Bilateral transauricular vagus nerve modulation device, electronic device and storage medium

Abstract

The application discloses a bilateral transaural vagus nerve regulation device, an electronic device and a storage medium. The nerve regulation device comprises an auditory stimulation module configured to apply multiple novel stimuli to a target object within an auditory stimulation period; a processing module configured to obtain an electroencephalogram signal corresponding to each electroencephalogram channel when each novel stimulus is applied to the target object; determine a target frequency when fitting the electroencephalogram signal with a target lag order based on the electroencephalogram signal corresponding to each electroencephalogram channel; determine a first proportion based on the information exchange intensity between different brain regions of the target object at each time point and the target frequency; determine a second stimulation frequency within a next electrical stimulation period adjacent to the auditory stimulation period based on the first proportion, the first stimulation frequency and the target frequency; and an electrical stimulation module configured to electrically stimulate the auricular vagus nerve of the binaural of the target object at the second stimulation frequency within the next electrical stimulation period.

Description

Technical Field

[0001] This application relates to the field of neuromodulation technology, specifically to a bilateral transauricular vagus nerve modulation device, electronic device, and storage medium. Background Technology

[0002] The vagus nerve is one of the longest and most complex mixed cranial nerves in the human body. Originating in the brainstem, it extends from the head and neck down to the chest and abdomen, innervating a vast area. It is a crucial anatomical pathway for bidirectional information transmission between the body and the brain. Transcutaneous auricular vagus nerve stimulation (TANS) activates the peripheral vagus nerve afferent pathway, modulating a wide range of arousal and consciousness-related networks from the brainstem to the thalamus and cortex. Based on the vagus nerve's key role in regulating consciousness and arousal, TNS is considered to have significant potential for enhancing levels of consciousness.

[0003] However, the stimulation parameters of existing percutaneous vagus nerve stimulation devices are fixed. The same stimulation parameters are used to adjust different disease types and severity, resulting in a mismatch between the stimulation parameters and the patient. In other words, the setting accuracy of the stimulation parameters is poor, and the treatment effect on the patient is poor. Summary of the Invention

[0004] This application provides a bilateral transauricular vagus nerve modulation device, electronic device, and storage medium, which can individually set a stimulation frequency that matches the individual subject, thereby improving the accuracy of stimulation frequency setting and thus improving the therapeutic effect on the subject.

[0005] In a first aspect, embodiments of this application provide a bilateral transauricular vagus nerve modulation device, comprising: an auditory stimulation module, a processing module, and an electrical stimulation module; The auditory stimulation module is used to apply multiple novel stimuli to the target object within the auditory stimulation cycle; The processing module is also used to acquire the EEG signal corresponding to each EEG channel when each novel stimulus is applied to the target object; The processing module is further configured to determine, based on the EEG signal corresponding to each EEG channel when applying each novel stimulus to the target object, the intensity of information exchange between different brain regions of the target object at each time point and at each frequency point when fitting the EEG signal with a target lag order. The processing module is further configured to determine the target frequency for fitting the EEG signal with the target lag order based on the information exchange intensity between different brain regions of the target object at each time point and at each frequency point when fitting the EEG signal with the target lag order, wherein the target frequency is the frequency point corresponding to the maximum information exchange intensity of the brain of the target object when fitting the EEG signal with the target lag order. The processing module is further configured to determine a first ratio based on the intensity of information exchange between different brain regions of the target object at each time point and at the target frequency, wherein the first ratio is the proportion of brain region pairs of the target object that have bidirectional information exchange at the target frequency; The processing module is further configured to determine a second stimulation frequency in the next electrical stimulation cycle adjacent to the auditory stimulation cycle based on the first ratio, the first stimulation frequency, and the target frequency, wherein the first stimulation frequency is the stimulation frequency when the vagus nerve of both ears of the target object is electrically stimulated in the previous electrical stimulation cycle adjacent to the auditory stimulation cycle. The electrical stimulation module is used to electrically stimulate the vagus nerves of both ears of the target subject at the second stimulation frequency in the next electrical stimulation cycle.

[0006] Secondly, embodiments of this application provide a method for modulating the vagus nerve, including: Apply multiple novel stimuli to the target object within the auditory stimulation cycle; Acquire the EEG signal corresponding to each EEG channel when each novel stimulus is applied to the target object; Based on the EEG signals corresponding to each EEG channel when applying each novel stimulus to the target object, the intensity of information exchange between different brain regions of the target object is determined at each time point and at each frequency point when fitting the EEG signals with the target lag order. Based on the information exchange intensity between different brain regions of the target object at each time point and at each frequency point when fitting the EEG signal with the target lag order, the target frequency when fitting the EEG signal with the target lag order is determined, wherein the target frequency is the frequency point corresponding to the maximum information exchange intensity of the brain of the target object when fitting the EEG signal with the target lag order. Based on the intensity of information exchange between different brain regions of the target object at each time point and at the target frequency, a first proportion is determined, wherein the first proportion is the percentage of brain region pairs of the target object that have bidirectional information exchange at the target frequency. Based on the first ratio, the first stimulation frequency, and the target frequency, a second stimulation frequency is determined in the next electrical stimulation cycle adjacent to the auditory stimulation cycle, wherein the first stimulation frequency is the stimulation frequency when the vagus nerve of both ears of the target object is electrically stimulated in the previous electrical stimulation cycle adjacent to the auditory stimulation cycle. In the next electrical stimulation cycle, the vagus nerves of both ears of the target subject are electrically stimulated at the second stimulation frequency.

[0007] Thirdly, embodiments of this application provide an electronic device, including: a processor and a memory, the processor being connected to the memory, the memory being used to store a computer program, and the processor being used to execute the computer program stored in the memory, so that the electronic device performs the method as described in the second aspect.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the second aspect.

[0009] Fifthly, embodiments of this application provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the method described in the second aspect.

[0010] Implementing the embodiments of this application has the following beneficial effects: As can be seen in this embodiment, when performing vagus nerve modulation on the target subject (i.e., the patient), the EEG signals of each EEG channel of the target subject during novel stimulation are used to calculate the information exchange intensity between different brain regions of the target subject at each time point and at each frequency point. Then, using the information exchange intensity between different brain regions of the target subject, the target frequency corresponding to the target subject is determined, that is, the frequency point corresponding to the maximum information exchange intensity of the target subject's brain, which is to say, the dominant frequency of the target subject is determined. Finally, using the information exchange intensity between different brain regions of the target subject at each time point and at the target frequency, a first proportion of brain region pairs with bidirectional information exchange at the target frequency is determined. Using this first proportion, it is possible to further verify whether the determined target frequency is the true dominant frequency, avoid the interference of brain region noise on the dominant frequency, and thus further ensure that the dominant frequency (i.e., the target frequency) is accurate and effective. Finally, using the first ratio, the first stimulation frequency of the previous electrical stimulation cycle, and the target frequency, the second stimulation frequency for the next electrical stimulation cycle is determined. The second stimulation frequency determined in this way is adapted to the target object and is targeted. Using the second stimulation frequency to electrically stimulate the vagus nerve of both ears of the target object can improve the control effect of the vagus nerve regulation of the target object. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A schematic diagram of vagus nerve modulation provided for an embodiment of this application; Figure 2 A schematic diagram of a bilateral transauricular vagus nerve modulation device provided in an embodiment of this application; Figure 3 This is a schematic diagram illustrating the processing of electroencephalogram (EEG) signals, provided as an embodiment of this application. Figure 4 A flowchart illustrating a method for modulating the vagus nerve of the ear, provided in an embodiment of this application; Figure 5 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0015] In this document, the term "embodiment" means that a particular feature, result, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0016] See Figure 1 , Figure 1 This is a schematic diagram of vagus nerve modulation provided in an embodiment of this application.

