CN112200912A - Brain imaging system and method based on electrical impedance imaging and electroencephalogram signals - Google Patents
Brain imaging system and method based on electrical impedance imaging and electroencephalogram signals Download PDFInfo
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Abstract
The invention discloses a brain imaging system and a method thereof based on electrical impedance imaging and electroencephalogram signals, comprising the following steps: the electroencephalogram measurement module is used for collecting electroencephalogram signals, generating electroencephalogram time domain waveforms according to the electroencephalogram signals, extracting electroencephalogram frequency domain characteristics based on the electroencephalogram waveforms, comparing the electroencephalogram frequency domain characteristics with a frequency domain preset threshold value, and sending a starting instruction to the control circuit if the electroencephalogram frequency domain characteristics are larger than the frequency domain preset threshold value; the control circuit is used for receiving the starting instruction and applying safe exciting current to the electrical impedance imaging module according to the starting instruction; the electrical impedance imaging module is used for acquiring voltage variation and impedance variation, calculating an impedance distribution function according to the voltage variation and the impedance variation, generating a finite element split image based on the impedance distribution function, and constructing a brain three-dimensional image according to the finite element split image. The system fuses electrical impedance imaging and electroencephalogram signals for brain imaging.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a brain imaging system and a method thereof based on electrical impedance imaging and electroencephalogram signals.
Background
The brain structure is complicated and comprises hundreds of millions of neurons which are connected with one another by millions of connections, so that the core functions of the brain, such as emotion and emotion, are still unsolved problems nowadays, which are the key for overcoming serious diseases of the nervous system seriously harming human physical and mental health, and also provide important basis for developing brain-like computing systems and devices and breaking through the constraint of traditional computer architectures, and determine the deep development direction of future artificial intelligence.
In recent years, brain imaging technology has made significant progress in 4 aspects of resolution, speed, depth and field of view of imaging. Aiming at the novel brain imaging technology of the brain loop multi-scale characteristic, the method provides key guidance and support for analyzing the structure and the function of the brain loop on a plurality of layers by the national brain plan, and the main research directions comprise: developing a high-throughput three-dimensional structure and function imaging and sample processing new technology and an image data processing and analyzing new method, and realizing rapid quantitative analysis of different biological whole brain neuron types, connections and activities with cell level resolution; developing new technologies such as in-vivo high-resolution optical imaging with large range and deep penetration, and realizing high space-time resolution analysis of the neural activities of conscious and freely moving animals; the method develops new ultramicro imaging technologies such as photoelectric correlation and the like, realizes ultramicro analysis and quantitative characterization on sub-cell structures such as nerve synapses and the like, further improves the imaging depth of living brain imaging in the future, carries out high-speed high-resolution three-dimensional reconstruction of nerve loops, explores accurate brain structure and functional imaging, is not only the development trend of brain imaging technology, but also one of the difficulties and key points of current international research
Some proposed measurement systems fusing multi-signal physiological information fuse characteristic information of electroencephalogram and other physiological parts, deep mining is not performed on physiological structures or functional information of a brain, an electrical impedance imaging means is mostly adopted in existing brain imaging systems, but the final brain imaging image quality is low, and research on the physiological structures and the functional information of the brain is difficult.
Therefore, it is an urgent need to solve the problem for those skilled in the art how to integrate electrical impedance imaging and electroencephalogram signals to perform brain imaging and further perform deep mining on physiological information and functional information of the brain.
Disclosure of Invention
In view of the above problems, the present invention aims to solve the problems that the existing brain imaging system mostly adopts an electrical impedance imaging means, but the final brain imaging image quality is low, and the study on the physiological structure and functional information of the brain is difficult, and to realize the fusion of electrical impedance imaging and electroencephalogram signals to perform brain imaging, and further perform deep mining on the physiological information and functional information of the brain.
