CN108158579B - Non-contact non-invasive epilepsy self-organization monitoring and early warning intervention system and method for acquiring interference electroencephalogram distribution - Google Patents
Non-contact non-invasive epilepsy self-organization monitoring and early warning intervention system and method for acquiring interference electroencephalogram distribution Download PDFInfo
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
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- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
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- A61B5/4094—Diagnosing or monitoring seizure diseases, e.g. epilepsy
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
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- A—HUMAN NECESSITIES
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
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- A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
Abstract
The invention discloses a non-contact non-invasive epilepsy self-organization monitoring and early warning intervention system and a method for acquiring interference electroencephalogram distribution, wherein the system comprises a non-contact acquisition module, a non-contact control module and a non-contact intervention module; the non-contact acquisition module comprises a plurality of signal acquisition electrodes which are not in contact with the scalp; the non-contact control module comprises a time prediction unit and a space prediction unit; the non-contact intervention module comprises a plurality of controllable pulse electromagnetic units, each controllable pulse electromagnetic unit is provided with an electromagnetic coil facing the scalp, and the electromagnetic coils and the signal acquisition electrodes are arranged in a staggered manner; the system can predict the seizure time and the seizure position before the epileptic seizure, intervenes the charge distribution and the current trend in the brain in a self-organizing way according to the prediction result, fully utilizes the initial sensitivity of the chaotic circuit, machine learning and the biological electromagnetic coupling principle to measure the electroencephalogram and intervene the epileptic seizure in a non-contact non-invasive way, and can be applied to the fields of wearable equipment and mobile medical treatment.
Description
Technical Field
The invention relates to the field of electromagnetic physiotherapy, in particular to a non-contact, non-invasive and nondestructive epilepsy monitoring and early warning intervention system and a method for acquiring interference electroencephalogram distribution.
Background
Epilepsy, commonly known as epilepsy or epilepsy, is a discharge phenomenon caused by internal network defects and caused by over-high pressure of partial network nodes.
In addition to the structural damage of the brain region of the patient, the convulsion and the loss of consciousness caused by the epileptic seizure also cause secondary damage such as falling injury, bump and the like to the patient. The main treatments at present are: the drug therapy is assisted by surgical treatment.
There is increasing evidence that: the electromagnetic field may act as an induction factor to affect the signal channels of the cells; the cell membrane is the main target site of the action of the environmental electromagnetic field and the cell, and the cell membrane receptor is one of the possible signal coupling points of the electromagnetic field. In addition, due to the extreme sensitivity of the dynamic behavior of the chaotic power system to initial parameters, people can detect and extract some weak signals through a nonlinear circuit.
Disclosure of Invention
The invention aims to provide a non-contact non-invasive epilepsy self-organization monitoring and early warning intervention system and a method for acquiring interference electroencephalogram distribution, aiming at the defects of the prior art.
The purpose of the invention is realized by the following technical scheme: a non-contact non-invasive epilepsy self-organization monitoring early warning intervention system is of a three-layer superposed structure and comprises the following components from inside to outside: the system comprises a non-contact acquisition module, a non-contact intervention module and a non-contact control module;
the non-contact acquisition module comprises n signal acquisition electrodes, the signal acquisition electrodes are not in contact with the scalp, the epileptic brain waves acquired by the signal acquisition electrodes are amplified by an amplifier, the relation between the brain waves and actual potential information is obtained through machine learning, and therefore the potential e 'is obtained in a non-contact mode'i1,2,3, n, followed by a filter pair potential e'iNoise reduction and filtration are carried out to obtain filtered potential ei,i＝1,2,3,...,n；
The non-contact control module comprises a time prediction unit and a space prediction unit; the time-space coupling control determines the moment of the epileptic burst and the position of the epileptic burst;
the time prediction unit passes the current t1Training potential sequences acquired at each time point in a set time interval before the time to obtain a prediction function P, obtaining the potential e (t) of each signal acquisition electrode prediction time t through the prediction function P, comparing the prediction potential e (t) with a set potential threshold value theta, and judging a position set A { (x, y) | e ≧ theta } of a signal acquisition electrode corresponding to the prediction potential e (t) mark e ≧ theta, e { (x, y) | e ≧ theta } as a time-predicted explosion position, wherein x represents the number of rows of the signal acquisition electrode in a non-contact acquisition module, y represents the number of columns of the signal acquisition electrode in the non-contact acquisition module, and e | is the epilepsy-predicted explosion time; sending the predicted outbreak position to a space prediction unit, starting the space prediction unit at the same time, and otherwise, continuing monitoring;
the spatial prediction unit passes the current time t of the n signal acquisition electrodes1The electric potential of the neighborhood time is combined with the positions (x, y) of the n signal acquisition electrodes in the non-contact acquisition module to obtain n (eta +1) voltages e of the n signal acquisition electrodes(i,t)(x,y),(t＝t1-η,...,t1-2,t1-1,t1(ii) a i 1, 2.. n), η +1 voltages e per signal acquisition point(i,t)In (x, y), adjacent voltages are subtracted to obtain eta voltage gradients D of each signal acquisition electrode(i,j)(i ═ 1, 2., n, j ═ 1, 2.,. η); averaging the eta voltage gradients of each signal acquisition point to obtain a voltage gradient average value
By comparing the average voltage gradient values of n signal-collecting electrodes
Obtaining a set B of voltage gradient maximum value coordinates (x ', y ') and a set C of minimum value coordinates (x ', y ");
obtaining a set P of brain excitation region coordinates (x, y) by calculating the intersection of the sets A and B, and obtaining a set P of brain excitation region coordinates (x, y) by calculating the set
Obtaining a non-excitatory area coordinate (x ^ y ^) set N of the brain by the intersection of the coordinate and the coordinate C, and sending coordinate set information to an intervention module;
the non-contact intervention module comprises m controllable pulse electromagnetic units, each controllable pulse electromagnetic unit is provided with an electromagnetic coil facing the scalp, and the electromagnetic coils and the signal acquisition electrodes are arranged in a staggered mode and do not overlap in the vertical direction; the electromagnetic coil generates an intervention magnetic field B with controllable size and polarity, the intervention magnetic field B performs electromagnetic stimulation massage on the non-excitation region set N in a self-organizing intervention mode, and a large amount of accumulated charges in a brain region where the excitation region set P is located are evacuated according to the brain energy conservation principle, so that the charge accumulation level in the brain is always kept in a balanced state;
human brain consumes energy every day, and each human brain consumes energy every day with a time-varying value and has a consumption peak value EmaxAnd consumption valley value Emin. The energy consumed at each moment is related to the oxygen consumption of the brain at each moment. The oxygen consumption of a person at any time is related to the functions of the person, such as the hemopoietic capacity of the heart,Muscle structure, etc., close to a constant value;
for convenience of description, the following energy value E means an average value of energy consumption in each time period of each day, oxygen consumption VocAverage brain consumption of oxygen for each time period of the day;
setting total energy consumed by the brain of a person at a certain moment as EtotalThe energy consumed by the excitation area at the same time is EaThe energy consumed by the non-excited region is EpThe rate of total energy consumption by the brain is
The velocity (gradient) of the energy consumed by the excitation area isThe non-excited region consumes energy at a rate (gradient) of
Oxygen consumption of brain is VocMaximum rate of energy consumption V in the brainEmaxThen, the following relationship is present:
Etotal∝Voc，VEmax∝Voc，Ea+Ep＝Etotal(1)
total energy E consumed in the braintotalDependent on cerebral oxygen consumption VocMaximum rate of energy consumption V in the brainEmaxAlso restricted to cerebral oxygen consumption Voc. Thus the consumption of energy in the brain is conserved and no energy mutation situation occurs. Based on the energy E of the non-excited area of the brainpWhen the excitation region becomes higher with a certain gradient, the excitation region energy EaWill become low with a certain gradient.
