CN113017653A - Steady-state visual evoked potential identification system and method based on chaos detection principle - Google Patents

Steady-state visual evoked potential identification system and method based on chaos detection principle Download PDF

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CN113017653A
CN113017653A CN202110276412.8A CN202110276412A CN113017653A CN 113017653 A CN113017653 A CN 113017653A CN 202110276412 A CN202110276412 A CN 202110276412A CN 113017653 A CN113017653 A CN 113017653A
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徐光华
张凯
杜成航
阎丽
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Institute Of Health Medicine Guangdong Academy Of Sciences
Xian Jiaotong University
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Xian Jiaotong University
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Abstract

A system and a method for identifying steady-state visual evoked potential based on chaos detection principle comprise a signal acquisition module: a measuring electrode, a reference electrode and a ground electrode are arranged on the head of a user, and electroencephalogram acquisition equipment is used for measuring electroencephalogram signals; the signal preprocessing module: preprocessing the acquired signals, extracting low-frequency-band signals by using a Chebyshev band-pass filter, and reducing the dimension of the multichannel electroencephalogram signals by means of common average reference; a chaotic system design module: establishing a chaotic system dynamic system, and training initial parameters of the chaotic system dynamic system by using the acquired template signals; a feature identification module: adjusting the chaotic system to a critical state, judging the state change of the chaotic system by adding a preprocessed SSVEP signal to be detected, and realizing the characteristic identification of the SSVEP signal to be detected; the invention effectively immunizes irrelevant noise in the identification process and realizes effective identification of BCI blind tested signals.

Description

Steady-state visual evoked potential identification system and method based on chaos detection principle
Technical Field
The invention relates to the technical field of neural engineering and brain-computer interfaces in biomedical engineering, in particular to a system and a method for identifying steady-state visual evoked potentials based on a chaos detection principle.
Background
The brain-computer interface (BCI) technology avoids the dependence on nerve-muscle channels when human beings interact with the external environment, and realizes the direct communication between the human brain and the external environment by decoding the electroencephalogram signals and converting the electroencephalogram signals into related instructions; among them, the steady-state visual evoked brain-computer interface is a method for evoking brain response by watching visual stimuli of specific frequency, and is widely used due to its characteristics of periodicity determination, stability control, and the like.
However, a Steady-State Visual Evoked Potential (SSVEP) is a signal with a weak amplitude and strong nonlinearity, a useful signal is often coupled with a multi-scale noise signal, attenuation of the useful signal is caused by excessive noise suppression, the characteristic signal cannot be identified even if noise influence is ignored, and how to effectively suppress noise and realize optimal identification is still one of the problems to be solved in the current field; secondly, numerous experimental studies have shown that even after a specific long training period, some users still cannot control a specific BCI system, i.e. BCI is blind; in the steady-state visual evoked potential detection process, the capture of a specific frequency peak in a power spectrum is often relied on, and a BCI blind weak peak is extremely easy to be submerged by background noise, so that an SSVEP brain-computer interface cannot function correctly. How to solve BCI blindness such feature recognition in these trials still requires further research.
In the current research, methods for decoding and identifying steady-state visual evoked potential signals are mostly based on the spatial filtering and template matching principle, good effects can be obtained within a certain range, and for decoding strong noise backgrounds and BCI blind tested electroencephalogram signals, the traditional methods cannot effectively finish feature extraction and identification.
In recent years, a weak signal detection technology (Chaos) based on a Chaos theory is widely applied to the field of bioelectricity signal detection and the like, but a steady-state visual evoked potential identification system and a method based on the Chaos theory are not disclosed.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a system and a method for identifying the steady-state visual evoked potential based on the chaos detection principle, which are used for effectively immunizing irrelevant noise in the process of identifying the characteristics of an electroencephalogram signal and can realize effective identification of a BCI blind tested signal.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a steady-state visual evoked potential signal identification system based on a chaos detection principle comprises the following modules:
the signal acquisition module: respectively arranging 8 measuring electrodes at different positions of a visual pillow area of the head of a user, wherein the 8 measuring electrodes are POz, PO3, PO4, PO5, PO6, Oz, O1 and O2, arranging a reference electrode R at the position of a single-side earlobe of the measuring electrode, arranging a ground electrode G at the position of forehead Fpz of the measuring electrode, and using electroencephalogram acquisition equipment to measure electroencephalogram signals;
the signal preprocessing module: preprocessing the signals acquired by the signal acquisition module, extracting low-frequency-band signals by using a Chebyshev band-pass filter, and reducing the dimension of the multichannel electroencephalogram signals by means of common average reference;
a chaotic system design module: establishing a chaotic system dynamic system by using a Duffing equation, and training initial parameters of the chaotic system dynamic system by using the acquired template signals;
a feature identification module: and adjusting the chaotic system to a critical state, and judging the state change of the chaotic system by adding the SSVEP signal to be detected which is processed by the signal preprocessing module, thereby realizing the feature identification of the SSVEP signal to be detected.
