CN114403896A - Method for removing ocular artifacts in single-channel electroencephalogram signal - Google Patents

Method for removing ocular artifacts in single-channel electroencephalogram signal Download PDF

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CN114403896A
CN114403896A CN202210042980.6A CN202210042980A CN114403896A CN 114403896 A CN114403896 A CN 114403896A CN 202210042980 A CN202210042980 A CN 202210042980A CN 114403896 A CN114403896 A CN 114403896A
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于宁波
韩建达
宋婷
孙玉波
舒智林
刘晋瑞
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Abstract

The method for removing the ocular artifacts in the single-channel electroencephalogram signal comprises the following steps: firstly, performing empirical wavelet transform processing on a single-channel electroencephalogram signal; step two, carrying out improved self-adaptive noise complete empirical mode decomposition on the brain electrical signals of the delta frequency band to obtain a plurality of inherent mode functions; step three, calculating the sample entropy of each inherent mode function, and setting a sample entropy threshold value to identify the inherent mode function containing the ocular artifacts; removing intrinsic mode functions containing ocular artifacts, and performing improved adaptive noise complete empirical mode decomposition inverse operation reconstruction on the remaining intrinsic mode functions to obtain filtered delta frequency band signals; and fifthly, performing inverse transformation on the filtered delta frequency band signal and the filtered high frequency band signal based on empirical wavelet transform, and finally reconstructing to obtain the electroencephalogram signal without the ocular artifacts. The method removes the ocular artifacts from the real electroencephalogram signals, simultaneously reserves the electroencephalogram information as much as possible, avoids distortion, and therefore accurately and effectively analyzes the brain activity.

