, and the graph Laplacian operator defined by the adjacency matrix is used to constrain the smoothness or local consistency across channels, so that the coherent oscillation characteristics across brain regions can still be retained under low sampling rate conditions;

[0093] If it is known that two brain regions often exhibit coupled oscillations in insomnia or anxiety scenarios, their corresponding matrix weights can be increased so that the reconstruction algorithm pays more attention to maintaining synchronization or common sparsity between the two regions.

[0094] This adjacency constraint can better extract EEG rhythms that are conducted or coupled across brain regions during reconstruction, reducing reconstruction distortion caused by single-point interference or local noise. When the system detects that the reconstruction differences between adjacent brain regions are too large, it can also trigger additional robustness correction steps within the algorithm, such as temporarily increasing the sampling frequency, using a denser measurement matrix, or adopting a stricter threshold suppression method, thereby enhancing resistance to environmental interference.

[0095] For example, if an abnormal jump occurs in the slow wave phase or alpha wave phase of the two adjacent channels when monitoring the border area between the forehead and the parietal lobe, the adjacency constraint regularization term can force the algorithm to bring the solutions of the two channels closer during the calculation, ensuring that the waveforms across regions are more consistent. Conversely, if there is indeed a pathological condition that causes an abnormal phase difference between the forehead and the parietal lobe, the system can compare the adjacency matrix and the global prior to determine whether the difference really exists. If it is confirmed that the difference is real brain activity rather than noise, the algorithm can distinguish between real signals and interference with the support of the remaining channels, thereby improving the accuracy of identifying abnormal pathological conditions.

[0096] When setting prior or subsequent stimulation parameters for different brain regions in subsequent transcranial alternating current stimulation control, if the processing unit detects a significant decrease in the power of multiple adjacent brain regions during the reconstruction phase through adjacency constraints, these brain regions can be simultaneously listed as high-priority stimulation targets to synchronously enhance their brain waves and avoid poor overall effects caused by stimulating only a single brain region. Another example is in an anxious state. If it is found that both the frontal lobe and the anterior parietal lobe have low alpha wave energy and have a large weight in the adjacency matrix, multi-channel stimulation with phase locking can be applied to these two regions to synchronize their alpha waves and jointly relieve anxiety symptoms.

[0097] In the above way, the new multi-channel electroencephalogram reconstruction method increases the utilization of the information connection between adjacent brain regions in the original scheme, and solves the technical problems that may be faced by single-channel or independent-channel methods, such as waveform fragmentation across brain regions, insufficient robustness in local noise processing, and difficulty in maintaining the phase consistency of adjacent brain regions. This improvement can form a complementarity with the aforementioned prior / subsequent stimulation mechanism:

[0098] The multi-channel electroencephalogram reconstruction phase uses adjacency constraints to provide more accurate spatial distribution and phase estimation;

[0099] In the stimulation phase, the prior pre-stimulation is used to lock the phase first, and then the subsequent stimulation is used to consolidate or enhance brain region synchronization, so as to more precisely regulate brain wave oscillation in insomnia or anxiety scenarios.

[0100] In this way, by introducing the correlation modeling between adjacent brain regions in the reconstruction phase, this application can further improve the fidelity and robustness of compressive sensing reconstruction, provide a more reliable brain wave feature basis for the subsequent partitioned stimulation strategy, and significantly improve the accuracy in cross-brain region fluctuation detection. This can not only technically solve the vulnerability of single-channel or independent-channel methods to complex noise interference, but also provide better support for synchronous closed-loop regulation of multiple functionally related brain regions.

[0101] As an optional implementation manner, the electroencephalogram channels arranged in multiple brain regions can be regarded as several graph nodes, neighbor nodes of each node are determined under the scalp electrode position or brain region function division, and an adjacency matrix is constructed based on this. The adjacency matrix is used to calculate the graph Laplacian operator to smooth or coherently constrain the cross-brain region distribution during the multi-channel electroencephalogram reconstruction process. After the compressed data obtained in the sampling phase is transmitted to the processing unit, the processing unit can combine the graph Laplacian operator with sparse regularization or low-rank prior during iterative solution, on the one hand, ensuring the restoration of key brain waves at a low sampling rate, and on the other hand, reducing unreasonable waveform mutations or phase jumps between adjacent brain regions by means of graph smoothing.

[0102] In the initialization stage, the electrode layout information or clinical data of the user can be read first, channel nodes are assigned to each brain region, and edge weights are determined between adjacent nodes. If two brain regions are close in location or show a high degree of synchronization during baseline monitoring, a larger weight can be assigned, thus forming a closer connection relationship in the graph structure. After obtaining the graph Laplacian operator through the adjacency matrix, when the processing unit performs multi-channel reconstruction, an additional smoothing constraint based on the graph structure is added in each iteration, suppressing the amplitude or phase differences of adjacent nodes in the same frequency band. When a significant deviation is detected in a certain channel and there is a large inconsistency with adjacent channels, the system can correct the reconstruction result of this channel according to the gradient information of the Laplacian term, thereby maintaining the possibility that local mutations may reflect real signals while minimizing outliers caused by noise or artifacts.

