CN105342604B - ICA artefacts identification based on brain electricity amplitude versus frequency characte and minimizing technology and device - Google Patents
ICA artefacts identification based on brain electricity amplitude versus frequency characte and minimizing technology and device Download PDFInfo
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- CN105342604B CN105342604B CN201510762746.0A CN201510762746A CN105342604B CN 105342604 B CN105342604 B CN 105342604B CN 201510762746 A CN201510762746 A CN 201510762746A CN 105342604 B CN105342604 B CN 105342604B
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Abstract
The invention discloses a kind of, and the ICA artefacts based on brain electricity amplitude versus frequency characte identify and minimizing technology and device, wherein method includes the following steps：Amplitude threshold, duration threshold and power threshold are obtained according to the amplitude of brain electricity artefact and frequency；The effective threshold value of amplitude, duration effective threshold value and the effective threshold value of power are obtained by being based on negentropy ICA；Automatic identification and removal are carried out to EEG signals midbrain electricity artefact according to the effective threshold value of amplitude, duration effective threshold value and power effective threshold value.The identification of the embodiment of the present invention and minimizing technology carry out artefact identification and removal by the amplitude and frequency of brain electricity artefact and the independent component analysis based on negentropy, improve accuracy and the efficiency of artefact identification, avoid the misrecognition of artefact, simple and convenient.
Description
Technical field
The present invention relates to artefact removal technology field, more particularly to a kind of ICA artefacts identification based on brain electricity amplitude versus frequency characte
With minimizing technology and device.
Background technology
Currently, eye electricity artefact minimizing technology is more, effect preferably has ICA (Independent Component
Analysis, independent component analysis), but the identification of artefact rely on mostly it is artificial or semi-artificial, cause using exist limitation, especially
Its this problem in the case where lead is more, data volume is big more highlights.In short, at present to eye electricity artefact, especially by conducting wire
Other artefacts that situations such as sliding, poor contact causes reply the mixing artefact (referred to as mixing artefact) of synthesis by cable with eye, not
There is highly effective artefact automatic removal method, it would be highly desirable to solve.
Invention content
The present invention is directed to solve at least to a certain extent it is above-mentioned in the related technology the technical issues of one of.
For this purpose, an object of the present invention is to provide a kind of, the ICA artefacts based on brain electricity amplitude versus frequency characte are identified and are removed
Method, accuracy and the efficiency of identification can be improved in this method, simple and convenient.
It is another object of the present invention to propose that a kind of ICA artefacts based on brain electricity amplitude versus frequency characte are identified to fill with removal
It sets.
In order to achieve the above objectives, one aspect of the present invention embodiment proposes a kind of ICA artefacts based on brain electricity amplitude versus frequency characte
Identification and minimizing technology, include the following steps：Amplitude threshold, duration threshold are obtained according to the amplitude of brain electricity artefact and frequency
With power threshold；Amplitude is obtained according to the amplitude threshold, duration threshold and power threshold, and by being based on negentropy ICA
Effective threshold value, duration effective threshold value and the effective threshold value of power；And it is effective according to the effective threshold value of the amplitude, duration
Threshold value carries out automatic identification and removal with the effective threshold value of power to EEG signals midbrain electricity artefact.
The ICA artefacts based on brain electricity amplitude versus frequency characte proposed according to embodiments of the present invention identify and minimizing technology, pass through brain
The amplitude and frequency of electric artefact obtain amplitude threshold, duration threshold and power threshold, secondly based on the isolated component of negentropy
Analysis obtains the effective threshold value of amplitude, duration effective threshold value and the effective threshold value of power, to carry out the identification and removal of artefact,
Accuracy and the efficiency for improving artefact identification, reduce leakage identification and the error of artefact, avoid the misrecognition of artefact, simply just
Victory, applicability are wide.
In addition, the identification of ICA artefacts and minimizing technology according to the above embodiment of the present invention based on brain electricity amplitude versus frequency characte are also
There can be following additional technical characteristic：
Further, in one embodiment of the invention, the above method further includes：Obtain the amplitude of the EEG signals
Threshold value and duration threshold；The independent element of the brain electricity artefact of identification is restored, with according to the width of the EEG signals
Value judges whether the brain electricity artefact of the identification is true and false mark with duration threshold, if the brain electricity artefact of the identification is true
Artefact then removes, and otherwise reverts to EEG signals.
Further, in one embodiment of the invention, the above method further includes：Sentenced according to default coherence factor threshold value
Whether the coherence factor of the brain electricity artefact of the disconnected identification meets the requirements；If it is satisfied, then being determined as true and false mark, institute is otherwise adjusted
State amplitude threshold, duration threshold and power threshold.
Further, in one embodiment of the invention, it is the true and false to preset coherence factor threshold decision in the basis
Before whether the coherence factor of the brain electricity artefact of the identification of mark meets the requirements, further include：The brain electricity artefact of the identification is carried out
The bandpass filtering treatment of predeterminated frequency.
Further, in one embodiment of the invention, the amplitude threshold, duration threshold and power threshold root
It is adjusted according to the brain electricity artefact of removal and the coherence in frequency domain coefficient of useful EEG signals.
Another aspect of the present invention embodiment propose it is a kind of based on brain electricity amplitude versus frequency characte ICA artefacts identification with removal fill
It sets, including：First acquisition module, for according to the amplitude and frequency of brain electricity artefact obtain amplitude threshold, duration threshold with
Power threshold；Decomposing module is used for according to the amplitude threshold, duration threshold and power threshold, and by being based on negentropy
ICA obtains the effective threshold value of amplitude, duration effective threshold value and the effective threshold value of power；And identify and remove module, it is used for root
EEG signals midbrain electricity artefact is carried out according to the effective threshold value of the amplitude, duration effective threshold value and power effective threshold value automatic
Identification and removal.
The ICA artefacts based on brain electricity amplitude versus frequency characte proposed according to embodiments of the present invention identify and removal device, pass through brain
The amplitude and frequency of electric artefact obtain amplitude threshold, duration threshold and power threshold, secondly based on the isolated component of negentropy
Analysis obtains the effective threshold value of amplitude, duration effective threshold value and the effective threshold value of power, to carry out the identification and removal of artefact,
Accuracy and the efficiency for improving artefact identification, reduce leakage identification and the error of artefact, avoid the misrecognition of artefact, simply just
Victory, applicability are wide.
