A personalized electrical stimulation signal generation system and its otDCS intervention method

Abstract

The application discloses a kind of individualized electric stimulation signal generation system and modulation gamma oscillation's space-frequency domain adaptive otDCS intervention method, including the EEG signal data of individual, the gamma-ASSR data collected are re-referenced, filtering, artifact removal, intercept data section, extract the resonance frequency of individual gamma oscillation as otDCS's stimulation frequency using spectrum analysis, extract the instantaneous phase of individual gamma oscillation as otDCS's stimulation phase using phase estimation, and extract the key node in the brain network of individual gamma oscillation as otDCS's stimulation site using network analysis method;And according to the resonance frequency, instantaneous phase and key node of individual gamma oscillation obtained by individualized stimulation parameter extraction module, lock stimulation frequency, stimulation phase and stimulation site, generate otDCS waveform, for subsequent individual electric stimulation output.

Description

Technical Field

[0001] This invention relates to the field of neuromodulation, and more particularly to a personalized electrical stimulation signal generation system and a spatial frequency domain adaptive otDCS (oscillatory transcranial direct current stimulation) intervention method for modulating gamma oscillations. Background Technology

[0002] Major depressive disorder (MDD) is a common mental disorder characterized by high prevalence, high relapse rate, high disability rate, and low cure rate. It has become a major public health problem threatening human mental health. Clinically, drug therapy remains the primary first-line treatment for MDD, but its effectiveness rate is less than 50%, its effects are slow, and it has certain side effects.

[0003] In recent years, scientists have confirmed that brain functional activity is closely related to the dynamic interaction of local and large-scale networks, reflected in neural oscillations at different frequencies. This has driven intervention strategies targeting brain oscillations to precisely improve brain function. Among these, brain gamma oscillations have become a research focus, leading a new wave of exploration into brain function. Previous studies have revealed that MDD patients showed significantly reduced gamma oscillation coherence in the right hemisphere when performing the emotional face task. A study by Liu Shuang's team at Tianjin University found spontaneous gamma activity inhibition in local brain regions of MDD patients (Xiaoya Liu, Shuang Liu, Meijuan Li, et al. Altered gamma oscillations and beta–gamma coupling in drug-naive first-episode major depressive disorder: association with sleep and cognitive disturbance[J]. Journal of Affective Disorders, 2022, 316: 99-108). Subsequently, by designing a 40Hz auditory steady-state response (ASSR) evoked paradigm, the team discovered for the first time a decrease in inter-trial consistency of 40Hz-ASSR in the right frontal and temporal lobes of MDD patients, suggesting abnormal gamma oscillation information encoding in MDD patients. Gamma oscillations are expected to become a potential biomarker for identifying MDD (Shuang Liu, Xiaoya Liu, Sitong Chen, et al. Neurophysiological markers of depression detection and severity prediction in first-episode major depressive disorder[J]. Journal of Affective Disorders, 2023, 331: 8-16). In addition, prospective studies in the field of MDD treatment have focused on patients' gamma oscillation activity and found that the EEG gamma oscillations of MDD patients also change during symptom relief.For example, Noda et al. found that during the recovery process, patients’ prefrontal gamma activity was enhanced when using transcranial magnetic stimulation to treat MDD, suggesting that gamma oscillation may become a new target for the treatment of MDD (Noda Y, Zomorrodi R, Saeki T, et al. Resting-state EEG gamma power and theta–gamma coupling enhancement following high-frequency left dorsolateral prefrontal TMS in patients with depression[J]. Clinical Neurophysiology, 2017, 128(3):424-432).

[0004] Transcranial electrical stimulation techniques, including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), have broad application prospects in the treatment of malignant neurodegenerative diseases (MDD) due to their advantages such as safety, portability, ease of operation, and low cost. tDCS modulates neuronal excitability through cathode or anodic stimulation, while tACS uses alternating current at a specific frequency to modulate neural oscillatory activity. In recent years, oscillatory tDCS (otDCS) has been developed as a special type of tDCS technique. It simulates or enhances endogenous neural oscillations in the brain by applying a periodically varying current intensity (frequency-modulated current) to the scalp. This modulation technique not only induces changes in neuronal excitability but also induces entrainment effects to modulate brain neural oscillatory activity, providing a new approach for effectively modulating the gamma oscillations of the brain in MDD patients.