[0103] In practical applications, a smoothing coefficient corresponding to the graph Laplacian operator can be set to adjust the trade-off between the smoothing degree of adjacent nodes and the global sparsity prior. If a reconstruction error is detected to be too high or the coupling degree between adjacent brain regions is insufficient, the system can appropriately increase the smoothing coefficient to enhance the maintenance of cross-brain region coherence. If the user has clear pathological features resulting in a real abnormal waveform in a certain brain region, after retrieving that the brain region has clinical features or prior annotations, the influence of adjacent smoothing on this channel can also be temporarily weakened to avoid confusing real abnormalities with noise. This can achieve a balance between global smoothing across brain regions and local specific changes.

[0104] When the reconstructed multi-channel signal better preserves the coherent oscillations of adjacent brain regions, subsequent pre-stimulus and post-stimulus phases can be regulated based on more accurate brain wave phase information. If slow wave enhancement is detected simultaneously in the frontal lobe and parietal lobe, during closed-loop control, similar stimulation phases or frequencies can also be set for these two brain regions. If a large difference in phase is detected between a single brain region and its surrounding brain regions and cannot be bridged after multiple iterations of reconstruction, the system can consider increasing the sampling frequency or replacing the measurement matrix to confirm whether this difference is noise or an actual local abnormality. During use, if insufficient bandwidth or computing resources are detected, the graph smoothing coefficient or the update frequency of some channels can be temporarily reduced to maintain the real-time performance of the overall algorithm.

[0105] Through this graphical signal processing approach, the recognition accuracy of cross-brain region synchronous activities can be enhanced under the condition of low sampling rate, which is especially applicable to the multi-brain region coupled oscillations commonly seen in insomniac and anxious populations. Compared with the existing simple penalty based on the adjacency matrix, the use of the graph Laplacian operator can more flexibly model the brain region network structure and is algorithmically compatible with the existing sparse or low-rank reconstruction modules. Combining the two-stage current output method of prior stimulation and post-stimulation, and cooperating with the multi-channel EEG reconstruction results based on graph smoothing, more accurate synchronous phase locking or phase adjustment can be achieved in the later closed-loop control, providing alternative implementation approaches for different clinical or home scenarios.

[0106] Exemplarily, several electrodes can be arranged at the frontal, parietal and occipital positions of the user's head respectively to form a total of twelve-channel EEG acquisition array. The acquisition hardware can perform linear projection by combining the ADS1299 analog front end and the FPGA, and use the STM32 series microcontroller to control data acquisition and wireless transmission. In the initial stage, the system reads the scalp electrode position and functional partition information, establishes corresponding graph nodes for each channel, and calculates the edge weights between adjacent nodes according to the geometric distance between the electrodes, the functional correlation reported in the literature or the phase synchronization degree measured in the pre-experiment. If certain electrode pairs show a high phase consistency during baseline monitoring (such as the coupling between the frontal and parietal lobes in the slow wave band), a larger weight will be assigned in the adjacency matrix; if two channels are far apart and lack synchronous characteristics after preliminary testing, the weight at the corresponding position in the adjacency matrix will be lower.

[0107] Exemplarily, if it is monitored that the leads in the frontal, parietal and occipital regions simultaneously show a significant decrease in slow wave energy, then in the subsequent closed-loop stimulation, the system can jointly mark these three channels as high-priority objects to ensure that a closer phase-locked frequency or higher stimulation intensity is allocated during transcranial alternating current stimulation. If it is detected that a certain channel continuously differs greatly from the surrounding channels, but the difference cannot be reduced through graph smoothing, the processing unit will perform supplementary sampling on this channel to determine whether it is indeed a real brain region abnormality. If it is confirmed as noise interference, the system will suppress this interference within the neighborhood range under the constraint of the graph Laplacian operator without affecting the overall phase determination; if it is confirmed as a pathological event, different prior or post-stimulation parameters will be set separately for this channel during subsequent stimulation output, so as to take into account both cross-brain region coupling and local personalized regulation.

[0108] Exemplarily, the fast iterative update of the graph Laplacian regular term can be implemented through fixed-point operations on the FPGA or DSP side, and offline verification can also be performed using high-precision floating-point operations in the Python or MATLAB environment. The smoothing coefficient can be dynamically adjusted between 0.01 and 0.1 according to the patient's condition or noise level: when the system detects that adjacent brain regions that should maintain synchronization show random deviations, the smoothing coefficient is appropriately increased; when there is an obvious lesion area and it is indeed necessary to retain the differences, the smoothing coefficient is decreased or the adjacent constraint on the lesion area is stopped. The multi-channel EEG signals obtained on this basis can better reflect the real correlation between brain regions in terms of phase and amplitude, laying a more reliable data foundation for the accurate phase locking and intensity allocation of prior and subsequent stimuli.

