CN116491960B

CN116491960B - Brain transient monitoring device, electronic device, and storage medium

Info

Publication number: CN116491960B
Application number: CN202310770534.1A
Authority: CN
Inventors: 白洋; 冯珍
Original assignee: First Affiliated Hospital of Nanchang University
Current assignee: First Affiliated Hospital of Nanchang University
Priority date: 2023-06-28
Filing date: 2023-06-28
Publication date: 2023-09-19
Anticipated expiration: 2043-06-28
Also published as: CN116491960A

Abstract

The application discloses a brain transient monitoring device, an electronic device and a storage medium. Collecting a plurality of original brain electrical signals of an object to be monitored at the current moment; tracing analysis is carried out on a plurality of original brain electrical signals based on the recombination matrix, so as to obtain a plurality of first brain power supply signals; intercepting brain electrical signals to be analyzed corresponding to the current moment from each first brain electrical source signal to obtain a plurality of brain electrical signals to be analyzed; processing a plurality of electroencephalogram signals to be analyzed to obtain a first delay autocovariance matrix; determining a plurality of first similarities between the first delay autocovariance matrix and a plurality of preset delay autocovariance matrices; determining the probability of various brain transients at the current moment according to the first similarity, the brain transients at the last moment, the transition probability matrixes of various brain transient states and the initial state probabilities of various brain transients; and determining the brain transient state at the current moment according to the probability of being in various brain transient states at the current moment.

Description

Brain transient monitoring device, electronic device, and storage medium

Technical Field

The application relates to the technical field of medical treatment, in particular to brain transient monitoring equipment, electronic equipment and a storage medium.

Background

Human brain is a complex dynamically changing nonlinear system, and since spontaneous brain activities can be presented in regular states and these states are closely related to the mechanisms and development of brain diseases, real-time monitoring of brain activity states is of great importance for evaluation and treatment of brain diseases.

The brain state monitoring technology based on brain electricity which is most widely applied clinically at present is a video brain electricity technology aiming at epilepsy and an anesthesia monitoring technology. The video brain electrical technology combines video recording with brain electrical, synchronously observes the behavior characteristics and brain electrical characteristics of epileptic seizures, and further determines the epileptic type. However, the technology is only limited to data acquisition, and the analysis of the brain state requires a neurologist with abundant experience to perform naked eye judgment in an off-line state, and the monitoring is affected by manual experience, so that the monitoring precision is low, the real-time monitoring of the brain state cannot be performed, and the on-line monitoring of the brain state cannot be performed. Anesthesia monitoring techniques typically evaluate the depth of anesthesia in real-time by calculation of brain electrical and guide anesthesiologists in adjusting the anesthetic drugs. However, the technology only monitors and calculates forehead electroencephalogram, does not evaluate the whole brain state, and only utilizes electroencephalogram data in a period of time to analyze the brain state, so that the brain state monitoring precision is low, and the brain state cannot be monitored in real time.

Therefore, how to monitor the brain state of the brain on line in real time and improve the monitoring accuracy of the brain state of the brain are technical problems to be solved currently.

Disclosure of Invention

The embodiment of the application provides a brain transient monitoring device, an electronic device and a storage medium, which improve the accuracy of brain transient monitoring and can monitor the brain state on line in real time by combining the parameters of off-line processing with the brain transient at the last moment.

In a first aspect, an embodiment of the present application provides a brain transient monitoring device, where the brain transient monitoring device includes an image acquisition module, an offline processing module, an electroencephalogram acquisition module, and a brain transient analysis module;

the image acquisition module is used for acquiring an original nuclear magnetic resonance image of the brain of the object to be monitored;

the electroencephalogram acquisition module is used for acquiring a plurality of off-line electroencephalograms of the object to be monitored on a plurality of channels in a preset time period;

the off-line processing module is used for obtaining a three-dimensional brain model of the object to be monitored based on the original nuclear magnetic resonance image;

acquiring a plurality of cortical grids in the three-dimensional brain model; aligning the positions of a plurality of electrodes used for acquiring brain electrical signals on the channels with the positions of the cortex grids in the brain of the object to be monitored to obtain a Lead-field matrix corresponding to the object to be monitored;

Acquiring covariance matrixes of the plurality of off-line electroencephalogram signals;

determining a reassembly matrix based on the covariance matrix and the Lead-field matrix;

performing inverse decomposition on the plurality of off-line electroencephalogram signals based on the Lead-field matrix to obtain a plurality of cortex signals corresponding to the cortex grids; dividing the three-dimensional brain model into a plurality of source sub-groups;

performing weighted mapping on the cortex signals to obtain a plurality of second brain power supply signals corresponding to the source electrodes;

windowing each second brain power supply signal for a plurality of times based on a preset time window so as to divide each second brain power supply signal and obtain a plurality of sub brain power supply signals corresponding to each second brain power supply signal;

obtaining a plurality of preset delay autocovariance matrixes, a state transition probability matrix among a plurality of brain transients and initial state probabilities corresponding to the plurality of brain transients based on a plurality of sub-brain power signals corresponding to each second brain power signal, wherein each preset delay autocovariance matrix is used for representing one brain transient;

the electroencephalogram acquisition module is used for acquiring a plurality of original electroencephalograms of an object to be monitored from a plurality of channels at the current moment;

The brain transient analysis module is used for carrying out traceability analysis on the plurality of original brain electrical signals based on the recombination matrix to obtain a plurality of first brain power supply signals;

intercepting brain electrical signals to be analyzed corresponding to the current moment from each first brain electrical source signal to obtain a plurality of brain electrical signals to be analyzed;

processing the plurality of electroencephalogram signals to be analyzed to obtain a first delay autocovariance matrix;

determining the similarity between the first delay autocovariance matrix and the plurality of preset delay autocovariance matrices to obtain a plurality of first similarities;

determining the probability of the object to be monitored in various brain transients at the current moment according to the first similarity, the brain transient of the object to be monitored at the last moment, the state transition probability matrix and the initial state probability;

and determining the brain transient state of the object to be monitored at the current moment according to the probability that the current moment is in various brain transient states.