The embodiment of the invention provides a brain imaging system based on electrical impedance imaging and electroencephalogram signals, which comprises: the electroencephalogram measurement module, the control circuit and the electrical impedance imaging module;
the electroencephalogram measurement module is connected with the control circuit and used for acquiring electroencephalogram signals, generating electroencephalogram time domain waveforms according to the electroencephalogram signals, extracting electroencephalogram frequency domain characteristics based on the electroencephalogram waveforms, comparing the electroencephalogram frequency domain characteristics with a frequency domain preset threshold value, and sending a starting instruction to the control circuit if the electroencephalogram frequency domain characteristics are larger than the frequency domain preset threshold value;
the control circuit is connected with the electroencephalogram measurement module and the electrical impedance imaging module, and is used for receiving the starting instruction and applying safe exciting current to the electrical impedance imaging module according to the starting instruction;
the electrical impedance imaging module is connected with the control circuit and used for collecting voltage variation and impedance variation, calculating an impedance distribution function according to the voltage variation and the impedance variation, generating a finite element split image based on the impedance distribution function, and constructing a brain three-dimensional image according to the finite element split image.
In one embodiment, the electroencephalogram measurement module includes: the electroencephalogram frequency domain feature extraction device comprises an electroencephalogram time domain waveform generation unit, an electroencephalogram frequency domain feature extraction unit and a judgment unit;
the electroencephalogram time domain waveform generating unit is used for collecting electroencephalogram signals and generating electroencephalogram time domain waveforms according to the electroencephalogram signals;
the electroencephalogram frequency domain feature extraction unit is used for extracting the electroencephalogram frequency domain features by utilizing wavelet transformation based on the electroencephalogram time domain waveform;
the judging unit is used for extracting a power spectrum entropy value by using a power spectrum entropy extraction algorithm based on the electroencephalogram frequency domain characteristics, comparing the power spectrum entropy value with the frequency domain preset threshold value, and sending a starting instruction to the control circuit if the power spectrum entropy value is larger than the frequency domain preset threshold value.
In one embodiment, the wavelet transform in the electroencephalogram frequency domain feature extraction unit is calculated as follows:
where α represents the wavelet scale, τ represents the amount of translation, f (t) represents the time signal function, t represents time,
representing the scale function of the wavelet transform.
In one embodiment, the electrical impedance imaging module comprises: the system comprises an acquisition unit, a calculation unit and a construction unit;
the acquisition unit is used for acquiring the voltage variation and the impedance variation in the three-dimensional current field after the safe excitation current is applied;
the calculation unit is used for calculating the impedance distribution function according to the voltage variation and the impedance variation;
the construction unit is used for carrying out radon transformation on the impedance distribution function according to a convolution back projection algorithm to generate the finite element sectional image and constructing a brain three-dimensional image according to the finite element sectional image.
In one embodiment, the calculation unit calculates the impedance distribution function according to the voltage variation and the impedance variation, and the calculation formula is as follows:
ΔV＝SΔC
where Δ V represents a voltage transformation amount, Δ C represents an impedance transformation amount, and S represents a sensitivity matrix.
In accordance with the above purposes, in a second aspect of the present application, there is also provided a method of brain imaging based on electrical impedance imaging and electroencephalogram signals, comprising:
the electroencephalogram measurement module acquires an electroencephalogram signal, generates an electroencephalogram time domain waveform according to the electroencephalogram signal, extracts an electroencephalogram frequency domain feature based on the electroencephalogram waveform, compares the electroencephalogram frequency domain feature with a frequency domain preset threshold, and sends a starting instruction to the control circuit if the electroencephalogram frequency domain feature is larger than the frequency domain preset threshold;
the control circuit receives the starting instruction and applies safe exciting current to the electrical impedance imaging module according to the starting instruction;
the electrical impedance imaging module collects voltage variation and impedance variation, calculates an impedance distribution function according to the voltage variation and the impedance variation, generates a finite element split image based on the impedance distribution function, and constructs a brain three-dimensional image according to the finite element split image.
In one embodiment, an electroencephalogram time-domain waveform generating unit acquires an electroencephalogram signal, generates an electroencephalogram time-domain waveform from the electroencephalogram signal;
based on the electroencephalogram time-domain waveform, an electroencephalogram frequency-domain feature extraction unit extracts the electroencephalogram frequency-domain features using wavelet transform;
based on the electroencephalogram frequency domain characteristics, the judging unit extracts a power spectrum entropy value by using a power spectrum entropy extraction algorithm, compares the power spectrum entropy value with the frequency domain preset threshold value, and sends a starting instruction to the control circuit if the power spectrum entropy value is larger than the frequency domain preset threshold value.
In one embodiment, the calculation formula of the wavelet transform is as follows:
where α represents the wavelet scale, τ represents the amount of translation, f (t) represents the time signal function, t represents time,
representing the scale function of the wavelet transform.
In one embodiment, the acquisition unit acquires the voltage variation and the impedance variation in the three-dimensional current field after the safe excitation current is applied;
calculating the impedance distribution function by a calculation unit according to the voltage variation and the impedance variation;
and according to a convolution back projection algorithm, a construction unit performs radon transformation on the impedance distribution function to generate the finite element sectional image, and constructs a brain three-dimensional image according to the finite element sectional image.
In one embodiment, the impedance distribution function is calculated as follows:
ΔV＝SΔC
where Δ V represents a voltage transformation amount, Δ C represents an impedance transformation amount, and S represents a sensitivity matrix.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
the brain imaging system and the method based on the electrical impedance imaging and electroencephalogram signals can acquire brain waveform signals in real time, extract brain electrical signal time domain waveforms according to the brain electrical waveform signals, realize switching between a brain electrical measurement module and an electrical impedance imaging module through a control circuit based on brain electrical time domain characteristics, realize high-time-space resolution monitoring on brain electrical signal changes, further perform three-dimensional imaging on the brain, and accurately reflect physiological information and functional information of the brain according to the three-dimensional imaging.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a block diagram of a brain imaging system based on electrical impedance imaging and electroencephalogram signals provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of a control circuit according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a 10-10EEG array provided by an embodiment of the present invention;
FIG. 4 is a schematic diagram of an EIT measurement electrode array provided by an embodiment of the invention;
FIG. 5 is a flow chart of a method for brain imaging based on electrical impedance imaging and electroencephalogram signals provided by an embodiment of the present invention;
fig. 6 is a flowchart of step S501 provided in the embodiment of the present invention;
fig. 7 is a flowchart of step S503 according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Referring to fig. 1, an embodiment of the present invention provides a brain imaging system based on electrical impedance imaging and electroencephalogram signals, including: the electroencephalogram measurement module 1, the control circuit 2 and the electrical impedance imaging module 3;
the electroencephalogram measurement module 1 is connected with the control circuit 2 and used for collecting electroencephalogram signals, generating electroencephalogram time domain waveforms according to the electroencephalogram signals, extracting electroencephalogram frequency domain features based on the electroencephalogram waveforms, comparing the electroencephalogram frequency domain features with frequency domain preset thresholds, and if the electroencephalogram frequency domain features are larger than the frequency domain preset thresholds, sending a starting instruction to the control circuit 2.
Specifically, the electroencephalogram frequency domain features include: a power spectrum transformation eigenvalue and a phase spectrum transformation eigenvalue.
Further, if the electroencephalogram frequency domain feature is larger than the frequency domain preset threshold, it is indicated that the electroencephalogram time domain waveform has abnormal oscillation.
The control circuit 2 is connected with the electroencephalogram measurement module 1 and the electrical impedance imaging module 3, and is used for receiving the starting instruction and applying safe exciting current to the electrical impedance imaging module 3 according to the starting instruction.
Specifically, referring to fig. 2, if the electroencephalogram time domain waveform has oscillation abnormality, the control circuit 2 positions the electrode for measuring the electroencephalogram signal of the electrode and the adjacent electrode thereof, turns on the switch of the electrode pair circuit, and applies a safe excitation current to the electrical impedance imaging module 3.
The electrical impedance imaging module 3 is connected with the control circuit 2 and used for collecting voltage variation and impedance variation, calculating an impedance distribution function according to the voltage variation and the impedance variation, generating a finite element split image based on the impedance distribution function, and constructing a brain three-dimensional image according to the finite element split image.
In the embodiment, brain waveform signals can be collected in real time, brain electrical signal time domain waveforms are extracted according to the brain electrical waveform signals, switching between a brain electrical measurement module and an electrical impedance imaging module is realized through a control circuit based on brain electrical time domain characteristics, high spatial and temporal resolution monitoring on brain electrical signal changes is realized, therefore, three-dimensional imaging of the brain is carried out, and physiological information and functional information of the brain are accurately reflected according to the three-dimensional imaging.
It should be noted that the measuring module comprises an electroencephalogram collecting device and an electrical impedance measuring device, the electroencephalogram measuring module 1 adopts a Quick-Cap electroencephalogram based on 32/64 channels and a Laplace electrode to collect electroencephalogram signals, as shown in fig. 3-4, the electrical impedance imaging module 3 employs a ring-like electrode array, with the electrode positions on the EIT (electrical impedance imaging) measurement electrode array corresponding to the positions of the 10-10EEG array, for example, the ring-shaped electrode wire of Fpz-Fp2-F8-T8-P8-O2-Oz-O1-P7-T7-F7-Fp1, the ring-shaped electrode wire forms an EIT imaging electrode array, the acquisition of two modes is controlled by using a control circuit 2, when EIT measurement is needed, safe current excitation corresponding to adjacent electrodes is started, and the conductivity distribution change of a specific part of an electric field is measured.
In one embodiment, the electroencephalogram measurement module 1 includes: an electroencephalogram time domain waveform generating unit 4, an electroencephalogram frequency domain feature extracting unit 5, and a judging unit 6;
the electroencephalogram time-domain waveform generating unit 4 is configured to collect electroencephalogram signals and generate electroencephalogram time-domain waveforms from the electroencephalogram signals.
Specifically, based on the electroencephalogram signal, an eeg time-domain waveform is generated by an eeg extraction algorithm, wherein the eeg algorithm specifically comprises the following steps:
1) adopting an asynchronous filter to filter signals, wherein 2-15Hz band-pass filtering is adopted, and 12dB linear gain is set;
2) correcting the absolute value of the amplitude, and converting the negative value of the electroencephalogram signal into a positive value;
3) extracting EEG amplitude envelope by using a 5-order butterworth filter;
4) and dividing the EEG envelope into time periods with duration of 10s, extracting upper and lower wave peak values of the amplitude as edge end points, and generating an electroencephalogram time domain waveform.
The electroencephalogram frequency domain feature extraction unit 5 is configured to extract the electroencephalogram frequency domain features based on the electroencephalogram time domain waveform by using wavelet transform.
Specifically, the calculation formula of the wavelet transform is as follows:
wherein WT (alpha, tau) represents frequency domain characteristics, alpha represents wavelet scale, tau represents translation amount, f (t) represents time signal function, t represents time,
representing the scale function of the wavelet transform.
Further, phase spectrum and power spectrum of the brain electricity frequency domain are extracted according to the frequency domain characteristics, namely power values and phase values of 4 frequency bands of (delta, 0.5-3Hz), (theta, 4-7Hz), (alpha, 8-13Hz), (beta, 14-30 Hz).
The judging unit 6 is configured to extract a power spectrum entropy value by using a power spectrum entropy extraction algorithm based on the electroencephalogram frequency domain feature, compare the power spectrum entropy value with the frequency domain preset threshold value, and send a start instruction to the control circuit 2 if the power spectrum entropy value is greater than the frequency domain preset threshold value.
Specifically, the power spectrum entropy extraction algorithm comprises the following specific steps:
1) calculating the Power spectral Density P (ω)i) The calculation formula is as follows:
wherein, | X (ω)i) I represents fastThe obtained frequency spectrum signal function of fast Fourier transform, N represents the number of frequency points;
2) normalizing PSE (port-system eigenphalopathy) values and extracting power spectral density distribution PiThe calculation formula is as follows:
3) and calculating the power spectrum entropy value H according to the following formula:
further, in the power spectrum of the electroencephalogram signal, when the peak value of the frequency spectrum is narrow, the power spectrum entropy value H is small, which indicates that the electroencephalogram signal has an obvious oscillation rhythm, otherwise, the peak value of the frequency spectrum is smoother, the power spectrum entropy value H is large, and if the power spectrum entropy value H is larger than a preset threshold value, the control circuit 2 is triggered.
In one embodiment, the electrical impedance imaging module 3 comprises: the device comprises an acquisition unit 7, a calculation unit 8 and a construction unit 9;
the acquisition unit 7 is used for acquiring the voltage variation and the impedance variation in the three-dimensional current field after the application of the safe excitation current;
the calculation unit 8 is configured to calculate the impedance distribution function according to the voltage variation and the impedance variation;
specifically, the impedance distribution function is calculated according to the voltage variation and the impedance variation, and the calculation formula is as follows:
ΔV＝SΔC
where Δ V represents a voltage transformation amount, Δ C represents an impedance transformation amount, and S represents a sensitivity matrix.
The construction unit 9 is configured to perform radon transform (radon transform) on the impedance distribution function according to a convolution back-projection algorithm, generate the finite element segmentation image, and construct a brain three-dimensional image according to the finite element segmentation image.
Specifically, the calculation formula of the convolution back projection algorithm is as follows:
where F (r, w) represents a finite element profile image, F (x, y) represents an impedance distribution function, δ (x) represents a dirac function, and δ (x) is equal to 1 when x is equal to 0 and δ (x) is equal to 0 when x is not equal to 0.
Further, the finite element subdivision image constructs a brain three-dimensional image according to a three-layer finite element model of the human head, namely the human head is divided into three layers, and the finite element subdivision image constructs the brain three-dimensional image according to different layers and parts and the layers and parts of the corresponding human head.
Referring to fig. 5, the brain imaging method based on electrical impedance imaging and electroencephalogram signals includes:
s501, an electroencephalogram measuring module collects electroencephalogram signals, electroencephalogram time domain waveforms are generated according to the electroencephalogram signals, electroencephalogram frequency domain features are extracted based on the electroencephalogram waveforms, the electroencephalogram frequency domain features are compared with a frequency domain preset threshold value, and if the electroencephalogram frequency domain features are larger than the frequency domain preset threshold value, a starting instruction is sent to a control circuit.
Specifically, the electroencephalogram frequency domain features include: a power spectrum transformation eigenvalue and a phase spectrum transformation eigenvalue.
Further, if the electroencephalogram frequency domain feature is larger than the frequency domain preset threshold, it is indicated that the electroencephalogram time domain waveform has abnormal oscillation.
And S502, the control circuit receives the starting instruction and applies safe exciting current to the electrical impedance imaging module according to the starting instruction.
Specifically, if the electroencephalogram time domain waveform has oscillation abnormality, the control circuit positions the electrode electroencephalogram signal measuring electrode and the adjacent electrode thereof, opens the electrode pair circuit switch, and applies safe excitation current to the electrical impedance imaging module.
S203, the electrical impedance imaging module collects voltage variation and impedance variation, an impedance distribution function is calculated according to the voltage variation and the impedance variation, a finite element split image is generated based on the impedance distribution function, and a brain three-dimensional image is constructed according to the finite element split image.
In one embodiment, as shown in FIG. 6, step 501, comprises:
s5011, an electroencephalogram time domain waveform generating unit collects electroencephalogram signals and generates electroencephalogram time domain waveforms according to the electroencephalogram signals.
Specifically, based on the electroencephalogram signal, an eeg time-domain waveform is generated by an eeg extraction algorithm, wherein the eeg algorithm specifically comprises the following steps:
1) adopting an asynchronous filter to filter signals, wherein 2-15Hz band-pass filtering is adopted, and 12dB linear gain is set;
2) correcting the absolute value of the amplitude, and converting the negative value of the electroencephalogram signal into a positive value;
3) extracting EEG amplitude envelope by using a 5-order butterworth filter;
4) and dividing the EEG envelope into time periods with duration of 10s, extracting upper and lower wave peak values of the amplitude as edge end points, and generating an electroencephalogram time domain waveform.
S5012, based on the electroencephalogram time domain waveform, the electroencephalogram frequency domain feature extraction unit extracts the electroencephalogram frequency domain features by utilizing wavelet transformation.
Specifically, the calculation formula of the wavelet transform is as follows:
where α represents the wavelet scale, τ represents the amount of translation, f (t) represents the time signal function, t represents time,
representing the scale function of the wavelet transform.
S5013, based on the electroencephalogram frequency domain characteristics, the judging unit extracts a power spectrum entropy value by using a power spectrum entropy extraction algorithm, compares the power spectrum entropy value with the frequency domain preset threshold value, and sends a starting instruction to the control circuit if the power spectrum entropy value is larger than the frequency domain preset threshold value.
Specifically, the power spectrum entropy extraction algorithm comprises the following specific steps:
1) calculating the Power spectral Density P (ω)i) The calculation formula is as follows:
wherein, | X (ω)i) I represents a frequency spectrum signal function obtained by fast Fourier transform, and N represents the number of frequency points;
2) normalizing PSE (port-system eigenphalopathy) values and extracting power spectral density distribution PiThe calculation formula is as follows:
3) and calculating the power spectrum entropy value H according to the following formula:
further, in the power spectrum of the electroencephalogram signal, when the peak value of the frequency spectrum is narrow, the power spectrum entropy value H is small, which indicates that the electroencephalogram signal has an obvious oscillation rhythm, otherwise, the peak value of the frequency spectrum is smoother, the power spectrum entropy value H is large, and if the power spectrum entropy value H is larger than a preset threshold value, the control circuit is triggered.
In one embodiment, as shown in fig. 7, step S503 includes:
s5031, the acquisition unit acquires the voltage variation and the impedance variation in the three-dimensional current field after the safe excitation current is applied.
S5032, calculating the impedance distribution function by a calculating unit according to the voltage variation and the impedance variation.
Specifically, the calculation formula of the impedance distribution function is as follows:
ΔV＝SΔC
where Δ V represents a voltage transformation amount, Δ C represents an impedance transformation amount, and S represents a sensitivity matrix.
S5033, according to a convolution back projection algorithm, the building unit performs radon transformation on the impedance distribution function to generate the finite element sectional image, and builds a brain three-dimensional image according to the finite element sectional image.
Specifically, the calculation formula of the convolution back projection algorithm is as follows:
where f (x, y) represents an impedance distribution function, δ (x) represents a dirac function, and δ (x) is 1 when x is equal to 0 and δ (x) is 0 when x is not equal to 0.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. Brain imaging system based on electrical impedance imaging and electroencephalogram signals, comprising: the electroencephalogram measurement module, the control circuit and the electrical impedance imaging module;
the electroencephalogram measurement module is connected with the control circuit and used for acquiring electroencephalogram signals, generating electroencephalogram time domain waveforms according to the electroencephalogram signals, extracting electroencephalogram frequency domain characteristics based on the electroencephalogram waveforms, comparing the electroencephalogram frequency domain characteristics with a frequency domain preset threshold value, and sending a starting instruction to the control circuit if the electroencephalogram frequency domain characteristics are larger than the frequency domain preset threshold value;
the control circuit is connected with the electroencephalogram measurement module and the electrical impedance imaging module, and is used for receiving the starting instruction and applying safe exciting current to the electrical impedance imaging module according to the starting instruction;
the electrical impedance imaging module is connected with the control circuit and used for collecting voltage variation and impedance variation, calculating an impedance distribution function according to the voltage variation and the impedance variation, generating a finite element split image based on the impedance distribution function, and constructing a brain three-dimensional image according to the finite element split image.
2. The electrical impedance imaging and electroencephalography signal based brain imaging system of claim 1, wherein the electroencephalography measurement module comprises: the electroencephalogram frequency domain feature extraction device comprises an electroencephalogram time domain waveform generation unit, an electroencephalogram frequency domain feature extraction unit and a judgment unit;
the electroencephalogram time domain waveform generating unit is used for collecting electroencephalogram signals and generating electroencephalogram time domain waveforms according to the electroencephalogram signals;
the electroencephalogram frequency domain feature extraction unit is used for extracting the electroencephalogram frequency domain features by utilizing wavelet transformation based on the electroencephalogram time domain waveform;
the judging unit is used for extracting a power spectrum entropy value by using a power spectrum entropy extraction algorithm based on the electroencephalogram frequency domain characteristics, comparing the power spectrum entropy value with the frequency domain preset threshold value, and sending a starting instruction to the control circuit if the power spectrum entropy value is larger than the frequency domain preset threshold value.
