CN117281480A - Brain imaging detection device, system, electronic equipment and storage medium - Google Patents
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Abstract
The invention provides a brain imaging detection device, a brain imaging detection system, an electronic device and a storage medium, wherein the brain imaging detection device specifically comprises: a signal receiving module configured to receive an initial brain activity signal sequence from the near infrared spectral imaging device; each brain activity signal in the initial brain activity signal sequence has a corresponding detection time respectively; the linear relation conversion module is configured to determine dynamic response relations corresponding to the voxels based on a preset linear relation fitting model aiming at each initial brain voxel corresponding to the initial brain activity signal sequence; the self-adaptive filtering module is configured to respectively filter all initial brain voxels based on the self-adaptive filtering model so as to obtain corresponding all target brain voxels; a brain imaging module configured to generate brain imaging results from respective target brain voxels. Therefore, the brain imaging quality can be remarkably improved by combining a functional near infrared spectrum imaging technology with a linear parameter change model and an adaptive filtering technology.
Description
Technical Field
The present invention relates to the field of brain imaging, and in particular, to a brain imaging detection device, a brain imaging detection system, an electronic device, and a storage medium.
Background
Brain imaging techniques and methods are important means for studying the human brain, and can be used to evaluate brain activity and functional connections, study neural mechanisms in cognitive, affective, behavioral, etc.
Currently, brain imaging techniques and methods mainly include functional magnetic resonance imaging (functional magnetic resonance imaging, fMRI), electroencephalogram (EEG), magnetoencephalography (MEG), and the like. fMRI is a brain imaging technique based on blood oxygen level dependent signals that can be used to assess brain activity and functional connectivity.
The principle of fMRI is to measure blood oxygen level-dependent signals by magnetic resonance imaging techniques to reflect metabolic activity of brain regions. fMRI has a high spatial resolution, where specific locations of brain activity can be determined, but a low temporal resolution, where transient changes in brain activity cannot be captured. EEG and MEG are brain imaging techniques based on electrical signals that can be used to assess brain activity and functional connectivity. The principle of EEG and MEG is to measure electrical or magnetic signals of brain regions through electrodes or magnetic sensors to reflect the temporal and spatial characteristics of brain activity. EEG and MEG have high temporal resolution, can capture transient changes in brain activity, but have low spatial resolution, cannot determine the specific location of brain activity, and often require expensive equipment and complex data analysis, limiting their range of application and feasibility.
In view of the above problems, currently, no preferred solution is proposed.
Disclosure of Invention
The invention provides a brain imaging detection device, a brain imaging detection system, electronic equipment and a storage medium, which are used for at least solving the problems that in the prior art, the time resolution of fMRI signals is low and the spatial resolutions of EEG and MEG are low.
The present invention provides a brain imaging detection apparatus, the apparatus comprising: a signal receiving module configured to receive an initial brain activity signal sequence from the near infrared spectral imaging device; each brain activity signal in the initial brain activity signal sequence has a corresponding detection time respectively; the linear relation conversion module is configured to determine the dynamic response relation corresponding to each initial brain voxel corresponding to the initial brain activity signal sequence based on a preset linear relation fitting model; the dynamic response relationship defines a hemodynamic response relationship over a plurality of consecutive time steps for the initial brain voxel; the linear relation fitting model comprises a cascade linear parameter change model part and an autoregressive moving average model part; the adaptive filtering module is configured to respectively filter each initial brain voxel based on the adaptive filtering model so as to obtain corresponding target brain voxels; a brain imaging module configured to generate brain imaging results from the respective target brain voxels.
According to the brain imaging detection device provided by the embodiment of the invention, the general expression of the linear parameter change model part is as follows:
x(k+1)＝A(p(k))x(k)+B(p(k))u(k)
y(k)＝C(p(k))x(k)+D(p(k))u(k)
wherein k represents the discrete time, state vector, corresponding to each signal in the initial brain activity signal sequenceInput vector corresponding to signal->Output vector->Model matrices A (p (k)), B (p (k)), C (p (k)) and D (p (k)) are matrices of parameter variations, +.>Representing a parameter vector, n, q, l and s represent the dimensions of a state vector, an input vector, an output vector and a parameter vector, respectively.
