Individualized mu rhythm electrical stimulation modulation device and related products

Abstract

The application discloses an individualized Mu rhythm electric stimulation regulation device and related products. The device includes an electroencephalogram acquisition module, a filtering module, an electric stimulation module, and a control module. The electroencephalogram acquisition module is used to obtain a plurality of first electroencephalogram signals of a target object at a first time through a plurality of electroencephalogram electrodes; the filtering module is used to filter the plurality of first electroencephalogram signals based on a target frequency of the Mu rhythm of the target object to obtain a plurality of first Mu rhythm signals; the control module is used to process the plurality of first Mu rhythm signals based on a target weight corresponding to each electroencephalogram electrode to obtain a second Mu rhythm signal of the target object at the first time; based on the second Mu rhythm signal, a third Mu rhythm signal of the target object at a second time is predicted; based on the third Mu rhythm signal, a stimulation signal of the target object at the second time is determined; and the electric stimulation module is used to apply the stimulation signal to the target object through an electroencephalogram electrode group corresponding to the target object at the second time.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of non-invasive neuromodulation technology, in particular to an individualized Mu rhythm electrical stimulation regulation device and related products. BACKGROUND

[0002] Mu rhythm is a specific brain rhythm generated by the cerebral cortex, with a frequency range of usually 8-13 Hz, mainly distributed in the central region of the brain (C3, C4, etc.), closely related to the activity of the motor cortex. The amplitude change of Mu rhythm is closely related to the individual's motor imagination, actual movement and motor preparation process. Therefore, Mu rhythm is considered as one of the important neurophysiological markers of brain motor function.

[0003] Non-invasive neuromodulation technology is a technology that adjusts brain neural activity through physical means, including transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), etc. These technologies can safely and effectively regulate the excitability of the cerebral cortex and promote neural plasticity, and have been widely used in the field of neural rehabilitation. For example, tACS is used to regulate the Mu rhythm of patients with movement disorders to restore the motor ability of patients.

[0004] However, the current Mu rhythm electrical stimulation is applied to the fixed position of the patient, and a fixed tACS signal is applied to the patient. The regulation precision of this Mu rhythm electrical stimulation method is poor, and the treatment effect on the patient is poor. SUMMARY

[0005] The embodiments of the present application provide an individualized Mu rhythm electrical stimulation regulation device and related products, which improve the precision of Mu rhythm enhancement for individuals to improve the stimulation effect of Mu rhythm electrical stimulation.

[0006] In a first aspect, the embodiments of the present application provide an individualized Mu rhythm electrical stimulation regulation device, comprising: an electroencephalogram acquisition module, a filtering module, an electrical stimulation module and a control module.

[0007] The electroencephalogram acquisition module is configured to acquire a plurality of first electroencephalogram signals of a target object at a first time through a plurality of electroencephalogram electrodes.

[0008] The filtering module is configured to filter the plurality of first electroencephalogram signals based on a target frequency of the Mu rhythm of the target object to obtain a plurality of first Mu rhythm signals.

[0009] The control module is configured to process the plurality of first Mu rhythm signals based on the target weight corresponding to each electroencephalogram electrode to obtain a second Mu rhythm signal of the target object at a first time point;

[0010] Based on the second Mu rhythm signal, a third Mu rhythm signal of the target object at a second time point is predicted, wherein the second time point is located after the first time point;

[0011] Based on the third Mu rhythm signal, a stimulation signal of the target object at the second time point is determined;

[0012] The electrical stimulation module is configured to apply the stimulation signal to the target object through the group of electroencephalogram electrodes corresponding to the target object at the second time point.

[0013] In a second aspect, the embodiments of the present application provide an individualized Mu rhythm electrical stimulation regulation method, comprising:

[0014] A plurality of first electroencephalogram signals of a target object at a first time point are obtained through a plurality of electroencephalogram electrodes;

[0015] Based on a target frequency of Mu rhythm of the target object, the plurality of first electroencephalogram signals are filtered to obtain a plurality of first Mu rhythm signals;

[0016] Based on a target weight corresponding to each electroencephalogram electrode, the plurality of first Mu rhythm signals are processed to obtain a second Mu rhythm signal of the target object at the first time point;

[0017] Based on the second Mu rhythm signal, a third Mu rhythm signal of the target object at a second time point is predicted, wherein the second time point is located after the first time point;

[0018] Based on the third Mu rhythm signal, a stimulation signal of the target object at the second time point is determined;

[0019] At the second time point, the stimulation signal is applied to the target object through the group of electroencephalogram electrodes corresponding to the target object.

[0020] In a third aspect, the embodiments of the present application provide an electronic device, comprising a processor and a memory, wherein the processor is connected with the memory, the memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory, so that the electronic device executes the method according to the second aspect.

[0021] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the method in the second aspect.

[0022] In a fifth aspect, an embodiment of the present application provides a computer program product, which comprises a computer program. The computer program is executed by a processor to implement the method in the second aspect.

