CN112364812A - aVEPs electroencephalogram identification method based on TRCA-WPTD - Google Patents

aVEPs electroencephalogram identification method based on TRCA-WPTD Download PDF

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CN112364812A
CN112364812A CN202011343524.2A CN202011343524A CN112364812A CN 112364812 A CN112364812 A CN 112364812A CN 202011343524 A CN202011343524 A CN 202011343524A CN 112364812 A CN112364812 A CN 112364812A
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matrix
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aveps
electroencephalogram
wptd
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CN112364812B (en
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杨帮华
周雨松
汪小帆
夏新星
高守玮
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University of Shanghai for Science and Technology
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    • G06F2218/12—Classification; Matching
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Abstract

The invention discloses a TRCA-WPTD-based aVEPs electroencephalogram identification method, which comprises the following stages: a training stage: filtering the training data respectively through WPTD algorithms with different threshold setting modes; respectively calculating SNR and RMSE parameters of the filtered training data, and selecting the data with the highest SNR value and the lowest RMSE value for data combing; calculating a generalized right eigenvector matrix W; reducing the dimension of the data to obtain a template matrix Temp; and (3) a testing stage: and (4) screening the test data, and calculating a correlation coefficient matrix r with the generalized right eigenvector matrix W and the template matrix Temp. And a decoding stage: and decoding the correlation coefficient matrix r obtained in the test stage, generating a predictive coding matrix, comparing the predictive coding matrix with the actual coding matrix, and outputting an identification result. According to the invention, the WPTD algorithm is used for filtering, the signal-to-noise ratio of the aVEPs electroencephalogram is improved, the root mean square error is reduced, the TRCA algorithm is used for carrying out template matching for identification, and the character identification accuracy and identification speed based on the aVEPs electroencephalogram are improved.

Description

aVEPs electroencephalogram identification method based on TRCA-WPTD
Technical Field
The invention relates to the field of electroencephalogram identification based on Event-Related Potential (ERP), in particular to an aVEPs electroencephalogram identification method based on TRCA-WPTD, which is a method for identifying asymmetric Visual Evoked Potentials (aVEPs) based on Task-Related Component Analysis (TRCA) in combination with Wavelet Packet Threshold Denoising (WTD) algorithm.
Background
With the continuous progress of computer-based biological communication technology, brain-computer interface technology has been developed as an important component thereof and is gradually applied clinically. In the research of brain-computer interface technology, the development of ERP-based electroencephalogram identification technology is stable and the application is wide, especially the ERP electroencephalogram based on P300 potential is most popular, such as: a character spelling system based on the P300 brain electricity, a target detection system based on the P300 combined with Rapid Serial Visual Presentation (RSVP), and the like. At present, a matrix flicker paradigm and an RSVP paradigm are commonly used for inducing P300 electroencephalograms, but both of the two paradigms require a testee to watch a flicker screen, and the generated stimulation appears in the central visual field of the testee, so that visual fatigue is easily caused, so that a new method for inducing the aVEPs electroencephalograms through coding by code division multiple access and space division multiple access by utilizing the space-to-side dominance characteristic of the human brain to the stimulation response is provided for character recognition. The existing method for processing the electroencephalograms of the aVEPs adopts the traditional typical Correlation Analysis (CCA) to calculate a Correlation coefficient and the Linear Discriminant Analysis (LDA) to output a classification result, the average Recognition Rate (RR) of the electroencephalograms of the aVEPs is not high, the Information Transfer Rate (ITR) of the electroencephalograms is slow, and the RR and ITR need to be further improved when the aVEPs are actually applied to life.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an aVEPs electroencephalogram identification method based on TRCA-WPTD, wherein the filtering is carried out by a WPTD algorithm, the Signal-to-Noise Ratio (SNR) of the aVEPs electroencephalogram is improved, the Root Mean Square Error (RMSE) is reduced, the identification is carried out by carrying out template matching by the TRCA algorithm, and the character identification accuracy and identification speed based on the aVEPs electroencephalogram are improved.
In order to achieve the purpose, the technical solution of the invention is as follows:
a TRCA-WPTD-based aVEPs electroencephalogram identification method comprises the following 3 stages:
(1) training phase
Importing preprocessed aVEPs electroencephalogram data TrainData1 for training, and respectively filtering by 8 WPTD algorithms with different threshold setting modes; generating 8 kinds of filtered TrainData2, calculating SNR and RMSE parameters for each TrainData2, selecting the TrainData2 with the highest SNR value and the lowest RMSE value for data combing, and generating TrainData 3; calculating a generalized right eigenvector matrix W by a TRCA algorithm; and reducing the dimension of the TrainData3 based on the matrix W to obtain a template matrix Temp.
(2) Testing phase
Importing preprocessed aVEPs electroencephalogram data TestData1 for testing, and respectively filtering by using 8 WPTD algorithms with different threshold setting modes to generate 8 filtered TestData 2; calculating SNR and RMSE parameters for each TestData2, selecting the TestData2 with the highest SNR value and the lowest RMSE value for data screening to generate TestData 3; calculating a correlation coefficient matrix r by the TestData3 and the generalized right eigenvector matrix W and the template matrix Temp generated in the training stage;
(3) decoding stage
And decoding the correlation coefficient matrix r to generate a predictive coding matrix, comparing the predictive coding matrix with an actual coding matrix set in an experimental paradigm, and outputting an identification result when the predictive coding matrix is completely matched with the actual coding matrix.
