CN105718953A - Single-time P300 detection method based on matrix grey modeling - Google Patents
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Abstract
The invention provides a single-time P300 detection method based on matrix grey modeling, belongs to the field of cognitive neuroscience and relates to a feature extraction and recognition detection method of an event related potential P300, in particular to a single-time P300 detection method based on matrix grey modeling in a grey theory.The method comprises the steps that 1, a original acquired electroencephalogram signal is preprocessed; 2, electrocardiographic lead combinations are selected, namely four electrocardiographic leads with most obvious top occipital region waveform differences are selected as optimal electrodes according to oscillograms of target stimulus and non-target stimulus of training set data; 3, segmented matrix grey modeling is conducted on the data of the four electrocardiographic leads, and model parameters are extracted to serve as feature vectors; 4, a Fisher ratio value method is utilized to perform optimal feature selection, and meanwhile the purpose of decreasing the number of feature vector dimensions is achieved; 5, a support vector machine classifier is utilized to classify feature vectors, and single-time P300 detection recognition is achieved.Experimental data tests show that the method can improve single-time P300 detection recognition rate, and the correct recognition rate can be further improved during less-time superposition.
Description
Technical Field
The invention belongs to the field of cognitive neuroscience, relates to a method for extracting, identifying and detecting characteristics of event-related potential P300, and particularly relates to a single P300 detection method based on matrix gray modeling in a gray theory.
Background
P300 (also known as P3b) is an endogenous component that reflects the process of advanced cognitive processing, and is the largest late positive wave recorded on the scalp with a latency of about 300ms when subject to "target stimuli" and evoked by visual, auditory and somatosensory stimuli.
The P300 potential is widely used in brain-computer interface (BCI) and psychological lie detection studies. The traditional P300 identification method adopts a time domain coherent averaging method, and background random noise can be effectively reduced through superposition averaging. This method can identify P300 well, but has significant drawbacks: the tested object is easy to feel tired after being averaged for many times, the difference between ERP of different test times is ignored, most importantly, the requirement of real-time performance cannot be met, and the method is not beneficial to the practical application of P300.
Researchers have proposed various methods to implement the feature extraction of the minority/single P300, such as a recognition algorithm based on wavelet transform, a recognition algorithm based on independent component analysis, a recognition algorithm based on principal component analysis, and the like. However, these methods have certain limitations, and the detection accuracy of a single extraction of P300 is not very high.
The Grey System theory (GreySystemTheory) was first introduced internationally in 1982 by Dunconlon, a scholaree in China. The method is particularly suitable for processing atypical regular signals (such as non-stationary, non-Gaussian distribution and non-white noise), and compared with other methods for processing data according to statistical rules and prior rules, the gray method has the obvious advantages of small required data volume, no need of prior knowledge and the like. The gray modeling facing the matrix is a further extension of the traditional gray modeling, and extends the point sequence modeling to the surface, namely the matrix sequence, which not only contains the time information, but also models the space information. Matrix-oriented gray modeling is a spatio-temporal analysis method.
The invention provides a single P300 feature extraction and identification method based on matrix gray modeling.
Disclosure of Invention
(I) technical problem to be solved
In order to overcome the defects of low accuracy and low practicability of a single P300 extraction method in the prior art, the invention provides a P300 feature extraction method based on matrix gray modeling, which models a P300 signal through matrix gray modeling and takes model parameters as features, so that the P300 classification and identification effects are better improved, and the detection and identification rate of the P300 signal is improved under the condition of reducing stimulation repetition times.
