CN115067944B - Eye movement state evaluation method and device, terminal equipment and storage medium - Google Patents
Eye movement state evaluation method and device, terminal equipment and storage medium Download PDFInfo
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
- A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
- A61B3/113—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for determining or recording eye movement
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
Abstract
The invention discloses an eye movement state evaluation method, an eye movement state evaluation device, terminal equipment and a storage medium, wherein the method comprises the following steps: acquiring electroencephalogram data, inputting the electroencephalogram data into a pre-trained eye movement evaluation model, and outputting eye movement score data based on the eye movement evaluation model, wherein the eye movement evaluation model is obtained by performing comparative analysis and training on the electroencephalogram data in different eye movement states; and determining eye movement state information corresponding to the eye movement scoring data according to the eye movement scoring data, wherein the eye movement state information is used for reflecting the blinking condition, the eyeball rotation condition or the eyeball sight line direction. The invention can evaluate the eye movement state information based on the electroencephalogram data, the whole analysis process is simple, the efficiency is high, and the eye movement state information obtained by analysis provides favorable data support for researching autism.
Description
Technical Field
The present invention relates to the field of eye movement analysis technologies, and in particular, to an eye movement state evaluation method, apparatus, terminal device, and storage medium.
Background
In the prior art, the eye movement state is the key for evaluating the autism, but for the analysis of the eye movement state, a manual observation method is basically adopted at present, such as observing the condition of eye blinking or the condition of eyeball rotation. However, the manual observation method is subjective, and is not accurate in analyzing the eye state, and is inefficient.
Thus, there is a need for improvements and enhancements in the art.
Disclosure of Invention
The present invention is to provide an eye movement state evaluation method, apparatus, terminal device and storage medium, aiming to solve the problems of subjectivity, inaccurate analysis and low efficiency when analyzing the eye movement state by using a manual observation method in the prior art.
In a first aspect, the present invention provides an eye movement state evaluation method, wherein the method comprises:
acquiring electroencephalogram data, inputting the electroencephalogram data into a pre-trained eye movement evaluation model, and outputting eye movement scoring data based on the eye movement evaluation model, wherein the eye movement evaluation model is obtained by carrying out comparative analysis and training on electroencephalogram data in different eye movement states;
and determining eye movement state information corresponding to the eye movement scoring data according to the eye movement scoring data, wherein the eye movement state information is used for reflecting the blinking condition, the eyeball rotation condition or the eyeball sight line direction.
In one implementation, the training process of the eye movement assessment model includes:
collecting a plurality of sample electroencephalogram data, classifying the sample electroencephalogram data, and determining the electroencephalogram data of the eyes under a normal eye movement state and the electroencephalogram data of the eyes under an abnormal eye movement state;
comparing and analyzing the electroencephalogram data of the eyes in a normal eye movement state and the electroencephalogram data of the eyes in an abnormal eye movement state, and determining electroencephalogram difference data and eye movement difference data corresponding to the electroencephalogram difference data;
and training a preset neural network model based on the electroencephalogram difference data and the eye movement difference data to obtain the eye movement evaluation model.
In one implementation, the classifying the sample electroencephalogram data to determine electroencephalogram data of the eye in a normal eye movement state and electroencephalogram data of the eye in an abnormal eye movement state includes:
acquiring eye movement information corresponding to all sample electroencephalogram data, wherein the eye movement information comprises: blink condition, eye rotation condition, or eye gaze direction;
and classifying the electroencephalogram data according to the eye movement information to obtain the electroencephalogram data of the eyes under the normal eye movement state and the electroencephalogram data of the eyes under the abnormal eye movement state.
In one implementation, the comparing and analyzing the electroencephalogram data of the eye in the normal eye movement state and the electroencephalogram data of the eye in the abnormal eye movement state to determine the electroencephalogram difference data and the eye movement difference data corresponding to the electroencephalogram difference data includes:
comparing the electroencephalogram data of the eyes in the normal eye movement state with the electroencephalogram data in the abnormal eye movement state to determine electroencephalogram difference data;
obtaining eye movement information of the eyes in a normal eye movement state and eye movement information of the eyes in an abnormal eye movement state;
and comparing the eye movement information of the eyes in the normal eye movement state with the eye movement information of the eyes in the abnormal eye movement state to obtain the eye movement difference data.