[0109] In this way, by incorporating the spatial or functional correlations of adjacent brain regions into the graph Laplacian constraint, the system can still maintain the overall coherence of cross-brain region oscillations in a low sampling rate and high noise environment, and provide more flexible multi-brain region collaborative regulation for states such as insomnia or anxiety in subsequent closed-loop transcranial alternating current stimulation.

[0110] As an alternative implementation, after the multi-channel EEG reconstruction is completed, the dominant frequency bands of regions such as the frontal lobe, parietal lobe, or occipital lobe can be first limited to the corresponding intervals according to the common functional attributes of the brain regions. For example, in the frontal lobe, the 0.5 - 1 Hz range of slow waves is preferentially considered, and in the parietal lobe or occipital lobe, the characteristic frequency bands of alpha waves or theta waves are emphasized. Through this general functional prior, it is possible to quickly screen out the relatively more noteworthy intervals from the full frequency band in a low sampling rate environment, reducing redundant calculations for irrelevant frequencies.

[0111] After the target interval is screened, for each brain region, the specific peak frequency and phase can be fine-tuned a second time in combination with the user's individual prior information. If it is detected that the real peak of a certain brain region deviates from the default prior, such as the common interval of alpha waves is 8 - 12 Hz while the user's actual peak is mostly concentrated around 12.5 Hz, then the attention bandwidth is appropriately expanded to 12 - 13 Hz in subsequent analysis and stimulation decisions to avoid ignoring the key waveforms brought about by individual offsets. If this offset only appears in a certain measurement, the system can perform more sample acquisitions in the subsequent time series window to verify whether there are stable energy peaks or phase synchronization characteristics, so as to prevent transient interference from being wrongly incorporated into the prior model.

[0112] When the general functional prior conflicts with the individualized prior or cannot cover the new characteristic peak, the system can temporarily mark the suspected frequency band of the brain region as a "suspicious segment" and increase the sampling density or iteratively reconstruct the weight in the next stage. If multiple verifications show that the new peak has a clear and lasting energy advantage, the processing unit will update the baseline prior of the brain region so that it always takes this new peak range into account in future analyses of the same user. In this way, even if abnormal oscillations or unique individual characteristics appear outside the common frequency band, they will not be ignored by the traditional fixed prior model.

[0113] Through this hierarchical prior fusion method, each brain region first relies on its functional attributes to perform large-band screening, and then combines the individualized data recorded by the user's baseline for local fine calibration, which can not only ensure consistency with the universal functional division of brain regions, but also capture the differentiated activity patterns of users. The extracted target brain wave features are therefore closer to the real physiological state, and whether it is energy, phase or peak frequency, it will more accurately reflect the actual performance of individual brain regions, providing a more reliable basis for the subsequent calculation of parameters for prior stimulation and post-stimulation.

[0114] If a new frequency band that does not match the original prior information and has a high amplitude or obvious phase synchronization characteristics is detected in a certain brain area during the application, the system will regard this as a potential pathological marker or unique brain wave activity, and increase the compressed sensing sampling frequency or switch to a measurement matrix more suitable for this feature in subsequent operations to confirm as soon as possible whether it is necessary to implement closed-loop regulation of this brain area that is different from the conventional frequency band. By layering and fusing common functions with individual differences, the entire feature extraction process takes into account stability and flexibility, and can still fully mine brain wave information related to the target state in a low sampling rate and noisy environment.

[0115] For example, see Figure 2 , Figure 2 A schematic diagram of a priori information provided for an embodiment of the present application; wherein the system stores two types of prior data for each brain region during initialization. The first type is functional prior, which is used to describe the common main frequency bands and waveform characteristics of the brain region, such as the preset slow wave interval of the frontal lobe is 0.5-1Hz, and the alpha wave interval of the occipital lobe or parietal lobe is 8-12Hz. The second type is individualized prior, which is used to record the actual peak frequency and time domain characteristics measured by the user during baseline acquisition or multiple previous uses. Baseline acquisition can be completed through continuous monitoring of multi-channel EEG signals, usually lasting for several hours and covering different states such as quiet rest or light sleep, so as to obtain the typical brain wave distribution of each brain region of the user.

[0116] When performing specific analysis, after the multi-channel reconstruction is completed, the system will first perform functional prior matching on the reconstructed signals of each brain region. The software module will preferentially search for the amplitude and phase peaks in the frequency spectrum of the frontal lobe signals within the range of 0.5 - 1 Hz. If effective peaks or phase synchronizations are found, they will be preliminarily marked as the main slow-wave activities of that brain region. Subsequently, combined with the individualized prior, if the user's past peaks in the slow-wave range were more concentrated around 0.7 Hz, the search within the sub-range of 0.6 - 0.8 Hz will be emphasized within the 0.5 - 1 Hz fine partition, and the difference between the actually detected peak frequency and the individual baseline will be compared. When the difference is within 0.05 Hz, the algorithm determines that it matches the user's prior, and the corresponding phase and energy are recorded as the core features of this monitoring; if the difference exceeds this range and the peak persists, this frequency range is marked as "suspected new peak", and a flag is set to prompt to increase the sampling density or lower the threshold during subsequent iterations or the next measurement to confirm whether the user's prior should be updated.