[0005] The modulatory effect of transcranial electrical stimulation (TCS) depends mainly on the configuration of different stimulation parameters such as stimulation frequency and stimulation site, and there is significant inter-individual heterogeneity in the effect, that is, different individuals respond significantly differently to stimulation. Therefore, establishing personalized stimulation parameter configuration patterns and constructing adaptive intervention methods in combination with closed-loop control is particularly important for improving the intervention effect. At present, some progress has been made in related research. For example, in terms of stimulation frequency, Kudo D et al. revealed that when the stimulation frequency is close to the resonant frequency of individual neural oscillation, the cortical oscillation shows a more obvious modulatory effect (Kudo D, Koseki T, Katagiri N, et al. Individualized beta-band oscillatory transcranial direct current stimulation over the primary motor cortex enhances corticomuscular coherence and corticospinal excitability in healthy individuals[J]. Brain Stimulation.2022,15(1):46-52). Regarding the stimulation phase, Chen Ni et al. have shown that when the stimulus appears at different phases of the EEG signal, it can produce excitatory or inhibitory neuromodulation effects. However, the exogenous stimulus needs to maintain a stable phase lock with the EEG signal to synchronize the excitatory state of oscillation activity in order to effectively regulate the neural oscillation (Chen Ni, Qin Yurong, Xiong Yanting, et al. EEG phase-locked stimulation method based on variational mode decomposition [J]. Journal of Instrumentation, 2020, 41(5):205-213). Regarding stimulation sites, given the spatial specificity of brain regions in MDD patients, the location of the optimal stimulation target plays a central role in the selection of stimulation sites. Yang Jun et al. proposed that brain network functional connectivity can predict the efficacy of magnetic stimulation therapy (Yang Jun, Yang Chunxia, ​​Liu Penghong, et al. Research progress on brain network mechanism of repetitive transcranial magnetic stimulation for antidepressant therapy based on magnetic resonance imaging [J]. Chinese Journal of Psychiatry, 2023, 56(2):144-149). This suggests that key nodes in the network may become lesion targets in MDD patients to improve network connectivity and thus alleviate depressive symptoms, providing a new approach to the localization of lesion targets in MDD.

[0006] In summary, given the urgent need for new technologies in MDD treatment, there is a need for an effective method to modulate gamma oscillations and achieve precise matching and adaptive intervention around the three key dimensions of frequency, phase, and location. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a personalized electrical stimulation signal generation system and a spatial frequency domain adaptive otDCS intervention method for modulating gamma oscillations. The generation system and intervention method aim to effectively modulate gamma oscillations. By designing a frequency-stepping gamma band ASSR evoked paradigm, and relying on electroencephalogram (EEG) technology, the resonant frequency of individual gamma oscillations is extracted using spectral analysis. Then, an autoregressive phase estimation algorithm is used to extract the instantaneous phase of individual gamma oscillations. Subsequently, a brain functional network model of individual gamma oscillations is constructed based on Granger causality analysis, and key nodes of the network are located by calculating indicators such as betweenness centrality. By combining EEG and otDCS technologies, closed-loop adaptive control is achieved, using a personalized and precise modulation method to regulate the gamma oscillation activity of MDD patients' brainwaves, improve their high-frequency auditory perception function, and alleviate their clinical symptoms, aiming to provide a new framework and technical support for targeted and precise treatment of MDD.

[0008] The objective of this invention is achieved through the following technical solution:

[0009] A personalized electrical stimulation signal generation system, comprising:

[0010] The signal acquisition module collects individual EEG signal data; in the signal acquisition module, the EEG signal data is collected by using a pre-set frequency-stepped gamma-ASSR paradigm as an auditory stimulation paradigm; wherein, the gamma frequency band range of the gamma-ASSR paradigm is 30Hz to 50Hz.

[0011] The signal preprocessing module performs rereference, filtering, and artifact removal on the gamma-ASSR data acquired by the signal acquisition module. Then, it extracts ASSR signals with different gamma frequencies from the data after removing artifact interference and extracts multiple data segments.