In a second aspect, an embodiment of the present application provides a brain transient monitoring method, where the method is applied to the brain transient monitoring device, and the brain transient monitoring device includes an image acquisition module, an offline processing module, an electroencephalogram acquisition module, and a brain transient analysis module; the method comprises the following steps:

Acquiring an original nuclear magnetic resonance image of the brain of a subject to be monitored;

collecting a plurality of off-line electroencephalogram signals of the object to be monitored on a plurality of channels within a preset time period;

based on the original nuclear magnetic resonance image, obtaining a three-dimensional brain model of the object to be monitored;

collecting a plurality of original electroencephalograms of an object to be monitored from a plurality of channels at the current moment;

performing traceability analysis on the plurality of original brain electrical signals based on the recombination matrix to obtain a plurality of first brain power supply signals;

In a third aspect, the present application provides an electronic device comprising: a processor and a memory, the processor being connected to the memory, the memory being for storing a computer program, the processor being for executing the computer program stored in the memory to cause the electronic device to perform the method as described in the second aspect.

In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program that causes a computer to perform the method according to the second aspect.

In a fifth aspect, the present application provides a computer program product comprising a non-transitory computer readable storage medium storing a computer program, the computer being operable to cause a computer to perform the method of the second aspect.

The embodiment of the application has the following beneficial effects:

it can be seen that in the embodiment of the application, firstly, a recombination matrix from an electroencephalogram signal to a brain power supply signal, a preset delay autocovariance matrix for representing brain transients, a state transition probability matrix among various brain transients and initial state probabilities of various brain transients are calculated offline, so that when real-time online monitoring is performed, the recombination matrix calculated offline can be utilized to directly trace a plurality of original brain power supply signals acquired online, and a plurality of first brain power supply signals are obtained; and intercepting the brain electrical signals to be analyzed corresponding to the current moment from each first brain electrical signal to obtain a plurality of brain electrical signals to be analyzed, and obtaining a first delay autocovariance matrix corresponding to the plurality of brain electrical signals to be analyzed. Then, a plurality of first similarities are determined directly using a plurality of preset delay autocovariance matrices. Finally, directly determining the probability that the object to be monitored is in various brain transients at the current moment based on a plurality of first similarities, the brain transients of the object to be monitored at the last moment, a state transition probability matrix among various brain transients and initial state probabilities corresponding to the various brain transients, and further determining the brain state at the current moment. Most of complex parameters monitored online are well calculated offline, so that the complexity of online calculation is greatly reduced, and the brain state at the current moment can be rapidly calculated during online monitoring, thereby realizing real-time online monitoring of the brain state of the brain. And when determining the brain transient at the current moment, the similarity between the brain electrical signal at the current moment and various brain transients is not only utilized, but also the brain transient at the last moment and the transition probabilities among various brain transients are combined, so that the brain transient at the current moment can be accurately determined, and the monitoring precision of the brain transient is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic diagram of a scenario for brain transient monitoring according to an embodiment of the present application;

fig. 2 is a schematic diagram of a brain transient monitoring device according to an embodiment of the present application;

fig. 3 is a schematic diagram of acquiring a plurality of sub-brain power signals corresponding to each second brain power signal based on a preset time window according to an embodiment of the present application;

FIG. 4 is a schematic diagram of a second time-delay autocovariance matrix for each time window according to an embodiment of the present application;

FIG. 5 is a schematic diagram of brain transient monitoring according to an embodiment of the present application;

fig. 6 is a schematic flow chart of a brain transient monitoring method according to an embodiment of the present application;

fig. 7 is a schematic diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

The terms "first," "second," "third," and "fourth" and the like in the description and in the claims and drawings are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, result, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

Referring to fig. 1, fig. 1 is a schematic view of a scene of brain transient monitoring according to an embodiment of the present application.

As shown in fig. 1, the brain transient monitoring apparatus acquires a plurality of original brain electrical signals of a subject to be monitored from a plurality of channels at the present moment by electrodes (circles of the brain cortex in fig. 1) placed on the brain cortex of the subject to be monitored; and then, performing traceability analysis on the plurality of original brain electrical signals based on the recombination matrix obtained through offline processing to obtain a plurality of first brain electrical signals. Then, intercepting brain electrical signals to be analyzed corresponding to the current moment from each first brain electrical source signal to obtain a plurality of brain electrical signals to be analyzed; processing the plurality of electroencephalogram signals to be analyzed to obtain a first delay autocovariance matrix; determining the similarity between the first delay autocovariance matrix and a plurality of preset delay autocovariance matrices to obtain a plurality of first similarities, wherein each preset delay autocovariance matrix is used for representing a brain transient; determining the probability of the object to be monitored in various brain transients at the current moment according to the first similarities, the brain transients of the object to be monitored at the last moment, a state transition probability matrix among various brain transients and initial state probabilities corresponding to the various brain transients; and determining the brain transient state of the object to be monitored at the current moment according to the probability that the current moment is in various brain transient states. Finally, the brain transient state of the object to be monitored at the current moment can be displayed on a brain transient state monitoring device display interface.

It should be noted that the brain transients referred to in this application may also be referred to as brain states, which are consistent in nature and may not be distinguishable.

