CN108095722A - Improvement EEMD algorithms based on EEG signals - Google Patents
Improvement EEMD algorithms based on EEG signals Download PDFInfo
- Publication number
- CN108095722A CN108095722A CN201810093526.7A CN201810093526A CN108095722A CN 108095722 A CN108095722 A CN 108095722A CN 201810093526 A CN201810093526 A CN 201810093526A CN 108095722 A CN108095722 A CN 108095722A
- Authority
- CN
- China
- Prior art keywords
- mrow
- eeg signals
- msub
- input signal
- function
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- 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]
-
- 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
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/22—Source localisation; Inverse modelling
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Surgery (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Physics & Mathematics (AREA)
- Animal Behavior & Ethology (AREA)
- Pathology (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Psychiatry (AREA)
- Psychology (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physiology (AREA)
- Signal Processing (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The invention discloses a kind of improvement EEMD algorithms based on EEG signals, this method is primarily based on the priori of EEG signals, chooses μ rhythm frequency range (8 12Hz) and beta response frequency range (18 26Hz) separately designs two bandpass filters；Full band white Gaussian noise is filtered using above-mentioned bandpass filter, obtains two band limit white Gaussian noises；Above-mentioned band limit white Gaussian noise w (t) is finally added in original EEG signals, carries out empirical mode decomposition.EEG signals are decomposed using inventive algorithm, have obtained the intrinsic mode function signal of frequency Relatively centralized, the intrinsic mode function of each frequency range is distinguished, and is greatly suppressed due to modal overlap problem caused by empirical mode decomposition.
Description
Technical field
The present invention relates to the improvement EEMD algorithms based on EEG signals, belong to intelligent information processing technology field.
Background technology
Empirical mode decomposition (Empirical Mode Decomposition, EMD) is used as a kind of NEW ADAPTIVE signal
Time frequency processing method, suitable for analyzing and processing non-linear, non-stationary signal.EMD algorithms are using signal local feature in itself as base
Complicated original signal can be resolved into a series of intrinsic mode functions limited, data volume is small by plinth by repeated screening
The sum of (Intrinsic Mode Functions, IMF) then carries out each IMF Hilbert conversion and solves its instantaneous frequency
Rate so that instantaneous frequency has actual physical significance, and then obtains non-linear, non-stationary signal time-frequency distributions.
The major defect of EMD algorithms is modal overlap problem.For the problem, gather empirical mode decomposition algorithm
(Ensemble EMD, EEMD) introduce noise auxiliary data analysis method (Noise-Assisted Data Analysis,
NA-DA).When original signal adds equally distributed white noise background, the signal area of different scale will be automatically mapped to
The relevant appropriate scale of background white noise, so as to effectively overcome modal overlap problem.EEMD is since proposition, in mechanical event
The numerous areas such as barrier diagnosis, Speech processing, image procossing, oceanography, geological exploration are widely used.
Mental imagery, which refers to, only carries out the limb motion imagination without actual limb motion.The motion perception rhythm and pace of moving things is saved by μ and β
Rule composition, they are the fluctuations that brain activity is located at μ frequency bands (7-13) and β frequency bands (19-26Hz).When the activity and movement of brain
During task correlation, sensorimotor rhythm (SMR) can change, it is even more important that sensation can be also reflected in by only carrying out Mental imagery
In the variation for moving the rhythm and pace of moving things.During Mental imagery, brain sensorimotor cortex can generate event-related design/desynchronize (ERS/
ERD) phenomenon, resulting EEG signals are a kind of signals of nonlinear and nonstationary.
The content of the invention
In view of the above shortcomings of the prior art, the present invention provides the improvement EEMD algorithms based on EEG signals, to traditional
EEMD algorithms are improved.
