CN114343675B - Electroencephalogram component extraction method - Google Patents
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Abstract
The invention provides an electroencephalogram component extraction method, which comprises the following steps: acquiring an original brain electrical signal, and acquiring a resting brain electrical signal and an excitation brain electrical signal from the original brain electrical signal; taking the resting electroencephalogram signal as a value basis to carry out parameter estimation of a Kalman filtering model to obtain state prediction parameters; and carrying out Kalman filtering on the excited electroencephalogram signals according to the state prediction parameters so as to obtain filtered electroencephalogram signals. Because the resting brain electrical signal and the excitation brain electrical signal are part of the original brain electrical signal, the noise components in the resting brain electrical signal and the excitation brain electrical signal are approximate. In addition, the resting brain electrical signal reflects the brain electrical signal of which the brain is in an unstimulated state, and the signal fluctuation is stable, thereby being suitable for being used as a value basis of the extraction state prediction parameters. The resting electroencephalogram signal is used as a value basis for parameter estimation, and a high-quality state prediction parameter can be provided for a filtering process of exciting the electroencephalogram signal, so that the accuracy of electroencephalogram component extraction is further improved.
Description
Technical Field
The invention relates to the field of computer information processing, in particular to an electroencephalogram component extraction method.
Background
Current studies on brain function cognition are mainly accomplished by extracting relevant components from brain electrical signals. Because the electroencephalogram signal is a complex non-stationary random signal, the data acquisition process is easily interfered by the external environment and the physiological condition of the subject. Therefore, the signals are filtered to eliminate the influence of interference, and the required cognitive components are obtained as much as possible.
In the related art, kalman filtering is often applied to the extraction of cognitive components of brain electrical signals, however, the development of theory is not mature enough. The application of Kalman filtering to the extraction of cognitive components of an electroencephalogram signal requires the calculation of a Jacobi (Jacobi) matrix, and the complex calculation of the Jacobi matrix plays a certain limiting role on practical application. Therefore, in order to improve the calculation efficiency of the kalman filtering and improve the numerical stability of the algorithm in the electroencephalogram component estimation, there is a need for an electroencephalogram component extraction method capable of more accurately estimating the electroencephalogram noise.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides an electroencephalogram component extraction method which can be used for more accurately estimating the electroencephalogram noise.
The electroencephalogram component extraction method according to the embodiment of the invention comprises the following steps:
collecting an original brain electrical signal, and obtaining a resting brain electrical signal and an excitation brain electrical signal from the original brain electrical signal, wherein the resting brain electrical signal reflects the brain electrical signal of which the brain is in an unstimulated state, and the excitation brain electrical signal reflects the brain electrical signal of which the brain is in a stimulated state;
taking the resting electroencephalogram signal as a value basis to perform parameter estimation of a Kalman filtering model to obtain state prediction parameters;
and carrying out Kalman filtering on the excited electroencephalogram signals according to the state prediction parameters so as to obtain filtered electroencephalogram signals.
The electroencephalogram component extraction method provided by the embodiment of the invention has at least the following beneficial effects:
the electroencephalogram component extraction method of the invention firstly collects the original electroencephalogram signals, and then obtains the resting electroencephalogram signals and the excitation electroencephalogram signals from the original electroencephalogram signals. Further, the rest electroencephalogram signal is used as a value basis to carry out Kalman filtering parameter estimation to obtain state prediction parameters, kalman filtering is carried out on the excited electroencephalogram signal according to the state prediction parameters, and finally the filtered electroencephalogram signal is obtained. Because the resting brain electrical signal and the excitation brain electrical signal are part of the original brain electrical signal, the noise components in the resting brain electrical signal and the excitation brain electrical signal are approximate. In addition, the resting brain electrical signal reflects the brain electrical signal of which the brain is in an unstimulated state, and the signal fluctuation is stable, thereby being suitable for being used as a value basis of the extraction state prediction parameters. Based on the reasons, the resting electroencephalogram signal is used as a value basis for parameter estimation, so that higher-quality state prediction parameters can be provided for the filtering process of the excited electroencephalogram signal, and the accuracy of electroencephalogram component extraction can be further improved.