Referring to fig. 2, fig. 2 is a schematic diagram of a brain transient monitoring device according to an embodiment of the present application. As shown in fig. 2, the brain transient monitoring apparatus includes: the device comprises an electroencephalogram acquisition module, a brain transient analysis module, an image acquisition module and an off-line processing module.

In order to facilitate understanding of the technical solution of the present application, an offline processing procedure of the present application will be explained and described first.

The offline processing of the present application is primarily intended to prepare parameters required for monitoring of the brain transient. Wherein, the parameters required for the brain transient at least include: the method comprises the steps of reorganizing a matrix, a preset delay autocovariance matrix corresponding to each brain transient, a state transition probability matrix among various brain transients and initial state probabilities corresponding to the various brain transients.

The off-line process of the present application is described below with reference to the accompanying drawings.

Illustratively, the image acquisition module is for acquiring a raw magnetic resonance image (Magnetic Resonance Imaging, MRI) of the brain of the subject to be monitored.

The electroencephalogram acquisition module is used for acquiring a plurality of off-line electroencephalograms of the object to be monitored on a plurality of channels in a preset time period; the preset time period may be a time period before the brain transient on-line monitoring of the subject to be monitored. For example, an electrode (i.e. an electroencephalogram electrode for acquiring electroencephalogram signals) is installed on the brain of the object to be monitored, and brain transient monitoring is to be performed on the object to be monitored at the time t, so that a plurality of electroencephalogram signals of the object to be monitored can be acquired within a preset time period before the time t and used as a plurality of off-line electroencephalogram signals for off-line analysis. Of course, the preset time period may be any time period before the brain transient monitoring is performed, for example, any preset time period before the time t, and the preset time period is not specifically limited by the present application. And the off-line processing module is used for determining a plurality of preset delay autocovariance matrixes, a reorganization matrix, a state transition probability matrix and initial state probabilities based on the nuclear magnetic resonance image and a plurality of off-line electroencephalogram signals.

Specifically, the offline processing module obtains the three-dimensional brain model of the object to be monitored based on the original nuclear magnetic resonance image. Specifically, the offline processing module re-slices the original nuclear magnetic resonance image to obtain a target nuclear magnetic resonance image. Then, the off-line processing module segments the target nuclear magnetic resonance image to obtain brain tissues, skull and scalp, and establishes a brain model based on the target nuclear magnetic resonance image of the segmented brain tissues, skull and scalp, namely establishes a signal conduction forward brain model based on the target nuclear magnetic resonance image and using conduction parameters with consistency in all directions to obtain a three-dimensional brain model of an object to be monitored. And then, acquiring all cortical grids in the three-dimensional brain model by the offline processing module, downsampling (namely reducing the number of the cortical grids) all the cortical grids, and carrying out matching calibration on the cortical network and the cortical grids of the standard brain model after downsampling to obtain a plurality of cortical grids. It should be noted that, with the continuous improvement of computing power, if the number of cortex grids that can be processed by the offline processing module is not limited, the cortex grids do not need to be downsampled, and only all the cortex grids need to be calibrated. The present application is mainly described by taking a plurality of cortical grids obtained after downsampling and calibration as an example.

Then, the off-line processing module obtains the positions of a plurality of electrodes for electroencephalogram signal acquisition on a plurality of channels in the brain of the object to be monitored, and the positions of a plurality of cortex grids in the brain of the object to be monitored, and aligns the two positions to obtain a Lead-field matrix (also called a Lead field matrix) corresponding to the object to be monitored. And finally, the off-line processing module acquires covariance matrixes of the plurality of off-line electroencephalogram signals. For example, the amplitude of each off-line electroencephalogram signal at each moment is taken as one row or one column of elements in the matrix, and a first amplitude matrix can be obtained. And obtaining covariance matrixes of the plurality of off-line electroencephalogram signals based on the first amplitude matrix. And finally, the offline processing module obtains a reorganization matrix based on the Lead-field matrix and the covariance matrix.

Illustratively, the reorganization matrix may be represented by formula (1):

formula (1);

wherein W is a reconstruction matrix, R is the covariance matrix, L is the Lead-field matrix, and T is a transpose operation.

It should be noted that the recombination matrix is mainly used for performing inverse decomposition on the brain electrical signals, that is, decomposing the brain electrical signals into a plurality of brain power supply signals corresponding to a plurality of source electrodes. Since for an individual the inverse decomposition of the brain electrical signal is mainly related to the forward brain model used, the electrode position, the Lead-field matrix and the noise level of the brain electrical signal. Therefore, for the same individual, under the same electroencephalogram environment (namely, the same electrode position and the same brain model are used), the recombination matrix used for reversely decomposing the electroencephalogram signals collected each time is the same, namely, the reversely decomposing from the electroencephalogram signals to the brain power supply signals can be completed by multiplying the electroencephalogram signals collected each time by the recombination matrix.

It should be noted that in the present application, in the online brain transient monitoring and offline processing, the brain electrical environment used by the object to be monitored is the same, i.e. the brain model and the electrode position used are the same. Therefore, when the application is used for carrying out brain transient monitoring on line, the recombination matrix analyzed off line can be used for directly carrying out inverse decomposition from brain electrical signals to brain power supply signals, and the recombination matrix is not required to be calculated again.

Further, after the Lead-field matrix is obtained, the offline processing module further performs inverse decomposition on a plurality of offline electroencephalogram signals based on the Lead-field matrix to obtain a plurality of cortex signals corresponding to a plurality of cortex grids, namely, the Lead-field matrix is used for performing inverse decomposition on the plurality of offline electroencephalogram signals through a linear constraint minimum variance algorithm to obtain a plurality of cortex signals.