To achieve the above object, the present invention adopts the following technical scheme that：
A kind of improvement EEMD algorithms based on EEG signals, include the following steps：
Step 1：A bandpass filter is separately designed out for the different rhythm and pace of moving things frequency ranges of EEG signals x (t)；
Step 2：Full band white Gaussian noise is filtered using the bandpass filter, is obtained with this white noise of limit for height
Sound sequence wi(t) (i=1,2 ..., N)；
Step 3：The band limit Gaussian sequence w is separately added into the EEG signals x (t)i(t) (i=1,
2 ..., N) in each single item, obtain input signal xi(t)=x (t)+wi(t), (i=1,2 ..., N), to xi(t) experience is carried out
Mode Decomposition, N number of input signal xi(t) n ranks intrinsic mode function and a survival function r are respectively obtainedi,n(t)；
Step 4：Ensemble average is carried out to obtained each rank intrinsic mode function of N number of input signal to get to brain telecommunications
The jth rank intrinsic mode function c of number x (t)j(t)；
The EEG signals x (t) is represented by：
Wherein, ci,j(t) represent that ith adds in EEG signals band limit white Gaussian noise progress empirical mode decomposition and obtains
Jth rank intrinsic mode function.
Preferably, the different rhythm and pace of moving things frequency ranges of the EEG signals x (t) include μ rhythm frequency range and beta response frequency range, frequency model
Enclose respectively 8-12Hz, 18-26Hz.
Preferably, in step 3, the step of empirical mode decomposition, includes：
Step 3.1：Judge each input signal xi(t) local extremum, is carried out curve fitting with cubic spline curve, office
Portion's maximum forms coenvelope emax(t), local minimum forms lower envelope emin(t)；
Step 3.2：Seek emax(t) and emin(t) average：
Step 3.3：Calculate input signal xi(t) and the difference of m (t)：
ci,1(t)=xi(t)-m(t) (4)
If ci,1(t) the definition cut-off condition of IMF cannot be met, repeat step 3.1-3.3, otherwise, extract ci,1(t) make
For the 1st rank intrinsic mode function, survival function ri,1(t) calculate as follows：
ri,1(t)=xi(t)-ci,1(t) (5)
Step 3.4：By survival function ri,1(t) step 3.1-3.3 is repeated as new input signal；
Step 3.5：Step 3.1-3.4 is repeated, until survival function ri,n(t) there are one for a monotonic function or only
During extreme value, decomposable process stops, at this time N number of input signal xi(t) n rank intrinsic mode functions c is respectively obtainedi,1(t),…,ci,n
(t) and a survival function ri,n(t)。
Preferably, the n=8.
Advantageous effect：EEG signals are decomposed using inventive algorithm, have obtained the natural mode of frequency Relatively centralized
State function signal, the intrinsic mode function of each frequency range are distinguished, greatly suppressed due to caused by empirical mode decomposition
Modal overlap problem.Being introduced into for band-limited noise efficiently extracts the mode of multiple aliasings in single IMF components in the present invention
In different IMF, efficiently separating for mode is realized to a certain extent, it was demonstrated that the innovatory algorithm is in this electric special dimension of brain
Use be feasible and effective, can preferably solve the problems, such as EEG signals EMD decompose modal overlap.By inventive algorithm
It, can be by each frequency of EEG signals area as far as possible using improved EEMD algorithms in Classification and Identification applied to imagination right-hand man
It separates, makes each component that its decomposition obtains all based on single group frequency, be beneficial to extract the distincter feature vector of feature,
Therefore the accuracy rate of Classification and Identification can be greatly improved.
Description of the drawings
Fig. 1 is the method for the present invention flow chart.
Fig. 2 is band limit white Gaussian noise generation figure in the method for the present invention.
Fig. 3 is original EEG signals time-frequency figure (being above time-domain diagram, lower is frequency domain figure).
Fig. 4 is the time-frequency figure that classics EMD decomposes EEG signals (left side is time-domain diagram, and the right side is frequency domain figure).
Fig. 5 is the time-frequency figure that inventive algorithm decomposes EEG signals (left side is time-domain diagram, and the right side is frequency domain figure).