Optionally, according to some embodiments of the present invention, the estimating parameters of the kalman filter model based on the resting electroencephalogram signal to obtain state prediction parameters includes:
analyzing the resting brain electrical signal to obtain an autoregressive model coefficient;
substituting the autoregressive model coefficient into a state equation and an observation equation, and estimating to obtain a state transition matrix and a measurement matrix in the state prediction parameters;
and obtaining an observation noise covariance matrix and a process noise covariance matrix in the state prediction parameters according to the autoregressive model coefficient, the state equation and the observation equation.
Optionally, according to some embodiments of the invention, the method further comprises:
establishing a P-order autoregressive model
Wherein a is 1 ，a 2 ，...，a p For the P-order autoregressive model coefficient, z s Representing said resting brain electrical signal without noise, v s K represents the time at which observation data is acquired for observation noise;
when x is p (k)＝[z s (k-p),z s (k-p+1),…z s (k-1),z s (k)] T Obtaining the state equation of Kalman filtering through the autoregressive model:
Optionally, according to some embodiments of the present invention, substituting the autoregressive model coefficient into a state equation and an observation equation and estimating to obtain a state transition matrix and a measurement matrix in the state prediction parameters includes:
p-order autoregressive model coefficient a extracted from resting electroencephalogram signals 1 ，a 2 ，...，a p Substituting the state equation and the observation equation, and estimating to obtain a Kalman filtering state transition matrix in the state prediction parameters
Measurement matrix h= [0 … 01 of kalman filter]。
Optionally, according to some embodiments of the invention, the deriving the observed noise covariance matrix and the process noise covariance matrix in the state prediction parameter includes:
the observed noise v s A multivariate normal distribution (0, R) expressed as mean 0 and covariance matrix R;
the resting electroencephalogram signal is used as an observation base to measure the observation noise v s And obtaining the observed noise covariance matrix R;
noise the process
A multivariate normal distribution (0, Q) expressed as a mean of 0 and a covariance matrix of Q; />
According to the P-order autoregressive model coefficient a 1 ，a 2 ，...，a p With the observed noise v s And obtaining the process noise covariance matrix Q.
Optionally, according to some embodiments of the invention, the performing kalman filtering on the excitation electroencephalogram signal according to the state prediction parameter includes:
according to the excitation electroencephalogram signal J (t-1) detected at the previous moment t-1 and the state prediction parameter, a priori state vector of the current moment t is calculated from the previous moment t-1 to the current moment t
Covariance matrix with prior error->
According to the prior state vector of the current time t
Covariance matrix with prior error->
Obtaining Kalman gain;
the excitation EEG signal J (t), the Kalman gain and the prior state vector detected according to the current moment t
Covariance matrix with the prior error +.>
Calculating a posterior state vector x of the current time t t Posterior error covariance matrix P t ；
According to said x t With said P t And generating an output value of the filtered electroencephalogram signal at the current time t.
Optionally, according to some embodiments of the invention, the method further comprises:
the x is set to t As a priori state vector at time t+1, the P is determined t As a priori error covariance matrix at the time t+1, calculating and updating the Kalman gain and updating the state prediction parameters;
according to the excitation electroencephalogram signal J (t+1) detected at the next moment t+1, the Kalman gain of the updated state and the state prediction parameters of the updated state, a posterior state vector x of the next moment t+1 is calculated t+1 Posterior error covariance matrix P t+1 ；
According to said x t+1 With said P t+1 And generating an output value of the filtered electroencephalogram signal at the next time t+1.
Optionally, according to some embodiments of the invention, the prior state vector of the current time t is calculated from the previous time t-1 to the current time t
Covariance matrix with prior error->
Comprising the following steps:
substituting a state transition matrix A and the process noise covariance matrix Q in the state prediction parameters into a state variable update equation
Update equation with error covariance->
Deriving said->
Is in contact with the->
Wherein x is t-1 P being the state variable at the last time t-1 t-1 Is the error covariance matrix of the last time t-1.
Optionally, according to some embodiments of the invention, the prior state vector according to the current time t
Covariance matrix with prior error->
Deriving a kalman gain comprising:
the measurement matrix H, the observed noise covariance matrix R and the prior state vector of the current moment t
Covariance matrix with prior error->
Substitution formula->
Obtaining Kalman gain K.