[0117] In this embodiment, if the user's frontal lobe signal is basically matched with the functional prior (0.5 - 1 Hz), but shows an obvious and stable peak at 1.05 Hz in multiple repeated samplings, the system will temporarily include the range of 1.0 - 1.1 Hz in the key search scope for the next round of reconstruction. After determining that it remains continuously active, the upper limit of the peak frequency for the frontal lobe in the user's baseline data will be automatically extended from 1.0 Hz to 1.1 Hz to ensure that this newly emerging peak will not be missed in future analysis. If this peak only appears briefly in one measurement and does not reappear in subsequent time series windows, the system will not make a permanent change to the individualized prior and regards this phenomenon as a short-term fluctuation or noise interference.

[0118] Through such a two-level prior fusion process, the system not only ensures the rapid positioning of the functional characteristics of the brain region, but also combines the user's historical records for step-by-step confirmation when peaks or phases deviating from the normal distribution are found. If such a deviation is determined to be a real feature, the baseline prior for subsequent analysis is updated; if it is determined to be noise or accidental fluctuation, the original prior is maintained. The entire implementation process technically relies on the hierarchical management of the prior library and the secondary calibration mechanism of local frequency band search, sets focusing strategies for the characteristic frequency bands of different brain regions respectively, so that the results of each electroencephalogram reconstruction can accurately extract the truly valuable target brain wave parameters with a relatively small sampling volume and calculation volume. When combining the parameters of the prior stimulus and the subsequent stimulus, these updated peaks and phases will directly determine the corresponding phase-locked frequency and signal output amplitude, thereby realizing a more personalized closed-loop regulation.

[0119] Furthermore, when there are imbalances or common abnormalities in multiple adjacent brain regions simultaneously, if the method of independently calculating the stimulation parameters for a single brain region is still used, it may not be able to coordinate the phase matching or intensity distribution between different brain regions, resulting in limited overall stimulation effects.

[0120] As an alternative implementation, when this application detects a high degree of correlation between adjacent brain regions and both exhibit abnormal target brain wave characteristics, these brain regions are regarded as a group of abnormally coupled brain regions. Combining the spatial or functional relationships reflected by the adjacency matrix, unified timing and parameter management are performed on them during the stimulation phase.

[0121] Specifically, before the prior stimulation, based on the dominant frequencies and phase difference degrees of each channel within the group of abnormally coupled brain regions, similar or identical initial stimulation parameters can be set for these brain regions, enabling them to receive pre-stimulation at similar frequency ranges or similar phase starting points. If it is monitored that the phase locking degree of some brain regions within the group is relatively high while that of other brain regions has not yet reached the threshold, the overall duration of the prior stimulation can be extended until all highly correlated brain regions achieve phase alignment or the phase locking indicators of most channels meet the preset standards, and then the subsequent stimulation phase can be entered.

[0122] In this way, instead of separately examining whether a single brain region has completed pre-stimulation, adjacent brain regions are regarded as a whole to evaluate whether the prior stimulation has achieved initial phase synchronization, which is particularly applicable to the joint regulation of the frontal lobe and parietal lobe or the parietal lobe and occipital lobe during abnormal slow waves or alpha waves.

[0123] In the subsequent subsequent stimulation, the processing unit can further utilize the weights of the adjacency matrix to determine whether multi-brain region synchronous stimulation needs to be maintained. If it is detected that the group of abnormally coupled brain regions has established good phase alignment for the prior stage, then for the subsequent stage, stimulation signals with the same frequency and extremely small phase difference can be applied to these brain regions to consolidate the cross-brain region synchronous oscillation and prevent each brain region from separately entering different frequency bands or phases and weakening the overall coupling degree. If one of these brain regions has returned to a state close to the baseline while other brain regions still have significant abnormalities, only the latter can have its stimulation intensity maintained or enhanced, and repeated stimulation of the brain regions that have already improved can be reduced. If it is detected that a certain brain region shows signs of over-stimulation or the user's comfort level decreases, the subsequent stimulation output of this brain region can be correspondingly reduced, and the abnormal conditions of the remaining adjacent regions can be temporarily monitored.

[0124] Through such an in-group dynamic allocation mechanism, while meeting the group synchronization requirements, the individual response situations of single brain regions can also be taken into account, and phase disorder caused by excessive or repeated stimulation can be avoided.

[0125] In specific implementation, the processing unit needs to perform timed or event-driven switching between the prior stimulation and the subsequent stimulation.