[0017] For example, such as Figure 1 As shown, the modulation of the vagus nerve is performed according to a standard paradigm.

[0018] Optionally, the vagus nerve modulation process is achieved through alternating cycles of auditory stimulation and electrical stimulation. Optionally, the durations of the auditory stimulation cycle and the electrical stimulation cycle can be the same or different, and this application does not impose any limitation.

[0019] For example, such as Figure 1As shown, novel stimuli and standard stimuli (also known as ordinary stimuli) are cyclically applied to the target subject within any auditory stimulation cycle. The proportion of novel stimuli and standard stimuli varies within a single auditory stimulation cycle, but their durations are the same. For example, if the duration of both novel and standard stimuli applied to the target subject within an auditory stimulation cycle is 200 ms, and the ratio of novel to standard stimuli is 1:4, then there is a time interval between each stimulus applied within the auditory stimulation cycle. For example, the time interval is 800 ms. Therefore, when the auditory stimulation cycle duration is 1 minute, 12 novel stimuli and 48 standard stimuli are applied within that cycle.

[0020] The frequencies of the auditory stimuli applied in the novel stimulus and the standard stimulus are different. Optionally, the standard stimulus is a high-frequency buzzing auditory stimulus of A Hz, while the novel stimulus is a low-frequency buzzing stimulus of B Hz, where A < B. That is to say, the novel stimulus is a rare and special auditory stimulus, mainly used to induce event-related potentials (ERPs) in the target object.

[0021] Furthermore, during vagus nerve modulation of the target subject, the target subject's electroencephalogram (EEG) signals are collected within the auditory stimulation cycle. Using the target subject's EEG signals from the time intervals before and after each application of a novel stimulus, the stimulation frequency of the next electrical stimulation cycle adjacent to the current auditory stimulation cycle is calculated. Therefore, the auditory stimulation cycle is primarily used for personalized calculation of the stimulation frequency of the next electrical stimulation cycle; hence, the auditory stimulation cycle can also be called the detection cycle.

[0022] Finally, during the electrical stimulation cycle, electrical stimulation is applied to the vagus nerve of the target's ears by using electrical stimulation electrodes worn on the target's ears to perform neural modulation on the target. The stimulation frequency of the electrical stimulation is the stimulation frequency determined within the aforementioned auditory stimulation cycle.

[0023] Optionally, the target group mentioned in this application is a person or patient who needs to undergo auricular vagus nerve modulation.

[0024] See Figure 2 , Figure 2 This is a schematic diagram of a bilateral transauricular vagus nerve modulation device provided in an embodiment of this application.

[0025] For example, such as Figure 2 As shown, the bilateral transauricular vagus nerve modulation device includes an auditory stimulation module, a processing module, and an electrical stimulation module. Optionally, the auditory stimulation module, processing module, and electrical stimulation module are specifically used to implement the following steps: The auditory stimulation module is used to apply multiple novel stimuli to the target object within an auditory stimulation cycle.

[0026] The auditory stimulation cycle mentioned above is the current auditory stimulation cycle.

[0027] For example, in combination Figure 1 As shown in the process, the auditory stimulation module applies multiple novel stimuli and multiple standard stimuli to the target object within the aforementioned auditory stimulation cycle, according to the ratio of novel stimuli to standard stimuli.

[0028] The processing module is used to acquire the EEG signal corresponding to each EEG channel when a novel stimulus is applied to the target object.

[0029] Optionally, the aforementioned bilateral transauricular vagus nerve modulation device also includes a data acquisition module.

[0030] For example, the data acquisition module described above includes multiple EEG electrodes, each corresponding to a single EEG channel. Therefore, during the auditory stimulation cycle, the data acquisition module continuously acquires the raw EEG signals of the target object through the EEG electrodes, obtaining the raw EEG signal corresponding to each EEG channel during that auditory stimulation cycle. Then, the data acquisition module sends the acquired raw EEG signal corresponding to each EEG channel to the processing module described above.

[0031] After obtaining the raw EEG signal corresponding to each EEG channel, the processing module preprocesses the raw EEG signal corresponding to each EEG channel to obtain the EEG signal corresponding to each EEG channel when a novel stimulus is applied to the target object.

[0032] For example, in order to avoid the problem of loss of single-channel EEG signal due to volume conduction effect, this application first rereferences the original EEG signal of each EEG channel.

[0033] Specifically, the data acquisition module further includes bilateral mastoid electrodes, each corresponding to a mastoid channel. Therefore, during the auditory stimulation cycle, the data acquisition module also acquires mastoid signals from the target object via the bilateral mastoid electrodes, obtaining mastoid signals corresponding to each mastoid channel, and sends these signals to the processing module. Further, the processing module uses the mastoid signals corresponding to the bilateral mastoid channels as reference channels to rereference the raw EEG signals of each EEG channel. For example, the processing module averages the mastoid signals corresponding to the bilateral mastoid channels to obtain a reference signal. Then, the processing module uses the reference signal to rereference the raw EEG signals of each EEG channel, obtaining a rereferenced EEG signal corresponding to each EEG channel.

[0034] For example, the rereferenced EEG signal corresponding to the i-th EEG channel can be represented by formula (1): Formula (1); in, , and These are the mastoid signals corresponding to the bilateral mastoid channels. For reference signal, This represents the original EEG signal corresponding to the i-th EEG channel. The rereferenced EEG signal corresponds to the i-th EEG channel.

[0035] Furthermore, in order to preserve EEG signals in specific frequency bands, the above processing module will also filter the rereferenced EEG signals corresponding to each EEG channel to obtain the filtered EEG signals corresponding to each EEG channel.

[0036] Furthermore, in order to reduce the length of the EEG signal, the above processing module will also downsample the filtered EEG signal corresponding to each EEG channel to obtain the sampled EEG signal corresponding to each EEG channel.

[0037] Then, the aforementioned processing module segments the sampled EEG signal corresponding to each EEG channel to obtain candidate EEG signals for each EEG channel when a novel stimulus is applied to the target object. Specifically, for the sampled EEG signal corresponding to each EEG channel, taking the moment of each novel stimulus as zero point, the EEG signal within a first time period is extracted forward, and the EEG signal within a second time period is extracted backward, to obtain candidate EEG signals for each EEG channel when a novel stimulus is applied to the target object. For example, the first time period can be 100ms, 120ms, or other values, and the second time period can be 300ms, 400ms, or other values. This application does not limit the first and second time periods.

[0038] Finally, the processing module performs baseline correction on the aforementioned EEG signals to obtain the EEG signal corresponding to each EEG channel when each novel stimulus is applied to the target object. For example, the processing module uses the EEG signals belonging to the first time period from the candidate EEG signals to perform baseline correction on the EEG signals in the second time period, and uses the corrected EEG signals in the second time period as the EEG signal corresponding to each EEG channel for each novel stimulus.

[0039] For example, such as Figure 3As shown, for EEG channel m, and k novel stimuli are applied to the target object within the above auditory stimulation cycle, the sampled EEG signals corresponding to EEG channel m are segmented and baseline corrected to obtain the EEG signals corresponding to EEG channel m for each novel stimulus, that is, the EEG signals corresponding to EEG channel m for novel stimulus 1, novel stimulus 2, ..., novel stimulus k are obtained.