Preferably, the WPTD algorithm of 8 different threshold setting manners in the stage (1) is specifically as follows:
decomposing aVEPs electroencephalogram data into various scales by wavelet packet decomposition, and generating 8 denoising methods by adopting a combination of a threshold value and a threshold value function, namely: the method comprises the steps of respectively removing wavelet coefficients belonging to noise in each scale by each method, retaining and enhancing the wavelet coefficients belonging to aVEPs electroencephalogram data, and finally reconstructing the processed wavelet coefficients by utilizing wavelet inverse transformation to obtain noise-reduced aVEPs electroencephalogram.
Preferably, the data combing process in the stage (1) is as follows: starting from the first character data, finding out the positions of sampling points with all label types of 0, taking the positions as initial positions, intercepting the set data segment length backwards, and sequentially storing all the data segments in an array named as Lefttemp according to the appearance order of the labels; finding out all data with the label type of 1 and storing the data in an array named as Righttemp; repeating the circulation until the data of all the characters are completely read; and merging the Data in the array of the Lefttemp and the array of the Righttemp, and sequentially putting the merged Data into an array named Data _ train for storage.
Preferably, the WPTD algorithm of 8 different threshold setting manners in the stage (1) is specifically as follows:
the WPTD algorithm process is that wavelet packet decomposition is utilized to decompose aVEPs electroencephalogram data into various scales, a threshold value is set, then wavelet coefficients belonging to noise in each scale are removed, the wavelet coefficients belonging to the aVEPs electroencephalogram data are reserved and enhanced, and finally the wavelet coefficients after processing are reconstructed by wavelet inverse transformation to obtain noise-reduced aVEPs electroencephalogram.
Matrix d for aVEPs electroencephalogram data TrainData1 used for traininglIs represented by dlThere are two dimensions: the method comprises the following steps of (1) channel number and sampling point number, and also label category information which does not participate in filtering calculation, wherein the specific data processing flow is as follows:
(1-1) wavelet packet decomposition TrainData1
To dlAnd (3) decomposing the nth frequency band of the j +1 layer into the 2 nth high-pass filtered frequency band and the 2n +1 th low-pass filtered frequency band of the j layer according to the selected wavelet base expansion, wherein the iterative calculation formula of the decomposition is as follows:
Figure BDA0002799184910000031
in the formula (I), the compound is shown in the specification,
Figure BDA0002799184910000032
is dlAt the 2 nth high-pass filtered sub-band of the j-th layer,
Figure BDA0002799184910000033
is dlAt the 2n +1 low-pass filtered sub-band of the j-th layer,
Figure BDA0002799184910000034
is dlThe k-th component, h, in the n bands of the (j + 1) -th layerk-2lDecomposing the high-pass filter coefficients for the wavelet packets of the corresponding component, gk-2lThe low-pass filter coefficients are decomposed for the wavelet packets of the corresponding component.
(1-2) selection of threshold function
The method is divided into the following 2 types:
(1-2-1) hard threshold denoising method
Let dlObtaining n frequency bands after wavelet decomposition, namely n filter coefficients k (including high-frequency coefficients and low-frequency coefficients), starting from item 1, when the absolute value of the filter coefficient k is smaller than a given threshold a, making the filter coefficient k be 0, otherwise, making the filter coefficient k be constant, and finally, the calculation result is represented by y until each item is calculated, and the mathematical expression of each calculation is as follows:
Figure BDA0002799184910000035
(1-2-2) Soft threshold denoising method
Let dlObtaining n frequency bands after wavelet decomposition, namely n filter coefficients k (including high-frequency coefficients and low-frequency coefficients), starting from item 1, when the absolute value of the filter coefficient k is smaller than a given threshold a, making the filter coefficient k be 0, otherwise, making the filter coefficient k subtract the threshold a, and finally, the calculation result is represented by y until each item is calculated, and the mathematical expression of each calculation is as follows:
Figure BDA0002799184910000036
(1-3) rule for selecting threshold a
The classification is as follows 4:
(1-3-1) general type
Let dlObtaining n filter coefficients after wavelet packet decomposition, dlThe mean square error of (a) is denoted as σ, the mathematical expression of the threshold value a is:
Figure BDA0002799184910000037
(1-3-2) unbiased form
Let dlAfter wavelet packet decomposition, n filter coefficients are obtained, and the squared values of all coefficients form a vector w, which is expressed as: w ═ w1，w2，···，wn]And w is1≤w2≤···≤wnLet a Risk vector Risk whose sub-element riThe expression of (a) is:
Figure BDA0002799184910000041
with the minimum value r in the Risk vector Risk elementjAs a risk value, j ∈ [1, n ]]Finding out the corresponding sub-element w in the wjThen the mathematical expression for threshold a is:
Figure BDA0002799184910000042
(1-3-3) Mixed type
Let dlObtaining n filter coefficients after wavelet packet decomposition, dlThe mean square error of (c) is represented as sigma, s is the sum of the squares of all coefficients, and then
Figure BDA0002799184910000043
Binding w described in (1-3-2)jThen the mathematical expression for threshold a is:
Figure BDA0002799184910000044
(1-3-4) very large and very small
Let dlAfter wavelet packet decomposition, n filter coefficients are obtained, and then the mathematical expression of the threshold value a is:
Figure BDA0002799184910000045
generating 8 denoising methods through the combination of the threshold values and the threshold function in the steps (1-2) and (1-3), namely: the method comprises a general hard threshold denoising method, a general soft threshold denoising method, an unbiased hard threshold denoising method, an unbiased soft threshold denoising method, a mixed hard threshold denoising method, a mixed soft threshold denoising method, a very small hard threshold denoising method and a very small soft threshold denoising method.