[0012] The personalized stimulation parameter extraction module extracts individualized stimulation parameters from the data segments obtained by the signal preprocessing module. Specifically, it uses spectral analysis to extract the resonant frequency of the individual's gamma oscillation as the stimulation frequency for otDCS, uses phase estimation to extract the instantaneous phase of the individual's gamma oscillation as the stimulation phase for otDCS, and uses network analysis to extract key nodes in the individual's gamma oscillation brain network as stimulation sites for otDCS. The otDCS stimulation module, based on the resonant frequency, instantaneous phase, and key nodes of the individual's gamma oscillation obtained by the personalized stimulation parameter extraction module, locks the stimulation frequency, stimulation phase, and stimulation site, and generates an otDCS waveform for subsequent individual electrical stimulation output.

[0013] Specifically, the signal preprocessing module further includes:

[0014] The obtained EEG data were rereferenced, and the average value of the two leads M1 and M2 of the bilateral mastoid process was selected as the reference.

[0015] The rereferenced EEG data were filtered, with a bandpass filter of 30-50Hz selected.

[0016] Independent component analysis was used to detect artifacts in the data and remove interference from artifacts such as electrooculography and electromyography.

[0017] The ASSR signals with different gamma frequencies are extracted from the data after removing artifact interference and further subdivided into epoch data segments with preset time periods.

[0018] Furthermore, the resonant frequencies of individual gamma oscillations are extracted, including:

[0019] The power spectrum curve of the gamma frequency band in each epoch data segment of the gamma-ASSR signal under each lead is calculated by using fast Fourier transform, and the average value of the individual gamma frequency is obtained by superimposing and averaging at the lead level and the epoch data segment level.

[0020] Using the built-in peak finding function findpeaks() and maximum value finding function max() in MATLAB, the resonance frequency F of an individual gamma oscillation is obtained. IGR .

[0021] Furthermore, the instantaneous phase of the individual gamma oscillation is extracted, including:

[0022] Construct an autoregressive phase estimation model and select F. IGR -ASSR data is used for modeling, and the least squares method is used to estimate the AR model coefficients; a voting decision method is used to obtain the instantaneous phase of individual gamma oscillations from the estimated phases of the data; wherein F IGR -ASSR data refers to the extracted individualized gamma resonance frequency F IGR With the center frequency as F IGR The bandwidth is ±2Hz, and the data is obtained by bandpass filtering the ASSR data using a Butterworth filter.

[0023] Furthermore, key nodes in the brain network that extract individual gamma oscillations include:

[0024] Constructing F-type causal analysis for MDD patients IGR-ASSR's brain functional network model extracts the betweenness centrality of an individual's brain functional network and selects the two electrodes with the highest betweenness centrality as key nodes of the individual's gamma oscillation brain network.

[0025] Preferably, the individual is an MDD patient, the obtained individualized gamma resonance frequency is determined as the individual's therapeutic stimulation frequency; the obtained instantaneous phase of the individualized gamma oscillation is determined as the individual's therapeutic stimulation phase; and the obtained key nodes of the brain network of the individualized gamma oscillation are determined as the individual's therapeutic targets.

[0026] Preferably, the frequency-stepped gamma-ASSR paradigm sets multiple gamma frequencies within a frequency band with a step size of 1Hz, and selects chirped signals as sound stimuli.

[0027] The spatial frequency domain adaptive otDCS intervention method utilizing the modulated gamma oscillation of the personalized electrical stimulation signal generation system includes:

[0028] Step 1: Collect individual EEG signal data according to the pre-set frequency-stepped gamma-ASSR paradigm to obtain gamma-ASSR data;

[0029] Step 2: After rereferencing, filtering, and removing artifacts from the collected gamma-ASSR data, extract ASSR signals of different gamma frequencies from the data after removing artifact interference and extract multiple data segments.

[0030] Step 3: Spectral analysis is used to extract the resonant frequency of the individual's gamma oscillations as the stimulation frequency for otDCS; phase estimation is used to extract the instantaneous phase of the individual's gamma oscillations as the stimulation phase for otDCS; and network analysis methods are used to extract key nodes in the individual's gamma oscillation brain network as stimulation sites for otDCS.

[0031] Step 4: Based on the resonant frequency, instantaneous phase, and key nodes of the individual gamma oscillation obtained in Step 3, lock the stimulation frequency, stimulation phase, and stimulation site to generate a personalized stimulation parameter otDCS waveform for subsequent individual electrical stimulation output.

[0032] Furthermore, step four specifically includes:

[0033] S401: Based on the individualized gamma resonance frequency F obtained in step three IGR The stimulation frequency of the stimulating current is determined. Based on the relationship between frequency and angular velocity ω=2πf, it is converted into a sine waveform expression, which yields:

[0034] s(t)=A×sin(2πF IGR·t)+DC

[0035] Where DC is the current intensity of the DC component of the preset stimulation current, and A is the current intensity of the AC component superimposed on the DC.

