CN114869303B - Method and system for removing electroencephalogram noise under transcranial direct current stimulation - Google Patents
Method and system for removing electroencephalogram noise under transcranial direct current stimulation Download PDFInfo
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- A61B5/377—Electroencephalography [EEG] using evoked responses
- A61B5/383—Somatosensory stimuli, e.g. electric stimulation
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- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
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- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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Abstract
The invention discloses a method and a system for removing electroencephalogram signal noise under transcranial direct current stimulation, wherein an electroencephalogram cap compatible with transcranial direct current stimulation is adopted, and electroencephalogram signals are synchronously acquired in the application process of transcranial direct current stimulation to a tested object; preprocessing the acquired electroencephalogram signals; extracting noise components of the preprocessed electroencephalogram signals by adopting an independent component analysis algorithm, and identifying specific noise components caused by transcranial electrical stimulation; removing corresponding noise components in the electroencephalogram signals by adopting a band-pass filtering method of a specific frequency band; and (4) carrying out independent component analysis on the filtered electroencephalogram signal, removing residual noise components in the electroencephalogram signal, and obtaining the denoised electroencephalogram signal. The invention firstly provides a method and a system for removing electroencephalogram signal noise under transcranial direct current stimulation, so that the processed electroencephalogram signals in task state and resting state under different transcranial direct current stimulation polarities have higher signal-to-noise ratio and can be used for further analysis.
Description
Technical Field
The invention relates to the technical field of non-invasive nerve regulation, in particular to a method and a system for removing electroencephalogram signal noise under transcranial direct current stimulation, mainly aiming at a denoising method of electroencephalogram signals (including event-related potentials and resting state signals) collected at a transcranial direct current stimulation (tDCS) stimulation stage, and mainly applied to optimization of tDCS regulation parameters, regulation of cognitive functions and intervention of neuropsychiatric diseases.
Background
Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technology for regulating and controlling brain excitability through current, previous researches show that the technology has effective effects on modulation and intervention of cognitive functions and mental diseases, but the clinical application and popularization of the technology are hindered due to the unclear regulation scheme and mechanism. the combination of the tDCS and the brain imaging technology is used for solving the problem, and on one hand, the combination is beneficial to observing and optimizing the regulation and control parameters in the tDCS stimulation process in real time; on the other hand, by regulating and controlling excitability of a target brain region and collecting brain imaging data under specific experimental design, the causal relationship between human cognition and brain activity is favorably revealed.
Electroencephalogram (EEG) is a non-invasive brain imaging technique that can acquire weak neuroelectrical signals on the scalp, but is also susceptible to noise interference. Because tDCS generates noise much larger than the neural source signal during stimulation (more than 1000 times under 2mA stimulation), the electroencephalogram signals collected during tDCS stimulation cannot be used for further analysis. Therefore, the previous research combining EEG and tDCS is limited to collecting and analyzing EEG signals before and after tDCS stimulation, and signals in the stimulation process cannot be effectively analyzed, so that the change of brain activities in the regulation and control process cannot be explored. Therefore, the evaluation of the noise characteristics of the electroencephalogram signals synchronously acquired in the tDCS stimulation process and the development of a corresponding denoising technology are important prerequisites for promoting the application of the tDCS in cognitive neuroscience and clinical neuroscience.
In the past, systematic review is carried out on noise components of electroencephalogram signals acquired in the tDCS stimulation process, but a targeted denoising technology is not developed in combination with noise characteristics, which probably is the most main reason for restricting the application of synchronously acquiring electroencephalogram (EEG-tDCS) in the tDCS stimulation process.
Disclosure of Invention
The invention provides a method and a system for removing electroencephalogram signal noise under transcranial direct current stimulation, in order to improve the signal-to-noise ratio of electroencephalogram signals collected in the transcranial direct current stimulation regulation process and promote the application of synchronously collecting electroencephalogram (EEG-tDCS) in the transcranial direct current stimulation regulation process.
The invention adopts the following technical scheme:
in one aspect, the present invention provides a method for removing noise of a brain electrical signal under direct current transcranial stimulation, the method comprising:
step 4, according to the extracted noise characteristics, removing corresponding noise components in the electroencephalogram signals by adopting a band-pass filtering method of a specific frequency band;
and 5, respectively carrying out independent component analysis on the filtered electroencephalogram signals, removing residual noise components in the electroencephalogram signals, and obtaining the denoised electroencephalogram signals.
