CN106344011B - A kind of evoked brain potential method for extracting signal based on factorial analysis - Google Patents
A kind of evoked brain potential method for extracting signal based on factorial analysis Download PDFInfo
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- 210000004556 brain Anatomy 0.000 title claims abstract description 28
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- 230000000763 evoking effect Effects 0.000 title claims abstract description 17
- 239000011159 matrix material Substances 0.000 claims abstract description 32
- 230000000638 stimulation Effects 0.000 claims abstract description 21
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- 238000002474 experimental method Methods 0.000 claims abstract description 12
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- 238000005259 measurement Methods 0.000 claims description 7
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- 230000002269 spontaneous effect Effects 0.000 abstract description 13
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- 210000005036 nerve Anatomy 0.000 abstract 1
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Abstract
The invention belongs to nerve information technical fields, a kind of evoked brain potential method for extracting signal based on factorial analysis are provided, to improve the efficiency and precision of faint evoked brain potential signal.The present invention carries out multiplicating stimulation test first, records multiple tracks measuring signal, obtains 2D signal；Then it is translated into two-dimensional matrix and carries out Factorization X=AF, obtain the factor matrix F of X；It subsequently determines the evoked brain potential factor, and other all factor zero setting that the evoked brain potential factor will be removed, obtains new factor matrix F ', restore to obtain EEG signals N ' using loading matrix A；Average acquisition evoked brain potential signal finally is overlapped to N '.The present invention passes through the statistic correlation between multiple tracks evoked brain potential signal and the independence between spontaneous brain electricity signal carries out the extraction of latent factor, obtains the evoked brain potential factor using statistical method, greatly reduces experiment number, shortens experimental period and cost；And further increase the efficiency and robustness of evoked brain potential signal extraction.
Description
Technical Field
The invention belongs to the technical field of neural information, relates to an induced electroencephalogram extraction method, and particularly relates to an induced electroencephalogram signal extraction method based on factor analysis.
Background
Spontaneous brain electrical activity is the spontaneous bioelectrical activity of brain cell populations recorded atraumatically at the scalp location using sophisticated electronics, with high temporal resolution (in the order of milliseconds) and low signal amplitude (within ± 100 microvolts). On the other hand, when a certain number of repeated stimuli (visual, auditory, somatosensory, etc.) are given to people, an induced electroencephalogram signal is generated, and the signal has a lower signal amplitude (within +/-10 microvolts), so that the induced electroencephalogram signal is considered as a window for researching higher functions of the human brain. However, when the induced electroencephalogram signal is measured, the spontaneous electroencephalogram signal can be measured and recorded at the same time, so that the model of signal recording is as follows: the measured data is induced brain wave + spontaneous brain wave. Because the induced electroencephalogram signals are weak and are submerged in the spontaneous electroencephalogram, the traditional method for extracting the induced electroencephalogram is a superposition technology, and the superposition times of different types of stimulation are different from 100-2000 times. The stacking technique has certain limitations in application: on one hand, the principle of extracting the evoked potential from the spontaneous brain electricity is to assume that the two are mutually independent, or that the spontaneous brain electricity and the experimental stimulation event are mutually independent, and to assume that the spontaneous brain electricity is random background white noise which can be weakened or eliminated by multiple superposition; on the other hand, repeated stimulation of multiple times is time consuming for clinical patients and less feasible.
A large number of researches show that spontaneous electroencephalogram and evoked electroencephalogram are not simple and statistically independent, and in addition, the evoked signals can change in the process of increasing the number of repetition times, so that the signals obtained by directly and repeatedly superposing for a plurality of times for a long time cannot truly reflect the bioelectricity activity generated by the central nervous system in the process of sensing external or internal stimulation. Aiming at the problem of extraction of induced electroencephalogram, different researchers provide various models and technical methods to attempt objective and single extraction of induced electroencephalogram, and the technical methods widely used in recent years include wiener posterior filtering, wavelet filtering, adaptive filtering, neural networks, independent component analysis methods, boot strap statistical methods and the like. However, due to the limitation of the method application, no known single extraction method is widely applied at present, and each induced electroencephalogram has various characteristics, so in practical application, a use method needs to be designed according to signal characteristics, namely prior knowledge, and how to acquire the prior knowledge becomes another problem, which is also the difficulty in developing the method. Therefore, the extraction method accepted by the researchers is also the traditional superposition average method.
Disclosure of Invention
In order to overcome the defects in the prior art, solve the technical problems and improve the efficiency and the precision of the weak induced electroencephalogram signal, so that the weak induced electroencephalogram signal can be widely applied to clinical and scientific research, the invention provides a factor analysis induced electroencephalogram signal extraction method. By utilizing the principle that the multiple induced electroencephalograms have correlation with each other and the multiple spontaneous electroencephalograms have weak correlation with each other, potential factors for inducing the electroencephalograms are extracted by adopting factor analysis, and other interference factors are removed, so that induced electroencephalogram signals can be obtained.