[0040] Furthermore, the aforementioned processing module is also used to determine the target frequency when fitting the EEG signal with the target lag order, based on the information exchange intensity between different brain regions of the target object at each time point and at each frequency point when fitting the EEG signal with the target lag order, wherein the target frequency is the frequency point corresponding to the maximum information exchange intensity of the target object's brain when fitting the EEG signal with the target lag order.

[0041] In one embodiment of this application, the target lag order is predetermined from a plurality of lag orders, wherein the plurality of lag orders are pre-set lag orders. For example, the lag order can be 1, 2, 3, etc. The lag order determines the number of historical time points selected when fitting the EEG signal.

[0042] For example, the above processing module will average the multiple EEG signals corresponding to each EEG channel when the target object is subjected to the multiple novel stimuli to obtain the target EEG signal of the target object under each EEG channel.

[0043] For example, such as Figure 3 As shown, the average of the EEG signals (i.e., potential signal values) corresponding to EEG channel m at the same time point is calculated when novel stimulus 1, novel stimulus 2, ..., novel stimulus k, to obtain the target EEG signal corresponding to EEG channel m, that is, to obtain the target EEG signal of the target object under EEG channel m.

[0044] Then, further, the aforementioned processing module is also used to form a multidimensional vector corresponding to each time point by combining the multiple target EEG signals of the target object under the multiple EEG channels at the same time point, wherein the multiple target EEG signals correspond one-to-one with the multiple EEG channels. Optionally, the aforementioned time point is the time point determined by sampling the target EEG signals according to a preset sampling interval. Therefore, the time point involved in this application can also be called the sampling time point. These two descriptions are essentially similar and do not need to be distinguished. Moreover, for the target EEG signal, there are multiple time points. For example, if the duration of the target EEG signal is 800ms and the preset sampling interval is 1ms, then for a target EEG signal with a length of 800ms, there are 800 sampling time points. The EEG signals (i.e., potential signal values) of different channels at the same time point are combined to form a multidimensional vector corresponding to each time point, resulting in 800 multidimensional vectors corresponding to the 800 time points.

[0045] It should be noted that for each brain region in the human brain, there are one or more EEG channels. For ease of understanding, this application uses one EEG channel corresponding to one brain region as an example. Since the multidimensional vector corresponding to each time point is composed of the potential signal values ​​of each EEG channel at that time point, the multidimensional vector corresponding to each time point contains the potential signal values ​​of each brain region at that time point. These potential signal values ​​can be understood as the activity of the brain region. Therefore, the multidimensional vector corresponding to each time point represents the activity of each brain region of the target object at that time point. Since the activity of all brain regions at the current moment can be considered as a linear combination of the activities of all brain regions over a past period, the activity of all brain regions of the target object at each time point can be considered as a linear combination of the activities of brain regions at previous historical time points. This allows us to fit the activity of a brain region at the current time point using the activity of the brain region at historical time points. The aforementioned lag order determines the number of historical time points that need to participate in the linear combination.

[0046] It should be noted that if the number of historical time points involved in the linear combination is relatively small, only historical time points closer to the current time point are considered, i.e., only the activity of the brain region in the recent period is considered, ignoring the activity of the brain region in the distant past. If the number of historical time points involved in the linear combination is large, since the activity of the brain region a long time ago has little correlation with the brain region's current activity, additional noise will be introduced. Therefore, it is necessary to determine the most suitable lag order (i.e., the target lag order of this application) from multiple lag orders in order to find the historical time points that are truly relevant at the current time point, so as to accurately fit the information exchange relationship between brain regions.

[0047] The following details the process by which this application comprehensively determines the target lag order in both the time and frequency domains.

[0048] For example, the EEG signal is initially fitted for each lag order to obtain the target lag order.

[0049] For example, using a first lag order, the EEG signals are fitted to the multidimensional vector corresponding to the first time point and the multidimensional vectors corresponding to multiple historical time points located before the first time point, to obtain a first connection matrix between the first time point and each of the multiple historical time points, wherein the number of the multiple historical time points is the first lag order, and the first lag order is any one of the multiple lag orders; the first connection matrix is ​​used to characterize the contribution of the target object's EEG signal at that historical time point to the EEG signal at the first time point when the EEG signal is fitted with the first lag order.

[0050] It is understood that the above-mentioned multiple historical time points are multiple time points located before the first time point among the multiple sampled time points. However, for the sake of easy distinction, this application refers to these time points as multiple historical time points.

[0051] Specifically, since brain activity at the current time point can be obtained by linearly combining brain activity at historical time points, the multidimensional vector corresponding to the first time point can be obtained by linearly combining multiple multidimensional vectors corresponding to multiple historical time points. Therefore, a multi-order linear model is constructed between the multidimensional vector corresponding to the first time point and the multiple multidimensional vectors corresponding to multiple historical time points.

[0052] For example, the above multi-order linear model can be represented by formula (2): Formula (2); in, Indicates the first point in time. Let represent the multidimensional vector corresponding to the first time point, h represent the h-th time point before the first time point, i.e., the h-th historical time point, and H be the first lag order mentioned above. This represents the multidimensional vector corresponding to the h-th historical time point. This represents the random noise corresponding to the first time point, i.e., the fitting error; yes and The regression coefficient matrix between them is used to analyze the regression coefficient matrix between them. Perform linear superposition, that is It is the h-th historical time point and the first time point The first connection matrix between them, where, The element in the i-th row and j-th column represents the EEG signal of the i-th brain region at the h-th historical time point, which is relevant to the prediction at the first time point. The contribution of the EEG signal from the j-th brain region is considered. This contribution reflects the intensity of information exchange between brain regions.

[0053] It can be seen that, in order to obtain the information exchange intensity between different brain regions at different times, it is only necessary to calculate the first connectivity matrix mentioned above to obtain the information exchange intensity between different brain regions at different times. Specifically, the least squares method is used to construct a recursive estimation equation corresponding to the above multi-order linear model. Based on the recursive estimation equation, a step-by-step recursive process is performed to obtain the first connectivity matrix between the first time point and each of the above multiple historical time points, that is, the recursive result... ,in, This represents the first connection matrix between the first historical time point and the first time point. This represents the first connection matrix between the Hth historical time point and the first time point.

[0054] Optionally, the above least squares method is recursive least squares with a forgetting factor (RLS with Forgetting Factor). The recursive process of using recursive least squares with a forgetting factor is existing technology and will not be described in detail.

[0055] Then, based on the multidimensional vector at the first time point and the first connection matrix between each historical time point, the fitting error corresponding to the first time point is determined when fitting the EEG signal with the first lag order.

[0056] For example, after recursively deriving the first connection matrix between the first time point and each historical time point, the multidimensional vectors of multiple historical time points are linearly superimposed using the first connection matrix to predict the multidimensional vector corresponding to the first time point. ,Right now .

[0057] Then, using the multidimensional vector corresponding to the first predicted time point Compared with the true multidimensional vector at the first time point ( ), calculate the fitting error at the first time point when fitting the EEG signal with the first lag order, i.e., the above-mentioned .

[0058] Furthermore, based on the fitting error corresponding to the first time point, the processing module determines the fitting error for each of the multiple time points when fitting the EEG signal with a first lag order. That is, similar to the first time point, EEG signals are fitted for each of the multiple time points to obtain the fitting error corresponding to each time point. Then, based on the fitting error for each time point, the number of time points, and the number of EEG channels, the processing module determines the Akaike information content corresponding to the first lag order. Specifically, the processing module combines the fitting errors for each time point to obtain a fitting error matrix, then obtains the covariance matrix of this fitting error matrix, and determines the Akaike information content corresponding to the first lag order based on the covariance matrix, the number of time points, and the number of EEG channels.