Considering that the amount of energy consumed by the brain does not change with time to a constant value, the total energy consumed by the brain is given as well as the rate at which it is consumed:
where t is any time and Δ t is a very small time interval. The following relations can be obtained from the expressions (1) and (2):
gradient of energy consumption in the excitation region according to formula (3)Gradient of energy consumption from non-excited regions
Gradient with sum equal to total energy consumed by brain
And because of the gradient of total energy consumption
Maximum consumption speed V less than total energyEmaxAnd V isEmaxWith oxygen consumption VocPositive correlation, so the total energy consumption rate
Oxygen consumption of brain VocProportional ratio, also limited by oxygen consumption of brain, so that the energy change gradientAlso no mutation occurred. Thus, the gradient of energy consumption when non-excited regions are not excited
When the energy of the original excitation area is reduced
It will rise and the rate of energy change will not change abruptly. Thus, the gradient of energy change can be achieved by controlling the increment of the gradient of energy consumption of the non-excited regionTo the maximum.
When the epilepsy is about to happen, the electromagnetic pulse B emitted by the controllable electromagnetic unit stimulates the non-excited area of the brain and excites the non-excited area of the brain through electromagnetic coupling, and a gradient is assumedAnd
at constant value, the energy of the non-excited region will be according to
Rise while the energy of the excitation area will follow a gradient
And (4) descending. Therefore, the energy in the brain is redistributed, a large amount of charges in an excitation area of the brain move to a non-excitation area, the brain electrical distribution in the brain is balanced, and the energy gradient of the excitation area can be maximized by controlling the frequency, the waveform and the like of the electromagnetic pulse B.
The self-organizing intervention mode is implemented as follows:
after the prediction of the time prediction unit and the space prediction unit is finished, obtaining a brain excitation area coordinate (x, y) set P and a brain non-excitation area coordinate (x, y), wherein the set P comprises P elements, and the set N comprises l elements; when the non-contact intervention module is started, the pulse electromagnetic units corresponding to the I coordinates in the set N start to emit electromagnetic pulses Bi(i＝1,2,…,l,l<m), electromagnetic pulse B)iRegulating between 0T and 2T, electromagnetic pulse BiRespectively acting on the scalp region sigma corresponding to each coordinate in the set Ni(i ═ 1,2, …, l) by biological electromagnetic coupling, σiThe brain charge in the region begins to accumulate and the potential e is measuredi(i ═ 1,2, …, l) is elevated; then according to the brain energy conservation principle, the scalp area lambda corresponding to each coordinate in the set Pk(k 1,2, …, p) charge begins to disperse and the potential e is measuredk(k ═ 1,2, …, p) begins to fall;after the action time lasts delta T, if the excitation area signal acquires the unit potential ekAnd when the potentials (k is 1,2, … and p) are all smaller than the potential threshold value theta, ending the operation of the non-contact intervention module, and otherwise, continuing the intervention operation.
The brain energy conservation principle is as follows: setting the total brain energy consumption of a person at a certain moment as EtotalThe energy consumed by the excitation area at the same time is EaThe energy consumed by the non-excited region is EpThe gradient of total energy consumed by the brain is
The gradient of the energy consumption of the excitation region is
The gradient of the energy consumption of the non-excited region isOxygen consumption of brain is VocMaximum rate of energy consumption V in the brainEmaxThen, there is the relation: etotal∝Voc，VEmax∝Voc，Ea+Ep＝Etotal(ii) a Total energy E consumed in the braintotalDepending on the oxygen consumption capacity of the brain, i.e. brain oxygen consumption VocMaximum rate of energy consumption V in the brainEmaxAlso restricted to cerebral oxygen consumption Voc(ii) a Thus, the consumption energy in the brain is conserved and no energy mutation is produced; based on the energy E of the non-excited area of the brainpWhen the excitation region becomes higher with a certain gradient, the excitation region energy EaWill become low at a certain gradient; if it represents the gradient of energy change
And
when constant, the energy of the non-excited region is according to
When rising, the energy of the excitation area will follow a gradient
Descending; so that the energy in the brain is redistributed and the large amount of charge accumulated in the excitation area of the brain will move to other areas, thereby balancing the charge distribution in the brain.
Further, the system is placed outside the brain and is hat-shaped in appearance.
Furthermore, the amplifier is a non-contact signal amplifier, which is different from the traditional method that the craniotomy is needed or the scalp is coated with saline water and other means to enhance signals, the signal acquisition electrode can be contacted with or not contacted with the scalp, a nonlinear circuit is adopted, the initial sensitivity of the chaotic circuit can be used for measuring tiny disturbance, and for example, a duffin circuit oscillator circuit can be used for measuring the change of weak signals; the non-contact signal amplifier is built by a duffing circuit formula:
where k is the damping ratio, x is the measurement signal, (x)3-x5) For the nonlinear restoring force, γ sin (ω t + π) is the built-in driving signal, γ is the amplitude of the built-in driving signal, and ω is the frequency of the built-in driving signal.