The technical scheme is as follows:
an induced electroencephalogram signal extraction method based on factor analysis comprises the following steps:
A. carrying out repeated stimulation experiments for many times, recording a plurality of measurement signals of the experiments by adopting electroencephalogram measurement equipment to obtain a two-dimensional signal (lead number multiplied by time sample point), and carrying out preprocessing including average reference, band-pass filtering and baseline calibration;
B. extracting any lead signal, converting the lead signal into a two-dimensional matrix X (time sample points multiplied by repeated stimulation times) by taking the occurrence time of repeated stimulation as an alignment point, and performing factorization X-AF; firstly, a correlation coefficient matrix R of X is calculated, and a corresponding factor load matrix is calculated by utilizing the correlation coefficient matrix RWherein,lambda is the eigenvalue of the correlation matrix R, U is the eigenvector corresponding to the eigenvalue, and a factor matrix F of X is obtained;
C. calculating total average signals of all leads and all repeated stimulations, then calculating the correlation coefficient of each factor and the total average signals, and finding out the factor corresponding to the maximum correlation coefficient in each factor, wherein the factor is determined as an induced electroencephalogram factor;
D. setting all factors except the induced electroencephalogram factor to zero to obtain a new factor matrix F ', restoring the electroencephalogram signal by using the load matrix A obtained in the step B, wherein the restoring mode is N' ═ AF ', and obtaining an electroencephalogram signal N'; carrying out superposition averaging on the N' to obtain an induced electroencephalogram signal;
E. repeating the steps B, C and D, sequentially obtaining the induced brain electrical signals of each lead signal, and finally obtaining induced brain electrical matrix data (the number of leads multiplied by the time sample point).
Further, the number of times of repeated stimulation experiments in the step A is 15-50; the electroencephalogram measuring equipment is a standard electroencephalogram signal recording system with 64-channel, 128-channel or 256-channel electrodes.
The invention has the beneficial effects that:
the induced electroencephalogram extraction method based on factor analysis can effectively remove spontaneous electroencephalogram, and meanwhile, high-quality induced electroencephalogram can be extracted only by repeated experiments for a few times. The method extracts potential factors through the statistical correlation among the multi-channel induced electroencephalogram signals and the weak correlation among the spontaneous electroencephalogram signals, obtains the induced electroencephalogram factors by using a statistical method, greatly reduces the experiment times, and shortens the experiment time and cost. The method utilizes the correlation matrix among the multiple channels of induced electroencephalograms to better acquire the statistical characteristics of the induced electroencephalograms, and further improves the extraction efficiency and robustness of the induced electroencephalogram signals.
Drawings
Fig. 1 is a main flow diagram of the present invention.
FIG. 2 is a correlation coefficient diagram of the evoked potential and the standard evoked potential extracted under different experimental times.
FIG. 3 is a diagram of the effect of extracting a real induced electroencephalogram according to the present invention.
Detailed Description
The technical solutions of the present invention will be described in further detail with reference to the accompanying drawings and the detailed description.
As shown in fig. 1, a method for extracting evoked brain signals based on factor analysis includes the following steps:
A. carrying out repeated stimulation experiments for many times, recording a plurality of measurement signals of the experiments by adopting electroencephalogram measurement equipment to obtain a two-dimensional signal (lead number multiplied by time sample point), and carrying out preprocessing including average reference, band-pass filtering and baseline calibration;
B. aiming at data measured by one electrode, taking the occurrence moment of repeated stimulation as an alignment point, converting the data into a two-dimensional matrix X (time sample point multiplied by repeated stimulation times), and factorizing X to AF; firstly, a correlation coefficient matrix R of X is calculated, and a corresponding factor load matrix is calculated by utilizing the correlation coefficient matrix RWherein, λ is the eigenvalue of the correlation matrix R, U is the eigenvector corresponding to the eigenvalue, and a factor matrix F of X is obtained;
C. calculating total average signals of all leads and all repeated stimulations, then calculating the correlation coefficient of each factor and the total average signals, and finding out the factor corresponding to the maximum correlation coefficient in each factor, wherein the factor is determined as an induced electroencephalogram factor;
D. setting all factors except the induced electroencephalogram factor to zero to obtain a new factor matrix F ', restoring the electroencephalogram signal by using the load matrix A obtained in the step B, wherein the restoring mode is N' ═ AF ', and obtaining an electroencephalogram signal N'; carrying out superposition averaging on the N' to obtain an induced electroencephalogram signal;
E. repeating the steps B, C and D, sequentially obtaining the induced brain electrical signals of each electrode, and finally obtaining induced brain electrical matrix data (the number of the lead multiplied by the time sample point).