Specific embodiment
With reference to Figure of description, the present invention is described in further detail.
As shown in Figure 1, the method for the invention includes the following steps：
Step 1：Priori based on EEG signals chooses μ rhythm frequency range (8-12Hz) and beta response frequency range (18-
26Hz) separately design two bandpass filters；
Step 2：Full band white Gaussian noise is filtered using above-mentioned bandpass filter, obtain two band limits for height this
White noise, as shown in Figure 2；
Step 3：Above-mentioned band limit white Gaussian noise is added in original EEG signals, carries out empirical mode decomposition；
EEG signals carry out empirical mode decomposition and are as follows：
(1) the band limit Gaussian sequence w is separately added into EEG signals x (t)i(t) (i=1,2 ..., N) in
Each single item, obtain input signal xi(t)=x (t)+wi(t), i=1,2 ..., N judge each xi(t) local extremum is used
Cubic spline curve carries out curve fitting, and local maximum forms coenvelope emax(t), local minimum forms lower envelope emin
(t)。
(2) e is soughtmax(t) and emin(t) average：
(3) input signal x is calculatedi(t) and the difference of m (t):
ci,1(t)=xi(t)-m(t) (2)
If ci,1(t) the definition cut-off condition of IMF cannot be met, repeat the above process (1)-(3), otherwise, extract ci,1
(t) as the 1st intrinsic mode function, surplus ri,1(t) calculate as follows：
ri,1(t)=xi(t)-ci,1(t) (3)
(4) the surplus data new as one are next more low-frequency intrinsic to obtain by identical screening process
Mode function.Until survival function ri,n(t) for a monotonic function or only there are one during extreme value, decomposable process stops.Assuming that
Input signal x at this timei(t) n intrinsic mode function and a survival function amount r are broken down intoi,n(t)。
Step 4：Ensemble average is carried out to obtained each rank intrinsic mode function；
Wherein, ci,j(t) represent that ith adds in original EEG signals band limit white Gaussian noise and carries out empirical mode decomposition
Obtained jth rank intrinsic mode function, cj(t) jth rank intrinsic mode function is represented.
The reconstruction signal of original input signal x (t) is：
The present embodiment takes preceding 8 rank intrinsic mode function to carry out algorithm comparison.As shown in figure 3, in original EEG signals with
Based on 12Hz and 24Hz, and aliasing has the component of other frequencies；As shown in figure 4, during classical EMD is decomposed, component IMF1 aliasings
Multiple frequency harmonics, predominantly 12Hz, aliasing 8Hz, 12Hz in 24Hz, component IMF2；And during inventive algorithm decomposes,
IMF1 components only include the high frequency harmonic components of 24Hz, the low-frequency harmonics of 12Hz are only included in IMF2 components, as shown in Figure 5.This
Being introduced into for band-limited noise efficiently extracts the mode of multiple aliasings in single IMF components in different IMF in invention, centainly
Efficiently separating for mode is realized in degree, it was demonstrated that use of the innovatory algorithm in this electric special dimension of brain is feasible and has
Effect, it can preferably solve the problems, such as the modal overlap that EEG signals EMD is decomposed.
In conclusion be merely preferred embodiments of the present invention, but protection scope of the present invention is not limited thereto.
In the disclosed technical scope of invention, the change or replacement that can readily occur in, should all cover disclosed herein technology
Within the scope of.Therefore, protection scope of the present invention should be subject to the protection domain of claims.