According to the brain imaging detection apparatus provided by the embodiment of the present invention, the linear parameter change model part is a model with affine parameter dependence, wherein the linear parameter change model part is a model depending on a parameter vector p (k) by:
wherein p is i (k) Is the i-th element of the parameter vector p (k),
according to the brain imaging detection device provided by the embodiment of the invention, the autoregressive moving average model part is used for converting the input-output relationship determined by the linear parameter change model part:
y(k)+a 1 (p(k))y(k―1)+a 2 (p(k))y(k―2)
+…+a n (p(k))y(k―n)
＝b 1 (p(k))u(k―1)+b 2 (p(k))u(k―2)
+…+b m (p(k))u(k―m),
wherein q=l=1,and->The output and input of the kth time step respectively,is a variable parameter vector, s represents an s-dimensional parameter vector, a i ,b j Is a parameter dependent coefficient i=1, 2, …, n, j=1, 2, …, m.
According to the brain imaging detection device provided by the embodiment of the invention, the dynamic response relation is as follows:
y(k)＝X(k)β；
wherein the conversion factor
a i ＝―[a i0 a i1 …a is ] T ；i＝1,2,3,…,n，
b i ＝[b i0 b i1 …b is ] T ；i＝1,2,…,m。
According to an embodiment of the present invention, there is provided a brain imaging detection apparatus, the adaptive filtering module being configured to perform operations comprising: determining a target filtering factor corresponding to a kth time step based on a preset weight updating algorithm, a history conversion factor and a history output vector; the historical conversion factor and the historical output vector are determined from the brain activity signal corresponding to at least one historical time step; and carrying out feedback filtering processing on the initial brain voxels corresponding to the kth time step according to the target filtering factor.
In another aspect, the present invention provides a brain imaging detection system, the system comprising: near infrared spectrum imaging equipment for acquiring initial brain activity signal sequence; each brain activity signal in the initial brain activity signal sequence has a corresponding detection time respectively; and a brain imaging detection apparatus as described in any one of the above.
Optionally, the near infrared spectrum imaging device comprises a brain wearing device.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which when executed causes the electronic device to implement a brain imaging detection apparatus as described in any one of the above.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to implement a brain imaging detection apparatus as described in any one of the above
The brain imaging detection device comprises a signal receiving module, a linear relation conversion module, an adaptive filtering module and a brain imaging module, and is beneficial to remarkably improving the brain imaging quality and accuracy of the brain imaging device by combining a functional Near Infrared (NIRS) imaging technology (functional near-infrared spectroscopy) with a linear parameter change model and an adaptive filtering technology.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in 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 shows a block diagram of an example of a brain imaging detection apparatus according to an embodiment of the present invention;
fig. 2 shows a schematic diagram of an adaptive filtering mechanism according to the adaptive filtering module in fig. 1;
FIG. 3 illustrates a block diagram of an example of a brain imaging detection system according to an embodiment of the present invention;
FIG. 4 illustrates an effect schematic of an example of a brain activity map determined by using a brain imaging detection system according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 shows a block diagram of a brain imaging detection apparatus according to an embodiment of the present invention.
As shown in fig. 1, the brain imaging detection apparatus 100 includes a signal receiving module 110, a linear relation converting module 120, an adaptive filtering module 130, and a brain imaging module 140.
The signal receiving module 110 is configured to receive an initial brain activity signal sequence from the near infrared spectral imaging device, each brain activity signal in the initial brain activity signal sequence having a respective detection time.
It should be noted that the near infrared spectrum imaging device may employ various near infrared spectrum imagers. In some embodiments, the near infrared spectral imaging device detects brain activity signals by a functional near infrared spectral imaging technique (fNIRS).
