CN117204859B - Dry electrode brain electrical system with common mode noise channel and active noise reduction method for signals - Google Patents
Dry electrode brain electrical system with common mode noise channel and active noise reduction method for signals Download PDFInfo
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Abstract
The invention discloses a dry electrode brain electrical system with a common mode noise channel and an active noise reduction method of signals, wherein the system comprises the following steps: the electrode channel is used for acquiring a target signal; the common mode noise channel is used for acquiring common mode noise homologous to the target signal; a processor for judging the periodicity of the common mode noise; the calculation module selects different calculation models according to the periodicity of the common mode noise so as to acquire the periodic common mode noise and/or the non-periodic common mode noise contained in the target signal based on the common mode noise; and the noise reduction module is used for removing the common mode noise in the target signal based on the fitted periodic common mode noise and/or non-periodic common mode noise. By introducing the common mode signal, the accuracy of noise reduction processing can be improved, and the periodicity of common mode noise existing in the target signal is judged, so that a corresponding noise reduction processing mode is selected, the pertinence of the noise reduction processing is improved, and the noise can be removed better.
Description
Technical Field
The invention belongs to the technical field of signal processing, and particularly relates to a dry electrode brain electrical system with a common mode noise channel and an active noise reduction method of signals.
Background
Common mode noise is the most common exogenous artifact in acquisition systems such as electroencephalogram, electrocardiograph, myoelectricity and the like, and is easy to cause the quality of acquired signals to be reduced, so that the accuracy of subsequent analysis, classification and detection tasks is greatly affected.
The common mode noise includes two types of periodic common mode noise and non-periodic common mode noise. In the prior art, notch or band-pass filtering is mostly adopted to remove periodic common mode noise, and an average reference or decomposition method is adopted to remove non-periodic common mode noise, but the method has the following defects:
(1) When the acquired signals have two common mode noises of non-period and period at the same time, the two common mode noises interfere with each other when noise reduction treatment is carried out, and the noise reduction effect is poor;
(2) The denoising method in the prior art is easy to cause the erroneous removal of the collected useful signals, and causes the loss of real signals.
Disclosure of Invention
The invention aims to solve the technical problems that: how to improve the noise reduction effect of the mixed common mode noise. Therefore, the invention provides a dry electrode brain electrical system with a common mode noise channel and an active noise reduction method of signals.
The technical scheme adopted for solving the technical problems is as follows: a dry electrode electroencephalogram system with common mode noise channels, comprising: the electrode channel is used for acquiring a target signal; the common mode noise channel is used for acquiring common mode noise homologous to the target signal; a processor for judging the periodicity of the common mode noise, namely, the periodicity common mode noise or the mixed common mode noise containing both the periodicity common mode noise and the aperiodic common mode noise; the calculation module is used for selecting different calculation models according to the periodicity of the common mode noise so as to acquire the periodic common mode noise and/or the non-periodic common mode noise contained in the target signal based on the common mode noise; and the noise reduction module is used for removing the common mode noise in the target signal based on the fitted periodic common mode noise and/or non-periodic common mode noise.
Further, when the processor determines that the common mode noise is periodic common mode noise, the processor controls the calculation module to execute a first calculation model, namely, adaptively filters the target signal based on the common mode noise, and fits the periodic common mode noise contained in the target signal.
Further, when the processor determines that the common mode noise is the mixed common mode noise, the processor controls the calculation module to execute a second calculation model, namely, adaptive filtering is performed on the target signal based on the common mode noise, periodic common mode noise contained in the target signal is fitted, adaptive filtering is performed on the target signal based on the common mode noise after inverse processing, and non-periodic common mode noise contained in the target signal is fitted.
Further, the second calculation model includes: the processor firstly controls the calculation module to execute preprocessing on the common mode noise and the target signal; the processor controls the calculation module to carry out self-adaptive filtering on the target signal based on the common mode noise, and the periodic common mode noise contained in the target signal is fitted; subtracting the fitted periodic common mode noise from the target signal to obtain a first target signal; performing inverse processing operation on the first target signal to obtain a second target signal; pre-denoising the common mode noise to obtain a first common mode signal; and carrying out self-adaptive filtering on the second target signal based on the first common mode signal, and fitting out aperiodic common mode noise contained in the second target signal.
