Electroencephalogram signal acquisition method

Abstract

The invention relates to an electroencephalogram signal acquisition method, and particularly provides an electroencephalogram signal feature extraction method, which is used for extracting available features from electroencephalogram signals acquired by an electroencephalogram filtering and amplifying circuit, can be realized in an embedded system, and solves the problem that data processing needs to be performed on a PC (Personal Computer) end after electroencephalogram acquisition in the prior art. And the efficiency of electroencephalogram acquisition and feature extraction is improved. The invention further provides an extremely simple electroencephalogram signal amplification filter circuit with high stability and extremely high signal-to-noise ratio, the complexity and difficulty of acquisition equipment are reduced, and the electroencephalogram acquisition equipment developed on the basis of the circuit is improved in comfort, convenience and stability and can be repeatedly used in daily life. And the extremely simple filtering and amplifying circuit is used for solving the problems that the electroencephalogram signals are easily interfered and the amplitude is low.

Description

Technical Field

[0001] The present invention relates to the technical field of electroencephalogram signal detection, and more specifically, to an electroencephalogram signal acquisition method. Background Art

[0002] Brainwave Power Spectrum is a method used to analyze and describe the frequency characteristics and energy distribution of electroencephalogram (EEG) signals. Brain waves are generated by the electrical activity of brain neurons. By measuring EEG signals, waveforms in different frequency ranges can be obtained. The brainwave power spectrum is usually obtained by Fourier transforming the EEG signal and converting the signal from the time domain to the frequency domain. In the energy spectrum, the horizontal axis represents the frequency, and the vertical axis represents the energy or power of each frequency component, usually expressed in logarithmic form, also known as spectral density. The energy spectrum can show the energy distribution in different frequency bands, which is of great significance for studying brain function, judging brain status and diagnosing some brain diseases.

[0003] Electroencephalography (EEG) is a technology that monitors brain activity by recording electrical signals on the scalp. EEG signals can objectively reflect the functional state of the human body. The monitoring of these signals can be used in a variety of applications such as brain-computer interfaces, neurorehabilitation, and long-term health care monitoring. In medicine, EEG signals can provide a means of diagnosing different types of intracranial lesions (such as stroke, brain tumors, encephalitis, metabolic brain lesions, and can also detect individuals' motor intentions to control rehabilitation robots). In daily life, EEG can be used to monitor an individual's level of concentration, emotional state, and assess an individual's stress level. EEG can also be used to reflect sleep status. By monitoring EEG waveforms during sleep, sleep quality and sleep stages can be determined. Some applications use EEG feedback to help users improve their concentration levels. In short, although EEG is mainly used in the fields of medicine and research, the development of some technologies in recent years has led to an increase in its application in daily life.

[0004] The acquisition of EEG signals often requires the use of conductive gel, which may make patients feel uncomfortable, thus limiting the application of physiological signal monitoring. During long-term physiological signal monitoring, the conductive gel will gradually dry up, resulting in a decrease in signal quality, making it the most important obstacle to traditional long-term healthcare monitoring. In addition, the use of conductive gel significantly increases the preparation time for physiological signal monitoring and may bring the risk of skin allergies. Therefore, researchers have developed different types of dry electrodes to measure signals by directly contacting the skin without using any conductive gel. However, due to the absence of conductive gel to ensure stable contact impedance, dry electrodes are very sensitive to skin conditions and motion artifacts. Especially for EEG measurements, it is quite challenging for dry electrodes to make good direct contact with the head skin through thick hair.

[0005] The frequency range of useful EEG signals is between 0.5 and 64 Hz, and the amplitude varies between 20 and 100 μV, which is very weak. The observed frequencies are subdivided into different groups: α (8-13 Hz), β (13-30 Hz), δ (0.5-4 Hz), θ (4-7 Hz) and γ (30-64 Hz). EEG signals are susceptible to various interferences, and common EEG artifact sources can be classified as follows: 50 / 60 Hz, impedance fluctuations, and cable movement. Based on the above characteristics of EEG signals, it is necessary to use amplifier circuits and filter circuits with extremely high signal-to-noise ratios to amplify and filter EEG signals.

[0006] Medical EEG machine is composed of high-performance EEG software and hardware (integrated amplifier input box) with extended functions, and equipped with The medical EEG machine is composed of a PC with XP operating system. It provides high-quality performance not only for routine EEG examinations, but also for higher-level sleep studies or EEG studies. Smooth waveforms can be obtained in any environment, and high-quality EEG recordings can be guaranteed even when there is a lot of electrical interference or in the ICU room. The medical EEG machine has a high-performance, high-sensitivity amplifier that can perform continuous high-frequency sampling. It also has a high-performance, USB-connected electrode input box and an ECG artifact rejection filter. It can be equipped with an optional electrode cable with a shielding device. However, this solution has the following defects: the medical EEG machine is large and expensive, which is not conducive to use in daily life. The large amount of wiring involved may cause discomfort to patients, especially during long-term recordings (for example, diagnosing sleep disorders, performing preoperative planning to map epileptic lesions in epileptic patients, etc.). The cables distributed on the scalp affect mechanical stability, resulting in motion artifacts during EEG recording.