The linear relationship conversion module 120 is configured to determine, for each initial brain voxel corresponding to the initial brain activity signal sequence, a dynamic response relationship corresponding to the voxel based on a preset linear relationship fitting model.
It should be understood that a voxel is an abbreviation for Volume element (Volume Pixel), which may represent a Pixel of 3D space. Specifically, by three-dimensional modeling of the received initial brain activity signal sequence, a corresponding three-dimensional model of the brain consisting of a plurality of voxels is obtained.
Here, the dynamic response relationship defines a hemodynamic response relationship over a plurality of consecutive time steps for an initial brain voxel, and the linear relationship fitting model comprises a cascaded linear parametric variation model part and an autoregressive moving average model part.
The adaptive filtering module 130 is configured to filter each of the initial brain voxels, respectively, based on the adaptive filtering model to obtain a corresponding each of the target brain voxels.
Specifically, filtering with different degrees or modes is adopted for different initial brain voxels respectively, and a better filtering effect is realized through a personalized filtering mode for different voxels.
Brain imaging module 140 is configured to generate brain imaging results from each target brain voxel.
Note that fNIRS is an optical signal-based brain imaging technique that enables assessment of brain activity and functional connectivity. The principle of fNIRS is to reflect the temporal and spatial characteristics of brain activity by measuring blood oxygen levels and hemodynamics of brain regions with an optical probe. The fNIRS has the advantages of low cost, portability, good time resolution and the like, and can be used for researching the aspects of functional connection, blood oxygen level, blood flow dynamics and the like of the brain.
However, the fNIRS technique also has some problems and disadvantages. First, the spatial resolution of the fNIRS technique is low and the specific location of brain activity cannot be determined. Second, the fNIRS technique is affected by scalp and hair, which require treatment of the scalp and hair. In addition, the signal of the fNIRS technique is affected by physiological noise and motion noise, and requires signal processing and denoising.
According to the embodiment of the invention, aiming at the fNIRS signal, a statistical method in a linear relation conversion module is utilized by a linear relation conversion technology and an adaptive filtering technology, so that the method can be used for analyzing the time and space characteristics of the fNIRS signal, and the physiological noise and the motion noise in the fNIRS signal can be effectively removed by utilizing an adaptive processing mode of the signal in the adaptive filtering technology, so that the quality and the accuracy of the signal are improved, and the function of integrating the real-time accurate detection of the neural activity in brain imaging equipment is realized.
For the linear parameter variation model portion in the linear relation conversion module 120, an LPV (linear parameter variation) model may be employed.
In particular, the LPV is a linear parametric variation model that can be approximated by a set of linear models to describe the nonlinear model. In the LPV model, the matrix of the model is a function of parameters that can be measured. The general expression of the LPV model may be expressed as a discrete LPV model in the form of a state space:
x (k+1) =a (p (k)) x (k) +b (p (k)) u (k), formula (1)
y (k) =c (p (k)) x (k) +d (p (k)) u (k), equation (2)
Where k represents the discrete time (i.e., the discrete time corresponding to each signal in the initial brain activity signal sequence), the state vectorInput vector->Output vector->Model matrices A (p (k)), B (p (k)), C (p (k)) and D (p (k)) are matrices of parameter variations, +.>Representing a parameter vector, n, q, l and s represent the dimensions of a state vector, an input vector, an output vector and a parameter vector, respectively.
The LPV model may be considered as a nonlinear model that is linearized along a time-varying trajectory determined by a time-varying parameter vector p (k). In some examples of embodiments of the invention, the LPV model is a model with affine parameter dependencies.
An LPV model with affine parameter dependence refers to a model in which the model matrix depends on the parameter vector p (k) in the following manner:
wherein p is i (k) Is the i-th element of the parameter vector p (k),
for the autoregressive moving average model portion in the linear relationship conversion module 120, an autoregressive moving average (auto-regressive moving average, ARMA) model may be employed.