Further, the obtaining of the potential e (t) at the predicted time t of each signal collecting electrode through the prediction function P specifically includes:
for the ith signal acquisition electrode, the potential time sequence obtained by amplification and filtration is ei(t), (i ═ 1, 2.., n), where c (i) is a set of nodes adjacent to node i, m is the number of elements in c (i) and indicates the number of adjacent nodes, and ec(i)(t) is the potential of node set c (i); with ei(t) and ec(i)(t) training the prediction function P with the historical data as input and output samples;
the training process is as follows:
defining the input of the prediction function P as input, output is output; taking deltat as a time interval, the potential of the ith signal acquisition point at the moment t is ei(t), the potential at a time t before the time t is represented by ei(t-1),ei(t-2)...,ei(t- τ); the potential of the adjacent acquisition point of the ith signal acquisition point is ec(i)The potential of (t), tau time intervals is denoted as ec(i)(t-1),ec(i)(t-2)...,e(i)(t- τ); with m +1 samples of length 1+ τ as input, the input is expressed as:
input＝{ei(t),ei(t-1),...,ei(t-τ),ec(i)(t),ec(i)(t-1),...,ec(i)(t-τ)}；
the output of the prediction function P is the potential e at time t over a time interval Δ ti(t +1), then the output is expressed as: output ═ ei(t+1)}；
When training the kth generation, the input length is (1+ m) × (1+ τ), and the output length is 1;
if t takes a certain time t before0Inputting training into
input＝{ei(t0),ei(t0-1),...,ei(t0-τ),ec(i)(t0),ec(i)(t0-1),...,ec(i)(t0- τ) } and a training output
output＝{ei(t0+1) } as a set of training samples; take the time interval Δ t' and from t0Taking N time intervals after the moment, i.e. t0,t0+1,...,t0Taking input and output corresponding to + N-1 as N groups of training samples, taking a training algebra k from 1000 times to 10000 times, and obtaining a training function P according to a training result, namely, output is P (input);
when t is the current time t1Then, the next time t is obtained by predicting through the prediction function P1+ Δ t acquisition point voltage amplitude ei(t1+ Δ t); let the predicted time t ═ t1+ Δ t, the predicted potential e (t) at the predicted time t is obtained.
Further, in the spatial prediction unit, a voltage ladderDegree average value
The acquisition method comprises the following steps:
current time t through n signal acquisition electrodes1The collected electric potentials of n signal collecting electrodes at n (eta +1) moments are obtained by combining the electric potentials of the neighborhood moments with the positions (x, y) of the n signal collecting electrodes in the non-contact collecting module
e(i,t)(x,y),(t＝t1-η,...,t1-2,t1-1,t1(ii) a 1,2, n), mixing e(i,t)(x, y) is denoted as e(i,t)Then, there is a collection potential matrix:
each row of the collection potential matrix represents eta +1 collection potential sequences at shorter time, and adjacent two rows of the matrix are subjected to difference to obtain eta potential gradients of the n signal collection electrodes
D(i,j)＝e(i,t)(x,y)-e(i,t-1)(x,y)，(t＝t1-η+1,...,t1-2,t1-1,t 11, 2.. n, j ═ 1, 2.. η), then there is a potential gradient matrix:
the ith row of the voltage gradient matrix represents a potential time difference sequence of the ith signal acquisition point, the jth column represents the jth adjacent potential time gradient, and then the potential time difference sequences of each row are averagedObtaining the average value of voltage gradient
Further, the controllable pulse electromagnetic unit comprises a PWM wave control module and a full-control switchPower-off electronic device module, electromagnetic coil; the fully-controlled turn-off controllable power electronic device module adopts an IGBT, the output end of the PWM wave control module is connected with a gate pole G of the IGBT, a collector electrode C of the IGBT is respectively connected with one end of a capacitor C1 and a first fixed end of a slide rheostat R1, a second fixed end of the slide rheostat R1 is connected with the positive pole of a power supply, an emitter electrode E of the IGBT is connected with one end of an electromagnetic coil, and the other end of the electromagnetic coil is connected with the negative pole of the power supply after being connected with the other end of the capacitor C1; the slide rheostat R1 can change the current in the electromagnetic coil; the on and off of the IGBT are controlled by the PWM wave control module, so that the current in the electromagnetic coil is changed, and the interference magnetic field is changed
Direction and strength.
A non-contact non-invasive self-organizing method for obtaining interference electroencephalogram distribution is characterized in that a non-contact non-invasive method is used for obtaining and self-organizing the distribution of electric charges and the trend of current; the method comprises the steps of measuring the distribution of charges and currents in the brain in a non-contact mode, locating a block with more charges accumulated and low accumulation speed or a region with less charges accumulated and low accumulation speed by combining the temporal prediction with the spatial prediction, and intervening the distribution of the charges in the brain and the current trend in neurons based on a self-organizing method; the method comprises the following specific steps:
step 1: acquiring brain waves of a subject by n signal acquisition electrodes, obtaining the relation between the brain waves and actual potential information by machine learning without contacting the signal acquisition electrodes with the scalp, and obtaining the potential e 'by a non-contact method'i1,2,3, n, followed by a filter pair potential e'iNoise reduction and filtration are carried out to obtain filtered potential ei,i＝1,2,3,...,n；
Step 2: and (3) time prediction: training to obtain a prediction function P through potential sequences acquired at each time point in a set time interval before the time t, obtaining the potential e (t) of each signal acquisition electrode at the prediction time t through the prediction function P, comparing the predicted potential e (t) with a set potential threshold theta, and setting the position set of the signal acquisition electrodes which is larger than or equal to theta as A { (x, y) | e ≧ theta }, wherein (x, y) is the position coordinate of the signal acquisition electrodes;
and 3, step 3: spatial prediction: current time t through n signal acquisition electrodes1The electric potential of the neighborhood time is combined with the positions (x, y) of the n signal acquisition electrodes to obtain n (eta +1) voltages of the n signal acquisition electrodes
e(i,t)(x,y),(t＝t1-η,...,t1-2,t1-1,t1(ii) a i 1, 2.. n), η +1 voltages e per signal acquisition point(i,t)In (x, y), adjacent voltages are subtracted to obtain eta voltage gradients D of each signal acquisition electrode(i,j)(i ═ 1, 2., n, j ═ 1, 2.,. η); averaging the eta voltage gradients of each signal acquisition point to obtain a voltage gradient average value
By comparing the average voltage gradient values of n signal-collecting electrodesObtaining a set B of voltage gradient maximum value coordinates (x ', y ') and a set C of minimum value coordinates (x ', y ");
and 4, step 4: obtaining a set P of brain excitation region coordinates (x, y) through the intersection of the sets A and B, and obtaining a set through aggregation
Obtaining a non-excitation area coordinate (x ^ y ^) set N of the brain by the intersection of the X and the C;
and 5, step 5: brain electrical interference: there are P elements in the brain excitation area coordinate (x, y) set P, there are l elements in the brain non-excitation area coordinate (x, y) set N; when the electroencephalogram interference starts, an electromagnetic pulse B is sent out to the coordinates I in the set Ni(i＝1,2,…,l,l<m), electromagnetic pulse B)iRegulating between 0T and 2T, electromagnetic pulse BiRespectively acting on the scalp region sigma corresponding to each coordinate in the set Ni(i ═ 1,2, …, l) by biological electromagnetic coupling, σiThe brain charge in the region begins to accumulate,measuring potential ei(i ═ 1,2, …, l) is elevated; then according to the brain energy conservation principle, the scalp area lambda corresponding to each coordinate in the set Pk(k 1,2, …, p) charge begins to disperse and the potential e is measuredk(k ═ 1,2, …, p) begins to fall; after the action time lasts delta T, if the excitation area signal acquires the unit potential ekWhen the potentials (k is 1,2, …, p) are all smaller than the potential threshold value theta, the interference is stopped, otherwise, the interference operation is continued.