In addition, the identification of ICA artefacts and removal device according to the above embodiment of the present invention based on brain electricity amplitude versus frequency characte are also
There can be following additional technical characteristic：
Further, in one embodiment of the invention, above-mentioned apparatus further includes：Second acquisition module, for obtaining
The amplitude thresholds and duration threshold of the EEG signals；Recovery module, the independent element for the brain electricity artefact to identification
Restored, to judge whether the brain electricity artefact of the identification is true with duration threshold according to the amplitude of the EEG signals
Artefact removes if the brain electricity artefact of the identification is true and false mark, otherwise reverts to EEG signals.
Further, in one embodiment of the invention, above-mentioned apparatus further includes：Judgment module, for according to default
Whether the coherence factor of the brain electricity artefact identified described in coherence factor threshold decision meets the requirements；Module is adjusted, if conditions are not met,
It is then used to adjust the amplitude threshold, duration threshold and power threshold.
Further, in one embodiment of the invention, above-mentioned apparatus further includes：Filter module, for the knowledge
Other brain electricity artefact carries out the bandpass filtering treatment of predeterminated frequency.
Further, in one embodiment of the invention, the amplitude threshold, duration threshold and power threshold root
It is adjusted according to the brain electricity artefact of removal and the coherence in frequency domain coefficient of useful EEG signals.
The additional aspect of the present invention and advantage will be set forth in part in the description, and will partly become from the following description
Obviously, or practice through the invention is recognized.
Description of the drawings
The above-mentioned and/or additional aspect and advantage of the present invention will become in the description from combination following accompanying drawings to embodiment
Obviously and it is readily appreciated that, wherein：
Fig. 1 is the flow according to ICA the artefacts identification and minimizing technology based on brain electricity amplitude versus frequency characte of the embodiment of the present invention
Figure；
Fig. 2 is the schematic diagram containing eye electricity artefact eeg data according to one embodiment of the invention；
Fig. 3 be ICA artefacts automatic identification according to one embodiment of the invention based on eye electricity peak value and duration and
The schematic diagram of the probability of misrecognition of minimizing technology；
Fig. 4 is the schematic diagram of the probability of misrecognition identified according to the first time of one embodiment of the invention；
The schematic diagram of signal after Fig. 5 is the false artefact according to one embodiment of the invention and its restores；
Fig. 6 is the schematic diagram of signal after being restored according to the non-reinforced true and false mark of one embodiment of the invention；
Fig. 7 is the schematic diagram of signal after being restored according to the true and false mark of one embodiment of the invention enhanced；
Fig. 8 is the schematic diagram compared according to signal power before and after the enhancing of one embodiment of the invention；
Fig. 9 is the schematic diagram according to the effect enhancing rate of one embodiment of the invention；
Figure 10 is the schematic diagram of EEG signals comparison before and after the removal artefact according to one embodiment of the invention；
Figure 11 is to be identified and minimizing technology according to the ICA artefacts based on brain electricity amplitude versus frequency characte of one embodiment of the invention
Flow chart；And
Figure 12 is the structure according to ICA the artefacts identification and removal device based on brain electricity amplitude versus frequency characte of the embodiment of the present invention
Schematic diagram.
Specific implementation mode
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, it is intended to for explaining the present invention, and is not considered as limiting the invention.
In addition, term " first ", " second " are used for description purposes only, it is not understood to indicate or imply relative importance
Or implicitly indicate the quantity of indicated technical characteristic.Define " first " as a result, the feature of " second " can be expressed or
Implicitly include one or more this feature.In the description of the present invention, the meaning of " plurality " is two or more,
Unless otherwise specifically defined.
In the present invention unless specifically defined or limited otherwise, term " installation ", " connected ", " connection ", " fixation " etc.
Term shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or be integrally connected；It can be machine
Tool connects, and can also be electrical connection；It can be directly connected, can also can be indirectly connected through an intermediary two members
Connection inside part.For the ordinary skill in the art, above-mentioned term can be understood in this hair as the case may be
Concrete meaning in bright.
In the present invention unless specifically defined or limited otherwise, fisrt feature the "upper" of second feature or "lower"
It may include that the first and second features are in direct contact, can also not be to be in direct contact but pass through it including the first and second features
Between other characterisation contact.Moreover, fisrt feature second feature " on ", " top " and " above " include first special
Sign is right over second feature and oblique upper, or is merely representative of fisrt feature level height and is higher than second feature.Fisrt feature exists
Second feature " under ", " lower section " and " following " include fisrt feature immediately below second feature and obliquely downward, or be merely representative of
Fisrt feature level height is less than second feature.
The ICA artefacts identification based on brain electricity amplitude versus frequency characte for describing to propose according to embodiments of the present invention with reference to the accompanying drawings with
Minimizing technology and device describe the ICA based on brain electricity amplitude versus frequency characte proposed according to embodiments of the present invention with reference to the accompanying drawings first
Artefact identifies and minimizing technology.Shown in referring to Fig.1, this approach includes the following steps：
S101 obtains amplitude threshold, duration threshold and power threshold according to the amplitude of brain electricity artefact and frequency.
Wherein, in an embodiment of the present invention, brain electricity artefact is mainly eye electricity artefact, also includes similar with eye electricity artefact
Other artefacts and mixing artefact, eye electricity artefact, brain electricity artefact, artefact hereafter do not distinguish especially.
S102 according to amplitude threshold, duration threshold and power threshold, and obtains amplitude by being based on negentropy ICA and has
Imitate threshold value, duration effective threshold value and the effective threshold value of power.
Specifically, according to pertinent literature, such as EEG signals artifact Separation Research based on extended informax algorithm is ground
Study carefully as a result, and in conjunction with the fastICA algorithms based on negentropy can be applied to super-Gaussian and sub-Gaussian signal the characteristics of, the present invention implement
Example carries out artefact removal using the fastICA algorithms based on negentropy to the EEG signals containing eye electrical interference.
Wherein, negentropy (J) is between Independent Sources with Any Probability Density Function function and Gaussian probability density function with same variance
KL divergences, for basic conception, negentropy should be the optimal measurement of signal non-gaussian degree, and using the fixation of negentropy
Point algorithm have ensure convergence, in iterative process without introducing the setting for adjusting the artificial parameter such as step-length, simple and convenient feature,
It is more more extensive than using the quick ICA methods application of quadravalence Higher Order Cumulants using negentropy.