In one implementation, the training a preset neural network model based on the electroencephalogram difference data and the eye movement difference data to obtain the eye movement evaluation model includes:
assigning values to eye movement information of the eyes in a normal eye movement state in advance to respectively obtain basic scores corresponding to a blinking condition, an eyeball rotation condition or an eyeball sight direction when the eyes are in the normal eye movement state;
constructing a scoring function based on the eye movement difference data and a basic score corresponding to the blinking condition, the eyeball rotation condition or the eyeball sight direction when the eye part is in the normal eye movement state;
and training the neural network model based on the eye movement difference data, the electroencephalogram difference data and the scoring function to obtain the eye movement evaluation model.
In one implementation, the determining, according to the eye movement score data, eye movement state information corresponding to the eye movement score data includes:
determining a score gear corresponding to the eye movement scoring data according to the eye movement scoring data;
and determining eye movement state information corresponding to the score gear based on the score gear.
In one implementation, the method further comprises:
counting eye movement state information in a preset time period, and screening out abnormal eye movement states in the eye movement state information;
and acquiring time information corresponding to the abnormal eye movement state, and outputting prompt information according to the abnormal eye movement state and the corresponding time information, wherein the prompt information is used for reflecting that the frequency of the abnormal eye movement state exceeds a preset frequency.
In a second aspect, an embodiment of the present invention further provides an eye movement state evaluation device, where the device includes:
the eye movement evaluation module is used for acquiring electroencephalogram data, inputting the electroencephalogram data into a pre-trained eye movement evaluation model, and outputting eye movement evaluation data based on the eye movement evaluation model, wherein the eye movement evaluation model is obtained by performing comparative analysis and training on electroencephalogram data in different eye movement states;
and the eye movement state information determining module is used for determining eye movement state information corresponding to the eye movement scoring data according to the eye movement scoring data, and the eye movement state information is used for reflecting the blinking condition, the eyeball rotation condition or the eyeball sight direction.
In one implementation, the apparatus includes a model training module that includes:
the sample acquisition unit is used for acquiring a plurality of sample electroencephalogram data, classifying the sample electroencephalogram data and determining the electroencephalogram data of the eyes under the normal eye movement state and the electroencephalogram data of the eyes under the abnormal eye movement state;
the contrast analysis unit is used for performing contrast analysis on the electroencephalogram data of the eyes in the normal eye movement state and the electroencephalogram data of the eyes in the abnormal eye movement state to determine electroencephalogram difference data and the eye movement difference data corresponding to the electroencephalogram difference data;
and the model training unit is used for training a preset neural network model based on the electroencephalogram difference data and the eye movement difference data to obtain the eye movement evaluation model.
In one implementation, the sample acquisition unit includes:
the information determining subunit is configured to acquire eye movement information corresponding to all sample electroencephalogram data, where the eye movement information includes: blink condition, eye rotation condition, or eye gaze direction;
and the data classification subunit is used for classifying the electroencephalogram data according to the eye movement information to obtain electroencephalogram data of the eyes under a normal eye movement state and electroencephalogram data of the eyes under an abnormal eye movement state.
In one implementation, the comparative analysis unit includes:
the first comparison subunit is used for comparing the electroencephalogram data of the eyes in the normal eye movement state with the electroencephalogram data in the abnormal eye movement state to determine the electroencephalogram difference data;
the information acquisition subunit is used for acquiring the eye movement information of the eyes in the normal eye movement state and the eye movement information of the eyes in the abnormal eye movement state;
and the second comparison subunit is used for comparing the eye movement information of the eyes in the normal eye movement state with the eye movement information of the eyes in the abnormal eye movement state to obtain the eye movement difference data.
In one implementation, the model training unit includes:
the assignment determining subunit is used for assigning the eye movement information of the eyes in the normal eye movement state in advance to respectively obtain basic scores corresponding to the blinking condition, the eyeball rotation condition or the eyeball sight direction when the eyes are in the normal eye movement state;
a function construction subunit, configured to construct a scoring function based on the eye movement difference data and a base score corresponding to a blinking condition, an eyeball rotation condition, or an eyeball sight line direction when the eye portion is in a normal eye movement state;
and the evaluation function training subunit is used for training the neural network model based on the eye movement difference data, the electroencephalogram difference data and the scoring function to obtain the eye movement evaluation model.
In one implementation, the eye movement state information determining module includes:
the score gear determining subunit is used for determining a score gear corresponding to the eye movement scoring data according to the eye movement scoring data;
and the state information determining subunit is used for determining eye movement state information corresponding to the score gear based on the score gear.
In one implementation, the apparatus further comprises:
the information counting module is used for counting the eye movement state information in a preset time period and screening out abnormal eye movement states in the eye movement state information;
and the information prompting module is used for acquiring time information corresponding to the abnormal eye movement state and outputting prompting information according to the abnormal eye movement state and the corresponding time information, wherein the prompting information is used for reflecting that the frequency of the abnormal eye movement state exceeds a preset frequency.
In a third aspect, an embodiment of the present invention further provides a terminal device, where the terminal device includes a memory, a processor, and an eye movement state evaluation program that is stored in the memory and is executable on the processor, and when the processor executes the eye movement state evaluation program, the steps of the eye movement state evaluation method in any one of the foregoing schemes are implemented.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where an eye movement state evaluation program is stored on the computer-readable storage medium, and when the eye movement state evaluation program is executed by a processor, the steps of the eye movement state evaluation method according to any one of the above-mentioned schemes are implemented.
Has the beneficial effects that: compared with the prior art, the invention provides an eye movement state evaluation method, firstly, electroencephalogram data are obtained, the electroencephalogram data are input into a pre-trained eye movement evaluation model, and the eye movement evaluation model is obtained by carrying out comparative analysis and training on the electroencephalogram data in different eye movement states, so that the eye movement evaluation model can determine corresponding eye movement information based on the electroencephalogram data, and further output eye movement scoring data. And then according to the eye movement score data, determining eye movement state information corresponding to the eye movement score data, wherein the eye movement state information is used for reflecting the blinking condition, the eyeball rotation condition or the eyeball sight line direction. The method can analyze the electroencephalogram data and evaluate the eye movement state information based on the eye movement evaluation model, the whole analysis process is simple, the efficiency is high, the accuracy is high, and the eye movement state information obtained by analysis provides favorable data support for researching the autism.
Drawings
Fig. 1 is a flowchart of a detailed implementation of an eye movement state evaluation method according to an embodiment of the present invention.
Fig. 2 is a functional schematic diagram of an eye movement state evaluation device according to an embodiment of the present invention.
Fig. 3 is a schematic block diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and effects of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The present embodiment provides an eye movement state evaluation method, which can efficiently and accurately evaluate an eye movement state. In specific implementation, firstly, electroencephalogram data are acquired, the electroencephalogram data are input into a pre-trained eye movement evaluation model, and the eye movement evaluation model is obtained by performing comparative analysis and training on the electroencephalogram data in different eye movement states, so that the eye movement evaluation model can determine corresponding eye movement information based on the electroencephalogram data, and further output eye movement scoring data. Then, according to the eye movement score data, eye movement state information corresponding to the eye movement score data is determined, and the eye movement state information is used for reflecting the blinking condition, the eyeball rotation condition or the eyeball sight direction. The method and the device can analyze the electroencephalogram data and evaluate the eye movement state information based on the eye movement evaluation model, the whole analysis process is simple, the efficiency is high, the accuracy is high, and the eye movement state information obtained through analysis provides favorable data support for researching autism.
For example, the eye movement state evaluation method of the embodiment may be implemented by a computer, after acquiring electroencephalogram data of a user, the computer may input the electroencephalogram data into a pre-trained eye movement evaluation model, the eye movement evaluation model may determine, according to the electroencephalogram data, whether the electroencephalogram data are acquired when eyes are in a normal eye movement state or an abnormal eye movement state, and the eye movement evaluation model may further output eye movement score data, which may indicate a level of the eye movement condition of the user at this time. After the computer outputs the eye movement score data, the eye movement state information corresponding to the eye movement score data can be further analyzed, so that information such as blinking condition, eyeball rotation condition or eyeball sight line direction and the like can be determined, and a corresponding report file is output.
Exemplary method
The eye movement state evaluation method can be applied to terminal equipment, and the terminal equipment can be intelligent product terminals such as computers, mobile phones and tablet computers. Specifically, as shown in fig. 1, the eye movement state evaluation method of the present embodiment includes the steps of:
s100, acquiring electroencephalogram data, inputting the electroencephalogram data into a pre-trained eye movement evaluation model, and outputting eye movement scoring data based on the eye movement evaluation model, wherein the eye movement evaluation model is obtained by performing comparative analysis and training on the electroencephalogram data in different eye movement states.
