CN106344011A - Evoked electroencephalogram signal extraction method based on factor analysis - Google Patents
Evoked electroencephalogram signal extraction method based on factor analysis Download PDFInfo
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- CN106344011A CN106344011A CN201610918415.6A CN201610918415A CN106344011A CN 106344011 A CN106344011 A CN 106344011A CN 201610918415 A CN201610918415 A CN 201610918415A CN 106344011 A CN106344011 A CN 106344011A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
- A61B5/377—Electroencephalography [EEG] using evoked responses
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
Abstract
The invention belongs to the technical field of neural information, provides an evoked electroencephalogram signal extraction method based on factor analysis and aims to improve efficiency and accuracy for weak evoked electroencephalogram signals. The evoked electroencephalogram signal extraction method comprises the following steps: firstly carrying out repeated stimulation experiment repeatedly and recording a plurality of measurement signals to obtain two-dimensional signals; then converting the two-dimensional signals into two-dimensional matrixes, carrying out factorization X=AF to obtain a factor matrix F of X; then determining an evoked electroencephalogram factor, setting all the other factors except the evoked electroencephalogram factor to zero to obtain a novel factor matrix F', and reducing a load matrix A to obtain electroencephalogram signals N'; and finally overlaying and averaging the N' to obtain the evoked electroencephalogram signals. The evoked electroencephalogram signal extraction method is used for extracting potential factors through statistical correlation between the plurality of evoked electroencephalogram signals and disrelation between spontaneous electroencephalogram signals; the evoked electroencephalogram factors are obtained by a statistical method; the experiment frequency, the experiment time and the experiment cost can be greatly reduced; meanwhile, the efficiency and the robustness of extraction of the evoked electroencephalogram signals can be further improved.
Description
Technical field
The invention belongs to nerve information technical field, it is related to a kind of evoked brain potential extracting method, more particularly, to one kind is based on
The evoked brain potential method for extracting signal of factorial analyses.
Background technology
Spontaneous brain electricity be using sophisticated electronics scalp location hurtless measure record brain cell group from
The property sent out bioelectric, this signal has high temporal resolution (millisecond magnitude), low signal amplitude (within ± 100 microvolts).
Relative, then can produce evoked brain potential signal when giving a number of repetitive stimulation of people (vision, audition, body-sensing etc.), should
Signal has lower signal amplitude (within ± 10 microvolts) it is considered to be studying the window of people's higher brain function.But surveying
During amount evoked brain potential signal simultaneously can surveying record to spontaneous brain electricity signal, thus the model of signal record is: " measurement data=
Evoked brain potential+spontaneous brain electricity ".Due to evoked brain potential weak output signal, it is submerged in spontaneous brain electricity, tradition extracts the side of evoked brain potential
Method is superimposing technique, for different types of stimulation stacking fold from 100～2000 times.Superimposing technique has one in the application
Foregone conclusion is sex-limited: on the one hand, the principle that evoked brain potential is extracted from spontaneous brain electricity assumes that both are separate, or
Spontaneous brain electricity is separate with experiment stimulation event, and assumes that spontaneous brain electricity is random background white noise, can pass through many
Secondary superposition and weaken or eliminate；On the other hand, time-consuming for clinical patient for multiple repetitive stimulation, and feasibility reduces.
Numerous studies show spontaneous and evoked brain potential between be not simple statistical iteration relation, in addition number of repetition
During increase, induction signal also can change, thus be directly repeatedly superimposed the signal obtaining for a long time can not be true
The bioelectric that reflection central nervous system produces during the impression external world or intrinsic stimuli.Extract for evoked brain potential and ask
Topic, different researcheres propose various models and technical method attempt objectively, the extraction evoked brain potential of single, in recent years extensively make
Technical method has the filtering of wiener posteriority, wavelet filtering, adaptive-filtering, neutral net, Independent Component Analysis, bootlace
Statistical method etc..But the limitation being suitable for due to method, be currently there are no a kind of generally acknowledged single-trial extraction method and obtains extensively should
With, and each have their own feature of every kind of evoked brain potential, so in actual applications, be that priori sets according to signal characteristic
How meter using method, obtain priori and become another problem again, this be also the development difficulty that faced of this kind of method it
It is located.Therefore obtain the extracting method of vast researcher approval or traditional superposed average method.