Claims (4)
1. a kind of improvement EEMD algorithms based on EEG signals, which is characterized in that include the following steps：
Step 1：A bandpass filter is separately designed out for the different rhythm and pace of moving things frequency ranges of EEG signals x (t)；
Step 2：Full band white Gaussian noise is filtered using the bandpass filter, obtains band limit white Gaussian noise sequence
Arrange wi(t) (i=1,2 ..., N)；
Step 3：The band limit Gaussian sequence w is separately added into the EEG signals x (t)i(t) (i=1,2 ..., N)
In each single item, obtain input signal xi(t)=x (t)+wi(t), (i=1,2 ..., N), to input signal xi(t) experience is carried out
Mode Decomposition, N number of input signal xi(t) n ranks intrinsic mode function and a survival function r are respectively obtainedi,n(t)；
Step 4：To obtained N number of input signal xi(t) n ranks intrinsic mode function carries out ensemble average to get to EEG signals
The jth rank intrinsic mode function c of x (t)j(t) it is：
<mrow>
<msub>
<mi>c</mi>
<mi>j</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>N</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<msub>
<mi>c</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
The EEG signals x (t) is represented by：
<mrow>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<msub>
<mi>c</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>r</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>n</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, ci,j(t) represent that ith adds in EEG signals the jth for carrying out empirical mode decomposition with limit white Gaussian noise and obtaining
Rank intrinsic mode function.
A kind of 2. improvement EEMD algorithms based on EEG signals according to claim 1, which is characterized in that the brain telecommunications
The different rhythm and pace of moving things frequency ranges of number x (t) include μ rhythm frequency range and beta response frequency range, and frequency range is respectively 8-12Hz, 18-26Hz.
3. a kind of improvement EEMD algorithms based on EEG signals according to claim 1, which is characterized in that in step 3, institute
The step of stating empirical mode decomposition includes：
Step 3.1：Judge each input signal xi(t) local extremum, is carried out curve fitting with cubic spline curve, local pole
Big value forms coenvelope emax(t), local minimum forms lower envelope emin(t)；
Step 3.2：Seek emax(t) and emin(t) average：
<mrow>
<mi>m</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>e</mi>
<mi>min</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>e</mi>
<mi>max</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
<mn>2</mn>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
Step 3.3：Calculate input signal xi(t) and the difference of m (t)：
ci,1(t)=xi(t)-m(t) (4)
If ci,1(t) the definition cut-off condition of IMF cannot be met, repeat step 3.1-3.3, otherwise, extract ci,1(t) as the
1 rank intrinsic mode function, survival function ri,1(t) calculate as follows：
ri,1(t)=xi(t)-ci,1(t) (5)
Step 3.4：By survival function ri,1(t) step 3.1-3.3 is repeated as new input signal；
Step 3.5：Step 3.1-3.4 is repeated, until survival function ri,n(t) for a monotonic function or only, there are one extreme values
When, decomposable process stops, at this time N number of input signal xi(t) n rank intrinsic mode functions c is respectively obtainedi,1(t),…,ci,n(t) and
One survival function ri,n(t)。
A kind of 4. improvement EEMD algorithms based on EEG signals according to claim 3, which is characterized in that the n=8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810093526.7A CN108095722B (en) | 2018-01-31 | 2018-01-31 | Improved EEMD algorithm based on electroencephalogram signals |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810093526.7A CN108095722B (en) | 2018-01-31 | 2018-01-31 | Improved EEMD algorithm based on electroencephalogram signals |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108095722A true CN108095722A (en) | 2018-06-01 |
| CN108095722B CN108095722B (en) | 2021-05-28 |
Family
ID=62221428