[0036] S402: Phase value estimated based on step three The initial output phase of the stimulation current is selected based on the value rules of the up and down curves, and the waveform formula is as follows:

[0037]

[0038] in, This represents the initial phase of the AC component, based on the phase value estimated in step three. Sure.

[0039] S403: Select betweenness centrality N from the betweenness centralities obtained in step three. BC (k) The two highest key nodes are used as stimulation sites for electrical stimulation.

[0040] S404: Based on the stimulation frequency, stimulation phase and stimulation site obtained in steps S401-403, obtain the otDCS waveform with personalized stimulation parameter configuration for subsequent individual electrical stimulation output.

[0041] An apparatus includes a processor and a memory storing program instructions that, when executed by the processor, cause the processor to perform steps of the spatial frequency domain adaptive otDCS intervention method for modulated gamma oscillations.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] 1. This invention designs a frequency-stepping gamma band auditory steady-state response paradigm to record the electroencephalogram (EEG) signals under the individual gamma-ASSR evoked paradigm, thereby providing a key paradigm and data support for obtaining electrical stimulation parameters such as personalized stimulation frequency, stimulation phase, and stimulation site for modulating gamma oscillations.

[0044] 2. By utilizing spectral analysis, phase estimation, and network analysis methods, the resonant frequency of individual gamma oscillations is extracted as the stimulation frequency of otDCS, the instantaneous phase of individual gamma oscillations is extracted as the stimulation phase of otDCS, and key nodes in the individual gamma oscillation brain network are extracted as stimulation sites of otDCS. Combined with the rapid feedback and closed-loop control of EEG-otDCS technology, adaptive parameter configuration is achieved, thereby realizing personalized and precise regulation of individual EEG gamma oscillations, effectively improving the clinical symptoms of MDD patients and assisting in precise clinical treatment. Attached Figure Description

[0045] Figure 1 This is a flowchart of the spatial frequency domain adaptive otDCS intervention method for modulating gamma oscillations as described in this invention;

[0046] Figure 2 This is a flowchart of the frequency-stepping gamma-ASSR paradigm designed in step one of the adaptive otDCS intervention method described in this invention.

[0047] Figure 3 In step three, S301 of the adaptive otDCS intervention method described in this invention, F IGR Extract the example image;

[0048] Figure 4 This is an example diagram of instantaneous phase extraction in step three, S302, of the adaptive otDCS intervention method described in this invention;

[0049] Figure 5 This is an example diagram of key node extraction in step three, S303, of the adaptive otDCS intervention method described in this invention.

[0050] Figure 6 This is an otDCS waveform diagram of a personalized stimulus parameter configuration pattern obtained by the adaptive otDCS intervention method according to the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, beneficial effects, and significant advancements of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings provided in the examples of the present invention. Obviously, all the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] The personalized electrical stimulation signal generation system and the spatial frequency domain adaptive otDCS intervention method using the generation device to modulate gamma oscillations, as described in this invention, can achieve personalized and precise control of EEG gamma oscillations, thereby alleviating the clinical symptoms of depressed patients. This can assist in precise clinical treatment, possessing personalization, targeting, and intelligence, and can yield considerable social and economic benefits. The optimal implementation scheme is proposed to be achieved through patent transfer, technological cooperation, or product development. The working process of this method will be further explained below with reference to the accompanying drawings.

[0053] A personalized electrical stimulation signal generation system, comprising:

[0054] The signal acquisition module collects individual EEG signal data; in the signal acquisition module, the EEG signal data is collected by using a pre-set frequency-stepped gamma-ASSR paradigm as an auditory stimulation paradigm; wherein, the gamma frequency band range of the gamma-ASSR paradigm is 30Hz to 50Hz.

[0055] The signal preprocessing module performs rereference, filtering, and artifact removal on the gamma-ASSR data acquired by the signal acquisition module. Then, it extracts ASSR signals with different gamma frequencies from the data after removing artifact interference and extracts multiple data segments.