Alternatively, according to some embodiments of the invention, the derivingThe posterior state vector x of the current time t is obtained t Posterior error covariance matrix P t Comprising:
the measurement matrix H, the Kalman gain K, the excitation electroencephalogram signal J (t) detected at the current moment t and the prior state vector at the current moment t are processed
Substitution formula->
Obtaining posterior state vector x at current time t t ；
The measurement matrix H, the Kalman gain K and the prior error covariance matrix of the current moment t are obtained
Substitution formula->
Obtaining a posterior error covariance matrix P of the current moment t t 。/>
Drawings
The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a schematic flow chart of an electroencephalogram component extraction method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another method for extracting brain electrical components according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of another method for extracting brain electrical components according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of another method for extracting brain electrical components according to an embodiment of the present invention;
FIG. 5 is a schematic flow chart of another method for extracting brain electrical components according to an embodiment of the present invention;
FIG. 6 is a flow chart of another method for extracting brain electrical components according to an embodiment of the present invention;
FIG. 7 is a flow chart of another method for extracting brain electrical components according to an embodiment of the present invention;
FIG. 8 is a flow chart of another method for extracting brain electrical components according to an embodiment of the present invention;
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
In the description of the present invention, a number means one or more, a number means two or more, and greater than, less than, exceeding, etc. are understood to not include the present number, and above, below, within, etc. are understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, it should be understood that the direction or positional relationship indicated with respect to the description of the orientation, such as up, down, left, right, front, rear, etc., is based on the direction or positional relationship shown in the drawings, is merely for convenience of describing the present invention and simplifying the description, and does not indicate or imply that the apparatus or element to be referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
In the description of the present specification, reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
In the description of the present invention, unless explicitly defined otherwise, terms such as arrangement, installation, connection, etc. should be construed broadly and the specific meaning of the terms in the present invention can be determined reasonably by a person skilled in the art in combination with the specific content of the technical solution.
Current studies on brain function cognition are mainly accomplished by extracting relevant components from brain electrical signals. Because the electroencephalogram signal is a complex non-stationary random signal, the data acquisition process is easily interfered by the external environment and the physiological condition of the subject. Therefore, the signals are filtered to eliminate the influence of interference, and the required cognitive components are obtained as much as possible.
In the related art, kalman filtering is often applied to the extraction of cognitive components of brain electrical signals, however, the development of theory is not mature enough. The application of Kalman filtering to the extraction of cognitive components of an electroencephalogram signal requires the calculation of a Jacobi (Jacobi) matrix, and the complex calculation of the Jacobi matrix plays a certain limiting role on practical application. Therefore, in order to improve the calculation efficiency of the kalman filtering and improve the numerical stability of the algorithm in the electroencephalogram component estimation, there is a need for an electroencephalogram component extraction method capable of more accurately estimating the electroencephalogram noise.
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides an electroencephalogram component extraction method which can be used for more accurately estimating the electroencephalogram noise.
The following is further described with reference to the accompanying drawings.
Referring to fig. 1, an electroencephalogram component extraction method according to an embodiment of the present invention includes:
step S101, acquiring an original brain electrical signal, and acquiring a resting brain electrical signal and an excitation brain electrical signal from the original brain electrical signal, wherein the resting brain electrical signal reflects the brain electrical signal of which the brain is in an unstimulated state, and the excitation brain electrical signal reflects the brain electrical signal of which the brain is in a stimulated state;
in some embodiments of the invention, the acquisition of the original brain electrical signal includes a resting brain electrical signal reflecting that the brain is in an unstimulated state, and includes an stimulated brain electrical signal reflecting that the brain is in a stimulated state. The whole original brain electrical signal records the brain from the unstimulated state to the stimulated state, and the brain electrical signal change process and the continuous process.
Step S102, parameter estimation of a Kalman filtering model is carried out by taking a resting electroencephalogram signal as a value basis, and state prediction parameters are obtained;
the rest brain electrical signal and the excitation brain electrical signal are part of the original brain electrical signal, so that noise components in the rest brain electrical signal and the excitation brain electrical signal are approximate. In addition, the resting brain electrical signal reflects the brain electrical signal of the brain in the unstimulated state, and the signal fluctuation is stable. Therefore, the resting electroencephalogram signal is suitable to be used as a value basis for extracting state prediction parameters, and the extracted state prediction parameters are used for carrying out Kalman filtering on the excited electroencephalogram signal subsequently so as to extract the filtered electroencephalogram signal.