Further, the adaptive filtering includes: the input signal being x (n), passing through the desired response d 1 The error between (n) and the output signal y (n) automatically updates the adaptive filter coefficient h to adapt to the next input signal x (n+1) such that the next output signal y (n+1) approximates the next expected response d 1 (n+1) to fit an expected response to the input signal to obtain an output signal, i.e. periodic or non-periodic common mode noise contained by the target signal;
the algorithm of the adaptive filtering comprises the following steps: a least mean square error algorithm, a recursive least square algorithm.
Further, the pre-noise reduction includes: removing power frequency and low-pass filtering; the pretreatment comprises the following steps: one or more of differential operation, PCA decomposition, SVD decomposition; the inverse processing includes: one or more of differential operation, PCA decomposition, SVD decomposition.
Further, the determining that the common mode noise is periodic common mode noise or mixed common mode noise includes: when the first characteristic value is larger than a first set value and the second characteristic value is smaller than a second set value, the common mode noise is periodic common mode noise; otherwise, judging the common mode noise as mixed common mode noise; wherein the first characteristic value is: zero crossing points Zc of the common mode noise; the second characteristic value is: the ratio r=RMS1/RMS2, r > 1, between the root mean square RMS1 and the root mean square RMS2 of the common mode noise after the first-order difference of the common mode noise.
Further, the method further comprises the following steps: an electroencephalogram cap; the electrode cap is provided with the electrode channel and the common mode noise channel; an electrode assembly is arranged on the electrode channel and used for collecting the target signal; and setting the signal gain of any common mode noise channel to be smaller than or equal to the maximum integer of the ratio of the maximum input range of the electrode channel to the common mode noise range so as to acquire common mode noise homologous to the target signal.
Further, when the common mode noise channel shares the same circuit module as the electrode channel, the common mode noise channel is configured not to connect the electrode channel of the electrode assembly; the dry electrode electroencephalogram system further comprises: a host on which the circuit module is disposed; the upper computer is communicated with the host computer and is provided with the processor, the computing module and the noise reduction module; wherein the electroencephalogram cap is provided with a first interface connected with the circuit module; the host is provided with a second interface which is detachably and electrically connected with the first interface.
Further, the electrode assembly comprises claw-type electrodes arranged on the electroencephalogram cap and used for collecting electroencephalogram signals; the dry electrode brain electrical system also comprises an additional electrode; the additional electrode adopts an ear clip type active electrode for collecting electrocardio or electrooculogram.
Further, the circuit module includes: the signal amplifying module is used for amplifying the collected target signal and common mode noise; and the digital-analog conversion module is used for converting the amplified target signal and the common mode noise into digital signals and transmitting the digital signals to the upper computer.
Further, when the common mode noise channel and the electrode channel do not share the same circuit module, that is, the common mode noise channel and the electrode channel are independently provided with corresponding circuit modules to adjust respective signal gains.
The invention also provides an active noise reduction method for signals, which uses the dry electrode brain electrical system, and comprises the following steps: setting an independent common mode noise channel to collect common mode noise; setting the gain of the common mode noise channel to be smaller than or equal to the maximum integer of the ratio of the maximum input range of the acquisition channel to the common mode noise range so as to acquire common mode noise homologous to a target signal; judging the common mode noise as periodic common mode noise or mixed common mode noise; selecting different calculation models according to the periodicity of the common mode noise; acquiring periodic common mode noise and/or non-periodic common mode noise contained in a target signal based on the common mode noise; and removing the common mode noise in the target signal based on the fitted periodic common mode noise and/or non-periodic common mode noise.
The invention has the beneficial effects that the dry electrode brain electrical system with the common mode noise channel and the active noise reduction method of the signal can improve the accuracy of noise reduction processing by introducing the common mode noise which is acquired by the common mode noise channel and is homologous to the target signal, and the periodicity of the common mode noise in the target signal is judged, so that the corresponding noise reduction processing mode is selected, thereby being beneficial to improving the pertinence of the noise reduction processing and being capable of better removing the noise.
Drawings
The invention will be further described with reference to the drawings and examples.
Fig. 1 is a schematic structural diagram of a dry electrode electroencephalogram system of the present invention.
FIG. 2 is a flow chart of a process of noise reduction for a dry-click brain electrical system of the present invention.
Fig. 3 is a schematic diagram of the adaptive filtering of the present invention.
Fig. 4 is a schematic structural view of the electroencephalogram cap of the present invention.
Fig. 5 is a schematic diagram of a first interface of the present invention.
Fig. 6 is a schematic structural diagram of a host according to the present invention.
FIG. 7 is a graph showing the comparison of the results of the treatments of example one and comparative example one.
FIG. 8 is a graph comparing PSD processing results of example one and comparative example one.