In order to further reduce the number of repeated stimulation, the extraction effect of 6-25 repeated stimulation is calculated and compared. Fig. 2 shows the influence of different repetition times on the method, the average correlation coefficients are respectively 0.63, 0.63, 0.63, 0.64, 0.64, 0.74, 0.75, 0.74, 0.76, 0.79, 0.80, 0.80, 0.80, 0.82, 0.82, 0.82, 0.83, 0.86, 0.85, and 0.87, the more accurate the extraction effect is with the increase of the times, the correlation coefficient of 15 times has reached 0.80, and the extraction effect has basically reached the application requirement. In order to compare the effect of induced electroencephalogram extraction, the extracted signals are compared with the traditional superposition average result, namely the result is superposed by 60 times of repeated data and then is used as a comparison standard, a graph of induced electroencephalogram extraction effect is shown in fig. 3, 20 times of repeated stimulation is adopted, and the correlation coefficient of the standard induced electroencephalogram reaches 0.9431, so that the induced electroencephalogram can be effectively extracted.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited thereto, and any simple modifications or equivalent substitutions of the technical solutions that can be obviously obtained by those skilled in the art within the technical scope of the present invention are within the scope of the present invention.
Claims (3)
1. An induced electroencephalogram signal extraction method based on factor analysis comprises the following steps:
A. carrying out repeated stimulation experiments for multiple times, recording multiple measurement signals of the experiments by adopting electroencephalogram measurement equipment to obtain a two-dimensional signal, wherein the two-dimensional signal is a lead number and a time sample point, and carrying out pretreatment including average reference, band-pass filtering and baseline calibration;
B. extracting any lead signal, converting the lead signal into a two-dimensional matrix X by taking the occurrence time of repeated stimulation as an alignment point, and performing factorization X-AF; first computing XA correlation coefficient matrix R, and calculating corresponding factor load matrix by using the correlation coefficient matrix RWherein, λ is the eigenvalue of the correlation matrix R, and U is the eigenvector corresponding to the eigenvalue, so as to obtain a factor matrix F of X;
C. calculating total average signals of all leads and all repeated stimulations, then calculating correlation coefficients of all factors and the total average signals, finding out factors corresponding to the maximum correlation coefficients in all the factors, and determining the factors corresponding to the maximum correlation coefficients as induced electroencephalogram factors;
D. setting all factors except the induced electroencephalogram factor to zero to obtain a new factor matrix F ', restoring the electroencephalogram signal by using the load matrix A obtained in the step B, wherein the restoring mode is N' ═ AF ', and obtaining an electroencephalogram signal N'; carrying out superposition averaging on the N' to obtain an induced electroencephalogram signal;
E. repeating the steps B, C and D, sequentially obtaining the induced brain electrical signals of each lead signal, and finally obtaining induced brain electrical matrix data.
2. The method for extracting evoked brain signals based on factor analysis as set forth in claim 1, wherein the number of times of repeated stimulation experiments in step a is 15-50 times.
3. The method for extracting evoked brain signals based on factor analysis as in claim 1, wherein said brain electrical measurement device is a standard 64-channel, 128-channel or 256-channel electrode brain electrical signal recording system.
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| CN102098639A (en) * | 2010-12-28 | 2011-06-15 | 中国人民解放军第三军医大学野战外科研究所 | Brain-computer interface short message sending control device and method |
| WO2013153798A1 (en) * | 2012-04-12 | 2013-10-17 | Canon Kabushiki Kaisha | Brain activity and visually induced motion sickness |
| CN103720471A (en) * | 2013-12-24 | 2014-04-16 | 电子科技大学 | Factor analysis based ocular artifact removal method |
| JP2018000396A (en) * | 2016-06-30 | 2018-01-11 | 国立研究開発法人産業技術総合研究所 | Method for evaluating and reading transient intracerebral information from brain wave |
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| CN102098639A (en) * | 2010-12-28 | 2011-06-15 | 中国人民解放军第三军医大学野战外科研究所 | Brain-computer interface short message sending control device and method |
| WO2013153798A1 (en) * | 2012-04-12 | 2013-10-17 | Canon Kabushiki Kaisha | Brain activity and visually induced motion sickness |
| CN103720471A (en) * | 2013-12-24 | 2014-04-16 | 电子科技大学 | Factor analysis based ocular artifact removal method |
| JP2018000396A (en) * | 2016-06-30 | 2018-01-11 | 国立研究開発法人産業技術総合研究所 | Method for evaluating and reading transient intracerebral information from brain wave |
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