Then, the off-line processing module divides the three-dimensional brain model into a plurality of source sub-areas, namely, a cortex area in the brain model is divided into one source sub-area, and a plurality of source sub-areas can be obtained. Then, the plurality of cortex signals are weighted and mapped to obtain a plurality of second brain power signals corresponding to the plurality of source electrodes. Alternatively, a principal component analysis method may be used to weight map the plurality of cortical signals to obtain a plurality of second electroencephalograms. Optionally, after the weighted mapping is performed on the cortex signals, spatial leakage and source sub-polarities of the electroencephalogram signals obtained by the weighted mapping are also required to be corrected, and the corrected electroencephalogram signals are used as the second electroencephalogram signals.

Finally, the offline processing module obtains a plurality of preset delay autocovariance matrixes, a state transition probability matrix among a plurality of brain transients and initial state probabilities corresponding to the plurality of brain transients based on a preset time window and the plurality of second brain power signals, wherein each preset delay autocovariance matrix is used for representing one brain transient.

Specifically, the offline processing module performs windowing on each second brain power supply signal for multiple times based on a preset time window so as to divide each second brain power supply signal and obtain a plurality of sub brain power supply signals corresponding to each second brain power supply signal. As shown in fig. 3, the sliding is performed on each second brain power signal with a preset time window, so that a plurality of time windows are generated during the sliding process, and each time window divides one sub-brain power signal from the second brain power signal. Based on a plurality of sub-brain power signals corresponding to each second brain power signal, a plurality of preset delay autocovariance matrixes, a state transition probability matrix and initial state probabilities are determined.

Specifically, the off-line processing module acquires a second time-delay autocovariance matrix corresponding to a plurality of sub-brain power signals of a plurality of second brain power signals under each time window, namely, acquires the autocovariance matrix of the plurality of sub-brain power signals, wherein the second time-delay autocovariance matrix simultaneously contains brain rhythm information in a space dimension and a time dimension because the plurality of sub-brain power signals are acquired from different brain cortex areas and the plurality of sub-brain power signals are brain electrical signals under one time window. For example, as shown in fig. 4, for a first time window, the sub-brain power signals of each second brain power signal under the first time window are acquired, and a plurality of sub-brain power signals under the first time window may be obtained. Then, the amplitude of each sub-brain power supply signal at each moment is taken as an element of one row or one column in the matrix, and a second amplitude matrix corresponding to the first time window can be obtained. Then, a second delayed autocovariance matrix corresponding to the first time window is obtained based on the second amplitude matrix. Finally, for each time window, a second time delay autocovariance matrix under each time window can be obtained, and then a plurality of second time delay autocovariance matrices under a plurality of time windows corresponding to a plurality of windowing can be obtained.

Further, the offline processing module groups a plurality of second delay autocovariance matrices to obtain a plurality of delay autocovariance matrix groups, wherein each delay autocovariance matrix group comprises one or more of the plurality of second delay autocovariance matrices. For example, the offline processing module may cluster the plurality of second delay autocovariance matrices to obtain a plurality of delay autocovariance matrix sets. Then, the center of each delay autocovariance matrix group is obtained, the center of each delay autocovariance matrix group is used as a preset delay autocovariance matrix group, and a plurality of preset delay autocovariance matrix groups are obtained. For example, a cluster center corresponding to each delay autocovariance matrix group is taken as a center of the delay autocovariance matrix group, or an average value of the second delay autocovariance matrix in each delay autocovariance matrix group is obtained and taken as a center of each delay autocovariance matrix group. The offline processing module then characterizes a brain transient using each of the pre-set delayed autocovariance matrices. In the application, each preset delay autocovariance matrix can be called a Gaussian observation model, so that each brain transient can be characterized by one Gaussian observation model, and various brain transients can be characterized by a plurality of Gaussian observation models, namely Gaussian observation model groups. In one embodiment of the present application, after obtaining a preset delay autocovariance matrix corresponding to each brain transient, in order to generalize subsequent on-line brain transient monitoring, a principal component analysis may be used to dimension-reduce the preset delay autocovariance matrix, only key time information and key space information in the preset delay autocovariance matrix are reserved, a dimension-reduced delay autocovariance matrix is obtained, and then the dimension-reduced delay autocovariance matrix may be used as the preset delay autocovariance matrix corresponding to the brain transient. The application is mainly described by taking the example of not carrying out dimension reduction on a preset delay autocovariance matrix.

Illustratively, the various brain transients in the present application include, but are not limited to: forehead transients, sensory motor transients, parietal transients and vision transients. Further, after obtaining a plurality of preset delay autocovariance matrices, in order to express each brain transient, the brain transient represented by each preset delay autocovariance matrix may be numbered, for example, may be respectively numbered as: brain transient 1, brain transient 2, … …. For convenience of explanation, the application is mainly described by taking 4 kinds of brain transients as examples, and the four kinds of brain transients are respectively numbered as brain transient 1, brain transient 2, brain transient 3 and brain transient 4.

Further, the offline processing module obtains a plurality of second similarities between the second delay autocovariance matrix and a plurality of preset delay autocovariance matrices under each time window. And then, the off-line processing module determines the brain transient state of the object to be monitored under each time window according to a plurality of second similarities under each time window, namely, the brain transient state represented by a preset delay autocovariance matrix corresponding to the maximum second similarity is used as the brain transient state under each time window. And then, the off-line processing module arranges the plurality of brain transients under the plurality of time windows according to the time sequence to obtain a brain transient sequence, namely, the plurality of brain transients under the plurality of time windows are combined according to the time sequence to obtain the brain transient sequence. Finally, based on the brain transient sequence, a state transition probability matrix among various brain transients and initial state probabilities corresponding to the various brain transients are determined.