S103, according to the effective threshold value of amplitude, duration effective threshold value and the effective threshold value of power to EEG signals midbrain electricity
Artefact carries out automatic identification and removal.
It should be noted that manual identified eye electricity is mainly by personal experience, basic criterion has the following：
(1) waveform, wave amplitude, shape etc.；
(2) frequency of wave；
(3) duration of single wave.
Above three standard meets the fundamental characteristics of eye electricity.
(1) eye electric frequency is 1-8Hz；
(2) eye electricity maximum value is usually all more than 100 μ v；
(3) single eye electricity peak value sustainable a period of time, EEG signals cannot keep higher amplitude for a long time.
With reference to shown in Fig. 2, Fig. 2 is the schematic diagram containing eye electricity artefact eeg data, and abscissa is the time in figure, and unit is
Second, ordinate is signal amplitude, and unit is μ v, and left side is numbered for lead.It is visible in 0-1,1-2,3-4,5-6 from Fig. 2 (a),
There are apparent artefact ingredients between 7-9 seconds, and based on eye electricity, eye electricity artefact amplitude is larger, frequency 1-8Hz, from enlarged drawing 2
(b) (the eye electricity of 5-7 leads between this section is derived from 1-2 seconds) visible in, duration of peak value is about 0.1-0.2 seconds.
Further, by the way that amplitude threshold, power threshold corresponding with frequency, the duration for reaching above-mentioned threshold value is arranged
Threshold value identifies eye electricity artefact.Amid all these factors and ICA resolution characteristics, the detailed setting of threshold value are as follows：
Amplitude threshold
The electric artefact amplitude of eye is larger, and EEG signals relative weak, electro-ocular signal belong to the larger minority of amplitude, and should
It is more highlighted in the data of feature after decomposing, therefore, it is considered that：The larger fraction data of amplitude are eye electricity, by calculating eye
The ratio of ratio-dependent eye electricity ingredient of the electric duration of peak value within the unit interval.In order to more effectively remove eye electricity artefact,
The embodiment of the present invention can be using 35 percentiles as initial threshold.
If duration of peak value is Ts, unit is millisecond, with 1 second for unit length, calculates the percentile threshold value of amplitude.
Frequency corresponds to power threshold
After being decomposed through isolated component, eye electricity artefact ingredient is more prominent, its amplitude maximum on corresponding direction, and it is other at
Framing value very little, power and energy are also larger.I.e. with eye electricity artefact ingredient separating vector the parts 1-8Hz performance number very
Greatly, the ratio that the embodiment of the present invention can account for all power by calculating the power of the part is threshold value.
If general power is Pall, 1-8Hz power and be P1-8, power ratio (RateP)：RateP=P1-8/Pall；
Theoretically this ratio should be approximately 1 in eye electricity artefact ingredient, since the data segment of decomposition is entirely not
The electric artefact of eye, therefore the embodiment of the present invention selects ThR according to data length is calculated>0.4 (power threshold), you can it meets the requirements,
Because other two threshold values are more accurate, to effectively ensure to accurately identify artefact ingredient.
Duration threshold
Yan electricity artefacts duration of peak value is indicated by the number at consecutive numbers strong point, it is assumed that Yan electricity duration of peak value is
Ts, unit ms, sample frequency fsThen the data length (DataLenth) of duration of peak value is：
DataLenth=fs×Ts×1000 (2)
Determine whether original signal meets wanting for duration of peak value according to the consecutive numbers strong point number for reaching amplitude threshold
It asks.The artefact duration caused by blink is usually no more than 200-300ms, when the embodiment of the present invention can select peak follow
Between initial threshold be 60ms.
The setting of above three initial threshold is affected by many factors, can according to actual needs combine subject at that time state with
And the characteristic of eeg data is calculated and is suitably adjusted.Three threshold values are conditional, and amplitude threshold, duration threshold are essences
Exact figures evidence, there is certain elasticity in power threshold, while the data for meeting three threshold values are just judged as eye electricity artefact, therefore have
The power threshold of certain elasticity can't cause the erroneous judgement of eye electricity artefact, can avoid failing to judge on the contrary.
Further, in one embodiment of the invention, the method for the embodiment of the present invention further includes：Obtain EEG signals
Amplitude thresholds and duration threshold；The independent element of the brain electricity artefact of identification is restored, with according to EEG signals
Amplitude judges whether the brain electricity artefact of identification is true and false mark with duration threshold, if the brain electricity artefact of identification is true and false mark,
It then removes, otherwise reverts to EEG signals.
It should be noted that may be eye electricity artefact ingredient by useful EEG's Recognition based on above-mentioned threshold value.Reason is such as
Under：
(1) ICA components is Gaussian similar, and amplitude is not much different, it is understood that there may be and amplitude percentile meets threshold value, but
It is not the component of eye electricity artefact；
(2) reason is based on duration of peak value's threshold value with (1), then the part EEG signals for artefact being identified as in (1),
It is judged as artefact；
(3) it is based on the corresponding power threshold of frequency, amplitude and the identified artefact of duration threshold and frequency will be based on
It is artefact in the EEG's Recognition of 1-8Hz.
Therefore, the true EEG signals for meeting above-mentioned threshold requirement may be judged as by artefact based on above-mentioned threshold value.
In view of this, the embodiment of the present invention devises true and false artefact recognition methods, it is as follows：
S1, the independent element to being identified as artefact restore；
The amplitude and duration threshold of original signal is arranged in S2；
S3, to the original signal restored, the threshold value based on (2) identifies, if meeting condition, artefact be it is true, otherwise
It is false, and it is EEG signals to restore false artefact.
In one embodiment of the invention, in order to analyze artefact false recognition rate, the embodiment of the present invention has randomly selected 5
The data of at least each 1 experiment in subject, totally 6 groups of data, have calculated separately the artefact false recognition rate of two kinds of automatic identifying methods,
As a result as follows：The artefact correctly identified is reference with manual identified, is 2000 points per segment data length.The side of the embodiment of the present invention
Method refers to the artefact false recognition rate of identification (carry out true and false artefact identification before) for the first time.