(1-4) wavelet packet reconstruction TrainData2
After the 8 wavelet packet threshold denoising methods are adopted for calculation, 8 decomposed d are respectively subjected tolAnd reconstructing, wherein the reconstruction calculation formula is as follows:
Figure BDA0002799184910000046
in the formula (I), the compound is shown in the specification,
Figure BDA0002799184910000051
for the data of the n-th band of the j +1 th layer reconstructed after the wavelet packet filtering,
Figure BDA0002799184910000052
is dlThe kth component in the 2n high-pass filtered subbands of the jth layer after the wavelet packet filtering,
Figure BDA0002799184910000053
is dlThe kth component, h, in the 2n +1 low-pass filtered sub-band of the jth layer after wavelet packet filteringk-2lReconstructing the high-pass filter coefficients, g, for the wavelet packets of the corresponding componentk-2lLow-pass filter coefficients are reconstructed for the wavelet packets of the corresponding component.
Preferably, the calculation expression of the SNR and RMSE parameters in the stage (1) is as follows:
Figure BDA0002799184910000054
Figure BDA0002799184910000055
in the formula (d)l(i) Representing the original aVEPs electroencephalogram data components,
Figure BDA0002799184910000056
representing the reconstructed aVEPs electroencephalogram data component after denoising, wherein N is the signal length.
Preferably, the data combing process in the stage (1) is as follows:
denoised reconstructed aVEPs electroencephalogram data
Figure BDA0002799184910000057
There are two dimensions: the channel number and the sampling point number simultaneously contain label category information, the positions of all the sampling points with the label category of 0 are found from the first character data, the set data segment length is intercepted backwards by taking the position as the initial position, and all the data segments are sequentially stored in an array named as Lefttemp according to the label appearance sequence;
similarly, the positions of the sampling points with the label category of 1 are found, the set data segment length is intercepted backwards by taking the positions as initial positions, and all the data segments are sequentially stored in an array named as Righttemp according to the appearance sequence of the labels;
repeating the circulation until the data of all the characters are completely read; merging the Data in the array of the Lefttemp and the array of the Righttemp, and sequentially putting the merged Data into an array named as Data _ train for storage, wherein the Data _ train array has four dimensions: the number of channels, the number of sampling points, the total number of labels and the number of label categories.
Preferably, the TRCA algorithm in the stage (1) is specifically as follows:
selecting a type of tag Data matrix X from the array of Data _ train, X having three dimensions: the number of channels, the number of sampling points and the total number of labels are set as X ═ X1，X2，···Xn]N is the total number of the labels in the single-type label data;
for the ith element X in XiAnd the jth element XjThe matrix S is calculated as follows:
Figure BDA0002799184910000058
and performing transposition operation on the matrix S to obtain a matrix S', and further solving a matrix T, wherein the calculation formula is as follows:
T＝S+S′
combining the matrix X on the dimension of the total number of the labels to obtain a matrix Y only containing two dimensions of the number of channels and the number of sampling points, and setting Y to expand according to columns to be expressed as: y ═ Y1，Y2，···Ym]M is the column number of the matrix Y, namely the number of sampling points, and each element of the matrix Z is calculated according to the following formula:
Figure BDA0002799184910000061
wherein Z isiDenotes the ith element in the matrix Z, so that Z ═ Z1，Z2，···Zm](ii) a And performing transposition operation on Z to obtain Z', and further solving a matrix Q, wherein the calculation formula is as follows:
Q＝Z+Z′
and calculating the generalized eigenvalue diagonal matrix D and the corresponding generalized right eigenvector matrix V for the solved T and Q by the following calculation formula:
T×V＝Q×V×D
extracting eigenvalue from diagonal of matrix D, sorting obtained vectors in descending mode, sorting elements of generalized right eigenvector matrix V in same descending mode and recording the sorted elements as matrix Wi；
Repeating the steps until all the category label data are calculated, and obtaining the generalized right eigenvector matrix W, wherein W is [ W ]1，W2，···Wt]，WiIs the ith element of W, and t is the tag class number.
Preferably, the dimension reduction algorithm in the stage (1) is specifically as follows:
for sub-elements X in the same type label data matrix XiSaid W obtained by TRCA algorithmiIs transferred Wi' with said XiMultiplying to obtain a single-class label data matrix E after dimension reductionkSub-element E ofkiNamely:
′
Eki＝Wi×Xi
repeating the steps until all the data of all the labels in the same class are calculated, and obtaining the reduced dimension single-class label data matrix EkCan be represented as Ek＝[Ek1，Ek2，···Ekn]，EkiIs EkN is the total number of labels in the single-class label data; then all label category data matrix E may be denoted as E ═ E1，E2，···Et]，EkIs the kth sub-element in E, and t is the label category number;
and averaging the obtained matrix E on the dimension of the total number of the labels to obtain the template matrix Temp.
Preferably, the WPTD algorithms with 8 different threshold setting modes in the stage (2) are the same as the WPTD algorithms with 8 different threshold setting modes in the step (1), and filtering is performed on the imported aVEPs electroencephalogram data TestData for testing through the WPTD algorithms.
Preferably, the SNR and RMSE parameters in the stage (2) are calculated in the same manner as the SNR and RMSE parameters in the step (1), and the corresponding SNR and RMSE parameters are calculated for the 8 filtered TestData.