Description

Method for removing ocular artifacts in single-channel electroencephalogram signal
Technical Field
The invention relates to the field of electroencephalogram signal preprocessing, in particular to a method for removing ocular artifacts in a single-channel electroencephalogram signal.
Background
The Brain-Computer Interface (BCI) technology can realize the interactive control between the human Brain and the external environment, and is a brand new direct control channel established between the Brain and a Computer or other devices. For the convenience of users, the BCI products are portable, and the BCI products are few channels or even single channels. The electroencephalogram signals have the characteristics of being weak, strong in randomness and non-stable, and are extremely easily influenced by movement, blinking and muscle activity in the acquisition process, wherein the ocular artifacts caused by blinking or eyeball movement with high amplitude and low frequency are most obvious and common, so that the removal of the ocular artifacts is very important for the BCI technology.
In recent years, researchers have proposed various methods for removing ocular artifacts from single channel brain signals. The regression method is the most direct method, but needs an additional ocular electrical reference channel, namely, the electroencephalogram signal is collected and the ocular electrical signal is collected at the same time, so that the hardware complexity is increased, and the problem of bidirectional pollution of the electroencephalogram signal and the ocular electrical signal exists. The blind source separation algorithm based on independent component analysis has a good effect on removing ocular artifacts of multi-channel electroencephalogram signals, and is not suitable for single-channel electroencephalogram signals due to the fact that the number of channels is necessarily greater than or equal to the prior condition of the number of sources. Aiming at the defects of a single method, an electroencephalogram signal eye artifact removing algorithm combining two or more methods is proposed at present. Liu Shi Yong provides an algorithm combining wavelet transformation, ensemble empirical mode decomposition and independent component analysis, and is used for removing ocular artifacts in single-channel electroencephalogram signals. Although the prior condition of blind source separation is met by decomposing a single signal into multi-dimensional data through set empirical mode decomposition, electroencephalogram information and electro-oculogram information are acquired by a single sensor, and the electroencephalogram information and the electro-oculogram information can be concentrated into the same component in the decomposition process and cannot be completely distinguished from each other, so that more useful information is removed. With the rapid development of brain-computer interfaces, how to remove ocular artifacts in single-channel electroencephalogram signals becomes extremely important.
Disclosure of Invention
The invention provides a method for removing ocular artifacts in a single-channel electroencephalogram signal, aiming at overcoming the defects of the prior art. The method is based on the combination of empirical wavelet transform and improved adaptive noise complete empirical mode decomposition, and the ocular artifacts are automatically removed.
The method for removing the ocular artifacts in the single-channel electroencephalogram signal comprises the following steps:
step one, performing empirical wavelet transform processing on a single-channel electroencephalogram signal to obtain a delta frequency band electroencephalogram signal and a high-frequency band electroencephalogram signal;
step two, carrying out improved self-adaptive noise complete empirical mode decomposition on the brain electrical signals of the delta frequency band to obtain a plurality of inherent mode functions;
step three, calculating the sample entropy of each inherent mode function, and setting a sample entropy threshold value to identify the inherent mode function containing the ocular artifacts;
removing intrinsic mode functions containing ocular artifacts, and performing improved adaptive noise complete empirical mode decomposition inverse operation reconstruction on the remaining intrinsic mode functions to obtain filtered delta frequency band signals;
and fifthly, performing inverse transformation on the filtered delta frequency band signal and the filtered high frequency band signal based on empirical wavelet transform, and finally reconstructing to obtain the electroencephalogram signal without the ocular artifacts.
Compared with the prior art, the invention has the beneficial effects that:
the method is based on the combination of empirical wavelet transform and improved self-adaptive noise complete empirical mode decomposition, automatically removes the ocular artifacts, can effectively remove the ocular artifacts, can furthest retain the original electroencephalogram information, and is suitable for the treatment of removing the ocular artifacts of the single-channel electroencephalogram signals.
The technical scheme of the invention is specifically explained by combining the drawings and the embodiment as follows:
drawings
FIG. 1 is a flow chart of ocular artifact removal in a single-channel electroencephalogram signal according to the present invention;
FIG. 2 is a schematic illustration of the placement of an International Standard 10-20 electrode system;
FIG. 3 is a diagram of a half-simulated electroencephalogram signal;
FIG. 4 is a schematic diagram of a half-simulated electroencephalogram signal after removing ocular artifacts;
FIG. 5 is a comparison graph of the real brain electrical signal and the brain electrical signal after the method of the present invention is adopted to remove the ocular artifacts.
Detailed Description
Referring to fig. 1, the method for removing ocular artifacts in a single-channel electroencephalogram signal in the embodiment includes the following steps:
step one, performing empirical wavelet transform processing on a single-channel electroencephalogram signal to obtain a delta frequency band electroencephalogram signal Sδ(t) and high-band electroencephalogram signal Shigh(t)；
Step two, the EEG signal S of delta frequency rangeδ(t) carrying out improved self-adaptive noise complete empirical mode decomposition to obtain a plurality of inherent mode functions;
step three, calculating the sample entropy of each inherent mode function, and setting a sample entropy threshold value to identify the inherent mode function containing the ocular artifacts;
removing the intrinsic mode function containing the ocular artifacts, and performing improved adaptive noise complete empirical mode decomposition inverse operation reconstruction on the remaining intrinsic mode function to obtain the filtered delta frequency band signal
Figure BDA0003471024430000021
Step five, the delta frequency band signal after filtering
Figure BDA0003471024430000031
And a high band signal Shigh(t) performing an inverse empirical wavelet transform-based transformFinally reconstructing to obtain the EEG signal without the ocular artifacts
Figure BDA0003471024430000032
The embodiment removes ocular artifact signals from single-channel electroencephalogram signals based on Empirical Wavelet Transform (EWT) and improved adaptive noise complete empirical mode decomposition (icemdan), removes ocular artifacts from real electroencephalogram signals, and simultaneously retains electroencephalogram information as much as possible to avoid electroencephalogram signal distortion, thereby accurately and effectively analyzing brain activities.
In the present embodiment, the acquired electroencephalogram signal containing ocular artifacts is s (T) ([ s (1), s (2), …, s (T) ], where s (T) ([ 1,2, …, T) ] represents the value of the electroencephalogram signal at time T.