The data acquisition method in the step 1 is as follows:
adopting a transcranial direct current stimulation compatible electroencephalogram cap to acquire data; a high-pass online filter of 0.1Hz is adopted in the application process of transcranial direct current stimulation, so that electroencephalogram signals are prevented from being saturated due to the application of transcranial direct current stimulation.
The method for preprocessing the electroencephalogram signals in the step 2 comprises the following steps:
performing zero setting operation on an electroencephalogram signal generated by the rising and falling of current in transcranial direct current stimulation; carrying out interpolation bad lead and re-reference operation on the brain electrical signal after zero processing to obtain a preprocessed brain electrical signal matrix of x (t) = [ x = [) 1 (t),x 2 (t),......x n (t)] T Wherein T ∈ R is a time point, n is the number of electroencephalogram signal channels, and T is the transpose of the matrix.
The method for extracting the noise component of the preprocessed electroencephalogram signal in the step 3 comprises the following steps:
step 3.1, solving a source signal matrix s (t) forming the electroencephalogram observation signal (x (t) = A.s (t)) by using an independent component analysis algorithm, and setting a reduction matrix W = A -1 Assuming that n sources contribute to the observed values of n electroencephalograms, the reduction matrix W belongs to R n×n For an n-dimensional square matrix, the independent component analysis algorithm reconstructs a source signal matrix s (t) by solving a reduction matrix W;
step 3.2, linear mapping of weight multiplication of the electroencephalogram observation signals x (t) of n channels and each time point is carried out: s (t) = W · x (t), and finally, a source signal matrix s (t) = [ s ]' composed of n source components is obtained 1 (t),s 2 (t),......s n (t)] T Where n is the number of components and T is the transpose of the matrix.
The electroencephalogram signals recorded from the rest state and the task state in the step 1 in the transcranial direct current stimulation process are subjected to band-pass filtering operation of 2Hz-30Hz in the step 4, noise components DC-offset which are characterized by high amplitude of a frequency range of 0-0.5Hz and noise components DC-drift of which the amplitude peak is concentrated at 1Hz and is rapidly attenuated along with the frequency increase are removed.
The method for removing the residual noise component in the electroencephalogram signal in the step 5 comprises the following steps:
step 5.1, performing independent component analysis on the filtered electroencephalogram signals to obtain a reconstructed source signal matrix s (T) = [ s1 (T), s2 (T),. Once.;
step 5.2, carrying out zero setting on j component matrixes of noise characteristics contained in the electroencephalogram signals to obtain a source signal matrix s with noise removed n-j (t)＝[s 1 (t),s 2 (t),......s n-j (t)] T ；
Step 5.3, the source signal matrix with the independent components removed is multiplied by the inverse matrix W of the reduction matrix -1 Obtaining the EEG signal X with the noise component removed (pure) ＝W -1 S n-j (t)。
In another aspect, the present invention further provides a system for removing noise of an electroencephalogram signal under transcranial direct current stimulation, the system comprising:
a transcranial direct current stimulator for applying transcranial direct current stimulation to a subject;
the electroencephalogram signal acquisition device synchronously acquires electroencephalogram signal data in the process of applying transcranial direct current stimulation;
the signal analysis and processing device comprises a signal preprocessing module, a noise extraction module and a noise removal module, wherein the signal preprocessing module receives and preprocesses the acquired electroencephalogram signals; the noise extraction module receives the electroencephalogram signal preprocessed by the signal preprocessing module, analyzes and extracts typical noise components contained in the electroencephalogram signal; the noise removing module comprises a band-pass filter and an independent component analysis denoising module, according to the characteristics of the extracted noise signal, firstly, the band-pass filter with a specific frequency band is adopted to remove typical noise components, secondly, the independent component analysis denoising module is adopted to further remove the residual noise components processed by the band-pass filter, and the denoised electroencephalogram signal is obtained.
Preferably, the frequency band of the band-pass filter is 2Hz to 30Hz.
The technical scheme of the invention has the following advantages:
A. the invention firstly provides a method and a system for removing electroencephalogram signal noise under transcranial direct current stimulation, which collect, analyze and process electroencephalogram signals in the application process of transcranial direct current stimulation. The system enables the task state electroencephalogram signals and the resting state electroencephalogram signals under different transcranial direct current stimulation polarities to have higher signal-to-noise ratio and can be used for further analysis. In general, the invention has the potential of substantially promoting the application and popularization of synchronous acquisition of electroencephalogram (tDCS-EEG) in the application stage of transcranial direct current stimulation.