Content of the invention
In order to overcome defect of the prior art, solve above-mentioned technical problem, improve the efficiency of faint evoked brain potential signal
With precision so that it can be widely used in clinic and scientific research, the present invention proposes a kind of evoked brain potential signal extraction of factorial analyses
Method.Mutually there is using multiple tracks evoked brain potential the principle of dependency, the mutual weak dependence of multiple tracks spontaneous brain electricity simultaneously, adopt
With factorial analyses, extract the latent factor of evoked brain potential, reject other interference factors, you can obtain evoked brain potential signal.
Its technical scheme is as follows:
A kind of evoked brain potential method for extracting signal based on factorial analyses, comprises the following steps:
A. carry out stimulation test is repeated several times, using the multiple tracks measurement signal of EEG measuring equipment record experiment, obtain one
Individual 2D signal (number × time samples of leading point), carries out the pretreatment including average reference, bandpass filtering and baseline calibration；
B. extract arbitrary lead signals, current moment is gone out as snap point with repetitive stimulation, be translated into a two-dimensional matrix x
(time samples point × repetitive stimulation number of times), carries out factorisation x=af；Calculate the correlation matrix r of x first, using phase
Close coefficient matrix r and calculate corresponding Factor load-matrixWherein, λ is the eigenvalue of correlation matrix r, and u is characterized
It is worth corresponding characteristic vector, obtain the factor matrix f of x；
C. calculate all lead and all repetitive stimulations total average signal, then the phase calculating each factor and total average signal
Close coefficient, find out the corresponding factor of maximum correlation coefficient in each factor, this factor is defined as the evoked brain potential factor；
D. by except other all factor zero setting of the evoked brain potential factor, obtain new factor matrix f ', obtained using step b
Loading matrix a reduction EEG signals, reduction mode be n '=af ', obtain EEG signals n '；N ' is overlapped averagely obtain
Evoked brain potential signal；
E. repeat above-mentioned b, c, Step d, obtain the evoked brain potential signal of each lead signals successively, finally obtain one and lure
Carbuncle in the occipital region electric matrix data (number × time samples of leading point).
Further, in described step a, the number of times of repetitive stimulation experiment is 15～50 times；Described EEG measuring equipment is mark
Zhun 64 road, the EEG signals record system of 128 Dao Huo 256 road electrode.
Beneficial effects of the present invention:
Using proposed by the present invention, spontaneous brain electricity effectively can be removed based on the evoked brain potential extracting method of factorial analyses,
Only need to minority to repeat several times to test just to extract high-quality evoked brain potential simultaneously.The method passes through multiple tracks evoked brain potential
Statistic correlation between signal and the weak dependence between spontaneous brain electricity signal carry out the extraction of latent factor, are obtained using statistical method
Obtain the evoked brain potential factor, greatly reduce experiment number, shorten experimental period and cost.The method utilizes between multiple tracks evoked brain potential
Correlation matrix preferably knows the statistical property of evoked brain potential, improves efficiency and the robust of evoked brain potential signal extraction further
Property.
Brief description
Fig. 1 is the main flow chart of the present invention.
Fig. 2 is the correlation coefficient figure of the evoked brain potential that extracts under different experiments number of times of the present invention and standard evoked brain potential.
Fig. 3 is the present invention to true evoked brain potential extraction effect figure together.
Specific embodiment
With reference to the accompanying drawings and detailed description technical scheme is described in more detail.
As shown in figure 1, a kind of evoked brain potential method for extracting signal based on factorial analyses, comprise the following steps:
A. carry out stimulation test is repeated several times, using the multiple tracks measurement signal of EEG measuring equipment record experiment, obtain one
Individual 2D signal (number × time samples of leading point), carries out the pretreatment including average reference, bandpass filtering and baseline calibration；
B. it is directed to the data of one electrode measurement, current moment is gone out as snap point with repetitive stimulation, is translated into one two
Dimension matrix x (time samples point × repetitive stimulation number of times), factorisation x=af；Calculate the correlation matrix r of x first, utilize
Correlation matrix r calculates corresponding Factor load-matrixWherein λ is the eigenvalue of correlation matrix r, and u is characterized
It is worth corresponding characteristic vector, obtain the factor matrix f of x；
C. calculate all lead and all repetitive stimulations total average signal, then the phase calculating each factor and total average signal
Close coefficient, find out the corresponding factor of maximum correlation coefficient in each factor, this factor is defined as the evoked brain potential factor；
D. by except other all factor zero setting of the evoked brain potential factor, obtain new factor matrix f ', obtained using step b
Loading matrix a reduction EEG signals, reduction mode be n '=af ', obtain EEG signals n '；N ' is overlapped averagely obtain
Evoked brain potential signal；
E. repeat above-mentioned b, c, Step d, obtain the evoked brain potential signal of per pass electrode successively, finally obtain an induction brain
Electric matrix data (number × time samples of leading point).