[0126] Among them, timed switching means that within the initially set prior stimulation time window, if the group of abnormally coupled brain regions has not achieved common phase alignment, the prior stimulation continues to be maintained; if synchronization has not been achieved after the time has elapsed, the protection state can be entered and the user or system administrator can be prompted to avoid discomfort caused to the user under long-term high-intensity pre-stimulation.

[0127] Event-driven switching emphasizes real-time monitoring even more. Once the phase consistency of the coupled abnormal brain region group exceeds a certain preset threshold, it immediately enters the post-stimulation and performs individual or small-range adjustments according to the remaining difference degrees of each brain region. If new interference or phase shift occurs during the post-stimulation, the processing unit can identify the synchronization mismatch between the affected brain region and its adjacent brain regions based on the adjacency matrix, and briefly fallback to the pre-stimulation mode again to re-lock the common phase when necessary, and then switch back to the post-stimulation again.

[0128] By also referring to the adjacency matrix during the stimulation phase to coordinate the pre- and post-regulation of multiple brain regions, the in-group synergy effect can be better exerted when there are indeed abnormal coupling fluctuations in adjacent brain regions, avoiding phase disconnection or intensity dispersion caused by independent stimulation of a single brain region.

[0129] For the abnormalities of multiple brain regions commonly observed in insomnia or anxiety scenarios, such as insufficient slow-wave regulation in the frontal lobe and parietal lobe or weakened α-wave synchronization in the occipital lobe and parietal lobe, adopting this coupled stimulation strategy helps to restore the coordinated oscillation of the brain region network on a large scale. In addition, when the abnormal amplitude or phase shift of a certain brain region is much larger than that of other brain regions, the system can perform stronger or more intensive stimulation on it during the post-stage while still maintaining the overall phase consistency with other adjacent brain regions, without destroying the integrity of the group due to strengthening individual brain regions. In this way, the combination of synchronous pre-locking of the pre-stimulation and flexible individual regulation of the post-stimulation is realized, enabling the present application to further exert its guiding role of cross-brain region correlation degree through the adjacency matrix during the stimulation phase outside the reconstruction stage, thereby providing a better closed-loop stimulation strategy for the situation of abnormal coupling of multiple brain regions.

[0130] Exemplarily, there are adjacent electrodes in both the prefrontal region and the parietal region of the user's head. The processing unit determines that these two have a high coupling degree during baseline monitoring according to the adjacency matrix. If the system detects low energy or abnormal phase in both the prefrontal and parietal regions in the α band (about 8 - 12 Hz) from the multi-channel electroencephalogram reconstruction result, these two brain regions can be jointly marked as a coupled abnormal brain region group, and cooperative pre-stimulation and post-stimulation are performed on them when determining the stimulation output.

[0131] Specifically:

[0132] Prior stimulation stage: The processing unit sets similar initial frequencies (e.g., 10 Hz) and medium-intensity currents (e.g., 0.5 mA) for the frontal and parietal lobes, and initiates the pre-stimulation process to observe the phase locking degree of the two brain regions. If it is detected that the phase locking of one of the brain regions is faster, while there is still no obvious synchronization sign in the other brain region, the system automatically extends the duration of the prior stimulation or slightly adjusts the phase shift of the brain region with slower phase locking, so as to make the two brain regions tend to be phase-aligned as much as possible during the prior period. Only when the processing unit observes that the phase difference between these two brain regions is less than the preset threshold (e.g., 5 degrees), and the subjective comfort of the user does not exceed the acceptable range, it is determined that the prior stage ends and enters the subsequent stage.

[0133] Subsequent stimulation stage: There is already preliminary phase synchronization between the frontal and parietal lobes. The processing unit can further increase the stimulation intensity to 0.8 - 1.0 mA and maintain the phase-consistent stimulation at about 10 Hz to consolidate the coupled oscillation. If it is detected that both of them maintain stable synchronization and the EEG signal has approached the reference level, the stimulation intensity of one of the brain regions can be gradually reduced, so as to reduce unnecessary power consumption and the burden on the user. If at this time the other brain region has not reached the standard, the system will separately extend the subsequent stimulation time for it, or fine-tune the frequency again to 9.8 - 10.2 Hz to match the optimal phase window of this brain region, and will not force the other recovered brain region to also bear the same intensity. During this process, if it is monitored that the phase of one of the brain regions suddenly unlocks or the user feels uncomfortable, the processing unit can temporarily reduce the intensity and observe whether it is necessary to go back to the short-term prior stimulation again to re-align the phases of the two.

[0134] In this way, by knowing from the adjacency matrix that the frontal and parietal lobes have a high cross-brain region correlation, pre-stimulation can be synchronized as much as possible in the prior stage, and then individual strengthening or maintenance can be carried out more flexibly in the subsequent stage. Compared with the method of stimulating the frontal and parietal lobes independently, this coupled prior stimulation can effectively reduce the time of initial phase locking, and can also targetedly process the brain regions that do not reach the standard or show deviation locally in the subsequent subsequent stimulation, so as to more efficiently enhance the user's α-wave activity and help relieve anxiety or improve the state of insufficient attention.