B. The method and the system for removing the electroencephalogram signal noise under the transcranial direct current stimulation have the popularization on different stimulation polarities, electroencephalogram task paradigms and electroencephalogram analysis technologies. By denoising time domain (ERP) and frequency domain (frequency spectrum) data of resting state and task state electroencephalogram signals of a cathode and an anode in a transcranial direct current stimulation stage, the electroencephalogram signals processed by the method and the system provided by the invention have higher signal-to-noise ratio and can be used for further analysis.
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In order to more clearly illustrate the embodiments of the present invention, the drawings which are needed to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained from the drawings without inventive labor to those skilled in the art.
FIG. 1 is a block diagram of the method for removing the noise of the electroencephalogram signal during the stimulation of the transcranial direct current stimulation provided by the present invention.
Fig. 2 is a flow chart of the method for removing the noise of the electroencephalogram signal in the transcranial direct current stimulation process provided by the present invention (taking the collection of the resting state electroencephalogram signal and the task state electroencephalogram signal as an example).
FIG. 3 is a layout of transcranial direct current stimulation sites of the present invention; where the black circles represent the center electrode and the gray circles represent the peripheral electrodes of opposite polarity to the center electrode.
FIG. 4 is a graph of the individual components representing noise characteristics reconstructed from raw data during the tDCS stimulation phase using an individual component analysis algorithm; wherein the first line component 1 (DC-offset) and the second line component 2 (DC-drift); the first column is the noise component under cathodic tDCS stimulation and the second column is the noise component under anodic tDCS stimulation. The upper left of each sub-image is a time domain image of a continuous signal obtained by reconstruction, and the upper right of each sub-image is head topographic map distribution of weighted values.
FIG. 5-1 is the preprocessed data of task state electroencephalogram signals acquired during transcranial direct current stimulation;
FIG. 5-2 is a graphical representation of brain electrical signals provided by the present invention after being band-pass filtered at 2-30 Hz;
FIG. 5-3 is a diagram of an EEG signal after ICA dehumification based on FIG. 5-2;
the left side is used for carrying out denoising treatment on the electroencephalogram signals collected in the anode tDCS stimulation process; the right side is the cathode.
FIG. 6-1 is data after preliminary pretreatment of the anode at rest;
6-2 are data after anode 2-30Hz filtering;
FIG. 6-3 is data after denoising by an anode independent component analysis algorithm (ICA);
FIGS. 6-4 are data after preliminary pretreatment of the cathode at rest;
FIGS. 6-5 are data after cathodic 2-30Hz filtering;
FIGS. 6-6 are data after denoising by the cathode independent component analysis algorithm (ICA).
Each subgraph includes a topological map of the head of the magnitudes of the top delta, theta, alpha, and theta rhythms; the mean frequency amplitude spectra of each of the 5 electrodes around the stimulation site on the left side of the middle-lower part; and the mean frequency amplitude spectrum of each contralateral to the stimulation site on the right side of the middle lower portion.
Fig. 7 is a block diagram of the system components provided by the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1 and fig. 2, the invention provides a method for removing noise of an electroencephalogram signal under transcranial direct current stimulation, which comprises the following steps:
[ S1 ] an electroencephalogram cap compatible with transcranial direct current stimulation is adopted, and electroencephalogram signals are synchronously collected in the process of applying transcranial direct current stimulation to a tested person.
Taking electroencephalogram signals collected in rest and task states in the tDCS stimulation process as an example, a specially-made tDCS compatible electroencephalogram cap is adopted, and stimulation sites are shown in figure 3. Specifically, in a resting state, a tested object needs to be kept quiet and looks at a fixation point on a screen for four minutes; in the task state, 3.25J of laser stimulation was applied to the left hand of the test for a total of 30 trials. In the resting and task state, the subject was applied with 2mA of transcranial direct current stimulation and simultaneously recorded brain electrical signals.
And (S2) preprocessing the acquired electroencephalogram signals to obtain preprocessed electroencephalogram signals.
