Eye movement training evaluation method and eye movement training evaluation device

Abstract

The invention is suitable for the technical field of eye movement training, and provides an eye movement training evaluation method and an eye movement training evaluation device.The method comprises the steps that in the eye movement training process, an electro-oculogram signal of a user is obtained; based on the electrooculogram signal, determining a target eye movement frequency band of the user, the target eye movement frequency band being a rapid eye movement frequency band of the user; determining a first power spectral density of the electrooculogram signal in the target eye movement frequency band; based on the first power spectral density, the extraocular muscle movement intensity of the user is determined, the extraocular muscle movement intensity refers to the contraction intensity of the extraocular muscle of the user, and the extraocular muscle movement intensity reflects the extraocular muscle state of the user. According to the invention, the problems of poor real-time performance, insufficient objectivity and the like of eye movement training effect evaluation can be solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of eye movement training, and particularly relates to an eye movement training evaluation method and an eye movement training evaluation device. BACKGROUND

[0002] The extraocular muscles are six muscles (i.e., four straight muscles and two oblique muscles) that control the movement of eyeballs. The health of the extraocular muscles is often related to problems such as abnormal eyeball movement, diplopia, and strabismus. In the current clinical and rehabilitation training, eye movement training is often used to improve the function of the extraocular muscles, alleviate symptoms such as visual fatigue, strabismus, amblyopia, and neurological eye movement disorders in patients. The existing training methods mostly rely on the self-completion of specific fixation or eye movement tasks by the trainer, such as inducing the trainer to perform eye movement training through visual guidance methods such as light, image, light heat, and flicker mode, and coordinating the buzzer, light, or voice to realize training prompts and training rhythm control. Most devices only rely on external stimuli or key performance indicators (such as the number of completions and time) to judge the effect of eye movement training, lack of measurement of the real physiological movement characteristics of the eyeball, and thus result in problems such as poor real-time performance and insufficient objectivity in the evaluation of the effect of eye movement training. SUMMARY

[0003] The embodiments of the application provide an eye movement training evaluation method and an eye movement training evaluation device, which can solve the problems of poor real-time performance and insufficient objectivity in the evaluation of the effect of eye movement training.

[0004] In a first aspect, the embodiments of the application provide an eye movement training evaluation method, which includes the following steps. During the eye movement training process, an electrooculogram signal of a user is acquired; Based on the electrooculogram signal, a target eye movement frequency band of the user is determined, and the target eye movement frequency band refers to a rapid eye movement frequency band of the user; A first power spectral density of the electrooculogram signal in the target eye movement frequency band is determined; Based on the first power spectral density, an extraocular muscle movement intensity of the user is determined, the extraocular muscle movement intensity refers to the contraction intensity of the extraocular muscles of the user, and the extraocular muscle movement intensity reflects the state of the extraocular muscles of the user.

[0005] In the embodiment of the present application, in the eye movement training process, by acquiring the electrooculogram signal of the user, the fast eye movement frequency band (i.e., the target eye movement frequency band) of the user can be determined based on the electrooculogram signal. Since the eye movement training requires the user to perform fast eye movement, the target eye movement frequency band contains more eye movement related information, and the power spectral density (i.e., the first power spectral density) in the target eye movement frequency band can indirectly measure the eye muscle movement intensity (i.e., the contraction intensity of the eye muscle of the user) of the user, thereby achieving the measurement of the real physiological movement characteristics of the eyeball. The movement of the eyeball mainly relies on the control of the extraocular muscle, and each eye movement requires the coordinated contraction and relaxation of the extraocular muscle. The eye muscle movement intensity refers to the contraction intensity of the extraocular muscle, which is a comprehensive performance of the contraction speed, contraction amplitude and tension of the extraocular muscle, and can reflect the state of the extraocular muscle of the user. Since the eye movement training directly mobilizes the activity of the extraocular muscle, the state of the extraocular muscle can be immediately reflected in the quantitative indicator of the eye muscle movement intensity, so that the state of the extraocular muscle of the user can be reflected by the quantitative indicator of the eye muscle movement intensity acquired in the eye movement training process, and the effect of the eye movement training can be evaluated in a timely and objective manner, thereby solving the problems of poor real-time performance and insufficient objectivity in the evaluation of the training effect.

[0006] In a second aspect, the embodiment of the present application provides an eye movement training evaluation device, comprising: a signal acquisition module, configured to acquire an electrooculogram signal of a user in an eye movement training process; a target determination module, configured to determine a target eye movement frequency band of the user based on the electrooculogram signal, the target eye movement frequency band being a fast eye movement frequency band of the user; a density determination module, configured to determine a first power spectral density of the electrooculogram signal in the target eye movement frequency band; an intensity determination module, configured to determine an eye muscle movement intensity of the user based on the first power spectral density, the eye muscle movement intensity being a contraction intensity of the eye muscle of the user, and the eye muscle movement intensity reflecting a state of the eye muscle of the user.

[0007] In a third aspect, the embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, the electronic device implements the eye movement training evaluation method of the first aspect described above.

[0008] In a fourth aspect, the embodiment of the present application provides a computer readable storage medium, which stores a computer program. When the computer program is executed by a computer, the eye movement training evaluation method of the first aspect described above is implemented.

[0009] In a fifth aspect, an embodiment of the present application provides a computer program product, comprising a computer program, which, when executed, causes the eye movement training evaluation method according to the first aspect to be performed.

[0010] It can be understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description in the first aspect, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0012] Figure 1 is a flowchart of an eye movement training evaluation method provided by an embodiment of the present application; Figure 2 is a setting example diagram of an electrooculogram electrode provided by an embodiment of the present application; Figure 3 is another flowchart of an eye movement training evaluation method provided by an embodiment of the present application; Figure 4 is an example diagram of a frequency spectrum density of a user provided by an embodiment of the present application; Figure 5 is an example diagram of a frequency spectrum density of a user 1 provided by an embodiment of the present application; Figure 6 is an example diagram of a frequency spectrum density of a user 2 provided by an embodiment of the present application; Figure 7 is a structural schematic diagram of an eye movement training evaluation device provided by an embodiment of the present application; Figure 8 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures, techniques, etc. are presented in order to thoroughly understand the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits and methods are omitted in order not to obscure the description of the present application with unnecessary details.

[0014] It should be understood that when used in the specification and the appended claims, the term "comprise" indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0015] In addition, in the description of the specification and the appended claims of the present application, the terms "first", "second", "third", etc. are only used for differentiation of description, and cannot be understood as indicating or implying relative importance.

[0016] The eye movement training evaluation method provided by the embodiments of the present application can be applied to electronic devices such as mobile phones, tablet computers, wearable devices, notebook computers, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA), etc. The embodiments of the present application do not make any limitation on the specific type of electronic devices.

[0017] For example, the electronic device in the embodiments of the present application can be a mobile phone. An electrooculogram (EOG) electrode can be embedded in the eye training glasses. During the eye movement training process, the user wears the eye training glasses. The eye training glasses can collect the electrooculogram signals of the user and send the collected electrooculogram signals to the electronic device. Based on the electrooculogram signals, the electronic device can evaluate the eye movement training effect of the user. As a non-invasive method for reflecting eye movement by detecting the cornea-retina potential difference changes caused by eye movement, the electrooculogram electrode has the advantages of simple operation, strong real-time performance, low cost, etc. and is widely used in eye movement monitoring, sleep analysis, fatigue detection, etc.

[0018] Please refer to Figure 1 , Figure 1 The flowchart of the eye movement training evaluation method provided by the embodiments of the present application is shown. As an example but not limitation, the method comprises the following steps: Step 101, during the eye movement training process, the electrooculogram signals of the user are acquired.

[0019] The electrooculogram signals record the potential changes of the extraocular muscles when the user's eyeballs move.

[0020] In this embodiment, during the eye movement training (such as attention improvement training, hand-eye coordination improvement training, rehabilitation training, etc.) of the user, the electrooculogram electrode can be used to collect the electrooculogram signals of the user's eyes in real time, and the collected electrooculogram signals can be sent to the electronic device in real time. Thus, the electronic device can acquire the electrooculogram signals of the user and evaluate the eye movement training effect of the user based on the electrooculogram signals of the user.

[0021] As an example but not limitation, as shown in Figure 2 The setting example of the electrooculogram electrode can include five electrodes, such as the upper electrode, the lower electrode, the right electrode, the left electrode, and the ground electrode. The upper electrode and the lower electrode detect the vertical direction of the eyeball movement. The right electrode and the left electrode detect the horizontal direction of the eyeball movement. The ground electrode can effectively suppress common-mode interference to ensure the stability and accuracy of the measured electrooculogram signal.

[0022] In order to enable the electrooculogram electrode to effectively distinguish the electrooculogram signal and the electromyographic interference, the sampling frequency of the electrooculogram electrode can be set to 250 ~ 1000Hz.

[0023] In order to improve the evaluation accuracy of the electronic device on the eye movement training effect, after obtaining the electrooculogram signal, the electronic device can first preprocess the electrooculogram signal, and then perform subsequent steps 102 to 104 based on the electrooculogram signal after preprocessing.

[0024] In this embodiment, the preprocessing of the electrooculogram signal includes but is not limited to: band-pass filtering the electrooculogram signal to remove direct current drift and high frequency noise; using sliding mean or wavelet denoising algorithm to smooth the electrooculogram signal to improve the quality of the electrooculogram signal; baseline correction of the electrooculogram signal to eliminate the resting potential offset before eye movement. Optionally, the above band-pass filtering allows signals within 0 ~ 30Hz to pass and suppresses signals of other frequencies.

[0025] When the user performs eye movement training, the user usually performs eye movement training based on the pre-set target eye movement direction. On this basis, in order to accurately obtain the target eye movement frequency band of the user, in an embodiment, the electronic device can first determine the actual eye movement direction of the user based on the electrooculogram signal; if the actual eye movement direction matches the target eye movement direction, the target eye movement frequency band is determined based on the electrooculogram signal; if the actual eye movement direction does not match the target eye movement direction, steps 102 to 104 are not performed. Wherein, the actual eye movement direction matching the target eye movement direction can mean that the actual eye movement direction is consistent or identical with the target eye movement direction. The actual eye movement direction not matching the target eye movement direction can mean that the actual eye movement direction is not consistent or identical with the target eye movement direction.

[0026] In this embodiment, the electronic device can use the time domain feature method to extract the waveform of the electrooculogram signal in real time, and judge the actual eye movement direction of the user through the potential difference related data between the electrodes. The judgment process is as follows: Calculate the potential difference EOG H between the right electrode and the left electrode; calculate the potential difference EOG V between the upper electrode and the lower electrode. if | EOG H | < θ H or | EOG V | < θ V , it is determined that the user has no eye movement, i.e., the user's eyes do not move; if EOG H > θ H and | EOG H | > | EOG V |, it is determined that the actual eye movement direction is to the right; if EOG H < - θ H and | EOG H | > | EOG V |, it is determined that the actual eye movement direction is to the left; if EOG V > θ V and | EOG H | < | EOG V |, it is determined that the actual eye movement direction is upward; if EOG V < - θ V and | EOG H | < | EOG V |, it is determined that the actual eye movement direction is downward.

[0027] wherein θ H represents a horizontal direction threshold value; and θ V represents a vertical direction threshold value.

[0028] Optionally, the horizontal direction threshold value and the vertical direction threshold value can be set based on empirical values, for example, the horizontal direction threshold value or the vertical direction threshold value is 50 µV; or the horizontal direction threshold value and the vertical direction threshold value can be determined based on the potential difference of the user in a resting state, which is not limited in the present application. The resting state can refer to a state without eye movement.

[0029] In the present embodiment, the formula for determining the horizontal direction threshold value and the vertical direction threshold value based on the potential difference of the user in the resting state is as follows: θ H = μ restH + 3σ H θ V = μ restV + 3σ V μ restH represents the potential difference between the right electrode and the left electrode of the user in the resting state; σ H represents the standard deviation of the potential difference between the right electrode and the left electrode of the user in the resting state; and 3σ H represents 3 times σH ; μ restV represents the potential difference between the upper electrode and the lower electrode in the resting state of the user; σ V represents the standard deviation of the potential difference between the upper electrode and the lower electrode in the resting state of the user, 3σ V represents 3 times σ V .

[0030] In this embodiment, in the case that the actual eye movement direction matches the target eye movement direction, it can be determined that the actual eye movement direction is correct, and the electronic device can issue a first prompt information to prompt the user to continue the eye movement training. In the case that the actual eye movement direction does not match the target eye movement direction, it can be determined that the actual eye movement direction is incorrect, and the electronic device can issue a second prompt information different from the first prompt information to prompt the user to correct the error. As an example but not limited to, the first prompt information is a green prompt mark or a prompt sound representing that the actual eye movement direction is correct; the second prompt information is a red prompt mark or a warning sound.

[0031] In this embodiment, the electronic device can realize instant judgment of the actual eye movement direction, real-time feedback prompt of the actual eye movement direction error, and target eye movement frequency band when the actual eye movement direction is correct during the eye movement training, so as to objectively evaluate the eye muscle movement intensity of each target eye movement direction. The eye muscle movement intensity can refer to the contraction intensity of the eye muscle.

[0032] In step 102, based on the electrooculogram signal, the target eye movement frequency band of the user is determined.

[0033] The target eye movement frequency band refers to the rapid eye movement frequency band of the user. The rapid eye movement can be understood as the rapid movement of the eyeball.

[0034] The electrooculogram signal can not only judge the movement direction of the eyeball (i.e. eye movement direction), but also indirectly measure the eye muscle movement intensity. It is found through research that the typical eye movement duration of human is in the range of 20~200ms, which is converted to a frequency of 5~50Hz, but the real stable and significant signal energy value is mostly distributed in a lower frequency range, especially in the frequency band of about 3~10Hz. When the eye muscle movement intensity driven by the eyeball increases, the signal energy value in this frequency band increases. The higher the signal energy value, the greater the eye movement amplitude, and the faster the eye movement. The eye movement training usually requires the user to perform rapid eye movement, so the signal in the frequency band of about 3~10Hz can indirectly measure the eye muscle movement intensity.

[0035] It should be understood that although signals with frequencies less than 3 Hz are also related to eye movement, they are similar in frequency to some noise (such as blinking, head movement, slow eye drift, and other artifacts), and eye movement training requires users to perform rapid eye movement rather than slow fixation drift. Although signals with frequencies greater than 10 Hz are also related to eye movement, the energy values of signals in the frequency band greater than 10 Hz gradually decrease, and are not easy to distinguish from noise. Therefore, signals in the frequency band of about 3-10 Hz can not only contain most typical rapid eye movement signals, but also effectively exclude low-frequency drift and high-frequency noise.

[0036] Since there are individual differences in the strength of the extraocular muscle movement, not all individuals' rapid eye movement frequency bands are completely within the range of 3-10 Hz, so in order to more accurately measure the strength of the user's extraocular muscle movement, the target eye movement frequency band of the user can be determined based on the electrooculogram signal.

[0037] It should be understood that when the user performs eye movement training based on the target eye movement direction, the number of target eye movement directions can be one or more, the number of training times for each target eye movement direction can be one or more, and each training of each target eye movement direction corresponds to an actual eye movement direction. Therefore, when the electronic device completes one training, it can determine the electrooculogram signal of the training from the acquired electrooculogram signal, determine the actual eye movement direction of the user in the training based on the electrooculogram signal of the training, and match the actual eye movement direction with the target eye movement direction of the training. If the actual eye movement direction matches the target eye movement direction of the training, the target eye movement frequency band can be determined based on the electrooculogram signal of the training, or the electrooculogram signal of the eye movement start to end window of the training can be cut from the electrooculogram signal of the training (for example, cut according to a fixed window length of 800 ms), and the target eye movement frequency band is determined based on the electrooculogram signal.

[0038] In one possible implementation, the electronic device can determine the target eye movement frequency band through steps 301-303 as shown in Figure 3

[0039] Step 301, determine the second power spectral density of the electrooculogram signal in a specific frequency band.

[0040] The specific frequency band refers to the frequency band of the eye movement signal. The second power spectral density includes the corresponding relationship between each frequency and the power spectral density value in the specific frequency band.

[0041] ​Research has found that the frequency band of rapid eye movement (REM) is usually in the range of 3 to 10 Hz. Considering individual differences, the frequency band where the eye movement signal is located can be defined as a specific frequency band, which covers 3 to 10 Hz. This allows electronic devices to adaptively determine the user's target eye movement frequency band within the specific frequency band based on the electrooculogram (EOG) signal.

[0042] Studies have found that eye-tracking signals are typically distributed within the range of 0.5 to 30 Hz, therefore, 0.5 to 30 Hz can be defined as a specific frequency band. Of course, it is understandable that the specific frequency band can be modified based on empirical values ​​or actual needs, and this application does not impose any limitations on this.

[0043] In this embodiment, the Welch method can be used to calculate the second power spectral density. The specific calculation formula is as follows:

[0044] in, Indicates a specific frequency band. Indicates frequency, It's an electrooculogram (EOG) signal. Indicates the sampling rate. Indicates the segment length. Represents the window function. This represents the power normalization term of the window function.

[0045] In order to accurately obtain the target eye movement frequency band for the user's current eye movement training in the target eye movement direction, the electronic device can determine the second power spectral density and execute subsequent steps when the actual eye movement direction of the current eye movement training matches the target eye movement direction, so as to evaluate the training effect of the user's current eye movement training.

[0046] Step 302: Determine the target frequency based on the second power spectral density.

[0047] The target frequency is the frequency at which the rate of decrease of the power spectral density value in the second power spectral density changes the most.

[0048] The second power spectral density is a curve representing the relationship between frequency and power spectral density value. By solving for the local maxima of the second derivative of this curve, the location of the maximum rate of decrease in the power spectral density value can be obtained; the frequency at this location is the target frequency. Specifically, the target frequency can be determined using the following formula. :

[0049] like Figure 4 The image shown is an example plot of the power spectral density. Figure 4 The mild and strong eye movements in the text refer to the same user's eye movements at different intensities.Figure 4 Taking the power spectral density of mild eye movement as an example, the power spectral density value (i.e., signal energy value) at low frequencies is relatively low during mild eye movement. As the frequency increases, the power spectral density value drops sharply. After exceeding a certain frequency, the decrease in power spectral density value slows down. This change process establishes an inflection point, which is the position where the rate of decrease in power spectral density value changes the most (i.e., the position where the rate of decay of power spectral density value changes significantly).

[0050] Depend on Figure 4 The power spectral density of mild and strong eye movements in electrooculograms (EOG) reveals that the power spectral density is typically higher at low frequencies and gradually decreases at high frequencies. The inflection point reflects the boundary between rapid eye movement signals and high-frequency noise in the EOG signal, and can also be seen as the boundary of the main energy bandwidth of the EOG signal. Therefore, the frequency at the inflection point can be determined as the target frequency to more accurately determine the user's target eye movement frequency band.

[0051] Depend on Figure 4 It is known that the frequency corresponding to the inflection point in the power spectral density of mild eye movements is lower than that of strong eye movements, and the eye movement velocity of mild eye movements is lower than that of strong eye movements. Therefore, it can be determined that eye movement velocity affects the position of the inflection point in the power spectral density; different inflection point positions correspond to different frequencies, with higher eye movement velocities corresponding to higher frequencies. Thus, the frequency at the inflection point can be used as a basis for determining the frequency band of rapid eye movements, thereby achieving adaptive determination of the target frequency based on electrooculogram (EOG) signals.

[0052] like Figure 5 and Figure 6 The image shows an example plot of the frequency spectral density for two different users (e.g., user 1 and user 2). Figure 5 and Figure 6 It is known that the power spectral density value at the same frequency is different for the same user under mild eye movement and strong eye movement. The inflection point is different for different users under mild eye movement and also different under strong eye movement. Therefore, by determining the frequency at the inflection point as the target frequency, the target eye movement frequency band can be accurately determined, thereby accurately determining the intensity of extraocular muscle movement.

[0053] The individual difference of the inflection point in the power spectral density is caused by the physiological structure difference, the innervation difference, the eye habit difference, etc. For example, some users have stronger extraocular muscles and faster eye movements, so the power spectral density value is high and the frequency at the inflection point is high. Some users have weaker or fatigued extraocular muscles and slower eye movements, so the high power spectral density value is concentrated at a lower frequency, and the frequency at the inflection point is low. Some users have more sensitive neural responses and faster extraocular muscle contractions, so the frequency at the inflection point is high. If the neural response is sluggish, the frequency at the inflection point is low. Some users habitually make large and fast saccades when they are fixating, so the frequency at the inflection point is high. Some users are used to slow eye movements, so the frequency at the inflection point is low. In addition, as age increases, the strength of the extraocular muscles decreases, and the eye movement speed also slows down, thereby affecting the position of the inflection point. Therefore, determining the inflection point based on the power spectral density of the individual and determining the frequency at the inflection point as the target frequency can more accurately determine the effective eye movement frequency range (i.e., the target eye movement frequency band), thereby accurately determining the strength of the extraocular muscle movement.

[0054] It should be understood that, Figure 4 , Figure 5 and Figure 6 The PSD in the above formula represents the power spectral density value.

[0055] Step 303, determining the target eye movement frequency band based on the target frequency.

[0056] Since the target frequency reflects the demarcation point of the rapid eye movement signal and the high-frequency noise in the electrooculogram signal, the target eye movement frequency band of the user can be more accurately determined based on the target frequency.

[0057] In order to improve the efficiency of obtaining the target eye movement frequency band of any target eye movement direction, in an embodiment, when the user has performed multiple times (for example, twice) of training for the target eye movement direction, the electronic device can calculate the average value of the target eye movement frequency bands obtained when the multiple times of training are performed, and determine the average value as the target eye movement frequency band for the next time of training for the target eye movement direction.

[0058] In a possible implementation, the above step 303 can include: Determining the lowest frequency of the target eye movement frequency band based on the target frequency, and determining the target frequency as the highest frequency of the target eye movement frequency band.

[0059] Since the target frequency reflects the demarcation point of the rapid eye movement signal and the high-frequency noise in the electrooculogram signal, determining the target frequency as the highest frequency of the target eye movement frequency band can avoid misjudging the high-frequency noise as the eye movement signal.

[0060] In an embodiment, the electronic device subtracts a preset value from the target frequency to obtain a frequency difference value, and determines the frequency difference value as the lowest frequency of the target eye movement frequency band.

[0061] Optionally, the preset value can be set according to experience value or actual demand. As an example but not limitation, the preset value is 5 Hz.

[0062] In order to avoid the interference of noise (e.g. low frequency noise), in an embodiment, after determining the target eye movement frequency, the electronic device can determine whether the target eye movement frequency band contains noise frequency band, and if the target eye movement frequency band contains noise frequency band, remove the noise frequency band from the target eye movement frequency band.

[0063] Optionally, the noise frequency band can be set according to actual demand. As an example but not limitation, the noise frequency band is 0~3 Hz.

[0064] Step 103, determine the first power spectral density of the electrooculogram signal in the target eye movement frequency band.

[0065] The first power spectral density includes the corresponding relationship between each frequency in the target eye movement frequency band and the power spectral density value.

[0066] It should be understood that the first power spectral density can be calculated by Welch method, and the specific calculation formula can be referred to the second power spectral density, which will not be described here.

[0067] Step 104, determine the eye muscle movement intensity of the user based on the first power spectral density.

[0068] The eye muscle movement intensity refers to the contraction intensity of the eye muscle of the user. The eye muscle movement intensity reflects the eye muscle state of the user, and the eye muscle state can be quantitatively evaluated through the eye muscle movement intensity.

[0069] Eye movement mainly relies on six extraocular muscles including medial rectus, lateral rectus, superior rectus, inferior rectus, superior oblique and inferior oblique. Each eye movement needs the coordinated contraction and relaxation of these extraocular muscles. The eye muscle movement intensity is a comprehensive performance of the contraction speed, contraction amplitude and tension of the extraocular muscles. When the eye muscle state is poor (e.g. long time use of eyes or some eye muscle diseases cause the eye muscle to be in fatigue state), the contraction speed and contraction amplitude of the eye muscle decrease, which causes the eye muscle to be unable to drive the eyeball to move quickly and with high amplitude, i.e. the eye movement amplitude decreases and the eye movement speed slows down, and the corresponding power spectral density value in the frequency domain decreases, especially the middle and high frequency components decrease. In this case, the eye muscle movement intensity obtained is small; when the eye muscle state is good, the eye muscle can drive the eyeball to move quickly and with high amplitude. In this case, the eye muscle movement intensity obtained is large. Therefore, the eye muscle movement intensity can reflect the eye muscle state.

[0070] Since the coordination of the extraocular muscles is related to the strength of the extraocular muscle movement in different target eye movement directions, in normal circumstances, the strength of the extraocular muscle movement in different target eye movement directions is balanced. If one or more extraocular muscles are limited (for example, the eye turns to the right is limited due to abducens nerve palsy), the strength of the extraocular muscle movement in different target eye movement directions is unbalanced, so in the case that the actual eye movement direction matches the target eye movement direction, the strength of the extraocular muscle movement of the user in the target eye movement direction can be determined based on the first power spectral density, and on this basis, the state of the extraocular muscles of the user in different target eye movement directions can be analyzed, and different personalized training plans can be developed for different target eye movement directions.

[0071] The electrooculogram signal is easily affected by physiological artifacts (such as blinking, electromyography, electrocardiogram interference), and if the amplitude of the electrooculogram signal is directly used to determine the strength of the extraocular muscle movement, the strength of the extraocular muscle movement is easily affected by these transient fluctuations and baseline drifts, and cannot stably and objectively reflect the strength of the extraocular muscle movement. The extraocular muscles produce energy enhancement in the target eye movement frequency band when moving, the first power spectral density can quantify the power change in the target eye movement frequency band, distinguish eye movement related signals and other artifacts, and reflect the strength of the extraocular muscle movement, avoiding the interference of high amplitude but non-eye movement related signals. Therefore, based on the first power spectral density, the strength of the extraocular muscle movement of the user can be accurately determined.

[0072] Since the eye movement training directly mobilizes the activity of the extraocular muscles, the contraction speed, strength, coordination and other states of the extraocular muscles can be reflected in the power spectral density in a timely manner. Therefore, after each training is performed, the electronic device can observe the changes of the inflection point and the changes of the power spectral density value in real time from the power spectral density, determine the strength of the extraocular muscle movement in a timely manner, and thus realize the timely evaluation of the eye movement training effect.

[0073] Since the electrooculogram signal and the strength of the extraocular muscle movement generated by the extraocular muscles during the eye movement training are quantifiable indicators, which do not depend on the subjective report of the user, the electronic device can objectively evaluate the eye movement training effect based on these quantifiable indicators.

[0074] In one possible implementation, the electronic device can determine the strength of the extraocular muscle movement of the user through steps a1 and a2.

[0075] Step a1, based on the first power spectral density, determining the first total power of the electrooculogram signal in the target eye movement frequency band.

[0076] The electronic device integrates the first power spectral density in the entire target eye movement frequency band to obtain the first total power.

[0077] Step a2, based on the first total power, determining the strength of the extraocular muscle movement.

[0078] In one embodiment, the electronic device can directly determine the first total power as the intensity of the user's extraocular muscle movement.

[0079] Considering that directly determining the first total power as the extraocular muscle movement intensity is easily affected by individual and environmental differences, in another embodiment, the electronic device can first determine the second total power of the electrooculogram signal in a specific frequency band based on the second power spectral density before determining the extraocular muscle movement intensity; then, the extraocular muscle movement intensity is determined based on the first total power and the second total power.

[0080] Optionally, the percentage of the first total power to the second total power can be calculated, and this percentage can be determined as the extraocular muscle movement intensity; alternatively, the ratio of the first total power to the second total power can be calculated, and this ratio can be determined as the extraocular muscle movement intensity.

[0081] The percentage of the first total power to the second total power and the ratio of the first total power to the second total power both reflect individual eye movement characteristics. They can standardize the intensity of extraocular muscle movement, eliminate the influence of fluctuations in the second total power, and make the intensity of extraocular muscle movement reflect only the relative changes in the target eye movement frequency band, thereby enhancing the stability and comparability of the intensity of extraocular muscle movement.

[0082] As an example, not a limitation, the specific frequency band is 0.5~30Hz, and the target eye-tracking frequency band is... First total power The calculation formula is as follows:

[0083] in, Indicates frequency resolution. Represents frequency The power spectral density value at that location, The value range is the frequency corresponding to each frequency point within the target eye movement frequency band.

[0084] Second total power The calculation formula is as follows:

[0085] in, Represents frequency The power spectral density value at that location, The value range is the frequency corresponding to each frequency point within a specific frequency band.

[0086] extraocular muscle movement intensity The calculation formula is as follows:

[0087] In an embodiment, after the user completes the eye movement training based on the current stage of the eye movement training scheme, the electronic device can generate the eye movement training scheme of the next stage of the current stage through steps b1 and b2, thereby generating a personalized training scheme for the user.

[0088] Step b1, obtaining the training execution of the user.

[0089] The training execution includes at least one of the following: the training completion degree of the user in the current stage of the eye movement training scheme, the training accuracy of each target eye movement direction in the current stage of the eye movement training scheme, the average eye muscle movement intensity in the current stage of the eye movement training scheme, and the average eye muscle movement intensity in at least one historical stage of the eye movement training scheme. The eye movement training scheme of each stage includes training parameters of at least one target eye movement direction, including but not limited to training times, training speed, eye movement amplitude, etc.

[0090] During the process of the user performing eye movement training based on the current stage of the eye movement training scheme, the electronic device can count the number of actual eye movement directions that are correct for each target eye movement direction. The training completion degree can be the ratio of the total number of actual eye movement directions that are correct to the total training times of the current stage of the eye movement training scheme, or the percentage of the total number of actual eye movement directions that are correct in the total training times. The total number of actual eye movement directions that are correct is the sum of the number of actual eye movement directions that are correct for each target eye movement direction. The total training times of the current stage of the eye movement training scheme is the sum of the training times of all target eye movement directions in the current stage of the eye movement training scheme. The training accuracy of any target eye movement direction can be the ratio of the number of actual eye movement directions that are correct for the target eye movement direction to the training times of the target eye movement direction, or the percentage of the number of actual eye movement directions that are correct in the training times of the target eye movement direction.

[0091] As an example but not limited to, the target eye movement directions of the current stage of the eye movement training scheme include four directions: left, right, up, and down, and the training times of the four directions are all 25. The number of actual eye movement directions that are correct for the left direction is 25, the number of actual eye movement directions that are correct for the right direction is 20, the number of actual eye movement directions that are correct for the up direction is 15, and the number of actual eye movement directions that are correct for the down direction is 20. Then the training completion degree can be calculated as 80%, the training accuracy of the left direction is 100%, the training accuracy of the right direction is 80%, the training accuracy of the up direction is 60%, and the training accuracy of the down direction is 80%.

[0092] In an embodiment, the average eye muscle movement intensity of the eye movement training scheme of any stage can be determined without distinguishing the target eye movement directions. That is, the average eye muscle movement intensity of the eye movement training scheme of any stage can refer to the average of all eye muscle movement intensities corresponding to each target eye movement direction in the eye movement training scheme of the stage. Any eye muscle movement intensity corresponding to any target eye movement direction can refer to the eye muscle movement intensity calculated when the actual eye movement direction of the user matches the target eye movement direction.

[0093] In another embodiment, in order to facilitate the evaluation of the training effect of different target eye movement directions, the average eye muscle movement intensity of the eye movement training scheme of any stage can be determined by distinguishing different target eye movement directions. That is, the average eye muscle movement intensity of the eye movement training scheme of any stage includes the average eye muscle movement intensities of each target eye movement direction in the eye movement training scheme of the stage. The average eye muscle movement intensity of any target eye movement direction can refer to the average of all eye muscle movement intensities corresponding to the target eye movement direction.

[0094] Step b2, updating the training parameters in the eye movement training scheme of the current stage based on the training execution, to obtain the eye movement training scheme of the next stage.

[0095] Based on the eye movement execution conditions such as the training completion degree, the training accuracy, and the average eye muscle movement intensity of the user in the training process, the electronic device can dynamically adjust the training parameters of the user in the next stage, thereby formulating a personalized training scheme for the user and improving the training effect and compliance.

[0096] In a possible implementation, the average eye muscle intensity of the user in the eye movement training scheme of the current stage includes the average eye muscle movement intensities of each target eye movement direction in the eye movement training scheme of the current stage, and each target eye movement direction includes the left eye movement direction and the right eye movement direction. The above updating the training parameters in the eye movement training scheme of the current stage based on the training execution can include: determining whether there is a training imbalance between the left eye movement direction and the right eye movement direction based on the average eye muscle movement intensity of the left eye movement direction and the average eye muscle movement intensity of the right eye movement direction; if there is a training imbalance between the left eye movement direction and the right eye movement direction, then the training parameters of the left eye movement direction and the right eye movement direction in the eye movement training scheme of the current stage are updated differently.

[0097] Since the intensity distribution of the eye muscle movement of the user in the left eye movement direction and the right eye movement direction is balanced under normal circumstances, if the intensity distribution is unbalanced, it indicates that there is an unbalanced training condition in the left eye movement direction and the right eye movement direction, and if the intensity distribution is balanced, it indicates that there is no unbalanced training condition in the left eye movement direction and the right eye movement direction. Therefore, based on the average eye muscle movement intensity in the left eye movement direction and the average eye muscle movement intensity in the right eye movement direction, it can be determined whether there is an unbalanced training condition in the left eye movement direction and the right eye movement direction.

[0098] The electronic device can calculate the absolute value of the difference between the average eye muscle movement intensity in the left eye movement direction and the average eye muscle movement intensity in the right eye movement direction, and if the absolute value of the difference is greater than a first preset threshold, it is determined that there is an unbalanced training condition in the left eye movement direction and the right eye movement direction; if the absolute value of the difference is less than or equal to the first preset threshold, it is determined that there is no unbalanced training condition in the left eye movement direction and the right eye movement direction.

[0099] In the case of an unbalanced training condition in the left eye movement direction and the right eye movement direction, the electronic device can determine a first eye movement direction and a second eye movement direction from the left eye movement direction and the right eye movement direction, the first eye movement direction being the eye movement direction with the maximum average eye muscle movement intensity in the left eye movement direction and the right eye movement direction, and the second eye movement direction being the eye movement direction with the minimum average eye muscle movement intensity in the left eye movement direction and the right eye movement direction. The electronic device can update the first training number of the first target eye movement direction in the current stage of the eye movement training scheme to a second training number and update the first eye movement amplitude to a second eye movement amplitude, and update the third training number of the second target eye movement direction in the current stage of the eye movement training scheme to a fourth training number and update the third eye movement amplitude to a fourth eye movement amplitude, the second training number being greater than the first training number, the second eye movement amplitude being greater than the first eye movement amplitude, the fourth training number being less than the third training number, and the fourth eye movement amplitude being less than the third eye movement amplitude.

[0100] In this embodiment, when the average eye muscle movement intensity of the user is unbalanced left and right, by increasing the training number and eye movement amplitude of the first target eye movement direction and appropriately reducing the training number and eye movement amplitude of the second target eye movement direction, different training parameters can be formulated for the left and right directions. In another possible implementation, each target eye movement direction includes at least one of the four directions of left, right, up, and down, and on this basis, the above step b2 can further include the following four embodiments.

[0101] In the embodiment one, if the training completion degree is greater than or equal to the second preset threshold value, and the third target eye movement direction exists in the target eye movement directions, the first training speed of the third target eye movement direction in the eye movement training scheme of the current stage is updated to the second training speed, or the fifth eye movement amplitude of the third target eye movement direction in the eye movement training scheme of the current stage is updated to the sixth eye movement amplitude, the second training speed is greater than the first training speed, the sixth eye movement amplitude is greater than the fifth eye movement amplitude, and the third target eye movement direction is the target eye movement direction with the average eye muscle movement intensity within the preset intensity range in the eye movement training scheme of the current stage. Optionally, the preset intensity range can be set according to actual needs.

[0102] In the embodiment one, if the training completion degree is greater than or equal to the second preset threshold value, it indicates that the user has a high training completion degree in the eye movement training scheme of the current stage, and the average eye muscle movement intensity of the third target eye movement direction is within the preset intensity range. In this case, the training speed of the third target eye movement direction is increased or the eye movement amplitude of the third target eye movement direction is increased, so as to shorten the training time of the user in the next stage and improve the training efficiency of the user in the next stage.

[0103] In the embodiment two, if the fourth target eye movement direction exists in the target eye movement directions, the third training speed of the fourth target eye movement direction in the eye movement training scheme of the current stage is updated to the fourth training speed, the fourth training speed is less than the third training speed, and the fourth target eye movement direction is the target eye movement direction with the training accuracy less than the third preset threshold value or the average eye muscle movement intensity in the eye movement training scheme of the current stage greater than or equal to the fourth preset threshold value than the average eye muscle movement intensity in the historical eye movement training schemes.

[0104] In the embodiment two, since the training accuracy of the fourth target eye movement direction is less than the third preset threshold value, or the average eye muscle movement intensity of the fourth target eye movement direction in the current stage is greater than or equal to the fourth preset threshold value than the average eye muscle movement intensity in the historical stages, it indicates that the training accuracy of the fourth target eye movement direction is low or the average eye muscle movement intensity of the fourth target eye movement direction in the current stage is decreased more than the average eye muscle movement intensity in the previous stage. In this case, the training speed of the fourth target eye movement direction is reduced, so as to improve the training enthusiasm of the user in the fourth target eye movement direction.

[0105] In the third embodiment, if the training accuracy of the fifth target eye movement direction is less than the training accuracy of other eye movement directions, and the absolute value of the difference between the training accuracy of the fifth target eye movement direction and the training accuracy of other eye movement directions is greater than the fifth preset threshold, it indicates that the fifth target eye movement direction has more biased errors. By increasing the training times of the fifth target eye movement direction, the training proportion of the fifth target eye movement direction in the next stage can be adjusted, so that the training of the fifth target eye movement direction is more biased in the next stage.

[0106] In the third embodiment, if the training accuracy of the fifth target eye movement direction is less than the training accuracy of other eye movement directions, and the absolute value of the difference between the training accuracy of the fifth target eye movement direction and the training accuracy of other eye movement directions is greater than the fifth preset threshold, it indicates that the fifth target eye movement direction has more biased errors. By increasing the training times of the fifth target eye movement direction, the training proportion of the fifth target eye movement direction in the next stage can be adjusted, so that the training of the fifth target eye movement direction is more biased in the next stage.

[0107] In the fourth embodiment, if the sixth target eye movement direction exists in the target eye movement directions, the seventh training times of the sixth target eye movement direction in the eye movement training scheme of the current stage are updated to the eighth training times, the eighth training times are less than the seventh training times and greater than zero, and the sixth target eye movement direction is the target eye movement direction whose average eye extraocular muscle movement intensity gradually increases and tends to be stable in the eye movement training schemes of the plurality of training stages.

[0108] In the fourth embodiment, the average eye extraocular muscle movement intensity of the sixth target eye movement direction gradually increases and tends to be stable, indicating that the training of the sixth target eye movement direction is effective, and there is no need to train the sixth target eye movement direction a lot, so the training times of the sixth target eye movement direction can be gradually reduced until the training is terminated. Optionally, for any target eye movement direction, the training speed of the target eye movement direction can be increased by shortening the fixation time of the target eye movement direction; the training speed of the target eye movement direction can be reduced by lengthening the fixation time of the target eye movement direction.

[0109] Optionally, the second training speed, the second eye movement amplitude, the fourth training speed, the second training times, the fourth training times, the fourth eye movement amplitude, the sixth training times, the sixth eye movement amplitude, and the eighth training times can be set according to actual needs.

[0110] In an embodiment, the electronic device can store the first power spectral density and the second power spectral density of each target eye movement direction during the training process, regularly (for example, daily, weekly, monthly) statistics the eye movement execution conditions such as training completion degree, training accuracy, average eye extraocular muscle movement intensity of the same target eye movement direction, analyze the muscle movement ability changes before and after long-term training, and objectively evaluate the improvement effect of the eye extraocular muscle function.

[0111] This application embodiment acquires and quantifies electrooculogram signals in real time during eye training, which can obtain the intensity of extraocular muscle movement in each training session. Based on the intensity of extraocular muscle movement, a personalized training plan can be formulated to avoid excessive fatigue or ineffective training, thereby improving training efficiency and effectiveness. It makes up for the shortcomings of existing eye movement training programs in terms of real-time performance and objectivity, and is suitable for personalized rehabilitation plans for patients of different ages and pathological types. It can scientifically formulate rehabilitation cycles and training intensity, and improve compliance and rehabilitation efficiency.

[0112] The eye-tracking training and assessment method provided in this application is non-invasive and portable, and its applications can be expanded by combining it with virtual reality (VR) devices / mobile device training platforms. For example, electrooculography electrodes can be embedded in vision training glasses and connected to a mobile phone via Bluetooth, allowing training data to be recorded and integrated in real time through an application on the mobile phone.

[0113] In this embodiment, during eye-tracking training, by acquiring the user's electrooculogram (EOG) signal, the target eye-tracking frequency band (i.e., the rapid eye movement frequency band) can be determined based on the EEG signal. Since eye-tracking training requires the user to perform rapid eye movements, the power spectral density (i.e., the first power spectral density) within the target eye-tracking frequency band can indirectly measure the user's extraocular muscle movement intensity, thus achieving the measurement of the real physiological movement characteristics of the eyeball. Eye movement is mainly controlled by the extraocular muscles. Each eye movement requires coordinated contraction and relaxation of the extraocular muscles. Extraocular muscle movement intensity refers to the contraction intensity of the extraocular muscles, which is a comprehensive expression of the contraction speed, contraction amplitude, and tension of the extraocular muscles, and can reflect the user's extraocular muscle state. Since eye-tracking training directly mobilizes extraocular muscle activity, the extraocular muscle state can be immediately reflected in the quantitative indicator of extraocular muscle movement intensity. Therefore, this solution reflects the user's extraocular muscle state by using the quantitative indicator of extraocular muscle movement intensity acquired during eye-tracking training, which can timely and objectively evaluate the eye-tracking training effect and solve the problems of poor real-time performance and insufficient objectivity in training effect evaluation.

[0114] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0115] Corresponding to the eye-tracking training assessment method described in the above embodiments, Figure 7 A schematic diagram of the eye-tracking training and assessment device provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0116] Reference Figure 7 The device includes: The signal acquisition module 701 is configured to acquire an electrooculogram signal of a user in an eye movement training process. The target determination module 702 is configured to determine a target eye movement frequency band of the user based on the electrooculogram signal, where the target eye movement frequency band refers to a rapid eye movement frequency band of the user. The density determination module 703 is configured to determine a first power spectral density of the electrooculogram signal in the target eye movement frequency band. The intensity determination module 704 is configured to determine an extraocular muscle movement intensity of the user based on the first power spectral density, where the extraocular muscle movement intensity refers to a contraction intensity of an extraocular muscle of the user, and the extraocular muscle movement intensity reflects an extraocular muscle state of the user.

[0117] Optionally, the target determination module 702 includes: A first determination unit configured to determine a second power spectral density of the electrooculogram signal in a specific frequency band, where the second power spectral density includes a corresponding relationship between each frequency and a power spectral density value in the specific frequency band, and the specific frequency band refers to a frequency band of an eye movement signal. A second determination unit configured to determine a target frequency based on the second power spectral density, where the target frequency is a frequency with a maximum change in a power spectral density value drop rate in the second power spectral density. A third determination unit configured to determine the target eye movement frequency band based on the target frequency.

[0118] Optionally, the third determination unit is specifically configured to: determine a lowest frequency of the target eye movement frequency band based on the target frequency, and determine the target frequency as a highest frequency of the target eye movement frequency band.

[0119] Optionally, the device further includes: A noise judgment module configured to judge whether the target eye movement frequency band includes a noise frequency band. A noise removal module configured to remove the noise frequency band from the target eye movement frequency band if the target eye movement frequency band includes the noise frequency band.

[0120] Optionally, the intensity determination module 704 includes: A fourth determination unit configured to determine a first total power of the electrooculogram signal in the target eye movement frequency band based on the first power spectral density. A fifth determination unit configured to determine the extraocular muscle movement intensity based on the first total power.

[0121] Optionally, the device further includes: a power determination module, configured to determine a second total power of the electrooculogram signal in the specific frequency band based on the second power spectral density; The fifth determination unit is specifically used for: determining the eye muscle movement strength based on the first total power and the second total power.

[0122] Optionally, the device further includes: a direction determination module, configured to determine an actual eye movement direction of the user based on the electrooculogram signal; The target determination module 702 is specifically used for: if the actual eye movement direction matches the target eye movement direction, determining the target eye movement frequency band based on the electrooculogram signal.

[0123] Optionally, after the user completes the eye movement training based on the eye movement training scheme of the current stage, the device further includes: a data acquisition module, configured to acquire a training execution of the user, the training execution including at least one of the following: a training completion degree of the user in the eye movement training scheme of the current stage, a training accuracy of each target eye movement direction in the eye movement training scheme of the current stage, an average eye muscle movement strength of the eye movement training scheme of the current stage, and an average eye muscle movement strength of the eye movement training scheme of at least one historical stage; a parameter updating module, configured to update a training parameter in the eye movement training scheme of the current stage based on the training execution, to obtain an eye movement training scheme of a next stage.

[0124] Optionally, the average eye muscle strength of the user in the eye movement training scheme of the current stage includes an average eye muscle movement strength of each target eye movement direction in the eye movement training scheme of the current stage, and each target eye movement direction includes a left eye movement direction and a right eye movement direction. The parameter updating module is specifically used for: judging whether there is a training imbalance situation of the left eye movement direction and the right eye movement direction based on the average eye muscle movement strength of the left eye movement direction and the average eye muscle movement strength of the right eye movement direction; if there is a training imbalance situation of the left eye movement direction and the right eye movement direction, performing different updates on the training parameter of the left eye movement direction and the training parameter of the right eye movement direction in the eye movement training scheme of the current stage, respectively.

[0125] It should be noted that the information interaction, execution process and the like between the above device / unit are based on the same concept as the method embodiments of the present application. For specific functions and technical effects brought by the same, refer to the method embodiments part, which will not be repeated here.

[0126] Figure 8 The structure schematic diagram of the electronic device provided in the embodiment of the present application is shown in the figure. The electronic device 8 of the embodiment includes at least one processor 80 (only one is shown in the figure), a memory 81, and a computer program 82 stored in the memory 81 and executable on the at least one processor 80, wherein the processor 80 implements the steps in any of the method embodiments described above when executing the computer program 82. Figure 8 Figure 8 The electronic device can include, but is not limited to, the processor 80 and the memory 81. Those skilled in the art can understand that the electronic device 8 shown in the figure is only an example and does not constitute a limitation on the electronic device 8, which can include more or fewer components than shown in the figure, or combine certain components, or different components, for example, can also include input / output devices, network access devices, etc.

[0127] The electronic device can include, but is not limited to, the processor 80 and the memory 81. Those skilled in the art can understand that the electronic device 8 shown in the figure is only an example and does not constitute a limitation on the electronic device 8, which can include more or fewer components than shown in the figure, or combine certain components, or different components, for example, can also include input / output devices, network access devices, etc. Figure 8 The processor 80 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, etc. The general-purpose processor can be a microprocessor or can also be any conventional processor.

[0128] The memory 81 can be an internal storage unit of the electronic device 8 in some embodiments, for example, a hard disk or a memory of the electronic device 8. The memory 81 can also be an external storage device of the electronic device 8 in other embodiments, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 81 can include both the internal storage unit and the external storage device of the electronic device 8. The memory 81 is used to store an operating system, application programs, a boot loader, data, and other programs, for example, program codes of the computer program, etc. The memory 81 can also be used to temporarily store data that has been output or will be output.

[0129] The electronic device can include, but is not limited to, the processor 80 and the memory 81. Those skilled in the art can understand that the electronic device 8 shown in the figure is only an example and does not constitute a limitation on the electronic device 8, which can include more or fewer components than shown in the figure, or combine certain components, or different components, for example, can also include input / output devices, network access devices, etc.​

[0130] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software. In addition, the specific names of each functional unit and module are only for easy distinction, and do not limit the protection scope of the application. The specific working process of the unit and module in the system can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0131] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware, and the computer program can be stored in a computer-readable storage medium. When the processor executes the computer program, the steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium at least includes any entity or device capable of carrying the computer program code to the device / equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, U disk, mobile hard disk, magnetic disk or optical disk, etc.

[0132] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0133] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0134] In the embodiments provided by the present application, it should be understood that the disclosed apparatus / equipment and method can be implemented in other manners. For example, the embodiments of the apparatus / equipment described above are merely illustrative. For example, the division of the modules or units is only a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0135] The units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0136] The above-described embodiments are merely used to illustrate the technical solutions of the present application, but not limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalent; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. An eye movement training assessment method, characterized in that, The method comprises the following steps: During the eye movement training process, an electrooculogram signal of a user is acquired; Based on the electrooculogram signal, a target eye movement frequency band of the user is determined, the target eye movement frequency band being a rapid eye movement frequency band of the user; A first power spectral density of the electrooculogram signal in the target eye movement frequency band is determined; Based on the first power spectral density, an extraocular muscle movement intensity of the user is determined, the extraocular muscle movement intensity being a contraction intensity of an extraocular muscle of the user, and the extraocular muscle movement intensity reflecting an extraocular muscle state of the user.

2. The eye training evaluation method of claim 1, wherein, The step of determining the target eye movement frequency band of the user based on the electrooculogram signal comprises the following steps: A second power spectral density of the electrooculogram signal in a specific frequency band is determined, the second power spectral density comprising a corresponding relationship between each frequency and a power spectral density value in the specific frequency band, and the specific frequency band being a frequency band in which an eye movement signal is located; Based on the second power spectral density, a target frequency is determined, the target frequency being a frequency in the second power spectral density at which a power spectral density value has a maximum change in a falling rate; Based on the target frequency, the target eye movement frequency band is determined.

3. The eye training evaluation method of claim 2, wherein, The step of determining the target eye movement frequency band of the user based on the target frequency comprises the following steps: Based on the target frequency, a lowest frequency of the target eye movement frequency band is determined, and the target frequency is determined as a highest frequency of the target eye movement frequency band.

4. The eye training evaluation method of claim 3, wherein, After the target eye movement frequency band is determined, the method further comprises the following steps: It is judged whether the target eye movement frequency band contains a noise frequency band; If the target eye movement frequency band contains the noise frequency band, the noise frequency band is removed from the target eye movement frequency band.

5. The eye training evaluation method of claim 2, wherein, The step of determining the extraocular muscle movement intensity of the user based on the first power spectral density comprises the following steps: Based on the first power spectral density, a first total power of the electrooculogram signal in the target eye movement frequency band is determined; Based on the first total power, the extraocular muscle movement intensity is determined.

6. The eye training evaluation method of claim 5, wherein, Before the step of determining the extraocular muscle movement intensity based on the first total power, the method further comprises the following steps: Based on the second power spectral density, a second total power of the electrooculogram signal in the specific frequency band is determined; The step of determining the extraocular muscle movement intensity based on the first total power comprises the following steps: Based on the first total power and the second total power, the extraocular muscle movement intensity is determined.

7. The eye movement training assessment method according to any one of claims 1 to 6, characterized in that, Before the step of determining the target eye movement frequency band of the user based on the electrooculogram signal, the method further comprises the following steps: Based on the electrooculogram signal, an actual eye movement direction of the user is determined; The step of determining the target eye movement frequency band of the user based on the electrooculogram signal comprises the following steps: If the actual eye movement direction matches the target eye movement direction, the target eye movement frequency band is determined based on the electrooculogram signal.

8. The eye training evaluation method according to any one of claims 1 to 6, characterized in that, After the user completes the eye movement training based on the eye movement training scheme of the current stage, the method further comprises the following steps: obtaining training execution of the user, the training execution including at least one of training completion degree of the user in the eye movement training scheme of the current stage, training accuracy of each target eye movement direction in the eye movement training scheme of the current stage, average eye extraocular muscle movement intensity of the eye movement training scheme of the current stage, average eye extraocular muscle movement intensity of the eye movement training scheme of at least one historical stage; updating the training parameters in the eye movement training scheme of the current stage based on the training execution, to obtain the eye movement training scheme of the next stage.

9. The eye training evaluation method of claim 8, wherein, The average eye extraocular muscle intensity of the user in the eye movement training scheme of the current stage includes average eye extraocular muscle movement intensity of each target eye movement direction in the eye movement training scheme of the current stage, and each target eye movement direction includes a left eye movement direction and a right eye movement direction. The updating of the training parameters in the eye movement training scheme of the current stage based on the training execution includes: determining whether there is a training imbalance between the left eye movement direction and the right eye movement direction based on the average eye extraocular muscle movement intensity of the left eye movement direction and the average eye extraocular muscle movement intensity of the right eye movement direction; if there is a training imbalance between the left eye movement direction and the right eye movement direction, then the training parameters of the left eye movement direction and the training parameters of the right eye movement direction in the eye movement training scheme of the current stage are updated differently.

10. An eye movement training assessment device, characterized in that, including: a signal acquisition module, configured to acquire an electrooculogram signal of a user in an eye movement training process; a target determination module, configured to determine a target eye movement frequency band of the user based on the electrooculogram signal, the target eye movement frequency band being a rapid eye movement frequency band of the user; a density determination module, configured to determine a first power spectral density of the electrooculogram signal in the target eye movement frequency band; an intensity determination module, configured to determine an eye extraocular muscle movement intensity of the user based on the first power spectral density, the eye extraocular muscle movement intensity being a contraction intensity of an eye extraocular muscle of the user, and the eye extraocular muscle movement intensity reflecting an eye extraocular muscle state of the user.

Images