CN112089415A - Electroencephalogram signal denoising method based on wavelet analysis - Google Patents
Electroencephalogram signal denoising method based on wavelet analysis Download PDFInfo
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Abstract
The invention discloses an electroencephalogram signal denoising method based on wavelet analysis, which comprises the following steps: A. collecting an electroencephalogram signal by a signal collector; B. the signal characteristic extraction unit extracts an electroencephalogram signal; C. carrying out signal segmentation on the extracted electroencephalogram characteristic signal, and segmenting the electroencephalogram characteristic signal into a plurality of electroencephalogram signal segments; D. respectively transmitting each electroencephalogram signal segment to a denoising unit for denoising to obtain a denoised signal; E. the method has the advantages that the denoising method is simple in principle, the EEG characteristic extraction and denoising units extract the EEG characteristics, denoising efficiency of the EEG can be improved, and acquisition precision of the EEG is improved.
Description
Technical Field
The invention relates to the technical field of electroencephalogram signal denoising, in particular to an electroencephalogram signal denoising method based on wavelet analysis.
Background
The brain of human being is a very complex biological tissue composed of hundreds of different kinds of hundreds of billions of nerve cells, electroencephalogram signal analysis and processing are one of the most challenging subjects in signal processing and machine learning research, and the analysis and processing of electroencephalogram signals in the past decades play a great role in promoting the research of brain science and the application of brain science in various fields. However, because of the specificity of the electroencephalogram signal, i.e. the very weak signal-to-noise ratio of the signal is very small, the analysis and the research of the electroencephalogram signal are greatly interfered, and therefore the problem of filtering the electroencephalogram signal is always a great obstacle which troubles the analysis and the research of the electroencephalogram signal.
The existing electroencephalogram signal denoising method is complex in principle and low in denoising efficiency, and therefore the electroencephalogram signal output precision is low, and improvement is needed.
Disclosure of Invention
The invention aims to provide an electroencephalogram signal denoising method based on wavelet analysis, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: the electroencephalogram signal denoising method based on wavelet analysis comprises the following steps:
A. collecting an electroencephalogram signal by a signal collector;
B. the signal characteristic extraction unit extracts an electroencephalogram signal;
C. carrying out signal segmentation on the extracted electroencephalogram characteristic signal, and segmenting the electroencephalogram characteristic signal into a plurality of electroencephalogram signal segments;
D. respectively transmitting each electroencephalogram signal segment to a denoising unit for denoising to obtain a denoised signal;
E. and synthesizing the plurality of sections of denoised electroencephalograms to obtain the denoised electroencephalograms.
Preferably, the method for extracting features of the electroencephalogram signal in step B is as follows:
a. firstly, extracting rhythm of electroencephalogram signals generated by a user due to auditory induction;
b. b, carrying out inverse operation on the rhythm extracted in the step a to obtain an electroencephalogram auditory signal;
c. b, performing energy calculation on the electroencephalogram auditory signals obtained in the step b;
d. b, quantitatively solving information entropy of the energy calculated in the step c;
e. and d, extracting characteristic dipoles from the information entropy obtained in the step d to obtain characteristic vectors, namely realizing the characteristic extraction of the electroencephalogram signals.
Preferably, the signal dividing method in step C is as follows:
a. firstly, dividing a time period for acquiring the electroencephalogram signals into N +1 time periods;
b. marking each time period to obtain N +1 marked time periods;
c. setting the central points of adjacent time periods as dividing points to obtain N dividing points;
d. the N division points divide the electroencephalogram signal into N-1 electroencephalogram signal segments.
Preferably, the denoising unit in the step D comprises a triode a, a triode B, an amplifier a, and an amplifier B, wherein the base of the triode a is connected to the collector of the triode B, the emitter of the triode B is grounded, the base is connected to the node of the resistor a and the resistor B, the resistor B is grounded, one end of the resistor a is connected to the collector of the triode a, the emitter of the triode a is respectively connected to one end of a resistor D, one end of a resistor E, and one end of a resistor F, the other end of the resistor D is respectively connected to the collector of the triode B and one end of a resistor C, the other end of the resistor C is grounded, the other end of the resistor E is connected to one end of a resistor G, the other end of the resistor G is connected to the negative input end of the amplifier a, the other end of the, the resistor J, the resistor K, the resistor L, the resistor M, the amplifier B and the capacitor form a level conversion circuit.