Firstly, because wide-band and global signal distortion is caused in the rising and falling stages of tDCS current, the data in the rising and falling stages are subjected to zero setting operation; and secondly, after zero setting processing, carrying out interpolation bad-leading and re-reference operation on the data. Let the processed EEG signal matrix be x (t) = [ x ] 1 (t),x 2 (t),......x n (t)] T Wherein T ∈ R is a time point, n is the number of electroencephalogram signal channels, and T is the transpose of the matrix.
And (S3) extracting noise components of the preprocessed electroencephalogram signals by adopting an independent component analysis algorithm, analyzing the characteristics of the extracted noise components, and identifying specific noise components caused by transcranial electrical stimulation.
Specifically, [ S3.1 ] noise component extraction is performed on the signal matrix x (t) after the preliminary preprocessing by using an independent component analysis algorithm. An Independent Component Analysis (ICA) algorithm can solve an independent source signal s (t) forming an electroencephalogram observation signal (x (t) = A · s (t)), and a reduction matrix W = A -1 Assuming that n sources contribute to the observed values of n electroencephalograms, the reduction matrix W belongs to R n×n For an n-dimensional square matrix, remolding a source signal matrix s (t) by solving a reduction matrix W through an independent component analysis algorithm; (S3.2) by multiplying the weight of the electroencephalogram observation signals x (t) of the n channels by the linear mapping of each time point: s (t) = W · x (t), and finally a matrix s (t) = [ s ] composed of n source components is obtained 1 (t),s 2 (t),......s n (t)] T Where n is the number of components and T is the transpose of the matrix.
And (S4) according to the characteristics of the noise components extracted in the step (S3), performing band-pass filtering on the electroencephalogram signals, and removing corresponding noise components.
As shown in fig. 4, the time domain signal, the frequency spectrum and the weight values of the weight matrix are visualized for each reconstructed independent component, and the independent components representing the noise characteristics in the cathodal and anodal tDCS stimuli are identified. As can be seen from fig. 4, the most significant noise generated to the electroencephalogram signal during the tDCS stimulation with different polarities is a DC-offset component below 0.5Hz, the weight value is the largest at the stimulation site, and the spatial distribution decreases from the stimulation site to the surrounding electrodes. Secondly, the second component causing the pollution of the brain electrical signals is a DC-drift component generated by maintaining a constant current by the tDCS stimulator, the frequency spectrum amplitude of the DC-drift component is maximum near 1Hz, and the DC-drift component is rapidly attenuated along with the increase of the frequency.
Based on the analysis of the extracted noise characteristics, the invention develops a denoising method and parameter setting different from the traditional electroencephalogram research. Firstly, according to the extracted noise characteristics, carrying out band-pass filtering operation of 2Hz-30Hz on the electroencephalogram signals, aiming at removing two typical noise signals, (1) noise component 1 (DC-offset) which is characterized by high amplitude of 0-0.5Hz frequency band; (2) The peak amplitude is centered at 1Hz and the amplitude decays rapidly with increasing frequency, noise component 2 (DC-drift).
And (S5) respectively carrying out independent component analysis on the filtered electroencephalogram signals, and removing residual noise components in the electroencephalogram signals to obtain the denoised electroencephalogram signals.
Aiming at the components still remained in the noise component 2, the method for removing the residual noise components in the electroencephalogram signal in the step (S5) is as follows:
(S5.1) carrying out independent component analysis on the filtered electroencephalogram signals to obtain a reconstructed source signal matrix S (t) = [ S ] 1 (t),s 2 (t),......s n (t)] T And restoring the matrix W;
s5.2, zeroing j component matrixes of noise characteristics contained in electroencephalogram signals to obtain a source signal matrix S with noise removed n-j (t)＝[s 1 (t),s 2 (t),......s n-j (t)] T ；
(S5.3) multiplying the source signal matrix from which the independent component is removed by an inverse W of a restoration matrix -1 Obtaining the EEG signal X without noise component (pure) ＝W -1 S n-j (t)。
As shown in FIGS. 5-1 to 5-3, the task state electroencephalogram signal denoising process and effect diagram are collected in the transcranial direct current stimulation process. The left side is used for carrying out denoising treatment on the electroencephalogram signals collected in the process of stimulating the anode tDCS; the right side is the cathode. As can be seen from the figure, the change of the amplitude of the ERP signal after filtering and ICA component elimination under two stimulation conditions is smoother along with the time, and the signal-to-noise ratio is further improved.