[0059] For example, the Akaike information content corresponding to the first lag order can be expressed by formula (3): Formula (3); in, This refers to the Akaike Information Criterion (AIC). S represents the Akaike information quantity corresponding to the first lag order, S represents the covariance matrix mentioned above, det represents the determinant operation, N represents the number of the multiple time points mentioned above, and M represents the number of EEG channels.

[0060] Further, the processing module determines the Akaike information quantity corresponding to each lag order based on the Akaike information quantity corresponding to the first lag order. Optionally, the method for obtaining the Akaike information quantity corresponding to each lag order is similar to the method for obtaining the Akaike information quantity corresponding to the first lag order, and will not be described again. Then, based on the Akaike information quantity corresponding to each lag order, the processing module determines the target lag order among the plurality of lag orders.

[0061] Optionally, based on the criterion of minimizing Akaike information content, the processing module directly uses the lag order corresponding to the minimum Akaike information content among multiple lag orders as the target lag order.

[0062] Optionally, since minimizing the Akaike information content only evaluates the stability and accuracy of fitting each lag order from the time dimension, the target lag order selected by the above-mentioned criterion of minimizing the Akaike information content only meets the requirements for accuracy and complexity in the time dimension, but does not evaluate the stability and accuracy of fitting EEG signals using the target lag order from the frequency domain dimension. Therefore, in order to take into account the frequency domain, the processing module selects multiple candidate lag orders from the multiple lag orders based on the Akaike information content corresponding to each lag order. For example, the processing module obtains the minimum Akaike information content among the multiple lag orders. Then, based on the preset adjustment range, the minimum Akaike information content is adjusted to obtain the reference Akaike information content, and the lag orders with Akaike information content less than the reference Akaike information content among the multiple lag orders are used as the multiple candidate lag orders. Among them, the multiple candidate lag orders can be expressed by formula (4): Formula (4); in, The set consisting of the above candidate lag orders. For preset adjustment range, This represents the minimum amount of Akaike information mentioned above. Let P be the Pth lag order among the aforementioned multiple lag orders.

[0063] Furthermore, the target EEG signal of the target object under each EEG channel is windowed multiple times to obtain multiple EEG signal segments of the target object under each EEG channel. That is, the target EEG signal is windowed multiple times using preset time windows. The reason for windowing is mainly to analyze the information exchange of the target object at different time periods.

[0064] Then, based on multiple EEG signal segments of the target object under the first window, the peak frequency of the target object under the first window is determined when fitting the EEG signal with the first candidate lag order. The first window is any one of multiple windows with multiple windowing operations; the first candidate lag order is any one of the multiple candidate lag orders; the peak frequency of the target object under the first window refers to the frequency point corresponding to the maximum information communication intensity of the target object's brain within the time period corresponding to the first window when fitting the EEG signal with the first candidate lag order; the multiple EEG signal segments correspond one-to-one with multiple EEG channels.

[0065] Optionally, the above calculation of the peak frequency of the target object within the first window can be interpreted as the EEG signal segment of each EEG channel within the first window being considered as the target EEG signal for each EEG channel when calculating the target frequency subsequently. Therefore, firstly, when fitting the EEG signal with the first candidate lag order, the first connection matrix between any time point within the first time window and historical time points before that time point is calculated. Then, a Fourier transform is performed based on the first connection matrix to obtain the second connection matrices corresponding to each time point and frequency point, respectively. The elements in the second connection matrix are then summed to obtain the information exchange intensity of the target object's brain at each frequency and each time point within the first time window. This determines the frequency point corresponding to the maximum information exchange intensity of the target object's brain within the time period corresponding to the first window; this frequency point is the peak frequency corresponding to the first window. Therefore, determining the peak frequency of the first window can refer to the method used to calculate the target frequency when fitting the EEG signal with the target lag order subsequently, and will not be described again.

[0066] Furthermore, based on the peak frequency of the target object in the first window when fitting the EEG signal with the first candidate hysteresis order, the processing module determines the peak frequency of the target object in each window when fitting the EEG signal with the first candidate hysteresis order. The method for obtaining the peak frequency of the target object in each window is similar to the method for obtaining the peak frequency of the target object in the first window, and will not be described again.

[0067] Then, the processing module determines the stability of the first candidate lag order based on the peak frequency of the target object in each window when fitting the EEG signal with the first candidate lag order. The stability is used to measure the stability of the first candidate lag order when fitting the EEG signal with the first candidate lag order, that is, to obtain the stability of fitting the first candidate lag order in the frequency domain dimension.

[0068] For example, the processing module obtains the brain communication intensity of the target object at the peak frequency of each window when fitting the EEG signal with the first candidate lag order. This is achieved by using a second connection matrix based on the target object's frequency points and peak frequencies within each window to obtain the brain communication intensity of the target object at the peak frequency of that window and at each time point within that window when fitting the EEG signal with the first candidate lag order. Then, the module averages the brain communication intensity of the target object at the peak frequency of that window and at each time point within that window when fitting the EEG signal with the first candidate lag order, obtaining the brain communication intensity of the target object at the peak frequency of each window. Finally, the module averages the brain communication intensities of the target object at the peak frequencies of multiple windows to obtain the average brain communication intensity of the target object at the first candidate lag order.

[0069] For example, the average information exchange intensity of the target subject's brain at the first candidate lag order can be expressed by formula (5): Formula (5); Where K represents the number of windows, k represents the k-th window, and P represents the first candidate order. This indicates the peak frequency of the target object in the k-th window when fitting the EEG signal with the first candidate order P. This indicates the intensity of information exchange in the target object's brain at the peak frequency of the k-th window. The average information exchange intensity of the target subject's brain at the first candidate lag order.

[0070] Further, the variance of the peak frequency of the target object in each window is determined when fitting the EEG signal with the first candidate hysteresis order. Finally, based on this variance and the aforementioned average information exchange intensity, the stability corresponding to the first candidate hysteresis order is obtained.

[0071] For example, the stability corresponding to the first candidate lag order can be expressed by formula (6): Formula (6); in, The stability level corresponding to the first candidate lag order. Var is the operation for calculating variance. They respectively represent the first candidate lag order. When fitting EEG signals, the target object's K peak frequencies are measured within K windows. Preset parameters to prevent the denominator from being zero. and These are the preset weighting coefficients.

[0072] Furthermore, the processing module determines the stability level corresponding to each candidate lag order based on the stability level corresponding to the first candidate lag order. The method of obtaining the stability level corresponding to each candidate lag order is similar to the method of determining the stability level corresponding to the first candidate lag order, and will not be described again.

[0073] Furthermore, the processing module determines the target lag order based on the stability of each candidate lag order, wherein the target lag order is the candidate lag order with the highest stability among the plurality of candidate lag orders.

[0074] At this point, the processing module has determined the optimal lag order. Therefore, the target lag order can be used to calculate the intensity of information exchange between different brain regions. Specifically, the processing module uses the first connectivity matrix between any time point and historical time points when fitting EEG signals using the target lag order to calculate the intensity of information exchange between different brain regions.

[0075] For example, the above processing module is also used to determine a frequency range based on the brain's rhythm. For example, the frequency range is 0~40Hz. Then, based on a preset frequency interval, frequency sampling is performed within the frequency range to obtain multiple frequency points. For example, if the frequency interval is 1Hz, 40 frequency points are obtained. Therefore, the frequency points mentioned in this application can also be called frequency sampling points; these two descriptions are similar and do not need to be distinguished.