Step S103, kalman filtering is carried out on the excited electroencephalogram signals according to the state prediction parameters so as to obtain filtered electroencephalogram signals.
The theoretical basis of the kalman filtering is to filter noise from an observed signal containing noise, and to recover the target signal itself or approach the target signal. The input of the filter is the observed quantity of the system, the calculated estimated value is the output of the filter by utilizing the statistical characteristics of system noise and observation noise, the time updating equation and the observation updating equation link the input and the output of the filter together, and all signals to be processed are estimated according to the two equations. The Kalman filtering design method is simple and easy to implement and wide in application range, but the theory development is not mature enough. Firstly, a Jacobi matrix needs to be calculated, and the complex calculation of the Jacobi matrix has a certain limiting effect on practical application; in addition, the assumption that the probability distribution function has gaussian characteristics makes it unusable for non-linear non-gaussian random systems. Therefore, the state prediction parameters obtained by taking the resting electroencephalogram signals as the value basis can improve the numerical stability of the algorithm on electroencephalogram component estimation.
The electroencephalogram component extraction method of the invention firstly collects the original electroencephalogram signals, and then obtains the resting electroencephalogram signals and the excitation electroencephalogram signals from the original electroencephalogram signals. Further, the rest electroencephalogram signal is used as a value basis to carry out Kalman filtering parameter estimation to obtain state prediction parameters, kalman filtering is carried out on the excited electroencephalogram signal according to the state prediction parameters, and finally the filtered electroencephalogram signal is obtained. Because the resting brain electrical signal and the excitation brain electrical signal are part of the original brain electrical signal, the noise components in the resting brain electrical signal and the excitation brain electrical signal are approximate. In addition, the resting brain electrical signal reflects the brain electrical signal of which the brain is in an unstimulated state, and the signal fluctuation is stable, thereby being suitable for being used as a value basis of the extraction state prediction parameters. Based on the reasons, the resting electroencephalogram signal is used as a value basis for parameter estimation, so that higher-quality state prediction parameters can be provided for the filtering process of the excited electroencephalogram signal, and the accuracy of electroencephalogram component extraction can be further improved.
Referring to fig. 2, in step S102, parameter estimation of a kalman filter model is performed based on a resting electroencephalogram signal as a value basis to obtain a state prediction parameter, which includes:
step S201, analyzing a resting brain electrical signal and obtaining an autoregressive model coefficient;
step S202, substituting the autoregressive model coefficient into a state equation and an observation equation, and estimating to obtain a state transition matrix and a measurement matrix in the state prediction parameters;
step S203, according to the autoregressive model coefficient, the state equation and the observation equation, an observation noise covariance matrix and a process noise covariance matrix in the state prediction parameters are obtained.
The state prediction parameters include a state transition matrix, a measurement matrix, an observation noise covariance matrix, and a process noise covariance matrix. In some embodiments of the present invention, a state transition matrix, a measurement matrix, an observation noise covariance matrix, and a process noise covariance matrix, which are obtained by taking a resting electroencephalogram signal as a value basis, are used to construct a priori estimates for a subsequent kalman filtering process of an excitation electroencephalogram signal.
Referring to fig. 3, a kalman filtered state equation and a kalman filtered observation equation are obtained according to some embodiments of the invention by:
step S301, establishing a P-order autoregressive model
Wherein a is 1 ，a 2 ，...，a p For the P-order autoregressive model coefficient, z s Representing a noise-free resting brain electrical signal, v s K represents the time at which observation data is acquired for observation noise;
the autoregressive model (Autoregressive model, AR model) is a method of statistically processing time series, and uses the previous phases of the same variable to predict the performance of the phase, and assumes that they are in a linear relationship. Since this is developed from linear regression in regression analysis, the variables of each previous period are used to predict the current phase behavior of the variables themselves, and are therefore called autoregressions.
And step S302, when the state variable takes a value from the resting brain electrical signal, obtaining a Kalman filtering state equation and an observation equation through an autoregressive model.