Most of the information obtained from the surrounding environment comes from vision, and the retina receives external information and transmits the information to the nerve center through the visual pathway, thereby triggering the mental activities of the brain. It is particularly noted that in the process of hand movement, people often need the coordination of the brain, eyes and hands, and the eye movement is naturally accompanied by some eye movement. Therefore, the eye movement and the brain electrical data are in a certain relation, and therefore the embodiment can realize the evaluation of the eye movement state based on the brain electrical data. The terminal device of the embodiment needs to firstly acquire the electroencephalogram data, then inputs the electroencephalogram data into the eye movement evaluation model trained in advance, and since the eye movement evaluation model is obtained by performing comparative analysis and training on the electroencephalogram data in different eye movement states, the eye movement evaluation model can determine corresponding eye movement information based on the electroencephalogram data, and then outputs the eye movement scoring data.
In one implementation, the present embodiment requires to train an eye movement evaluation model in advance, and specifically, the eye movement evaluation model includes the following steps:
s101, collecting a plurality of sample electroencephalogram data, classifying the sample electroencephalogram data, and determining the electroencephalogram data of the eyes in a normal eye movement state and the electroencephalogram data of the eyes in an abnormal eye movement state;
step S102, comparing and analyzing the electroencephalogram data of the eyes in a normal eye movement state and the electroencephalogram data of the eyes in an abnormal eye movement state, and determining electroencephalogram difference data and eye movement difference data corresponding to the electroencephalogram difference data;
step S103, training a preset neural network model based on the electroencephalogram difference data and the eye movement difference data to obtain the eye movement evaluation model.
Specifically, in this embodiment, a plurality of sample electroencephalogram data are collected first, and then eye movement information corresponding to all the sample electroencephalogram data is obtained, where the eye movement information includes: blink conditions, eye rotation conditions, or eye gaze directions. The blinking condition comprises the blinking times, the blinking frequency and the like, the eyeball rolling condition comprises the rotating frequency and the like, and the eyeball sight line direction comprises the upward or downward looking time length. The eye movement information is synchronously acquired when the sample electroencephalogram data are acquired, and the eye movement information and the corresponding sample electroencephalogram data are bound, so that the corresponding eye movement information exists in each sample electroencephalogram data. After the eye movement information corresponding to the sample electroencephalogram data is obtained, the electroencephalogram data can be classified according to the eye movement information, which eye movement information belongs to the eye movement information that the eyes are in the normal eye movement state, which eye movement information belongs to the eye movement information that the eyes are in the abnormal eye movement state, and then the electroencephalogram data can be classified, so that the electroencephalogram data that the eyes are in the normal eye movement state and the electroencephalogram data that the eyes are in the abnormal eye movement state are obtained. Of course, when the classification is performed, if the eye movement information cannot be distinguished as normal or abnormal, the eye movement information and the corresponding electroencephalogram data are deleted. Of course, in another implementation manner, during the classification, the embodiment may first distinguish the eye movement information of the eye in the normal eye movement state from the corresponding electroencephalogram data, and thus the remaining part of the electroencephalogram data is the eye movement information of the eye in the abnormal eye movement state and the corresponding electroencephalogram data.
Because the electroencephalogram data of the eyes in the normal eye movement state and the electroencephalogram data of the eyes in the abnormal eye movement state are different before, in the embodiment, the electroencephalogram data of the eyes in the normal eye movement state and the electroencephalogram data of the eyes in the abnormal eye movement state are compared to determine the electroencephalogram difference data. Meanwhile, the present embodiment obtains eye movement information of the eye in the normal eye movement state and eye movement information in the abnormal eye movement state. And then comparing the eye movement information of the eyes in the normal eye movement state with the eye movement information of the eyes in the abnormal eye movement state to obtain the eye movement difference data. Since the sample electroencephalogram data corresponds to the eye movement information, the eye movement difference data also corresponds to the electroencephalogram difference data. That is, the present embodiment can know what difference the electroencephalogram data corresponding to the normal eye movement and the abnormal eye movement are, and how the difference is reflected on the corresponding eye movement information.
Then, the present embodiment assigns the eye movement information of the eye in the normal eye movement state in advance, and obtains the basic score corresponding to the blinking condition, the eyeball rolling condition, or the eyeball sight direction when the eye is in the normal eye movement state. That is, the present embodiment sets the base scores for the blinking condition, the eyeball rolling condition, or the eyeball sight line direction of the eye in the normal eye movement state. When the eye is in the abnormal eye movement state, the score of the blinking condition, the eyeball rolling condition or the eyeball sight direction is definitely lower than the corresponding basic score, and the eye movement difference data reflects the difference between the eye movement information of the eye in the abnormal eye movement state and the eye movement information of the eye in the normal state, so that the specific score of the blinking condition, the eyeball rolling condition or the eyeball sight direction when the eye is in the abnormal eye movement state can be determined based on the eye movement difference information. Since the present embodiment finally outputs the eye movement score data determined based on the eye blinking condition, the eye rotation condition, or the specific score of the eye sight direction, the present embodiment may construct a score function, and the eye movement score data may be calculated through the score function.
In one implementation, the scores of the blinking condition, the eyeball rolling condition or the eyeball sight direction when the eye is in the normal eye movement state are definitely lower than the corresponding base scores of W1, W2 and W3 respectively in the present embodiment. The difference data between the eye movement difference data and the blinking condition of the eye in the normal eye movement state is a1, the eyeball rolling condition of the eye in the normal eye movement state is a2, and the eyeball sight line direction a3 of the eye in the normal eye movement state. In this embodiment, corresponding weights are set for the blinking condition, the eyeball rolling condition or the eyeball sight direction, which are b1, b2 and b3 respectively. The scoring function may thus be: y =100- (W1 × a1 × b1+ W2 × a2 × b 2). Because the eye movement difference data correspond to the electroencephalogram difference data, the neural network model can be trained to obtain the eye movement evaluation model based on the eye movement difference data, the electroencephalogram difference data and the scoring function during training.
Therefore, after the computer of this embodiment acquires the electroencephalogram data, the electroencephalogram data is input to a pre-trained eye movement evaluation model, the eye movement evaluation model compares the electroencephalogram data at this time with the electroencephalogram data of the eyes in the normal eye movement state to determine electroencephalogram abnormal data, the corresponding eye movement abnormal data can be determined based on the electroencephalogram abnormal data, after the eye movement abnormal data is obtained, the present embodiment can obtain the difference data of the eye movement information of the user at this time from the blink condition of the eyes in the normal eye movement state as a1, from the eyeball rotation condition of the eyes in the normal eye movement state as a2, and from the eyeball sight direction a3 of the eyes in the normal eye movement state, and then, through the constructed scoring function, the eye movement scoring data can be automatically output.
Step S200, according to the eye movement score data, determining eye movement state information corresponding to the eye movement score data, wherein the eye movement state information is used for reflecting a blinking condition, an eyeball rotation condition or an eyeball sight line direction.
In one implementation manner, the embodiment includes the following steps when determining the eye movement state information:
step S201, according to the eye movement scoring data, determining a score gear corresponding to the eye movement scoring data;
step S202, based on the score gear, determining eye movement state information corresponding to the score gear.
After the eye movement score data is obtained, the eye movement state information corresponding to the eye movement score data can be determined, and the score gear corresponding to the eye movement score data can be determined according to the eye movement score data. In the embodiment, a plurality of score gears are divided in advance, each score gear corresponds to the eye movement score data, corresponding eye movement state information is set for each score gear, and at the moment, after the eye movement score data are obtained, the corresponding eye movement state information can be matched. The eye movement state information of the present embodiment includes a blinking condition, an eyeball rolling condition, or an eyeball sight line direction. Specifically, the eye movement information includes: blinking condition, eye rotation condition, or eye gaze direction. The blinking condition comprises the blinking times, the blinking frequency and the like, the eyeball rolling condition comprises the rotating frequency and the like, and the eyeball sight direction comprises the upward looking or downward looking time duration.
In an implementation manner, after the terminal device of this embodiment determines the eye movement state information, the terminal device may count the eye movement state information within a preset time period (for example, within one week), and screen out an abnormal eye movement state in the eye movement state information, that is, screen out data of a blink condition, an eyeball rotation condition, or an eyeball sight line direction abnormality. Then, the embodiment acquires time information corresponding to the abnormal eye movement state, determines the occurrence frequency of the abnormal eye movement state according to the abnormal eye movement state and the corresponding time information, and if the occurrence frequency of the abnormal eye movement state exceeds a preset frequency, the terminal device outputs a prompt message. The prompting information prompts the user that the frequency of the abnormal eye movement state exceeds the preset frequency, so that favorable data support can be provided for researching the autism. In addition, the present embodiment performs micro-electrical stimulation when determining the abnormal eye movement state, and the micro-electrical stimulation may send a stimulation signal to a wearable device (such as an intelligent eye) of the user to prompt the user to pay attention to the eye movement at this time, so as to avoid the abnormal eye movement state. Of course, the present embodiment may also adjust the information of the area, intensity, duration, and the like of the micro-electrical stimulation according to the score value corresponding to the eye movement score data, so as to better remind the user.