(1) with reference to shown in Fig. 3, Fig. 3 is ICA artefacts automatic identification and minimizing technology based on eye electricity peak value and duration
Probability of misrecognition schematic diagram, it can be obtained from the figure that, exist in the automatic identifying method based on eye electricity peak value and duration very big
False recognition rate.
(2) with reference to shown in Fig. 4, Fig. 4 is the schematic diagram of the probability of misrecognition of identification for the first time.
From the above it is found that being still remained in first time identification process based on the method for the embodiment of the present invention prodigious
Artefact false recognition rate, therefore visible true and false artefact recognition methods is introduced for avoiding the importance of the misrecognition of artefact.
(3) the final artefact recognition accuracy analysis of the method for the embodiment of the present invention.
The embodiment of the present invention does not count the artefact probability of misrecognition finally identified especially, but in above-mentioned 6 processing procedures
In, all it is to refer to determine final artefact component with the artefact of manual identified, it is as a result consistent with automatic identification, from the above
Understand that artefact probability of misrecognition is very low, while it is 0 to calculate the leakage identification probability of artefact during (2) statistics false recognition rate,
In summary, the method for the embodiment of the present invention has higher accuracy rate.
General effect is shown in Fig. 5 and Fig. 6, Fig. 8.Fig. 5 (a) is the artefact of identification for the first time, wherein the 4th leads, the 6th to lead be false pseudo-
Mark, Fig. 5 (b), (c) are the recovery signals of the artefact ingredient of identification for the first time, can determine whether that the ingredient is extensive from amplitude and frequency characteristic
The useful EEG signals that signal after multiple is 1-8Hz (based on theta waves).Fig. 6 and Fig. 7 is the true brain electricity artefact ingredient of identification
And its signal after restoring.
The result shows that true and false artefact recognition methods, can avoid the misrecognition of artefact, simultaneously because threshold value initial value is corresponding
The minimum value of characteristic, and adaptively adjust, it can be ensured that identification will not be leaked.
Wherein, in one embodiment of the invention, amplitude threshold, duration threshold and power threshold are according to removal
Brain electricity artefact and the coherence in frequency domain coefficient of useful EEG signals are adjusted.
Further, in one embodiment of the invention, the method for the embodiment of the present invention further includes：According to default relevant
Coefficient threshold judges whether the coherence factor of the brain electricity artefact of identification meets the requirements；If it is satisfied, then being determined as true and false mark, otherwise
Adjust amplitude threshold, duration threshold and power threshold.
Specifically, how to evaluate artefact removal effect is the difficulties in the research of artefact minimizing technology.Currently, not yet
Weigh the signal that goes after puppet whether be true brain electricity direct method, being normally applied data simulation, expert, manually evaluation etc. is indirect
Method is as standard.
Wherein, coherence factor reflects the common situation of change of two signal phases, shows the correlation of signal.
In an embodiment of the present invention, since the method for the embodiment of the present invention is mainly for the ingredient of 1-8Hz, the present invention is real
The evaluation index that example selects coherence factor as artefact removal effect is applied, and as adjusting thresholds in artefact identification and removal process
Criterion, while in order to further verify artefact removal effect, after each data processing is complete, comparing the front and back signal wave of artefact removal
The variation of shape.
Show that coherence in frequency domain coefficient is lower through real data verification, artefact removal effect is better, the distortion factor of EEG signals
It is smaller.
Further, in one embodiment of the invention, it is being true and false mark according to coherence factor threshold decision is preset
Before whether the coherence factor of the brain electricity artefact of identification meets the requirements, further include：Predeterminated frequency is carried out to the brain electricity artefact of identification
Bandpass filtering treatment.
Specifically, the Enhancement Method of artefact removal effect includes the following steps：
S1 carries out the true and false mark bandpass filtering of 1-8Hz；
Wherein, bandpass filtering threshold value can adjust according to actual needs
S2 therefrom extracts true and false mark ingredient and sets to 0, and non-artefact ingredient reverts to EEG signals.
In signal after being mainly reflected in brain electricity artefact ingredient with the difference for not carrying out effect enhancing processing and its restore, by
The visible the two difference in Fig. 6,7,8；Fig. 6 is the signal for not carrying out effect enhancing, wherein the useful signal containing more other frequencies；And
Other frequency signals are seldom in the artefact ingredient of Fig. 7, show the useful brain electricity ingredient of Enhancement Method Effective selection.
From power spectrumanalysis, the signal power Fig. 8 (a), (c) that do not carry out effect enhancing processing have higher magnitude in 1-30Hz
(relatively figure (d)) distribution, the power of 8-30Hz is obviously reduced in Fig. 8 (b), (d), shows the artefact ingredient for not carrying out effect enhancing
Contain more EEG signals.
Effect enhancing rate refers to the power ratio of the useful signal and useful signal before not reinforcing of enhancing.By comparing
The changed power in the front and back non-eye electric frequency region of effect enhancing weighs effect enhancing effect.If certain one piece of data does not have artefact point
Amount, then enhancing rate is 0, and result of calculation is with reference to shown in Fig. 9.
With reference to shown in Fig. 9, by the effect enhancing rate in the visible data segment simply by the presence of eye electricity artefact component of result figure compared with
Greatly, available signal power increases obviously after reflecting enhancing.Wherein it is tested in 1,2 that there are imitated in the data segment of eye electricity artefact component
Fruit enhancing rate is nearly all more than 0.8, and is tested enhancing rate minimum in 3 also greater than 0.6, is tested 4,5 effect enhancing rate mean value
More than 0.5.
In one particular embodiment of the present invention, practical eeg data is carried out using the method for the embodiment of the present invention pseudo-
Mark identifies and removal, eeg data of the data from certain experiment acquisition of taikonaut training centre, and data acquisition equipment is
FLY-2 nervous physiology information works station (16 channel eeg amplifiers, silver chloride electrode), brain electrode are distributed according to 10-20 systems.
Each section of decomposition data length is 3000 in calculating, as a result as shown in Fig. 5, Fig. 6, Fig. 7, Fig. 8, Figure 10 and table 2.Ordinate in figure
For amplitude, unit is μ v.
With reference in Fig. 6, Fig. 7, Fig. 8, Figure 10 as it can be seen that eye electricity is concentrated mainly on the lead of frontal region, in pillow, top, temporo area lead
Amplitude is smaller.(lead number with 10-20 system leads correspondences is shown in Table 1) meets eye electricity and is concentrated mainly on forehead and can be from
Back-propagating at forehead traverses entire head, but its intensity is with the traditional view of square decaying of distance.
Table 1
| Title | Fp1 | Fp2 | F3 | F4 | C3 | C4 | P3 | P4 |
| Number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Title | O1 | O2 | F7 | F8 | T3 | T4 | T5 | T6 |
| Number | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 |
Table 2 is 1 maximum value and mean value for testing all data coherency coefficients, data length 109794.
Table 2
| Lead | Fp1 | Fp2 | F3 | F4 | C3 | C4 | P3 | P4 |
| Mean | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Max | 11 | 17 | 16 | 14 | 17 | 14 | 10 | 14 |
| Lead | O1 | O2 | F7 | F8 | T3 | T4 | T5 | T6 |
| Mean | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Max | 23 | 11 | 13 | 13 | 16 | 11 | 25 | 21 |
With reference to Fig. 6, Fig. 7, Fig. 8, Figure 10 as it can be seen that the probability of misrecognition of artefact is smaller, the identification accurate and effective of artefact.From figure
It is substantially free of artefact in EEG signals after 10 high-visible removal artefacts, and waveform keeps preferable.In Figure 10 (c), P4 is led
There are very big interference between being associated in 71-72 seconds, and the interference of the visible corresponding position of comparison diagram 10 (d) has been removed, and waveform
Keep preferable；In Figure 10 (e), C4 leads nearby disturbed at 105 seconds, and the visible corresponding position of comparison diagram 10 (f) is done
It disturbs and has been removed, and waveform keeps preferable.Show that context of methods also has better effects to the removal of non-eye electricity artefact.
In an embodiment of the present invention, the artefact automatic identification of the embodiment of the present invention and minimizing technology have effect well
Fruit can not only remove eye electricity artefact, and also have preferable removal effect to other interference.
It is all based on theory analysis with the bigger of other methods below, and artefact discrimination, effect enhancing rate etc. can be found in
Fig. 3, Fig. 4 and Fig. 9 other than theory analysis, while comparing each front and back letter of artefact removal in addition with the comparison of manual method
Number waveform.
Compared with manual method：Artefact automatic removal method and the difference on effect of artificial artefact minimizing technology depend primarily on
Identifying whether for the electric artefact of eye is accurate, accurate.Artefact identification is more accurate, then the effect of artefact removal is better.This is also current base
The greatest problem existing for the artefact automatic identification and minimizing technology of ICA.
The threshold parameter of the method design of the embodiment of the present invention is adaptive, and initial parameter is both configured to greatly artefact characteristic
Minimum value, it is ensured that the most artefacts (based on eye electricity) of identification range covering avoid artefact leakage identification, at the same in order to avoid
Excessively, calculation amount is excessive for " artefact " of identification, in conjunction with the threshold value of coherence in frequency domain coefficient, can adjust threshold parameter in due course, reduces " pseudo-
Mark " ingredient reduces calculation amount.Simultaneously in order to avoid being eye electricity artefact by useful EEG's Recognition, in calculating process, design
True and false brain electricity artefact recognition methods, avoids the misrecognition of artefact, improves the accuracy rate of artefact identification, effectively remains
Useful EEG signals.Therefore, the artefact recognition accuracy based on above-mentioned two strategy is suitable with manual method, can be accurately and effectively
Identify brain electricity artefact.
The method of the embodiment of the present invention introduces effect reinforcing method, avoids and removes in existing ICA artefacts minimizing technology
The shortcomings that useful signal, the effect of artefact removal is significantly improved, the effect more existing artefact minimizing technology based on ICA significantly carries
It is high.It is specifically shown in Fig. 4 effect enhancing rates.
Compared with automated process：Currently, a large amount of eye movement artefact automatic removal methods have been produced, such as based on independent element point
The method of analysis, the method based on regression iterative, and the method etc. based on Wavelet transformation.From Computing Principle and algorithm thinking, meter
For calculating step, it is to refer to (statistical property for only needing eye electricity) that the method for the embodiment of the present invention, which does not need electro-ocular signal,
Artefact eradicating efficacy is good, while having the characteristics that clear thinking, step are simple.
Compared with the automated process for being not based on ICA：Method based on regression iterative and Wavelet transformation is for artefact signal
Processing mainly does average treatment replacement eye electricity artefact by acquiring adjacent signal.Eye may be contained in actually adjacent signal
Electric artefact (or other artefacts), reason is：Algorithm is when carrying out eye electricity artefact automatic identification, just for eye electricity peak region,
Do not consider the signal of eye electricity beginning and end, this be cause adjacent signals may contain eye electricity artefact the reason of one of, in addition lead
The factors such as line drift, hair instantaneous interference may also cause above-mentioned consequence.
The method of the embodiment of the present invention is in artefact identification process, although with works such as the duration of eye electricity peak value and amplitudes
For the reference of threshold design, but according to ICA characteristics it is found that in identification process not only an identification eye electricity peak region (this be with
The maximum difference of other methods).Effect reinforcing method is introduced again in artefact removal process, improves the effect of artefact removal,
More useful informations are remained, show that context of methods can preferably remove artefact through real data verification.
Compared with the automatic year method based on ICA：ICA artefacts automatic identification based on head model (dipole) and removal side
The effect of method depends on the accuracy of head model.Head model accurately whether determine artefact identification accuracy rate, individual difference and
Deviation existing for brain electrode is worn, causing practical eye electricity artefact, there are deviations in the distribution on head and head model, and artefact is caused to be known
Other accuracy rate reduces, and artefact is easy to identify does not go out for some electricity, and the signal that some is not eye electricity is identified as eye electricity puppet
Mark, artefact identification is inaccurate, and artefact eradicating efficacy is bad.
In terms of artefact eradicating efficacy, the processing currently based on the artefact minimizing technology of ICA to artefact is all by artefact ingredient
It sets to 0, then reverse isolated component is the EEG signals after removing artefact in patient for scalp projection, is caused non-puppet in artefact ingredient
Mark signal also eliminates together, is easily lost useful information.