When x is p (k)＝[z s (k-p),z s (k-p+1),…z s (k-1),z s (k)] T And obtaining a Kalman filtering state equation through an autoregressive model:
Referring to fig. 4, according to some embodiments of the present invention, substituting the autoregressive model coefficients into the state equation and the observation equation in step S202 and estimating the state transition matrix and the measurement matrix in the state prediction parameters includes:
step S401, extracting P-order autoregressive model coefficient a from resting brain electrical signal 1 ，a 2 ，...，a p Substituting a state equation and an observation equation;
step S402, estimating a Kalman filtering state transition matrix in the state prediction parameters
Measurement matrix h= [0 … 01 of kalman filter]。
Referring to fig. 5, according to some embodiments of the present invention, deriving the observed noise covariance matrix and the process noise covariance matrix in the state prediction parameters in step S203 includes:
step S501, observe noise v s The process noise is represented by a multivariate normal distribution (0, R) with a mean value of 0 and a covariance matrix of R
A multivariate normal distribution (0, Q) expressed as a mean of 0 and a covariance matrix of Q;
step S502, measuring observation noise v by taking resting brain electrical signal as observation base s Obtaining an observation noise covariance matrix R;
step S503, according to the P-order autoregressive model coefficient a 1 ，a 2 ，...，a p And observation noise v s And obtaining a process noise covariance matrix Q.
Referring to fig. 6, step S103 performs kalman filtering on the excitation electroencephalogram signal according to the state prediction parameters, including:
step S601, detecting according to the previous time t-1The obtained excitation EEG signal J (t-1) and the state prediction parameters calculate the prior state vector of the current time t from the previous time t-1 to the current time t
Covariance matrix with prior error->
According to some embodiments of the present invention, if the current time t is the starting time of the excitation electroencephalogram, the excitation electroencephalogram does not exist at the previous time t-1, and the prior state vector of the current time t is performed according to the state prediction parameters acquired by taking the resting electroencephalogram as the value basis
Covariance matrix with prior error->
It is to be understood that the case where the current time t is the excitation electroencephalogram signal start time is included in the literal meaning of step S601.
Step S602, according to the prior state vector of the current time t
Covariance matrix with prior error->
Obtaining Kalman gain;
step S603, according to the excitation EEG signal J (t), kalman gain and prior state vector detected at the current time t
Covariance matrix with prior error->
Estimating the posterior state direction of the current time tQuantity x t Posterior error covariance matrix P t ；
The excitation brain signal J (t), kalman gain estimation and prior state vector detected according to the current time t
Covariance matrix with prior error->
Obtaining posterior state vector x at current time t t Posterior error covariance matrix P t A priori state vector for the current time t is intended to be reduced +.>
Covariance matrix with prior error->
Error from actual value, thereby obtaining posterior state vector x closer to actual value t Posterior error covariance matrix P t 。
Step S604, according to x t And P t And generating an output value of the filtered electroencephalogram signal at the current time t.
It should be noted that, based on the posterior state vector x t Posterior error covariance matrix P t The generated output value is the value of the filtered electroencephalogram signal at the current time t. In general, posterior state vectors and posterior error covariance matrixes of all moments obtained by processing observed values of excitation electroencephalogram signals at all moments are obtained, output values of all moments are obtained, and finally the output values of all moments are combined to form waveforms of the filtered electroencephalogram signals.
Referring to fig. 7, step S103 performs kalman filtering on the excitation electroencephalogram signal according to the state prediction parameters, and further includes:
step S701, X is calculated t As a priori state vector at time t+1, P is taken as t As tCalculating an updated Kalman gain and an updated state prediction parameter by using the prior error covariance matrix at the moment +1;
in some embodiments of the present invention, updating the state prediction parameters mainly refers to updating the process noise covariance Q. Specifically, through Q t+1 ＝K 1 Q t +K 2 K (J (t+1) -HX) to update the process noise covariance Q, where K 1 ，K 2 Are all weighting coefficients, and 0<K 1 <1,0<K 2 <1, K is Kalman filtering gain, J (t+1) is an excitation electroencephalogram signal detected at the next moment t+1, H is a Kalman filtering measurement matrix, and X is a state variable matrix formed by state variables. Along with the update of the Kalman gain according to the state prediction parameters in each round of circulation, the Kalman filtering process can generate self-adaptive adjustment along with the change of the excitation electroencephalogram signals, so that the accuracy of the electroencephalogram component extraction method is further improved.