The chaotic system design module firstly establishes an initial chaotic dynamic system by a Duffing equation, wherein the equation expression is as follows:
Figure BDA0002976793590000031
wherein k is a damping coefficient; ax + bx3-a non-linear restoring force; a-linear stiffness coefficient of the spring; b-nonlinear spring rate; γ cos (ω t) -built-in periodic driving force; γ — magnitude of driving force;
then, by adding an electroencephalogram template signal as s (t), and then configuring an initial chaotic dynamical system equation by using empirical parameters, wherein the equation expression is as follows:
Figure BDA0002976793590000032
and judging phase points of the chaotic dynamics system from the chaotic state to the periodic state mutation by using the phase locus and the bifurcation diagram of the chaotic dynamics system, and determining the magnitude of the gamma parameter, thereby completing the establishment of the chaotic system.
The characteristic identification module firstly sets the chaotic system to a critical state of chaotic state to large-scale periodic state mutation, indexes the stimulation frequency of a single checkerboard motion stimulation unit corresponding to a signal to be tested, and sets the frequency omega of the built-in driving force to be the same as the target frequency;
introducing the preprocessed SSVEP signals, and judging whether the chaotic system has state change or not by observing a phase locus diagram of the chaotic system; if the chaotic state is mutated into a large-scale periodic state, the target frequency characteristic exists in the signal to be detected; if not, the phase of the built-in driving force signal is adjusted in fixed step length until one cycle is finished, and if the state mutation occurs in the cycle, the target frequency characteristic of the signal to be measured is judged to exist, and if not, the target frequency characteristic is not induced.
After the single recognition of the feature recognition module is finished, the recognition process is repeated, the SSVEP signals of multiple trials are recognized, and recognition results are recorded.
A method for identifying steady-state visual evoked potential based on chaos detection principle comprises the following steps:
step 1, a signal acquisition module acquires electroencephalogram signals:
1.1) respectively placing 8 measuring electrodes at different positions of the visual pillow area of the head of a user, wherein the 8 measuring electrodes are POz, PO3, PO4, PO5, PO6, Oz, O1 and O2, the reference electrode R is placed at the position of a single-side earlobe, and the ground electrode G is placed at the position of the forehead Fpz;
1.2) opening the visual stimulation device, wherein a stimulation interface is a single checkerboard movement stimulation unit displayed on a display screen, and the stimulation unit contracts and expands in a sine or cosine modulation mode to form visual stimulation; the visual stimulation frequency range is 3 Hz-20 Hz, the frequency interval is 0.5Hz, one stimulation target appears on the screen of the display each time, and the motion frequency of each stimulation target is randomly selected in the range but does not repeatedly appear;
1.3) starting the electroencephalogram acquisition equipment, setting the acquisition starting time to be 0.3s before the visual stimulation appears, setting the visual stimulation to last for 3s, and setting the acquisition stopping time to be 0.7s after the visual stimulation is finished;
step 2, the signal preprocessing module carries out electroencephalogram signal preprocessing:
filtering the electroencephalogram signal to 2-30 Hz by using a Chebyshev I-type filter; and then through common average reference, reducing the dimension of the multi-channel electroencephalogram signals, adopting a difference thought, carrying out superposition average on each channel electroencephalogram signal data to be used as a virtual reference electrode, and subtracting signals measured by other electrodes from the virtual reference electrode signals to eliminate noise interference, wherein the expression is as follows:
Figure BDA0002976793590000041
in the formula:
Figure BDA0002976793590000042
the potential difference between electrode i and the reference electrode, ViCentral electrode, n-number of leads, Vj-a reference electrode, selecting Oz as a central electrode for processing;
step 3, the chaotic system design module establishes a chaotic system:
3.1) establishing an initial chaotic dynamic system by using Duffing equation, wherein the equation expression is as follows:
Figure BDA0002976793590000051
wherein k is a damping coefficient; ax + bx3-a non-linear restoring force; a-linear stiffness coefficient of the spring; b-nonlinear spring rate; γ cos (ω t) -built-in periodic driving force; γ — magnitude of driving force;
3.2) configuring the initial chaotic dynamical system by using an additional electroencephalogram template signal as s (t) and using empirical parameters, wherein the equation expression is as follows:
Figure BDA0002976793590000052
3.3) judging phase points of the chaotic dynamic system from the chaotic state to the periodic state mutation by using the phase locus and the bifurcation diagram of the chaotic dynamic system, and determining the size of a gamma parameter so as to complete the establishment of the chaotic system;
step 4, the feature recognition module performs SSVEP feature recognition:
4.1) setting the chaotic system to be in a critical state of sudden change from a chaotic state to a large-scale periodic state by adjusting a parameter gamma, indexing the stimulation frequency of a single checkerboard motion stimulation unit corresponding to a signal to be tested, and setting the frequency omega of the built-in driving force to be the same as the target frequency;
4.2) introducing the SSVEP signal preprocessed in the step 2, and judging whether the chaotic system has state change or not by observing a phase locus diagram of the chaotic system; if the chaotic state is mutated into a large-scale periodic state, the target frequency characteristic exists in the signal to be detected; if not, the phase of the built-in driving force signal is adjusted in a fixed step length until a cycle is finished, and if the state mutation occurs in the cycle, the target frequency characteristic of the signal to be detected is judged to exist, and if not, the target frequency characteristic is not induced;
4.3) repeating the step 4.2) after the single identification is finished, realizing the identification of the SSVEP signals of multiple tests, and recording the identification result.
The invention has the beneficial effects that: the invention constructs a chaotic system through a mathematical model, introduces the acquired signals into the chaotic system, and determines the existence of weak signals through whether the system has state change from a chaotic state to a large-scale periodic state. The weak signal chaotic detection technology is different from other methods, the detection is not carried out by inhibiting noise, but the detection is finished by utilizing that a chaotic system is not influenced by the noise and the system has sudden state change under the disturbance of weak signals. From the principle of steady-state visual evoked potential, when a person receives visual stimulation in a blinking or conversion mode with a fixed frequency, the cerebral cortex generates a periodic rhythm similar to that of the visual stimulation. Therefore, the weak signal detection technology based on the chaos theory has basic conditions for detecting the steady-state visual evoked potential, effectively immunizes irrelevant noise in the electroencephalogram signal feature identification process, and can realize effective identification of BCI blind tested signals.
Drawings
FIG. 1 is a block diagram of a system and method according to an embodiment of the invention.
FIG. 2 is a steady state visual evoked experimental signal acquisition diagram according to an embodiment of the present invention.
FIG. 3 is a phase diagram of the chaotic system with sudden state change according to the embodiment of the present invention.
FIG. 4 is a block diagram of steady-state visual evoked potential signature frequency identification in accordance with an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Referring to fig. 1, a system for identifying a steady-state visual evoked potential signal based on a chaos detection principle comprises the following modules:
the signal acquisition module: respectively arranging 8 measuring electrodes at different positions of a visual pillow area of the head of a user, wherein the 8 measuring electrodes are POz, PO3, PO4, PO5, PO6, Oz, O1 and O2, arranging a reference electrode R at the position of a single-side earlobe of the measuring electrode, arranging a ground electrode G at the position of forehead Fpz of the measuring electrode, and using electroencephalogram acquisition equipment to measure electroencephalogram signals;
the signal preprocessing module: preprocessing the signals acquired by the signal acquisition module, extracting low-frequency-band signals by using a Chebyshev band-pass filter, and reducing the dimension of the multichannel electroencephalogram signals by means of common average reference;
a chaotic system design module: establishing a chaotic dynamical system by using a Duffing equation, and training initial parameters of the chaotic dynamical system by using the acquired template signals;
a feature identification module: and adjusting the chaotic system to a critical state, and judging the state change of the chaotic system by adding the SSVEP signal to be detected which is processed by the signal preprocessing module, thereby realizing the feature identification of the SSVEP signal to be detected.
Referring to fig. 1, a method for identifying a steady-state visual evoked potential based on a chaos detection principle includes the following steps:
step 1, a signal acquisition module acquires electroencephalogram signals:
1.1) respectively placing 8 measuring electrodes at different positions of the visual pillow area of the head of a user, wherein the 8 measuring electrodes are POz, PO3, PO4, PO5, PO6, Oz, O1 and O2, the reference electrode R is placed at the position of a single-side earlobe, and the ground electrode G is placed at the position of the forehead Fpz;
1.2) turning on the visual stimulation device, wherein a stimulation interface is a single checkerboard motion stimulation unit displayed on a display screen, and the stimulation unit contracts and expands in a sine or cosine modulation mode to form visual stimulation; the visual stimulation frequency range is 3 Hz-20 Hz, the frequency interval is 0.5Hz, one stimulation target appears on the screen of the display each time, and the motion frequency of each stimulation target is randomly selected in the range but does not repeatedly appear;
1.3) starting the electroencephalogram acquisition equipment, setting the acquisition starting time to be 0.3s before the visual stimulation appears, setting the visual stimulation to last for 3s, and setting the acquisition stopping time to be 0.7s after the visual stimulation is finished;
step 2, the signal preprocessing module carries out electroencephalogram signal preprocessing:
filtering the electroencephalogram signal to 2-30 Hz by using a Chebyshev I-type filter; and then through common average reference, reducing the dimension of the multi-channel electroencephalogram signals, adopting a difference thought, carrying out superposition average on each channel electroencephalogram signal data to be used as a virtual reference electrode, and subtracting signals measured by other electrodes from the virtual reference electrode signals to eliminate noise interference, wherein the expression is as follows:
Figure BDA0002976793590000071
in the formula:
Figure BDA0002976793590000081
the potential difference between electrode i and the reference electrode, ViCentral electrode, n-number of leads, Vj-a reference electrode, selecting Oz as a central electrode for processing;
step 3, the chaotic system design module establishes a chaotic system:
3.1) chaotic theory research in a determined nonlinear system, the occurred track is complex and unpredictable chaotic motion, the chaotic motion is always limited in a limited area but the track is never repeated and the behaviour is complex, wherein, the initial value sensitivity and the noise immunity are one of the main characteristics of the chaotic motion, which is also the research foundation based on the chaotic theory weak signal detection; the research shows that the chaotic dynamics system based on the Duffing equation has better effect in the field of weak signal detection, so that the invention selects the Duffing equation to build the chaotic dynamics system, and uses the Duffing equation to build the initial chaotic dynamics system, and the equation expression is as follows:
Figure BDA0002976793590000082
wherein k is a damping coefficient; ax + bx3-a non-linear restoring force; a-linear stiffness coefficient of the spring; b-nonlinear spring rate; γ cos (ω t) -built-in periodic driving force; γ — magnitude of driving force;
3.2) configuring the initial chaotic dynamical system by using an additional electroencephalogram template signal as s (t) and using empirical parameters, wherein the equation expression is as follows:
Figure BDA0002976793590000083
3.3) judging phase points of the chaotic dynamic system from the chaotic state to the periodic state mutation by using the phase locus and the bifurcation diagram of the chaotic dynamic system, and determining the size of a gamma parameter so as to complete the establishment of the chaotic system;
step 4, the feature recognition module performs SSVEP feature recognition:
4.1) setting the chaotic system to be in a critical state with sudden change from the chaotic state to a large-scale periodic state by adjusting a parameter gamma, as shown in FIG. 3; indexing the stimulation frequency of a single checkerboard movement stimulation unit corresponding to a signal to be detected, and setting the frequency omega of the built-in driving force to be the same as the target frequency;
4.2) referring to FIG. 4, introducing the SSVEP signal preprocessed in the step 2, and judging whether the chaotic system has state change or not by observing a phase locus diagram of the chaotic system; if the chaotic state is mutated into a large-scale periodic state, the target frequency characteristic exists in the signal to be detected; if not, the phase of the built-in driving force signal is adjusted in a fixed step length until a cycle is finished, and if the state mutation occurs in the cycle, the target frequency characteristic of the signal to be detected is judged to exist, and if not, the target frequency characteristic is not induced;
4.3) repeating the step 4.2) after the single identification is finished, realizing the identification of the SSVEP signals of multiple tests, and recording the identification result.
In order to verify the feasibility of the method (Chaos) provided by the invention, data of 32 tested persons (male-female ratio is 1:1) are collected, the tested persons are 20-26 years old and all have normal vision or normal vision after correction; each test subject needs 3 times of experiments, and each time of experiment has 25 times of stimulation; the cursor prompt with the duration of 0.3s appears in the center of the display screen at the beginning of each visual stimulation so as to be convenient for a subject to focus on the attention, then the visual stimulation with the duration of 3s is carried out, and the rest is carried out for 0.7s after the completion, so the duration of each experiment is 4 s.
Before formal test, processing the experimental data through typical correlation analysis (CCA), and setting the tested object with the target identification accuracy rate of more than 70% as an A group for simulating a normal tested object; while less than 70% are set as group B for simulating BCI blindness; through test grouping, the tested persons of the group A and the group B are exactly 13 persons respectively, and in order to compare the reliability of the chaos detection method, a typical correlation analysis (CCA) and a multivariate synchronous index Method (MSI) are used as comparison methods:
TABLE 1 Classification accuracy and information transfer Rate results (group A)
Figure BDA0002976793590000091
Figure BDA0002976793590000101
TABLE 2 Classification accuracy and information transfer Rate results (group B)
Figure BDA0002976793590000102
Figure BDA0002976793590000111
For a normal group of tested objects (group A), the three methods can obtain ideal classification accuracy and information transmission rate, wherein Chaos detection (Chaos) obtains optimal classification performance.
Unlike the normal group of subjects, blind subjects (group B) for BCI performed poorly in CCA and MSI and made a large gap from performance in group a. The Chaos method still has good performance and achieves optimal information transmission rate, and still can maintain classification level equivalent to that of normal tested. Therefore, it can be concluded that the chaotic detection technology can accurately decode the steady-state visual evoked potential signals to complete the feature identification no matter whether the normal test or the BCI blind test is performed.
In order to verify the classification performance of the chaotic detection system under a strong noise level, Gaussian noises with different intensity levels are added to the data of the group A, and classification is carried out by using different methods. Four groups of noise levels are set in the experiment respectively, no noise, 1 time noise, 2 times noise and 4 times noise, and the result shows that the two classification accuracy rates of MSI and CCA are greatly reduced along with the rise of the noise level, and the classification accuracy rate is maintained at about 40% under the 4 times noise intensity. In the Chaos method, the classification accuracy rate is reduced to a low extent under different noise levels, and 79.50% accuracy rate can be obtained under 4 times of noise intensity. Therefore, for the traditional steady-state visual evoked potential identification method, the identification technology based on the chaos detection principle has excellent noise resistance and can obtain reliable and ideal performance in BCI blind subject group decoding.

Claims (5)

1. A system for identifying steady-state visual evoked potential signals based on a chaos detection principle is characterized by comprising the following modules:
the signal acquisition module: respectively arranging 8 measuring electrodes at different positions of a visual pillow area of the head of a user, wherein the 8 measuring electrodes are POz, PO3, PO4, PO5, PO6, Oz, O1 and O2, arranging a reference electrode R at the position of a single-side earlobe of the measuring electrode, arranging a ground electrode G at the position of forehead Fpz of the measuring electrode, and using electroencephalogram acquisition equipment to measure electroencephalogram signals;
the signal preprocessing module: preprocessing the signals acquired by the signal acquisition module, extracting low-frequency-band signals by using a Chebyshev band-pass filter, and reducing the dimension of the multichannel electroencephalogram signals by means of common average reference;
a chaotic system design module: establishing a chaotic system dynamic system by using a Duffing equation, and training initial parameters of the chaotic system dynamic system by using the acquired template signals;
a feature identification module: and adjusting the chaotic dynamics system to a critical state, and judging the state change of the chaotic dynamics system by adding the SSVEP signal to be detected, which is processed by the signal preprocessing module, so as to realize the feature identification of the SSVEP signal to be detected.
2. The system of claim 1, wherein the system comprises: the chaotic dynamics system design module firstly establishes an initial chaotic dynamics system by a Duffing equation, wherein the equation expression is as follows:
Figure FDA0002976793580000011
wherein k is a damping coefficient; ax + bx3-a non-linear restoring force; a-linear stiffness coefficient of the spring; b-nonlinear spring rate; γ cos (ω t) -built-in periodic driving force; γ — magnitude of driving force;
then, by adding an electroencephalogram template signal as s (t), and then configuring an initial chaotic dynamical system equation by using empirical parameters, wherein the equation expression is as follows:
Figure FDA0002976793580000021
and judging phase points of the chaotic dynamics system from the chaotic state to the periodic state mutation by using the phase locus and the bifurcation diagram of the chaotic dynamics system, and determining the magnitude of the gamma parameter, thereby completing the establishment of the chaotic system.
3. The system of claim 1, wherein the system comprises: the characteristic identification module firstly sets the chaotic system to a critical state of chaotic state to large-scale periodic state mutation, indexes the stimulation frequency of a single checkerboard motion stimulation unit corresponding to a signal to be tested, and sets the frequency omega of the built-in driving force to be the same as the target frequency;
introducing the preprocessed SSVEP signals, and judging whether the chaotic system has state change or not by observing a phase locus diagram of the chaotic system; if the chaotic state is mutated into a large-scale periodic state, the target frequency characteristic exists in the signal to be detected; if not, the phase of the built-in driving force signal is adjusted in fixed step length until one cycle is finished, and if the state mutation occurs in the cycle, the target frequency characteristic of the signal to be measured is judged to exist, and if not, the target frequency characteristic is not induced.
4. The system of claim 3, wherein the system comprises: after the single recognition of the feature recognition module is finished, the recognition process is repeated, the SSVEP signals of multiple trials are recognized, and recognition results are recorded.
5. A method for identifying steady-state visual evoked potential based on chaos detection principle is characterized by comprising the following steps:
step 1, a signal acquisition module acquires electroencephalogram signals:
1.1) respectively placing 8 measuring electrodes at different positions of the visual pillow area of the head of a user, wherein the 8 measuring electrodes are POz, PO3, PO4, PO5, PO6, Oz, O1 and O2, the reference electrode R is placed at the position of a single-side earlobe, and the ground electrode G is placed at the position of the forehead Fpz;
1.2) opening the visual stimulation device, wherein a stimulation interface is a single checkerboard movement stimulation unit displayed on a display screen, and the stimulation unit contracts and expands in a sine or cosine modulation mode to form visual stimulation; the visual stimulation frequency range is 3 Hz-20 Hz, the frequency interval is 0.5Hz, one stimulation target appears on the screen of the display each time, and the motion frequency of each stimulation target is randomly selected in the range but does not repeatedly appear;
1.3) starting the electroencephalogram acquisition equipment, setting the acquisition starting time to be 0.3s before the visual stimulation appears, setting the visual stimulation to last for 3s, and setting the acquisition stopping time to be 0.7s after the visual stimulation is finished;
step 2, the signal preprocessing module carries out electroencephalogram signal preprocessing:
filtering the electroencephalogram signal to 2-30 Hz by using a Chebyshev I-type filter; and then through common average reference, reducing the dimension of the multi-channel electroencephalogram signals, adopting a difference thought, carrying out superposition average on each channel electroencephalogram signal data to be used as a virtual reference electrode, and subtracting signals measured by other electrodes from the virtual reference electrode signals to eliminate noise interference, wherein the expression is as follows:
Figure FDA0002976793580000031
in the formula:
Figure FDA0002976793580000032
the potential difference between electrode i and the reference electrode, ViCentral electrode, n-number of leads, Vj-a reference electrode, selecting Oz as a central electrode for processing;
step 3, the chaotic system design module establishes a chaotic system:
3.1) establishing an initial chaotic dynamic system by using Duffing equation, wherein the equation expression is as follows:
Figure FDA0002976793580000033
wherein k is a damping coefficient; ax + bx3-a non-linear restoring force; a-linear stiffness coefficient of the spring; b-nonlinear spring rate; γ cos (ω t) -built-in periodic driving force; γ — magnitude of driving force;
3.2) configuring the initial chaotic dynamical system by using an additional electroencephalogram template signal as s (t) and using empirical parameters, wherein the equation expression is as follows:
Figure FDA0002976793580000041
3.3) judging phase points of the chaotic dynamic system from the chaotic state to the periodic state mutation by using the phase locus and the bifurcation diagram of the chaotic dynamic system, and determining the size of a gamma parameter so as to complete the establishment of the chaotic system;
step 4, the feature recognition module performs SSVEP feature recognition:
4.1) setting the chaotic system to be in a critical state of sudden change from a chaotic state to a large-scale periodic state by adjusting a parameter gamma, indexing the stimulation frequency of a single checkerboard motion stimulation unit corresponding to a signal to be tested, and setting the frequency omega of the built-in driving force to be the same as the target frequency;
4.2) introducing the SSVEP signal preprocessed in the step 2, and judging whether the chaotic system has state change or not by observing a phase locus diagram of the chaotic system; if the chaotic state is mutated into a large-scale periodic state, the target frequency characteristic exists in the signal to be detected; if not, the phase of the built-in driving force signal is adjusted in a fixed step length until a cycle is finished, and if the state mutation occurs in the cycle, the target frequency characteristic of the signal to be detected is judged to exist, and if not, the target frequency characteristic is not induced;
4.3) repeating the step 4.2) after the single identification is finished, realizing the identification of the SSVEP signals of multiple tests, and recording the identification result.
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