Fig. 9 is a graph showing the comparison of the results of the treatment of the second embodiment and the second comparative embodiment.
FIG. 10 is a graph comparing PSD processing results of example two and comparative example two.
Fig. 11 is a comparative graph of the processing results of the third example and the third comparative example.
FIG. 12 is a graph comparing PSD processing results of example III and comparative example III.
In the figure: 1. an electrode channel; 2. a common mode noise channel; 3. a processor; 4. a computing module; 5. a noise reduction module; 6. an electroencephalogram cap; 7. a host; 8. an upper computer; 11. an electrode assembly; 111. claw-type electrodes; 9. an additional electrode; 61. a first interface; 71. and a second interface.
Detailed Description
The invention will now be described in further detail with reference to the accompanying drawings. The drawings are simplified schematic representations which merely illustrate the basic structure of the invention and therefore show only the structures which are relevant to the invention.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, features defining "first", "second" may include one or more such features, either explicitly or implicitly. In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
As shown in fig. 1 to 6, the dry electrode brain electrical system with common mode noise channel of the present invention comprises: an electrode channel 1 for acquiring a target signal. And a common mode noise channel 2 for acquiring common mode noise homologous to the target signal. The processor 3 determines the periodicity of the common mode noise, i.e. the periodic common mode noise or the mixed common mode noise containing both periodic common mode noise and non-periodic common mode noise. The calculation module 4 selects different calculation models according to the periodicity of the common mode noise to obtain the periodic common mode noise and/or the non-periodic common mode noise contained in the target signal based on the common mode noise. And the noise reduction module 5 is used for removing the common mode noise in the target signal based on the fitted periodic common mode noise and/or non-periodic common mode noise.
The invention is provided with the common mode noise channel 2, so that common mode noise homologous to a target signal can be collected, and the common mode noise is taken as a physical premise of discrimination and noise reduction. The type of common mode noise (i.e. periodic common mode noise or mixed common mode noise) can be analyzed by the processor 3 and matched to different calculation models to remove the common mode noise in the target signal. The invention introduces the common mode noise channel 2, can directly collect the homologous common mode noise of the target signal, so as to improve the accuracy of removing the common mode noise in the target signal and reduce the loss of effective signals.
Specifically, when the processor 3 determines that the common mode noise is periodic common mode noise, the processor 3 controls the calculation module 4 to execute the first calculation model, that is, adaptively filters the target signal based on the common mode noise, and fits the periodic common mode noise included in the target signal.
For example, adaptive filtering includes: the input signal being x (n), passing through the desired response d 1 The error between (n) and the output signal y (n) automatically updates the adaptive filter coefficient h to adapt to the next input signal x (n+1) such that the next output signal y (n+1) approximates the next expected response d 1 (n+1) to fit the desired response to the output signal based on the input signal, i.e. the target signal contains periodic or non-periodic common mode noise. The algorithm of the adaptive filtering includes: a least mean square error algorithm, a recursive least square algorithm. For example, the number of the cells to be processed,，m/>[0,L-1]n represents the nth sample point, n=1, 2,..n, N represents the number of sample points, L represents the order of the adaptive filtering, and error e (N) =d 1 (n) -y (n). In this scheme, common mode noise is used as the input signal x (n), and the target signal is used as the desired response d 1 (n) the fitted periodic common mode noise is the output signal y (n). And subtracting the fitted periodic common mode noise from the target signal to remove the periodic common mode noise in the target signal.
Specifically, when the processor 3 determines that the common mode noise is the mixed common mode noise, the processor 3 controls the calculation module 4 to execute the second calculation model, that is, first, adaptively filters the target signal based on the common mode noise, fits the periodic common mode noise contained in the target signal, and then adaptively filters the target signal based on the inverse processed common mode noise, and fits the non-periodic common mode noise contained in the target signal.
For example, the second computational model includes: the processor 3 firstly controls the calculation module 4 to execute preprocessing on the common mode noise and the target signal; the processor 3 then controls the calculation module 4 to carry out self-adaptive filtering on the target signal based on the common mode noise, and the periodic common mode noise contained in the target signal is fitted; subtracting the fitted periodic common mode noise from the target signal to obtain a first target signal; performing inverse processing operation on the first target signal to obtain a second target signal; pre-denoising common mode noise to obtain a first common mode signal; and carrying out self-adaptive filtering on the second target signal based on the first common mode signal, and fitting out aperiodic common mode noise contained in the second target signal. The process of adaptive filtering is the same as described above. That is, the periodic common mode noise has been removed from the first target signal, and then the fitted non-periodic common mode noise is subtracted from the second target signal to obtain the target signal from which the mixed common mode noise has been removed.
For example, pre-noise reduction includes: and removing power frequency and low-pass filtering. The pretreatment comprises the following steps: one or more of differential operation, PCA decomposition, SVD decomposition. The inverse process includes: one or more of differential operation, PCA decomposition, SVD decomposition. The processing method of the reverse processing corresponds to the processing method of the preprocessing. For example, if the preprocessing selects a differential operation, the inverse processing also selects a differential operation. The aim of preprocessing is to separate the non-periodic common mode noise in the target signal first, so that only the periodic common mode noise exists in the target signal, and the subsequent fitting effect is improved.
For example, taking the first order difference as an example, d2[ n ]]=x[n]-x[n-1]，n=2，3，...，N；d2[1]=x[1]Wherein x [ n ]]Representing the input signal, d2 n]Representing the result of the first order difference. After the target signal is subjected to first-order differential processing, non-periodic common mode noise in the target signal is separated, and at the moment, main noise in the target signal is periodic common mode noise. The preprocessing is simply to separate the aperiodic common mode noise and is not completely removed. Therefore, it is also necessary to remove the non-periodic common mode noise, and the separated non-periodic common mode noise needs to be restored by an inverse operation before the removal. The reverse first order difference is:n=1, 2,. -%, K; y (n) is the adaptively filtered output signal, s [ n ]]Representing the inverse first order difference result for y (n).
For example, the processor 3 determines the periodicity of the common mode noise based on: when the first characteristic value is larger than the first set value and the second characteristic value is smaller than the second set value, the common mode noise is periodic common mode noise; otherwise, the common mode noise is judged to be the mixed common mode noise. Wherein, the first eigenvalue is: zero crossing Zc of common mode noise. The second characteristic value is: the ratio r=rms 1/RMS2, r > 1, between the root mean square RMS1 of the common mode noise and the root mean square RMS2 of the common mode noise after the first order difference.
It should be noted that, when only the periodic common mode noise exists in the target signal, the zero-crossing point Zc is a stable value related to the frequency of the periodic common mode noise, and at this time, the zero-crossing point Zc is greater than the first set value. When the target signal contains non-periodic common mode noise, the zero crossing point Zc is sharply reduced. When only periodic common mode noise exists in the target signal, the second characteristic value is smaller than 1 and smaller than the second set value. When the target signal contains non-periodic common mode noise, the second characteristic value increases sharply. Therefore, based on the joint judgment of the first characteristic value and the second characteristic value, whether the noise of the target signal is the periodic common mode noise or the mixed common mode noise can be analyzed.
For example, when calculating the characteristic value of the target signal, a sliding window process is performed on the target signal to obtain a plurality of target signal segments. Let s (i) represent common mode noise within the target signal segment, i=1, 2, N; n represents the sample length of the signal segment,，/>，/>mean value of the signal,/">And the result of subtracting the overall mean value of the signal from the common mode noise in the ith target signal segment in the signal is shown. />J=1, 2, N; z (j) represents the product of the front data point and the rear data point after the common mode noise is subjected to the mean value removal, the sign of z (j) is negative, zero crossing points exist, and the zero crossing points Zc are the numbers smaller than 0 in z (j).
For example root mean squareA (j) =s (j+1) -s (j), a (j) representing the result of the common mode noise first order difference, +.>，/>，/>Represents the average value of the first order difference result,representing the result of subtracting the mean value of the first-order differential result from the common-mode noise first-order differential result,。
for example, the dry electrode electroencephalogram system of the present invention further includes: the brain electricity cap 6, host computer 7 and host computer 8 are provided with circuit module on the brain electricity cap 6, and host computer 7 is connected with circuit module electricity, and host computer 8 and host computer 7 intercommunication are provided with treater 3, calculation module 4 and noise reduction module 5 on the host computer 8. The electrode channel 1 comprises a plurality of electrode assemblies 11 arranged on the electroencephalogram cap 6 and used for collecting target signals. The electrode assembly 11 is electrically connected with the circuit module. When the common mode noise channel 2 and the electrode channel 1 share the same circuit module, the common mode noise channel 2 is arranged on the circuit module and is not externally connected with the electrode assembly 11, that is, the common mode noise channel 2 is configured as the electrode channel 1 which is not connected with the electrode assembly, the signal gain of the common mode noise channel 2 can be differently arranged, so as to collect the common mode noise which is homologous to the target signal.
That is, the electrode assembly 11 is used for collecting the target signal, and the common mode noise channel 2 is a separate collection channel reserved, which occupies the original electrode channel 1 but is not connected with any electrode, that is, the common mode noise channel 2 and the electrode channel 1 belong to the same sensor, and the common mode noise channel 2 is mainly used for collecting the common mode noise homologous to the target signal for the subsequent noise reduction processing. For example, the circuit module includes: the signal amplifying module is used for amplifying the collected target signal and the common mode noise, and the digital-analog converting module is used for converting the amplified target signal and the common mode noise into digital signals and transmitting the digital signals to the upper computer 8. The signal amplifying module comprises devices such as an operational amplifier and the like, and can amplify the collected signals to a proper voltage range. The digital-to-analog conversion module is used for converting an analog signal into a digital signal. Of course, the common-mode noise channel 2 and the electrode channel 1 may not share the same circuit module, that is, the common-mode noise channel 2 and the electrode channel 1 belong to different sensors, and the corresponding circuit modules are respectively and independently provided to adjust the respective signal gains, so as to realize that the signal gain of the common-mode noise channel 2 is smaller than or equal to the maximum integer of the ratio of the maximum input range of the electrode channel 1 to the common-mode noise range.
For example, the electroencephalogram cap 6 can be fixed to the brain of a target subject for signal acquisition, and the perimeter and height of the electroencephalogram cap 6 can be adapted to different target subjects. The electroencephalogram cap 6 is provided with a first interface 61 connected with the circuit module. The host computer 7 is provided with a second interface 71, the second interface 71 is detachably and electrically connected with the first interface 61 in a wired mode, and signal transmission between the electroencephalogram cap 6 and the host computer 7 can be achieved through connection of the first interface 61 and the second interface 71.
For example, the electrode assembly 11 includes a claw electrode 111 provided on the electroencephalogram cap 6 for acquiring an electroencephalogram signal. The claw electrode 111 is in contact with the scalp of the target, the claw electrode 111 includes a cavity, an electrode claw, and a bottom knob, and the claw electrode 111 includes two forms: one end of the claw electrode 111 is connected with the frame of the electroencephalogram cap 6 in a fixed mode and in an overhanging mode, the claw electrode 111 in the fixed mode is kept in stable contact with the scalp through a pressure spring, and the claw electrode 111 in the overhanging mode can obtain a large range of rotation through a torsion spring so as to realize that the claw electrode 111 can keep good contact with the rugged head. The electrode claw is made of conductive material and is additionally provided with a conductive electroplated layer, and the electrode claw is in direct contact with the scalp. For example, the pressure spring is located in the cavity and connected with the electrode claw, so that the electrode claw has certain elasticity, the head is not damaged by pressure, and the comfort level of a target object is improved. The bottom knob can be manually rotated, a circle of non-conductive claws are arranged on the periphery of the electrode claw, the claws are connected with the bottom knob, and the claws can be enabled to dial hair by rotating the bottom knob so as to ensure that the electrode claw better contacts scalp.
The electrode claw adopts flexible conductive materials and is matched with elastic design, so that the electrode claw can be better contacted with the scalp on one hand; on the other hand, the comfort of the user can be improved, and the pain can not appear in the long-time collection process.
For example, the dry electrode electroencephalogram system further comprises an additional electrode 9, and the additional electrode 9 adopts an ear clip type active electrode for acquiring electrocardio or electrooculogram. The additional electrode 9 is introduced, so that more kinds of signals can be acquired for analysis, the acquisition scheme is convenient to flexibly adjust, and the method is not limited to electroencephalogram acquisition.
For example, the signal amplification module has 33 channels, of which 32 channels are used for amplification of the target signal and 1 channel is used for amplification of common mode noise. When the electroencephalogram signals are collected, the external noise such as power frequency and motion artifacts can be contained, and the common-mode noise channel 2 only collects the external noise, so that the common-mode noise channel 2 can be used as a collection channel of the common-mode noise, and the removal of the external noise of 32 electroencephalogram channels is facilitated, so that the signal-to-noise ratio of signals is improved. The input impedance of the electrode can be improved through the active signal amplification module, so that the signal-to-noise ratio of the signal is improved. The invention inserts the circuit module behind the electrode assembly 11, the mode can improve the input impedance of the electrode by tens of times, and the higher input impedance means that more voltage can be separated in the contact with the scalp, so that more brain electrical signals are acquired, and the signal-to-noise ratio is improved.
The host computer 7 is used for receiving various signals acquired, and the host computer 7 adopts a hot plug technology, so that the duration of 5 minutes can be ensured in the process of battery replacement, and signal acquisition is not interrupted. In addition, to prevent the phenomenon that WiFi transmission may cause packet loss of data, the host 7 is further provided with an SD card, so that data can be actively stored.
For example, the host computer 8 has functions of real-time display, recording, playback, and the like. The upper computer 8 also has the SQI (signal quality index ) function for evaluating the target signal quality and the real-time impedance display function, and can assist in adjusting the wearing of the electrode cap 6. The SQI function can evaluate the signal quality in real time from the frequency domain, evaluate the data of each channel in five dimensions, integrate the results of the five dimensions into a value of 0-100, and intuitively evaluate the results. The SQI values of all channels of each electroencephalogram cap 6 are again unified into one value, and the quality of the data acquired by the electroencephalogram cap 6 at the moment can be evaluated as a whole through the value. Meanwhile, under the condition that the parameters of five dimensions of each channel are low, the upper computer 8 can judge the reasons for low signal quality, including four types of power frequency noise, electrode falling, motion artifacts and myoelectric noise. The real-time impedance display function can display the impedance value of each channel, wherein the impedance value is in the unit of MΩ and reaches 0.1MΩ at the highest precision. The data forwarding function supports that electrode data can be invoked in real time. The user can view the time domain waveform of the common mode noise channel 2 in real time in the use process, and the motion artifact and the power frequency interference displayed by the waveform can reflect various exogenous noises in the current dry electrode brain electrical system acquisition environment. The user forwards the data of the brain electrical signals of the 32 paths of channels and the data of the common mode noise channel 2 in real time in a data forwarding mode, and the upper computer 8 can perform denoising processing.
The technical effects of the present invention will be described below by way of specific examples.
Scene one: only periodic common mode noise is present in the target signal.
Embodiment one: the processing mode of the invention is adopted.
Comparative example one: an existing notch filtering method is adopted.
As shown in fig. 7, the abscissa represents time, the ordinate represents voltage amplitude, curve a represents an original signal, curve B represents a signal obtained by the process of example one, and curve C represents a signal obtained by the process of comparative example one. As shown in fig. 8, the abscissa represents frequency, the ordinate represents PSD (power spectral density), curve a ' represents an original signal, curve B ' represents a signal obtained by the process of example one, and curve C ' represents a signal obtained by the process of comparative example one. In fig. 7, the periodic common mode noise appears in the whole signal section of the original signal, the corresponding protrusion at the position of the arrow a' in fig. 8 is the PSD of the periodic common mode noise, and the comparative example one has a large recess at the frequency of 50Hz, which means that the comparative example removes not only the periodic common mode noise but also the 50Hz signal in the original signal, and the loss of the method of the embodiment one at the 50Hz is very small. As can be seen from fig. 7 and 8, both the first embodiment and the first comparative embodiment can remove the periodic common mode noise, but the first comparative embodiment loses some effective signal.
Scene II: there is mixed common mode noise in the target signal, in which case the periodic common mode noise is removed.
Embodiment two: the processing mode of the invention is adopted.
Comparative example two: an existing notch filtering method is adopted.
As shown in fig. 9, the abscissa represents time, the ordinate represents voltage amplitude, curve D represents an original signal, curve E represents a signal obtained by the process of example two, and curve F represents a signal obtained by the process of comparative example two. As shown in fig. 10, the abscissa represents frequency, the ordinate represents PSD (power spectral density), the curve D ' represents an original signal, the curve E ' represents a signal obtained by the process of example two, and the curve F ' represents a signal obtained by the process of comparative example two. In fig. 9, the saw-tooth shaped large waveform of the original signal is non-periodic common mode noise, the periodic common mode noise is 50Hz oscillation superimposed on the non-periodic common mode, it can be seen from fig. 10 that the original signal PSD has a large bump at 50Hz, the bump is reduced and tends to be flat after the second processing of the embodiment, and the reduction of the bump of the second pair of the comparison example is smaller. As can be seen from fig. 9 and 10, the noise reduction effect of the present method is due to the comparative example.
Scene III: the target signal has mixed common mode noise, and the mixed common mode noise is removed.
Embodiment III: the processing mode of the invention is adopted.
Comparative example three: the existing notch filtering and PCA denoising methods are adopted.
As shown in fig. 11, the abscissa represents time, the ordinate represents voltage amplitude, the curve G represents an original signal, the curve H represents a signal obtained by the process of example three, and the curve I represents a signal obtained by the process of comparative example three. As shown in fig. 12, the abscissa represents frequency, the ordinate represents PSD (power spectral density), the curve G ' represents an original signal, the curve H ' represents a signal obtained by the process of example three, and the curve I ' represents a signal obtained by the process of comparative example three. The large amplitude oscillating waveform of the original signal in fig. 11 is non-periodic common mode noise, the 50Hz signal superimposed thereon is periodic common mode noise, and it is evident in fig. 12 that there is a significant periodic common mode noise bump at 50Hz and its frequency multiplication, the bump tends to be flat after the treatment of example three, and the treatment effect of comparative example three at 50Hz is more significant without taking the noise at the frequency multiplication into consideration, and at the alpha activity (8 Hz-12 Hz) range, the energy after the treatment of comparative example three is lower than that of example three, which can be interpreted as signal loss. As can be seen from fig. 11 and 12, the present method is capable of effectively removing mixed common mode noise and reducing loss of effective signals (e.g., α activity).
The invention also provides an active noise reduction method of the signal, which comprises the following steps: an independent common mode noise channel 2 is arranged to collect common mode noise, namely, the common mode noise channel 2 is connected to a collecting circuit of a dry electrode sensor, and the common mode signal collecting channel is not externally connected with the dry electrode sensor or a new common mode noise collecting sensor is independently arranged; setting the gain of the common mode noise channel 2 to be smaller than or equal to the maximum integer of the ratio of the maximum input range of the electrode channel 1 to the common mode noise range so as to acquire common mode noise homologous to a target signal; judging the common mode noise as periodic common mode noise or mixed common mode noise; selecting different calculation models according to the periodicity of the common mode noise; acquiring periodic common mode noise and/or non-periodic common mode noise contained in a target signal based on the common mode noise; and removing the common mode noise in the target signal based on the fitted periodic common mode noise and/or non-periodic common mode noise. Reference is made to the foregoing for a specific process, and details are not repeated here.
In summary, according to the active noise reduction method for the dry electrode electroencephalogram system and the signal with the common mode noise channel 2, the common mode noise channel 2 is introduced to collect the common mode noise homologous to the target signal, so that the accuracy of noise reduction processing can be improved, the periodicity of the common mode noise existing in the target signal is judged, and a corresponding noise reduction processing mode is selected, so that the pertinence of noise reduction processing is improved, and the noise can be removed better.
With the above-described preferred embodiments according to the present invention as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined as the scope of the claims.
Claims (9)
1. A dry electrode electroencephalogram system with common mode noise channels, comprising:
a plurality of electrode channels (1) for acquiring target signals;
at least one common mode noise channel (2) for acquiring common mode noise homologous to the target signal;
a processor (3) that judges the periodicity of the common mode noise, that is, the periodic common mode noise or the mixed common mode noise containing both periodic common mode noise and aperiodic common mode noise;
a calculation module (4) for selecting different calculation models according to the periodicity of the common mode noise so as to acquire the periodic common mode noise and/or the non-periodic common mode noise contained in the target signal based on the common mode noise;
the noise reduction module (5) is used for removing common mode noise in the target signal based on the periodic common mode noise and/or the aperiodic common mode noise obtained by fitting;
further comprises: an electroencephalogram cap (6);
the electroencephalogram cap (6) is provided with the electrode channel (1) and the common mode noise channel (2);
an electrode assembly (11) is arranged on the electrode channel (1) and is used for collecting the target signal;
the common mode noise channels (2) only collect exogenous noise, and the signal gain of any common mode noise channel (2) is set to be smaller than or equal to the maximum integer of the ratio of the maximum input range of the electrode channel to the common mode noise range so as to collect common mode noise homologous to a target signal;
when the common mode noise channel (2) and the electrode channel (1) share the same circuit module, the common mode noise channel (2) is configured as an electrode channel which is not connected with the electrode assembly, and the common mode noise channel (2) is a collection channel which is reserved separately and occupies the original electrode channel (1) but is not connected with any electrode;
when the common mode noise channel (2) and the electrode channel (1) do not share the same circuit module, namely the common mode noise channel (2) and the electrode channel (1) are independently provided with corresponding circuit modules so as to adjust respective signal gains;
the judging that the common mode noise is periodic common mode noise or mixed common mode noise comprises:
when the first characteristic value is larger than a first set value and the second characteristic value is smaller than a second set value, the common mode noise is periodic common mode noise; otherwise, judging the common mode noise as mixed common mode noise; wherein,
the first characteristic value is as follows: zero crossing points Zc of the common mode noise;
the second characteristic value is: the ratio r=RMS1/RMS2, r > 1, between the root mean square RMS1 and the root mean square RMS2 of the common mode noise after the first-order difference of the common mode noise.
2. The dry electrode electroencephalogram system of claim 1 wherein,
when the processor (3) judges that the common mode noise is periodic common mode noise, the processor (3) controls the calculation module (4) to execute a first calculation model, namely, the target signal is subjected to self-adaptive filtering based on the common mode noise, and periodic common mode noise contained in the target signal is fitted.
3. The dry electrode electroencephalogram system of claim 1 wherein,
when the processor (3) judges that the common mode noise is mixed common mode noise, the processor (3) controls the calculation module (4) to execute a second calculation model,
the second computational model includes:
the processor (3) firstly controls the calculation module (4) to execute preprocessing on the common mode noise and the target signal;
the processor (3) controls the calculation module (4) to carry out self-adaptive filtering on the target signal based on the common mode noise, and the periodic common mode noise contained in the target signal is fitted;
subtracting the fitted periodic common mode noise from the target signal to obtain a first target signal;
performing inverse processing operation on the first target signal to obtain a second target signal;
pre-denoising the common mode noise to obtain a first common mode signal;
and carrying out self-adaptive filtering on the second target signal based on the first common mode signal, and fitting out aperiodic common mode noise contained in the second target signal.
4. A dry electrode electroencephalogram system as claimed in claim 2 or 3,
the adaptive filtering includes: the input signal being x (n), passing through the desired response d 1 The error between (n) and the output signal y (n) automatically updates the adaptive filter coefficient h to adapt to the next input signal x (n+1) such that the next output signal y (n+1) approximates the next expected response d 1 (n+1) to fit the expected response based on the input signal to an output signal, i.e. the periodic common mode contained in the target signalNoise or non-periodic common mode noise;
the algorithm of the adaptive filtering comprises the following steps: a least mean square error algorithm, a recursive least square algorithm.
5. The dry electrode electroencephalogram system of claim 3 wherein,
the pre-noise reduction includes: removing power frequency and low-pass filtering;
the pretreatment comprises the following steps: one or more of differential operation, PCA decomposition, SVD decomposition;
the inverse processing includes: one or more of differential operation, PCA decomposition, SVD decomposition.
6. The dry electrode electroencephalogram system of claim 1 wherein,
the dry electrode electroencephalogram system further comprises:
a host (7) on which the circuit module is arranged;
the upper computer (8) is communicated with the host computer (7), and the processor (3), the computing module (4) and the noise reduction module (5) are arranged on the upper computer; wherein the method comprises the steps of
The electroencephalogram cap (6) is provided with a first interface (61) connected with the circuit module;
the host (7) is provided with a second interface (71) for detachable wired electrical connection with the first interface (61).
7. The dry electrode electroencephalogram system of claim 6 wherein,
the electrode assembly (11) comprises claw-type electrodes (111) arranged on the electroencephalogram cap (6) and used for acquiring electroencephalogram signals;
the dry electrode electroencephalogram system further comprises an additional electrode (9); the additional electrode (9) adopts an ear clip type active electrode and is used for collecting electrocardio or oculogram electricity.
8. The dry electrode electroencephalogram system of claim 6 wherein,
the circuit module includes:
the signal amplifying module is used for amplifying the collected target signal and common mode noise;
and the digital-analog conversion module is used for converting the amplified target signal and the common mode noise into digital signals and transmitting the digital signals to the upper computer (8).
9. A method of active noise reduction of a signal, characterized in that a dry electrode electroencephalogram system according to any one of claims 1 to 8 is used, the method comprising:
setting an independent common mode noise channel (2) to collect common mode noise;
setting the gain of the common mode noise channel (2) to be smaller than or equal to the maximum integer of the ratio of the maximum input range to the common mode noise range of the electrode channel (1) so as to acquire common mode noise homologous to a target signal;
judging the common mode noise as periodic common mode noise or mixed common mode noise;
selecting different calculation models according to the periodicity of the common mode noise;
acquiring periodic common mode noise and/or aperiodic common mode noise contained in a target signal based on the common mode noise;
and removing the common mode noise in the target signal based on the fitted periodic common mode noise and/or the aperiodic common mode noise.
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