In order to reduce repetitive stimulation number of times further, we calculate the extraction effect comparing 6 times～25 repetitive stimulations.
Fig. 2 shows the impact to this method for the different numbers of repetition, and average correlation coefficient is 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,
0.87, the increase extraction effect with number of times is more accurate, and the correlation coefficient of 15 times has reached 0.80, and extraction effect is basic
Reach application demand.In order to compare the effect of evoked brain potential extraction, the signal extracting is carried out with traditional superposed average result
Compare, show one evoked brain potential extraction effect figure, adopt 20 as the standard of comparison, Fig. 3 after 60 repeated data superpositions
Secondary repetitive stimulation, reaches 0.9431 with the correlation coefficient of standard evoked brain potential, illustrates that the present invention can efficiently extract out induction brain
Electricity.
The above, the only present invention preferably specific embodiment, protection scope of the present invention not limited to this, any ripe
Know those skilled in the art in the technical scope of present disclosure, the letter of the technical scheme that can become apparent to
Altered or equivalence replacement each fall within protection scope of the present invention.
Claims (3)
1. a kind of evoked brain potential method for extracting signal based on factorial analyses, comprises the following steps:
A. carry out stimulation test is repeated several times, using the multiple tracks measurement signal of EEG measuring equipment record experiment, obtain one two
Dimensional signal, carries out the pretreatment including average reference, bandpass filtering and baseline calibration；
B. extract arbitrary lead signals, current moment is gone out as snap point with repetitive stimulation, be translated into a two-dimensional matrix x, enter
Row factorisation x=af；Calculate the correlation matrix r of x first, calculate the corresponding factor using correlation matrix r and carry
Lotus matrixWherein, λ is the eigenvalue of correlation matrix r, and u is characterized the corresponding characteristic vector of value, obtains the factor square of x
Battle array f；
C. calculate all lead and all repetitive stimulations total average signal, then the phase relation calculating each factor and total average signal
Number, finds out the corresponding factor of maximum correlation coefficient in each factor, this factor is defined as the evoked brain potential factor；
D. by except other all factor zero setting of the evoked brain potential factor, new factor matrix f ', the load obtaining using step b are obtained
Lotus matrix a reduces EEG signals, and reduction mode is n '=af ', obtains EEG signals n '；N ' is overlapped averagely obtain induction
EEG signals；
E. repeat above-mentioned b, c, Step d, obtain the evoked brain potential signal of each lead signals successively, finally obtain an induction brain
Electric matrix data.
2. as described in claim 1 the evoked brain potential method for extracting signal based on factorial analyses it is characterised in that described step a
The number of times of middle repetitive stimulation experiment is 15～50 times.
3. as described in claim 1 the evoked brain potential method for extracting signal based on factorial analyses it is characterised in that described brain electrical measurement
Amount equipment is 64 roads of standard, the EEG signals record system of 128 Dao Huo 256 road electrode.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107657278A (en) * | 2017-09-26 | 2018-02-02 | 电子科技大学 | A kind of polytypic preferably sample number methods of sampling of EEG signals pattern |
| CN109512394A (en) * | 2018-12-06 | 2019-03-26 | 深圳技术大学（筹） | Multichannel Evoked ptential detection method and system based on independent component analysis |
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| 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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| CN107657278A (en) * | 2017-09-26 | 2018-02-02 | 电子科技大学 | A kind of polytypic preferably sample number methods of sampling of EEG signals pattern |
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| CN109512394A (en) * | 2018-12-06 | 2019-03-26 | 深圳技术大学（筹） | Multichannel Evoked ptential detection method and system based on independent component analysis |
| CN109512394B (en) * | 2018-12-06 | 2021-07-13 | 深圳技术大学（筹） | Multichannel evoked potential detection method and system based on independent component analysis |
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