Specifically, the offline processing module determines the number of occurrences of each brain transient in the sequence of brain transients, and determines the probability of occurrence of each brain transient based on the number of occurrences of each brain transient in the sequence of brain transients and the number of brain transients in the sequence of brain transients (i.e., the number of the plurality of brain transients or the number of time windows as can be understood), i.e., the ratio between the number of occurrences of each brain transient and the number of brain transients in the sequence of brain transients is taken as the probability of occurrence of each brain transient. Finally, the occurrence probability of each brain transient is used as the initial state probability corresponding to various brain transients.

Specifically, for each brain transient, an offline processing module determines a brain transient adjacent to the brain transient at a next time in the sequence of brain transients, and determines the number of times each brain transient transitions to various brain transients at the next time based on the brain transients adjacent to the brain transient at the next time; the probability of each brain transient transitioning to a variety of brain transients is determined based on the number of times each brain transient transitions to a variety of brain transients at the next time and the number of times each brain transient occurs. For example, the ratio between the number of times each brain transient is transferred to various brain transients at the next time and the number of times each brain transient occurs is taken as the probability of each brain transient being transferred to various brain transients; based on the probability of each brain transient transition to various brain transients, determining a state transition probability matrix among various brain transients, namely, taking the probability of each brain transient transition to various brain transients as each row element in the state transition probability matrix to obtain the state transition probability matrix.

For example, if the number of time windows is 8, 8 brain transients are included in the brain transient sequence, e.g., brain transient sequence [ brain transient 1, brain transient 2, brain transient 1, brain transient 3, brain transient 4, brain transient 2, brain transient 1, brain transient 4]. The number of occurrences of the brain transient 1 is 3, the number of occurrences of the brain transient 2 is 2, the number of occurrences of the brain transient 3 is 1, and the number of occurrences of the brain transient 4 is 2, so that the initial state probabilities corresponding to the four brain transients are respectively: [3/8, 1/4, 1/8, 1/4].

Then, for brain transient 1, the number of times of transition to brain transient 1 at the next time is 0, the number of times of transition to brain transient 2 at the next time is 1, the number of times of transition to brain transient 3 at the next time is 1, the number of times of transition to brain transient 4 at the next time is 1, and the probabilities of transition to four brain transients at the next time of brain transient 1 are respectively: 0. 1/3, 1/3. For brain transient 2, the number of times of transition to brain transient 1 at the next time is 2, the number of times of transition to brain transient 2 at the next time is 0, the number of times of transition to brain transient 3 at the next time is 0, the number of times of transition to brain transient 4 at the next time is 0, and the probabilities of transition to four brain transients at the next time of brain transient 2 are respectively: 1. 0, 0. For brain transient 3, the number of times of transition to brain transient 1 at the next time is 0, the number of times of transition to brain transient 2 at the next time is 0, the number of times of transition to brain transient 3 at the next time is 0, the number of times of transition to brain transient 4 at the next time is 1, and the probabilities of transition to four brain transients at the next time of brain transient 3 are respectively: 0. 0, 1. For brain transient 4, the number of times of transition to brain transient 1 at the next time is 0, the number of times of transition to brain transient 2 at the next time is 1, the number of times of transition to brain transient 3 at the next time is 0, the number of times of transition to brain transient 4 at the next time is 0, and the probabilities of transition to four brain transients at the next time of brain transient 4 are respectively: 0. 1, 0.

The state transition probability matrix between these four brain transients is:

the process of acquiring parameters required for online brain transient monitoring by offline processing is described above, and the online brain transient monitoring process of the present application is described in detail below in conjunction with the parameters acquired by offline processing.

In an exemplary embodiment, during online monitoring, the electroencephalogram acquisition module is configured to acquire a plurality of original electroencephalograms of an object to be monitored from a plurality of channels at a current moment. Specifically, the brain transient monitoring device may be provided with a brain transient monitoring button, and when the button is pressed, the brain electrical signal of the object to be monitored is collected by the brain electrical collecting module all the time. The application is mainly described by taking the analysis of brain transient at the current moment as an example, so the application is mainly described by taking the brain electrical signal acquired at the current moment as an example, wherein the current moment can be any moment after on-line monitoring.

Further, the brain transient analysis module is configured to perform traceable analysis on a plurality of brain electrical signals to be analyzed based on the recombination matrix to obtain a plurality of first brain electrical signals, that is, perform inverse decomposition on a plurality of original brain electrical signals based on the recombination matrix to obtain a plurality of first brain electrical signals corresponding to a plurality of source electrodes, that is, map a plurality of original brain electrical signals based on the recombination matrix, so as to obtain a plurality of first brain electrical signals. Then, the brain transient analysis module intercepts brain electrical signals to be analyzed corresponding to the current moment from each first brain power supply signal to obtain a plurality of brain electrical signals to be analyzed. Starting at the current moment, respectively intercepting the brain electrical signals from each first brain power supply signal forwards to obtain a plurality of brain electrical signals to be analyzed, wherein the intercepted length is the length corresponding to a preset window. It should be noted that, because the application intercepts the signal through the preset time window and analyzes the brain transient at each moment, when the length of the brain electrical signal before a certain moment is insufficient to window, the monitoring of the brain transient at the moment can not be performed. For example, if the preset time window is 30ms, after the on-line monitoring function is started, the brain electrical signal of the first 30ms cannot be windowed, so that the brain transient at each moment can not be output in the first 30ms, the brain transient of the object to be monitored at each moment can be monitored in real time from 30ms, and the brain transient at each moment can be output and displayed.