[0135] Exemplarily, Table 1 gives an exemplary adjacency matrix for representing the adjacent relationship and its weight between four brain region channels. Assume that four electrodes are arranged on the scalp, named C1, C2, P1, and P2 (only for example marking), and each element in the matrix represents the channel and the channel The greater the value, the closer the two are in geometric position or function, and a zero value indicates no direct association. This matrix is usually a symmetric matrix, and the main diagonal is zero.

[0136] Table 1 Exemplary Table of Adjacency Matrix

[0137]

[0138] In this example:

[0139] The correlation degree between C1 and C2 is 0.65, which means that these two channels (possibly both located in the adjacent frontal or central regions) show a relatively high degree of phase coupling functionally or in the baseline test;

[0140] The correlation degree between C1 and P1 is only 0.20, indicating that they are far apart in location or have insignificant functional correlation;

[0141] The correlation degree between P1 and P2 reaches 0.70, suggesting that an obvious synchronization relationship can be observed between these channels (possibly both located near the parietal or occipital lobes); if the value is zero at other positions, it means that there is almost no direct coupling between them or no strong correlation is found in the monitoring data.

[0142] When this adjacency matrix is cited in multi-channel EEG reconstruction or during the pre- / post-stimulus phases, the algorithm can determine which channels need synchronous pre-stimulation or subsequent enhanced regulation based on these correlation degrees. Also, after detecting that a certain channel is interfered by noise, the algorithm can refer to the reconstruction results of its adjacent channels to constrain or correct the solution of this channel, thereby retaining the overall consistency across brain regions.

[0143] In specific implementation, multiple types of information and steps can be adopted to construct the adjacency matrix, thereby quantifying the correlation relationship between brain region channels.

[0144] Exemplarily, please refer to Figure 3 , Figure 3 which is a flowchart for constructing an adjacency matrix provided by an embodiment of this application, where:

[0145] The first step is to collect the position information of scalp electrodes:

[0146] Record the electrode layout coordinates of the user's head in a database. For example, use the international 10 - 20 system or measure the distances between electrodes and anatomical landmark points at custom positions. If the international 10 - 20 system is selected, the geometric distances between electrodes can be approximately estimated through the mapping of simplified scalp coordinates and a reference template. If high-precision positioning tools or 3D scanning are used, the coordinate differences between electrodes can be measured more accurately.

[0147] Among them, the international 10 - 20 system is an internationally common method for standardizing the placement positions of electroencephalogram (EEG) electrodes. This system ensures consistent and reproducible EEG recordings among different individuals and different laboratories, facilitating data comparison, analysis, and diagnosis.

[0148] The second step is to calculate the geometric distance weights:

[0149] Based on the electrode position coordinates from the previous step, obtain the interval between each pair of electrodes according to Euclidean distance or other distance metrics. For the convenience of subsequent fusion, it can be converted into a normalized weight between 0 and 1. An example approach is to take the reciprocal of the distance d or use exponential decay, etc.:

[0150] ;

[0151] where represents the distance between the i-th electrode and the j-th electrode, is a factor for adjusting the distance sensitivity. reflects the geometric proximity between electrode i and electrode j. The closer the value is to 1, the shorter the physical distance between the electrodes; a value close to 0 indicates a relatively far distance or almost no spatial correlation between the two.

[0152] Step 3: Obtain functional or physiological baseline data:

[0153] In the initial baseline test, perform multi-channel EEG monitoring on the user for a certain duration (from several minutes to several hours). The user can be in a resting state, eyes closed / open, light sleep, or other typical states to observe the coupling degree, phase synchronization degree, or mutual information size between each pair of electrodes in the main frequency bands (such as alpha waves, slow wave intervals).

[0154] Step 4: Extract the functional correlation weight:

[0155] According to the brain wave phase synchronization indicators (such as coherence coefficient, PLV, mutual information, Granger causality, etc.) or energy correlation in the baseline data, assign a functional coupling degree between 0 and 1 to each pair of electrodes, denoted as . If two electrodes continuously show a high degree of synchronization or energy correlation in the same target frequency band, then the higher the value, and vice versa.

[0156] Step 5: Combine the distance and functional information:

[0157] Combine the geometric distance weight and the functional coupling degree in a certain form to obtain the final adjacency matrix weight .

[0158] For example, weighted average:

[0159] ;

[0160] where is used to control the relative importance of functional coupling and geometric distance;

[0161] Another example is product fusion:

[0162] ;

[0163] If any one of them is very small, the final weight will also decrease accordingly;

[0164] For another example, other custom functions: Different forms of non-linear combinations can be assigned to distance and functional coupling according to practical experience or experimental results.