The invention has the beneficial effects that:
(1) because the traditional electroencephalogram measurement means needs to open the skull of a tested person to insert an electrode sheet or paste an electrode sheet by smearing physiological saline close to the scalp to obtain required data, great trouble is brought to the tested person. The invention adopts non-contact means such as duffin signal detection circuit and the like to detect the electroencephalogram data of the user of the system. The electroencephalogram signals can be obtained in a non-invasive and non-destructive method by utilizing the initial sensitivity of the chaotic circuit;
(2) the general invention only predicts the epileptic outbreak time to provide early warning, and the early warning function is not perfect. The invention provides a method for predicting an epilepsia position by utilizing an electroencephalogram gradient field, and a time and a space position of a large amount of accumulated charges in electroencephalogram to a critical value are obtained by a combined-empty prediction method.
(3) When electric charge flows in the brain, a large amount of electric charge is accumulated due to defects on certain structures, and the symptoms of brain discharge such as epilepsy are caused. The invention provides an intervention method based on a self-organization mode through the intervention effect of a controllable magnetic field, so that the charges in a charge accumulation area are transferred to an area with less charge accumulation.
(4) The invention is light and fast, and can be applied to the fields of wearable equipment and mobile medical treatment.
Drawings
FIG. 1 is a conceptual diagram of the appearance of the present system;
FIG. 2 is a diagram of a distribution of a controllable pulse electromagnetic unit and a duffin electroencephalogram acquisition unit;
FIG. 3 is a duffin electroencephalogram acquisition circuit diagram of the system;
FIG. 4 is a schematic diagram of time series prediction;
FIG. 5 is a graph of a spatially predicted voltage fit surface;
FIG. 6 is a circuit diagram of a controlled pulse electromagnetic unit;
FIG. 7 is a schematic diagram of a self-organizing intervention mode;
FIG. 8 is a block diagram of the system;
fig. 9 is a complete operational flow diagram of the present system.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described with reference to the accompanying drawings.
The invention provides a non-contact non-invasive epilepsy self-organization monitoring and early warning intervention system, as shown in figure 1, the appearance of the system is in a hat shape or a shape wrapping a brain, the system has three layers of structures, and the three layers are respectively as follows from inside to outside: the system comprises a non-contact acquisition module, a non-contact intervention module and a non-contact control module; the duffin electroencephalogram acquisition units and the controllable pulse electromagnetic units are distributed as shown in figure 2, are not overlapped with each other and are arranged in a staggered manner.
The non-contact acquisition module comprises n signal acquisition electrodes, the signal acquisition electrodes are not in contact with the scalp, the epileptic brain waves acquired by the signal acquisition electrodes are amplified by an amplifier, the relation between the brain waves and actual potential information is obtained through machine learning, and therefore the potential e 'is obtained in a non-contact mode'i1,2,3, n, followed by a filter pair potential e'iNoise reduction and filtration are carried out to obtain filtered potential e i1,2,3, ·, n; as shown in fig. 3, the signal acquisition electrode circuit is implemented by the duffing circuit equation.
As shown in fig. 4, the time prediction unit obtains a prediction function P through training of a potential sequence acquired at each time point within a set time interval before the time t;
for the ith signal acquisition electrode, the potential time sequence obtained by amplification and filtration is ei(t), (i ═ 1, 2.., n), where c (i) is a set of nodes adjacent to node i, m is the number of elements in c (i) and indicates the number of adjacent nodes, and ec(i)(t) is the potential of node set c (i);
defining the input of the prediction function P as input and the output as output; taking deltat as a time interval, the potential of the ith signal acquisition point at the moment t is ei(t), the potential at a time t before the time t is represented by ei(t-1),ei(t-2)...,ei(t- τ); the potential of the adjacent acquisition point of the ith signal acquisition point is ec(i)The potentials of (t) and (tau) time intervals are expressed as
ec(i)(t-1),ec(i)(t-2)...,e(i)(t- τ); with 1+ m samples of length 1+ τ as input, the input is expressed as:
input＝{ei(t),ei(t-1),...,ei(t-τ),ec(i)(t),ec(i)(t-1),...,ec(i)(t-τ)}；
the output of the prediction function P is the potential e at time t over a time interval Δ ti(t +1), then the output is expressed as: output ═ ei(t+1)}；
When training the kth generation, the input length is (1+ m) × (1+ τ), and the output length is 1;
if t takes a certain time t before0Inputting training into
input＝{ei(t0),ei(t0-1),...,ei(t0-τ),ec(i)(t0),ec(i)(t0-1),...,ec(i)(t0- τ) } and a training output
output＝{ei(t0+1) } as a set of training samples; take the time interval Δ t' and from t0Taking N time intervals after the moment, i.e. t0,t0+1,...,t0Taking input and output corresponding to + N-1 as N groups of training samples, taking a training algebra k from 1000 times to 10000 times, and obtaining a training function P according to a training result, namely, output is P (input);
when t is the current time, the voltage amplitude e of the acquisition point at the next time t + delta t is obtained through prediction of a prediction function Pi(t + Δ t); let the prediction time t be t + Δ t, and obtain the prediction potential e (t) at the prediction time t.