[0023] The embodiments of the present application have the following beneficial effects:

[0024] It can be seen that, in the embodiments of the present application, the second time can be understood as a current stimulation time, so the present application will first collect a plurality of first brain electrical signals of the target object before the second time, i.e., at the first time, when performing Mu rhythm electrical stimulation on the target object; then, based on a target frequency of the Mu rhythm of the target object, a plurality of first Mu rhythm signals of the target object are filtered out from each first brain electrical signal. Then, based on the target weight corresponding to each electroencephalogram electrode, the plurality of first Mu rhythm signals are processed to obtain a second Mu rhythm signal of the target object at the first time, i.e., the real Mu rhythm signal of the target object at the first time. Then, based on the second Mu rhythm signal, a third Mu rhythm signal of the target object at the second time is predicted. Further, based on the third Mu rhythm signal, a stimulation signal of the target object at the second time is determined, so that the stimulation signal at the second time is the stimulation signal adapted to the individual target object, rather than a preconfigured fixed stimulation signal. Since calculation requires time, after the stimulation signal is calculated, the clock reaches the second time, so the stimulation signal can be directly used to perform electrical stimulation on the target object at the second time. And the stimulation signal is applied to the target object through the electroencephalogram electrode group corresponding to the target object, i.e., the stimulation signal is applied to the target object in the brain region adapted to the target object, so that the present application can use the stimulation signal precisely matched with the Mu rhythm of the target object at the stimulation time, i.e., the second time, and apply the stimulation signal to the target object in the brain region adapted to the target object, thereby improving the precision of Mu rhythm electrical stimulation and improving the stimulation effect of Mu rhythm electrical stimulation. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0026] Figure 1 A schematic diagram of an individualized Mu rhythm electrical stimulation regulation device provided for an embodiment of the present application;

[0027] Figure 2 A schematic diagram of calculating a third Mu rhythm signal provided for an embodiment of the present application;

[0028] Figure 3 A schematic diagram of an individualized Mu rhythm electrical stimulation regulation method provided for an embodiment of the present application;

[0029] Figure 4 A schematic diagram of an electronic device provided for an embodiment of the present application. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without any creative work fall within the protection scope of the present application.

[0031] The terms “first”, “second”, “third”, and “fourth” and the like in the specification of the present application and the claims and the drawings are used to distinguish different objects, rather than to describe a particular order. In addition, the terms “include” and “have” and any variations thereof are intended to cover the inclusions without exclusivity. For example, a process, method, system, product, or device including a series of steps or units is not limited to the listed steps or units, but can optionally include other steps or units not listed, or can optionally include other steps or units inherent to the process, method, product, or device.

[0032] In this document, the term “embodiment” means that a particular feature, result, or characteristic described in connection with an embodiment can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily mutually exclusive of other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0033] Reference Figure 1 , Figure 1 A schematic diagram of an individualized Mu rhythm electrical stimulation regulation device provided for an embodiment of the present application. As Figure 1As shown, the individualized Mu rhythm electrical stimulation regulation device comprises an electroencephalogram acquisition module, a filtering module, an electrical stimulation module, and a control module. Moreover, the individualized Mu rhythm electrical stimulation regulation device is connected with a plurality of electroencephalogram electrodes and a stimulation electrode, wherein the plurality of electroencephalogram electrodes are used to acquire electroencephalogram signals, and the stimulation electrode is used to send electrical stimulation signals. In this application, 64 general electroencephalogram electrodes are taken as examples for description.

[0034] Firstly, the stimulation frequency of the target object can be set, so that the time of applying the stimulation signal to the target object can be determined. The second time mentioned in this application is one time of applying the stimulation signal to the target object. It should be understood that this application will calculate the Mu rhythm signal of the target object at each stimulation time in real time. However, the calculation needs time, so when the stimulation signal needs to be applied to the target object at the second time, the stimulation signal to be applied at the second time needs to be calculated at the first time. When the stimulation signal to be applied at the second time is calculated, the current time has just reached or not reached the second time, so the corresponding stimulation signal can be applied to the target object at the second time. The time length between the second time and the first time is greater than or equal to the preset time length, wherein the preset time length is the calculation time required for calculating the stimulation signal.

[0035] For the convenience of description, the time length between the second time and the first time is taken as the preset time length as an example for description.

[0036] For example, the electroencephalogram acquisition module obtains a plurality of first electroencephalogram signals of the target object at the first time through a plurality of electroencephalogram electrodes, that is, the electroencephalogram signals of the target object are acquired through the electroencephalogram electrodes, and then the plurality of first electroencephalogram signals of the target object at the first time are obtained from the electroencephalogram signals acquired by the electroencephalogram electrodes, such as adding a window to the electroencephalogram signals acquired by the electroencephalogram electrodes from the first time. The plurality of first electroencephalogram signals correspond one-to-one to the plurality of electroencephalogram electrodes.

[0037] Optionally, the target object involved in this application can be a patient to be treated. Optionally, the target object can be a patient with impaired movement, such as a stroke patient or a vegetable, etc. The type of target object is not limited in this application.

[0038] Then, the filtering module filters the plurality of first electroencephalogram signals based on the target frequency of the Mu rhythm of the target object to obtain a plurality of first Mu rhythm signals, wherein the plurality of first electroencephalogram signals correspond one-to-one to the plurality of first Mu rhythm signals.

[0039] For example, the filtering module first performs Fourier transform on each first electroencephalogram signal, such as, performing Fast Fourier Transform (FFT) on each first electroencephalogram signal to obtain a first frequency spectrum of each first electroencephalogram signal. Then, the filtering module filters the first frequency spectrum of each first electroencephalogram signal based on the target frequency to obtain a second frequency spectrum corresponding to each first electroencephalogram signal.