In summary, in this embodiment, firstly, electroencephalogram data is acquired, and the electroencephalogram data is input into a pre-trained eye movement evaluation model, and since the eye movement evaluation model is obtained by performing comparative analysis and training on electroencephalogram data in different eye movement states, the eye movement evaluation model can determine corresponding eye movement information based on the electroencephalogram data, and then output eye movement score data. Then, according to the eye movement score data, the present embodiment determines eye movement state information corresponding to the eye movement score data, where the eye movement state information is used to reflect a blinking condition, an eyeball rotation condition, or an eyeball sight line direction. The embodiment can analyze the electroencephalogram data and evaluate the eye movement state information based on the eye movement evaluation model, the whole analysis process is simple, the efficiency is high, the accuracy is high, and the eye movement state information obtained through analysis provides favorable data support for researching the autism.
Exemplary devices
Based on the above-described embodiments, the present invention provides an eye movement state evaluation device, as shown in fig. 2, the device including: the eye movement score data determination module 10 and the eye movement state information determination module 20. Specifically, the eye movement score data determination module 10 is configured to acquire electroencephalogram data, input the electroencephalogram data to a pre-trained eye movement evaluation model, and output the eye movement score data based on the eye movement evaluation model, where the eye movement evaluation model is obtained by performing comparative analysis and training on electroencephalogram data in different eye movement states. The eye movement state information determining module 20 is configured to determine, according to the eye movement score data, eye movement state information corresponding to the eye movement score data, where the eye movement state information is used to reflect a blinking condition, an eyeball rotation condition, or an eyeball sight line direction.
In one implementation, the apparatus includes a model training module that includes:
the sample acquisition unit is used for acquiring a plurality of sample electroencephalogram data, classifying the sample electroencephalogram data and determining the electroencephalogram data of the eyes under the normal eye movement state and the electroencephalogram data of the eyes under the abnormal eye movement state;
the contrast analysis unit is used for performing contrast analysis on the electroencephalogram data of the eyes in the normal eye movement state and the electroencephalogram data of the eyes in the abnormal eye movement state to determine electroencephalogram difference data and the eye movement difference data corresponding to the electroencephalogram difference data;
and the model training unit is used for training a preset neural network model based on the electroencephalogram difference data and the eye movement difference data to obtain the eye movement evaluation model.
In one implementation, the sample acquisition unit includes:
the information determining subunit is configured to obtain eye movement information corresponding to all sample electroencephalogram data, where the eye movement information includes: blink conditions, eye rotation conditions, or eye gaze directions;
and the data classification subunit is used for classifying the electroencephalogram data according to the eye movement information to obtain electroencephalogram data of the eyes under a normal eye movement state and electroencephalogram data of the eyes under an abnormal eye movement state.
In one implementation, the comparative analysis unit includes:
the first comparison subunit is used for comparing the electroencephalogram data of the eyes in the normal eye movement state with the electroencephalogram data in the abnormal eye movement state to determine the electroencephalogram difference data;
the information acquisition subunit is used for acquiring the eye movement information of the eyes in the normal eye movement state and the eye movement information of the eyes in the abnormal eye movement state;
and the second comparison subunit is used for comparing the eye movement information of the eyes in the normal eye movement state with the eye movement information of the eyes in the abnormal eye movement state to obtain the eye movement difference data.
In one implementation, the model training unit includes:
the assignment determining subunit is used for assigning the eye movement information of the eyes in the normal eye movement state in advance to respectively obtain basic scores corresponding to the blinking condition, the eyeball rotation condition or the eyeball sight direction when the eyes are in the normal eye movement state;
a function construction subunit, configured to construct a scoring function based on the eye movement difference data and a base score corresponding to a blinking condition, an eyeball rotation condition, or an eyeball sight line direction when the eye portion is in a normal eye movement state;
and the evaluation function training subunit is used for training the neural network model based on the eye movement difference data, the electroencephalogram difference data and the scoring function to obtain the eye movement evaluation model.
In one implementation, the eye movement status information determining module includes:
the score gear determining subunit is used for determining a score gear corresponding to the eye movement scoring data according to the eye movement scoring data;
and the state information determining subunit is used for determining eye movement state information corresponding to the score gear based on the score gear.
In one implementation, the apparatus further comprises:
the information counting module is used for counting the eye movement state information in a preset time period and screening abnormal eye movement states in the eye movement state information;
and the information prompting module is used for acquiring time information corresponding to the abnormal eye movement state and outputting prompting information according to the abnormal eye movement state and the corresponding time information, wherein the prompting information is used for reflecting that the frequency of the abnormal eye movement state exceeds a preset frequency.
The working principle of each module in the eye movement state evaluation device of this embodiment is the same as the principle of each step in the above method embodiments, and is not described herein again.
Based on the above embodiment, the present invention further provides a terminal device, and a schematic block diagram of the terminal device may be as shown in fig. 3. The terminal device may include one or more processors 100 (only one shown in fig. 3), a memory 101, and a computer program 102, e.g., a program for eye state assessment, stored in the memory 101 and executable on the one or more processors 100. The various steps in method embodiments of eye state assessment may be implemented by one or more processors 100 executing computer program 102. Alternatively, the functions of the modules/units in the device embodiment for eye movement state evaluation may be implemented by one or more processors 100 executing the computer program 102, which is not limited herein.
In one embodiment, processor 100 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In one embodiment, the storage 101 may be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device. The memory 101 may also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) card, a flash memory card (flash card), and the like provided on the electronic device. Further, the memory 101 may also include both an internal storage unit and an external storage device of the electronic device. The memory 101 is used for storing computer programs and other programs and data required by the terminal device. The memory 101 may also be used to temporarily store data that has been output or is to be output.
It will be understood by those skilled in the art that the block diagram of fig. 3 is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation to the terminal equipment to which the solution of the present invention is applied, and a specific terminal equipment may include more or less components than those shown in the figure, or may combine some components, or have different arrangements of components.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, operational databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual operation data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link (Synchlink) DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct bused dynamic RAM (DRDRAM), and bused dynamic RAM (RDRAM), among others.
In summary, the present invention discloses an eye movement state evaluation method, apparatus, terminal device and storage medium, wherein the method comprises: acquiring electroencephalogram data, inputting the electroencephalogram data into a pre-trained eye movement evaluation model, and outputting eye movement score data based on the eye movement evaluation model, wherein the eye movement evaluation model is obtained by performing comparative analysis and training on the electroencephalogram data in different eye movement states; and determining eye movement state information corresponding to the eye movement scoring data according to the eye movement scoring data, wherein the eye movement state information is used for reflecting the blinking condition, the eyeball rotation condition or the eyeball sight line direction. The method can evaluate the eye movement state information based on the electroencephalogram data, the whole analysis process is simple, the efficiency is high, and the eye movement state information obtained by analysis provides favorable data support for researching the autism.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. An ocular state assessment method, the method comprising:
acquiring electroencephalogram data, inputting the electroencephalogram data into a pre-trained eye movement evaluation model, and outputting eye movement score data based on the eye movement evaluation model, wherein the eye movement evaluation model is obtained by performing comparative analysis and training on the electroencephalogram data in different eye movement states;
determining eye movement state information corresponding to the eye movement scoring data according to the eye movement scoring data, wherein the eye movement state information is used for reflecting the blinking condition, the eyeball rotation condition or the eyeball sight line direction;
the training process of the eye movement evaluation model comprises the following steps:
collecting a plurality of sample electroencephalogram data, classifying the sample electroencephalogram data, and determining the electroencephalogram data of the eyes under the normal eye movement state and the electroencephalogram data of the eyes under the abnormal eye movement state;
comparing and analyzing the electroencephalogram data of the eyes in a normal eye movement state and the electroencephalogram data of the eyes in an abnormal eye movement state, and determining electroencephalogram difference data and eye movement difference data corresponding to the electroencephalogram difference data;
training a preset neural network model based on the electroencephalogram difference data and the eye movement difference data to obtain the eye movement evaluation model;
the comparing and analyzing the electroencephalogram data of the eyes under the normal eye movement state and the electroencephalogram data of the eyes under the abnormal eye movement state to determine the electroencephalogram difference data and the eye movement difference data corresponding to the electroencephalogram difference data comprises the following steps:
comparing the electroencephalogram data of the eyes in the normal eye movement state with the electroencephalogram data in the abnormal eye movement state to determine electroencephalogram difference data;
obtaining eye movement information of the eyes in a normal eye movement state and eye movement information of the eyes in an abnormal eye movement state;
and comparing the eye movement information of the eyes in the normal eye movement state with the eye movement information of the eyes in the abnormal eye movement state to obtain the eye movement difference data.
2. The method of claim 1, wherein the classifying the sample electroencephalogram data to determine electroencephalogram data of the eye in a normal state of eye movement and electroencephalogram data of the eye in an abnormal state of eye movement comprises:
acquiring eye movement information corresponding to all sample electroencephalogram data, wherein the eye movement information comprises: blink conditions, eye rotation conditions, or eye gaze directions;
and classifying the electroencephalogram data according to the eye movement information to obtain the electroencephalogram data of the eyes under the normal eye movement state and the electroencephalogram data of the eyes under the abnormal eye movement state.
3. The method for evaluating an eye movement state according to claim 1, wherein the training a preset neural network model based on the electroencephalogram difference data and the eye movement difference data to obtain the eye movement evaluation model comprises:
assigning values to eye movement information of the eyes in a normal eye movement state in advance to respectively obtain basic scores corresponding to a blinking condition, an eyeball rotation condition or an eyeball sight direction when the eyes are in the normal eye movement state;
constructing a scoring function based on the eye movement difference data and a basic score corresponding to the blinking condition, the eyeball rotation condition or the eyeball sight direction when the eye part is in the normal eye movement state;
and training the neural network model based on the eye movement difference data, the electroencephalogram difference data and the scoring function to obtain the eye movement evaluation model.
4. The method for evaluating an eye movement state according to claim 1, wherein the determining eye movement state information corresponding to the eye movement score data according to the eye movement score data includes:
determining a score gear corresponding to the eye movement scoring data according to the eye movement scoring data;
and determining eye movement state information corresponding to the score gear based on the score gear.
5. The eye movement state assessment method according to claim 1, further comprising:
counting eye movement state information in a preset time period, and screening out abnormal eye movement states in the eye movement state information;
and acquiring time information corresponding to the abnormal eye movement state, and outputting prompt information according to the abnormal eye movement state and the corresponding time information, wherein the prompt information is used for reflecting that the frequency of the abnormal eye movement state exceeds a preset frequency.
6. An eye movement state evaluation device, characterized in that the device comprises:
the eye movement evaluation data determining module is used for acquiring electroencephalogram data, inputting the electroencephalogram data into a pre-trained eye movement evaluation model, and outputting the eye movement evaluation data based on the eye movement evaluation model, wherein the eye movement evaluation model is obtained by carrying out comparative analysis and training on the electroencephalogram data under different eye movement states;
the eye movement state information determining module is used for determining eye movement state information corresponding to the eye movement scoring data according to the eye movement scoring data, and the eye movement state information is used for reflecting the blinking condition, the eyeball rotation condition or the eyeball sight line direction;
the apparatus includes a model training module, the model training module comprising:
the sample acquisition unit is used for acquiring a plurality of sample electroencephalogram data, classifying the sample electroencephalogram data and determining the electroencephalogram data of the eyes under the normal eye movement state and the electroencephalogram data of the eyes under the abnormal eye movement state;
the contrast analysis unit is used for performing contrast analysis on the electroencephalogram data of the eyes in a normal eye movement state and the electroencephalogram data of the eyes in an abnormal eye movement state to determine electroencephalogram difference data and eye movement difference data corresponding to the electroencephalogram difference data;
the model training unit is used for training a preset neural network model based on the electroencephalogram difference data and the eye movement difference data to obtain the eye movement evaluation model;
the comparative analysis unit comprises:
the first comparison subunit is used for comparing the electroencephalogram data of the eyes in the normal eye movement state with the electroencephalogram data in the abnormal eye movement state to determine the electroencephalogram difference data;
an information acquisition subunit, configured to acquire eye movement information of an eye in a normal eye movement state and eye movement information of an eye in an abnormal eye movement state;
and the second comparison subunit is used for comparing the eye movement information of the eyes in the normal eye movement state with the eye movement information of the eyes in the abnormal eye movement state to obtain the eye movement difference data.
7. A terminal device, characterized in that the terminal device comprises a memory, a processor and an eye movement state evaluation program stored in the memory and operable on the processor, and the processor implements the steps of the eye movement state evaluation method according to any one of claims 1 to 5 when executing the eye movement state evaluation program.
8. A computer-readable storage medium, characterized in that an eye movement state evaluation program is stored on the computer-readable storage medium, which when executed by a processor, implements the steps of the eye movement state evaluation method according to any one of claims 1 to 5.
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