[0076] It should be noted that, in the process of selecting candidate lag orders, a first connection matrix between each time point and a previous historical time point was already calculated when fitting the EEG signal with the target lag order. Therefore, based on the first connection matrix between the first time point and each historical time point when fitting the EEG signal with the target lag order, the processing module obtains a second connection matrix for the target object at the first time point and each frequency point when fitting the EEG signal with the target lag order. The second connection matrix is ​​used to characterize the information exchange intensity between different brain regions of the target object at the first time point and each frequency point when fitting the EEG signal with the target lag order.

[0077] For example, when the above processing module fits the EEG signal with the target lag order based on each of the above frequency points, it performs a Fourier transform on the first connection matrix between the first time point and each historical time point to obtain the frequency domain coefficient matrix of the target object at the first time point and each frequency point when fitting the EEG signal with the target lag order.

[0078] For example, the frequency domain coefficient matrix of the target object at the first time point and at each frequency point is represented by formula (7): Formula (7); Where U is the target lag order, and f represents a frequency point. When fitting EEG signals with the target lag order, the frequency domain coefficient matrix of the target object at the first time point and frequency point f is given, where j is the imaginary unit. For the preset time interval, Let h be the identity matrix, and h take values ​​from 1 to U.

[0079] Furthermore, when the above processing module fits the EEG signal with the target lag order, it inverts the frequency domain coefficient matrix of the target object at the first time point and at each frequency point to obtain the transfer function matrix of the target object at the first time point and at each frequency point when fitting the EEG signal with the target lag order.

[0080] For example, when fitting the EEG signal with the target lag order, the transfer function matrix of the target object at the first time point and at each frequency point can be represented by formula (8): Formula (8); in, When fitting EEG signals with the target lag order, the transfer function matrix of the target object at the first time point and frequency point f, the element in the i-th row and j-th column of the transfer function matrix, represents the information exchange intensity and direction between the i-th brain region and the j-th brain region at time point t and frequency point f when fitting EEG signals with the target lag order.

[0081] The direction of information exchange is determined by the sign of the element in the i-th row and j-th column. If the sign is greater than 0, the direction of information exchange is from the i-th brain region to the j-th brain region; if the sign is less than 0, the direction of information exchange is from the j-th brain region to the i-th brain region.

[0082] Furthermore, in the aforementioned processing module, when fitting the EEG signal with the target lag order, the elements in the transfer function matrix of the target object at the first time point and each frequency point are squared modulo-squared to obtain the second connectivity matrix of the target object at the first time point and each frequency point when fitting the EEG signal with the target lag order. It can be understood that after square-squared modulo-squared, the elements in the second connectivity matrix only represent intensity information; therefore, the second connectivity matrix is ​​used to characterize the intensity of information exchange between different brain regions of the target object at the first time point and each frequency point.

[0083] Furthermore, the aforementioned processing module is also used to obtain, based on the second connectivity matrix of the target object at the first time point and each frequency point when fitting the EEG signal with the target lag order, the information exchange intensity between different brain regions of the target object at each time point and each frequency point. The method for obtaining the second connectivity matrix corresponding to each time point is similar to that for obtaining the second connectivity matrix corresponding to the first time point, and will not be described again.

[0084] Then, further, the above-mentioned processing module is also used to determine the target frequency based on the information exchange intensity between different brain regions of the target object at each time point and each frequency point when fitting the EEG signal with the target lag order.

[0085] For example, when the above processing module fits the EEG signal with the target lag order, it averages the elements in the second connectivity matrix at each time point and each frequency point to obtain the information communication intensity of the target object's brain at each time point and each frequency point. Since the diagonal elements in the second connectivity matrix represent the information communication intensity between brain regions, not the information communication intensity between brain regions, the diagonal elements cannot reflect the information communication intensity between brain regions. Since the information communication of the entire brain is mainly caused by information communication between brain regions, in practical applications, it is only necessary to average the off-diagonal elements in the second connectivity matrix to obtain the information communication intensity of the target object's brain at each time point and each frequency point.

[0086] For example, the intensity of information exchange in the target subject's brain at each time point and at each frequency point can be represented by formula (9): Formula (9); in, The second connectivity matrix represents the brain of the target object at time point t and frequency point f, where M is the number of EEG channels, and i and j represent the elements in the i-th row and j-th column of the second connectivity matrix.

[0087] Furthermore, after determining the information exchange intensity of the target object's brain at each time point and frequency point, the processing module can construct a two-dimensional matrix of these information exchange intensities. The dimension of this two-dimensional matrix is ​​determined by the number of time points and the number of frequency points; that is, the rows of the two-dimensional matrix correspond to time points, and the columns correspond to frequency points. Then, the processing module determines the maximum value in this two-dimensional matrix and uses the frequency point corresponding to the maximum value as the target frequency, thus obtaining the target frequency for fitting the EEG signal with the target lag order. .

[0088] It should be noted that, at the aforementioned target frequency At this frequency, the information exchange intensity in the target subject's brain regions is the greatest, meaning the information exchange between brain regions is most frequent, and the brain regions are most active. However, because the information in the human brain and the communication between information are quite complex, and the noise signals in the human brain are also quite complex, although at the target frequency... The intensity of information exchange is greatest in the lower brain region. However, this does not directly indicate that... The question is whether the communication between brain regions in the target subject is truly effective information exchange or merely noise. Therefore, this application will not directly use the target frequency. Instead of setting the stimulation frequency of electrical stimulation during vagal nerve modulation, the aim is to further verify the target frequency. The effectiveness.

[0089] For example, the above processing module is further configured to determine a first ratio based on the intensity of information exchange between different brain regions of the target object at each time point and at the target frequency, wherein the first ratio is the proportion of brain region pairs of the target object that have bidirectional information exchange at the target frequency.

[0090] To understand two-way information exchange, the information sending end and information receiving end of this application are first defined.

[0091] For any frequency point and any time point, the bidirectional information exchange intensity between the i-th brain region and the j-th brain region can be obtained from the second connectivity matrix. This means obtaining the information exchange intensity between the i-th and j-th brain regions (i.e., the information exchange intensity when the i-th brain region transmits information to the j-th brain region), and the information exchange intensity between the j-th and i-th brain regions (i.e., the information exchange intensity when the j-th brain region transmits information to the i-th brain region). Then, based on the bidirectional information exchange intensity, the information sender and receiver in the i-th and j-th brain regions are determined.

[0092] For example, the difference between the information exchange intensities of bidirectional interaction is obtained; based on this difference, the information sender and receiver in the i-th and j-th brain regions are determined. For example, the difference between the information exchange intensities of bidirectional interaction between the i-th and j-th brain regions can be expressed by formula (10): Formula (10); in, This represents the intensity of information exchange between the i-th and j-th brain regions at frequency point f and time point t. This represents the intensity of information exchange between the j-th brain region and the i-th brain region at frequency point f and time point t. This represents the difference in the intensity of bidirectional information exchange between the i-th and j-th brain regions at frequency point f and time point t.

[0093] In response to If the value is greater than 0, then the i-th brain region is determined as the information transmitter and the j-th brain region as the information receiver; in response to If the value is less than 0, then the i-th brain region is determined as the information receiver and the j-th brain region is determined as the information sender.

[0094] For example, the above processing module determines the information exchange endpoint in each brain region pair at each time point and at the target frequency based on the information exchange intensity between each brain region pair at each time point and at the target frequency.

[0095] Optionally, brain regions are pre-paired to obtain multiple brain region pairs. Then, based on the information exchange intensity between each brain region pair at each time point and the target frequency, the processing module determines the information exchange endpoint within each brain region pair at each time point and the target frequency. The information exchange endpoint includes an information sender or an information receiver; the information sender is the brain region in the brain region pair that sends information, and the information receiver is the brain region in the brain region pair that receives information. The information exchange intensity between each brain region pair includes the information exchange intensity during bidirectional interaction between the two brain regions in each pair.