[0040] For example, the filtering module constructs a first filtering interval based on the target frequency. Specifically, the filtering module constructs the first filtering interval based on the first frequency and the target frequency. For example, the first filtering interval is fw1=[fmu-f1, fmu+f1], where f1 is the first frequency. Finally, the filtering module filters the first frequency spectrum of each first electroencephalogram signal based on the first filtering range to obtain a second frequency spectrum corresponding to each first electroencephalogram signal, that is, filtering out the frequency spectrum belonging to the first filtering range in the first frequency spectrum of each first electroencephalogram signal to obtain a second frequency spectrum corresponding to each first electroencephalogram signal, and performing inverse Fourier transform on the second frequency spectrum corresponding to each first electroencephalogram signal to obtain a first Mu rhythm signal corresponding to each first electroencephalogram signal.

[0041] Further, the control module processes the plurality of first Mu rhythm signals based on the target weight corresponding to each electroencephalogram electrode to obtain a second Mu rhythm signal of the target object at the first time. That is, the plurality of first Mu rhythm signals are weighted and fused based on the target weight corresponding to each electroencephalogram electrode to obtain the second Mu rhythm signal. For example, the first Mu rhythm signal 1, the first Mu rhythm signal 2, …, and the first Mu rhythm signal n are weighted to obtain the second Mu rhythm signal. Figure 2

[0042] Further, the control module predicts a third Mu rhythm signal of the target object at a second time based on the second Mu rhythm signal.

[0043] For example, the control module first performs windowing on the second Mu rhythm signal to obtain a fourth Mu rhythm signal of the target object at the second time, as shown in Figure 2 ​As shown, the second Mu rhythm signal is windowed with the first time t1 as the end position of the time window, so as to intercept the fourth Mu rhythm signal corresponding to the first time t1. Then, the fourth Mu rhythm signal is intercepted to obtain the fifth Mu rhythm signal corresponding to the first time t1. For example, based on the first preset time length and the first time t1, the fourth time t4 is determined, wherein the fourth time t4 is located before the first time t1, and the time difference between the fourth time t4 and the first time t1 is the first preset time length. Then, the Mu rhythm signal between the fourth time t4 and the first time t1 in the fourth Mu rhythm signal is filtered out to obtain the fifth Mu rhythm signal, so as to eliminate the edge effect.

[0044] Further, the control module performs forward prediction based on the fifth Mu rhythm signal to obtain the third Mu rhythm signal. For example, as shown, the forward prediction model can be used to perform forward prediction on the fifth Mu rhythm signal to obtain the third Mu rhythm signal, wherein the third Mu rhythm signal can be the Mu rhythm signal between the first time t1 and the second time t2. Specifically, the fifth Mu rhythm signal is used for forward prediction to predict the Mu rhythm signal between the fourth time t4 and the second time t2, and the signal between the first time t1 and the second time t2 is intercepted from the Mu rhythm signal as the third Mu rhythm signal. Of course, the Mu rhythm signal between the fourth time t4 and the second time t2 can also be directly used as the third Mu rhythm signal, as long as the second time is included in the third Mu rhythm signal. Figure 2

[0045] Further, the control module transforms the third Mu rhythm signal to obtain the phase and amplitude of the third Mu rhythm signal at the second time. For example, the Hilbert transform is performed on the third Mu rhythm signal to obtain the phase and amplitude of the third Mu rhythm at the second time. Then, the inverse Fourier transform is performed on the phase and amplitude of the third Mu rhythm signal at the second time to obtain the stimulation signal at the second time, and the phase and amplitude of the stimulation signal at the second time are the same as the phase and amplitude of the third Mu rhythm signal at the second time.

[0046] Finally, the control module applies the stimulation signal to the target object through the electrostimulation module and the electroencephalogram electrode group corresponding to the target object at the second time. For example, the control module instructs the control module with the phase and amplitude of the stimulation signal at the second time, and then the control module applies the stimulation signal to the target object through the electroencephalogram electrode group according to the phase and amplitude of the stimulation signal at the second time at the second time.

[0047] ​It can be seen that in the embodiment of the present application, the second time can be understood as the current stimulation time, so the present application will first collect the plurality of first brain electrical signals of the target object at the first time before the second time when performing Mu rhythm electrical stimulation on the target object; then, based on the target frequency of the Mu rhythm of the target object, the plurality of first Mu rhythm signals of the target object are filtered out from each first brain electrical signal. Then, based on the target weight corresponding to each electroencephalogram electrode, the plurality of first Mu rhythm signals are processed to obtain the second Mu rhythm signal of the target object at the first time, that is, the real Mu rhythm signal of the target object at the first time. Then, based on the second Mu rhythm signal, the third Mu rhythm signal of the target object at the second time is predicted. Further, based on the third Mu rhythm signal, the stimulation signal of the target object at the second time is determined, so that the stimulation signal at the second time is the stimulation signal adapted to the individual target object, rather than the preconfigured fixed stimulation signal. Since calculation requires time, after the stimulation signal is calculated, the clock reaches the second time, so the stimulation signal can be directly used to perform electrical stimulation on the target object at the second time. And when electrical stimulation is performed, the stimulation signal is applied to the target object through the electroencephalogram electrode group corresponding to the target object, that is, the stimulation signal is applied to the target object in the brain region adapted to the target object, so that the present application can use the stimulation signal precisely matched with the Mu rhythm of the target object at the stimulation time, that is, the second time, and apply the stimulation signal to the target object in the brain region adapted to the target object, thereby improving the precision of Mu rhythm electrical stimulation and improving the stimulation effect of Mu rhythm electrical stimulation.