Specifically, the ARMA model is used to represent the input-output relationship of the LPV model. It should be noted that the ARMA model is a common discrete-time data representation model, which can be used to describe features of time-series data and predict future values. The LPV model is expressed in the form of an ARMA model based on its good performance on a single-input single-output model, where the parameters of the model are varied.
Here, the ARMA model is used to transform the input-output relationship determined by the LPV model:
wherein q=l=1,and->The output and input of the kth time step respectively,is a variable parameter vector, a i ,b j (i=1, 2, …, n and j=1, 2, …, m) are coefficients related to parameters.
The discrete LPV model and the ARMA model are combined to achieve information characterization of the time data. The similarity in terms of parameters between the discrete LPV model and the ARMA model can be expressed as:
substituting (8) and (9) into (7) to obtain:
for simplicity, the following variables were introduced:
a i ＝―[a i0 a i1 …a is ] T the method comprises the steps of carrying out a first treatment on the surface of the i=1, 2,3, …, n, equation (13)
b i ＝[b i0 b i1 …b is ] T The method comprises the steps of carrying out a first treatment on the surface of the i=1, 2, …, m, equation (14)
The subscript s in formulas (11) and (12) represents an s-dimensional parameter vector. According to formulas (11) - (15), formula (10) may be reduced to:
from equation (16), it can be seen that the hemodynamic response for a particular time step can be expressed in terms of an equation.
Thus, the hemodynamic response of a particular voxel over N consecutive time steps can be represented by N discrete equations, resulting in the following matrix equation:
the general form of equation (17) can be expressed as:
y (k) =x (k) β, equation (18)
Wherein β is a conversion factor.
According to the embodiment of the invention, aiming at the problem that the fNIRS signal is interfered by scalp and hair in the fNIRS brain imaging technology, the fNIRS technology is combined with the linear parameter change model and the adaptive filtering technology to detect the neural activity, so that the limitations in the aspects including time resolution, spatial resolution, cost, complexity and the like in the prior art are eliminated, physiological noise and motion noise can be effectively removed, the quality and accuracy of the signal are improved, the time resolution and the spatial resolution of the brain imaging technology are improved, and the equipment cost and the design complexity are reduced.
Fig. 2 shows a schematic diagram of the adaptation mechanism according to the adaptive filtering module 130 in fig. 1.
Specifically, during operation of adaptive filtering module 130 to perform adaptive filtering, adaptive filtering module 130 may determine a target filter factor corresponding to the kth time step based on a preset weight update algorithm, a historical conversion factor, and a historical output vector (e.g.,). Here, the history conversion factor and the history output vector are determined from the brain activity signal corresponding to at least one history time step (e.g., the kth-1 time step). Furthermore, the adaptive filtering module 130 performs feedback filtering processing on the initial brain voxel corresponding to the kth time step according to the target filtering factor.
The brain imaging detection system provided by the invention is described below, and the brain imaging detection system described below and the brain imaging detection device described above can be referred to correspondingly.
Fig. 3 shows a block diagram of an example of a brain imaging detection system according to an embodiment of the present invention.
As shown in fig. 3, the brain imaging detection system 300 includes a near infrared spectrum imaging device 310 and a brain imaging detection apparatus 320. For more details on the brain imaging detection device 320, reference may be made to the description of the other parts above, and further description is omitted here. In addition, near infrared spectral imaging device 310 may acquire an initial sequence of brain activity signals, each brain activity signal in the initial sequence of brain activity signals having a respective detection time.
In some embodiments, the near infrared spectral imaging device comprises a brain donning device. Thus, the user may more conveniently provide brain imaging functionality to the user by wearing the near infrared spectral imaging device in the brain.
Fig. 4 shows an effect schematic of an example of a brain activity map determined by using the brain imaging detection system according to the embodiment of the present invention.
