CN114403896B - Method for removing ocular artifacts in single-channel electroencephalogram signals - Google Patents
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Abstract
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 single-channel electroencephalogram signals; step two, performing improved self-adaptive noise complete empirical mode decomposition on the delta-band electroencephalogram signals to obtain a plurality of inherent mode functions; step three, calculating sample entropy of each natural mode function, and setting a sample entropy threshold to identify the natural mode function containing ocular artifacts; removing an inherent mode function containing ocular artifacts, and performing improved self-adaptive noise complete empirical mode decomposition inverse operation reconstruction on the rest inherent mode function to obtain a delta frequency band signal after filtering; and fifthly, performing inverse transformation based on empirical wavelet transformation on the filtered delta frequency band signal and the high frequency band signal, and finally reconstructing to obtain the electroencephalogram signal with the ocular artifacts removed. The invention removes the ocular artifacts from the real brain signals, and simultaneously keeps the brain electrical artifacts as much as possible, avoids distortion, and thus, accurately and effectively analyzes brain activities.
Description
Technical Field
The invention relates to the field of electroencephalogram signal preprocessing, in particular to a method for removing ocular artifacts in single-channel electroencephalogram signals.
Background
The Brain-computer interface (BCI) technology can realize the interactive control of 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 convenience of use, portable BCI products are often used, and these products are small-channel or even single-channel. Electroencephalogram signals have the characteristics of weak, strong randomness and non-stability, are extremely susceptible to movement, blinking and muscle activity during acquisition, and among them, ocular artifacts caused by blinking or eyeball movement of high amplitude and low frequency are most remarkable and common, so that the removal of ocular artifacts is crucial to BCI technology.
In recent years, researchers have proposed various methods for removing ocular artifacts from single-channel brain signal. The regression method is the most direct method, but requires an extra electro-oculogram reference channel, namely, the electro-oculogram signal is acquired at the same time of acquiring the electro-brain signal, so that the hardware complexity is increased, and the problem of bidirectional pollution of the electro-brain signal and the electro-oculogram signal exists. The blind source separation algorithm based on independent component analysis has a good effect on removing the ocular artifacts of the multi-channel electroencephalogram signals, and is not applicable to single-channel electroencephalogram signals because the prior condition that the number of channels is more than or equal to the number of sources is needed. Aiming at the defects of a single method, an electroencephalogram signal and eye artifact removal algorithm combining two or more methods is proposed. Liu Zhiyong proposes an algorithm combining wavelet transformation, ensemble empirical mode decomposition and independent component analysis for removing electro-oculogram artifacts in single-channel electroencephalogram signals. Although the prior condition of blind source separation is satisfied by decomposing a single signal into multidimensional data through ensemble empirical mode decomposition, electroencephalogram information and electrooculogram information are acquired by a single sensor, and the electroencephalogram information and the electrooculogram information may be concentrated into the same component in the decomposition process and cannot be completely distinguished, so that more useful information is removed. With the rapid development of brain-computer interfaces, how to remove electro-oculogram artifacts in single-channel brain-electrical signals becomes extremely important.
Disclosure of Invention
The invention provides a method for removing ocular artifacts in single-channel electroencephalogram signals, which aims to overcome the defects in the prior art. The method is based on the combination of empirical wavelet transformation and improved adaptive noise complete empirical mode decomposition, and can automatically remove the ocular artifacts.
The method for removing the ocular artifacts in the single-channel electroencephalogram signal comprises the following steps:
step one, performing empirical wavelet transformation processing on a single-channel electroencephalogram signal to obtain a delta-band electroencephalogram signal and a high-band electroencephalogram signal;
step two, performing improved self-adaptive noise complete empirical mode decomposition on the delta-band electroencephalogram signals to obtain a plurality of inherent mode functions;
step three, calculating sample entropy of each natural mode function, and setting a sample entropy threshold to identify the natural mode function containing ocular artifacts;
removing an inherent mode function containing ocular artifacts, and performing improved self-adaptive noise complete empirical mode decomposition inverse operation reconstruction on the rest inherent mode function to obtain a delta frequency band signal after filtering;
and fifthly, performing inverse transformation based on empirical wavelet transformation on the filtered delta frequency band signal and the high frequency band signal, and finally reconstructing to obtain the electroencephalogram signal with the ocular artifacts removed.
Compared with the prior art, the invention has the beneficial effects that:
the method is based on the combination of empirical wavelet transformation and improved self-adaptive noise complete empirical mode decomposition, and can automatically remove the electro-oculogram artifacts, so that the method not only can effectively remove the electro-oculogram artifacts, but also can reserve the original electroencephalogram artifacts to the greatest extent, and is suitable for single-channel electroencephalogram signal electro-oculogram artifact removal processing.
The technical scheme of the invention is specifically described below with reference to the accompanying drawings and examples:
drawings
FIG. 1 is a flow chart of removing electro-oculogram artifacts in a single-channel electroencephalogram signal according to the present invention;
FIG. 2 is a schematic diagram of the placement of an international standard 10-20 electrode system;
FIG. 3 is a semi-simulated electroencephalogram;
FIG. 4 is a schematic diagram of the semi-model EEG after removing ocular artifacts;
fig. 5 is a diagram showing a comparison of a real brain electrical signal and a brain electrical signal after removing an electro-oculogram artifact by using the method of the present invention.
Detailed Description
Referring to fig. 1, the method for removing ocular artifacts in a single-channel electroencephalogram signal according to the present embodiment includes the following steps:
step one, performing empirical wavelet transformation processing on single-channel electroencephalogram signals to obtain delta-frequency-band electroencephalogram signals S δ (t) and high-band EEG signals S high (t)；
Step two, for the EEG signal S of delta frequency band δ (t) performing improved adaptive noise complete empirical mode decomposition to obtain a plurality of inherent mode functions;
step three, calculating sample entropy of each natural mode function, and setting a sample entropy threshold to identify the natural mode function containing ocular artifacts;
removing the inherent mode function containing the ocular artifacts, and performing improved self-adaptive noise complete empirical mode decomposition inverse operation reconstruction on the rest inherent mode function to obtain a filtered delta frequency band signal
Step five, filtering the delta frequency band signalAnd a high-frequency band signal S high (t) performing an inverse transformation based on empirical wavelet transformation, and finally reconstructing to obtain an electroencephalogram signal with ocular artifacts removed +.>
The embodiment removes the electro-oculogram artifact signals from the single-channel electroencephalogram signals based on the empirical wavelet transform (empirical wavelet transform, EWT) and the improved adaptive noise complete empirical mode decomposition (improved complete ensemble empirical mode decomposition with adaptive noise, icemdan), and retains the electro-cerebral information as much as possible while removing the electro-oculogram artifact from the real electroencephalogram signals, thereby avoiding the distortion of the electroencephalogram signals and accurately and effectively analyzing the brain activities.
In the present embodiment, the acquired electroencephalogram signal including the ocular artifacts is set to be S (T) = [ S (1), S (2), …, S (T) ], where S (T) (t=1, 2, …, T) represents the value of the electroencephalogram signal at the time T.
Further, in the second step: the improved self-adaptive noise complete empirical mode decomposition of the delta-band electroencephalogram signal is expressed as the sum of N intrinsic mode functions and residual errors;
wherein: IMF (inertial measurement unit) n (t) is the nth natural mode function, r n (t) is a residual signal;
based on the above inventive concept, the present invention is further described in the following embodiments, and software for automatically removing the ocular artifacts in the single-channel electroencephalogram signal is designed, and related signal diagrams are shown in fig. 3, fig. 4 and fig. 5.
Example 1:
in the embodiment, the brain electrical signal is acquired by using a Neuroscan brain electrical acquisition device, and the electrode position is placed by using an international 10-20 lead system electrode placement method as shown in fig. 2. The EEG signal of FP1 channel of the subject during eye closure is first acquired for 10 seconds and pre-processed, including down-converting the EEG signal at 2000Hz to 200Hz, and band-pass filtering at 0.5-40Hz and notch filtering at 50 Hz. And then collecting the vertical eye electric signals and the horizontal eye electric signals of the subject during the opening period, and also reducing the frequency of the 2000Hz eye electric signals to 200Hz, and carrying out band-pass filtering with the frequency of 0.5-5 Hz. Set S EEG 、S VEOG And S is HEOG The pure electroencephalogram signals, the vertical electro-oculogram signals and the horizontal electro-oculogram signals are respectively, and the semi-simulation electroencephalogram signals containing electro-oculogram artifacts are:
S con ＝S EEG +αS VEOG +βS HEOG
wherein: alpha and beta are pollution coefficients of vertical and horizontal electro-ocular signals, respectively. And obtaining the electroencephalogram signal containing the ocular artifacts with the signal-to-noise ratio of-5 dB by adjusting the parameters alpha and beta. Fig. 3 (a) shows a pure electroencephalogram signal S EEG And (b) is vertical electrooculogramSignal S VEOG (c) is the horizontal electro-oculogram signal S HEOG (d) is the synthesized signal of the three, namely the EEG signal S containing the ocular artifacts con Is a waveform of (a);
semi-analog brain electrical signal S con Carrying out EWT decomposition to obtain an electroencephalogram signal in delta frequency band and high frequency band; then ICEEMDANs are decomposed on the delta frequency band electroencephalogram signals to obtain a plurality of IMFs; calculating a sample entropy value of each IMF, removing IMFs with the sample entropy value smaller than 0.4, and carrying out inverse ICEEMDAN reconstruction on the remaining IMFs to obtain an electroencephalogram signal of delta frequency band without ocular artifacts; and (3) carrying out inverse EWT (electro-magnetic resonance) conversion on the delta-band electro-optical signal without the electro-optical artifacts and the high-band electro-optical signal to obtain an electro-optical signal without the electro-optical artifacts, wherein fig. 4 is a comparison graph before and after electro-optical artifacts are removed, wherein (a) is a pure electro-optical signal waveform graph, (b) is an electro-optical signal waveform graph with the electro-optical artifacts, and (c) is an electro-optical signal waveform graph after the electro-optical artifacts are removed.
The embodiment quantitatively analyzes the electroencephalogram data after removing the ocular artifacts and the corresponding pure electroencephalogram signals, so as to evaluate the effectiveness of an ocular artifact removal algorithm. A correlation coefficient (correlation coefficient, CC) and a Relative Root Mean Square Error (RRMSE) are used as evaluation criteria.
Wherein S is EEG Is a pure EEG signal, S clean To remove the electroencephalogram signals after the ocular artifacts. And the CC quantitatively evaluates the correlation between the pure electroencephalogram signal and the electroencephalogram signal with the ocular artifacts removed, 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 reserved. The RRMSE quantitatively evaluates the amplitude distortion condition of the electroencephalogram signal, and the smaller the RRMSE value is, namely, the more the pure electroencephalogram signal is close to the electroencephalogram signal with the ocular artifacts removed, the ocular artifacts are removedThe more complete the trace removal.
Fig. 4 shows the effect of removing the electro-oculogram artifacts, the CC value is 0.9534, the relative root mean square error is 0.3016, which indicates that the electro-oculogram artifacts are removed, and meanwhile, the electro-oculogram signals are reserved to the greatest extent, and the effectiveness of the embodiment for removing the electro-oculogram artifacts in the single-channel electro-cerebral signals is illustrated.
Example 2:
in the embodiment, the Neuroscan electroencephalogram acquisition equipment is adopted to acquire the electroencephalogram signals of the FP1 channel of the subject in the period of opening eyes, the signal length is 10 seconds, and the obvious intraocular artifacts are included as shown in (a) of fig. 5.
By adopting the EWT-ICEEMDAN method provided by the embodiment, the electro-oculogram artifact is automatically removed from the FP1 channel electroencephalogram signal based on the software of the electro-oculogram artifact automatic removal method in the single channel electroencephalogram signal. The eye electrical artifact removal result is shown in fig. 5 (b), and it can be seen that the eye electrical artifact is removed.