[0056] The personalized stimulation parameter extraction module extracts individualized stimulation parameters from the data segments obtained by the signal preprocessing module. Specifically, it uses spectral analysis to extract the resonant frequency of the individual's gamma oscillation as the stimulation frequency for otDCS, uses phase estimation to extract the instantaneous phase of the individual's gamma oscillation as the stimulation phase for otDCS, and uses network analysis to extract key nodes in the individual's gamma oscillation brain network as stimulation sites for otDCS. The OtDCS stimulation module, based on the resonant frequency, instantaneous phase, and key nodes of the individual's gamma oscillation obtained by the personalized stimulation parameter extraction module, locks the stimulation frequency, stimulation phase, and stimulation site, and generates an otDCS waveform for subsequent individual electrical stimulation output.

[0057] like Figure 1 As shown, a spatial frequency domain adaptive otDCS intervention method using the personalized electrical stimulation signal generation system with modulated gamma oscillations includes:

[0058] Step 1: Acquire EEG signals using the gamma-ASSR induced paradigm

[0059] S101: Design of Auditory Stimulation Paradigm. This invention selects a frequency-stepped gamma-ASSR paradigm, wherein the gamma frequency band ranges from 30Hz to 50Hz, and multiple gamma frequencies are set in 1Hz increments, including 31Hz, 32Hz, ..., 49Hz. Simultaneously, a chirp signal is selected as the sound stimulus. The chirp signal is designed based on the cochlear traveling wave theorem, which can enhance the synchronous firing of neurons in the cochlea, improve the ASSR response intensity, and induce a more stable and effective ASSR.

[0060] like Figure 2As shown, the gamma-ASSR paradigm workflow is as follows: This part defaults to 3 sets of chirp sound stimulation tasks (the number of sets can be increased according to clinical needs). In each stimulation task, the frequency of the chirp sound presentation is a different gamma frequency (31Hz, 32Hz, ..., 49Hz), and the presentation order of the chirp sound stimuli of different gamma frequencies is randomized. Each sound stimulus is presented 5 times, and each time is one trial. In a single trial, a 2-second chirp stimulus is presented with a 1-second stimulus interval. The 1-second stimulus interval is set to ensure that the information between different trials does not cross-influence. The generation and control of the evoked paradigm are implemented by writing code using the Matlab dedicated toolbox (Psychtoolbox), and synchronization event information is sent to the EEG amplifier to ensure data synchronization.

[0061] S102: EEG signal acquisition. The MDD patient wears a wired noise-canceling headset (Edifier W820NB) to receive sound. The sound is played in dual channels at a sound pressure level of 45dB. Using a 10-20 international standard lead system and a 62-channel EEG acquisition system with dedicated software, 62-lead EEG data are continuously recorded in the gamma-ASSR paradigm at a sampling rate of 1000Hz. The reference electrode is the left mastoid M1. During stimulation, the user only needs to remain as still as possible and does not need to perform any other operations.

[0062] Step 2: Preprocess the collected gamma-ASSR data. This includes:

[0063] S201: Bilateral mastoid average rereference (left and right mastoid M1, M2), obtaining 60-lead EEG data (leads remaining after removing M1 and M2), with bandpass filtering of 30-50Hz;

[0064] S202: Independent components analysis (ICA) is used to perform artifact detection processing on the data to remove artifacts such as electrooculography and electromyography.

[0065] S203: Extract ASSR signals with different gamma frequencies from the data after removing artifact interference and subdivide them into 2-second epoch data segments.

[0066] Step 3: Calculate individualized stimulus parameters based on the preprocessed data.

[0067] S301: Calculate the individualized gamma resonance frequency (F... IGR )

[0068] The specific steps are as follows: use Fast Fourier Transform to calculate the power spectrum curve of the gamma (30-50Hz) frequency band range of each epoch data segment in the gamma-ASSR signal of each lead, and then superimpose and average the gamma frequency at the lead level and epoch level to obtain the individual average ASSR energy value of the gamma frequency.

[0069] Using MATLAB's built-in peak finding function `findpeaks()` and maximum value finding function `max()`, the resonant frequency of an individual gamma oscillation, i.e., the gamma frequency corresponding to the maximum response value, is obtained. A schematic diagram of the individualized gamma resonant frequency is shown below. Figure 3 As shown, the individualized gamma resonance frequency F obtained in this example IGR It is 37Hz.

[0070] S302: Construct an autoregressive (AR) phase estimation model to calculate the instantaneous phase of individual gamma oscillations.