3. The electrical impedance imaging and electroencephalogram signal-based brain imaging system according to claim 2, wherein the calculation formula of the wavelet transform in the electroencephalogram frequency-domain feature extraction unit is as follows:
wherein, alpha represents wavelet scale, tau represents translation quantity, f (t) represents time signal function, and t represents timeIn the middle of the furnace, the gas-liquid separation chamber,
representing the scale function of the wavelet transform.
4. The electrical impedance imaging and electroencephalogram signal-based brain imaging system of claim 1, wherein the electrical impedance imaging module comprises: the system comprises an acquisition unit, a calculation unit and a construction unit;
the acquisition unit is used for acquiring the voltage variation and the impedance variation in the three-dimensional current field after the safe excitation current is applied;
the calculation unit is used for calculating the impedance distribution function according to the voltage variation and the impedance variation;
the construction unit is used for carrying out radon transformation on the impedance distribution function according to a convolution back projection algorithm to generate the finite element sectional image and constructing a brain three-dimensional image according to the finite element sectional image.
5. The electrical impedance imaging and electroencephalogram signal-based brain imaging system according to claim 4, wherein the impedance distribution function is calculated in the calculation unit according to the voltage variation and the impedance variation, and the calculation formula is as follows:
ΔV＝SΔC
where Δ V represents a voltage transformation amount, Δ C represents an impedance transformation amount, and S represents a sensitivity matrix.
6. A method of brain imaging based on electrical impedance imaging and electroencephalogram signals, comprising:
the electroencephalogram measurement module acquires an electroencephalogram signal, generates an electroencephalogram time domain waveform according to the electroencephalogram signal, extracts an electroencephalogram frequency domain feature based on the electroencephalogram waveform, compares the electroencephalogram frequency domain feature with a frequency domain preset threshold, and sends a starting instruction to the control circuit if the electroencephalogram frequency domain feature is larger than the frequency domain preset threshold;
the control circuit receives the starting instruction and applies safe exciting current to the electrical impedance imaging module according to the starting instruction;
the electrical impedance imaging module collects voltage variation and impedance variation, calculates an impedance distribution function according to the voltage variation and the impedance variation, generates a finite element split image based on the impedance distribution function, and constructs a brain three-dimensional image according to the finite element split image.
7. The electrical impedance imaging and electroencephalogram signal-based brain imaging method of claim 6, wherein the electroencephalogram time-domain waveform generating unit acquires an electroencephalogram signal from which the electroencephalogram signal is generated;
based on the electroencephalogram time-domain waveform, an electroencephalogram frequency-domain feature extraction unit extracts the electroencephalogram frequency-domain features using wavelet transform;
based on the electroencephalogram frequency domain characteristics, the judging unit extracts a power spectrum entropy value by using a power spectrum entropy extraction algorithm, compares the power spectrum entropy value with the frequency domain preset threshold value, and sends a starting instruction to the control circuit if the power spectrum entropy value is larger than the frequency domain preset threshold value.
8. The method for brain imaging based on electrical impedance imaging and electroencephalogram signals of claim 7, wherein the wavelet transform is calculated as follows:
where α represents the wavelet scale, τ represents the amount of translation, f (t) represents the time signal function, t represents time,
representing the scale function of the wavelet transform.
9. The method for brain imaging based on electrical impedance imaging and electroencephalogram signals of claim 6, wherein the acquisition unit acquires the amount of voltage change and the amount of impedance change in a three-dimensional current field after applying a safe excitation current;
calculating the impedance distribution function by a calculation unit according to the voltage variation and the impedance variation;
and according to a convolution back projection algorithm, a construction unit performs radon transformation on the impedance distribution function to generate the finite element sectional image, and constructs a brain three-dimensional image according to the finite element sectional image.
10. A method of electrical impedance imaging and electroencephalogram signal based brain imaging according to claim 9, wherein the impedance distribution function is calculated as follows:
ΔV＝SΔC
where Δ V represents a voltage transformation amount, Δ C represents an impedance transformation amount, and S represents a sensitivity matrix.
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