[0165] Step 6, threshold and symmetrization processing:

[0166] To ensure the convenience of matrix use and meet the algorithm requirements, the following processing is performed on the generated adjacency matrix:

[0167] For example, symmetrization:

[0168] Let A(i,j) = A(j,i). If it is not naturally symmetric, take the average value, such as:

[0169] ;

[0170] Among them, is the adjacency matrix after symmetrization processing.

[0171] For example, threshold truncation:

[0172] If some weights are less than the preset threshold (such as 0.05 or 0.1), they are regarded as having no obvious correlation and set to 0 to reduce noise edges or unnecessary weak connections.

[0173] For example, normalization:

[0174] All elements of the matrix can be normalized to [0, 1] as a whole to facilitate unified processing by subsequent algorithms.

[0175] After completing the above construction process, an adjacency matrix A that conforms to the actual electrode distribution and physiological coupling characteristics can be obtained, providing a basis for multi-channel EEG reconstruction, prior / post stimulation parameter scheduling, and cross-brain region coupling regulation.

[0176] Based on the same inventive concept, an transcranial alternating current stimulation system with an automatic adjustment function corresponding to the transcranial alternating current stimulation method with an automatic adjustment function is also provided in the embodiments of the present application. Since the principle of solving problems in the system in the embodiments of the present application is similar to that of the above transcranial alternating current stimulation method with an automatic adjustment function in the embodiments of the present application, the implementation of the system can refer to the implementation of the method, and the repeated parts will not be described again.

[0177] Referring to Figure 4 as shown, it is a schematic diagram of the transcranial alternating current stimulation system with an automatic adjustment function provided by the embodiments of the present application. The system includes: an acquisition module 10, a processing module 20, and an output module 30;

[0178] The acquisition module 10 is configured to acquire multi-channel electroencephalogram signals arranged in multiple brain regions of a target user, and perform compressive sensing sampling on the multi-channel electroencephalogram signals based on the characteristic frequency bands of each brain region to generate compressed data;

[0179] The processing module 20 is configured to perform multi-channel electroencephalogram reconstruction on the compressed data, and extract target brain wave characteristic parameters corresponding to a target state in combination with the physiological priors of each brain region;

[0180] The output module 30 is configured to respectively determine transcranial alternating current stimulation output parameters for each target brain region based on the target brain wave characteristic parameters, and perform signal output;

[0181] The output module 30 is further configured to divide the transcranial alternating current stimulation output parameters into prior stimulation parameters and subsequent stimulation parameters;

[0182] Among them, the prior stimulation parameters are used to perform pre-stimulation on the target brain region to determine the preliminary phase information of the brain waves of the target brain region; the subsequent stimulation parameters are dynamically adjusted based on the preliminary phase information.

[0183] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

Claims

1. A transcranial alternating current stimulation method with an automatic adjustment function, characterized in that Including: Obtaining multi-channel electroencephalogram signals arranged in multiple brain regions of a target user, and performing compressive sensing sampling on the multi-channel electroencephalogram signals based on the characteristic frequency bands of each brain region to generate compressed data; Performing multi-channel electroencephalogram reconstruction on the compressed data, and combining the physiological priors of each brain region to extract target brain wave characteristic parameters corresponding to a target state; Based on the target brain wave characteristic parameters, respectively determining transcranial alternating current stimulation output parameters for each target brain region, and performing signal output; Dividing the transcranial alternating current stimulation output parameters into prior stimulation parameters and posterior stimulation parameters; Among them, the prior stimulation parameters are used to pre-stimulate the target brain region to determine the preliminary phase information of the brain waves in the target brain region; the posterior stimulation parameters are dynamically adjusted based on the preliminary phase information.

2. The transcranial alternating current stimulation method with an automatic adjustment function according to claim 1, characterized in that, The performing multi-channel electroencephalogram reconstruction on the compressed data includes: Based on the arrangement positions of scalp electrodes or brain region function divisions, constructing an adjacency matrix to represent the association relationship between adjacent brain region channels; Introducing a penalty term corresponding to the adjacency matrix into the reconstruction optimization objective, so that when the phase or amplitude difference between the reconstructed signals of each pair of adjacent channels exceeds a preset range, a penalty value is increased, and the penalty value, the data consistency term, and the sparse regularization term are jointly incorporated into the objective function for solution.

3. The transcranial alternating current stimulation method with an automatic adjustment function according to claim 2, wherein, Also including: When performing the multi-channel electroencephalogram reconstruction, detecting the reconstruction difference between any two channels marked as adjacent according to the adjacency matrix; When the reconstruction difference exceeds a preset reconstruction difference threshold, adjusting the measurement matrix to obtain new compressed data in subsequent iterations, and continuing to perform the multi-channel electroencephalogram reconstruction based on the new compressed data.