Comparing the predicted potential e (t) with a set potential threshold value theta, and judging a position set A { (x, y) | e ≧ theta } of the signal acquisition electrode corresponding to the predicted potential e (t) mark e, e ≧ theta as a time-predicted burst position, wherein x represents the number of rows of the signal acquisition electrode in the acquisition module, y represents the number of columns of the signal acquisition electrode in the acquisition module, and t corresponding to e is the epilepsy predicted burst time; sending the predicted outbreak position to a space prediction unit, starting the space prediction unit at the same time, and otherwise, continuing monitoring;
as shown in FIG. 5, the spatial prediction unit passes the current time t of the n signal collecting electrodes1The electric potential of the neighborhood time is combined with the positions (x, y) of the n signal acquisition electrodes in the non-contact acquisition module to obtain n (eta +1) voltages e of the n signal acquisition electrodes(i,t)(x,y),(t＝t1-η,...,t1-2,t1-1,t1(ii) a i 1, 2.. n), η +1 voltages e per signal acquisition point(i,t)In (x, y), adjacent voltages are subtracted to obtain eta voltage gradients D of each signal acquisition electrode(i,j)(i ═ 1, 2., n, j ═ 1, 2.,. η); averaging the eta voltage gradients of each signal acquisition point to obtain a voltage gradient average valueBy comparing the average voltage gradient values of n signal-collecting electrodes
Obtaining a set B of voltage gradient maximum value coordinates (x ', y ') and a set C of minimum value coordinates (x ', y ");
obtaining a set P of brain excitation region coordinates (x, y) by calculating the intersection of the sets A and B, and obtaining a set P of brain excitation region coordinates (x, y) by calculating the set
Obtaining a non-excitatory area coordinate (x ^ y ^) set N of the brain by the intersection of the coordinate and the coordinate C, and sending coordinate set information to an intervention module;
the non-contact intervention module comprises m controllable pulse electromagnetic units, as shown in fig. 6, the controllable pulse electromagnetic units are provided with electromagnetic coils facing the scalp, the electromagnetic coils and the signal acquisition electrodes are arranged in a staggered manner, and no overlapping exists in the vertical direction; the electromagnetic coil generates an intervention magnetic field B with controllable size and polarity, the intervention magnetic field B performs electromagnetic stimulation massage on the non-excitation region set N in a self-organizing intervention mode, and a large amount of accumulated charges in a brain region where the excitation region set P is located are evacuated according to the brain energy conservation principle, so that the charge accumulation level in the brain is always kept in a balanced state;
the human brain consumes energy every day, and the energy consumed at each moment is related to the oxygen consumption of the brain at each moment. The oxygen consumption of a person at each moment is related to the functions of the person, such as the hemopoietic capacity of the heart, the muscle structure and the like, and is close to a constant value. It can therefore be deduced that when the stimulated non-excitatory region becomes active, the accumulated charge in the excitatory region will be dispersed and the charge will reach a lower level.
As shown in fig. 7, the excitation region P and the non-excitation region N belong to different regions of the brain, the electromagnetic pulse B emitted by the controllable electromagnetic unit stimulates the non-excitation region of the brain through electromagnetic coupling and excites the non-excitation region of the brain, and due to the conservation of energy consumption in the brain and no sudden change of energy, the energy of the non-excitation region rises according to a certain gradient, and the energy of the excitation region falls according to a certain energy. Therefore, the energy in the brain is redistributed, and a large amount of charges in an excitation area of the brain move to a non-excitation area, so that the brain electricity distribution in the brain is balanced.
The system composition diagram is shown in fig. 8, and the complete work flow diagram is shown in fig. 9.
Example 1:
the following describes the method of using the system, taking a subject wearing the system as an example:
specifically, when the system is worn by the subject, the signal collecting electrode is not in contact with the scalp, the electroencephalogram of the epileptic patient collected by the signal collecting electrode is amplified by an amplifier, and the relationship between the electroencephalogram and the actual potential information is obtained by machine learning, whereby the potential e 'is obtained in a non-contact manner'i1,2,3, n, followed by filteringCounter potential e'iNoise reduction and filtration are carried out to obtain filtered potential ei,i＝1,2,3,...,n；
For the ith signal acquisition electrode, the potential time sequence obtained by amplification and filtration is ei(t), (i ═ 1, 2.., n), where c (i) is a set of nodes adjacent to node i, m is the number of elements in c (i) and indicates the number of adjacent nodes, and ec(i)(t) is the potential of node set c (i); the moment when the tested object wears the system is set as t0Inputting training into
input＝{ei(t0),ei(t0-1),...,ei(t0-τ),ec(i)(t0),ec(i)(t0-1),...,ec(i)(t0- τ) } and a training output
output＝{ei(t0+1) } as a set of training samples; take the time interval Δ t' and from t0Taking N time intervals after the moment, i.e. t0,t0+1,...,t0Taking input and output corresponding to + N-1 as N groups of training samples, taking a training algebra k 5000 times, and obtaining a training function P according to a training result, namely output ═ P (input);
when t is the wearing time, the voltage amplitude e of the acquisition point of the next time t + delta t is obtained through prediction of a prediction function Pi(t + Δ t); let the prediction time t be t + Δ t, and obtain the prediction potential e (t) at the prediction time t.