[0048] The above describes the process of how to perform electrical stimulation on the target object online to enhance the Mu rhythm of the target object. How to measure the Mu rhythm of the target object and the electroencephalogram electrode group corresponding to the target object offline will be introduced below.

[0049] For example, the electroencephalogram acquisition module obtains a plurality of second brain electrical signals of the target object at a fourth time through the plurality of electroencephalogram electrodes, wherein the fourth time is before the first time. Optionally, the plurality of second brain electrical signals and the first brain electrical signals can be collected in the same measurement, or can be collected in different measurements, that is, before performing electrical stimulation on the target object, a plurality of second brain electrical signals of the target object can be collected offline, wherein the method of obtaining the plurality of second brain electrical signals is similar to that of obtaining the plurality of first brain electrical signals, which will not be described again.

[0050] Further, the control module performs Fourier transform on each second electroencephalogram signal to obtain a frequency spectrum of each second electroencephalogram signal. Then, the target frequency is determined based on the frequency spectrum corresponding to the first electroencephalogram electrode in the plurality of electroencephalogram electrodes. It is known from experience that the Mu rhythm signal is generally located in the bilateral motor area, and the electroencephalogram signal of the bilateral motor area is generally obtained through the C3 electrode and the C4 electrode in the plurality of electroencephalogram electrodes. Therefore, it is generally considered that the electroencephalogram signal collected by the C3 electrode and the C4 electrode is a pure Mu rhythm signal. Therefore, the above-mentioned first electroencephalogram electrode is the C3 electrode and / or the C4 electrode. Whether it is the C3 electrode or the C4 electrode is determined based on the motor area to be adjusted. If the motor area to be adjusted is the left motor area, the above-mentioned first electroencephalogram electrode is the C3 electrode. If the motor area to be adjusted is the right motor area, the above-mentioned first electroencephalogram electrode is the C4 electrode. If the motor area to be adjusted is the left motor area and the right motor area, the above-mentioned first electroencephalogram electrode is the C3 electrode and the C4 electrode. This application is described for convenience, and the first electroencephalogram electrode is mainly taken as one of the C3 electrode and the C4 electrode as an example for description.

[0051] Specifically, based on the experience of electroencephalogram research, the frequency of the Mu rhythm of each person appears at the frequency corresponding to the second peak in the frequency spectrum of the Mu rhythm signal of each person. Therefore, for the second electroencephalogram signal corresponding to the first electroencephalogram electrode, the second peak in the first frequency spectrum of the second electroencephalogram signal is determined, and the frequency corresponding to the second peak in the first frequency spectrum is taken as the target frequency.

[0052] Further, the filtering module filters the plurality of second electroencephalogram signals based on the target frequency to obtain a plurality of sixth Mu rhythm signals. For example, the filtering module filters the first frequency spectrum of each second electroencephalogram signal based on the target frequency to obtain a second frequency spectrum of each second electroencephalogram signal. For example, the filtering module constructs a second filtering interval based on the target frequency. Specifically, the filtering module constructs the second filtering interval based on the second frequency and the target frequency. For example, the second filtering interval is fw2=[fmu-f2, fmu+f2], where f2 is the second frequency. Finally, the filtering module filters the first frequency spectrum of each second electroencephalogram signal based on the second filtering range to obtain the second frequency spectrum corresponding to each second electroencephalogram signal, that is, the first frequency spectrum of each second electroencephalogram signal is filtered out to obtain the second frequency spectrum corresponding to each second electroencephalogram signal. Finally, the second frequency spectrum corresponding to each second electroencephalogram signal is inverse Fourier transformed to obtain the sixth Mu rhythm signal corresponding to each second electroencephalogram signal, that is, a plurality of sixth Mu rhythm signals are obtained. That is, by using the real frequency of the Mu rhythm of the target object, the Mu rhythm signal related to the target object can be filtered out from the second electroencephalogram signal collected by each electroencephalogram electrode, avoiding the interference of other electroencephalogram signals collected by the electroencephalogram electrode.

[0053] Further, the control module determines a spatial filter of the whole brain corresponding to the target object based on the plurality of sixth Mu rhythm signals.

[0054] For example, the control module obtains an original nuclear magnetic resonance image of the target object, and then re-slices the original nuclear magnetic resonance image to obtain a target nuclear magnetic resonance image. Then, the control module segments the target nuclear magnetic resonance image to obtain brain tissue, skull and scalp, and establishes a brain model based on the target nuclear magnetic resonance image from which the brain tissue, skull and scalp are segmented, that is, establishes a signal conduction forward brain model based on the target nuclear magnetic resonance image and using isotropic conduction parameters to obtain a three-dimensional brain model of the target object. Then, the control module segments the three-dimensional brain model of the target object to obtain a plurality of original dipoles, that is, divides the three-dimensional brain model into a plurality of regions with small granularity to obtain a plurality of original dipoles. Then, the control module down-samples the plurality of original dipoles (i.e., reduces the number of dipoles), and matches and calibrates the dipoles after down-sampling with dipoles of a standard brain model to obtain a plurality of dipoles corresponding to the target object in the present application. It should be noted that with the continuous improvement of computing power, if the number of dipoles that the control module can process is not limited, the dipoles do not need to be down-sampled, and only calibration of all dipoles is required. In the present application, the plurality of dipoles obtained after down-sampling and calibration are mainly used as examples for description.

