Non-invasive electrical stimulation regulation and control system and method thereof

Abstract

The invention discloses a non-invasive electrical stimulation regulation and control system and a method thereof. The regulation and control system comprises an electroencephalogram acquisition module, an individualized parameter extraction module and a regulation and control module, wherein the individualized parameter extraction module is composed of a frequency extraction unit and a site determination unit; the electroencephalogram acquisition module is used for acquiring and recording resting-state electroencephalogram signals of an anxiety disorder patient; the individualized parameter extraction module is used for extracting an individual alpha frequency and an individualized stimulation site of a patient; wherein the frequency extraction unit selects a frequency at a peak value of a power spectrum as an individual alpha frequency through spectrum analysis; the site determination unit selects a site with the weakest function connection as a personalized stimulation site by calculating the PLV value of the frontal apex lobe network; the regulation and control module is used for outputting a personalized stimulation scheme and implementing non-invasive transcranial electrical stimulation, and more personalized and accurate regulation and control on anxiety disorder patients are achieved by considering the difference between individuals.

Description

Technical Field

[0001] The present invention belongs to the field of neuromodulation biomedical engineering, and specifically belongs to the field of neuromodulation technology. Background Art

[0002] Anxiety disorder is a type of mental illness characterized by anxious mood and physical discomfort. It is one of the common mental disorders in psychiatry. According to the American Diagnostic and Statistical Manual of Mental Disorders (5th edition) (DSM-5), anxiety disorders are divided into generalized anxiety disorder, panic disorder, agoraphobia, specific phobia, social anxiety disorder and separation anxiety disorder. The latest mental health survey shows that the incidence of anxiety disorders in my country is as high as 4.98%. Anxiety disorders have the clinical characteristics of high incidence and high prevalence, which seriously affect the quality of life of patients and increase the socioeconomic burden. At present, the treatment methods for anxiety disorders include drug therapy, psychotherapy and physical therapy. However, drug therapy may be accompanied by a series of adverse reactions and side effects, and psychotherapy usually takes a long time to see significant effects. Therefore, it is urgent to develop a new technology for personalized regulation of anxiety symptoms.

[0003] Studies have shown that patients with anxiety disorders have attentional cognitive impairments, and changes in EEG alpha rhythms are closely related to the activity of the attention system. Patients with anxiety disorders have lower EEG alpha activity, and an increase in alpha activity can lead to a decrease in anxiety. [1] In addition, studies have shown that neurofeedback training to increase alpha can effectively reduce anxiety. [2] Therefore, the present invention focuses on the EEG alpha frequency band to develop a fast and efficient alpha frequency extraction method; In addition, anxiety can disrupt the balance between the goal-oriented system and the stimulus-driven system in the brain, thereby affecting the individual's attention control [3] The frontoparietal attention network plays an important role in attention control. Studies have shown that anxiety disorders are closely related to the reduction and inactivity of the frontoparietal network function. [4] The study showed that alpha-tACS treatment enhanced alpha band connectivity from the posterior to the anterior part of the brain, and this enhancement was accompanied by a reduction in anxious arousal. [5] This suggests that by regulating neural oscillations and enhancing functional connectivity, a positive improvement effect can be achieved on emotional state. Therefore, the present invention focuses on the fronto-parietal network to develop a specific target location method.

[0004] tACS is a low-intensity, non-invasive transcranial electrical stimulation method that modulates the oscillations of the nervous system by attaching electrodes to the subject's scalp and applying a small amount of alternating current stimulation. It can modulate endogenous brain oscillations of a specific frequency and induce synaptic plasticity changes, thereby affecting brain function and providing key technical support for regulating alpha activity. The efficacy of tACS depends on the setting of stimulation parameters, but the tACS parameter settings in existing studies are generally limited to fixed frequencies and fixed sites. Because there are individual differences in the individual alpha frequency (IAF) of different patients and the lesion targets with abnormal functional connectivity [6] Therefore, it is necessary to select personalized stimulation frequency and stimulation site for different patients. Electroencephalogram (EEG) is a technology that records electrical signals generated by brain neuron activity. By placing electrodes on the scalp, it captures and records the brain's electrical activity, thereby reflecting the brain's rhythmic activity and providing direct and objective electrophysiological feedback for the setting of personalized tACS parameters. Summary of the invention

[0005] In response to the difficulties of the existing technology, the present invention provides a non-invasive electrical stimulation regulation system and its application. The invention combines tACS and EEG technology to select specific individual alpha frequencies for patients with anxiety disorders, and determines specific stimulation sites by calculating the functional connectivity of the frontal and parietal networks. This method achieves more personalized and precise regulation of patients with anxiety disorders through individual differences.