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810093526.7A Active CN108095722B (en) | 2018-01-31 | 2018-01-31 | Improved EEMD algorithm based on electroencephalogram signals |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108095722B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109117775A (en) * | 2018-08-02 | 2019-01-01 | 南京邮电大学 | Based on polynomial improvement EMD algorithm |
| CN109598222A (en) * | 2018-11-26 | 2019-04-09 | 南开大学 | Wavelet neural network Mental imagery brain electricity classification method based on the enhancing of EEMD data |
| CN110013249A (en) * | 2019-03-19 | 2019-07-16 | 西北大学 | A kind of Portable adjustable wears seizure monitoring instrument |
| CN110261305A (en) * | 2019-06-18 | 2019-09-20 | 南京东南建筑机电抗震研究院有限公司 | Based on across footpaths continuous bridge damnification recognition methods such as the multispan for influencing line |
| CN112115851A (en) * | 2020-09-16 | 2020-12-22 | 北京邮电大学 | CMEEMD-GAIW-SW-DFA-based distributed optical fiber signal auditory information fusion method |
| CN112200069A (en) * | 2020-09-30 | 2021-01-08 | 山东大学 | Tunnel filtering method and system combining time-frequency domain spectral subtraction and empirical mode decomposition |
| CN116318437A (en) * | 2023-03-16 | 2023-06-23 | 中国科学院空天信息创新研究院 | Cross-medium communication interference suppression method and system |
| CN117030268A (en) * | 2023-10-07 | 2023-11-10 | 太原科技大学 | Rolling bearing fault diagnosis method |
| CN117390531A (en) * | 2023-11-20 | 2024-01-12 | 兰州交通大学 | Automatic depression identification method based on IEEMD electroencephalogram signal decomposition |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104091172A (en) * | 2014-07-04 | 2014-10-08 | 北京工业大学 | Characteristic extraction method of motor imagery electroencephalogram signals |
| WO2016004186A1 (en) * | 2014-07-01 | 2016-01-07 | The University Of New Hampshire | Empirical mode decomposition for spectrum sensing in communication systems |
| CN106951835A (en) * | 2017-03-03 | 2017-07-14 | 东华大学 | A kind of EEG signals noise remove method |
-
2018
- 2018-01-31 CN CN201810093526.7A patent/CN108095722B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016004186A1 (en) * | 2014-07-01 | 2016-01-07 | The University Of New Hampshire | Empirical mode decomposition for spectrum sensing in communication systems |
| CN104091172A (en) * | 2014-07-04 | 2014-10-08 | 北京工业大学 | Characteristic extraction method of motor imagery electroencephalogram signals |
| CN106951835A (en) * | 2017-03-03 | 2017-07-14 | 东华大学 | A kind of EEG signals noise remove method |
Non-Patent Citations (3)
| Title |
|---|
| ZHANG J,YAN R,GAO R X , ET AL.: "Performance enhancement of ensemble empirical mode decomposition", 《MECHANICAL SYSTEMS & SIGNAL PROCESSING》 * |
| 何星，王宏力，姜伟，王林: "改进的自适应EEMD方法及其应用", 《系统仿真学报》 * |
| 黄丽亚,笪铖璐,杨晨,等.: "基于脑电EEG的改进EEMD算法", 《计算机科学》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109117775A (en) * | 2018-08-02 | 2019-01-01 | 南京邮电大学 | Based on polynomial improvement EMD algorithm |
| CN109598222A (en) * | 2018-11-26 | 2019-04-09 | 南开大学 | Wavelet neural network Mental imagery brain electricity classification method based on the enhancing of EEMD data |
| CN109598222B (en) * | 2018-11-26 | 2023-04-07 | 南开大学 | EEMD data enhancement-based wavelet neural network motor imagery electroencephalogram classification method |
| CN110013249A (en) * | 2019-03-19 | 2019-07-16 | 西北大学 | A kind of Portable adjustable wears seizure monitoring instrument |
| CN110013249B (en) * | 2019-03-19 | 2022-02-18 | 西北大学 | Portable adjustable head-mounted epilepsy monitor |
| CN110261305A (en) * | 2019-06-18 | 2019-09-20 | 南京东南建筑机电抗震研究院有限公司 | Based on across footpaths continuous bridge damnification recognition methods such as the multispan for influencing line |
| CN112115851A (en) * | 2020-09-16 | 2020-12-22 | 北京邮电大学 | CMEEMD-GAIW-SW-DFA-based distributed optical fiber signal auditory information fusion method |
| CN112115851B (en) * | 2020-09-16 | 2022-02-08 | 北京邮电大学 | CMEEMD-GAIW-SW-DFA-based distributed optical fiber signal auditory information fusion method |