In the embodiment of the invention, the linear parameter change model and the adaptive filtering technology are adopted to process the fNIRS signal, so that the yield and quality of the fNIRS signal can be improved. Note that, the fnigs technique has good time resolution, but the signal quality is affected by the scalp and hair, and the scalp and hair treatment is required. By adopting the embodiment of the invention, the physiological noise and the motion noise can be removed by adopting the self-adaptive filtering technology, thereby improving the quality and the accuracy of the signal. In addition, in processing the temporal and spatial characteristics of brain activity, and the relationship of brain activity to behavior, the yield and quality of signals can be improved. Further, by the embodiment of the invention, the precision and efficiency of the signal can be improved. The linear parametric variation model is a statistical-based method that can be used to analyze the temporal and spatial characteristics of the fnrs signal. The self-adaptive filtering technology is a method suitable for signal processing, and can remove physiological noise and motion noise, thereby improving the precision and efficiency of signals.
In some business scenes of the embodiment of the invention, the system supports an online processing mode, can process signals in real time, improves the brain imaging efficiency, and can be applied to research on aspects of functional connection, blood oxygen level, blood flow dynamics and the like of the brain.
It should be noted that, the fNIRS technology has advantages of low cost, portability, and good time resolution, but the existing brain imaging technologies and methods generally require expensive equipment and complex data analysis, which limits their application range and feasibility. According to the embodiment of the invention, the fNIRS signal is processed by adopting a linear parameter change model and an adaptive filtering technology, so that the cost and complexity can be reduced, and the brain imaging technology is more popular and feasible.
Fig. 5 illustrates a physical schematic diagram of an electronic device, as shown in fig. 5, which may include: processor 510, communication interface (Communications Interface) 520, memory 530, and communication bus 540, wherein processor 510, communication interface 520, memory 530 complete communication with each other through communication bus 540. Processor 510 may invoke logic instructions in memory 530 to cause the electronic device to implement brain imaging detection apparatus comprising: a signal receiving module configured to receive an initial brain activity signal sequence from the near infrared spectral imaging device; each brain activity signal in the initial brain activity signal sequence has a corresponding detection time respectively; the linear relation conversion module is configured to determine the dynamic response relation corresponding to each initial brain voxel corresponding to the initial brain activity signal sequence based on a preset linear relation fitting model; the dynamic response relationship defines a hemodynamic response relationship over a plurality of consecutive time steps for the initial brain voxel; the linear relation fitting model comprises a cascade linear parameter change model part and an autoregressive moving average model part; the adaptive filtering module is configured to respectively filter each initial brain voxel based on the adaptive filtering model so as to obtain corresponding target brain voxels; a brain imaging module configured to generate brain imaging results from the respective target brain voxels.
Further, the logic instructions in the memory 530 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention 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 invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program storable on a non-transitory computer readable storage medium, the computer program when executed by a processor being capable of implementing a brain imaging detection apparatus, the apparatus comprising: a signal receiving module configured to receive an initial brain activity signal sequence from the near infrared spectral imaging device; each brain activity signal in the initial brain activity signal sequence has a corresponding detection time respectively; the linear relation conversion module is configured to determine the dynamic response relation corresponding to each initial brain voxel corresponding to the initial brain activity signal sequence based on a preset linear relation fitting model; the dynamic response relationship defines a hemodynamic response relationship over a plurality of consecutive time steps for the initial brain voxel; the linear relation fitting model comprises a cascade linear parameter change model part and an autoregressive moving average model part; the adaptive filtering module is configured to respectively filter each initial brain voxel based on the adaptive filtering model so as to obtain corresponding target brain voxels; a brain imaging module configured to generate brain imaging results from the respective target brain voxels.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, enables the processor to implement a brain imaging detection apparatus, the apparatus comprising: a signal receiving module configured to receive an initial brain activity signal sequence from the near infrared spectral imaging device; each brain activity signal in the initial brain activity signal sequence has a corresponding detection time respectively; the linear relation conversion module is configured to determine the dynamic response relation corresponding to each initial brain voxel corresponding to the initial brain activity signal sequence based on a preset linear relation fitting model; the dynamic response relationship defines a hemodynamic response relationship over a plurality of consecutive time steps for the initial brain voxel; the linear relation fitting model comprises a cascade linear parameter change model part and an autoregressive moving average model part; the adaptive filtering module is configured to respectively filter each initial brain voxel based on the adaptive filtering model so as to obtain corresponding target brain voxels; a brain imaging module configured to generate brain imaging results from the respective target brain voxels.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A brain imaging detection apparatus, the apparatus comprising:
a signal receiving module configured to receive an initial brain activity signal sequence from the near infrared spectral imaging device; each brain activity signal in the initial brain activity signal sequence has a corresponding detection time respectively;
the linear relation conversion module is configured to determine the dynamic response relation corresponding to each initial brain voxel corresponding to the initial brain activity signal sequence based on a preset linear relation fitting model; the dynamic response relationship defines a hemodynamic response relationship over a plurality of consecutive time steps for the initial brain voxel; the linear relation fitting model comprises a cascade linear parameter change model part and an autoregressive moving average model part;
the adaptive filtering module is configured to respectively filter each initial brain voxel based on the adaptive filtering model so as to obtain corresponding target brain voxels;
a brain imaging module configured to generate brain imaging results from the respective target brain voxels.