Step S702, estimating the posterior state vector x of the next time t+1 according to the excitation electroencephalogram signal J (t+1) detected at the next time t+1, the Kalman gain of the updated state, and the state prediction parameters of the updated state t+1 Posterior error covariance matrix P t+1 ；
Step S703, according to x t+1 And P t+1 And generating an output value of the filtered electroencephalogram signal at the next time t+1.
It should be noted that, in this embodiment, steps S701 to S703 form a cycle of the kalman filtering process, and the kalman gain and the state prediction parameter can be updated once after each cycle, and an output value of the filtered electroencephalogram signal is obtained until the kalman filtering is completed to excite all the electroencephalogram signals to be processed, and the filtered electroencephalogram signals are randomly generated. The output of each cycle is obtained by the posterior state vector and the posterior error covariance matrix corresponding to the cycle, so that the output value close to the actual value can be obtained for each cycle, and the accuracy of the finally formed filtered electroencephalogram signal is correspondingly improved.
Referring to FIG. 8, step S601 is from the last time instant according to some embodiments of the present inventiont-1 calculates a priori state vector of the current time t from the current time t
Covariance matrix with prior error->
Comprising the following steps:
step S801, substituting the state transition matrix A and the process noise covariance matrix Q in the state prediction parameters into the state variable update equation
Update equation with error covariance->
Wherein x is t-1 P being the state variable at the last time t-1 t-1 An error covariance matrix of the last time t-1;
step S802, obtaining a priori state vector of the current time t
Covariance matrix with prior error->
According to some embodiments of the invention, step S602 is based on an a priori state vector at the current time t
Covariance matrix with prior error->
Deriving a kalman gain comprising:
the prior state vector of the measurement matrix H, the observed noise covariance matrix R and the current moment t
With a prioriError covariance matrix->
Substitution formula->
Obtaining Kalman gain K.
According to some embodiments of the invention, a posterior state vector x of the current time t is calculated in step S603 t Posterior error covariance matrix P t Comprising:
the measurement matrix H, the Kalman gain K, the excitation electroencephalogram signal J (t) detected at the current moment t and the prior state vector at the current moment t are measured
Substitution formula->
Obtaining posterior state vector x at current time t t ；
The prior error covariance matrix of the current moment t is obtained by measuring the matrix H, the Kalman gain K and the prior error covariance matrix of the current moment t
Substitution formula
Obtaining a posterior error covariance matrix P of the current moment t t 。
It should be appreciated that the various embodiments provided by the embodiments of the present invention may be arbitrarily combined to achieve different technical effects.
While the preferred embodiment of the present invention has been described in detail, the present invention is not limited to the above embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit and scope of the present invention, and these equivalent modifications or substitutions are included in the scope of the present invention as defined in the appended claims.