[0055] Then, the control module obtains the positions of a plurality of electroencephalogram electrodes for electroencephalogram signal acquisition on the brain of the target object and the positions of the plurality of dipoles on the brain of the target object, and aligns the two positions to obtain a Lead-field matrix (also referred to as a lead field matrix) corresponding to the target object.

[0056] Further, the control module determines a covariance matrix of the plurality of sixth Mu rhythm signals based on the plurality of sixth Mu rhythm signals. For example, the amplitude of each sixth Mu rhythm signal at each time is taken as an element in a row or a column of the matrix to obtain a first amplitude matrix. Based on the first amplitude matrix, the covariance matrix of the plurality of sixth Mu rhythm signals is obtained. Finally, the control module obtains the spatial filter of the whole brain of the target object based on the Lead-field matrix and the covariance matrix.

[0057] For example, the above spatial filter can be represented by formula (1):

[0058] Formula (1);

[0059] Wherein, W is a spatial filter, Lead-field matrix, C is the above covariance matrix, and T is a transposition operation.

[0060] Further, based on the spatial filter, the sixth Mu rhythm signals are subjected to source analysis to obtain a brain electrical source signal corresponding to each of the plurality of dipoles.

[0061] It can be understood that the sixth Mu rhythm signal collected by each electrode is a superposition of the brain electrical source signals of the plurality of dipoles at the channel corresponding to the electrode, and the spatial filter represents the superposition relationship of the source brain electrical signals. Therefore, the sixth Mu rhythm signal can be inversely decomposed by using the filter to obtain the brain electrical source signal corresponding to each of the plurality of dipoles. Therefore, the brain electrical source signals corresponding to the plurality of dipoles can be represented by formula (2):

[0062] Formula (2);

[0063] Wherein S is a signal matrix composed of the brain electrical source signals corresponding to the plurality of dipoles, is a signal matrix composed of the plurality of sixth Mu rhythm signals, W is the spatial filter, and T is a transposition operation.

[0064] It can be understood that the brain electrical source signal corresponding to each of the plurality of dipoles in the inverse decomposition can be understood as a source Mu rhythm signal generated at each of the plurality of dipoles.

[0065] Further, the control module performs Fourier transform on the brain electrical source signal corresponding to each of the plurality of dipoles to obtain the energy corresponding to each of the plurality of dipoles. For example, the Fourier transform is performed on the brain electrical source signal corresponding to each of the plurality of dipoles to obtain a frequency spectrum corresponding to the brain electrical source signal of each of the plurality of dipoles. Then, the energy corresponding to each of the plurality of dipoles is calculated by using the frequency spectrum. For example, the frequency spectrum is an energy spectrum, and the energy corresponding to each frequency point in the frequency spectrum is summed to obtain the energy corresponding to each of the plurality of dipoles.

[0066] Further, the control module determines a target dipole from the plurality of dipoles based on the energy corresponding to each of the plurality of dipoles. It can be understood that the brain electrical source signal corresponding to each of the plurality of dipoles can be understood as a source Mu rhythm signal generated at each of the plurality of dipoles, and the energy corresponding to each of the plurality of dipoles can be understood as the energy of the generated Mu rhythm signal. Therefore, the energy corresponding to each of the plurality of dipoles can be understood as the confidence of the dipole in generating the Mu rhythm, and the greater the energy, the greater the probability of the dipole in generating the Mu rhythm. Therefore, the control module can select the target dipole from the plurality of dipoles in descending order of energy. For example, the control module can select a first proportion of dipoles from the plurality of dipoles in descending order of energy as the target dipole, and the selected target dipole can be understood as a region in the brain of the target object generating the Mu rhythm signal.

[0067] It should be noted that although the target dipoles are the regions in the brain most likely to generate Mu rhythm signals, the Mu rhythm signals generated by the target dipoles are collected by the electroencephalogram electrodes, and the components of the Mu rhythm signals generated by each target dipole decomposed to the channels corresponding to different electroencephalogram electrodes can be different, that is, the Mu rhythm signals generated by each target dipole decomposed to signals at different positions are different, so it is necessary to calculate the decomposition weight of the Mu rhythm signals generated by each target dipole on each electroencephalogram electrode. These decomposition weights can represent the total components of the Mu rhythm signals generated by all target dipoles on each electroencephalogram electrode, that is, each electroencephalogram electrode can obtain the weight of the Mu rhythm signals generated by all target dipoles, so as to determine a plurality of electroencephalogram electrodes that can truly collect Mu rhythm signals, thereby screening out the electroencephalogram electrode group corresponding to the target object.

[0068] For example, the control module determines the target weight corresponding to each electroencephalogram electrode based on the target dipoles and the spatial filter, where the target weight corresponding to each electroencephalogram electrode can be understood as the decomposition weight of the Mu rhythm signals generated by the target dipoles on the electroencephalogram electrode. Specifically, since the decomposition coefficient of each dipole on each electroencephalogram electrode is included in the spatial filter, the decomposition coefficient is essentially the decomposition weight of each dipole on each electroencephalogram electrode. Therefore, for any one electroencephalogram electrode, the decomposition coefficients of the target dipoles on the electroencephalogram electrode can be summed to obtain the total decomposition weight of all target dipoles on the electroencephalogram electrode.