[0006] In order to solve the problems of the prior art, the present invention adopts the following technical solutions:

[0007] A non-invasive electrical stimulation control system, the control system comprising an EEG acquisition module, an individualized parameter extraction module consisting of a frequency extraction unit and a site determination unit, and a control module; wherein:

[0008] The EEG acquisition module is used to acquire and record resting EEG signals of patients with anxiety disorders;

[0009] The individualized parameter extraction module is used to extract the patient's individual alpha frequency and personalized stimulation site; wherein:

[0010] The frequency extraction unit selects the frequency at the peak of the power spectrum as the individual alpha frequency through spectrum analysis; the site determination unit selects the site with the weakest functional connection as the personalized stimulation site by calculating the PLV value of the frontal parietal network;

[0011] The control module is used to output a personalized stimulation plan and implement non-invasive transcranial electrical stimulation.

[0012] Furthermore, the frequency extraction unit selects the frequency at the peak of the power spectrum as the individual alpha frequency process through spectrum analysis; comprising:

[0013] The frequency extraction unit selects an alpha frequency for each subject, wherein the alpha frequency is determined by a resting-state electroencephalogram in an eyes-open state or an eyes-closed state;

[0014] The collected EEG data were analyzed according to the eye-opening and eye-closing states, and divided into 2-second segments after preprocessing. The frequency domain signal was obtained according to the following formula;

[0015]

[0016] in, a n is the nth sample of the signal in the time domain, N is the total number of signal samples, and k is the index frequency component in the frequency domain;

[0017] The power spectrum density of the EEG signal is obtained according to the following formula based on the frequency domain signal;

[0018]

[0019] where f s is the sampling rate of the signal;

[0020] The power spectrum density in the frequency range of 8-13Hz is selected by the following formula:

[0021]

[0022] In the alpha wave frequency band, find the frequency f corresponding to the maximum value of the power spectrum IAF ,Right now:

[0023]

[0024] Among them, f IAF It is the frequency corresponding to the maximum value of the power spectrum in the alpha wave band.

[0025] Furthermore, the site determination unit selects the site with the weakest functional connection as the personalized stimulation site process by calculating the PLV value of the frontal-parietal network; including:

[0026] According to the collected resting-state EEG signals, three sites were selected in the frontal lobe and parietal lobe respectively.

[0027] The phase lock value PLV of each pair of points is calculated by the following formula:

[0028]

[0029] Where: n represents the time point, φxt and φyt represent the phase angles of signals x and y at time point t respectively; the value range of PLV is [0 1], and the larger the value, the stronger the phase synchronization between the two signals;

[0030] Calculate the PLV values ​​of each site from the frontal lobe to the parietal lobe, and select a pair of sites with the smallest PLV value as personalized stimulation sites;

[0031] The personalized stimulation site is set as the stimulation electrode of tACS and output to the control module.

[0032] In order to solve the technical problem, the present invention also adopts the following technical solution:

[0033] A non-invasive electrical stimulation control method comprises the following steps:

[0034] Collect and record resting-state EEG signals from patients with anxiety disorders;

[0035] The frequency at the peak of the power spectrum is selected as the individual alpha frequency; where:

[0036] The power spectrum density of the EEG signal is obtained according to the following formula based on the frequency domain signal;

[0037]

[0038] The power spectrum density in the frequency range of 8-13Hz is selected by the following formula:

[0039]

[0040] In the alpha wave frequency band, find the frequency f corresponding to the maximum value of the power spectrum IAF ,Right now:

[0041]

[0042] Among them, f IAF It is the frequency corresponding to the maximum value of the power spectrum in the alpha wave band;

[0043] By calculating the PLV value of the frontoparietal network, the site with the weakest functional connection is selected as the personalized stimulation site; among them:

[0044] According to the collected resting-state EEG signals, three sites were selected in the frontal lobe and parietal lobe respectively.