The embodiment carries out quantitative evaluation on the effect of removing the ocular artifacts of the real brain electrical data. Since the actual 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 electroencephalogram signal distortion condition, evaluation is performed by calculating the power spectrum distortion condition of the high frequency band before and after the removal of the ocular artifacts, that is, the average absolute error (meanabsolute error, MAE) of the power spectrum (power spectral density, PSD) of the θ, α, β, γ frequency bands. For the theta frequency band,is defined as:
in the method, in the process of the invention,power spectrum of EEG signal containing ocular artifacts in theta frequency band>Represents the power spectrum, K of the EEG signal in the theta frequency band after removing the ocular artifacts 1 And K 2 The frequency points of PSD in theta frequency band are respectively shown. The average absolute error of the power spectrum in the alpha, beta, gamma frequency bands can also be obtained.
Fig. 5 (c) is a power spectrum of an electroencephalogram before and after removing an ocular artifact. The average absolute errors of the power spectrums of the theta, alpha, beta and gamma frequency bands before and after the ocular artifacts are removed 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 electro-oculogram artifacts in the real electroencephalogram signals, and is suitable for removing the electro-oculogram artifacts of the single-channel electroencephalogram signals.
The present invention has been described in terms of preferred embodiments, but is not limited to the invention, and any equivalent embodiments can be made by those skilled in the art without departing from the scope of the invention, as long as the equivalent embodiments are possible using the above-described structures and technical matters.
Claims (6)
1. The method for removing the ocular artifacts in the single-channel electroencephalogram signals is characterized by comprising the following steps of: comprises the following steps:
step one, performing empirical wavelet transformation processing on single-channel electroencephalogram signals to obtain delta-frequency-band electroencephalogram signals S δ (t) and high-band EEG signals S high (t)；
Step two, for the EEG signal S of delta frequency band δ (t) performing improved adaptive noise complete empirical mode decomposition to obtain a plurality of inherent mode functions;
step three, calculating sample entropy of each natural mode function, and setting a sample entropy threshold to identify the natural mode function containing ocular artifacts;
removing the inherent mode function containing the ocular artifacts, and performing improved self-adaptive noise complete empirical mode decomposition inverse operation reconstruction on the rest inherent mode function to obtain a filtered delta frequency band signal
Step five, filtering the delta frequency band signalAnd a high-frequency band signal S high (t) performing an inverse transformation based on empirical wavelet transformation, and finally reconstructing to obtain an electroencephalogram signal with ocular artifacts removed +.>
2. The method for removing electro-oculogram artifacts in single-channel electroencephalogram signals according to claim 1, characterized in that: the intrinsic mode function in the second step is expressed as:
wherein: IMF (inertial measurement unit) n (t) is the nth natural mode function, r n And (t) is a residual signal.
3. The method for removing ocular artifacts in single-channel electroencephalogram signals according to claim 1, characterized in that: and in the third step, setting the sample entropy threshold to be 0.4.
4. The method for removing electro-oculogram artifacts in single-channel electroencephalogram signals according to claim 1, characterized in that: a Neuroscan brain electrical acquisition device is adopted to acquire single-channel brain electrical signals, and an international 10-20 lead system electrode placement method is adopted for electrode position placement.
5. The method for removing electro-oculogram artifacts in single-channel electroencephalogram signals according to claim 1, characterized in that: the effectiveness indexes for evaluating the removal of the ocular artifacts are a correlation coefficient CC and a relative root mean square error RRMSE respectively;
wherein S is EEG Is a pure EEG signal, S clean To remove the electroencephalogram signals after the ocular artifacts.
6. The method for removing electro-oculogram artifacts in single-channel electroencephalogram signals according to claim 1, characterized in that: for the distortion condition of the brain electrical signal, the evaluation is carried out by calculating the power spectrum distortion condition of the high frequency band before and after the removal of the ocular artifacts, and for the theta frequency band,is defined as:
in the method, in the process of the invention,power spectrum of EEG signal containing ocular artifacts in theta frequency band>Represents the power spectrum, K of the EEG signal in the theta frequency band after removing the ocular artifacts 1 And K 2 The frequency points of the power spectrum in the theta frequency band are respectively shown.
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