Comparing the predicted potential e (t) with a set potential threshold value theta, and judging a position set A { (x, y) | e ≧ theta } of the signal acquisition electrode corresponding to the predicted potential e (t) mark e, e ≧ theta as a time-predicted burst position, wherein x represents the number of rows of the signal acquisition electrode in the acquisition module, y represents the number of columns of the signal acquisition electrode in the acquisition module, and t corresponding to e is the epilepsy predicted burst time; sending the predicted outbreak position to a space prediction unit, starting the space prediction unit at the same time, and otherwise, continuing monitoring;
the current time t of the spatial prediction unit passing through the n signal acquisition electrodes1The electric potential of the neighborhood time is combined with n signal acquisition electrodes in a non-contact wayAcquiring the position (x, y) of the module to obtain n (eta +1) voltages e of n signal acquisition electrodes(i,t)(x,y),(t＝t1-η,...,t1-2,t1-1,t1(ii) a i 1, 2.. n), η +1 voltages e per signal acquisition point(i,t)In (x, y), adjacent voltages are subtracted to obtain eta voltage gradients D of each signal acquisition electrode(i,j)(i ═ 1, 2., n, j ═ 1, 2.,. η); averaging the eta voltage gradients of each signal acquisition point to obtain a voltage gradient average value
By comparing the average voltage gradient values of n signal-collecting electrodes
Obtaining a set B of voltage gradient maximum value coordinates (x ', y ') and a set C of minimum value coordinates (x ', y ");
obtaining a set P of brain excitation region coordinates (x, y) by calculating the intersection of the sets A and B, and obtaining a set P of brain excitation region coordinates (x, y) by calculating the set
Obtaining a non-excitatory area coordinate (x ^ y ^) set N of the brain by the intersection of the coordinate and the coordinate C, and sending coordinate set information to an intervention module;
after the prediction of the time prediction unit and the space prediction unit is finished, obtaining a brain excitation area coordinate (x, y) set P and a brain non-excitation area coordinate (x, y), wherein the set P comprises P elements, and the set N comprises l elements; when the intervention module is started, the pulse electromagnetic units corresponding to the coordinates in the set N start to emit electromagnetic pulses Bi(i＝1,2,…,l,l<m), electromagnetic pulse B)iRegulating between 0T and 2T, electromagnetic pulse BiRespectively acting on the scalp region sigma corresponding to each coordinate in the set Ni(i ═ 1,2, …, l) by biological electromagnetic coupling, σiThe brain charge in the region begins to accumulate and the potential e is measuredi(i ═ 1,2, …, l) is elevated; then according to the brain energy conservation principle, the scalp area lambda corresponding to each coordinate in the set Pk(k 1,2, …, p) charge begins to disperse and is measuredVolume potential ek(k ═ 1,2, …, p) begins to fall; after the action time lasts delta T, if the excitation area signal acquires the unit potential ekAnd when the (k is 1,2, …, p) potentials are all smaller than the potential threshold value theta, ending the operation of the intervention module, and otherwise, continuing the intervention operation.
Example 2:
the following uses a system user using the system to predict the effect of using the time-space coupling method of the system compared with a system only using the time prediction method:
an outdoor scene is assumed, an epileptic wears the system to take a bus to go to a certain place, and t is assumed0The time being the time of wearing the system, t1The time is the current time and the time when the time prediction unit sends out the early warning signal. From t0Starting from the moment, the non-contact acquisition module starts to detect the electroencephalogram voltage value of the electroencephalogram patient, and if only simple time prediction is available, namely at t1The epileptic outbreak is early warned at any moment and a signal is sent to the outside, and a patient can find a place to sit down or lie on the ground in the time period, but still cannot avoid the epileptic outbreak, and still can cause injury to the patient in an outdoor scene or a scene without accompanying people;
when the time t of the epileptic outbreak and the exciting area P and the non-exciting area N of the outbreak point are early warned by adopting the time-space coupling method, the patient can be enabled to be at t1The system receives the alarm of the system all the time, and receives the interference of the system on the charge distribution in the brain by adopting a self-organizing method, when the charge in the brain of the patient reaches balance, the alarm is released, the system continuously monitors the electroencephalogram of the patient, and the operation of early warning and interference is sent to the patient and the medical data of the patient for further analysis and diagnosis by a doctor later.
Example 3:
according to the magnitude of the electroencephalogram potential threshold value theta, the method can be used for inhibiting the accumulation of overhigh charges in the brain or the accumulation of overlow charges in the brain; the threshold value theta is adjusted to be a higher value when the cerebral neurons are excited, and is adjusted to be a lower value when the cerebral neurons are unconscious;
the following two users wearing the system are used to realize the method of using the system to obtain and interfere with the brain electrical distribution in a non-contact non-invasive way:
the system user A is a user who sleeps at night, neurons are excited at the sleeping time point, and the electroencephalogram voltage value of an excitation area P obtained through non-contact measurement is higher than a threshold value theta;
when the system is worn by a user, firstly, the charge distribution in the brain is obtained by a non-contact non-invasive and non-destructive method, when the condition that the voltage value of the brain electricity is overhigh is detected, a time prediction unit prompts that the brain electricity is abnormal, the neuron of the brain is shown to be in a high state at the moment, an excitation area set P with the overhigh voltage value of the brain electricity and a non-excitation area set N with a lower voltage value of the brain electricity are determined by a time-space coupling control method, the potential of the excitation area P is reduced through electromagnetic massage on the non-excitation area N of the brain, the brain restores to a calm state again, and then the stable sleep state can be entered;
the system user B is a user who is tired to drive at night, neurons are in a low state most of the time in the driving process, and the non-excitation area N electroencephalogram voltage value obtained through non-contact measurement is lower than a threshold value theta;
when the system is worn by a user, firstly, the charge distribution in the brain is obtained by a non-contact non-invasive and non-destructive method, and when the condition that the electroencephalogram voltage value is too low is detected, the time prediction unit prompts that the electroencephalogram is abnormal and possibly needs to be kept clear; determining a neuron non-excitation region set N and a normal excitation region P by using a time-space coupling control method, and gathering charges and increasing the potential of the non-excitation region N through electromagnetic massage on the brain non-excitation region; the brain can recover the waking state again, and the harm of fatigue driving can be prevented.