Compared with other artefact automatic removal methods based on ICA, due to the threshold value of the method setting of the embodiment of the present invention
An electrical characteristics (amplitude, frequency, duration) are covered comprehensively, and are adaptive threshold, while being introduced true and false artefact and being known
Other method avoids leakage identification and the misrecognition of artefact, improves the accuracy rate of artefact identification.And in artefact removal process
In, and effect reinforcing method is introduced, the thinking that traditional IC A artefacts minimizing technology handles artefact ingredient is changed, artefact is gone
Except effect significantly improves than existing methods.
Specifically, referring to Fig.1 shown in 1, the method for the embodiment of the present invention is based on brain electricity artefact (mainly for eye electricity puppet
Mark) amplitude and frequency characteristic, design amplitude, duration and power threshold, and the characteristic for combining ICA to decompose dexterously turn
Be changed to still effective threshold value after disassembly, carry out the automatic identification of artefact, and in view of ICA is decomposed can with the characteristic of threshold value
The misrecognition of artefact can be caused, therefore, false brain electricity artefact that may be present, devises true and false puppet after being identified for first time artefact
False brain electricity artefact effectively can be reverted to useful signal, avoid the misrecognition of artefact by mark recognition methods.
Further, in an embodiment of the present invention, the artefact recognition threshold of design adaptively adjusts, and initial value is big
It is both configured to the minimum value of correlation properties, in calculating process, calculates the coherence in frequency domain coefficient of the artefact and useful brain electricity of removal,
And be compared with threshold value, to recognize the need for readjust threshold value；Adaptive threshold not only can effectively avoid artefact
Leakage identification, and ensure that the effect of artefact removal.
Further, effect reinforcing method is introduced in artefact removal process, converted existing ICA artefacts minimizing technology
To the thinking of artefact ingredient component processing, artefact removal effect is significantly improved.
The method of the embodiment of the present invention has many advantages, such as that accurate automatic identification artefact, distortion is small, arithmetic speed is fast, breaks away from
Dependence to eye recognition artefact method greatly reduces workload, saves the time.Real data verification shows that artefact identifies
It is preferable with removal effect, the interference of eye electricity artefact (containing other mixing artefacts) is effectively removed, and remained well
With EEG signals, automation of the ICA methods in brain electricity artefact identifies and removes is realized, to promoting ICA methods in the direction
Popularization and application provide theoretical, methods and techniques foundation.
Wherein, the effect of artefact identification and removal additionally depends on the decomposable process of isolated component and the selection of criterion, can tie
The amplitude versus frequency characte of syncerebrum electricity designs related criteria and constraints, is improved to ICA methods, realizes constraint ICA methods, from
And improve identification and the removal effect of artefact.
The ICA artefacts based on brain electricity amplitude versus frequency characte proposed according to embodiments of the present invention identify and minimizing technology, based on puppet
Mark amplitude versus frequency characte, in conjunction with the characteristics of independent component analysis, devise isolated component decompose after still effective amplitude, frequency correspond to
The artefacts automatic identification threshold value such as power, duration, and coherence factor is combined to realize the adaptive adjustment of threshold value, reduce puppet
The leakage of mark identifies, and innovatively devises true and false artefact recognition methods for issuable false artefact, avoids artefact
Misrecognition, above-mentioned two strategy ensures the artefact recognition accuracy of this method, and for being still remained in artefact component
The characteristics of effective information, it is proposed that artefact removal effect Enhancement Method is effectively extracted useful brain electric information therein, and artefact is gone
Except significant effect improves, roadmap of the traditional IC A artefacts minimizing technology to artefact is changed, remains more useful brain electricity
Information, not only reliability is high, and the distortion factor is small, and is provided for automation application of the ICA methods in terms of eye electricity artefact removal
Important theoretical reference and effective way.
The ICA artefacts identification based on brain electricity amplitude versus frequency characte of proposition according to the ... of the embodiment of the present invention is described referring next to attached drawing
With removal device.Referring to Fig.1 shown in 2, artefact identification includes with removal device 10：First acquisition module 100, decomposing module
200 with identification with removal module 300.
Wherein, the first acquisition module 100 is used to obtain amplitude threshold, duration according to the amplitude and frequency of brain electricity artefact
Threshold value and power threshold.Decomposing module 200 is used for according to amplitude threshold, duration threshold and power threshold, and by being based on
Negentropy ICA obtains the effective threshold value of amplitude, duration effective threshold value and the effective threshold value of power.Identification is used for removal module 300
EEG signals midbrain electricity artefact is known automatically according to the effective threshold value of amplitude, duration effective threshold value and power effective threshold value
Not with removal.The device 10 of the embodiment of the present invention is by the amplitude and frequency of brain electricity artefact and based on the independent component analysis of negentropy
Artefact identification and removal are carried out, accuracy and the efficiency of identification is improved, avoids the misrecognition of artefact.
Further, in one embodiment of the invention, the device 10 of the embodiment of the present invention can also include：Second obtains
Modulus block and recovery module.
Wherein, the second acquisition module is used to obtain the amplitude thresholds and duration threshold of EEG signals.Recovery module is used
Restore in the independent element of the brain electricity artefact to identification, to judge to know according to the amplitude of EEG signals and duration threshold
Whether other brain electricity artefact is true and false mark, if the brain electricity artefact of identification is true and false mark, removes, otherwise reverts to brain telecommunications
Number.
Further, in one embodiment of the invention, the device 10 of the embodiment of the present invention can also include：Judge mould
Block and adjustment module.
Wherein, whether the coherence factor for the brain electricity artefact that judgment module is used to be identified according to default coherence factor threshold decision
It meets the requirements.If conditions are not met, then adjusting module for adjusting amplitude threshold, duration threshold and power threshold.
Specifically, in one embodiment of the invention, amplitude threshold, duration threshold and power threshold are according to removal
Brain electricity artefact and the coherence in frequency domain coefficient of useful EEG signals be adjusted.
Further, in one embodiment of the invention, the device 10 of the embodiment of the present invention can also include：Filter mould
Block.Wherein, filter module is used to carry out the brain electricity artefact of identification the bandpass filtering treatment of predeterminated frequency.
It should be understood that the tool of ICA artefacts identification and removal device according to the ... of the embodiment of the present invention based on brain electricity amplitude versus frequency characte
Body realizes that process can be with the workflow of ICA the artefacts identification and minimizing technology based on brain electricity amplitude versus frequency characte of the embodiment of the present invention
It is identical, it is no longer described in detail herein.
The ICA artefacts based on brain electricity amplitude versus frequency characte proposed according to embodiments of the present invention identify and removal device, based on puppet
Mark amplitude versus frequency characte, in conjunction with the characteristics of independent component analysis, devise isolated component decompose after still effective amplitude, frequency correspond to
The artefacts automatic identification threshold value such as power, duration, and coherence factor is combined to realize the adaptive adjustment of threshold value, reduce puppet
The leakage of mark identifies, and innovatively devises true and false artefact recognition methods for issuable false artefact, avoids artefact
Misrecognition, above-mentioned two strategy ensures the artefact recognition accuracy of this method, and for being still remained in artefact component
The characteristics of effective information, it is proposed that artefact removal effect Enhancement Method is effectively extracted useful brain electric information therein, and artefact is gone
Except significant effect improves, roadmap of the traditional IC A artefacts minimizing technology to artefact is changed, remains more useful brain electricity
Information, not only reliability is high, and the distortion factor is small, and is provided for automation application of the ICA methods in terms of eye electricity artefact removal
Important theoretical reference and effective way.
Any process described otherwise above or method description are construed as in flow chart or herein, and expression includes
It is one or more for realizing specific logical function or process the step of executable instruction code module, segment or portion
Point, and the range of the preferred embodiment of the present invention includes other realization, wherein can not press shown or discuss suitable
Sequence, include according to involved function by it is basic simultaneously in the way of or in the opposite order, to execute function, this should be of the invention
Embodiment person of ordinary skill in the field understood.
Expression or logic and/or step described otherwise above herein in flow charts, for example, being considered use
In the order list for the executable instruction for realizing logic function, may be embodied in any computer-readable medium, for
Instruction execution system, device or equipment (system of such as computer based system including processor or other can be held from instruction
The instruction fetch of row system, device or equipment and the system executed instruction) it uses, or combine these instruction execution systems, device or set
It is standby and use.For the purpose of this specification, " computer-readable medium " can any can be included, store, communicating, propagating or passing
Defeated program is for instruction execution system, device or equipment or the dress used in conjunction with these instruction execution systems, device or equipment
It sets.The more specific example (non-exhaustive list) of computer-readable medium includes following：Electricity with one or more wiring
Interconnecting piece (electronic device), portable computer diskette box (magnetic device), random access memory (RAM), read-only memory
(ROM), erasable edit read-only storage (EPROM or flash memory), fiber device and portable optic disk is read-only deposits
Reservoir (CDROM).In addition, computer-readable medium can even is that the paper that can print described program on it or other are suitable
Medium, because can be for example by carrying out optical scanner to paper or other media, then into edlin, interpretation or when necessary with it
His suitable method is handled electronically to obtain described program, is then stored in computer storage.
It should be appreciated that each section of the present invention can be realized with hardware, software, firmware or combination thereof.Above-mentioned
In embodiment, software that multiple steps or method can in memory and by suitable instruction execution system be executed with storage
Or firmware is realized.It, and in another embodiment, can be under well known in the art for example, if realized with hardware
Any one of row technology or their combination are realized：With the logic gates for realizing logic function to data-signal
Discrete logic, with suitable combinational logic gate circuit application-specific integrated circuit, programmable gate array (PGA), scene
Programmable gate array (FPGA) etc..
Those skilled in the art are appreciated that realize all or part of step that above-described embodiment method carries
Suddenly it is that relevant hardware can be instructed to complete by program, the program can be stored in a kind of computer-readable storage medium
In matter, which includes the steps that one or a combination set of embodiment of the method when being executed.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing module, it can also
That each unit physically exists alone, can also two or more units be integrated in a module.Above-mentioned integrated mould
The form that hardware had both may be used in block is realized, can also be realized in the form of software function module.The integrated module is such as
Fruit is realized in the form of software function module and when sold or used as an independent product, can also be stored in a computer
In read/write memory medium.
Storage medium mentioned above can be read-only memory, disk or CD etc..
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, schematic expression of the above terms are not
Centainly refer to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be any
One or more embodiments or example in can be combined in any suitable manner.
Although the embodiments of the present invention has been shown and described above, it is to be understood that above-described embodiment is example
Property, it is not considered as limiting the invention, those skilled in the art are not departing from the principle of the present invention and objective
In the case of can make changes, modifications, alterations, and variations to the above described embodiments within the scope of the invention.
Claims (10)
1. a kind of identification of ICA artefacts and minimizing technology based on brain electricity amplitude versus frequency characte, which is characterized in that include the following steps：
Amplitude threshold, duration threshold and power threshold are obtained according to the amplitude of brain electricity artefact and frequency；
The effective threshold of amplitude is obtained according to the amplitude threshold, duration threshold and power threshold, and by being based on negentropy ICA
Value, duration effective threshold value and the effective threshold value of power；And
According to the effective threshold value of the amplitude, duration effective threshold value and the effective threshold value of power to EEG signals midbrain electricity artefact into
Row automatic identification and removal；The independent element of the brain electricity artefact of identification is restored, according to the amplitude of the EEG signals with
Duration threshold judges whether the brain electricity artefact of the identification is true and false mark.
2. ICA automatic identifications and minimizing technology according to claim 1 based on brain electricity amplitude versus frequency characte, automatic identification it
Afterwards, further include：
Obtain the EEG signals based on the amplitude thresholds and duration threshold before negentropy ICA separation；
The independent element of the brain electricity artefact of identification is restored, with according to the amplitude and duration threshold of the EEG signals
Judge whether the brain electricity artefact of the identification is true and false mark,
If the brain electricity artefact of the identification is true and false mark, removes, otherwise revert to EEG signals.
3. the identification of ICA artefacts and minimizing technology according to claim 2 based on brain electricity amplitude versus frequency characte, which is characterized in that
Further include：
Whether met the requirements according to the coherence factor of the brain electricity artefact identified described in default coherence factor threshold decision；Wherein, institute
The common situation of change that coherence factor reflects two signal phases is stated, the correlation of described two signals is shown；
If it is satisfied, then being determined as true and false mark, otherwise based on the amplitude threshold after negentropy ICA separation, duration described in adjustment
Threshold value and power threshold.
4. the identification of ICA artefacts and minimizing technology according to claim 3 based on brain electricity amplitude versus frequency characte, which is characterized in that
Whether the coherence factor that the brain electricity artefact for the identification that coherence factor threshold decision is true and false mark is preset in the basis meets the requirements
Before, further include：
The bandpass filtering treatment of predeterminated frequency is carried out to the brain electricity artefact of the identification.
5. the identification of ICA artefacts and minimizing technology according to claim 3 based on brain electricity amplitude versus frequency characte, which is characterized in that
Amplitude threshold, duration threshold and power threshold after the separation based on negentropy ICA according to the brain electricity artefact of removal with have
It is adjusted with the coherence in frequency domain coefficient of EEG signals.
6. a kind of identification of ICA artefacts and removal device based on brain electricity amplitude versus frequency characte, which is characterized in that including：
First acquisition module, for obtaining amplitude threshold, duration threshold and power according to the amplitude and frequency of brain electricity artefact
Threshold value；
Decomposing module, for being obtained according to the amplitude threshold, duration threshold and power threshold, and by being based on negentropy ICA
To the effective threshold value of amplitude, duration effective threshold value and the effective threshold value of power；And
Identification and removal module, for according to the effective threshold value of the amplitude, duration effective threshold value and the effective threshold value pair of power
EEG signals midbrain electricity artefact carries out automatic identification and removal；The independent element of the brain electricity artefact of identification is restored, according to
The amplitude of the EEG signals judges whether the brain electricity artefact of the identification is true and false mark with duration threshold.
7. the identification of ICA artefacts and removal device according to claim 6 based on brain electricity amplitude versus frequency characte, which is characterized in that
Further include：
Second acquisition module, the amplitude thresholds for obtaining the EEG signals before the ICA separation based on negentropy and duration
Threshold value；
Recovery module, the independent element for the brain electricity artefact to identification restores, with according to the amplitude of the EEG signals
Judge whether the brain electricity artefact of the identification is true and false mark with duration threshold, if the brain electricity artefact of the identification is the true and false
Mark then removes, and otherwise reverts to EEG signals.
8. the identification of ICA artefacts and removal device according to claim 7 based on brain electricity amplitude versus frequency characte, which is characterized in that
Further include：
Judgment module, for whether being met according to the coherence factor for presetting the brain electricity artefact identified described in coherence factor threshold decision
It is required that；Wherein, the coherence factor reflects the common situation of change of two signal phases, shows the phase of described two signals
Guan Xing；
Module is adjusted, if conditions are not met, being then used to adjust the amplitude threshold after the ICA separation based on negentropy, duration threshold
Value and power threshold.
9. the identification of ICA artefacts and removal device according to claim 8 based on brain electricity amplitude versus frequency characte, which is characterized in that
Further include：
Filter module, the bandpass filtering treatment for carrying out predeterminated frequency to the brain electricity artefact of the identification.
10. the identification of ICA artefacts and removal device according to claim 8 based on brain electricity amplitude versus frequency characte, which is characterized in that
Amplitude threshold, duration threshold and power threshold after the separation based on negentropy ICA according to the brain electricity artefact of removal with have
It is adjusted with the coherence in frequency domain coefficient of EEG signals.
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| CN109247935B (en) * | 2018-10-31 | 2020-10-16 | 山东大学 | Human body abnormal sleep state monitoring system at night |
| CN112244872B (en) * | 2020-09-28 | 2021-09-07 | 北京创新智源科技有限公司 | Electroencephalogram signal artifact identification, removal and evaluation method and device and electronic equipment |
| CN114469135A (en) * | 2022-01-17 | 2022-05-13 | 北京脑陆科技有限公司 | Artifact removing method and device for multi-lead electroencephalogram signal and brain-computer interface |
| CN114886388B (en) * | 2022-07-12 | 2022-11-22 | 浙江普可医疗科技有限公司 | Evaluation method and device for quality of electroencephalogram signal in anesthesia depth monitoring process |
| CN117503162B (en) * | 2023-11-30 | 2024-06-04 | 中国人民解放军总医院 | Method for determining position of ocular artifacts in single-channel electroencephalogram signals |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102697493A (en) * | 2012-05-03 | 2012-10-03 | 北京工业大学 | Method for rapidly and automatically identifying and removing ocular artifacts in electroencephalogram signal |
| EP2515744A2 (en) * | 2009-12-23 | 2012-10-31 | DELTA, Dansk Elektronik, Lys & Akustik | A monitoring device |
| CN102835955A (en) * | 2012-09-08 | 2012-12-26 | 北京工业大学 | Method of automatically removing ocular artifacts from electroencephalogram signal without setting threshold value |
| CN103690163A (en) * | 2013-12-21 | 2014-04-02 | 哈尔滨工业大学 | ICA (independent component analysis) and HHT (Hilbert-Huang transform) fusion based automatic electrooculogram interference eliminating method |
-
2015
- 2015-11-10 CN CN201510762746.0A patent/CN105342604B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2515744A2 (en) * | 2009-12-23 | 2012-10-31 | DELTA, Dansk Elektronik, Lys & Akustik | A monitoring device |
| CN102697493A (en) * | 2012-05-03 | 2012-10-03 | 北京工业大学 | Method for rapidly and automatically identifying and removing ocular artifacts in electroencephalogram signal |
| CN102835955A (en) * | 2012-09-08 | 2012-12-26 | 北京工业大学 | Method of automatically removing ocular artifacts from electroencephalogram signal without setting threshold value |
| CN103690163A (en) * | 2013-12-21 | 2014-04-02 | 哈尔滨工业大学 | ICA (independent component analysis) and HHT (Hilbert-Huang transform) fusion based automatic electrooculogram interference eliminating method |
Non-Patent Citations (3)
| Title |
|---|
| 一种基于盲源分离的眼电伪迹自动去除方法;计瑜;《浙江大学学报(工学版)》;20130430;第47卷(第3期);415-421 * |
| 基于独立元分析和非线性指数分析的脑电信号中伪迹分量的自动去除;卞宁艳;《生物物理学报》;20060430;第22卷(第2期);150-155 * |
| 脑电信号中眼电伪迹自动去除方法的研究;李明爱;《电子学报》;20130630;第41卷(第6期);1207-1213 * |
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