[0007] At the 6th International Conference on Biomedical Engineering and Informatics in 2013, conference participants presented a single-channel, low-cost EEG signal acquisition circuit for BCI systems. The circuit consists of a protection circuit, an instrumentation amplifier, a common-mode rejection (CMR) circuit, a gain-adjustable amplifier, and a filter. It is very simple, reliable, and low-cost. Experimental results show that the circuit works well, has good amplification capabilities and high CMR, and is very suitable for online BCI systems. However, this system has shortcomings in signal recognition and processing. Further processing of the collected EEG signals requires additional data analysis, and the stability of the system needs to be evaluated.

[0008] Therefore, in view of the advantages and disadvantages mentioned above, the applicant hopes to develop a set of convenient wearable devices that can collect EEG signals in daily life and calculate the energy of different bands in real time. Summary of the invention

[0009] In view of the shortcomings of the prior art, the purpose of the present invention is to provide an EEG signal acquisition method to overcome the shortcomings of the prior art that the EEG signal acquisition equipment is bulky and expensive, and a large amount of wiring may cause artifacts during EEG recording.

[0010] The above technical objectives of the present invention are achieved through the following technical solutions: A method for collecting EEG signals, comprising:

[0011] S1, placing the electrode sheet on the forehead to collect EEG signals;

[0012] S2, using the EEG signal filtering and amplification circuit to filter and amplify the EEG signal to obtain an enhanced signal;

[0013] S3, decomposing the enhanced signal using a wavelet packet decomposition algorithm to obtain a corresponding wavelet packet decomposition tree;

[0014] S4, removing the baseline data with a frequency less than 0.5 Hz in the wavelet packet decomposition tree, and reconstructing the signals of different bands after removing the baseline data according to the frequency band, and correspondingly obtaining a δ wave signal, a θ wave signal, an α wave signal, a β wave signal and a γ wave signal;

[0015] S5. Perform time domain signal integration on the δ wave signal, θ wave signal, α wave signal, β wave signal and γ wave signal respectively to obtain corresponding δ wave energy, θ wave energy, α wave energy, β wave energy and γ wave energy.

[0016] Optionally, the using a wavelet packet decomposition algorithm to decompose the enhanced signal to obtain a corresponding wavelet packet decomposition tree includes:

[0017] wpt EEG =wpdec(XEEG ,9,′db5′);

[0018] Among them, wpdec is the one-dimensional wavelet packet decomposition function; X EEG is the enhanced signal; 9 is the decomposition level of the wavelet packet function; db5 is the wavelet basis function; wpt EEG Is a wavelet packet tree object, corresponding to the enhanced signal X EEG 9 layers of decomposition.

[0019] Optionally, baseline data with a frequency less than 0.5 Hz in the wavelet packet decomposition tree is removed, and signals of different bands after the baseline data are removed are reconstructed according to the frequency bands, and corresponding delta wave signals, theta wave signals, alpha wave signals, beta wave signals, and gamma wave signals are obtained, including:

[0020] baseline EEG =wprcoef(wpt EEG ,[9 0]);

[0021] delta EEG =wprcoef(wpt EEG ,[6 0])-baseline EEG ;

[0022] theta EEG =wprcoef(wpt EEG ,[6 1]);

[0023] alpha EEG =wprcoef(wpt EEG ,[6 2])+wprcoef(wpt EEG ,[7 6]);

[0024] beta EEG =wprcoef(wpt EEG ,[7 7])+wprcoef(wpt EEG ,[4 1])

[0025] -wprcoef(wpt EEG ,[7 15]);

[0026] gamma EEG =wprcoef(wpt EEG ,[7 15])+wprcoef(wpt EEG ,[3 1]);

[0027] Among them, wprcoef is the wavelet packet reconstruction function; baseline EEGis the baseline data; [9 0] indicates the first node of the 9th level decomposition; [6 0] indicates the first node of the 6th level decomposition; [6 1] indicates the second node of the 6th level decomposition; [6 2] indicates the third node of the 6th level decomposition; [7 6] indicates the seventh node of the 7th level decomposition; [7 7] indicates the eighth node of the 7th level decomposition; [4 1] indicates the second node of the 4th level decomposition; [7 15] indicates the sixteenth node of the 7th level decomposition; [3 1] indicates the second node of the 3rd level decomposition; delta EEG Theta is the delta wave signal; EEG is theta wave signal; alpha EEG For alpha wave signal, beta EEG is the beta wave signal; gamma EEG It is a gamma wave signal.

[0028] Optionally, performing time domain signal integration on the delta wave signal, theta wave signal, alpha wave signal, beta wave signal and gamma wave signal respectively to obtain corresponding delta wave energy, theta wave energy, alpha wave energy, beta wave energy and gamma wave energy comprises:

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] Among them, DeltaEnergy is delta wave energy; ThetaEnergy is theta wave energy; AlphaEnergy is alpha wave energy; BetaEnergy is beta wave energy; GammaEnergy is gamma wave energy.

[0035] Optionally, the EEG signal filtering and amplifying circuit comprises: an electrode sheet for fitting with the skin, a front-stage low-pass filtering module, a high-pass amplifying module, an adding amplifying module and a rear-stage low-pass filtering module; the input end of the front-stage low-pass filtering module is connected to the electrode sheet; the output end of the front-stage low-pass filtering module is connected to the input end of the high-pass amplifying module; the output end of the high-pass amplifying module is connected to the input end of the adding amplifying module; the output end of the adding amplifying module is connected to the output end of the EEG signal filtering and amplifying circuit after passing through the rear-stage low-pass filtering module.

[0036] Optionally, the front-stage low-pass filter module includes: a first resistor, a second resistor, a first capacitor and a second capacitor; one end of the first resistor is connected to the first metal electrode of the electrode sheet; the other end of the first resistor is connected to the first input end of the high-pass amplifier module; the other end of the first resistor is also grounded after passing through the first capacitor; one end of the second resistor is connected to the third metal electrode of the electrode sheet; the other end of the second resistor is connected to the second input end of the high-pass amplifier module; the other end of the second resistor is also grounded after passing through the second capacitor.

[0037] Optionally, the high-pass amplifier module includes: an instrument amplifier, an adjustment resistor, a first operational amplifier, a third capacitor and a third resistor; one end of the adjustment resistor is connected to the first adjustment end of the instrument amplifier; the other end of the adjustment resistor is connected to the second adjustment end of the instrument amplifier; the in-phase input end and the inverting input end of the instrument amplifier are both connected to the output end of the front-stage low-pass filter module; the negative power input port of the instrument amplifier is connected to V ss The positive power supply input port of the instrument amplifier is connected to V dd The output end of the instrument amplifier is connected to the inverting input end of the first operational amplifier after passing through the third resistor; the output end of the instrument amplifier is also connected to the input end of the summing amplifier module; the output end of the first operational amplifier is connected to the reference voltage end of the instrument amplifier; the output end of the first operational amplifier is also connected to the inverting input end of the first operational amplifier after passing through the third capacitor; the negative power supply input port of the first operational amplifier is connected to V ss The positive power input port of the first operational amplifier is connected to V dd The voltage terminal is connected; the non-inverting input terminal of the first operational amplifier is grounded.

[0038] Optionally, the addition amplifier module includes: a second operational amplifier, a fourth resistor, a sixth resistor, a ninth resistor and a tenth resistor; the inverting input terminal of the second operational amplifier is connected to the output terminal of the instrumentation amplifier after passing through the fourth resistor; the non-inverting input terminal of the second operational amplifier is connected to V dd The voltage terminal is connected; the in-phase input terminal of the second operational amplifier is grounded after passing through the ninth resistor; the negative power input port of the second operational amplifier is connected to V ss The positive power input port of the second operational amplifier is connected to V dd The output end of the second operational amplifier is connected to the inverting input end of the second operational amplifier after passing through the sixth resistor.

[0039] Optionally, the post-stage low-pass filter module includes: a fifth resistor and a fifth capacitor; the output end of the second operational amplifier is connected to the output end of the EEG signal filter amplifier circuit after passing through the fifth resistor; the output end of the second operational amplifier is grounded after passing through the fifth resistor and the fifth capacitor in turn.

[0040] Optionally, the electrode sheet includes: 3M double-sided tape, PI flexible board, PET insulating film, a first metal electrode, a second metal electrode and a third metal electrode; the 3M double-sided tape is fixedly connected to one side of the PI flexible board; the PET insulating film is fixedly connected to the other side of the PI flexible board; the first metal electrode, the second metal electrode and the third metal electrode are all embedded in the PET insulating film; the first metal electrode and the third metal electrode are respectively connected to the front-stage low-pass filter module through wires; the second metal electrode is grounded.

[0041] In summary, the present invention has the following beneficial effects: The present application provides an EEG signal feature extraction method, which extracts usable features from the EEG signal collected by the EEG filter amplifier circuit. This method can be implemented in an embedded system, which solves the complexity of the need to go to the PC for data processing after EEG collection in the past, and improves the efficiency of EEG collection and feature extraction. The present application further provides a minimalist EEG signal amplification and filtering circuit with high stability and extremely high signal-to-noise ratio, which reduces the complexity and difficulty of the collection equipment: the EEG collection equipment developed based on this improves comfort, convenience and stability, and can be reused in daily life. The use of a minimalist filter amplifier circuit makes up for the problem that EEG signals are susceptible to interference and have low amplitude. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a circuit principle diagram of an electroencephalogram signal filtering and amplifying circuit of the present invention;

[0043] Figure 2 This is a schematic diagram of the electrode sheet structure of the present invention;

[0044] Figure 3 This is a flow chart of the method for collecting EEG signals of the present invention;

[0045] Figure 4 This is a time domain signal diagram of the EEG signal collected by the present invention;

[0046] Figure 5 This is a frequency domain signal diagram of the EEG signal collected by the present invention.

[0047] In the figure: 1. 3m double-sided tape; 2. PI flexible board; 3. PET insulating film; 4. first metal electrode; 5. second metal electrode; 6. third metal electrode; 7. wire; 8. instrument amplifier; 9. first operational amplifier; 10. second operational amplifier. DETAILED DESCRIPTION

[0048] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings. Several embodiments of the present invention are shown in the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein.

[0049] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. The terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features.

[0050] In the present invention, unless otherwise clearly specified and limited, the first feature being "above" or "below" the second feature may include the first and second features being in direct contact, or may include the first and second features being in contact not directly but through another feature between them. Moreover, the first feature being "above", "above" and "above" the second feature includes the first feature being directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. The first feature being "below", "below" and "below" the second feature includes the first feature being directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature. The terms "vertical", "horizontal", "left", "right", "above", "below" and similar expressions are for illustrative purposes only, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0051] The present invention is described in detail below in conjunction with the accompanying drawings and embodiments.

[0052] Example 1

[0053] The present application provides a specific implementation method. Figure 3 As shown, a method for collecting EEG signals comprises the following steps:

[0054] S1, placing the electrode sheet on the forehead to collect EEG signals;

[0055] S2, using the EEG signal filtering and amplifying circuit to filter and amplify the EEG signal to obtain an enhanced signal;

[0056] S3, decomposing the enhanced signal using a wavelet packet decomposition algorithm to obtain a corresponding wavelet packet decomposition tree;

[0057] S4, removing the baseline data with a frequency less than 0.5 Hz in the wavelet packet decomposition tree, and reconstructing the signals of different bands after removing the baseline data according to the frequency band, and correspondingly obtaining a δ wave signal, a θ wave signal, an α wave signal, a β wave signal and a γ wave signal;

[0058] S5. Perform time domain signal integration on the δ wave signal, θ wave signal, α wave signal, β wave signal and γ wave signal respectively to obtain corresponding δ wave energy, θ wave energy, α wave energy, β wave energy and γ wave energy.

[0059] Furthermore, the use of a wavelet packet decomposition algorithm to decompose the enhanced signal to obtain a corresponding wavelet packet decomposition tree includes:

[0060] wpt EEG =wpdec(X EEG ,9,′db5′);

[0061] Among them, wpdec is the one-dimensional wavelet packet decomposition function; X EEG is the enhanced signal; 9 is the decomposition level of the wavelet packet function; db5 is the wavelet basis function; wpt EEG Is a wavelet packet tree object, corresponding to the enhanced signal X EEG 9 layers of decomposition.

[0062] Furthermore, the baseline data with a frequency less than 0.5 Hz in the wavelet packet decomposition tree is removed, and the signals of different bands after removing the baseline data are reconstructed according to the frequency band, and correspondingly, a delta wave signal, a theta wave signal, an alpha wave signal, a beta wave signal and a gamma wave signal are obtained, including:

[0063] baseline EEG =wprcoef(wpt EEG ,[9 0]);

[0064] delta EEG =wprcoef(wpt EEG ,[6 0])-baseline EEG ;

[0065] theta EEG =wprcoef(wpt EEG ,[6 1]);

[0066] alphaEEG =wprcoef(wpt EEG ,[6 2])+wprcoef(wpt EEG ,[7 6]);

[0067] beta EEG =wprcoef(wpt EEG ,[7 7])+wprcoef(wpt EEG ,[4 1])

[0068] -wprcoef(wpt EEG ,[7 15]);

[0069] gamma EEG =wprcoef(wpt EEG ,[7 15])+wprcoef(wpt EEG ,[3 1]);

[0070] Among them, wprcoef is the wavelet packet reconstruction function; baseline EEG is the baseline data; [9 0] indicates the first node of the 9th level decomposition; [6 0] indicates the first node of the 6th level decomposition; [6 1] indicates the second node of the 6th level decomposition; [6 2] indicates the third node of the 6th level decomposition; [7 6] indicates the seventh node of the 7th level decomposition; [7 7] indicates the eighth node of the 7th level decomposition; [4 1] indicates the second node of the 4th level decomposition; [7 15] indicates the sixteenth node of the 7th level decomposition; [3 1] indicates the second node of the 3rd level decomposition; delta EEG Theta is the delta wave signal; EEG is theta wave signal; alpha EEG For alpha wave signal, beta EEG is the beta wave signal; gamma EEG It is a gamma wave signal.

[0071] Further, the time domain signal integration is performed on the delta wave signal, theta wave signal, alpha wave signal, beta wave signal and gamma wave signal respectively to obtain the corresponding delta wave energy, theta wave energy, alpha wave energy, beta wave energy and gamma wave energy, including:

[0072]

[0073]

[0074]

[0075]

[0076]

[0077] Among them, DeltaEnergy is delta wave energy; ThetaEnergy is theta wave energy; AlphaEnergy is alpha wave energy; BetaEnergy is beta wave energy; GammaEnergy is gamma wave energy.

[0078] Example 2

[0079] Furthermore, the present invention provides an electroencephalogram signal filtering and amplification circuit, such as Figure 1 As shown, an electroencephalogram signal filtering and amplifying circuit includes: an electrode sheet for fitting with the skin, a front-stage low-pass filtering module, a high-pass amplifying module, an adding amplifying module and a rear-stage low-pass filtering module; the input end of the front-stage low-pass filtering module is connected to the electrode sheet;

[0080] The output end of the front-stage low-pass filtering module is connected to the input end of the high-pass amplification module;

[0081] The output end of the high-pass amplifier module is connected to the input end of the addition amplifier module;

[0082] The output end of the addition amplifier module is connected to the output end of the EEG signal filtering and amplifying circuit after passing through the post-stage low-pass filtering module.

[0083] Furthermore, the pre-stage low-pass filter module includes: a first resistor R1, a second resistor R2, a first capacitor C1 and a second capacitor C2; one end of the first resistor R1 is connected to the first metal electrode 4 of the electrode sheet; the other end of the first resistor R1 is connected to the first input end of the high-pass amplifier module (that is, the inverting input end of the instrument amplifier of the present application); the other end of the first resistor R1 is also grounded after passing through the first capacitor C1; one end of the second resistor R2 is connected to the third metal electrode 6 of the electrode sheet; the other end of the second resistor R2 is connected to the second input end of the high-pass amplifier module (that is, the in-phase input end of the instrument amplifier of the present application); the other end of the second resistor R2 is also grounded after passing through the second capacitor C2.

[0084] Specifically, taking the left EEG signal input as an example, let the EEG input signal be V L , the output of the previous passive low-pass filter is V1. Then the transfer function and cutoff frequency of the previous passive low-pass filter are:

[0085]

[0086]

[0087] Since the highest effective range of EEG signals is 64Hz, fL Should be around 64Hz, In the low-pass filter, 1>>ωC1R1. The input of all electrodes should pass through the same front-stage low-pass filter, let R1=R2, C1=C2, and similarly, the input V of the right electrode can be obtained. R After the same filtering, the output of the right electrode after the previous passive low-pass filter is assumed to be V2.

[0088] Furthermore, the high-pass amplifier module includes: an instrument amplifier 8, an adjustment resistor R g , a first operational amplifier 9, a third capacitor C3 and a third resistor R3; the adjusting resistor R g One end of the adjusting resistor R is connected to the first adjusting end of the instrument amplifier 8; g The other end is connected to the second adjustment end of the instrument amplifier 8; the in-phase input end and the inverting input end of the instrument amplifier 8 are both connected to the output end of the front-stage low-pass filter module; the negative power input port of the instrument amplifier 8 is connected to V ss The positive power input port of the instrument amplifier 8 is connected to V dd The output end of the instrument amplifier 8 is connected to the inverting input end of the first operational amplifier 9 after passing through the third resistor R3; the output end of the instrument amplifier 8 is also connected to the input end of the addition amplifier module; the output end of the first operational amplifier 9 is connected to the reference voltage end of the instrument amplifier 8; the output end of the first operational amplifier 9 is also connected to the inverting input end of the first operational amplifier 9 after passing through the third capacitor C3; the negative power input port of the first operational amplifier 9 is connected to V ss The positive power input port of the first operational amplifier 9 is connected to V dd The voltage terminal is connected; the non-inverting input terminal of the first operational amplifier 9 is grounded;

[0089] Specifically, the selected instrument amplifier should have electrostatic protection (ESD protection) function. The input voltage of this high-pass amplifier is the difference V1-V2 between the input voltage of the two electrodes and the output voltage of the previous passive low-pass filter. The output voltage is V3, and the amplification factor is K. g . Then the transfer function and cutoff frequency of this high-pass amplifier are:

[0090]

[0091]

[0092] Among them, K g With R g The relationship between the two is determined by the selected instrumentation amplifier specifications. Since the minimum effective range of EEG signals is 0.5Hz, f LShould be around 0.5Hz, In the high-pass filter, 1<<ωC3R3.

[0093] Furthermore, the addition amplification module includes: a second operational amplifier 10, a fourth resistor R4, a sixth resistor R6, a ninth resistor R9 and a tenth resistor R 10 The inverting input terminal of the second operational amplifier 10 is connected to the output terminal of the instrumentation amplifier 8 through the fourth resistor R4; the non-inverting input terminal of the second operational amplifier 10 is connected to the output terminal of the instrumentation amplifier 8 through the tenth resistor R 10 After V dd The in-phase input terminal of the second operational amplifier 10 is connected to the ground after passing through the ninth resistor R9; the negative power input port of the second operational amplifier 10 is connected to V ss The positive power input port of the second operational amplifier 10 is connected to V dd The output end of the second operational amplifier 10 is connected to the inverting input end of the second operational amplifier 10 after passing through the sixth resistor R6.

[0094] Specifically, the transfer function of this summing amplifier is:

[0095]

[0096] Among them, V dd is the voltage between boards, usually 3.3V. This adder adds one to the EEG signal. The DC component of the EEG voltage is shifted to Up and down, to facilitate the subsequent conversion of analog-to-digital data. The amplification factor of this amplifier is

[0097] Furthermore, the post-stage low-pass filter module includes: a fifth resistor R5 and a fifth capacitor C5; the output end of the second operational amplifier is connected to the output end of the EEG signal filter amplifier circuit after passing through the fifth resistor R5; the output end of the second operational amplifier is grounded after passing through the fifth resistor R5 and the fifth capacitor C5 in turn.

[0098] Specifically, the transfer function and cutoff frequency of this subsequent passive low-pass filter are:

[0099]

[0100]

[0101] Since the highest effective range of EEG signals is 64Hz, f L Should be around 64Hz, The latter passive low-pass filter can use the same resistors and capacitors as the former passive low-pass filter, that is, R5 = R1, C5 = C1. Similarly, 1>>ωC5R5.

[0102] In practical applications, Figure 1 This is an EEG signal filtering and amplification circuit described in this application, which includes an instrumentation amplifier and two operational amplifiers. The circuit mainly includes a front-stage passive low-pass filter, a high-pass amplifier, an addition amplifier, and a rear-stage passive low-pass filter. The different parts of the circuit are introduced below:

[0103] In summary, the transfer function and signal-to-noise ratio formula of this circuit are calculated and simplified as follows:

[0104]

[0105]

[0106] Where V n_Da is the input voltage noise power spectral density of the instrumentation amplifier, V n_Op is the input voltage noise power spectral density of the operational amplifier, I n_Da is the input current noise power spectral density of the instrumentation amplifier, I n_On is the input current noise power spectral density of the operational amplifier, k b =1.38*10 -23 J / K, T represents thermodynamic temperature, the unit is Kelvin.

[0107] Furthermore, the present application also provides a specific implementation method, including specific values ​​of the resistors and capacitors in the circuit; specifically, since the amplitude of the EEG signal is about 100μV, in order to facilitate the subsequent analog-to-digital conversion, it should be amplified 4000 times to about 400mV. Usually, the instrumentation amplifier can amplify the signal to 1000 times, so From the perspective of power, the voltage between boards is 3.3V, and the current between boards should not exceed 1mA, so a resistor of 3.3KΩ or more should be selected. From the perspective of noise, R1 / R4 / R9 should be as small as possible, and C3 should be as large as possible. The general specifications of capacitors are 1nF to 1μF. Therefore, R1 can be selected from 10KΩ to 2.5MΩ, R4 can be selected from 10KΩ to 2.5MΩ, R9 can be selected from 10KΩ to 1MΩ, and C3 can be selected from 0.1μF to 1μF.

[0108] When selecting the resistor and capacitor parameters, the noise and power consumption should be balanced. Through simulation experiments with different parameters, we obtain the following embodiments, which can achieve the minimum noise with low power consumption.

[0109] 1. In the previous passive low-pass filter, take R1=R2=25KΩ, C1=C2=0.1μF, so that the cutoff frequency of this previous passive low-pass filter is 63.69Hz.

[0110] 2. In the high-pass amplifier, the operational amplifier for realizing the active high-pass filter is OP2177 from ADI. R3 = 500KΩ, C3 = 1μF, so that the cutoff frequency of this active high-pass filter is 0.31Hz. The instrument amplifier (IA) is AD8421 from ADI, R g Used to adjust the gain K of the instrumentation amplifier IA g , the relationship between them is described as:

[0111]

[0112] This instrumentation amplifier can amplify the signal by about 1000 times. If Rg = 10Ω, this instrumentation amplifier can amplify the EEG signal by 991 times, from 100μV to about 100mV.

[0113] 3. In the adding amplifier, the operational amplifier is OP2177 from ADI. Take R4 = R6 / 4 = 10KΩ, R 10 =9R9=90KΩ, this combination of adder amplifier can shift the signal amplitude to V dd / 2, which facilitates analog-to-digital conversion and amplifies the signal by 4 times.

[0114] 4. In the post-stage passive low-pass filter, take R5 = 2.5KΩ, C5 = 1μF, so that the cutoff frequency of the post-stage passive low-pass filter is 63.69Hz.

[0115] According to the above analysis, in this embodiment, the transfer function formula of this circuit is calculated and simplified as follows:

[0116]

[0117] This amplification and filtering circuit can amplify the EEG signal to about 4000 times, and process the EEG signal through low-pass filtering and high-pass filtering to obtain the signal of the frequency band of interest.

[0118] Furthermore, the electrode sheet includes: 3M double-sided tape 1, PI flexible board 2, PET insulating film 3, a first metal electrode 4, a second metal electrode 5 and a third metal electrode 6; the 3M double-sided tape 1 is fixedly connected to one side of the PI flexible board 2; the PET insulating film 3 is fixedly connected to the other side of the PI flexible board 2; the first metal electrode 4, the second metal electrode 5 and the third metal electrode 6 are all embedded in the PET insulating film 3; the first metal electrode 4 and the third metal electrode 6 are respectively connected to the front-stage low-pass filter module through a wire 7; the second metal electrode 5 is grounded.

[0119] In practical applications, Figure 2 This is the structural diagram of the electrode sheet proposed in this embodiment. The electrode sheet can be divided into four layers, which are respectively: 3m double-sided tape, PI flexible board, circuit and electrode, PET insulating film according to the distance from the skin. The thickness of the electrode is 0.03μm. The PET insulating film plays a role in protecting the circuit, and the electrode protrudes from the PET insulating film and can be in direct contact with the skin. Human skin is often not flat, but bends with the shape of the bones. The flexible electrode produced according to this structure can adapt to uneven skin, and this flexible electrode can be fixed on the headband to achieve the purpose of convenience and wearability. The electrode part is made of metal material, which can be pure gold / platinum / alloy / copper plated with gold. The selection of electrode materials should meet the following two conditions: ① The contact impedance is as small as possible, for example, less than (45±5)KΩ at 10Hz. ② Tested by salt spray test, the steps of the salt spray test are as follows: The electrode is immersed in salt water with a concentration of 10% for 7 days, without dirt, corrosion and shedding, and the impedance degradation does not exceed 10%. Electrode materials that meet both conditions can be selected as flexible electrode materials. The optional electrode size is (8±3)×(12±3)mm, and the spacing is (12±5)mm.

[0120] Specifically, after the EEG signal is collected by the circuit in Example 2, the collected amplified EEG signal can be decomposed into δ wave energy, θ wave energy, α wave energy, β wave energy and γ wave energy of different frequencies through the relevant signal processing method in Example 1, and then the EEG signal can be analyzed. Figure 4 , Figure 5 As shown, the collected EEG is divided into different frequency bands using this method. From this result, it can be seen that the circuit structure and related signal processing method disclosed in this application can be used to divide the signals of different bands of EEG signals and calculate the energy of different bands, which is quite accurate compared with traditional EEG signal acquisition equipment.

[0121] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0122] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for collecting electroencephalogram signals, characterized in that: include: S1, placing the electrode sheet on the forehead to collect EEG signals; S2, using the EEG signal filtering and amplification circuit to filter and amplify the EEG signal to obtain an enhanced signal; S3, decomposing the enhanced signal using a wavelet packet decomposition algorithm to obtain a corresponding wavelet packet decomposition tree; S4, removing the baseline data with a frequency less than 0.5 Hz in the wavelet packet decomposition tree, and reconstructing the signals of different bands after removing the baseline data according to the frequency band, and correspondingly obtaining a δ wave signal, a θ wave signal, an α wave signal, a β wave signal and a γ wave signal; S5. Perform time domain signal integration on the δ wave signal, θ wave signal, α wave signal, β wave signal and γ wave signal respectively to obtain corresponding δ wave energy, θ wave energy, α wave energy, β wave energy and γ wave energy.

2. The method for collecting electroencephalogram signals according to claim 1, characterized in that: The step of using a wavelet packet decomposition algorithm to decompose the enhanced signal to obtain a corresponding wavelet packet decomposition tree includes: wpt EEG =wpdec(X EEG ,9,′db5′); Among them, wpdec is the one-dimensional wavelet packet decomposition function; X EEG is the enhanced signal; 9 is the decomposition level of the wavelet packet function; db5 is the wavelet basis function; wpt EEG Is a wavelet packet tree object, corresponding to the enhanced signal X EEG 9 layers of decomposition.

3. The method for collecting electroencephalogram signals according to claim 2, characterized in that: The method removes the baseline data with a frequency less than 0.5 Hz in the wavelet packet decomposition tree, and reconstructs the signals of different bands after removing the baseline data according to the frequency band, and correspondingly obtains a delta wave signal, a theta wave signal, an alpha wave signal, a beta wave signal, and a gamma wave signal, including: baseline EEG =wprcoef(wpt EEG ,[90]); delta EEG =wprcoef(wpt EEG ,[60])-baseline EEG ; theta EEG =wprcoef(wpt EEG ,[61]); alpha EEG =wprcoef(wpt EEG ,[62])+wprcoef(wpt EEG ,[76]); beta EEG =wprcoef(wpt EEG ,[77])+wprcoef(wpt EEG ,[41])-wprcoef(wpt EEG ,[715]); gamma EEG =wprcoef(wpt EEG ,[715])+wprcoef(wpt EEG ,[31]); Among them, wprcoef is the wavelet packet reconstruction function; baseline EEG is the baseline data; [90] represents the first node of the 9th level decomposition; [60] represents the first node of the 6th level decomposition; [61] represents the second node of the 6th level decomposition; [62] represents the third node of the 6th level decomposition; [76] represents the seventh node of the 7th level decomposition; [77] represents the eighth node of the 7th level decomposition; [41] represents the second node of the 4th level decomposition; [715] represents the sixteenth node of the 7th level decomposition; [31] represents the second node of the 3rd level decomposition; delta EEG Theta is the delta wave signal; EEG is theta wave signal; alpha EEG For alpha wave signal, beta EEG is the beta wave signal; gamma EEG It is a gamma wave signal.

4. The method for collecting electroencephalogram signals according to claim 3, characterized in that: The step of performing time domain signal integration on the delta wave signal, theta wave signal, alpha wave signal, beta wave signal and gamma wave signal to obtain corresponding delta wave energy, theta wave energy, alpha wave energy, beta wave energy and gamma wave energy comprises: Among them, DeltaEnergy is delta wave energy; ThetaEnergy is theta wave energy; AlphaEnergy is alpha wave energy; BetaEnergy is beta wave energy; GammaEnergy is gamma wave energy.

5. The method for collecting electroencephalogram signals according to claim 1, characterized in that: The EEG signal filtering and amplifying circuit comprises: an electrode sheet for fitting with the skin, a front-stage low-pass filtering module, a high-pass amplifying module, an adding amplifying module and a rear-stage low-pass filtering module; The input end of the front-stage low-pass filter module is connected to the electrode sheet; The output end of the front-stage low-pass filtering module is connected to the input end of the high-pass amplification module; The output end of the high-pass amplifier module is connected to the input end of the addition amplifier module; The output end of the addition amplifier module is connected to the output end of the EEG signal filtering and amplifying circuit after passing through the post-stage low-pass filtering module.

6. The method for collecting electroencephalogram signals according to claim 5, characterized in that: The front-stage low-pass filter module includes: a first resistor, a second resistor, a first capacitor and a second capacitor; One end of the first resistor is connected to the first metal electrode of the electrode sheet; the other end of the first resistor is connected to the first input end of the high-pass amplifier module; the other end of the first resistor is also grounded after passing through the first capacitor; one end of the second resistor is connected to the third metal electrode of the electrode sheet; the other end of the second resistor is connected to the second input end of the high-pass amplifier module; the other end of the second resistor is also grounded after passing through the second capacitor.

7. The method for collecting electroencephalogram signals according to claim 6, characterized in that: The high-pass amplifier module includes: an instrument amplifier, an adjustment resistor, a first operational amplifier, a third capacitor and a third resistor; One end of the regulating resistor is connected to the first regulating end of the instrument amplifier; the other end of the regulating resistor is connected to the second regulating end of the instrument amplifier; The in-phase input terminal and the inverting input terminal of the instrument amplifier are both connected to the output terminal of the front-stage low-pass filter module; The negative power supply input of the instrumentation amplifier is connected to V ss The positive power supply input port of the instrument amplifier is connected to V dd Voltage terminal connection; The output end of the instrument amplifier is connected to the inverting input end of the first operational amplifier after passing through the third resistor; the output end of the instrument amplifier is also connected to the input end of the addition amplifier module; The output terminal of the first operational amplifier is connected to the reference voltage terminal of the instrumentation amplifier; the output terminal of the first operational amplifier is also connected to the inverting input terminal of the first operational amplifier after passing through a third capacitor; The negative power input port of the first operational amplifier is connected to V ss The positive power input port of the first operational amplifier is connected to V dd Voltage terminal connection; A non-inverting input terminal of the first operational amplifier is grounded.

8. The method for collecting electroencephalogram signals according to claim 7, characterized in that: The addition amplification module includes: a second operational amplifier, a fourth resistor, a sixth resistor, a ninth resistor and a tenth resistor; The inverting input terminal of the second operational amplifier is connected to the output terminal of the instrumentation amplifier after passing through a fourth resistor; The non-inverting input terminal of the second operational amplifier is connected to V dd The voltage terminal is connected; the non-inverting input terminal of the second operational amplifier is grounded after passing through a ninth resistor; The negative power input port of the second operational amplifier is connected to V ss The positive power input port of the second operational amplifier is connected to V dd Voltage terminal connection; The output terminal of the second operational amplifier is connected to the inverting input terminal of the second operational amplifier after passing through a sixth resistor.

9. The method for collecting electroencephalogram signals according to claim 8, characterized in that: The post-stage low-pass filter module includes: a fifth resistor and a fifth capacitor; The output end of the second operational amplifier is connected to the output end of the EEG signal filtering and amplifying circuit after passing through a fifth resistor; The output terminal of the second operational amplifier is grounded after passing through a fifth resistor and a fifth capacitor in sequence.

10. The method for collecting EEG signals according to claim 9, characterized in that: The electrode sheet includes: 3M double-sided adhesive, PI flexible board, PET insulating film, a first metal electrode, a second metal electrode and a third metal electrode; The 3M double-sided adhesive tape is fixedly connected to one side of the PI flexible board; the PET insulating film is fixedly connected to the other side of the PI flexible board; The first metal electrode, the second metal electrode and the third metal electrode are all embedded inside the PET insulating film; The first metal electrode and the third metal electrode are connected to the front-stage low-pass filter module through wires respectively; the second metal electrode is grounded.

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