2. The brain imaging detection apparatus according to claim 1, wherein the general expression of the linear parameter variation model section is:
x(k+1)＝A(p(k))x(k)+B(p(k))u(k)
y(k)＝C(p(k))x(k)+D(p(k))u(k)
wherein k represents the discrete time, state vector, corresponding to each signal in the initial brain activity signal sequenceInput vector corresponding to signal->Output vector->Model matrices A (p (k)), B (p (k)), C (p (k)) and D (p (k)) are matrices of parameter variations, +.>Representing a parameter vector, n, q, l and s represent the dimensions of a state vector, an input vector, an output vector and a parameter vector, respectively.
3. The brain imaging detection apparatus according to claim 2, wherein said linear parameter change model section is a model having affine parameter dependence,
wherein the linear parameter variation model part is a model depending on the parameter vector p (k) by:
wherein p is i (k) Is the i-th element of the parameter vector p (k),
4. a brain imaging detection apparatus according to claim 3, wherein said autoregressive moving average model portion is configured to convert the input-output relationship determined by said linear parameter change model portion:
y(k)+a 1 (p(k))y(k-1)+a 2 (p(k))y(k-2)+…+a n (p(k))y(k-n)
＝b 1 (p(k))u(k-1)+b 2 (p(k))u(k-2)+…+b m (p(k))u(k-m)，
wherein q=l=1,and->The output and input of the kth time step, respectively,/->Is a variable parameter vector, s represents an s-dimensional parameter vector, a i ，b j Is a coefficient related to a parameter, i=1, 2,..n, j=1, 2,..m.
5. The brain imaging detection apparatus according to claim 4, wherein the dynamic response relationship is:
y(k)＝X(k)β；
wherein the conversion factor
a i ＝-[a i0 a i1 ...a is ] T ；i＝1，2，3，...，n，
b i ＝[b i0 b i1 ...b is ] T ；i＝1，2，...，m。
6. The brain imaging detection apparatus according to claim 5, wherein said adaptive filtering module is configured to perform operations comprising:
determining a target filtering factor corresponding to a kth time step based on a preset weight updating algorithm, a history conversion factor and a history output vector; the historical conversion factor and the historical output vector are determined from the brain activity signal corresponding to at least one historical time step;
and carrying out feedback filtering processing on the initial brain voxels corresponding to the kth time step according to the target filtering factor.
7. A brain imaging detection system, the system comprising:
near infrared spectrum imaging equipment for acquiring initial brain activity signal sequence; each brain activity signal in the initial brain activity signal sequence has a corresponding detection time respectively; and
the brain imaging detection apparatus according to any one of claims 1 to 6.
8. The brain imaging detection system according to claim 7, wherein said near infrared spectral imaging device comprises a brain donning device.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program causing the electronic device to implement the brain imaging detection apparatus as claimed in any one of claims 1-6.
10. A non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to implement the brain imaging detection apparatus of any one of claims 1-6.
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