(II) technical scheme
The invention is realized by adopting the following technical scheme. The overall flow is shown in figure 1:
1. preprocessing an EEG signal which is originally collected, firstly carrying out 1-15Hz band-pass filtering, and then segmenting according to a data label; then, artifact elimination is carried out on the segmented Epoch; finally, taking the average value of the data 100ms before stimulation as a baseline, and performing baseline correction on the data after stimulation;
2. lead selection, which is to draw a oscillogram of target stimulation and non-target stimulation by observing the collected training data and select 4 leads with the most obvious waveform difference as the optimal electrodes;
3. segmenting the selected 4 lead preprocessed epochs, establishing a matrix gray model of each small segment of data, extracting the gray action quantity of each segmented model as a characteristic parameter, and connecting all segmented model parameters of each Epoch as the characteristic vector of the Epoch, as shown in fig. 2;
4. and (3) feature selection, namely connecting all feature parameters of the Epoch obtained by the method into a feature vector, then calculating the Fisher ratio, and sequencing according to the ratio value to obtain the sequence number corresponding to each sequenced feature. Finally, selecting an optimal feature space according to the feature sorting;
5. and (3) SVM classification and identification, namely selecting a support vector machine as a classifier, selecting a radial basis kernel function by the kernel function, and inputting the feature vector obtained by the steps into the SVM to realize the identification and classification of the P300.
(III) advantageous effects
The invention provides a single P300 feature extraction method based on matrix gray modeling, which can well extract the characteristic feature quantity of P300. Feature vectors are classified by combining feature selection based on a Fisher ratio and a Support Vector Machine (SVM) classifier, and the accuracy of single P300 extraction and identification is effectively improved. The actual measurement of experimental data shows that the method obtains higher identification accuracy on the actual measurement data of most objects, has the advantages of high online operation speed and high precision, and can be suitable for practical application.
Drawings
FIG. 1 is a general flow chart of the process of the present invention
FIG. 2 is a flowchart of DTMGM modeling with Epoch data
FIG. 3 is a characteristic parameter distribution of data segments under target stimulation and non-target stimulation after matrix gray modeling
FIG. 4P 300 recognition accuracy at single extraction and low trial overlap
Detailed description of the preferred embodiments
The invention is further described with reference to the following figures and detailed description.
The invention provides a P300 feature extraction method based on matrix gray modeling, which is used for feature extraction and identification of P300 signals by combining a Fisher ratio feature selection method and a Support Vector Machine (SVM), and extracting model parameters from an EEG data segment corresponding to each stimulus to be used as feature vectors, so that the identification accuracy is improved when P300 is extracted once, and further, the character transmission rate of a BCI system based on P300 and the identification efficiency of P300 in lie detection application can be improved.
The main steps of the invention are shown in figure 1. The method comprises the following steps:
step S1: the EEG data is pre-processed, including filtering, artifact removal and baseline calibration. Firstly, performing 1-15Hz band-pass filtering on acquired original data, and selecting a Butterworth filter as a filter; then, segmenting according to the data labels, selecting data from 0.1s before stimulation to 0.7s after stimulation, and segmenting into epochs of 0.8 s; and then, carrying out artifact elimination on the segmented Epoch, marking the data segment with the difference between the maximum amplitude and the minimum amplitude larger than 140 μ v as a noise artifact, and carrying out elimination without participating in subsequent data processing. Finally, taking the average value of the data 100ms before stimulation as a baseline, and performing baseline correction on the data after stimulation;
step S2: lead selection, the P300 component, is more pronounced in the parietal occipital region, so that a portion of the leads in this region are selected for analysis to select the best lead combination for subsequent processing classification. By observing the acquired training data, target and non-target oscillograms are plotted, and the 4 leads with the most distinct waveform differences are selected as the optimal electrodes.
Step S3: and performing matrix gray modeling on the preprocessed Epoch by k segmentation, and extracting the gray action quantity of each segmented model as a characteristic parameter. All the segment model parameters of each Epoch are concatenated as the feature vector of this one Epoch, as shown in FIG. 2;
step S4: and selecting the features, namely splicing the 2 k-dimensional feature vectors obtained by the method into 2 k-dimensional feature vectors, then calculating the Fisher ratio, and sequencing according to the ratio value to obtain the sequence number corresponding to each sequenced feature. Finally, selecting an optimal feature space according to the feature sorting;
step S5: and (4) classifying and identifying, namely selecting a support vector machine as a classifier, selecting a radial basis kernel function by the kernel function, and inputting the feature vector obtained by the steps into the SVM to realize the identification and classification of the P300.