Preferably, the signal synthesis method in step E is as follows:
a. synthesizing signals at two adjacent ends by using a signal synthesizer;
b. and synthesizing the synthesized signal and the head and tail signals to obtain the final synthesized electroencephalogram signal.
Compared with the prior art, the invention has the beneficial effects that: the denoising method adopted by the invention is simple in principle, and can improve the denoising efficiency of the electroencephalogram signal and improve the acquisition precision of the electroencephalogram signal by extracting the characteristics of the electroencephalogram signal and denoising the electroencephalogram signal in a segmented manner by the denoising unit; the adopted denoising unit can further improve the anti-interference capability of the signal in the transmission process and increase the stability of signal transmission.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a circuit diagram of a denoising unit according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front", "rear", "both ends", "one end", "the other end", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "disposed," "connected," and the like are to be construed broadly, such as "connected," which may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Referring to fig. 1-2, the present invention provides a technical solution: the invention provides the following technical scheme: the electroencephalogram signal denoising method based on wavelet analysis comprises the following steps:
A. collecting an electroencephalogram signal by a signal collector;
B. the signal characteristic extraction unit extracts an electroencephalogram signal;
C. carrying out signal segmentation on the extracted electroencephalogram characteristic signal, and segmenting the electroencephalogram characteristic signal into a plurality of electroencephalogram signal segments;
D. respectively transmitting each electroencephalogram signal segment to a denoising unit for denoising to obtain a denoised signal;
E. and synthesizing the plurality of sections of denoised electroencephalograms to obtain the denoised electroencephalograms.
In the invention, the method for extracting the characteristics of the electroencephalogram signal in the step B is as follows:
a. firstly, extracting rhythm of electroencephalogram signals generated by a user due to auditory induction;
b. b, carrying out inverse operation on the rhythm extracted in the step a to obtain an electroencephalogram auditory signal;
c. b, performing energy calculation on the electroencephalogram auditory signals obtained in the step b;
d. b, quantitatively solving information entropy of the energy calculated in the step c;
e. and d, extracting characteristic dipoles from the information entropy obtained in the step d to obtain characteristic vectors, namely realizing the characteristic extraction of the electroencephalogram signals.
In the invention, the signal segmentation method in the step C is as follows:
a. firstly, dividing a time period for acquiring the electroencephalogram signals into N +1 time periods;
b. marking each time period to obtain N +1 marked time periods;
c. setting the central points of adjacent time periods as dividing points to obtain N dividing points;
d. the N division points divide the electroencephalogram signal into N-1 electroencephalogram signal segments.
In the invention, the denoising unit in the step D comprises a triode A1B, a triode B2B, an amplifier A1C and an amplifier B2C, wherein the base electrode of the triode A1B is connected with the collector electrode of the triode B2B, the emitter electrode of the triode B2B is grounded, the base electrode is connected with the nodes of a resistor A1 a and a resistor B2 a, the resistor B2 a is grounded, one end of the resistor A1 a is connected with the collector electrode of the triode A1B, the emitter electrode of the triode A1B is respectively connected with one end of a resistor D4 a, one end of a resistor E5 a and one end of a resistor F6 a, the other end of the resistor D4 a is respectively connected with the collector electrode of the triode B2B and one end of a resistor C3 a, the other end of the resistor C3 a is grounded, the other end of the resistor, the other end of the resistor F6 a is connected to the anode input end of the amplifier A1 c, the resistor I9 a is connected between the anode input end and the output end of the amplifier A1 c, one end of the resistor H8 a is connected to the cathode input end of the amplifier A1 c, the other end of the resistor H8 a is grounded, and the resistor J10 a, the resistor K11 a, the resistor L12 a, the resistor M13 a, the amplifier B2 c and the capacitor 1d form a level conversion circuit.
In addition, in the invention, the signal synthesis method in the step E is as follows:
a. synthesizing signals at two adjacent ends by using a signal synthesizer;
b. and synthesizing the synthesized signal and the head and tail signals to obtain the final synthesized electroencephalogram signal.