Preferably, the data screening process in the stage (2) is as follows:
because the test Data hides the label category information, the positions of the electroencephalogram sampling points when all characters appear are marked by using the same label, the positions of the electroencephalogram sampling points when the label appears are found out firstly, the set Data segment lengths are intercepted backwards by taking the positions as initial positions, all the Data segments are sequentially stored in an array named as Data _ test according to the appearance sequence of the label,
preferably, the algorithm for calculating the correlation coefficient matrix r in the stage (2) is specifically as follows:
selecting 1 tag Data matrix M from the array of Data _ test, M having three dimensions: the number of channels, the number of sampling points and the total number of labels are set as M ═ M1，M2，···Mn]，MiIs the ith sub-element in M, n is the total number of labels in the single-class label data, MiIs transposed Mi' with said WiIs transferred Wi' multiplication, generating matrix AiiThe calculation formula is as follows:
Aii＝M′i×W′i
the result of the calculation of all the data in M according to the formula is recorded as matrix A, AiiIs the ith sub-element in A, calculate AiiWith a sub-element Temp of said template matrix TempiiTo obtain the sub-elements R of the correlation coefficient matrix RiThe calculation formula is as follows:
Figure BDA0002799184910000071
wherein the content of the first and second substances,
Figure BDA0002799184910000072
is the average of a over the column dimension,
Figure BDA0002799184910000073
is the average of Temp over the column dimension.
Repeating the steps until all the label data are calculated, and obtaining a correlation coefficient matrix R ═ R1，R2，···Rt]，RiIs the ith sub-element in R, and t is the label category number.
And rearranging and combining the correlation coefficient matrix R on the dimensionality of the trial time, and averaging on the dimensionality of the trial time to obtain the correlation coefficient matrix R.
Preferably, the decoding calculation in the stage (3) is specifically as follows:
reading the element values in the correlation coefficient matrix r in sequence, reading 2 adjacent elements each time, recording the element values as 10 if the first element value is larger than the second element value, otherwise recording the element values as 01, and sequentially putting the recorded results into the predictive coding matrix until all the values are read.
The actual coding matrix in stage (3) is composed of a plurality of vectors containing only "0" and "1" elements, each vector representing a character, which are specified in an experimental paradigm.
The invention has obvious and prominent substantive features and remarkable advantages:
1. the invention is based on the mixed use of TRCA algorithm and WPTD algorithm, filters the aVEPs electroencephalogram, improves SNR and reduces RMSE, and finally realizes the improvement of the overall recognition rate and the recognition speed of character data based on the aVEPs electroencephalogram by a template matching method;
2. according to the invention, a dynamic WPTD algorithm is adopted, filtering is carried out on each group of aVEPs electroencephalograms under different threshold setting methods, the SNR and the RMSE after filtering are calculated, and the filtered electroencephalograms with the maximum SNR and the minimum RMSE are selected for template matching, so that the overall identification rate of the aVEPs electroencephalograms is improved;
3. the invention adopts TRCA algorithm to match the template, compared with the traditional CCA algorithm, the ITR can be effectively improved, and the invention is beneficial to the further practical application of aVEPs electroencephalogram;
4. the invention is beneficial to promoting the development of aVEPs electroencephalograms, perfects the brain-computer interface system based on ERP electroencephalograms, can be used for the auxiliary output of physically disabled people, can also be used in the fields of industrial control and automatic driving, and has good market application prospect and social and economic benefits.
Drawings
FIG. 1 is a general algorithmic flow chart of the present invention.
FIG. 2 is a flow chart of the training phase algorithm of the present invention.
FIG. 3 is a flow chart of the test phase algorithm of the present invention.
Fig. 4 is a flow chart of the decoding phase algorithm of the present invention.
Fig. 5 is a flow chart of the WPTD algorithm of 8 different threshold setting modes according to the present invention.
FIG. 6 is a flow chart of the data combing algorithm of the present invention.
Fig. 7 is a flow chart of the TRCA algorithm of the present invention.
FIG. 8 is a flow chart of the dimension reduction algorithm of the present invention.
FIG. 9 is a flow chart of a data screening algorithm of the present invention.
Fig. 10 is a flow chart of the algorithm for calculating the correlation coefficient matrix r according to the invention.
Fig. 11 is a flow chart of the decoding algorithm of the present invention.
Detailed Description
The preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings:
the first embodiment is as follows:
a TRCA-WPTD-based aVEPs electroencephalogram identification method comprises the following 3 stages:
the acquisition of aVEPs electroencephalogram data uses a Neuroscan synomps 2 electroencephalogram recording system. When in electroencephalogram recording, 0.1-100Hz band-pass filtering is adopted, 50Hz notch is superposed, and the sampling frequency is 200 Hz.
Referring to fig. 1, a TRCA-WPTD based aVEPs electroencephalogram identification method comprises the following 3 stages:
(1) training phase
Referring to fig. 2, preprocessed aVEPs electroencephalogram data TrainData1 for training are imported, and filtering is performed by using 8 WPTD algorithms with different threshold setting modes; generating 8 kinds of filtered TrainData2, calculating SNR and RMSE parameters for each TrainData2, selecting the TrainData2 with the highest SNR value and the lowest RMSE value for data combing, and generating TrainData 3; calculating a generalized right eigenvector matrix W by a TRCA algorithm; and reducing the dimension of the TrainData3 based on the matrix W to obtain a template matrix Temp.
(2) Testing phase
Referring to fig. 3, preprocessed aVEPs electroencephalogram data TestData1 for testing are imported, and are filtered by using WPTD algorithms with 8 different threshold setting modes, so as to generate 8 filtered TestData 2; calculating SNR and RMSE parameters for each TestData2, selecting the TestData2 with the highest SNR value and the lowest RMSE value for data screening to generate TestData 3; calculating a correlation coefficient matrix r by the TestData3 and the generalized right eigenvector matrix W and the template matrix Temp generated in the training stage;
(3) decoding stage
Referring to fig. 4, the correlation coefficient matrix r is decoded and calculated to generate a predictive coding matrix, and the predictive coding matrix is compared with an actual coding matrix element set in an experimental paradigm, and an identification result can be output only when the predictive coding matrix element is completely matched with the actual coding matrix element set in the experimental paradigm. According to the embodiment, the WPTD algorithm is used for filtering, the signal-to-noise ratio of the aVEPs electroencephalogram is improved, the root mean square error is reduced, the TRCA algorithm is used for carrying out template matching for identification, and the character identification accuracy and identification speed based on the aVEPs electroencephalogram are improved.
Example two:
this embodiment is substantially the same as the first embodiment, and is characterized in that:
referring to fig. 5, the WPTD algorithm of 8 different threshold setting modes in the phase (1) is specifically as follows:
the WPTD algorithm process is that wavelet packet decomposition is utilized to decompose aVEPs electroencephalogram data into various scales, a threshold value is set, then wavelet coefficients belonging to noise in each scale are removed, the wavelet coefficients belonging to the aVEPs electroencephalogram data are reserved and enhanced, and finally the wavelet coefficients after processing are reconstructed by wavelet inverse transformation to obtain noise-reduced aVEPs electroencephalogram.
aVEPs electroencephalogram data Tra for trainingMatrix d for InData1lIs represented by dlThere are two dimensions: the method comprises the following steps of (1) channel number and sampling point number, and also label category information which does not participate in filtering calculation, wherein the specific data processing flow is as follows:
(1-1) wavelet packet decomposition TrainData1
To dlAnd (3) decomposing the nth frequency band of the j +1 layer into the 2 nth high-pass filtered frequency band and the 2n +1 th low-pass filtered frequency band of the j layer according to the selected wavelet base expansion, wherein the iterative calculation formula of the decomposition is as follows:
Figure BDA0002799184910000091
in the formula (I), the compound is shown in the specification,
Figure BDA0002799184910000101
is dlAt the 2 nth high-pass filtered sub-band of the j-th layer,
Figure BDA0002799184910000102
is dlAt the 2n +1 low-pass filtered sub-band of the j-th layer,
Figure BDA0002799184910000103
is dlThe k-th component, h, in the n bands of the (j + 1) -th layerk-2lDecomposing the high-pass filter coefficients for the wavelet packets of the corresponding component, gk-2lThe low-pass filter coefficients are decomposed for the wavelet packets of the corresponding component.
(1-2) selection of threshold function
The method is divided into the following 2 types:
(1-2-1) hard threshold denoising method
Let dlObtaining n frequency bands after wavelet decomposition, namely n filter coefficients k (including high-frequency coefficients and low-frequency coefficients), starting from item 1, when the absolute value of the filter coefficient k is smaller than a given threshold a, making the filter coefficient k be 0, otherwise, making the filter coefficient k be constant, and finally, the calculation result is represented by y until each item is calculated, and the mathematical expression of each calculation is as follows:
Figure BDA0002799184910000104
(1-2-2) Soft threshold denoising method
Let dlObtaining n frequency bands after wavelet decomposition, namely n filter coefficients k (including high-frequency coefficients and low-frequency coefficients), starting from item 1, when the absolute value of the filter coefficient k is smaller than a given threshold a, making the filter coefficient k be 0, otherwise, making the filter coefficient k subtract the threshold a, and finally, the calculation result is represented by y until each item is calculated, and the mathematical expression of each calculation is as follows:
Figure BDA0002799184910000105
(1-3) rule for selecting threshold a
The classification is as follows 4:
(1-3-1) general type
Let dlObtaining n filter coefficients after wavelet packet decomposition, dlThe mean square error of (a) is denoted as σ, the mathematical expression of the threshold value a is:
Figure BDA0002799184910000106
(1-3-2) unbiased form
Let dlAfter wavelet packet decomposition, n filter coefficients are obtained, and the squared values of all coefficients form a vector w, which is expressed as: w ═ w1，w2，···，wn]And w is1≤w2≤···≤wnLet a Risk vector Risk whose sub-element riThe expression of (a) is:
Figure BDA0002799184910000111
with the minimum value r in the Risk vector Risk elementjAs a risk value, j ∈ [1, n ]]Finding out the w middle pairsCorresponding sub-element wjThen the mathematical expression for threshold a is:
Figure BDA0002799184910000112
(1-3-3) Mixed type
Let dlObtaining n filter coefficients after wavelet packet decomposition, dlThe mean square error of (c) is represented as sigma, s is the sum of the squares of all coefficients, and then
Figure BDA0002799184910000113
Binding w described in (1-3-2)jThen the mathematical expression for threshold a is:
Figure BDA0002799184910000114
(1-3-4) very large and very small
Let dlAfter wavelet packet decomposition, n filter coefficients are obtained, and then the mathematical expression of the threshold value a is:
Figure BDA0002799184910000115
generating 8 denoising methods through the combination of the threshold values and the threshold function in the steps (1-2) and (1-3), namely: the method comprises a general hard threshold denoising method, a general soft threshold denoising method, an unbiased hard threshold denoising method, an unbiased soft threshold denoising method, a mixed hard threshold denoising method, a mixed soft threshold denoising method, a very small hard threshold denoising method and a very small soft threshold denoising method.