Further, the brain transient analysis module processes the plurality of brain electrical signals to be analyzed to obtain a first delay autocovariance matrix, namely, the autocovariance matrix corresponding to the plurality of brain electrical signals to be analyzed is obtained. For example, the amplitude of each electroencephalogram signal to be analyzed at each time may be used as one row or one column of elements in the matrix, so as to obtain a third amplitude matrix. Then, based on the magnitude matrix, a first delayed autocovariance matrix may be determined.

Further, the brain transient analysis module determines similarities between the first delay autocovariance matrix and a plurality of preset delay autocovariance matrices to obtain a plurality of first similarities. And then, determining the probability of the object to be monitored in various brain transients at the current moment according to a plurality of first similarities, the brain transients of the object to be monitored at the last moment, a state transition probability matrix among various brain transients and initial state probabilities corresponding to the various brain transients.

For example, if the current time is when the brain transient of the object to be monitored is determined for the first time, the brain transient analysis module determines probabilities that the current time of the object to be monitored is in various brain transients based on the initial state probabilities corresponding to the various brain transients and the plurality of first similarities. Optionally, the brain transient analysis module inputs the first similarities to nodes corresponding to the current moment in a hidden markov model to obtain probabilities that the current moment of the object to be monitored is in various brain transients, wherein initial state probabilities and state transition probability matrixes corresponding to various brain transients are embedded in the hidden markov model. After the first similarities are input to the nodes corresponding to the hidden Markov model, the initial state probability corresponding to the brain transients and the first similarities are subjected to dot multiplication to obtain the probability of determining that the current moment of the object to be monitored is in various brain transients, namely the first similarities corresponding to each brain transient and the initial state probability are multiplied to obtain the probability of determining that the current moment of the object to be monitored is in each brain transient.

For example, if the current moment is the first time of determining the brain transient of the object to be monitored, the brain transient analysis module determines the probability that the current moment of the object to be monitored is in various brain transients based on the state transition probability matrix, the first similarities and the brain transient of the object to be monitored at the previous moment. Optionally, the brain transient analysis module may input the plurality of first similarities to nodes corresponding to the current moment in the hidden markov model, so as to obtain probabilities that the current moment of the object to be monitored is in various brain transients. Specifically, after a plurality of first similarities are input to nodes corresponding to the hidden markov model, a brain transient of a previous node (i.e., a previous moment) can be obtained from the hidden markov model; then, obtaining a probability sequence of transition from the brain transient state of the object to be monitored to various brain transient states at the last moment from a state transition probability matrix; and determining the probability that the current moment of the object to be monitored is in various brain transients according to the probability sequence and the first similarities. For example, the probability sequence and the first similarities are subjected to dot multiplication to obtain probabilities that the object to be monitored is in various brain transients at the current moment, namely, the first similarities corresponding to each brain transient and the probabilities that the brain transient at the previous moment is transferred into the brain transient are multiplied to obtain probabilities that the object to be monitored is in various brain transients at the current moment.

Finally, the brain transient analysis module determines the brain transient of the object to be detected at the current moment based on the probability that the object to be monitored is at various brain transients at the current moment. For example, the brain transient analysis module takes the brain transient corresponding to the maximum probability as the brain transient of the object to be detected at the current moment.

In one embodiment of the application, the brain transient monitoring device further comprises a display module. After acquiring the brain transient state of the object to be detected at the current moment, the brain transient state of the object to be detected at the current moment can be displayed through the display module, so that a doctor can timely observe the brain transient state of the object to be detected at the current moment.

In one embodiment of the application, the brain transient monitoring device may further comprise an alarm module. After acquiring the brain transient state of the object to be detected at the current moment, comparing the brain transient state with a preset brain transient state to determine whether the brain transient state at the current moment is dangerous or not; if yes, the alarm module is used for alarming, for example, alarm information is sent to a far-end contact person or doctor, or an alarm signal is played.

In one embodiment of the application, the brain transient monitoring device can also output the brain transient acquired by the object to be monitored at each moment to other devices, so that the other devices can conveniently perform disease analysis, health monitoring, personalized neuromodulation treatment strategies and the like based on the brain transient of the object to be monitored at each moment. Alternatively, the brain transient monitoring device may also cache and archive brain transients of the subject to be monitored at various times for subsequent analysis.

The process of monitoring brain transients is described above from both an offline and an online perspective, respectively. The brain transient monitoring process of the present application is generally described below with reference to the accompanying drawings.

As shown in fig. 5, firstly, performing off-line processing, obtaining scalp electroencephalogram of a to-be-monitored object, obtaining off-line electroencephalogram signals, and obtaining a nuclear magnetic resonance image of the head of the to-be-monitored object; then, performing cerebral cortex reconstruction on the nuclear magnetic resonance image to obtain a three-dimensional brain model and cortical grids in the three-dimensional brain model, so as to construct a Lead-field matrix and a recombination matrix, and performing inverse decomposition on offline brain electrical signals based on the Lead-field matrix to obtain a plurality of brain power signals corresponding to a plurality of source electrodes; based on the plurality of brain power signals, a plurality of gaussian observation models may be constructed, wherein each gaussian observation model is used to characterize a brain transient. Likewise, based on a plurality of brain power signals, a brain transient sequence during offline processing may be constructed; based on the brain transient sequence, initial state probabilities corresponding to various brain transients and a state transition probability matrix between the various brain transients are determined.