[0045] The phase lock value PLV of each pair of points is calculated by the following formula:

[0046]

[0047] Where: n represents the time point, φxt and φyt represent the phase angles of signals x and y at time point t respectively; the value range of PLV is [0 1], and the larger the value, the stronger the phase synchronization between the two signals;

[0048] Calculate the PLV values ​​of each site from the frontal lobe to the parietal lobe, and select a pair of sites with the smallest PLV value as personalized stimulation sites;

[0049] The personalized stimulation sites were set as stimulation electrodes for tACS to implement non-invasive transcranial electrical stimulation.

[0050] Beneficial Effects

[0051] Compared with the traditional technical solution, the beneficial effects brought by the present invention are:

[0052] The present invention avoids the differences between individuals through the innovative use of individual alpha frequency, and combines functional connectivity analysis to select appropriate tACS stimulation sites based on individual characteristics and develop a personalized tACS stimulation plan. Compared with the traditional fixed-site, fixed-frequency stimulation method, the present invention allows personalized selection of stimulation frequency and site based on individual characteristics, thereby maximizing the therapeutic effect. This personalized treatment plan provides a new treatment approach for patients with anxiety disorders and also provides a new paradigm for personalized medicine. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The present invention relates to a 10-min open-eye and closed-eye resting EEG paradigm schematic diagram;

[0054] Figure 2 It is a schematic diagram of functional connection calculation of the present invention;

[0055] Figure 3 is an example diagram of a personalized stimulation scheme of the present invention;

[0056] Figure 4 It is a schematic diagram of the overall layout of the present invention. DETAILED DESCRIPTION

[0057] The following is combined with Figure 1 -Attached Figure 4 The present invention is described in detail:

[0058] The present invention combines the selection of individual alpha frequency and personalized stimulation site to propose a non-invasive electrical stimulation control system and method. Figure 4As shown, the invention includes an EEG acquisition module 101, an individualized parameter extraction module 201 composed of a frequency extraction unit 202 and a site determination unit 203; and a control module 301. The EEG acquisition module 101 is used to collect and record the resting EEG signals of patients with anxiety disorders; the individualized parameter extraction module is used to extract the individual alpha frequency and personalized stimulation site of the patient, the frequency extraction unit 202 selects the frequency at the peak of the power spectrum as the individual alpha frequency for the individual through spectrum analysis, and the site determination unit selects the site with the weakest functional connection as the personalized stimulation site by calculating the (Phase Locking Value, PLV) PLV value of the frontoparietal network; the control module 301 is responsible for outputting a personalized stimulation scheme, implementing tACS stimulation, so as to improve the level of attention and regulate anxiety. The present invention aims to provide a more targeted treatment method for patients with anxiety disorders through a non-invasive electrical stimulation control system and method, combined with the research results of electrophysiology and brain area network connectivity, and provide new theoretical support for research in the field of neuroscience.

[0059] The present invention is composed of three modules, namely an EEG acquisition module 101 , an individualized parameter extraction module 201 and a control module 301 .

[0060] (1) In the EEG acquisition module 101, the implementation example adopts a 10-minute resting-state EEG acquisition paradigm, and the acquisition process includes a 5-minute eye-opening (Open, O) and a 5-minute eye-closing (Close, C). When the voice prompts the subject to "open your eyes", the subject should keep his eyes open until the voice prompts "close your eyes", and the subject switches to the eye-closing state. Figure 1 shown.

[0061] While the subjects were doing a 10-minute open-eye and closed-eye resting EEG paradigm, EEG signals were collected. The EEG signals collected by the EEG cap were first transmitted to the amplifier, which amplified the weak signals to obtain the potential map of each electrode, so as to enter the subsequent signal processing steps, such as filtering, spectrum analysis, etc.

[0062] (2) The individualized parameter extraction module 201 is divided into two parts, namely, the extraction of IAF frequency and the extraction of personalized stimulation sites; the individualized parameter extraction module 201 is composed of a frequency extraction unit 202 and a site determination unit 203.

[0063] Before the tACS stimulation begins, the frequency extraction unit 202 selects an alpha frequency for each subject, and the alpha frequency is determined by the resting-state EEG in the eyes-open state or eyes-closed state. The collected EEG data are analyzed separately according to the eyes-open and eyes-closed state, and divided into 2-second segments after preprocessing. Subsequently, the fast Fourier transform (FFT) is applied to each segment of data to calculate the power spectrum, and the power spectrum peak in the frequency range of 8-13Hz is found. The frequency at the peak is the alpha frequency of the subject. During the experiment, the stimulation frequency is selected for the subject based on the eyes-open and eyes-closed state of the subject during the stimulation process, that is, if the subject keeps his eyes open during the stimulation process, the IAF frequency in the eyes-open state is selected as the stimulation frequency, and if the subject keeps his eyes closed, the IAF frequency in the eyes-closed state is selected as the stimulation frequency. The specific method for extracting the IAF frequency is as follows:

[0064] The collected EEG data is preprocessed, including removing artifacts (such as eye movement artifacts, electromyographic artifacts), filtering and re-referencing. Then the spectrum analysis is completed by fast Fourier transform (FFT) to obtain the frequency domain signal. Fast Fourier transform is an algorithm for efficiently calculating discrete Fourier transform (DFT) and its inverse transform. For a non-continuous signal of finite length a n =0,1,...,N-1, first perform discrete Fourier transform, the formula is [7] :

[0065]

[0066] in, a n is the time domain, the nth sample of the signal N is the total number of signal samples, and k is the index of the frequency domain (frequency component). The fast Fourier transform itself is a fast algorithm for the discrete Fourier transform, which reduces the algorithm complexity from the original O(N 2 ) becomes O(NlogN), which greatly improves the calculation speed.

[0067] Then calculate the power spectral density of the EEG signal:

[0068]

[0069] where f s is the sampling rate of the signal;

[0070] Furthermore, the power spectral density in the frequency range of 8-13 Hz is extracted:

[0071]

[0072] In the alpha wave frequency band, find the frequency f corresponding to the maximum value of the power spectrum IAF :

[0073]

[0074] Among them, f IAF It is the frequency corresponding to the maximum value of the power spectrum in the alpha wave band, that is, the individual alpha frequency (IAF).

[0075] The site determination unit 203 selects the F2, F4, and F6 sites of the frontal lobe and the P2, P4, and P6 sites of the parietal lobe according to the collected resting-state EEG signals, and calculates the phase-locking value PLV of each pair of sites, such as Figure 2 As shown. PLV is a phase-based functional connection method, which actually measures the phase difference between two channel signals. The specific calculation method is as follows:

[0076]

[0077] Where n represents the time point, φxt and φyt represent the phase angles of signals x and y at time point t respectively. The value range of PLV is [0 1]. The larger the value, the stronger the phase synchronization between the two signals.

[0078] Calculate the PLV values ​​of nine pairs of sites, F2-P2, F2-P4, F2-P6, F4-P2, F4-P4, F4-P6, F6-P2, F6-P4, and F6-P6, respectively, and find the pair of sites with the smallest PLV value, that is, the pair of sites with the lowest phase synchronization. Set these two sites as tACS stimulation electrodes, T8 as the return electrode, and apply tACS stimulation.

[0079] (3) In the control module, the stimulation frequency and stimulation site (taking F4-P4 as an example) are determined according to the feedback results of the parameter extraction module, and a personalized stimulation plan for the subject is formulated. The transcranial alternating current stimulator outputs stimulation to the patient, such as Figure 3 shown.

[0080] The present invention proposes a non-invasive electrical stimulation control system and method, which is expected to achieve considerable social and economic benefits. The best implementation method is to be promoted and utilized by way of patent transfer, technical cooperation or product development. This technology can not only play a role in the clinical treatment of anxiety disorders, but can also be further expanded to other fields, such as cognitive enhancement. Specific cooperation partners may include hospitals, neuroscience research institutions, technology companies, etc., to jointly explore the application potential of this technology in different fields. In addition, the present invention may also consider cooperating with relevant institutions to develop products with commercial potential, design and launch innovative products suitable for medical and scientific research fields according to the characteristics of personalized stimulation programs, expand the commercial application of technology, and improve the social and economic benefits of technology.

[0081] Although the present invention has been described above, the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can make many modifications without departing from the purpose of the present invention, which are all within the protection of the present invention.

Claims

1. A non-invasive electrical stimulation control system, characterized in that: The control system includes an EEG acquisition module, an individualized parameter extraction module consisting of a frequency extraction unit and a site determination unit, and a control module; wherein: The EEG acquisition module is used to acquire and record resting EEG signals of patients with anxiety disorders; The individualized parameter extraction module is used to extract the patient's individual alpha frequency and personalized stimulation site; wherein: The frequency extraction unit selects the frequency at the peak of the power spectrum as the individual alpha frequency through spectrum analysis; the site determination unit selects the site with the weakest functional connection as the personalized stimulation site by calculating the PLV value of the frontal parietal network; The control module is used to output a personalized stimulation plan and implement non-invasive transcranial electrical stimulation.

2. A non-invasive electrical stimulation control system according to claim 1, characterized in that: The frequency extraction unit selects the frequency at the peak of the power spectrum as the individual alpha frequency process through spectrum analysis; comprising: The frequency extraction unit selects an alpha frequency for each subject, wherein the alpha frequency is determined by a resting-state electroencephalogram in an eyes-open state or an eyes-closed state; The collected EEG data were analyzed according to the eye-opening and eye-closing states, and segmented into 2-second segments after preprocessing. The frequency domain signal was then obtained according to the following formula; in, k=0,1,...,N-1;a n is the nth sample of the signal in the time domain, N is the total number of signal samples, and k is the index frequency component in the frequency domain; The power spectrum density of the EEG signal is obtained according to the following formula based on the frequency domain signal; where f s is the sampling rate of the signal; The power spectrum density in the frequency range of 8-13Hz is selected by the following formula: In the alpha wave frequency band, find the frequency ′ corresponding to the maximum value of the power spectrum, that is: Among them, f IAF It is the frequency corresponding to the maximum value of the power spectrum in the alpha wave band.

3. A non-invasive electrical stimulation control system according to claim 1, characterized in that: The site determination unit selects the site with the weakest functional connection as a personalized stimulation site by calculating the PLV value of the frontal-parietal network; including: According to the collected resting-state EEG signals, three sites were selected in the frontal lobe and parietal lobe respectively, and the phase-locked value PLV of each pair of sites was calculated by the following formula; Where: n represents the time point, φxt and φyt represent the phase angles of signals x and y at time point t respectively; the value range of PLV is [0 1], and the larger the value, the stronger the phase synchronization between the two signals; Calculate the PLV values ​​of each site from the frontal lobe to the parietal lobe, and select a pair of sites with the smallest PLV value as personalized stimulation sites; The personalized stimulation site is set as the stimulation electrode of tACS and output to the control module.

4. A non-invasive electrical stimulation control method, characterized in that: The method uses the system of any one of claims 1 to 3 to realize personalized selection of a cranial electrical stimulation program for anxiety disorders, comprising the following steps: Collect and record resting-state EEG signals from patients with anxiety disorders; The frequency at the peak of the power spectrum is selected as the individual alpha frequency; where: The power spectrum density of the EEG signal is obtained according to the following formula based on the frequency domain signal; The power spectrum density in the frequency range of 8-13Hz is selected by the following formula: In the alpha wave frequency band, find the frequency f corresponding to the maximum value of the power spectrum IAF ,Right now: Among them, f IAF It is the frequency corresponding to the maximum value of the power spectrum in the alpha wave band; By calculating the PLV value of the frontoparietal network, the site with the weakest functional connection is selected as the personalized stimulation site; among them: According to the collected resting-state EEG signals, three sites were selected in the frontal lobe and parietal lobe respectively, and the phase-locked value PLV of each pair of sites was calculated by the following formula; Where: n represents the time point, φxt and φyt represent the phase angles of signals x and y at time point t respectively; the value range of PLV is [0 1], and the larger the value, the stronger the phase synchronization between the two signals; Calculate the PLV values ​​of each site from the frontal lobe to the parietal lobe, and select a pair of sites with the smallest PLV value as personalized stimulation sites; The personalized stimulation sites were set as stimulation electrodes for tACS to implement non-invasive transcranial electrical stimulation.

Images