[0096] It should be noted that although this application only identifies the information sender or receiver in each brain region pair, in practical applications, both the information sender and receiver in each brain region pair can be identified simultaneously.

[0097] Specifically, each brain region pair includes two brain regions, namely a first brain region and a second brain region, and the information exchange intensity between each brain region pair includes a first information exchange intensity between the first and second brain regions in the pair, and a second information exchange intensity between the second brain region and the first brain region. Therefore, in response to the first information exchange intensity being greater than the second information exchange intensity, the first brain region of each brain region pair is used as the information sender, and the second brain region of each brain region pair is used as the information receiver; in response to the first information exchange intensity being less than the second information exchange intensity, the second brain region of each brain region pair is used as the information sender, and the first brain region of each brain region pair is used as the information receiver.

[0098] Furthermore, based on the information exchange endpoints in each brain region pair of the target object at each time point and the target frequency, target brain region pairs with bidirectional information exchange are identified among all brain region pairs of the target object. These target brain region pairs are those whose information exchange endpoints are not completely identical at the target frequency and multiple time points. That is, the information exchange endpoints of each brain region pair are traversed at the target frequency and each time point, and it is determined whether the multiple information exchange endpoints of the brain region pair are completely identical at multiple time points. If they are not completely identical, the brain region pair is considered a target brain region pair.

[0099] For example, if the information exchange end is the information sending end, and this refers to a brain region pair consisting of the i-th and j-th brain regions, at the aforementioned target frequency, if the information sending end of this brain region pair is the i-th brain region at time point t1, but the information sending end is the j-th brain region at time point t2, then it is determined that the information exchange end of this brain region pair is different at time points t1 and t2. Therefore, the brain region pair consisting of the i-th and j-th brain regions is a target brain region pair.

[0100] Finally, the processing module uses the ratio of the number of target brain region pairs to the total number of brain region pairs as the first ratio.

[0101] For example, the first ratio mentioned above can be expressed by formula (11): Formula (11); in, The first ratio mentioned above, The number of target brain regions, The number of brain region pairs, The target frequency is as described above.

[0102] It is understandable that the aforementioned target brain region pairs are brain region pairs that actually exchange information at the target frequency; that is, target brain region pairs are brain region pairs that effectively exchange information among all brain region pairs. Therefore, the aforementioned processing module is further used to determine a second stimulation frequency in the next electrical stimulation cycle adjacent to the auditory stimulation cycle based on the first ratio, the first stimulation frequency, and the target frequency, wherein the first stimulation frequency is the stimulation frequency at which the vagus nerves of both ears of the target object are electrically stimulated in the previous electrical stimulation cycle adjacent to the auditory stimulation cycle.

[0103] For example, in response to the first ratio being greater than or equal to the first threshold, it indicates that the information exchange between brain regions of the target object is truly effective at the target frequency. That is, the intensity of information exchange between brain regions of the target object is caused by truly effective information exchange between brain regions, not by noise. Therefore, at the target frequency, there is truly effective information exchange between brain regions of the target object, and the above processing module determines the second stimulation frequency based on the target frequency. For example, the target frequency can be directly used as the second stimulation frequency; or, the second stimulation frequency can be determined based on the target frequency and a preset frequency step size. For example, the target frequency can be added to the preset frequency step size to obtain the second stimulation frequency; or, the target frequency can be subtracted from the preset frequency step size to obtain the second stimulation frequency. Whether to add or subtract the preset frequency step size can be set according to actual needs, and this application does not limit this.

[0104] For example, in response to the first ratio being less than the first threshold, it indicates that at the target frequency, the information exchange intensity in the brain regions of the target object is caused by noise, rather than by truly effective information exchange between brain regions. Therefore, even though the information exchange intensity in the brain regions is very high at the target frequency, the target frequency cannot be used as the second stimulation frequency. Thus, the first stimulation frequency is still used as the second stimulation frequency, that is, the stimulation frequency of the previous stimulation cycle is maintained.

[0105] Finally, the aforementioned processing module sends the second stimulation frequency to the electrical stimulation module. Accordingly, the electrical stimulation module is used in the next electrical stimulation cycle to electrically stimulate the vagus nerves of both ears of the target subject at the second stimulation frequency. Specifically, the electrical stimulation module applies electrical stimulation to the vagus nerves of both ears of the target subject at the second stimulation frequency through electrical stimulation electrodes worn on both ears, thereby performing neuromodulation on the target subject.

[0106] As can be seen in this embodiment, when performing vagus nerve modulation on the target subject (i.e., the patient), the EEG signals of each EEG channel of the target subject during novel stimulation are used to calculate the information exchange intensity between different brain regions of the target subject at each time point and at each frequency point. Then, using the information exchange intensity between different brain regions of the target subject, the target frequency corresponding to the target subject is determined, that is, the frequency point corresponding to the maximum information exchange intensity of the target subject's brain, which is to say, the dominant frequency of the target subject is determined. Finally, using the information exchange intensity between different brain regions of the target subject at each time point and at the target frequency, a first proportion of brain region pairs with bidirectional information exchange at the target frequency is determined. Using this first proportion, it is possible to further verify whether the determined target frequency is the true dominant frequency, avoid the interference of brain region noise on the dominant frequency, and thus further ensure that the dominant frequency (i.e., the target frequency) is accurate and effective. Finally, using the first ratio, the first stimulation frequency of the previous electrical stimulation cycle, and the target frequency, the second stimulation frequency for the next electrical stimulation cycle is determined. The second stimulation frequency determined in this way is adapted to the target object and is targeted. Using the second stimulation frequency to electrically stimulate the vagus nerve of both ears of the target object can improve the control effect of the vagus nerve regulation of the target object.

[0107] See Figure 4 , Figure 4 This is a flowchart illustrating a transauricular vagus nerve modulation method provided in an embodiment of this application. The method is applied to the aforementioned bilateral transauricular vagus nerve modulation device. The method includes, but is not limited to, the following steps: S401: Apply multiple novel stimuli to the target object within the auditory stimulation cycle.

[0108] S402: Acquire the EEG signal corresponding to each EEG channel when each novel stimulus is applied to the target object.

[0109] S403: Based on the EEG signal corresponding to each EEG channel when applying each novel stimulus to the target object, determine the information exchange intensity between different brain regions of the target object at each time point and at each frequency point when fitting the EEG signal with a target lag order.

[0110] S404: Based on the information exchange intensity between different brain regions of the target object at each time point and at each frequency point when fitting the EEG signal with the target lag order, determine the target frequency when fitting the EEG signal with the target lag order, wherein the target frequency is the frequency point corresponding to the maximum information exchange intensity of the target object's brain when fitting the EEG signal with the target lag order.

[0111] S405: Based on the intensity of information exchange between different brain regions of the target object at each time point and at the target frequency, a first proportion is determined, wherein the first proportion is the percentage of brain region pairs of the target object that have bidirectional information exchange at the target frequency.

[0112] S406: Based on the first ratio, the first stimulation frequency, and the target frequency, determine the second stimulation frequency in the next electrical stimulation cycle adjacent to the auditory stimulation cycle, wherein the first stimulation frequency is the stimulation frequency when the vagus nerve of both ears of the target object is electrically stimulated in the previous electrical stimulation cycle adjacent to the auditory stimulation cycle.

[0113] S407: In the next electrical stimulation cycle, the auricular vagus nerves of both ears of the target subject are electrically stimulated at the second stimulation frequency.