[0071] The specific steps are as follows: Based on step S301, extract F IGR -ASSR epoch data segment, i.e., the individualized gamma resonance frequency F obtained from S301 IGR As the center frequency of the narrowband filter, with F IGR With a bandwidth of ±2Hz, a Butterworth filter was used to perform narrowband filtering on the ASSR data to extract epoch data segments under 2s of sound stimulation. This yielded at least 5 trials * 3 groups * 60 leads of F-band data. IGR -ASSR data. Based on the principle of autoregressive modeling, the EEG signal time series {x n The correspondence between the nth x-value and the first m x-values ​​in the equation can be expressed as:

[0072] x n =a1x n-1 +a2x n-2 +…+a m x n-m +ε n (1)

[0073] Where a1, a2, ..., a m Here are the coefficients of the autoregressive model, m is the order of the AR model (default order is 10, determined based on experimental testing and can be adjusted as needed), and ε. n The residuals of the AR model are white noise sequences.

[0074] An AR model is constructed. Since the least squares method offers advantages such as high accuracy, simplicity, and unbiased estimation for parameter estimation, the AR model coefficients are estimated using the least squares method; the F-values ​​of 2s are extracted. IGR The ASSR data segment consists of 5 trials * 3 groups * 60 leads = 900 data segments. The first 2 / 3 of the data in each 2-second segment is extracted for AR modeling to obtain least squares estimates, i.e., model coefficients. Then, the remaining 1 / 3 of the data is used for prediction.

[0075] Next, the predicted data is smoothed, and four phase feature points are identified: the upper zero crossing point, the peak, the lower zero crossing point, and the trough (e.g., ...). Figure 4 As shown in the figure, their phase values ​​are 0, π / 2, π and 3π / 2 (–π / 2).

[0076] Then, using the time difference and phase difference between adjacent phase feature points, T is calculated. i Phase of time The specific calculation formula is as follows:

[0077]

[0078] in, For T n+1 Phase of time, For T n Phase.

[0079] like Figure 4 As shown, assume T n To cross zero, T n+1 If it is a peak, then It is π / 2.

[0080] The model's reliability was validated and its coefficients optimized by predicting the remaining one-third of the data. Furthermore, the phase of the next time step for the 2-second data was predicted; that is, the phase of the next time step for each of the 900 data segments was estimated. Then, the values ​​are determined according to the rules of the up and down curves. When the predicted phase value is on the up curve of "trough (–π / 2) → peak (π / 2)" (around zero), the output phase is 0°; when the predicted phase value is on the down curve of "peak (π / 2) → trough (3π / 2)" (around zero), the output phase is 180°.

[0081] Finally, using a voting decision-making method, based on the majority vote rule, the instantaneous phase of the individual gamma oscillation at the next moment after the sound stimulus ends is extracted as the estimated phase value.

[0082] S303: Key node information of brain functional networks for calculating individual gamma oscillations

[0083] The specific steps are as follows: using the 60-lead electrodes as network nodes, and constructing the F-type network for MDD patients based on Granger causality analysis. IGR -ASSR's brain functional network model extracts the betweenness centrality of the network. Betweenness centrality is a key indicator of the importance of nodes in a network. Research shows that nodes with high betweenness centrality play an important "bridging" role in the network, serving as crucial locations for the flow and integration of information and resources. The formula for calculating betweenness centrality is:

[0084]

[0085] Where, σ ij Let σ be the number of all shortest paths from node i to node j, and let σ be the number of shortest paths from node i to node j. ij (k) represents the number of paths passing through node k among these shortest paths. The two electrodes with the highest betweenness centrality are extracted as key nodes in the brain network of individual gamma oscillations.

[0086] Step 4: Output the otDCS waveform based on the personalized stimulation parameters obtained in Step 3.

[0087] S401: Stimulation frequency: Individualized gamma resonance frequency F obtained from S301 for different individuals. IGR To determine the stimulation frequency of the stimulation current, based on the relationship between frequency and angular velocity ω=2πf, taking a sinusoidal waveform as an example, we can obtain:

[0088] s(t)=A×sin(2πF IGR ·t)+DC (4)

[0089] Where A is the current intensity of the AC component of the stimulation current, and DC is the current intensity of the DC component of the stimulation current. This is based on the pre-defined framework commonly used in existing research (…). K, J, D, et al. Theta-modulated oscillation-latory transcranial direct current stimulation over posterior parietal cortex improves associative memory[J]. Scientific Reports, 2021, 11(1): 3013-3020.), set A to a constant value of 1mA (peak-to-peak value of 2mA), and DC to a constant value of 1.5mA, which can also be adjusted according to needs.