4. The transcranial alternating current stimulation method with an automatic adjustment function according to claim 2 or 3, characterized in that, When performing multi-channel electroencephalogram reconstruction on the compressed data, including: Calculating a graph Laplacian operator based on the adjacency matrix, and incorporating the graph Laplacian operator into the reconstruction optimization objective; During the iterative solution process, constraining the differences of each channel through the graph Laplacian operator, so that the reconstructed signals of adjacent channels are locally consistent in phase or amplitude.

5. The transcranial alternating current stimulation method with an automatic adjustment function according to claim 4, characterized in that, Also including: When performing multi-channel electroencephalogram reconstruction with graph Laplacian operator constraint, detecting the local difference between channels marked as adjacent according to the adjacency matrix; In response to determining that the local difference corresponds to real brain region characteristics rather than noise interference, reducing the constraint weight of the graph Laplacian operator to allow the corresponding channels to maintain reconstruction results different from adjacent channels; In response to determining that the local difference belongs to noise interference, maintaining or increasing the constraint weight of the graph Laplacian operator to reduce the reconstruction deviation between the interfered channels and adjacent channels.

6. The transcranial alternating current stimulation method with an automatic adjustment function according to claim 5, wherein, The combining the physiological priors of each brain region to extract target brain wave characteristic parameters corresponding to a target state includes: Based on the preset brain region function prior, setting corresponding target frequency bands for the frontal lobe, parietal lobe, and occipital lobe respectively; After the multi-channel electroencephalogram reconstruction is completed, performing the first round of frequency band screening on the reconstructed signals of each brain region according to the function prior; Combining the individualized prior established by the user during baseline acquisition or historical records, and locating the typical peak frequency or phase of the user within the target frequency band obtained in the first round of screening; In response to the difference between the individualized prior and the functional prior, locally expand the frequency band range of the corresponding brain region.

7. The transcranial alternating current stimulation method with an automatic adjustment function according to claim 6, characterized in that, It further includes: In response to detecting a strong peak or phase that cannot be covered by the individualized prior and the functional prior, mark the corresponding frequency band as a suspicious interval and increase the sampling density or iteration weight in the next reconstruction cycle; In response to repeatedly detecting the continuous existence of a strong peak or phase and being irrelevant to noise interference, update the peak range corresponding to the corresponding brain region in the individualized prior.

8. The transcranial alternating current stimulation method with an automatic adjustment function according to claim 2, wherein, It further includes: When it is detected that target brain wave abnormalities exist in multiple brain regions and the correlation weight between any two brain regions in the adjacency matrix is greater than a preset threshold, generate a coupled abnormal brain region group; According to the target brain wave characteristics of each brain region in the coupled abnormal brain region group, respectively determine and store the corresponding initial parameters of the prior stimulation, including the stimulation frequency, initial phase, and pre-stimulation duration; Simultaneously apply the prior stimulation to the coupled abnormal brain region group and generate a phase-locking index. If the phase-locking index is greater than or equal to the first threshold after the prior stimulation ends, output identification information to indicate entering the post-stimulation stage; In the post-stimulation stage, calculate and store the personalized stimulation intensity or frequency parameters for each brain region respectively, and perform closed-loop stimulation output for each brain region based on the frequency parameters.

9. The transcranial alternating current stimulation method with an automatic adjustment function according to claim 8, characterized in that, It further includes: In the post-stimulation stage, real-time obtain the phase difference or energy difference of each brain region in the coupled abnormal brain region group, and generate and update the coupling degree measurement result; When it is monitored that the target brain wave amplitude difference or phase deviation degree of any brain region is lower than the second threshold, downshift the stimulation intensity or frequency of this brain region based on the current coupling degree measurement result, and store the downshift parameters for subsequent iteration use; When it is monitored that the phase or energy of the entire coupled abnormal brain region group shifts again, use the downshift parameters and the coupling degree measurement result to determine whether to regenerate the initial parameters of the prior stimulation.

10. A transcranial alternating current stimulation system with an automatic adjustment function, characterized in that, It includes: An acquisition module, a processing module, and an output module; The acquisition module is used to obtain multi-channel electroencephalogram signals arranged in multiple brain regions of a target user, and perform compressive sensing sampling on the multi-channel electroencephalogram signals based on the characteristic frequency bands of each brain region to generate compressed data; The processing module is used to perform multi-channel electroencephalogram reconstruction on the compressed data, and combine the physiological priors of each brain region to extract the target brain wave characteristic parameters corresponding to the target state; The output module, based on the target brain wave characteristic parameters, respectively determines the transcranial alternating current stimulation output parameters for each target brain region and performs signal output; The output module is further used to divide the transcranial alternating current stimulation output parameters into prior stimulation parameters and post-stimulation parameters; Among them, the prior stimulation parameters are used to pre-stimulate the target brain region to determine the preliminary phase information of the brain wave of the target brain region; the post-stimulation parameters are dynamically adjusted based on the preliminary phase information.

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