[0114] As can be seen in this embodiment, when performing vagus nerve modulation on the target subject (i.e., the patient), the EEG signals of each EEG channel of the target subject during novel stimulation are used to calculate the information exchange intensity between different brain regions of the target subject at each time point and at each frequency point. Then, using the information exchange intensity between different brain regions of the target subject, the target frequency corresponding to the target subject is determined, that is, the frequency point corresponding to the maximum information exchange intensity of the target subject's brain, which is to say, the dominant frequency of the target subject is determined. Finally, using the information exchange intensity between different brain regions of the target subject at each time point and at the target frequency, a first proportion of brain region pairs with bidirectional information exchange at the target frequency is determined. Using this first proportion, it is possible to further verify whether the determined target frequency is the true dominant frequency, avoid the interference of brain region noise on the dominant frequency, and thus further ensure that the dominant frequency (i.e., the target frequency) is accurate and effective. Finally, using the first ratio, the first stimulation frequency of the previous electrical stimulation cycle, and the target frequency, the second stimulation frequency for the next electrical stimulation cycle is determined. The second stimulation frequency determined in this way is adapted to the target object and is targeted. Using the second stimulation frequency to electrically stimulate the vagus nerve of both ears of the target object can improve the control effect of the vagus nerve regulation of the target object.

[0115] See Figure 5 , Figure 5 This is a schematic diagram of an electronic device provided in an embodiment of this application. The electronic device 500 can be the aforementioned bilateral transauricular vagus nerve modulation device.

[0116] Electronic device 500 includes a memory 501, a processor 502, a communication interface 503, and a bus 504. The memory 501, processor 502, and communication interface 503 are interconnected via the bus 504.

[0117] The memory 501 can be a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 501 can store programs; when the electronic device 500 is the aforementioned bilateral transauricular vagus nerve modulation device, when the program stored in the memory 501 is executed by the processor 502, the processor 502 and the communication interface 503 are used to execute the various steps performed by the bilateral transauricular vagus nerve modulation device in the transauricular vagus nerve modulation method of this application embodiment.

[0118] The processor 502 may be a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), graphics processing unit (GPU), or one or more integrated circuits, used to execute relevant programs to implement the auricular vagus nerve modulation method in the method embodiments of this application.

[0119] The processor 502 can also be an integrated circuit chip with signal processing capabilities. In implementation, each step of the vagus nerve modulation method of this application can be completed by the integrated logic circuitry in the hardware of the processor 502 or by software instructions. The processor 502 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 501, and processor 502 reads the information in memory 501 to execute the various steps of the vagus nerve modulation method.

[0120] The communication interface 503 can be a transceiver device such as a transceiver to enable communication between the electronic device 500 and other devices or communication networks. The communication interface 503 can also be an input-output interface to enable data transmission between the electronic device 500 and input-output devices, including but not limited to keyboards, mice, displays, USB flash drives, and hard drives. For example, the processor 502 can receive signals through the communication interface 503.

[0121] Bus 504 may include a pathway for transmitting information between various components of device electronics 500 (e.g., memory 501, processor 502, communication interface 503).

[0122] It should be noted that, although Figure 5 The illustrated electronic device 500 only shows the memory, processor, and communication interface. However, those skilled in the art should understand that in specific implementations, the electronic device 500 may also include other devices necessary for normal operation. Furthermore, depending on specific needs, those skilled in the art should understand that the electronic device 500 may also include hardware devices for implementing other additional functions. Moreover, those skilled in the art should understand that the electronic device 500 may only include the devices necessary for implementing the embodiments of this application, and may not necessarily include... Figure 5 All the devices shown.

[0123] This application also provides a computer-readable storage medium storing a computer program that is executed by a processor to implement some or all of the steps of any of the ear vagus nerve modulation methods described in the above method embodiments.

[0124] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the ear vagus nerve modulation methods described in the above method embodiments.

[0125] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0126] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0127] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0128] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0129] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0130] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0131] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0132] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A bilateral transauricular vagus nerve modulation device, characterized in that, include: Auditory stimulation module, processing module, and electrical stimulation module; The auditory stimulation module is used to apply multiple novel stimuli to the target object within the auditory stimulation cycle; The processing module is used to acquire the EEG signal corresponding to each EEG channel when each novel stimulus is applied to the target object; The processing module is further configured to determine, based on the EEG signal corresponding to each EEG channel when applying each novel stimulus to the target object, the intensity of information exchange between different brain regions of the target object at each time point and at each frequency point when fitting the EEG signal with a target lag order. The processing module is further configured to determine the target frequency for fitting the EEG signal with the target lag order based on the information exchange intensity between different brain regions of the target object at each time point and at each frequency point when fitting the EEG signal with the target lag order, wherein the target frequency is the frequency point corresponding to the maximum information exchange intensity of the brain of the target object when fitting the EEG signal with the target lag order. The processing module is further configured to determine a first ratio based on the intensity of information exchange between different brain regions of the target object at each time point and at the target frequency, wherein the first ratio is the proportion of brain region pairs of the target object that have bidirectional information exchange at the target frequency; The processing module is further configured to determine a second stimulation frequency in the next electrical stimulation cycle adjacent to the auditory stimulation cycle based on the first ratio, the first stimulation frequency, and the target frequency, wherein the first stimulation frequency is the stimulation frequency when the vagus nerve of both ears of the target object is electrically stimulated in the previous electrical stimulation cycle adjacent to the auditory stimulation cycle. The electrical stimulation module is used to electrically stimulate the vagus nerves of both ears of the target subject at the second stimulation frequency in the next electrical stimulation cycle.

2. The device according to claim 1, characterized in that, In determining the first proportion based on the intensity of information exchange between different brain regions of the target object at each time point and the target frequency, the processing module is specifically used for: Based on the intensity of information exchange between different brain regions of the target object at each time point and at the target frequency, the intensity of information exchange between each pair of brain regions in the multiple pairs of brain regions of the target object is determined. Based on the information exchange intensity between each brain region pair at each time point and the target frequency, the information exchange end in each brain region pair at each time point and the target frequency is determined, wherein the information exchange end includes an information sending end or an information receiving end, the information sending end is the brain region in the brain region pair that sends information, and the information receiving end is the brain region in the brain region pair that receives information; The first ratio is determined based on the information exchange endpoints in each brain region pair of the target object at each time point and at the target frequency.

3. The device according to claim 2, characterized in that, Based on the information exchange points in each brain region pair of the target object at each time point and the target frequency, determining the first proportion aspect, the processing module is specifically used for: Based on the information communication endpoints in each brain region pair of the target object at each time point and at the target frequency, target brain region pairs with bidirectional information communication are identified among all brain region pairs of the target object. The target brain region pair is a brain region pair among all brain region pairs whose information communication endpoints are not completely the same at the target frequency and multiple time points. The ratio of the number of target brain region pairs to the total number of brain region pairs is used as the first ratio.

4. The device according to claim 3, characterized in that, Each brain region pair includes a first brain region and a second brain region, and the information exchange intensity between each brain region pair includes a first information exchange intensity between the first brain region and the second brain region in the brain region pair, and a second information exchange intensity between the second brain region and the first brain region. In determining the communication endpoints in each brain region pair at each time point and at the target frequency based on the intensity of communication between each brain region pair at each time point and at the target frequency, the processing module is specifically used for: In response to the first information exchange intensity being greater than the second information exchange intensity, the first brain region of each brain region pair is used as the information sending end, and the second brain region of each brain region pair is used as the information receiving end. In response to the first information exchange intensity being less than the second information exchange intensity, the second brain region of each brain region pair is used as the information sending end, and the first brain region of each brain region pair is used as the information receiving end.