Further, in the second step: after the electroencephalogram signals of the delta frequency band are decomposed by an improved self-adaptive noise complete empirical mode, the electroencephalogram signals are expressed as the sum of N inherent mode functions and residual errors;
Figure BDA0003471024430000033
wherein: IMFn(t) is the n-th natural mode function, rn(t) is a residual signal;
based on the above inventive concept, the present invention is further described below by embodiments, and software for automatically removing ocular artifacts in a single-channel electroencephalogram signal is designed, and the diagrams of the related signals are shown in fig. 3, fig. 4 and fig. 5.
Example 1:
in the embodiment, Neuroscan brain electrical acquisition equipment is adopted to acquire brain electrical signals, and the electrode position placement adopts an international 10-20 lead system electrode placement method as shown in figure 2. The electroencephalogram signal of the FP1 channel during the eye closure of the subject was first acquired for 10 seconds and pre-processed, including the 2000Hz electroencephalogram signal down-converted to 200Hz, and filtered with a 0.5-40Hz band pass and 50Hz notch. Recapturing the eye open period of the subjectAnd (3) reducing the frequency of the 2000Hz eye electric signal to 200Hz by using the vertical eye electric signal and the horizontal eye electric signal between the two, and filtering by using a band pass of 0.5-5 Hz. Let SEEG、SVEOGAnd SHEOGRespectively are pure electroencephalogram signals, vertical electro-oculogram signals and horizontal electro-oculogram signals, and the half-mode simulated electroencephalogram signals containing the electro-oculogram artifacts are as follows:
Scon＝SEEG+αSVEOG+βSHEOG
wherein: alpha and beta are the contamination coefficients of the vertical and horizontal ocular signals, respectively. The EEG signal containing the ocular artifacts with the signal-to-noise ratio of-5 dB is obtained by adjusting the parameters alpha and beta. FIG. 3 (a) shows a clean electroencephalogram signal SEEGThe waveform of (a), (b) is a vertical electro-oculogram signal SVEOGIs a horizontal eye electrical signal SHEOGThe waveform of (d) is a synthesized signal of the three, namely an electroencephalogram signal S containing ocular artifactsconThe waveform of (a);
half-and-half analog electroencephalogram signal SconPerforming EWT decomposition to obtain an electroencephalogram signal of a delta frequency band and a high frequency band; carrying out ICEEMDAN decomposition on the brain electrical signals of the delta frequency band to obtain a plurality of IMFs; calculating a sample entropy value of each IMF, removing IMFs of which the sample entropy value is less than 0.4, and carrying out inverse ICEEMDAN reconstruction on the rest IMFs to obtain an electroencephalogram signal of a delta frequency band without ocular artifacts; carrying out inverse EWT conversion on the brain electrical signal of delta frequency band without ocular artifacts and the brain electrical signal of high frequency band to obtain the brain electrical signal without ocular artifacts, and fig. 4 is a comparison graph before and after the ocular artifacts are removed, wherein (a) is a pure brain electrical signal waveform graph, (b) is a brain electrical signal waveform graph with ocular artifacts, and (c) is a brain electrical signal waveform graph after the ocular artifacts are removed.
The method carries out quantitative analysis on the electroencephalogram data after the ocular artifact is removed and the corresponding pure electroencephalogram signals, so as to evaluate the effectiveness of the ocular artifact removal algorithm. The Correlation Coefficient (CC) and the relative root-mean-squared error (RRMSE) are used as evaluation criteria.
Figure BDA0003471024430000041
Figure BDA0003471024430000042
Wherein S isEEGIs a pure brain electrical signal, ScleanThe method is used for removing the electroencephalogram signals after the ocular artifacts are removed. The CC quantitatively evaluates the correlation between the pure electroencephalogram signal and the electroencephalogram signal without the ocular artifacts, and the larger the CC value is, the larger the correlation is, namely, the more complete the electroencephalogram signal information after the ocular artifacts are removed is kept. RRMSE quantitatively evaluates the amplitude distortion condition of the electroencephalogram signals, and the smaller the RRMSE value is, the closer the pure electroencephalogram signals are to the electroencephalogram signals without the ocular artifacts, and the more complete the ocular artifacts are removed.
Fig. 4 shows the effect of removing the ocular artifacts, the CC value is 0.9534, the relative root mean square error is 0.3016, which illustrates that the electroencephalogram signal is retained to the maximum extent while the ocular artifacts are removed, and illustrates the effectiveness of the present embodiment in removing the ocular artifacts in the single-channel electroencephalogram signal.
Example 2:
in the present embodiment, a Neuroscan electroencephalogram acquisition device is used to acquire an electroencephalogram signal of the FP1 channel during the eye opening period of the subject, the length of the signal is 10 seconds, and as shown in fig. 5 (a), it can be seen that the electroencephalogram signal contains significant ocular artifacts.
By adopting the EWT-ICEEMDAN method provided by the embodiment, the software based on the method for automatically removing the ocular artifacts in the single-channel electroencephalogram signals is used for automatically removing the ocular artifacts from the electroencephalogram signals of the FP1 channel. The result of the removal of the ocular artifacts is shown in fig. 5 (b), and it can be seen that the ocular artifacts are removed.
The method carries out quantitative evaluation on the eye electrical artifact removal effect of the real electroencephalogram data. Because the real electroencephalogram data cannot provide a pure electroencephalogram signal, CC and RRMSE cannot be calculated to evaluate the effect of removing the ocular artifacts. Therefore, for the distortion of the electroencephalogram signal, the power spectrum distortion of the high frequency band before and after the removal of the ocular artifacts is evaluated by calculating the mean absolute error (PSD) of the Power Spectrum (PSD) in the theta, alpha, beta, gamma frequency bands,MAE). For the frequency band of theta,
Figure BDA0003471024430000051
is defined as:
Figure BDA0003471024430000052
in the formula (I), the compound is shown in the specification,
Figure BDA0003471024430000053
representing the power spectrum of the EEG signal containing the ocular artifacts in a theta frequency band,
Figure BDA0003471024430000054
expressing the power spectrum of the EEG signal without ocular artifacts in the theta frequency band, K1And K2Respectively, the frequency points of the PSD in the theta band. The average absolute error of the power spectrum in the α, β, γ frequency bands can also be obtained.
Fig. 5 (c) is a power spectrum of the electroencephalogram signal before and after the ocular artifact removal. The average absolute errors of the power spectrums of theta, alpha, beta and gamma frequency bands before and after the removal of the ocular artifacts are 2.761, 0.1154, 0.002299 and 0.0009138 respectively, which shows that the distortion of the electroencephalogram signals in the high frequency band is very small. The result shows that the method is effective in removing the ocular artifacts in the real electroencephalogram signals, and is suitable for removing the ocular artifacts of the single-channel electroencephalogram signals.
The present invention is not limited to the above embodiments, and those skilled in the art can make various changes and modifications without departing from the scope of the invention.

Claims (6)

1. The method for removing the ocular artifacts in the single-channel electroencephalogram signal is characterized by comprising the following steps: comprises the following steps:
step one, performing empirical wavelet transform processing on a single-channel electroencephalogram signal to obtain a delta frequency band electroencephalogram signal Sδ(t) and high-band electroencephalogram signal Shigh(t)；
Step two, the EEG signal S of delta frequency rangeδ(t) carrying out improved self-adaptive noise complete empirical mode decomposition to obtain a plurality of inherent mode functions;
step three, calculating the sample entropy of each inherent mode function, and setting a sample entropy threshold value to identify the inherent mode function containing the ocular artifacts;
removing the intrinsic mode function containing the ocular artifacts, and performing improved adaptive noise complete empirical mode decomposition inverse operation reconstruction on the remaining intrinsic mode function to obtain the filtered delta frequency band signal
Figure FDA0003471024420000011
Step five, the delta frequency band signal after filtering
Figure FDA0003471024420000012
And a high band signal Shigh(t) performing inverse transformation based on empirical wavelet transform, and finally reconstructing to obtain the electroencephalogram signal without the ocular artifacts
Figure FDA0003471024420000013
2. The method for removing the ocular artifacts in the single-channel electroencephalogram signal according to claim 1, characterized in that: the inherent mode function in the second step is expressed as:
Figure FDA0003471024420000014
wherein: IMFn(t) is the n-th natural mode function, rn(t) is a residual signal.
3. The method for removing the ocular artifacts in the single-channel electroencephalogram signal according to claim 1, characterized in that: the sample entropy threshold is set to 0.4 in step three.
4. The method for removing the ocular artifacts in the single-channel electroencephalogram signal according to claim 1, characterized in that: acquiring single-channel electroencephalogram signals by adopting Neuroscan electroencephalogram acquisition equipment, and placing the electrodes by adopting an international 10-20 lead system electrode placement method.
5. The method for removing the ocular artifacts in the single-channel electroencephalogram signal according to claim 1, characterized in that: evaluating the effectiveness indexes of the removal of the ocular artifacts to be a correlation coefficient CC and a relative root mean square error RRMSE respectively;
Figure FDA0003471024420000015
Figure FDA0003471024420000016
wherein S isEEGIs a pure brain electrical signal, ScleanThe method is used for removing the electroencephalogram signals after the ocular artifacts are removed.
6. The method for removing the ocular artifacts in the single-channel electroencephalogram signal according to claim 1, characterized in that: for the distortion condition of the electroencephalogram signal, the power spectrum distortion condition of the high frequency band before and after the removal of the ocular artifact is calculated to evaluate, for the theta frequency band,
Figure FDA0003471024420000021
is defined as:
Figure FDA0003471024420000022
in the formula (I), the compound is shown in the specification,
Figure FDA0003471024420000023
representing the power spectrum of the EEG signal containing the ocular artifacts in a theta frequency band,
Figure FDA0003471024420000024
expressing the power spectrum of the EEG signal without ocular artifacts in the theta frequency band, K1And K2Respectively, are frequency points of the power spectrum in the theta band.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115510692A (en) * 2022-11-09 2022-12-23 齐鲁工业大学 Electroencephalogram signal artifact removing method based on variational modal decomposition and second-order blind identification
CN115836867A (en) * 2023-02-14 2023-03-24 中国科学技术大学 Dual-branch fusion deep learning electroencephalogram noise reduction method, device and medium

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101869477A (en) * 2010-05-14 2010-10-27 北京工业大学 Self-adaptive EEG signal ocular artifact automatic removal method
CN104809434A (en) * 2015-04-22 2015-07-29 哈尔滨工业大学 Sleep staging method based on single-channel electroencephalogram signal ocular artifact removal
CN106333678A (en) * 2016-09-21 2017-01-18 广州视源电子科技股份有限公司 Method and system for detecting brain wave at sleep state in electroencephalogram
WO2018053968A1 (en) * 2016-09-21 2018-03-29 广州视源电子科技股份有限公司 Device for removing electrooculograph artifact in sleep condition analysis
CN109157214A (en) * 2018-09-11 2019-01-08 河南工业大学 A method of the online removal eye electricity artefact suitable for single channel EEG signals
CN110269609A (en) * 2019-07-08 2019-09-24 江苏理工学院 Based on eye electricity artefact separation method in single pass EEG signals
CN110292376A (en) * 2018-03-22 2019-10-01 深圳先进技术研究院 Remove method, apparatus, equipment and the storage medium of eye electricity artefact in EEG signals
CN111956217A (en) * 2020-07-15 2020-11-20 山东师范大学 Blink artifact identification method and system for real-time electroencephalogram signals

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101869477A (en) * 2010-05-14 2010-10-27 北京工业大学 Self-adaptive EEG signal ocular artifact automatic removal method
CN104809434A (en) * 2015-04-22 2015-07-29 哈尔滨工业大学 Sleep staging method based on single-channel electroencephalogram signal ocular artifact removal
CN106333678A (en) * 2016-09-21 2017-01-18 广州视源电子科技股份有限公司 Method and system for detecting brain wave at sleep state in electroencephalogram
WO2018053968A1 (en) * 2016-09-21 2018-03-29 广州视源电子科技股份有限公司 Device for removing electrooculograph artifact in sleep condition analysis
CN110292376A (en) * 2018-03-22 2019-10-01 深圳先进技术研究院 Remove method, apparatus, equipment and the storage medium of eye electricity artefact in EEG signals
CN109157214A (en) * 2018-09-11 2019-01-08 河南工业大学 A method of the online removal eye electricity artefact suitable for single channel EEG signals
CN110269609A (en) * 2019-07-08 2019-09-24 江苏理工学院 Based on eye electricity artefact separation method in single pass EEG signals
CN111956217A (en) * 2020-07-15 2020-11-20 山东师范大学 Blink artifact identification method and system for real-time electroencephalogram signals

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
杨磊等: ""采用样本熵自适应噪声完备经验模态分解的脑电信号眼电伪迹去除算法"", 《西安交通大学学报》, vol. 54, no. 8, pages 177 - 184 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115510692A (en) * 2022-11-09 2022-12-23 齐鲁工业大学 Electroencephalogram signal artifact removing method based on variational modal decomposition and second-order blind identification
CN115836867A (en) * 2023-02-14 2023-03-24 中国科学技术大学 Dual-branch fusion deep learning electroencephalogram noise reduction method, device and medium
CN115836867B (en) * 2023-02-14 2023-06-16 中国科学技术大学 Deep learning electroencephalogram noise reduction method, equipment and medium with double-branch fusion

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