In conclusion, the denoising method adopted by the invention has a simple principle, and can improve the denoising efficiency of the electroencephalogram signal and improve the acquisition precision of the electroencephalogram signal by extracting the characteristics of the electroencephalogram signal and denoising the electroencephalogram signal in a segmented manner by the denoising unit; the adopted denoising unit can further improve the anti-interference capability of the signal in the transmission process and increase the stability of signal transmission.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (5)
1. The electroencephalogram signal denoising method based on wavelet analysis is characterized by comprising the following steps: the method comprises the following steps:
A. collecting an electroencephalogram signal by a signal collector;
B. the signal characteristic extraction unit extracts an electroencephalogram signal;
C. carrying out signal segmentation on the extracted electroencephalogram characteristic signal, and segmenting the electroencephalogram characteristic signal into a plurality of electroencephalogram signal segments;
D. respectively transmitting each electroencephalogram signal segment to a denoising unit for denoising to obtain a denoised signal;
E. and synthesizing the plurality of sections of denoised electroencephalograms to obtain the denoised electroencephalograms.
2. The wavelet analysis-based electroencephalogram signal denoising method according to claim 1, characterized in that: the method for extracting the characteristics of the electroencephalogram signals in the step B comprises the following steps:
a. firstly, extracting rhythm of electroencephalogram signals generated by a user due to auditory induction;
b. b, carrying out inverse operation on the rhythm extracted in the step a to obtain an electroencephalogram auditory signal;
c. b, performing energy calculation on the electroencephalogram auditory signals obtained in the step b;
d. b, quantitatively solving information entropy of the energy calculated in the step c;
e. and d, extracting characteristic dipoles from the information entropy obtained in the step d to obtain characteristic vectors, namely realizing the characteristic extraction of the electroencephalogram signals.
3. The wavelet analysis-based electroencephalogram signal denoising method according to claim 1, characterized in that: the signal segmentation method in the step C is as follows:
a. firstly, dividing a time period for acquiring the electroencephalogram signals into N +1 time periods;
b. marking each time period to obtain N +1 marked time periods;
c. setting the central points of adjacent time periods as dividing points to obtain N dividing points;
d. the N division points divide the electroencephalogram signal into N-1 electroencephalogram signal segments.
4. The wavelet analysis-based electroencephalogram signal denoising method according to claim 1, characterized in that: the denoising unit in the step D comprises a triode A, a triode B, an amplifier A and an amplifier B, wherein the base of the triode A is connected with the collector of the triode B, the emitter of the triode B is grounded, the base is connected with a node of the resistor A and the resistor B, the resistor B is grounded, one end of the resistor A is connected with the collector of the triode A, the emitter of the triode A is respectively connected with one end of a resistor D, one end of a resistor E and one end of a resistor F, the other end of the resistor D is respectively connected with the collector of the triode B and one end of a resistor C, the other end of the resistor C is grounded, the other end of the resistor E is connected with one end of a resistor G, the other end of the resistor G is connected with the negative input end of the amplifier A, the other end of the resistor F is connected, The resistor K, the resistor L, the resistor M, the amplifier B and the capacitor form a level conversion circuit.
5. The wavelet analysis-based electroencephalogram signal denoising method according to claim 1, characterized in that: the signal synthesis method in the step E is as follows:
a. synthesizing signals at two adjacent ends by using a signal synthesizer;
b. and synthesizing the synthesized signal and the head and tail signals to obtain the final synthesized electroencephalogram signal.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113208622A (en) * | 2021-04-07 | 2021-08-06 | 北京脑陆科技有限公司 | Electroencephalogram EEG signal denoising method and system based on deep neural network technology |
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| CN106953660A (en) * | 2017-02-17 | 2017-07-14 | 浙江氢创投资有限公司 | A kind of information exchange client and method |
| CN108236464A (en) * | 2017-12-29 | 2018-07-03 | 重庆邮电大学 | Feature extracting method and its Detection and Extraction system based on EEG signals |
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| CN106953660A (en) * | 2017-02-17 | 2017-07-14 | 浙江氢创投资有限公司 | A kind of information exchange client and method |
| CN108236464A (en) * | 2017-12-29 | 2018-07-03 | 重庆邮电大学 | Feature extracting method and its Detection and Extraction system based on EEG signals |
Cited By (1)
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| CN113208622A (en) * | 2021-04-07 | 2021-08-06 | 北京脑陆科技有限公司 | Electroencephalogram EEG signal denoising method and system based on deep neural network technology |
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