5. The device according to any one of claims 1-4, characterized in that, In determining the second stimulation frequency in the next electrical stimulation cycle adjacent to the auditory stimulation cycle based on the first ratio, the first stimulation frequency, and the target frequency, the processing module is specifically configured to: In response to the first ratio being greater than or equal to the first threshold, the second stimulation frequency is determined based on the target frequency; In response to the first ratio being less than the first threshold, the first stimulation frequency is used as the second stimulation frequency.

6. The device according to claim 1, characterized in that, When determining the EEG signal fitting with a target lag order based on the EEG signal corresponding to each EEG channel when applying each novel stimulus to the target object, before determining the information exchange intensity between different brain regions of the target object at each time point and each frequency point, the processing module is further configured to: When the target object is subjected to the multiple novel stimuli, the average of the multiple EEG signals corresponding to each EEG channel is calculated to obtain the target EEG signal of the target object under each EEG channel. The target object's multiple EEG signals under multiple EEG channels are combined with the EEG signals at the same time point to form a multi-dimensional vector corresponding to the time point, thus obtaining a multi-dimensional vector corresponding to each time point. Using a first lag order, the EEG signals are fitted to the multidimensional vector corresponding to the first time point and the multidimensional vectors corresponding to multiple historical time points preceding the first time point, to obtain a first connection matrix between the first time point and each of the multiple historical time points. The number of the multiple historical time points is the first lag order, which is any one of multiple lag orders. The first connection matrix is ​​used to characterize the contribution of the target object's EEG signal at that historical time point to the EEG signal at the first time point when the EEG signal is fitted using the first lag order. Based on the multidimensional vector at the first time point and the first connection matrix between each historical time point, the fitting error corresponding to the first time point is determined when fitting the EEG signal with the first lag order. Based on the fitting error corresponding to the first time point, the fitting error corresponding to each time point is determined when fitting the EEG signal with the first lag order. Based on the fitting error at each time point, the number of time points, and the number of EEG channels, the Akaike information quantity corresponding to the first lag order is determined. Based on the Akaike information quantity corresponding to the first lag order, determine the Akaike information quantity corresponding to each lag order. Based on the Akaike information quantity corresponding to each lag order, the target lag order among the plurality of lag orders is determined.

7. The device according to claim 6, characterized in that, In determining the target lag order among the plurality of lag orders based on the Akaike information content corresponding to each lag order, the processing module is specifically used for: Based on the Akaike information quantity corresponding to each lag order, multiple candidate lag orders are selected from the multiple lag orders; The target EEG signal of the target object under each EEG channel is windowed multiple times to obtain multiple EEG signal segments of the target object under each EEG channel; Based on multiple EEG signal segments of the target object under the first window, the peak frequency of the target object under the first window is determined when fitting the EEG signal with a first candidate lag order. The first window is any one of multiple windows that are windowed multiple times. The first candidate lag order is any one of the multiple candidate lag orders. The peak frequency of the target object in the first window refers to the frequency point corresponding to the maximum information communication intensity of the target object's brain within the time period corresponding to the first window when fitting the EEG signal with the first candidate hysteresis order. The multiple EEG signal segments correspond one-to-one with multiple EEG channels; Based on the peak frequency of the target object in the first window when fitting the EEG signal with the first candidate lag order, the peak frequency of the target object in each window when fitting the EEG signal with the first candidate lag order is determined. Based on the peak frequency of the target object in each window when fitting the EEG signal with the first candidate lag order, the stability degree corresponding to the first candidate lag order is determined, wherein the stability degree is used to measure the stability of the first candidate lag order when fitting the EEG signal with the first candidate lag order. Based on the stability level corresponding to the first candidate lag order, determine the stability level corresponding to each candidate lag order; Based on the stability of each candidate lag order, the target lag order is determined, wherein the target lag order is the candidate lag order with the highest stability among the plurality of candidate lag orders.

8. The device according to claim 7, characterized in that, In determining the EEG signal fitting with a target lag order based on the EEG signal corresponding to each EEG channel when applying each novel stimulus to the target object, the processing module is specifically used for, regarding the information exchange intensity between different brain regions of the target object at each time point and each frequency point: Based on the first connection matrix between the first time point and each historical time point when fitting the EEG signal with the target lag order, a second connection matrix of the target object at the first time point and each frequency point when fitting the EEG signal with the target lag order is obtained. The second connection matrix is ​​used to characterize the intensity of information exchange between different brain regions of the target object at the first time point and each frequency point when fitting the EEG signal with the target lag order; Based on the second connectivity matrix of the target object at the first time point and each frequency point when fitting the EEG signal with the target lag order, the information exchange intensity between different brain regions of the target object at each time point and each frequency point when fitting the EEG signal with the target lag order is obtained.

9. 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, such that the electronic device performs the following steps: Apply multiple novel stimuli to the target object within the auditory stimulation cycle; Acquire the EEG signal corresponding to each EEG channel when each novel stimulus is applied to the target object; Based on the EEG signals corresponding to each EEG channel when applying each novel stimulus to the target object, the intensity of information exchange between different brain regions of the target object is determined at each time point and at each frequency point when fitting the EEG signals with the target lag order. Based on the information exchange intensity between different brain regions of the target object at each time point and at each frequency point when fitting the EEG signal with the target lag order, the target frequency when fitting the EEG signal with the target lag order is determined, wherein the target frequency is the frequency point corresponding to the maximum information exchange intensity of the brain of the target object when fitting the EEG signal with the target lag order. Based on the intensity of information exchange between different brain regions of the target object at each time point and at the target frequency, a first proportion is determined, wherein the first proportion is the percentage of brain region pairs of the target object that have bidirectional information exchange at the target frequency. Based on the first ratio, the first stimulation frequency, and the target frequency, a second stimulation frequency is determined in the next electrical stimulation cycle adjacent to the auditory stimulation cycle, wherein the first stimulation frequency is the stimulation frequency when the vagus nerve of both ears of the target object is electrically stimulated in the previous electrical stimulation cycle adjacent to the auditory stimulation cycle. In the next electrical stimulation cycle, the vagus nerves of both ears of the target subject are electrically stimulated at the second stimulation frequency.

10. 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: Apply multiple novel stimuli to the target object within the auditory stimulation cycle; Acquire the EEG signal corresponding to each EEG channel when each novel stimulus is applied to the target object; Based on the EEG signals corresponding to each EEG channel when applying each novel stimulus to the target object, the intensity of information exchange between different brain regions of the target object is determined at each time point and at each frequency point when fitting the EEG signals with the target lag order. Based on the information exchange intensity between different brain regions of the target object at each time point and at each frequency point when fitting the EEG signal with the target lag order, the target frequency when fitting the EEG signal with the target lag order is determined, wherein the target frequency is the frequency point corresponding to the maximum information exchange intensity of the brain of the target object when fitting the EEG signal with the target lag order. Based on the intensity of information exchange between different brain regions of the target object at each time point and at the target frequency, a first proportion is determined, wherein the first proportion is the percentage of brain region pairs of the target object that have bidirectional information exchange at the target frequency. Based on the first ratio, the first stimulation frequency, and the target frequency, a second stimulation frequency is determined in the next electrical stimulation cycle adjacent to the auditory stimulation cycle, wherein the first stimulation frequency is the stimulation frequency when the vagus nerve of both ears of the target object is electrically stimulated in the previous electrical stimulation cycle adjacent to the auditory stimulation cycle. In the next electrical stimulation cycle, the vagus nerves of both ears of the target subject are electrically stimulated at the second stimulation frequency.

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