[0069] Therefore, based on the target dipoles, a weight vector is constructed, where the elements corresponding to the target dipoles in the weight vector are 1, and the elements corresponding to the dipoles other than the target dipoles in the plurality of dipoles are 0. Then, based on the weight vector and the spatial filter, the initial weight corresponding to each electroencephalogram electrode is determined. For example, the spatial filter and the weight vector are multiplied to obtain the initial weight corresponding to each electroencephalogram electrode, that is, the total decomposition weight of all target dipoles on the electroencephalogram electrode.

[0070] For example, the spatial filter is a 64x15000 matrix, where 15000 is the number of dipoles and 64 is the number of electroencephalogram electrodes. A row of elements in the matrix corresponds to an electroencephalogram electrode, and the row of elements is the decomposition coefficients of 15000 dipoles on the electroencephalogram electrode corresponding to the row of elements. The weight vector is 1500x1, and the number corresponding to the target dipole in the weight vector is 1, and the other elements are 0. Therefore, by multiplying the 64x15000 matrix and the 1500x1 vector, the decomposition coefficients of all dipoles on each electroencephalogram electrode can be summed to obtain the initial weight corresponding to each electroencephalogram electrode, that is, each element in the obtained 64x1 vector is the initial weight corresponding to an electroencephalogram electrode.

[0071] Further, the initial weights corresponding to the plurality of electroencephalogram electrodes are normalized to obtain the target weight of each electroencephalogram electrode.

[0072] Further, the control module determines a target electroencephalogram electrode in the plurality of electroencephalogram electrodes based on the target weight corresponding to each electroencephalogram electrode, where the target electroencephalogram electrode is the electroencephalogram electrode with the largest target weight in the plurality of electroencephalogram electrodes. Then, based on the target electroencephalogram electrode, a candidate electroencephalogram electrode corresponding to the target electroencephalogram electrode in the plurality of electroencephalogram electrodes is determined.

[0073] For example, the control module determines the candidate electroencephalogram electrode corresponding to the target electroencephalogram electrode in the plurality of electroencephalogram electrodes. For example, the control module obtains the distance between each electroencephalogram electrode in the plurality of electroencephalogram electrodes except the target electroencephalogram electrode and the target electroencephalogram electrode, and selects the candidate electroencephalogram electrode based on the distance. For example, the first number of electroencephalogram electrodes are selected as the candidate electroencephalogram electrode in order of increasing distance.

[0074] Finally, based on the target electroencephalogram electrode and the candidate electroencephalogram electrode, the electroencephalogram electrode group corresponding to the target object is determined. For example, the target electroencephalogram electrode is used as the anode when stimulated, and the selected candidate electroencephalogram electrode is used as the negative electrode, thereby constructing the electroencephalogram electrode group corresponding to the target object, and the electroencephalogram electrode group is used as the electrode group for applying electrical stimulation to the target object.

[0075] It can be seen that in the embodiments of the present application, the electroencephalogram electrode with the largest weight is selected as the target electroencephalogram electrode, so that the target object is stimulated by the target electroencephalogram electrode, thereby the stimulation signal can be maximally applied to the target dipole, and the Mu rhythm signal generated by the target dipole can be accurately regulated, thereby improving the stimulation effect of electrical stimulation.

[0076] Referring to Figure 3 , Figure 3A flowchart of an individualized Mu rhythm electrical stimulation regulation method provided by an embodiment of the present application is shown. The method is applied to the individualized Mu rhythm electrical stimulation regulation device described above. The method includes but is not limited to the following steps:

[0077] S301: Obtain a plurality of first electroencephalogram signals of a target object at a first time through a plurality of electroencephalogram electrodes.

[0078] S302: Filter the plurality of first electroencephalogram signals based on a target frequency of Mu rhythm of the target object to obtain a plurality of first Mu rhythm signals.

[0079] S303: Process the plurality of first Mu rhythm signals based on a target weight corresponding to each electroencephalogram electrode to obtain a second Mu rhythm signal of the target object at the first time.

[0080] S304: Predict a third Mu rhythm signal of the target object at a second time based on the second Mu rhythm signal, wherein the second time is after the first time.

[0081] S305: Determine a stimulation signal of the target object at the second time based on the third Mu rhythm signal.

[0082] S306: Apply the stimulation signal to the target object through an electroencephalogram electrode group corresponding to the target object at the second time.

[0083] It should be noted that, Figure 3 The specific implementation process of each step of the method can be referred to the above Figure 1 The functions of each module in the individualized Mu rhythm electrical stimulation regulation device are not repeated.

[0084] Reference Figure 4 , Figure 4 A schematic diagram of an electronic device provided by an embodiment of the present application is shown. The electronic device 400 can be the individualized Mu rhythm electrical stimulation regulation device described above.

[0085] The electronic device 400 includes a memory 401, a processor 402, a communication interface 403, and a bus 404. The memory 401, the processor 402, and the communication interface 403 are in communication connection with each other through the bus 404.

[0086] The memory 401 can be a Read Only Memory (ROM), a static storage device, a dynamic storage device or a Random Access Memory (RAM). The memory 401 can store programs; when the electronic device 400 is the individualized Mu rhythm electrical stimulation regulation device, the processor 402 and the communication interface 403 are used to execute each step performed by the individualized Mu rhythm electrical stimulation regulation device in the individualized Mu rhythm electrical stimulation regulation method of the embodiments of the present application when the programs stored in the memory 401 are executed by the processor 402.