Claims (7)
1. The utility model provides a non-contact non-invasive epilepsy self-organizing monitors early warning and intervenes system which characterized in that, this system is three-layer stack structure, from inside to outside respectively: the system comprises a non-contact acquisition module, a non-contact intervention module and a non-contact control module;
the non-contact acquisition module comprises n signal acquisition electrodes, the signal acquisition electrodes are not in contact with the scalp, the epileptic brain waves acquired by the signal acquisition electrodes are amplified by an amplifier, the relation between the brain waves and actual potential information is obtained through machine learning, and therefore the potential e 'is obtained in a non-contact mode'i1,2,3, n, followed by a filter pair potential e'iNoise reduction and filtration are carried out to obtain filtered potential ei，i＝1，2，3，...，n；
The non-contact control module comprises a time prediction unit and a space prediction unit;
the time prediction unit passes the current t1Training the potential sequences acquired at each time point in a set time interval before the moment to obtain a prediction function P, obtaining the potential e (t) of each signal acquisition electrode prediction moment t through the prediction function P, comparing the prediction potential e (t) with a set potential threshold theta, and judging a position set A { (x, y) | e ≧ theta } of a signal acquisition electrode corresponding to the prediction potential e (t) mark e ≧ theta, wherein x represents the number of rows of the signal acquisition electrode in a non-contact acquisition module, y represents the number of columns of the signal acquisition electrode in the non-contact acquisition module, and e corresponding time t is the epilepsy prediction explosion time; sending the predicted outbreak position to a space prediction unit, starting the space prediction unit at the same time, and otherwise, continuing monitoring;
the spatial prediction unit passes the current time t of the n signal acquisition electrodes1The electric potential of the neighborhood time is combined with the positions (x, y) of the n signal acquisition electrodes in the non-contact acquisition module to obtain n (eta +1) voltages e of the n signal acquisition electrodes(i，t)(x，y)，t＝t1-η，...，t1-2，t1-1，t1(ii) a 1, 2.. n, η +1 voltages e per signal acquisition point(i，t)In (x, y), adjacent voltages are subtracted to obtain eta voltage gradients D of each signal acquisition electrode(i，j)1,2,.. n, j ═ 1,2,. η; eta voltages for each signal acquisition pointAveraging the gradients to obtain a voltage gradient average value
By comparing the average voltage gradient values of n signal-collecting electrodes
Obtaining a set B of voltage gradient maximum coordinates (x ', y') and a set C of minimum coordinates (x ', y');
obtaining a set P of brain excitation region coordinates (x, y) by calculating the intersection of the sets A and B, and obtaining a set P of brain excitation region coordinates (x, y) by calculating the set
Obtaining a non-excitatory area coordinate (x ^ y ^) set N of the brain by the intersection of the coordinate and the coordinate C, and sending coordinate set information to an intervention module;
the non-contact intervention module comprises m controllable pulse electromagnetic units, each controllable pulse electromagnetic unit is provided with an electromagnetic coil facing the scalp, and the electromagnetic coils and the signal acquisition electrodes are arranged in a staggered mode and do not overlap in the vertical direction; the electromagnetic coil generates an interference magnetic field with controllable size and polarity
The intervening magnetic field
Carrying out electromagnetic stimulation massage on the non-excitation region set N in a self-organizing intervention mode, and evacuating a large amount of accumulated charges in a brain region where the excitation region set P is located according to the brain energy conservation principle, so that the charge accumulation level in the brain is always kept in a balanced state; the self-organizing intervention mode is implemented as follows:
after the prediction of the time prediction unit and the space prediction unit is finished, obtaining a brain excitation area coordinate (x, y) set P and a brain non-excitation area coordinate (x, y), wherein the set P comprises P elements, and the set N comprises l elements; when the non-contact intervention module is started, the setClosing pulse electromagnetic units corresponding to the coordinates in N to start to send out electromagnetic pulses Bi1,2, l, l < m, electromagnetic pulse BiRegulating between 0T and 2T, electromagnetic pulse BiRespectively acting on the scalp region sigma corresponding to each coordinate in the set Ni1, 2.. times.l, by bio-electromagnetic coupling, σiThe brain charge in the region begins to accumulate and the potential e is measurediHigh, i ═ 1,2,.., l; then according to the brain energy conservation principle, the scalp area lambda corresponding to each coordinate in the set PkThe electric charge starts to disperse, and the potential e is measuredkStart to fall, k 1, 2.., p; after the action time lasts delta T, if the excitation area signal acquires the unit potential ekAnd when the potentials are all smaller than the potential threshold value theta, ending the operation of the non-contact intervention module, otherwise, continuing the intervention operation, wherein k is 1, 2.
2. The system of claim 1, wherein the system is placed outside the brain and is hat-shaped in appearance.
3. The system of claim 1, wherein the amplifier is a duffing circuit amplifier and is constructed according to the following formula:
where k is the damping ratio, x is the measurement signal, (x)3-x5) For the nonlinear restoring force, γ sin (ω t + π) is the built-in driving signal, γ is the amplitude of the built-in driving signal, and ω is the frequency of the built-in driving signal.
4. The system for monitoring, warning and intervening non-contact, non-invasive and self-organizing epilepsy according to claim 1, wherein the potential e (t) of each signal collecting electrode at the predicted time t by the prediction function P is specifically:
for the ith signal acquisition electrode, the potential time sequence obtained by amplification and filtration is ei(t), i ═ 1, 2.·, n, where c (i) is a set of nodes adjacent to node i, m is the number of elements in c (i) and represents the number of adjacent nodes, ec(i)(t) is the potential of node set c (i); with ei(t) and ec(i)(t) training the prediction function P with the historical data as input and output samples;
the training process is as follows:
defining the input of the prediction function P as input and the output as output; taking deltat as a time interval, the potential of the ith signal acquisition point at the moment t is ei(t), the potential at a time t before the time t is represented by ei(t-1)，ei(t-2)...，ei(t- τ); the potential of the adjacent acquisition point of the ith signal acquisition point is ec(i)The potential of (t), tau time intervals is denoted as ec(i)(t-1)，ec(i)(t-2)...，e(i)(t- τ); with m +1 samples of length 1+ τ as input, the input is expressed as:
input＝{ei(t)，ei(t-1)，...，ei(t-τ)，ec(i)(t)，ec(i)(t-1)，...，ec(i)(t-τ)}；
the output of the prediction function P is the potential e at time t over a time interval Δ ti(t +1), then the output is expressed as: output ═ ei(t+1)}；
When training the kth generation, the input length is (1+ m) × (1+ τ), and the output length is 1;
if t takes a certain time t before0Input is given as { e ═ ei(t0)，ei(t0-1)，...，ei(t0-τ)，ec(i)(t0)，ec(i)(t0-1)，...，ec(i)(t0- τ) } and training output ═ ei(t0+1) } as a set of training samples; take the time interval Δ t' and from t0Taking N time intervals after the moment, i.e. t0，t0+1，...，t0Taking input and output corresponding to + N-1 as N groups of training samples, taking a training algebra k from 1000 times to 10000 times, and obtaining a training function P according to a training result, namely, output is P (input);
when t is the current time t1Then, the next time t is obtained by predicting through the prediction function P1+ Δ t acquisition point voltage amplitude ei(t1+ Δ t); let the predicted time t ═ t1+ Δ t, the predicted potential e (t) at the predicted time t is obtained.