[0090] S402: Stimulus Phase: The phase value of the next time step for each data segment estimated in S302. The initial phase of the stimulation current is selected, mainly including two types: stimulation phase of 0° and 180°. The waveform formula is as follows:

[0091]

[0092] in, The initial phase of the AC component is indicated, and the specific settings are as follows:

[0093]

[0094] Finally, the initial phase value of the stimulation current is obtained by using a voting decision method and according to the majority vote rule.

[0095] S403: Stimulation site: Select betweenness centrality N from the betweenness centralities obtained from S303. BC (k) The two highest key nodes are used as specific targets for modulating individual gamma oscillations. Figure 5 The two dark spots in the middle are the stimulation sites for electrical stimulation.

[0096] S404: Based on the individualized gamma resonance frequency, instantaneous phase, and key nodes obtained in steps S401-403, the stimulation frequency is locked as F. IGR =37Hz, stimulation phase is The stimulation site is F4-P4, thereby obtaining... Figure 6 The otDCS waveform of the personalized stimulation parameter configuration mode shown is obtained to obtain the otDCS waveform corresponding to the MDD treatment subject. Moreover, the otDCS waveform can be visualized by physicians or nurses through computer devices such as monitors, so as to incorporate its stimulation frequency, phase and site information into clinical decision-making, which is beneficial for the treatment subject to obtain its personalized stimulation treatment.

[0097] This invention proposes a novel otDCS adaptive intervention method with precise configuration of personalized parameters around three key dimensions: frequency, phase, and location. Addressing the issues of significant individual differences in brain gamma oscillations among MDD patients and insufficient targeting of otDCS modulation techniques, this invention innovatively proposes a targeted closed-loop otDCS adaptive intervention method with precise matching of personalized stimulation parameters. This solves the problems of high heterogeneity in MDD intervention responses and insufficient targeting in current methods, facilitating efficient and precise intervention of EEG gamma oscillations and providing new methods and pathways for improving cognitive function and alleviating depressive symptoms.

Claims

1. A personalized electrical stimulation signal generation system, comprising: The signal acquisition module collects individual EEG signal data; In the signal acquisition module, the EEG signal data is acquired by using a pre-set frequency-stepped gamma-ASSR paradigm as the auditory stimulation paradigm; wherein, the gamma frequency band of the gamma-ASSR paradigm is 30Hz~50Hz. The signal preprocessing module performs rereference, filtering, and artifact removal on the gamma-ASSR data acquired by the signal acquisition module. Then, it extracts ASSR signals with different gamma frequencies from the data after removing artifact interference and extracts multiple data segments. The personalized stimulation parameter extraction module extracts individualized stimulation parameters from the data segments obtained by the signal preprocessing module. Specifically, it uses spectral analysis to extract the resonant frequency of the individual's gamma oscillation as the stimulation frequency for otDCS, uses phase estimation to extract the instantaneous phase of the individual's gamma oscillation as the stimulation phase for otDCS, and uses network analysis to extract key nodes in the individual's gamma oscillation brain network as stimulation sites for otDCS. The otDCS stimulation module, based on the resonant frequency, instantaneous phase, and key nodes of the individual's gamma oscillation obtained by the personalized stimulation parameter extraction module, locks the stimulation frequency, stimulation phase, and stimulation site, and generates an otDCS waveform for subsequent individual electrical stimulation output.

2. The personalized electrical stimulation signal generation system according to claim 1, characterized in that, The signal preprocessing module further includes: The obtained EEG data were rereferenced, and the average value of the two leads M1 and M2 in the bilateral mastoid processes was selected as the reference. The rereferenced EEG data were filtered, with a bandpass filter of 30-50Hz selected. Independent component analysis was used to detect artifacts in the data and remove interference from electrooculography and electromyography artifacts. The ASSR signals with different gamma frequencies are extracted from the data after removing artifact interference and further subdivided into epoch data segments with preset time periods.