| CN112200069B (en) * | 2020-09-30 | 2022-11-04 | 山东大学 | Tunnel filtering method and system combining time-frequency domain spectral subtraction and empirical mode decomposition |
| CN112200069A (en) * | 2020-09-30 | 2021-01-08 | 山东大学 | Tunnel filtering method and system combining time-frequency domain spectral subtraction and empirical mode decomposition |
| CN116318437A (en) * | 2023-03-16 | 2023-06-23 | 中国科学院空天信息创新研究院 | Cross-medium communication interference suppression method and system |
| CN116318437B (en) * | 2023-03-16 | 2023-12-01 | 中国科学院空天信息创新研究院 | Cross-medium communication interference suppression method and system |
| CN117030268A (en) * | 2023-10-07 | 2023-11-10 | 太原科技大学 | Rolling bearing fault diagnosis method |
| CN117030268B (en) * | 2023-10-07 | 2024-01-23 | 太原科技大学 | Rolling bearing fault diagnosis method |
| CN117390531A (en) * | 2023-11-20 | 2024-01-12 | 兰州交通大学 | Automatic depression identification method based on IEEMD electroencephalogram signal decomposition |
| CN117390531B (en) * | 2023-11-20 | 2024-04-02 | 兰州交通大学 | Automatic depression identification method based on IEEMD electroencephalogram signal decomposition |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108095722B (en) | 2021-05-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108095722A (en) | Improvement EEMD algorithms based on EEG signals | |
| Sachs et al. | Effects of nonlinearities on speech encoding in the auditory nerve | |
| Chen et al. | Removal of power line interference from ECG signals using adaptive notch filters of sharp resolution | |
| CN105809124A (en) | DWT- and Parametric t-SNE-based characteristic extracting method of motor imagery EEG(Electroencephalogram) signals | |
| Estrada et al. | Wavelet-based EEG denoising for automatic sleep stage classification | |
| CN108714026A (en) | The fine granularity electrocardiosignal sorting technique merged based on depth convolutional neural networks and on-line decision | |
| CN106618561A (en) | High-robustness analysis method for electroencephalogram (ECG) signal of disabled people in motion state | |
| CN105677035A (en) | EEMD (Ensemble Empirical Mode Decomposition) and wavelet threshold based motor imagery electroencephalogram signal denoising method | |
| CN104810024A (en) | Double-path microphone speech noise reduction treatment method and system | |
| CN107729289A (en) | A kind of adaptive time-frequency conversion method of Polynomial Phase Signals based on genetic optimization | |
| Giorgio et al. | Improving ECG signal denoising using wavelet transform for the prediction of malignant arrhythmias | |
| Sohal et al. | FPGA implementation of Power-Efficient ECG pre-processing block | |
| Rostan et al. | Will Saudi Arabia get older? Will its pension system be sustainable? Spectral answers | |
| CN109117775A (en) | Based on polynomial improvement EMD algorithm | |
| CN106805968A (en) | A kind of brain electricity allowance recognition methods and device | |
| CN106503366B (en) | Method for inhibiting EMD endpoint effect | |
| CN104935292B (en) | A kind of surface electromyogram signal adaptive filter method based on identifying source | |
| Adochiei et al. | Comparative filtering methods for noisy ECG signals | |
| CN114757236B (en) | Electroencephalogram signal denoising optimization method and system based on TQWT and SVMD | |
| Li et al. | sEMG signal filtering study using synchrosqueezing wavelet transform with differential evolution optimized threshold | |
| CN106097264B (en) | Based on dual-tree complex wavelet and morphologic satellite telemetering data filtering method | |
| Zahan | Removing EOG artifacts from EEG signal using noise-assisted multivariate empirical mode decomposition | |
| CN105662391B (en) | A kind of feature extraction and classifying system and method for stomach magnetic signal | |
| Toshio | A'gammachirp'function as an optimal auditory filter with the Mellin transform | |
| Bhogeshwar et al. | Study of structural complexity of optimal order digital filters for de-noising ECG signal |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |