A method, device, equipment and medium for judging intention based on the combination of eye movement and electroencephalogram

Abstract

The present invention relates to an intention judgment method, device, equipment and medium based on the combination of eye movement and electroencephalogram, belonging to the field of brain-computer interface. The method includes extracting eye movement features; establishing an intention capture judgment model based on the eye movement features and outputting the intention capture judgment result, the intention capture judgment result includes the intention capture result and the intention non-capture result; when the intention capture result is output, a confirmation signal is sent to the user and the gaze drift rate is obtained, a confirmation signal reception judgment model is established and confirmation signal reception judgment information is output, the confirmation signal reception judgment information includes confirmation signal reception information and confirmation signal non-reception information; when the confirmation signal reception information is output, a steady-state visual evoked potential signal is obtained through a brain-computer interface and the user's intention is judged, so that the user's intention is judged comprehensively by eye movement and electroencephalogram signals, while improving efficiency and reducing the misjudgment rate.

Description

Technical Field

[0001] The present invention belongs to the technical field of brain-computer interface, and specifically relates to a method, device, equipment and medium for determining intention based on the combination of eye movement and electroencephalogram. Background Art

[0002] With the development of medicine, patients with stroke, craniocerebral trauma, cognitive impairment and other conditions that cannot communicate normally can convey their thoughts and intentions through brain-computer interface technology. Brain-computer interface technology decodes brain neural activity information, connects external components, and forms a loop system with communication or feedback functions. In rehabilitation practice, it can interpret the electrical, magnetic, metabolic and other EEG signals generated by the patient's brain activity, control external devices such as computers, manipulators, functional electrical stimulation, etc. to produce synchronized feedback, and realize closed-loop rehabilitation training with real-time connection between the central and peripheral. However, the current simple brain-computer interface technology has a high misjudgment rate, and when the patient has no obvious intention, such as in a state of emptying, or in a state of slow reaction due to illness, it is also easy to be identified as an expression of intention, resulting in a waste of resources. Existing EEG signal recognition methods also have certain defects. Some methods require the collection of the subject's EEG signal template in advance. Due to the time-varying characteristics and specificity of EEG signals, the cross-subject effect is poor, and the characteristics of the signal itself will be suppressed while suppressing noise. At the same time, due to the huge differences in EEG signals between different patients, the method has low universality. Summary of the invention

[0003] In order to solve the above problems existing in the prior art, the present invention provides a method, device, equipment and medium for determining intention based on the combination of eye movement and EEG.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A method for determining intention based on the combination of eye movement and electroencephalogram, the implementation of the method for determining intention includes the following steps:

[0006] S1: Preset an eye movement monitoring cycle, and extract eye movement features according to the eye movement monitoring cycle by an eye tracker, wherein the eye movement features include average fixation time, saccade amplitude, blink frequency, average blink time, and pupil diameter variation coefficient;

[0007] S2: establishing an intention capture judgment model based on the eye movement features and outputting an intention capture judgment result, wherein the intention capture judgment result includes an intention capture result and an intention non-capture result;

[0008] S3: when the intention capture judgment model outputs the intention capture result, sending a confirmation signal to the user and obtaining the gaze drift rate, establishing a confirmation signal reception judgment model and outputting confirmation signal reception judgment information, wherein the confirmation signal reception judgment information includes confirmation signal reception information and confirmation signal non-reception information;

[0009] S4: When the confirmation signal reception judgment model outputs the confirmation signal reception information, a steady-state visual evoked potential signal is acquired through a brain-computer interface and a user intention judgment is performed.

[0010] Preferably, the step S1 specifically includes:

[0011] S101: construct an eye movement coordinate system and obtain the scanning amplitude. The eye movement coordinate system takes the pupil center when both eyes are looking horizontally normally as the origin. The calculation formula of the scanning amplitude is:

[0012]

[0013] Among them, S r is the scanning amplitude, X s X is the horizontal coordinate of the start of the scan, e Y is the horizontal coordinate of the end of the scan, s Y is the vertical coordinate of the start of the scan, e is the ordinate of the end of the saccade;

[0014] S102: The calculation formula of the pupil diameter variation coefficient is:

[0015]

[0016] Among them, P cv is the coefficient of variation of pupil diameter, ψ is the standard deviation of pupil diameter, is the average pupil diameter.

[0017] Preferably, the step S2 specifically includes:

[0018] S201: Calculate the intention capture judgment index based on the eye movement feature and establish the intention capture judgment model. The calculation formula of the intention capture judgment index is:

[0019]

[0020] Among them, θ is the intention capture judgment index, S r is the scanning amplitude, BT max is the maximum average blink time, BT min is the minimum average blink time, t is time, FT is the average fixation time, BF is the blink frequency, P cv is the coefficient of variation of pupil diameter;

[0021] S202: Preset an intention capture judgment threshold, and obtain the intention capture judgment result by comparing the intention capture judgment index with the intention capture judgment threshold.

[0022] Preferably, the step S202 specifically includes:

[0023] Determining the intention capture judgment index, when the intention capture judgment index is greater than or equal to the intention capture judgment threshold, the intention capture judgment model outputs the intention capture result;

[0024] When the intention capture judgment index is less than the intention capture judgment threshold, the intention capture judgment model outputs the intention not captured result.

[0025] Preferably, the step S3 specifically includes:

[0026] S301: Obtaining an average power density of gaze drift rate according to the gaze drift rate, and the calculation formula is:

[0027]

[0028] Where PSD is the average power density of gaze drift rate, t1 is the first moment, t2 is the second moment, N(i) is the gaze drift rate at the i-th moment, and f s is the eye tracker frequency;

[0029] S302: Calculate the gaze drift rate differential entropy based on the gaze drift rate average power density and construct the confirmation signal reception judgment model, the calculation formula is:

[0030] DE = log2PSD,

[0031] Where DE is the differential entropy of gaze drift rate, PSD is the average power density of gaze drift rate;

[0032] S303: Preset a differential entropy threshold, and obtain the confirmation signal reception judgment information by comparing the gaze drift rate differential entropy with the differential entropy threshold.

[0033] Preferably, the step S303 specifically includes:

[0034] Determining the gaze drift rate differential entropy, when the gaze drift rate differential entropy is less than or equal to the differential entropy threshold, the confirmation signal reception determination model outputs the confirmation signal reception information;

[0035] When the gaze drift rate differential entropy is greater than the differential entropy threshold, the confirmation signal reception judgment model outputs the confirmation signal non-reception information.

[0036] Preferably, the step S4 specifically includes:

[0037] S401: Identify the steady-state visual evoked potential signal by using the Da Vinci chaos method. The expression of the Da Vinci chaos method is:

[0038]

[0039] Among them, S(κ) is the steady-state visual evoked potential signal, ω is the angular frequency of the periodic driving force, k is the damping coefficient, is the oscillator acceleration of the Da Vinci chaos method, is the oscillator velocity of the Darven chaos method, X(κ) is the oscillator displacement of the Darven chaos method, γ is the amplitude of the periodic driving force, is the initial phase of the periodic driving force, To drive the cycle;

[0040] S402: Convert the Da Vinci chaos method expression into an equation form and obtain the steady-state visual evoked potential signal. The conversion process is expressed as:

[0041]

[0042]

[0043] Where y is the actual displacement of the oscillator in the Da Vinci chaos method, is the actual speed of the oscillator in the Da Vinci chaos method, is the oscillator speed of the Dufferin chaos method, ω is the angular frequency of the periodic driving force, k is the damping coefficient, X(κ) is the oscillator displacement of the Dufferin chaos method, γ is the amplitude of the periodic driving force, S(κ) is the steady-state visual evoked potential signal, To drive the cycle.

[0044] An intention judgment device based on the combination of eye movement and EEG, used to execute the above-mentioned intention judgment method based on the combination of eye movement and EEG, characterized by comprising a feature extraction module, an intention capture judgment module, a confirmation signal reception judgment module, and an intention judgment module;

[0045] The feature extraction module is used to preset an eye movement monitoring cycle, and extract eye movement features according to the eye movement monitoring cycle through an eye tracker, wherein the eye movement features include average fixation time, saccade amplitude, blink frequency, average blink time, and pupil diameter variation coefficient;

[0046] The intention capture judgment module is used to establish an intention capture judgment model based on the eye movement features and output an intention capture judgment result, wherein the intention capture judgment result includes an intention capture result and an intention non-capture result;

[0047] The confirmation signal reception judgment module is used to send a confirmation signal to the user and obtain the gaze drift rate when the intention capture judgment model outputs the intention capture result, establish a confirmation signal reception judgment model and output confirmation signal reception judgment information, and the confirmation signal reception judgment information includes confirmation signal reception information and confirmation signal non-reception information;

[0048] The intention judgment module is used to obtain a steady-state visual evoked potential signal through a brain-computer interface and perform user intention judgment when the confirmation signal reception judgment model outputs the confirmation signal reception information.

[0049] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned intention judgment method based on the combination of eye movement and electroencephalogram is implemented.

[0050] A storage medium containing computer executable instructions, which, when executed by a computer processor, are used to execute the above-mentioned intention judgment method based on the combination of eye movement and electroencephalogram.

[0051] The beneficial effects of the present invention are:

[0052] (1) By combining eye movements with EEG signals, the patient's intentions can be comprehensively and accurately judged with a low misjudgment rate. In addition, the eye movement characteristics can be used to determine whether to proceed to the next step of EEG signal recognition, thereby reducing workload and improving work efficiency.

[0053] (2) By extracting eye movement features including average fixation time, saccade amplitude, blink frequency, average blink time, and pupil diameter variation coefficient, an intention capture judgment model is established and the intention capture judgment result is output. The judgment is more comprehensive, and it is more convenient and efficient to extract the above eye movement features through an eye tracker;

[0054] (3) Steady-state visual evoked potential signals are identified through the Da Vinci chaos method. Among various types of EEG signals, steady-state visual evoked potential signals have the advantages of low acquisition cost, convenient recording, and high temporal resolution. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0056] Figure 1 The present invention is a flowchart of the steps of a method for determining intention based on the combination of eye movement and EEG. DETAILED DESCRIPTION

[0057] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0058] The working principle and use process of the present invention:

[0059] See also Figure 1 , an intention judgment method based on the combination of eye movement and EEG, including:

[0060] S1: Preset an eye movement monitoring cycle, and extract eye movement features according to the eye movement monitoring cycle by an eye tracker, wherein the eye movement features include average fixation time, saccade amplitude, blink frequency, average blink time, and pupil diameter variation coefficient;

[0061] S2: establishing an intention capture judgment model based on the eye movement features and outputting an intention capture judgment result, wherein the intention capture judgment result includes an intention capture result and an intention non-capture result;

[0062] S3: when the intention capture judgment model outputs the intention capture result, sending a confirmation signal to the user and obtaining the gaze drift rate, establishing a confirmation signal reception judgment model and outputting confirmation signal reception judgment information, wherein the confirmation signal reception judgment information includes confirmation signal reception information and confirmation signal non-reception information;

[0063] S4: When the confirmation signal reception judgment model outputs the confirmation signal reception information, a steady-state visual evoked potential signal is acquired through a brain-computer interface and a user intention judgment is performed.

[0064] In this embodiment, an eye movement monitoring cycle is preset, and eye movement features are extracted by an eye tracker according to the eye movement monitoring cycle. The eye movement features include average fixation time, saccade amplitude, blink frequency, average blink time, and pupil diameter variation coefficient. The judgment is more comprehensive, and the extraction of the above eye movement features by an eye tracker is more convenient and more efficient. It can be implemented by the following steps:

[0065] S101: construct an eye movement coordinate system and obtain the scanning amplitude. The eye movement coordinate system takes the pupil center when both eyes are looking horizontally normally as the origin. The calculation formula of the scanning amplitude is:

[0066]

[0067] Among them, S r is the sweep amplitude, X s X is the horizontal coordinate of the start of the scan, e Y is the horizontal coordinate of the end of the scan, s Y is the vertical coordinate of the start of the scan, e is the ordinate of the end of the saccade;

[0068] S102: The calculation formula of the pupil diameter variation coefficient is:

[0069]

[0070] Among them, P cv is the coefficient of variation of pupil diameter, ψ is the standard deviation of pupil diameter, is the average pupil diameter.

[0071] In this embodiment, an intention capture judgment model is established based on the eye movement features and an intention capture judgment result is output to determine whether to proceed to the next step of EEG signal recognition, thereby reducing workload and improving work efficiency. The intention capture judgment result includes an intention capture result and an intention non-capture result, which can be implemented by the following steps:

[0072] S201: Calculate the intention capture judgment index based on the eye movement feature and establish the intention capture judgment model. The calculation formula of the intention capture judgment index is:

[0073]

[0074] Among them, θ is the intention capture judgment index, S r is the scanning amplitude, BT max is the maximum average blink time, BT min is the minimum average blink time, t is time, FT is the average fixation time, BF is the blink frequency, P cv is the coefficient of variation of pupil diameter;

[0075] S202: Preset an intention capture judgment threshold, and obtain the intention capture judgment result by comparing the intention capture judgment indicator with the intention capture judgment threshold;

[0076] Determining the intention capture judgment index, when the intention capture judgment index is greater than or equal to the intention capture judgment threshold, the intention capture judgment model outputs the intention capture result;

[0077] When the intention capture judgment index is less than the intention capture judgment threshold, the intention capture judgment model outputs the intention not captured result, indicating that the user has no obvious intention and no subsequent intention judgment is required.

[0078] In this embodiment, when the intention capture judgment model outputs the intention capture result, a confirmation signal is sent to the user and the gaze drift rate is obtained, a confirmation signal reception judgment model is established and confirmation signal reception judgment information is output, and the confirmation signal reception judgment information includes confirmation signal reception information and confirmation signal non-reception information, which can be specifically implemented by the following steps:

[0079] S301: Obtaining an average power density of gaze drift rate according to the gaze drift rate, and the calculation formula is:

[0080]

[0081] Where PSD is the average power density of gaze drift rate, t1 is the first moment, t2 is the second moment, N(i) is the gaze drift rate at the i-th moment, and f s is the eye tracker frequency;

[0082] S302: Calculate the gaze drift rate differential entropy based on the gaze drift rate average power density and construct the confirmation signal reception judgment model, the calculation formula is:

[0083] DE = log2PSD,

[0084] Where DE is the differential entropy of gaze drift rate, PSD is the average power density of gaze drift rate;

[0085] S303: Preset a differential entropy threshold, and obtain the confirmation signal reception judgment information by comparing the gaze drift rate differential entropy with the differential entropy threshold;

[0086] Determining the gaze drift rate differential entropy, when the gaze drift rate differential entropy is less than or equal to the differential entropy threshold, the confirmation signal reception determination model outputs the confirmation signal reception information;

[0087] When the gaze drift rate differential entropy is greater than the differential entropy threshold, the confirmation signal reception judgment model outputs the confirmation signal not received information, indicating that the user has not received the confirmation signal and needs to resend it.

[0088] In this embodiment, when the confirmation signal reception judgment model outputs the confirmation signal reception information, a steady-state visual evoked potential signal is obtained through a brain-computer interface and a user intention judgment is performed, which can be specifically implemented through the following steps:

[0089] S401: Identify the steady-state visual evoked potential signal by using the Da Vinci chaos method. Among various types of EEG signals, the steady-state visual evoked potential signal has the advantages of low acquisition cost, convenient recording, and high time resolution. The expression of the Da Vinci chaos method is:

[0090]

[0091] Among them, S(κ) is the steady-state visual evoked potential signal, ω is the angular frequency of the periodic driving force, k is the damping coefficient, is the oscillator acceleration of the Da Vinci chaos method, is the oscillator velocity of the Darven chaos method, X(κ) is the oscillator displacement of the Darven chaos method, γ is the amplitude of the periodic driving force, is the initial phase of the periodic driving force, To drive the cycle;

[0092] S402: Convert the Da Vinci chaos method expression into an equation form and obtain the steady-state visual evoked potential signal. The conversion process is expressed as:

[0093]

[0094]

[0095] Where y is the actual displacement of the oscillator in the Da Vinci chaos method, is the actual speed of the oscillator in the Da Vinci chaos method, is the oscillator speed of the Dufferin chaos method, ω is the angular frequency of the periodic driving force, k is the damping coefficient, X(κ) is the oscillator displacement of the Dufferin chaos method, γ is the amplitude of the periodic driving force, S(κ) is the steady-state visual evoked potential signal, To drive the cycle.

[0096] An intention judgment device based on the combination of eye movement and EEG, comprising a feature extraction module, an intention capture judgment module, a confirmation signal reception judgment module, and an intention judgment module;

[0097] The feature extraction module is used to preset an eye movement monitoring cycle, and extract eye movement features according to the eye movement monitoring cycle through an eye tracker, wherein the eye movement features include average fixation time, saccade amplitude, blink frequency, average blink time, and pupil diameter variation coefficient;

[0098] The intention capture judgment module is used to establish an intention capture judgment model based on the eye movement features and output an intention capture judgment result, wherein the intention capture judgment result includes an intention capture result and an intention non-capture result;

[0099] The confirmation signal reception judgment module is used to send a confirmation signal to the user and obtain the gaze drift rate when the intention capture judgment model outputs the intention capture result, establish a confirmation signal reception judgment model and output confirmation signal reception judgment information, and the confirmation signal reception judgment information includes confirmation signal reception information and confirmation signal non-reception information;

[0100] The intention judgment module is used to obtain a steady-state visual evoked potential signal through a brain-computer interface and perform user intention judgment when the confirmation signal reception judgment model outputs the confirmation signal reception information.

[0101] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device.

[0102] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0103] The program code included on the computer readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0104] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for determining intention based on the combination of eye movement and EEG, characterized in that: The implementation of the intention determination method includes the following steps: S1: Preset an eye movement monitoring cycle, and extract eye movement features according to the eye movement monitoring cycle by an eye tracker, wherein the eye movement features include average fixation time, saccade amplitude, blink frequency, average blink time, and pupil diameter variation coefficient; S2: establishing an intention capture judgment model based on the eye movement features and outputting an intention capture judgment result, wherein the intention capture judgment result includes an intention capture result and an intention non-capture result; S201: Calculate the intention capture judgment index based on the eye movement feature and establish the intention capture judgment model. The calculation formula of the intention capture judgment index is: ， in, S is the intention capture judgment indicator. r is the scanning amplitude, BT max is the maximum average blink time, BT min is the minimum average blink time, t is time, FT is the average fixation time, BF is the blink frequency, P cv is the coefficient of variation of pupil diameter; S202: Preset an intention capture judgment threshold, and obtain the intention capture judgment result by comparing the intention capture judgment indicator with the intention capture judgment threshold; S3: when the intention capture judgment model outputs the intention capture result, sending a confirmation signal to the user and obtaining the gaze drift rate, establishing a confirmation signal reception judgment model and outputting confirmation signal reception judgment information, wherein the confirmation signal reception judgment information includes confirmation signal reception information and confirmation signal non-reception information; S301: Obtaining an average power density of gaze drift rate according to the gaze drift rate, and the calculation formula is: ， Where PSD is the average power density of gaze drift rate, t1 is the first moment, t2 is the second moment, N(i) is the gaze drift rate at the i-th moment, f s is the eye tracker frequency; S302: Calculate the gaze drift rate differential entropy based on the gaze drift rate average power density and construct the confirmation signal reception judgment model, the calculation formula is: ， Where DE is the differential entropy of gaze drift rate, PSD is the average power density of gaze drift rate; S303: Preset a differential entropy threshold, and obtain the confirmation signal reception judgment information by comparing the gaze drift rate differential entropy with the differential entropy threshold; S4: When the confirmation signal reception judgment model outputs the confirmation signal reception information, a steady-state visual evoked potential signal is acquired through a brain-computer interface and a user intention judgment is performed.

2. The intention determination method according to claim 1, characterized in that: The step S1 specifically includes: S101: construct an eye movement coordinate system and obtain the scanning amplitude. The eye movement coordinate system takes the pupil center when both eyes are looking horizontally normally as the origin. The calculation formula of the scanning amplitude is: ， Among them, S r is the scanning amplitude, X s X is the horizontal coordinate of the start of the scan, e Y is the horizontal coordinate of the end of the scan, s Y is the vertical coordinate of the start of the scan, e is the ordinate of the end of the saccade; S102: The calculation formula of the pupil diameter variation coefficient is: ， Among them, P cv is the coefficient of variation of pupil diameter, is the standard deviation of pupil diameter, is the average pupil diameter.

3. The intention determination method according to claim 1, characterized in that: The step S202 specifically includes: Determining the intention capture judgment index, when the intention capture judgment index is greater than or equal to the intention capture judgment threshold, the intention capture judgment model outputs the intention capture result; When the intention capture judgment index is less than the intention capture judgment threshold, the intention capture judgment model outputs the intention not captured result.

4. The intention determination method according to claim 1, characterized in that: The step S303 specifically includes: Determining the gaze drift rate differential entropy, when the gaze drift rate differential entropy is less than or equal to the differential entropy threshold, the confirmation signal reception determination model outputs the confirmation signal reception information; When the gaze drift rate differential entropy is greater than the differential entropy threshold, the confirmation signal reception judgment model outputs the confirmation signal non-reception information.

5. The intention determination method according to claim 1, characterized in that: The step S4 specifically includes: S401: Identify the steady-state visual evoked potential signal by using the Da Vinci chaos method. The expression of the Da Vinci chaos method is: ， in, is the steady-state visual evoked potential signal, is the angular frequency of the periodic driving force, k is the damping coefficient, is the oscillator acceleration of the Da Vinci chaos method, is the oscillator velocity of the Da Vinci chaos method, is the oscillator displacement of the Darwin chaos method, is the period driving force amplitude, is the initial phase of the periodic driving force, To drive the cycle; S402: Convert the Da Vinci chaos method expression into an equation form and obtain the steady-state visual evoked potential signal. The conversion process is expressed as: ， ， Where y is the actual displacement of the oscillator in the Da Vinci chaos method, is the actual speed of the oscillator in the Da Vinci chaos method, is the oscillator velocity of the Da Vinci chaos method, is the angular frequency of the periodic driving force, k is the damping coefficient, is the oscillator displacement of the Darwin chaos method, is the period driving force amplitude, is the steady-state visual evoked potential signal, To drive the cycle.

6. An intention judgment device based on the combination of eye movement and EEG, characterized in that: It includes a feature extraction module, an intention capture judgment module, a confirmation signal reception judgment module, and an intention judgment module; The feature extraction module is used to preset an eye movement monitoring cycle, and extract eye movement features according to the eye movement monitoring cycle through an eye tracker, wherein the eye movement features include average fixation time, saccade amplitude, blink frequency, average blink time, and pupil diameter variation coefficient; The intention capture judgment module is used to establish an intention capture judgment model based on the eye movement features and output an intention capture judgment result, wherein the intention capture judgment result includes an intention capture result and an intention non-capture result; The intention capture judgment index is calculated based on the eye movement feature and the intention capture judgment model is established. The calculation formula of the intention capture judgment index is: ， in, S is the intention capture judgment indicator. r is the scanning amplitude, BT max is the maximum average blink time, BT min is the minimum average blink time, t is time, FT is the average fixation time, BF is the blink frequency, P cv is the coefficient of variation of pupil diameter; Preset an intention capture judgment threshold, and obtain the intention capture judgment result by comparing the intention capture judgment indicator with the intention capture judgment threshold; The confirmation signal reception judgment module is used to send a confirmation signal to the user and obtain the gaze drift rate when the intention capture judgment model outputs the intention capture result, establish a confirmation signal reception judgment model and output confirmation signal reception judgment information, and the confirmation signal reception judgment information includes confirmation signal reception information and confirmation signal non-reception information; The gaze drift rate average power density is obtained by the gaze drift rate, and the calculation formula is: ， Where PSD is the average power density of gaze drift rate, t1 is the first moment, t2 is the second moment, N(i) is the gaze drift rate at the i-th moment, f s is the eye tracker frequency; The gaze drift rate differential entropy is calculated based on the gaze drift rate average power density and the confirmation signal reception judgment model is constructed. The calculation formula is: ， Where DE is the differential entropy of gaze drift rate, PSD is the average power density of gaze drift rate; Presetting a differential entropy threshold, and obtaining the confirmation signal reception judgment information by comparing the gaze drift rate differential entropy with the differential entropy threshold; The intention judgment module is used to obtain a steady-state visual evoked potential signal through a brain-computer interface and perform user intention judgment when the confirmation signal reception judgment model outputs the confirmation signal reception information.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the intention judgment method as described in any one of claims 1-5 is implemented.

8. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to perform the intention determination method as described in any one of claims 1-5 when executed by a computer processor.

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