Further, in an embodiment of the present invention (with a sampling rate of Fs), step S3 includes the following specific steps:
step S31: each sample comprises 4 leads of 0.8s data, which can be represented in a 4 x (Fs x 0.8) matrix form, spatially transforming the 4 leads by a 2 x 2 matrix, so that they are finally written in a three-dimensional form of 2 x 2 (Fs x 0.8);
step S32: 0.8 sample points Fs in time are segmented, and each n points are divided into small segments which can be divided intoSegments, each segment in a data matrix of 2 x n;
step S33: DTMGM (1, 1+2) modeling was performed on the 2 x n data. The specific process is as follows:
(1) x (1), x (2),., x (n)), where x (i) represents a 2 × 2 matrix, i ∈ {1, 2., n }, x(0)A diagonal matrix transformation sequence being a matrix sequence x, x(1)＝MAGOx(0)And then:
x(1)＝(x(1)(1),x(1)(2),...,x(1)(n))(1)
wherein
(2) Establishing a diagonal transformation matrix sequence gray model (diagonaltransformational matrix sequence Grey model, DTMMG):
x(0)(i)+z(1)(i)＝diag(a1,a2)(2)
wherein z is(1)(i)＝0.5x(1)(i)+0.5x(1)(i-1)，z(1)(i)∈z(1)＝(z(1)(2),...,z(1)(n))。
(3) Calculating a parameter vector p according todm：
pdm＝[a,a1,a2]T＝(BTB)-1yn(3)
Wherein,
(4) the acting amount of ash is equal to [ a ]1,a2]As a characteristic parameter of this piece of data.
Step S34: and modeling and extracting parameter characteristics for the k segments according to the method given by S33, wherein k x 2 characteristic parameters can be extracted finally for each Epoch.
In one embodiment of the present invention, the sampling rate Fs is 200Hz, and when n is 8, k is 20. The distribution of the characteristic parameters extracted by the method is shown in figure 3.
The method fully applies the advantages of few data modeling of gray modeling, can effectively enhance the characteristics of P300 after segmented modeling, extracts model parameters as characteristic vectors, performs characteristic space dimensionality reduction by adopting a Fisher ratio, inputs the characteristic vectors into a support vector machine for classification and identification, and effectively improves the identification rate.
The feature vectors are put into an SVM classifier, the radial basis function is taken as a kernel function, cross validation is carried out by adopting a leave-one-out method, the average accuracy of all tested results can reach 91.43%, and the superiority of the method provided by the invention is shown. When the overlapping is performed with few trials, the classification recognition rate can be further improved, as shown in fig. 4.
Claims (2)
1. A single P300 detection method based on matrix gray modeling is characterized by comprising the following steps:
step S1: preprocessing EEG data including filtering, segmentation, artifact removal and baseline calibration;
step S2: drawing a target and non-target oscillogram by observing the collected training data, and selecting 4 leads with the most obvious waveform difference as optimal electrodes;
step S3: performing matrix gray modeling on each preprocessed sample by k segmentation, and extracting the gray action quantity of each segmented model as a characteristic parameter;
step S4: and connecting the 2 k-dimensional feature vectors obtained by the method into 2 k-dimensional feature vectors, then calculating Fisher ratio, and sorting according to the ratio value to obtain the serial number corresponding to each sorted feature. Finally, selecting an optimal feature space according to the feature sorting;
step S5: and (4) performing classification and identification, namely selecting a support vector machine as a classifier, selecting a radial basis kernel function by the kernel function, and inputting the feature vector obtained by the steps into the SVM (support vector machine), so as to realize the identification and classification of the single P300.
2. The single P300 detection method based on matrix gray modeling according to claim 1, wherein the step 3 comprises the following steps:
step S31: each test sample comprises 4 leads of 0.8s data, which can be expressed in a matrix form (sampling rate is Fs) of 4 x (Fs x 0.8), and the 4 leads are spatially subjected to 2 x 2 matrix transformation, so that the data are finally written in a three-dimensional form of 2 x (Fs x 0.8);
step S32: 0.8 sample points Fs in time are segmented, and each n points are divided into small segments which can be divided intoSegments, each segment in a data matrix of 2 x n;
step S33: carrying out DTMGM (1, 1+2) modeling on the 2 x n data, and extracting a model ash action quantity parameter;
step S34: and modeling and extracting parameter characteristics for the k segments according to the method given by S33, wherein k x 2 characteristic parameters can be extracted finally for each Epoch.
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