(1-4) wavelet packet reconstruction TrainData2
After the 8 wavelet packet threshold denoising methods are adopted for calculation, 8 decomposed d are respectively subjected tolAnd reconstructing, wherein the reconstruction calculation formula is as follows:
Figure BDA0002799184910000116
in the formula (I), the compound is shown in the specification,
Figure BDA0002799184910000117
for the data of the n-th band of the j +1 th layer reconstructed after the wavelet packet filtering,
Figure BDA0002799184910000118
is dlThe kth component in the 2n high-pass filtered subbands of the jth layer after the wavelet packet filtering,
Figure BDA0002799184910000119
is dlThe kth component, h, in the 2n +1 low-pass filtered sub-band of the jth layer after wavelet packet filteringk-2lReconstructing the high-pass filter coefficients, g, for the wavelet packets of the corresponding componentk-2lLow-pass filter coefficients are reconstructed for the wavelet packets of the corresponding component.
Preferably, the calculation expression of the SNR and RMSE parameters in the stage (1) is as follows:
Figure BDA0002799184910000121
Figure BDA0002799184910000122
in the formula (d)l(i) Representing the original aVEPs electroencephalogram data components,
Figure BDA0002799184910000123
representing the reconstructed aVEPs electroencephalogram data component after denoising, wherein N is the signal length.
Referring to fig. 6, the data combing process in stage (1) is as follows:
denoised reconstructed aVEPs electroencephalogram data
Figure BDA0002799184910000124
There are two dimensions: the channel number and the sampling point number simultaneously contain label category information, the positions of all the sampling points with the label category of 0 are found from the first character data, the set data segment length is intercepted backwards by taking the position as the initial position, and all the data segments are sequentially stored in an array named as Lefttemp according to the label appearance sequence;
similarly, the positions of the sampling points with the label category of 1 are found, the set data segment length is intercepted backwards by taking the positions as initial positions, and all the data segments are sequentially stored in an array named as Righttemp according to the appearance sequence of the labels;
repeating the circulation until the data of all the characters are completely read; merging the Data in the array of the Lefttemp and the array of the Righttemp, and sequentially putting the merged Data into an array named as Data _ train for storage, wherein the Data _ train array has four dimensions: the number of channels, the number of sampling points, the total number of labels and the number of label categories.
Referring to fig. 7, the TRCA algorithm in stage (1) is specifically as follows:
selecting a type of tag Data matrix X from the array of Data _ train, X having three dimensions: the number of channels, the number of sampling points and the total number of labels are set as X ═ X1，X2，···Xn]N is the total number of the labels in the single-type label data;
for the ith element X in XiAnd the jth element XjThe matrix S is calculated as follows:
Figure BDA0002799184910000125
and performing transposition operation on the matrix S to obtain a matrix S', and further solving a matrix T, wherein the calculation formula is as follows:
T＝S+S′
combining the matrix X on the dimension of the total number of the labels to obtain a matrix Y only containing two dimensions of the number of channels and the number of sampling points, and setting Y to expand according to columns to be expressed as: y ═ Y1，Y2，···Ym]M is a column of matrix YCounting, namely the number of sampling points, and calculating each element of the matrix Z according to the following formula:
Figure BDA0002799184910000131
wherein Z isiDenotes the ith element in the matrix Z, so that Z ═ Z1，Z2，···Zm](ii) a And performing transposition operation on Z to obtain Z', and further solving a matrix Q, wherein the calculation formula is as follows:
Q＝Z+Z′
and calculating the generalized eigenvalue diagonal matrix D and the corresponding generalized right eigenvector matrix V for the solved T and Q by the following calculation formula:
T×V＝Q×V×D
extracting eigenvalue from diagonal of matrix D, sorting obtained vectors in descending mode, sorting elements of generalized right eigenvector matrix V in same descending mode and recording the sorted elements as matrix Wi；
Repeating the steps until all the category label data are calculated, and obtaining the generalized right eigenvector matrix W, wherein W is [ W ]1，W2，···Wt]，WiIs the ith element of W, and t is the tag class number.
Referring to fig. 8, the dimension reduction algorithm in the stage (1) is specifically as follows:
for sub-elements X in the same type label data matrix XiSaid W obtained by TRCA algorithmiIs transferred Wi' with said XiMultiplying to obtain a single-class label data matrix E after dimension reductionkSub-element E ofkiNamely:
Eki＝W′i×Xi
repeating the steps until all the data of all the labels in the same class are calculated, and obtaining the reduced dimension single-class label data matrix EkCan be represented as Ek＝[Ek1，Ek2，···Ekn]，EkiIs EkN isTotal number of tags in the single type of tag data; then all label category data matrix E may be denoted as E ═ E1，E2，···Et]，EkIs the kth sub-element in E, and t is the label category number;
and averaging the obtained matrix E on the dimension of the total number of the labels to obtain the template matrix Temp.
Referring to fig. 5, the WPTD algorithms with 8 different threshold setting modes in the stage (2) are the same as the WPTD algorithms with 8 different threshold setting modes in the step (1), and the imported aVEPs electroencephalogram data TestData for testing needs to be filtered through the WPTD algorithms.