As shown in FIGS. 6-1 to 6-6, the method is a denoising process and effect diagram of resting state electroencephalogram signals acquired in the transcranial direct current stimulation process. FIG. 6-1 is the data after preliminary pretreatment of the anode in a resting state; FIG. 6-2 is data after 2-30Hz filtering at the anode; FIG. 6-3 is the denoised data of the anode independent component analysis algorithm; FIGS. 6-4 are data after preliminary pretreatment of the cathode at rest; FIGS. 6-5 are data from 2-30Hz cathode filters; FIGS. 6-6 are data after denoising by the cathode independent component analysis algorithm (ICA). Each subgraph includes a topological map of the head of the magnitudes of the top delta, theta, alpha, and theta rhythms; the average frequency amplitude spectra of each of the 5 electrodes around the stimulation site on the left side of the middle and lower part; and the average frequency amplitude spectrum of each of the stimulation electrode contralateral sides to the right of the middle and lower portions. As can be seen from the figure, the data after the preliminary pretreatment still retains the DC-offset noise with low frequency and high amplitude of 0-0.5 Hz; after 2-30Hz filtering, the DC-offset component is removed; further, the DC-drift noise in the electroencephalogram signal is removed through the identification and removal of the residual noise component by an independent component analysis algorithm (ICA). The denoised EEG signal retains a typical alpha component under a resting eye-open state.
In addition, as shown in fig. 7, the present invention also provides a system for removing noise of an electroencephalogram signal under transcranial direct current stimulation, comprising: a transcranial direct current stimulator, an electroencephalogram signal acquisition device and a signal analysis and processing device.
The transcranial direct current stimulator is used for applying transcranial direct current stimulation to a tested object; the electroencephalogram signal acquisition device comprises an electroencephalogram cap compatible with a transcranial direct current stimulator and a 0.1Hz high-pass online filter for preventing the electroencephalogram signal from being saturated, and is used for synchronously acquiring electroencephalogram signals which can be used for analysis in the process of performing transcranial direct current stimulation;
the signal analysis and processing device comprises a signal preprocessing module, a noise extraction module and a noise removal module.
The signal preprocessing module receives the acquired electroencephalogram signals and preprocesses the electroencephalogram signals; the noise extraction module receives the electroencephalogram signal preprocessed by the signal preprocessing module, analyzes and extracts typical noise components contained in the electroencephalogram signal; the noise removing module comprises a band-pass filter and an independent component analysis denoising module, according to the characteristics of the extracted noise signal, firstly the band-pass filter with a specific frequency band is adopted to remove typical noise components, secondly the independent component analysis denoising module is adopted to further remove the residual noise components processed by the band-pass filter, the denoised electroencephalogram signal is obtained, and a pure electroencephalogram signal waveform is presented for a user to check.
When the noise elimination is carried out on the resting state electroencephalogram signals and the task state electroencephalogram signals, the preferred bandwidth of the band-pass filter is 2Hz-30 Hz.
The invention is applicable to the prior art.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications derived therefrom are intended to be within the scope of the present invention.
Claims (7)
1. A system for removing noise from an electrical brain signal during transcranial direct current stimulation, the system comprising:
a transcranial direct current stimulator for applying transcranial direct current stimulation to a subject;
the electroencephalogram signal acquisition device adopts an electroencephalogram cap compatible with transcranial direct current stimulation, and synchronously acquires electroencephalogram signals in the process of applying the transcranial direct current stimulation;
the signal analysis and processing device comprises a signal preprocessing module, a noise extraction module and a noise removal module;
the signal preprocessing module receives the acquired electroencephalogram signals and preprocesses the electroencephalogram signals;
the noise extraction module receives the electroencephalogram signal preprocessed by the signal preprocessing module, noise component extraction is carried out on the preprocessed electroencephalogram signal by adopting an independent component analysis algorithm, the characteristics of the extracted noise component are analyzed, a specific noise component caused by transcranial electrical stimulation is identified, and a typical noise component contained in the extracted noise component is extracted;
the noise removing module comprises a band-pass filter and an independent component analysis denoising module, and according to the characteristics of the extracted noise signal, the band-pass filter with a specific frequency band is firstly adopted to remove typical noise components, and then the independent component analysis denoising module is adopted to further remove the residual noise components processed by the band-pass filter, so that the denoised electroencephalogram signal is obtained.
2. The system for removing EEG noise under transcranial direct current stimulation according to claim 1, wherein the frequency band of the band-pass filter is 2Hz-30 Hz.
3. The system for removing noise from an electroencephalogram signal under transcranial direct current stimulation according to claim 1, wherein the transcranial direct current stimulator employs a 0.1Hz high-pass on-line filter in the application process of the transcranial direct current stimulation for avoiding the saturation of the electroencephalogram signal caused by the application of the transcranial direct current stimulation.
4. The system for removing noise of electroencephalogram signals under transcranial direct current stimulation according to claim 1, wherein the method for preprocessing the electroencephalogram signals by the signal preprocessing module is as follows:
performing zero setting operation on an electroencephalogram signal generated by the rising and falling of current in transcranial direct current stimulation; carrying out interpolation bad lead and re-reference operation on the brain electrical signal after zero processing to obtain a preprocessed brain electrical signal matrix of x (t) = [ x = [) 1 (t),x 2 (t),......x n (t)] T Wherein T ∈ R is a time point, n is the number of electroencephalogram signal channels, and T is the transpose of the matrix.
5. The system for removing noise of an electroencephalogram signal under transcranial direct current stimulation according to claim 1, wherein the method for extracting noise components of the preprocessed electroencephalogram signal by the noise extraction module by adopting an independent component analysis algorithm is as follows:
by usingAn independent component analysis algorithm solves a source signal matrix s (t) forming an electroencephalogram observation signal (x (t) = A.s (t)), and a reduction matrix W = A -1 Assuming that n sources contribute to the observed values of n electroencephalograms, the reduction matrix W belongs to R n×n For an n-dimensional square matrix, the independent component analysis algorithm reconstructs a source signal matrix s (t) by solving a reduction matrix W;
linear mapping for each time point is multiplied by the weight of the electroencephalogram observation signals x (t) of n channels: s (t) = W · x (t), and finally, a source signal matrix s (t) = [ s ]' composed of n source components is obtained 1 (t),s 2 (t),......s n (t)] T Where n is the number of components and T is the transpose of the matrix.
6. The system for removing the noise of the electroencephalogram signal under the stimulation of the transcranial direct current according to claim 2, wherein the electroencephalogram signal acquisition device records the electroencephalogram signal in the process of the transcranial direct current stimulation from a resting state and a task state, the band-pass filter performs the band-pass filtering operation of 2Hz to 30Hz on the electroencephalogram signal, removes the noise component DC-offset which is characterized by the high amplitude of the frequency range of 0 to 0.5Hz, and the noise component DC-drift of which the amplitude peak is concentrated at 1Hz and is rapidly attenuated along with the increase of the frequency.
7. The system for removing noise of electroencephalogram signals under transcranial direct current stimulation according to any one of claims 1-6, wherein the method for further removing residual noise components processed by the band-pass filter by the independent component analysis denoising module is as follows:
carrying out independent component analysis on the filtered electroencephalogram signals to obtain a reconstructed source signal matrix s (t) = [ s ] 1 (t),s 2 (t),......s n (t)] T And restoring the matrix W;
zeroing j component matrixes of noise characteristics contained in the electroencephalogram signals to obtain a source signal matrix s with noise removed n-j (t)＝[s 1 (t),s 2 (t),......s n-j (t)] T ；
Will have a separate component removedMultiplying the source signal matrix by the inverse W of the restoration matrix -1 Obtaining the EEG signal X with the noise component removed (pure) ＝W -1 S n-j (t)。
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110368577A (en) * | 2019-07-03 | 2019-10-25 | 天津大学 | Programmable flexibility electrical transcranial stimulation and brain wave acquisition integrated system |
| CN112604163A (en) * | 2020-12-30 | 2021-04-06 | 杭州电子科技大学 | Auxiliary memory system based on transcranial direct current stimulation |
| CN113208626A (en) * | 2021-04-07 | 2021-08-06 | 北京脑陆科技有限公司 | Emotional state regulation and control method and system based on EEG signal |
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| CN113208626A (en) * | 2021-04-07 | 2021-08-06 | 北京脑陆科技有限公司 | Emotional state regulation and control method and system based on EEG signal |
| CN113694374A (en) * | 2021-08-31 | 2021-11-26 | 上海市精神卫生中心（上海市心理咨询培训中心） | Prediction system, method, device, processor and storage medium for predicting efficacy of treatment of obsessive-compulsive disorder based on EEG (electroencephalogram) |
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