5. The system of claim 1, wherein the spatial prediction unit is configured to predict the mean value of voltage gradient
The acquisition method comprises the following steps:
current time t through n signal acquisition electrodes1The electric potential of the neighborhood time is combined with the positions (x, y) of the n signal acquisition electrodes in the non-contact acquisition module to obtain the acquisition electric potential e of the n signal acquisition electrodes at n (eta +1) times(i，t)(x，y)，t＝t1-η，...，t1-2，t1-1，t1(ii) a 1,2, n, and e(i，t)(x, y) is denoted as e(i，t)Then, there is a collection potential matrix:
each row of the collection potential matrix represents eta +1 collection potential sequences at shorter time, and adjacent two rows of the matrix are subjected to difference to obtain eta potential gradients of the n signal collection electrodes
D(i，j)＝e(i，t)(x，y)-e(i，t-1)(x，y)，t＝t1-η+1，...，t1-2，t1-1，t11, 2.. n, j ═ 1, 2.. η, then there is a potential gradient matrix:
the ith row of the voltage gradient matrix represents a potential time difference sequence of the ith signal acquisition point, the jth column represents the jth adjacent potential time gradient, and then the potential time difference sequences of each row are averaged
Obtaining the average value of voltage gradient
6. The system for monitoring, early warning and intervening epilepsy in a non-contact, non-invasive and non-invasive manner according to claim 1, wherein the controllable pulse electromagnetic unit comprises a PWM wave control module, a fully controllable turn-off power electronics module, and an electromagnetic coil; the fully-controlled turn-off controllable power electronic device module adopts an IGBT, the output end of the PWM wave control module is connected with a gate pole G of the IGBT, a collector electrode C of the IGBT is respectively connected with one end of a capacitor C1 and a first fixed end of a slide rheostat R1, a second fixed end of the slide rheostat R1 is connected with the positive pole of a power supply, an emitter electrode E of the IGBT is connected with one end of an electromagnetic coil, and the other end of the electromagnetic coil is connected with the negative pole of the power supply after being connected with the other end of the capacitor C1; the slide rheostat R1 can change the current in the electromagnetic coil; the on and off of the IGBT are controlled by the PWM wave control module, so that the current in the electromagnetic coil is changed, and the interference magnetic field is changedDirection and strength.
7. A non-contact non-invasive self-organizing method for obtaining interference electroencephalogram distribution is characterized in that the distribution of charges and the trend of current are obtained by a non-contact non-invasive method and are interfered by a self-organizing method; the method comprises the steps of measuring the distribution of electric charge and current in the brain in a non-contact mode, locating a region with more electric charge accumulation and high accumulation speed or with less electric charge accumulation and low accumulation speed by combining the time prediction with the space prediction, and intervening the distribution of the electric charge in the brain and the current trend in neurons based on a self-organizing method; the method comprises the following specific steps:
step 1: acquiring brain waves of a subject by n signal acquisition electrodes, obtaining the relation between the brain waves and actual potential information by machine learning without contacting the signal acquisition electrodes with the scalp, and obtaining the potential e 'by a non-contact method'i1,2,3, n, followed by a filter pair potential e'iNoise reduction and filtration are carried out to obtain filtered potential ei，i＝1，2，3，...，n；
Step 2: and (3) time prediction: training to obtain a prediction function P through a potential sequence which is acquired at each time point in a set time interval before the time t and is subjected to noise reduction and filtering treatment, obtaining a potential e (t) of each signal acquisition electrode at the prediction time t through the prediction function P, comparing the predicted potential e (t) with a set potential threshold theta, and setting a position set of the signal acquisition electrodes which are larger than or equal to theta to be A { (x, y) | e ≧ theta }, wherein (x, y) is the position coordinates of the signal acquisition electrodes;
and 3, step 3: spatial prediction: current time t through n signal acquisition electrodes1The n (eta +1) voltages e of the n signal acquisition electrodes are obtained by combining the potentials of the neighborhood time with the positions (x, y) of the n signal acquisition electrodes(i，t)(x，y)，t＝t1-η，...，t1-2，t1-1，t1(ii) a 1, 2.. n, η +1 voltages e per signal acquisition point(i，t)In (x, y), adjacent voltages are subtracted to obtain eta voltage gradients D of each signal acquisition electrode(i，j)1,2,.. n, j ═ 1,2,. η; averaging the eta voltage gradients of each signal acquisition point to obtain a voltage gradient average value
By comparing the average voltage gradient values of n signal-collecting electrodes
Obtaining a set B of voltage gradient maximum coordinates (x ', y') and a set C of minimum coordinates (x ', y');
and 4, step 4: obtaining a set P of brain excitation region coordinates (x, y) through the intersection of the sets A and B, and obtaining a set through aggregation
Obtaining a non-excitation area coordinate (x ^ y ^) set N of the brain by the intersection of the X and the C;
and 5, step 5: brain electrical interference: there are P elements in the brain excitation area coordinate (x, y) set P, there are l elements in the brain non-excitation area coordinate (x, y) set N; when the electroencephalogram interference starts, an electromagnetic pulse B is sent out to the coordinates I in the set Ni1,2, l, l < m, electromagnetic pulse BiRegulating between 0T and 2T, electromagnetic pulse BiRespectively acting on the scalp region sigma corresponding to each coordinate in the set Ni1, 2.. times.l, by bio-electromagnetic coupling, σiThe brain charge in the region begins to accumulate and the potential e is measurediHigh, i ═ 1,2,.., l; then according to the brain energy conservation principle, the scalp area lambda corresponding to each coordinate in the set PkThe electric charge starts to disperse, and the potential e is measuredkStart to fall, k 1, 2.., p; after the action time lasts delta T, if the excitation area signal acquires the unit potential ekAnd when the potentials are all smaller than the potential threshold value theta, stopping the interference, otherwise, continuing the interference operation, wherein k is 1, 2.
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