Preferably, the SNR and RMSE parameters in the stage (2) are calculated in the same manner as the SNR and RMSE parameters in the step (1), and the corresponding SNR and RMSE parameters are calculated for the 8 filtered TestData.
Referring to fig. 9, the data screening process in stage (2) is as follows:
because the test Data hides the label type information, the same type label '1' is used for marking the sampling point positions of the electroencephalogram when all characters appear, the positions of the electroencephalogram sampling points when the label appears are found out firstly, the set Data segment length is intercepted backwards by taking the positions as the initial positions, all the Data segments are sequentially stored in an array named as Data _ test according to the appearance sequence of the label,
referring to fig. 10, the algorithm for calculating the correlation coefficient matrix r in the stage (2) is specifically as follows:
selecting 1 tag Data matrix M from the array of Data _ test, M having three dimensions: the number of channels, the number of sampling points and the total number of labels are set as M ═ M1，M2，···Mn]，MiIs the ith sub-element in M, n is the total number of labels in the single-class label data, MiIs transposed Mi' with said WiIs transferred Wi' multiplication, generating matrix AiiThe calculation formula is as follows:
Aii＝M′i×W′i
corresponding to total number of labels in single-class label dataThe result of some data calculated according to the formula is recorded as a matrix A, AiiIs a child element in A, calculate AiiWith a sub-element Temp of said template matrix TempiiTo obtain the sub-elements R of the correlation coefficient matrix RiThe calculation formula is as follows:
Figure BDA0002799184910000141
wherein the content of the first and second substances,
Figure BDA0002799184910000142
is the average of a over the column dimension,
Figure BDA0002799184910000143
is the average of Temp over the column dimension.
Repeating the steps until all the label data are calculated, and obtaining a correlation coefficient matrix R ═ R1，R2，···Rt]，RiIs the ith sub-element in R, and t is the label category number.
And rearranging and combining the correlation coefficient matrix R on the dimensionality of the trial time, and averaging on the dimensionality of the trial time to obtain the correlation coefficient matrix R.
Referring to fig. 11, the decoding calculation in the stage (3) is specifically as follows:
reading the element values in the correlation coefficient matrix r in sequence, reading 2 adjacent elements each time, recording the element values as 10 if the first element value is larger than the second element value, otherwise recording the element values as 01, and sequentially putting the recorded results into the predictive coding matrix until all the values are read.
The actual coding matrix in stage (3) is composed of a plurality of vectors containing only "0" and "1" elements, each vector representing a character, which are specified in an experimental paradigm.
In conclusion, the invention discloses an aVEPs electroencephalogram identification method based on TRCA-WPTD. The method comprises the following steps: firstly, importing preprocessed aVEPs electroencephalogram data TrainData1 for training, and respectively filtering by 8 WPTD algorithms with different threshold setting modes; generating 8 kinds of filtered TrainData2, calculating SNR and RMSE parameters for each TrainData2, selecting the TrainData2 with the highest SNR value and the lowest RMSE value for data combing, and generating TrainData 3; calculating a generalized right eigenvector matrix W by a TRCA algorithm; performing dimension reduction on the TrainData3 based on the matrix W to obtain a template matrix Temp; secondly, importing preprocessed aVEPs electroencephalogram data TestData1 for testing, and respectively filtering through 8 WPTD algorithms with different threshold setting modes to generate 8 filtered TestData 2; calculating SNR and RMSE parameters for each TestData2, selecting the TestData2 with the highest SNR value and the lowest RMSE value for data screening to generate TestData 3; calculating a correlation coefficient matrix r by the TestData3 and the generalized right eigenvector matrix W and the template matrix Temp generated in the training stage; and finally, decoding the correlation coefficient matrix r to generate a predictive coding matrix, comparing the predictive coding matrix with an actual coding matrix set in an experimental paradigm, and outputting an identification result when the predictive coding matrix is completely matched with the actual coding matrix. According to the invention, the WPTD algorithm is used for filtering, the signal-to-noise ratio of the aVEPs electroencephalogram is improved, the root mean square error is reduced, the TRCA algorithm is used for carrying out template matching for identification, and the character identification accuracy and identification speed based on the aVEPs electroencephalogram are improved.
The above detailed description merely describes preferred embodiments of the present invention and does not limit the scope of the invention. Without departing from the design concept and spirit of the present invention, various changes, substitutions and improvements of the technical solutions of the present invention made by the text description and the drawings provided by the ordinary skilled in the art shall fall into the protection scope of the present invention. The scope of the invention is defined by the claims.

Claims (8)

1. A TRCA-WPTD-based aVEPs electroencephalogram identification method is characterized by comprising the following steps: the method comprises the following 3 stages:
(1) a training stage:
filtering training data by using 8 WPTD algorithms with different threshold setting modes respectively; respectively calculating SNR and RMSE parameters of the filtered training data, and selecting the data with the highest SNR value and the lowest RMSE value for data combing; calculating a generalized right eigenvector matrix W by a TRCA algorithm; reducing the dimension of the data to obtain a template matrix Temp;
(2) and (3) a testing stage:
processing the test data by the same filtering and data selecting method as the training stage, then screening the data, and calculating a correlation coefficient matrix r with the generalized right eigenvector matrix W and the template matrix Temp;
(3) and a decoding stage:
and decoding the correlation coefficient matrix r obtained in the test stage, generating a predictive coding matrix, comparing the predictive coding matrix with the actual coding matrix, and outputting an identification result.