3. The personalized electrical stimulation signal generation system according to claim 1, characterized in that, The resonant frequencies for extracting individual gamma oscillations include: The power spectrum curve of the gamma frequency band in each epoch data segment of the gamma-ASSR signal under each lead is calculated by using fast Fourier transform, and the average value of the individual gamma frequency is obtained by superimposing and averaging at the lead level and the epoch data segment level. Using MATLAB's built-in peak finding function findpeaks() and maximum value finding function max(), the resonant frequency of an individual gamma oscillation is obtained. .

4. The personalized electrical stimulation signal generation system according to claim 3, characterized in that, The instantaneous phase of an individual gamma oscillation is extracted as follows: Construct an autoregressive phase estimation model and select... -ASSR data were used for modeling, and the least squares method was employed to estimate the AR model coefficients; a voting decision method was used to extract the instantaneous phase of individual gamma oscillations from the estimated phase of the data; wherein... -ASSR data refers to the extracted individualized gamma resonance frequencies. With the center frequency, The bandwidth is ±2Hz, and the data is obtained by bandpass filtering the ASSR data using a Butterworth filter.

5. The personalized electrical stimulation signal generation system according to claim 4, characterized in that, Key nodes in the brain network from which individual gamma oscillations were extracted include: MDD patients were constructed based on Granger causality analysis. -ASSR's brain functional network model extracts the betweenness centrality of an individual's brain functional network and selects the two electrodes with the highest betweenness centrality as key nodes in the brain network.

6. The personalized electrical stimulation signal generation system according to claim 1, characterized in that, The individual is an MDD patient, and the obtained individualized gamma resonance frequency is determined as the individual's treatment stimulation frequency; the obtained instantaneous phase of the individualized gamma oscillation is determined as the individual's MDD treatment stimulation phase; and the key nodes in the obtained individualized gamma oscillation brain network are determined as the individual's treatment targets.

7. The personalized electrical stimulation signal generation system according to claim 1, characterized in that, The frequency-stepping gamma-ASSR paradigm sets multiple gamma frequencies within a frequency band with a step size of 1 Hz, and selects chirped signals as sound stimuli.

8. A spatial-frequency domain adaptive otDCS intervention method utilizing modulated gamma oscillations of a personalized electrical stimulation signal generation system as described in claim 1, comprising: Step 1: Collect individual EEG signal data according to the pre-set frequency-stepped gamma-ASSR paradigm to obtain gamma-ASSR data; Step 2: After rereferencing, filtering, and removing artifacts from the collected gamma-ASSR data, extract ASSR signals of different gamma frequencies from the data after removing artifact interference and extract multiple data segments. Step 3: Use spectral analysis to extract the resonance frequency of the individual gamma oscillation as the stimulation frequency of otDCS, use phase estimation to extract the instantaneous phase of the individual gamma oscillation as the stimulation phase of otDCS, and use network analysis methods to extract key nodes in the individual gamma oscillation brain network as stimulation sites of otDCS. as well as Step 4: Based on the resonant frequency, instantaneous phase, and key nodes of the individual gamma oscillation obtained in Step 3, lock the stimulation frequency, stimulation phase, and stimulation site to generate a personalized stimulation parameter otDCS waveform for subsequent individual electrical stimulation output.

9. The spatial frequency domain adaptive otDCS intervention method for modulated gamma oscillation according to claim 8, characterized in that, Step four specifically includes: S401: Based on the individualized gamma resonance frequency obtained in step three Determine the stimulation frequency of the stimulation current based on the relationship between frequency and angular velocity. Converting this to a sine wave expression, we get: ； Where DC is the current intensity of the DC component of the preset stimulation current, and A is the current intensity of the AC component superimposed on the DC. S402: Phase value estimated in step three The initial output phase of the stimulation current is selected according to the value rules of the up and down curves, and the waveform formula is as follows: ； in, This represents the initial phase of the AC component, based on the phase value estimated in step three. Sure; S403: Select betweenness centrality from the betweenness centralities obtained in step three. The two highest key nodes are used as stimulation sites for electrical stimulation. S404: Based on the resonant frequency, instantaneous phase, and key nodes of the individual gamma oscillation obtained in steps S401-403, lock the stimulation frequency, stimulation phase, and stimulation site to obtain the otDCS waveform with personalized stimulation parameter configuration, which is used for subsequent individual electrical stimulation output.

10. An apparatus comprising a processor and a memory storing program instructions that, when executed by the processor, cause the processor to perform the steps of the spatial frequency domain adaptive otDCS intervention method for modulated gamma oscillations as described in any one of claims 8-9.

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