[0087] The processor 402 can be a general-purpose Central Processing Unit (CPU), a microprocessor, an Application Specific Integrated Circuit (ASIC), a graphics processing unit (GPU) or one or more integrated circuits, used to execute related programs to implement the individualized Mu rhythm electrical stimulation regulation method in the method embodiments of the present application.

[0088] The processor 402 can also be an integrated circuit chip with a signal processing capability. In the implementation process, each step in the individualized Mu rhythm electrical stimulation regulation method of the present application can be completed by the integrated logic circuit of hardware in the processor 402 or the instructions in the form of software. The processor 402 described above can also be a general-purpose processor, a Digital Signal Processing (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. Each method, step and logic block diagram disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as a hardware coding processor for execution, or a combination of hardware and software modules in the coding processor for execution. The software module can be located in a random access memory, a flash memory, a read only memory, a programmable read only memory or an electrically erasable programmable memory, a register or other mature storage medium in the art. The storage medium is located in the memory 401, and the processor 402 reads the information in the memory 401 to execute each step of the individualized Mu rhythm electrical stimulation regulation method.

[0089] The communication interface 403 can be a transceiver, such as a transceiver, to realize the communication between the electronic device 400 and other devices or communication networks; the communication interface 403 can also be an input-output interface to realize the data transmission between the electronic device 400 and input-output devices, wherein the input-output devices include but are not limited to keyboard, mouse, display screen, U disk and hard disk. For example, the processor 402 can receive a signal through the communication interface 403.

[0090] The bus 404 can include a path for transmitting information between various components (for example, the memory 401, the processor 402, the communication interface 403) of the device electronic device 400.

[0091] It should be noted that, although Figure 4 The electronic device 400 shown only shows the memory, processor, communication interface, but in the specific implementation process, those skilled in the art should understand that the electronic device 400 also includes other devices necessary for normal operation. At the same time, according to the specific needs, those skilled in the art should understand that the electronic device 400 can also include hardware devices for realizing other additional functions. In addition, those skilled in the art should understand that the electronic device 400 can also only include devices necessary for the implementation of the embodiments of the present application, and does not have to include all the devices shown in the above. Figure 4

[0092] The embodiments of the present application also provide a computer readable storage medium, the computer readable storage medium stores a computer program, the computer program is executed by a processor to realize part or all steps of any one of the individualized Mu rhythm electrical stimulation regulation methods described in the above method embodiments.

[0093] The embodiments of the present application also provide a computer program product, the computer program product includes a non-transitory computer readable storage medium storing a computer program, the computer program is operable to make a computer execute part or all steps of any one of the individualized Mu rhythm electrical stimulation regulation methods described in the above method embodiments.

[0094] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the action order described, because according to the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to optional embodiments, and the actions and modules involved are not necessarily the application.

[0095] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.​

[0096] In several embodiments provided in the present application, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the division of the apparatus embodiments described above is merely a logical division, and there can be another division manner in actual implementation. For example, two or more units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between units can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0097] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they can be located in one place, or distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0098] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can be physically present separately, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software program module.

[0099] The integrated unit, if realized in the form of a software program module and sold or used as an independent product, can be stored in a computer readable memory. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art that contributes to the technical solutions or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various program code storage media.

[0100] Those skilled in the art can understand that all or part of the steps of various methods in the above embodiments can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer readable memory, which can include a flash disk, a read-only memory (English: Read-Only Memory, ROM for short), a random access memory (English: Random Access Memory, RAM for short), a magnetic disk or an optical disk, etc.

[0101] The above describes the embodiments of the present application in detail, and the principles and implementation manners of the present application are described by applying specific examples; the above embodiment descriptions are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges will be changed, and the above descriptions should not be understood as limiting the present application.

Claims

1. A personalized Mu rhythm electrical stimulation modulation device, characterized in that, include: EEG acquisition module, filtering module, electrical stimulation module, and control module; The EEG acquisition module is used to acquire multiple first EEG signals of the target object at a first moment through multiple EEG electrodes; The filtering module is used to filter the plurality of first EEG signals based on the target frequency of the Mu rhythm of the target object to obtain a plurality of first Mu rhythm signals. The control module is used to process the plurality of first Mu rhythm signals based on the target weight corresponding to each EEG electrode to obtain the second Mu rhythm signal of the target object at the first moment; Based on the second Mu rhythm signal, predict the third Mu rhythm signal of the target object at a second time point, wherein the second time point is after the first time point; specifically used for: Windowing is applied to the second Mu rhythm signal to obtain the fourth Mu rhythm signal corresponding to the first time point; The fourth Mu rhythm signal is truncated to obtain the fifth Mu rhythm signal corresponding to the first time point; Based on the fifth Mu rhythm signal, forward prediction is performed to obtain the third Mu rhythm signal; Based on the third Mu rhythm signal, the stimulation signal of the target object at the second moment is determined; The electrical stimulation module is used to apply the stimulation signal to the target object at the second time by means of an EEG electrode group corresponding to the target object.

2. The device according to claim 1, characterized in that, In determining the stimulation signal of the target object at the second moment based on the third Mu rhythm signal, the control module is specifically used for: The third Mu rhythm signal is transformed to obtain the phase and amplitude of the third Mu rhythm signal at the second time. The stimulation signal is obtained by performing an inverse Fourier transform based on the phase and amplitude of the third Mu rhythm signal at the second time.

3. The device according to claim 1, characterized in that, The EEG acquisition module is also used to acquire multiple second EEG signals of the target object at a fourth time point through the multiple EEG electrodes, wherein the fourth time point is located before the first time point; The control module is further configured to perform a Fourier transform on each second EEG signal to obtain a first spectrum of each second EEG signal; and to determine the target frequency based on the first spectrum of the first EEG electrode among the plurality of EEG electrodes.

4. The device according to claim 3, characterized in that, The filtering module is also used to filter the plurality of second EEG signals based on the target frequency to obtain a plurality of sixth Mu rhythm signals; The control module is also used to determine the whole-brain spatial filter corresponding to the target object based on the plurality of sixth Mu rhythm signals; Based on the spatial filter, the source analysis of the sixth Mu rhythm signal is performed to obtain the brain power signal corresponding to each of the multiple dipoles; Perform a Fourier transform on the brain power signal corresponding to each dipole to obtain the energy corresponding to each dipole; Based on the energy corresponding to each dipole, the target dipole among the plurality of dipoles is determined; Based on the target dipole and the spatial filter, the target weight corresponding to each EEG electrode is determined.

5. The device according to claim 4, characterized in that, The control module is further configured to determine a target EEG electrode among the plurality of EEG electrodes based on the target weight corresponding to each EEG electrode, wherein the target EEG electrode is the EEG electrode with the largest target weight among the plurality of EEG electrodes; Identify candidate EEG electrodes among the plurality of EEG electrodes that correspond to the target EEG electrode; Based on the target EEG electrode and the candidate EEG electrodes, a set of EEG electrodes corresponding to the target object is determined.

6. The device according to claim 4 or 5, characterized in that, In determining the target weight corresponding to each EEG electrode based on the target dipole and the spatial filter, the control module is specifically used for: Based on the target dipole, construct a weight vector; Based on the weight vector and the spatial filter, the initial weights corresponding to each EEG electrode are determined. The initial weights corresponding to the multiple EEG electrodes are normalized to obtain the target weights for each EEG electrode.

7. An electronic device, characterized in that, include: A processor and a memory, the processor being connected to the memory for storing computer programs, the processor being configured to execute the computer programs stored in the memory, causing the electronic device to perform the following steps: Multiple first EEG signals of the target object at the first moment are obtained by using multiple EEG electrodes; Based on the target frequency of the Mu rhythm of the target object, the plurality of first EEG signals are filtered to obtain a plurality of first Mu rhythm signals; Based on the target weight corresponding to each EEG electrode, the plurality of first Mu rhythm signals are processed to obtain the second Mu rhythm signal of the target object at the first moment; Based on the second Mu rhythm signal, predict the third Mu rhythm signal of the target object at a second time point, wherein the second time point is after the first time point; including: Windowing is applied to the second Mu rhythm signal to obtain the fourth Mu rhythm signal corresponding to the first time point; The fourth Mu rhythm signal is truncated to obtain the fifth Mu rhythm signal corresponding to the first time point; Based on the fifth Mu rhythm signal, forward prediction is performed to obtain the third Mu rhythm signal; Based on the third Mu rhythm signal, the stimulation signal of the target object at the second moment is determined; At the second moment, the stimulation signal is applied to the target object through the EEG electrode group corresponding to the target object.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to perform the following steps: Multiple first EEG signals of the target object at the first moment are obtained by using multiple EEG electrodes; Based on the target frequency of the Mu rhythm of the target object, the plurality of first EEG signals are filtered to obtain a plurality of first Mu rhythm signals; Based on the target weight corresponding to each EEG electrode, the plurality of first Mu rhythm signals are processed to obtain the second Mu rhythm signal of the target object at the first moment; Based on the second Mu rhythm signal, predict the third Mu rhythm signal of the target object at a second time point, wherein the second time point is after the first time point; including: Windowing is applied to the second Mu rhythm signal to obtain the fourth Mu rhythm signal corresponding to the first time point; The fourth Mu rhythm signal is truncated to obtain the fifth Mu rhythm signal corresponding to the first time point; Based on the fifth Mu rhythm signal, forward prediction is performed to obtain the third Mu rhythm signal; Based on the third Mu rhythm signal, the stimulation signal of the target object at the second moment is determined; At the second moment, the stimulation signal is applied to the target object through the EEG electrode group corresponding to the target object.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, performs the following steps: Multiple first EEG signals of the target object at the first moment are obtained by using multiple EEG electrodes; Based on the target frequency of the Mu rhythm of the target object, the plurality of first EEG signals are filtered to obtain a plurality of first Mu rhythm signals; Based on the target weight corresponding to each EEG electrode, the plurality of first Mu rhythm signals are processed to obtain the second Mu rhythm signal of the target object at the first moment; Based on the second Mu rhythm signal, predict the third Mu rhythm signal of the target object at a second time point, wherein the second time point is after the first time point; including: Windowing is applied to the second Mu rhythm signal to obtain the fourth Mu rhythm signal corresponding to the first time point; The fourth Mu rhythm signal is truncated to obtain the fifth Mu rhythm signal corresponding to the first time point; Based on the fifth Mu rhythm signal, forward prediction is performed to obtain the third Mu rhythm signal; Based on the third Mu rhythm signal, the stimulation signal of the target object at the second moment is determined; At the second moment, the stimulation signal is applied to the target object through the EEG electrode group corresponding to the target object.

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