Claims (6)
1. An electroencephalogram component extraction method, characterized by comprising:
collecting an original brain electrical signal, and obtaining a resting brain electrical signal and an excitation brain electrical signal from the original brain electrical signal, wherein the resting brain electrical signal reflects the brain electrical signal of which the brain is in an unstimulated state, and the excitation brain electrical signal reflects the brain electrical signal of which the brain is in a stimulated state;
carrying out parameter estimation of a Kalman filtering model by taking the resting electroencephalogram signal as a value basis to obtain state prediction parameters, wherein the state prediction parameters comprise a state transition matrix, a measurement matrix, an observation noise covariance matrix and a process noise covariance matrix; the method for estimating parameters of the Kalman filtering model by taking the resting electroencephalogram signal as a value basis to obtain state prediction parameters comprises the following steps:
analyzing the resting electroencephalogram signals to obtain autoregressive model coefficients, wherein establishing
Order autoregressive model
Wherein->
Is->
Order autoregressive model coefficients,>
representing said resting brain electrical signal without noise, < >>
For observing noise +.>
Indicating the time at which the observed data was acquired; when (when)
And obtaining a Kalman filtering state equation through the autoregressive model:
substituting the autoregressive model coefficient into the state equation and the observation equation, and estimating to obtain a state transition matrix and a measurement matrix in the state prediction parameters, wherein the state transition matrix and the measurement matrix are extracted from the resting electroencephalogram signal
Coefficient of order autoregressive model
Substituting the state equation and the observation equation, and estimating to obtain a Kalman filtering state transition matrix in the state prediction parameters>
Measurement matrix of Kalman filtering>
；
Obtaining an observation noise covariance matrix and a process noise covariance matrix in the state prediction parameters according to the autoregressive model coefficient, the state equation and the observation equation, wherein the observation noise covariance matrix is obtained by using the method
Expressed as mean 0, covariance matrix +.>
Is->
The method comprises the steps of carrying out a first treatment on the surface of the Measuring the observation noise by taking the resting electroencephalogram signal as an observation basis>
And deriving an observed noise covariance matrix in said state prediction parameters>
The method comprises the steps of carrying out a first treatment on the surface of the The process noise is->
Expressed as mean 0, covariance matrix +.>
Is->
The method comprises the steps of carrying out a first treatment on the surface of the According to said->
Coefficient of order autoregressive model
Is +.>
Deriving a process noise covariance matrix in the state prediction parameters>
；/>
And carrying out Kalman filtering on the excited electroencephalogram signals according to the state prediction parameters so as to obtain filtered electroencephalogram signals.
2. The method of claim 1, wherein said kalman filtering said excitation electroencephalogram signal according to said state prediction parameters comprises:
according to the last time
The detected stimulated electroencephalogram signal +.>
The state prediction parameter, from the last moment +.>
To the current time->
Calculating the current moment +.>
Is>
Covariance matrix with prior error->
；
According to the current time
Is>
Covariance matrix with prior error->
Obtaining Kalman gain;
according to the current time
The detected stimulated electroencephalogram signal +.>
Said Kalman gain, said a priori state vector +.>
Covariance matrix with the prior error +.>
Calculating the current moment +.>
Posterior state vector->
Covariance matrix with posterior error->
；
According to the described
Is in contact with the->
GeneratingThe filtered EEG signal is +.>
Is a function of the output value of (a).
3. The method of claim 2, wherein said kalman filtering said excitation electroencephalogram signal according to said state prediction parameters further comprises:
the said
As->
A priori state vector of the moment of time, said +.>
As->
Calculating and updating the Kalman gain and the state prediction parameters by using the priori error covariance matrix of the moment;
according to the next time
The detected stimulated electroencephalogram signal +.>
The Kalman gain of the updated state and the state prediction parameter of the updated state are calculated to obtain the next moment +.>
Posterior state vector->
Covariance matrix with posterior error->
；
According to the described
Is in contact with the->
Generating said filtered brain electrical signal at the next moment +.>
Is a function of the output value of (a).
4. The method of claim 2, wherein the since the last time
To the current time->
Calculating the current moment +.>
Is>
Covariance matrix with prior error->
Comprising:
state transition matrix in the state prediction parameters
Covariance matrix with the process noise +.>
Substitution of the state variable update equation +.>
Update equation with error covariance->
=
+
Deriving said->
Is in contact with the->
Wherein->
For the last moment +.>
State variable of->
For the last moment +.>
Error covariance matrix of (a) is obtained.
5. The method according to claim 4, wherein said step of determining said current time
Prior state vector of (c)
Covariance matrix with prior error->
Obtaining the Kalman gainComprising:
the measurement matrix
The observed noise covariance matrix +.>
Current time->
Is>
Covariance matrix with prior error->
Substitution formula k= =>
The Kalman gain K is obtained.
6. The method according to claim 5, wherein the calculating results in a current time
Posterior state vector->
Covariance matrix with posterior error->
Comprising:
the measurement matrix
Said kalman gain K, current moment +.>
The detected stimulated electroencephalogramSignal->
Current time->
Is +.>
Substitution formula->
=
+
Obtaining the current moment +.>
Posterior state vector->
；
The measurement matrix
Said kalman gain K, current moment +.>
Is a priori error covariance matrix->
Substitution formula->
=
Obtaining the current moment +.>
Posterior error covariance matrix of->
。/>
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