2. The TRCA-WPTD-based aVEPs electroencephalogram identification method according to claim 1, which is characterized in that: the WPTD algorithms of 8 different threshold setting modes in the stages (1) and (2) are specifically as follows:
decomposing aVEPs electroencephalogram data into various scales by wavelet packet decomposition, and generating 8 denoising methods by adopting a combination of a threshold value and a threshold value function, namely: the method comprises the steps of respectively removing wavelet coefficients belonging to noise in each scale by each method, retaining and enhancing the wavelet coefficients belonging to aVEPs electroencephalogram data, and finally reconstructing the processed wavelet coefficients by utilizing wavelet inverse transformation to obtain noise-reduced aVEPs electroencephalogram.
3. The TRCA-WPTD-based aVEPs electroencephalogram identification method according to claim 1, which is characterized in that: the data carding process in the stage (1) is as follows: starting from the first character data, finding out the positions of sampling points with all label types of 0, taking the positions as initial positions, intercepting the set data segment length backwards, and sequentially storing all the data segments in an array named as Lefttemp according to the appearance order of the labels; finding out all data with the label type of 1 and storing the data in an array named as Righttemp; repeating the circulation until the data of all the characters are completely read; and merging the Data in the array of the Lefttemp and the array of the Righttemp, and sequentially putting the merged Data into an array named Data _ train for storage.
4. The TRCA-WPTD-based aVEPs electroencephalogram identification method according to claim 1, which is characterized in that: the TRCA algorithm in the stage (1) is specifically:
selecting a type of label Data matrix X from the Data _ train array, and setting X as [ X ]1，X2，···Xn]N is the total number of the labels in the single-type label data; for the ith element X in XiAnd the jth element XjThe matrix S is calculated as follows:
Figure FDA0002799184900000011
adding the matrix S and the transpose of the matrix S, and further solving a matrix T;
combining the matrix X on the dimension of the total number of the labels to obtain a matrix Y, and expanding Y according to columns to be expressed as: y ═ Y1，Y2，···Ym]M is the column number of the matrix Y, namely the number of sampling points, and each element of the matrix Z is calculated according to the following formula:
Figure FDA0002799184900000021
wherein Z isiDenotes the ith element in the matrix Z, so that Z ═ Z1，Z2，···Zm](ii) a m is the column number of the matrix Z, namely the number of sampling points, and the matrix Z are transposed and added to further solve Q; calculating generalized eigenvalue diagonal matrix D and corresponding generalized right eigenvector matrix V for the obtained T and Q, extracting eigenvalues from the diagonal of the matrix D, and comparing the obtained vectors in a descending mannerSorting is carried out, elements of the generalized right eigenvector matrix V are sorted in the same descending way and then recorded as a matrix Wi(ii) a Repeating the steps until all the category label data are calculated, and obtaining the generalized right eigenvector matrix W, wherein W is [ W ]1，W2，···Wt]，WiIs the ith element of W, and t is the tag class number.
5. The TRCA-WPTD-based aVEPs electroencephalogram identification method according to claim 1, which is characterized in that: the dimensionality reduction algorithm in the stage (1) is specifically as follows: for sub-elements X in the same type label data matrix XiWith said WiIs transposed with respect to the XiMultiplying to obtain a single-class label data matrix E after dimension reductionkSub-elements of
Figure FDA0002799184900000022
Repeating the steps until all the data of all the labels in the same class are calculated, and obtaining the reduced dimension single-class label data matrix EkCan be expressed as
Figure FDA0002799184900000023
Figure FDA0002799184900000024
Figure FDA0002799184900000025
Is EkN is the total number of labels in the single-class label data; then all label category data matrix E may be denoted as E ═ E1，E2，···Et]，EkIs the kth sub-element in E, and t is the label category number; and averaging the obtained matrix E on the dimension of the total number of the labels to obtain the template matrix Temp.
6. The TRCA-WPTD-based aVEPs electroencephalogram identification method according to claim 1, which is characterized in that: the data screening process in the stage (2) is as follows: the method comprises the steps of firstly finding out the position of an electroencephalogram sampling point when a label type appears, then taking the position as an initial position, intercepting the set Data segment length backwards, and sequentially storing all the Data segments in an array named as Data _ test according to the appearance sequence of labels.
7. The TRCA-WPTD-based aVEPs electroencephalogram identification method according to claim 1, which is characterized in that: the algorithm for calculating the correlation coefficient matrix r in the stage (2) is specifically as follows: selecting 1 tag Data matrix M from the Data _ test array, and setting M as [ M [ ]1，M2，···Mn]，MiIs the ith sub-element in M, n is the total number of labels in the single-class label data, MiIs transposed with respect to the WiIs multiplied by the transpose of (a) to generate a matrix aiiCalculating AiiWith a sub-element Temp of said template matrix TempiiTo obtain the sub-elements R of the correlation coefficient matrix Ri(ii) a Repeating the steps until all the label data are calculated, and obtaining a correlation coefficient matrix R ═ R1，R2，···Rt]，RiIs the ith sub-element in R, and t is the label category number; and rearranging and combining the correlation coefficient matrix R on the dimensionality of the trial time, and averaging on the dimensionality of the trial time to obtain the correlation coefficient matrix R.
8. The TRCA-WPTD-based aVEPs electroencephalogram identification method according to claim 1, which is characterized in that: the decoding calculation in the stage (3) is specifically: reading the element values in the correlation coefficient matrix r in sequence, reading 2 adjacent elements each time, recording the element values as 10 if the first element value is larger than the second element value, otherwise recording the element values as 01, and sequentially putting the recorded results into the predictive coding until all the values are read.
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