As shown in fig. 5, during online monitoring, an electroencephalogram signal at the current moment can be obtained, and the electroencephalogram signal is inversely decomposed (i.e. the electroencephalogram power signal is reconstructed) into a plurality of electroencephalogram power signals based on a recombination matrix; intercepting an electroencephalogram signal to be analyzed at the current moment from a brain power supply signal by using a preset time window, and calculating a first delay autocovariance matrix of the electroencephalogram signal to be analyzed; calculating first similarity between the first delay autocovariance matrix and each Gaussian observation model to obtain a plurality of first similarities; and inputting the first similarities into a hidden Markov model, and obtaining the brain transient of the object to be monitored at the current moment based on the initial state probabilities corresponding to the brain transients in the hidden Markov model, the state transition probability matrix among the brain transients and the historical brain transient sequence.

Referring to fig. 6, fig. 6 is a schematic flow chart of a brain transient monitoring method according to an embodiment of the application. The method is applied to the brain transient monitoring equipment. The method includes, but is not limited to, the following steps:

s601: the electroencephalogram acquisition module acquires a plurality of original electroencephalogram signals of an object to be monitored from a plurality of channels at the current moment.

S602: the brain transient analysis module performs traceable analysis on the plurality of original brain electrical signals based on the recombination matrix to obtain a plurality of first brain power supply signals.

S603: and the brain transient analysis module intercepts brain electrical signals to be analyzed corresponding to the current moment from each first brain power supply signal to obtain a plurality of brain electrical signals to be analyzed.

S604: and the brain transient analysis module processes the plurality of brain electrical signals to be analyzed to obtain a first delay autocovariance matrix.

S605: the brain transient analysis module determines similarities between the first delay autocovariance matrix and a plurality of preset delay autocovariance matrices to obtain a plurality of first similarities, wherein each preset delay autocovariance matrix is used for representing a brain transient.

S606: the brain transient analysis module determines the probability that the object to be monitored is in various brain transients at the current moment according to the first similarities, the brain transients of the object to be monitored at the last moment, a state transition probability matrix among various brain transients and initial state probabilities corresponding to the various brain transients.

S607: and the brain transient analysis module determines the brain transient of the object to be monitored at the current moment according to the probability of being in various brain transients at the current moment.

The specific implementation process of step S601 to step S607 may refer to specific functions of the offline analysis module, the image acquisition module, the electroencephalogram acquisition module and the brain transient analysis module, which are not described herein.

Referring to fig. 7, fig. 7 is a schematic diagram of an electronic device according to an embodiment of the application. The electronic device 700 shown in fig. 7 includes a memory 701, a processor 702, a communication interface 703, and a bus 704. The memory 701, the processor 702, and the communication interface 703 are connected to each other by a bus 704. The electronic device 700 may be the brain transient monitoring apparatus described above. The processor 702 may implement the functionality of the brain transient analysis module and the offline processing module described above. The communication interface 703 may implement the functions of the image acquisition module and the electroencephalogram acquisition module described above.

The processor 702 may integrate the functions of the above-mentioned brain transient analysis module and the offline processing module, and is used for offline computing (i.e. offline computing a reorganization matrix, a preset delay autocovariance matrix, a state transition probability matrix between brain transients, and initial state probabilities corresponding to various brain transients) and online computing (i.e. computing the brain transient at the current moment). The communication interface 703 may integrate the functions of the image acquisition module and the electroencephalogram acquisition module described above, for example, to acquire an electroencephalogram signal from a subject to be monitored, or to acquire a nuclear magnetic resonance image of the subject to be monitored.

The Memory 701 may be a Read Only Memory (ROM), a static storage device, a dynamic storage device, or a random access Memory (Random Access Memory, RAM). The memory 701 may store a program, and when the program stored in the memory 701 is executed by the processor 702, the processor 702 and the communication interface 703 are used to perform the respective steps of the brain transient monitoring method of the embodiment of the application.

The processor 702 may employ a general-purpose central processing unit (Central Processing Unit, CPU), microprocessor, application specific integrated circuit (Application Specific Integrated Circuit, ASIC), graphics processor (graphics processing unit, GPU) or one or more integrated circuits for executing associated programs to perform the functions required by the units in the audio feature compensation apparatus or the audio recognition apparatus of the present application or to perform the brain transient monitoring method of the method embodiment of the present application.

The processor 702 may also be an integrated circuit chip with signal processing capabilities. In implementation, various steps in the stimulus intensity setting method of the present application may be accomplished by instructions in the form of integrated logic circuits of hardware or software in the processor 702. The processor 702 may also be a general purpose processor, a digital signal processor (Digital Signal Processing, DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 701, and the processor 702 reads the information in the memory 701, and in combination with its hardware, performs functions to be executed by the units included in the audio feature compensation apparatus or the audio recognition apparatus according to the embodiment of the present application, or performs various steps in the brain transient monitoring method according to the embodiment of the method of the present application.

The communication interface 703 enables communication between the electronic device 700 and other devices or communication networks using transceiving means such as, but not limited to, transceivers, input-output devices, and the like. For example, brain electrical signals may be acquired through the communication interface 703.

A bus 704 may include a path that communicates information between various components of the electronic device 700 (e.g., memory 701, processor 702, communication interface 703).

It should be noted that while the electronic device 700 shown in fig. 7 illustrates only a memory, a processor, and a communication interface, those skilled in the art will appreciate that in a particular implementation, the electronic device 700 also includes other components necessary to achieve proper operation. Also, those skilled in the art will appreciate that the electronic device 700 may also include hardware components that perform other additional functions, as desired. Furthermore, it will be appreciated by those skilled in the art that the electronic device 700 may also include only the components necessary to implement embodiments of the present application, and not necessarily all of the components shown in FIG. 7.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Embodiments of the present application also provide a computer readable storage medium storing a computer program for execution by a processor to perform part or all of the steps of any one of the brain transient monitoring methods described in the method embodiments above.

Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform part or all of the steps of any one of the brain transient monitoring methods as described in the method embodiments above.

It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are alternative embodiments, and that the acts and modules referred to are not necessarily required for the present application.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, such as the division of the units, merely a logical function division, and there may be additional manners of dividing the actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units described above may be implemented either in hardware or in software program modules.

The integrated units, if implemented in the form of software program modules, may be stored in a computer-readable memory for sale or use as a stand-alone product. Based on this understanding, the technical solution of the present application may be embodied essentially or partly in the form of a software product, or all or part of the technical solution, which is stored in a memory, and includes several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.

The foregoing has outlined rather broadly the more detailed description of embodiments of the application, wherein the principles and embodiments of the application are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.

Claims

1. A brain transient monitoring device is characterized in that,

the brain transient monitoring equipment comprises an image acquisition module, an off-line processing module, an electroencephalogram acquisition module and a brain transient analysis module;

acquiring a plurality of cortical grids in the three-dimensional brain model;

aligning the positions of a plurality of electrodes used for acquiring brain electrical signals on the channels with the positions of the cortex grids in the brain of the object to be monitored to obtain a Lead-field matrix corresponding to the object to be monitored;

the electroencephalogram acquisition module is used for acquiring a plurality of original electroencephalograms of the object to be monitored from the channels at the current moment;

determining the probability of the object to be monitored in various brain transients at the current moment according to the first similarity, the brain transient of the object to be monitored at the last moment, the state transition probability matrix and the initial state probability; the method is particularly used for:

If the current moment is the brain transient of the object to be monitored, determining the probability that the current moment of the object to be monitored is in various brain transients based on the initial state probabilities corresponding to various brain transients and the first similarity;

if the current moment is not the first moment, determining a probability sequence of the brain transient of the object to be monitored to be changed into various brain transients at the last moment based on the state transition probability matrix; determining the probability of the current moment of the object to be monitored in various brain transients according to the probability sequence and the first similarity;

2. The apparatus of claim 1, wherein the device comprises a plurality of sensors,

the offline processing module is specifically configured to, based on a plurality of sub-brain power signals corresponding to each second brain power signal, obtain a plurality of preset delay autocovariance matrices, a state transition probability matrix between a plurality of brain transients, and initial state probabilities corresponding to the plurality of brain transients:

acquiring a plurality of sub-brain power supply signals of the plurality of second brain power supply signals under each time window;

Determining a second delayed autocovariance matrix under each time window based on the plurality of sub-brain power supply signals under each time window;

obtaining a plurality of second delay autocovariance matrixes under a plurality of time windows corresponding to the windowing for a plurality of times based on the second delay autocovariance matrixes of each time window;

grouping the plurality of second delay autocovariance matrixes to obtain a plurality of delay autocovariance matrix groups;

taking the center of each delay autocovariance matrix group as a preset delay autocovariance matrix to obtain a plurality of preset delay autocovariance matrices;

determining a plurality of second similarities between a second delay autocovariance matrix under each time window and the plurality of preset delay autocovariance matrices;

determining a brain transient of the subject to be monitored under each time window based on a plurality of second similarities under each time window;

arranging a plurality of brain transients under the plurality of time windows according to the sequence of time to obtain a brain transient sequence;

based on the brain transient sequence, a state transition probability matrix between the plurality of brain transients and an initial state probability corresponding to the plurality of brain transients are determined.

3. The apparatus of claim 2, wherein the device comprises a plurality of sensors,

in determining a state transition probability matrix between the plurality of brain transients and initial state probabilities corresponding to the plurality of brain transients based on the brain transient sequence, the offline processing module is specifically configured to:

determining the number of times each brain transient occurs in the sequence of brain transients;

determining the probability of occurrence of each brain transient based on the number of occurrences of each brain transient and the number of brain transients in the sequence of brain transients;

taking the occurrence probability of each brain transient as the initial state probability corresponding to the plurality of brain transients;

for each brain transient, determining a brain transient adjacent to the brain transient at a next moment in the sequence of brain transients, and determining the number of times the next moment of the brain transient is transferred to various brain transients based on the brain transient adjacent to the brain transient at the next moment;

determining a probability of each brain transient transitioning to a variety of brain transients based on the number of times that the brain transient transitions to the variety of brain transients at a next time and the number of times that each brain transient occurs;

a state transition probability matrix between each of the plurality of brain transients is determined based on the probability of the transition of the brain transient to the respective brain transient.

4. The apparatus of claim 1, wherein the device comprises a plurality of sensors,

in the aspect of obtaining the three-dimensional brain model of the object to be monitored based on the original nuclear magnetic resonance image, the offline processing module is specifically configured to:

re-slicing the original nuclear magnetic resonance image to obtain a target nuclear magnetic resonance image;

dividing the target nuclear magnetic resonance image to obtain brain tissue, skull and scalp;

and establishing a brain model based on the target nuclear magnetic resonance image of the brain tissue, the skull and the scalp to obtain the three-dimensional brain model of the object to be monitored.

5. An electronic device, comprising: the electronic device comprises a processor and a memory, wherein the processor is connected with the memory, the memory is used for storing a computer program, and the processor is used for executing the computer program stored in the memory, so that the electronic device executes the following steps:

Collecting a plurality of original brain electrical signals of the object to be monitored from the channels at the current moment;

determining the probability of the object to be monitored in various brain transients at the current moment according to the first similarity, the brain transient of the object to be monitored at the last moment, the state transition probability matrix and the initial state probability; comprising the following steps:

6. A computer readable storage medium storing a computer program, the computer program being executable by a processor to perform the steps of: