CN107296586A - Collimation error detection device/method and writing system/method based on the equipment - Google Patents
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Abstract
The invention discloses a kind of collimation error detection device based on the electric wearable device of brain, method and the writing system based on detection device, including sensor unit, wearable the wearing for gathering brain electricity；Visual display unit, including display screen, so that visual stimulus is presented to user in multiple sectors in the visual field, the visual stimulus wherein presented includes being mapped to the optical flicker effect of the selected frequency of each sector in the visual field, is configured to excite the stimulation of multiple spot Steady State Visual Evoked Potential by the sensor unit in the EEG signals that the user obtained is presented；Display stimulates synchronization unit；And data processing unit, it stimulates synchronization unit to communicate to analyze the assessment of acquired EEG signals and generation to the visual field with the sensor unit, the visual display unit and display.The present invention using higher density EEG record and multiple spot Steady State Visual Evoked Potential, compared to existing visible detection method, it can provide improvement signal to noise ratio, increase repeatability and diagnostic accuracy.
Description
Technical Field
The invention relates to wearable equipment, in particular to visual error detection equipment and method based on electroencephalogram wearable equipment and a writing system.
Background
In the prior art, optic neuropathy refers to a condition of optic nerve damage that can result in significant and irreversible loss of visual function and disability.
Glaucoma is associated with degeneration of Retinal Ganglion Cells (RGCs) and their axons, leading to a characteristic appearance of the optic disc and loss of visual field. Loss of visual function in glaucoma is usually irreversible and without adequate treatment, the disease can progress to disability and blindness. The disease generally remains relatively asymptomatic, usually until late stage. Therefore, early detection and monitoring of visual function impairment is crucial to prevent dysfunction and blindness.
It is estimated that glaucoma-like disease affects more than 7000 million individuals worldwide, of which about 10% is bilateral blindness, making it one of the leading causes of irreversible blindness in the world. However, since the disease generally remains asymptomatic until it is very severe, the number of affected individuals may be much greater than known numbers. Population level survey data indicate that only 10% to 50% of individuals are aware that they are suffering from glaucoma.
Visual dysfunction appears to be a strong predictor of cognitive dysfunction in subjects in many clinical neurological disorders. For example, functional deficits in glaucoma and alzheimer's disease are linked, including the extent of loss of low spatial frequency range at contrast sensitivity, which is similar in both diseases.
Traditionally, standard automated visual fields (SAP) are used to assess loss of function in disease. SAP is the current standard for assessing visual field loss. Methods of visual field assessment using SAP require considerable subjective feedback from the patient and for some patients obtaining reliable visual field measurements is very difficult or even impossible. SAP is also limited by large re-test variability and often requires extensive testing in order to distinguish true disease progression from noise situations. For example, SAP testing is limited by the subjectivity and large variability of patient responses, often requiring large numbers of tests, and effective detection over a period of time. These tests are typically performed on a clinical-based SAP facility and due to limited health data and hospital resources, the number of data acquired over a long period of time is insufficient, leading to untimely diagnosis and disease progression. The cost, complexity and non-portability of SAP testing to highly trained technicians also prevents its application to screening for visually deficient populations. For example, due to SAP, testing is typically performed on a clinical-based facility and requires highly trained technicians, which limits the scale-up of such methods. Moreover, the equipment is often expensive and not easily transportable, which has largely hindered the development of SAP technology for telemedicine and test screening populations.
Disorders such as macular degeneration, diabetic retinopathy, optic neuritis, papilloma edema, anterior ischemic optic neuropathy and tumors can be tracked by SAP diagnosis.
Attempts to evaluate visual field damage in glaucoma using Visual Evoked Potentials (VEPs) and multipoint VEP techniques have been pursued. These techniques break through signal-to-noise limitations and have shown potential in assessing diseases of visual field loss. For example, the conventional mode VEP receives primarily brain electrical data generated from the central retinal projection scalp region, with the central 2% of the visual field contributing 65% of the response. Thus, the conventional mode VEP has a limited ability to reflect the loss of non-central regions such as those that may occur with glaucoma.
While the multi-point VEP technique allows many regions of the retina to be stimulated simultaneously and separates responses from each part of the visual field, individual variability in the anatomy of the visual cortex leads to individual variability in test results in normal subjects, making it difficult to identify patients with mfVEP abnormalities. Furthermore, existing mfVEP recording techniques can only be performed in a clinical or laboratory based setting using non-portable equipment, requiring time and effort consuming electrode placement.
The present technology includes EEG-based detection methods, systems and devices for visual field inspection by using high density EEG to correlate the dynamics of multi-point steady-state visual evoked potentials with visual field defects, wherein the use of rapid blinking stimuli can produce a brain response characterized by a "quasi-sinusoidal" waveform, whose "frequency components are constant in amplitude and phase, the so-called steady-state response. The steady state VEP has characteristics for assessing the integrity of the visual system. For example, this technique is faster than mfVEP, less sensitive to artifacts produced by blinking and eye movement, more sensitive to electromyographic noise contamination, and may exhibit better signal-to-noise ratio (SNR). The multi-point steady-state visual evoked potential, which is a signal of the multi-frequency tag SSVEP, can be elicited by, for example, simultaneously presenting multiple successive, repeating black/white inverted visual patches that blink at different frequencies, as opposed to a normal SSVEP. Based on the nature of the multi-point steady-state visual evoked potentials, visual locations with defective visual fields will be less sensitive or unresponsive to cause weaker SSVEP or lack thereof.
Methods, systems, and apparatus for visual field inspection using multi-point steady-state visual evoked potential data include forming a spatial visual stimulus display having a plurality of regions or sectors at different spatial locations, wherein for each particular region an optical effect (e.g., light flicker) is included that changes at a unique frequency relative to at least a neighboring region or any other region of the visual stimulus display. For example, the visual stimulus display may include 20 regions, each region exhibiting its respective optical effect at a different frequency between 8.0Hz and 11.8Hz (e.g., 8.0 in sector 1, 8.2 in sector 2, 8.4 … in sector 3, 11.8Hz sector 20). Brain responses to visual stimuli are acquired and processed, for example, the visual stimuli are measured by at least one electrode placed on the head or a plurality of electrodes arranged in an arrangement that improves the spatiotemporal resolution of the EEG signal, to generate and evaluate multi-point steady-state visual evoked potential data relating to a frequency spectrum comprising specified frequencies mapped to spatial regions of the visual stimuli display. The processing includes comparing the multi-point steady-state visual evoked potential signal at a particular frequency to a predetermined threshold or relative to other multi-point steady-state visual evoked potential signals to determine if the signal falls below the predetermined threshold or to compare if the multi-point steady-state visual evoked potential signal is lower in value. The multi-point steady-state visual evoked potential signal is below the threshold or substantially below the standard visual field deficit value at the particular frequency and the visual deficit of the user in that area is displayed on the display.
In some embodiments, for example, the disclosed technology includes using a brain-computer interface (BCI) to bridge a human brain with a computer or external device. By detecting SSVEP frequencies from non-invasively recorded EEG, a user of the SSVEP based brain-machine interface can interact with or control an external device and/or environment by gazing at different frequency encoding targets. For example, SSVEP-based BCI can provide a promising communication vehicle for disabled patients because of their high signal-to-noise ratio on the visual cortex, which can be measured non-invasively by EEG in the occipital region. The disclosed techniques methods, devices and systems can be used for continuous monitoring of high temporal and spatial resolution noninvasive electroencephalographic acquisition platforms without the need for conductive gels. An exemplary system may employ dry electrode EEG sensors, low power signal acquisition, amplification and digitization, wireless communication, and real-time processing. In addition, the present techniques apply independent component analysis, which may improve the detectability of the SSVEP signal.
The technology describes an exemplary implementation of multi-point steady-state visual evoked potentials. Such embodiments demonstrate detection of local loss, as well as display assessment of optic nerve disease (e.g., glaucoma, amblyopia, age-related macular degeneration).
The diagnosis and detection of neurological disorders remains challenging. For example, an effective portable method for assessing visual field loss would have many advantages over current methods for assessing loss of functionality. EEG-based tests remove subjective tests and some unreliable tests. Under the unrestricted condition, can be at home long-range, quick, portable, objective test, reduced the number of times of going to the hospital. Furthermore, the availability of large amounts of data facilitates more accurate and earlier detection of potential risks over time. Furthermore, a more accurate assessment of the rate of progress can be obtained. The visual field assessment method of the examples can be used for screening in remote areas or areas with medical resource deficiencies, and for assessing visual field defects in other conditions.
There is currently little available reliable and efficient portable method for assessing visual function loss. Our solution includes an electroencephalogram acquisition platform integrating a wearable wireless EEG dry electrode system and a head-mounted display system, which allows a user to monitor the electroencephalogram activity associated with a field of view on a timely and continuous basis, such as at home. In addition, such devices provide innovative and potentially useful methods of screening for disease. The technology includes a portable brain-computer interface and methods for complex analysis of EEG data, including for example diagnostic and assessment methods to detect disease progression.
Disclosure of Invention
In view of the above disadvantages and shortcomings in the prior art, an object of the present invention is to provide a visual error detection device and method based on an electroencephalogram wearable device, and a writing system based on the detection device.
The purpose of the invention is realized by the following technical scheme:
a visual error detection device based on an electroencephalogram wearable device comprises a visual stimulation device 2 with a screen and an electroencephalogram processing device 1 with an electrode 3;
the visual stimulation device with screen 2 comprises:
a visual stimulus unit comprising a display screen to present visual stimuli to a user in a plurality of sectors of a field of view, wherein the presented visual stimuli comprise optical flicker effects at selected frequencies mapped to each sector of the field of view, stimuli configured to excite multi-point steady-state visual evoked potentials in brain electrical signals presented by the user acquired by the sensor unit;
the display synchronization unit is used for synchronously displaying signals and matching with the electroencephalogram signals to obtain accurate frequency results;
the electroencephalogram processing apparatus 1 with the electrodes 3 includes:
the electrode 3 acquires an electroencephalogram signal;
a brain electrical acquisition unit for acquiring brain electrical signals including one or more electrodes 3 attached to a head-worn wearable on a user's head;
an electroencephalogram processing unit in communication with the electroencephalogram acquisition unit and the visual stimulation unit to analyze the acquired electroencephalogram signals and produce an assessment of the user's field of view.
A visual error detection method based on electroencephalogram wearable equipment comprises the following steps:
presenting visual stimuli in a plurality of sectors of a field of view to a subject, wherein for each sector the presented visual stimuli comprises an optical flicker effect;
the display synchronization unit receives the display signal of the display and synchronously sends the display signal to the brain electrical signal processing unit for data processing;
the electroencephalogram data processing device with electrodes 3 acquires electroencephalogram signals from one or more electrodes 3 in contact with the head of the subject;
processing the acquired brain electrical signals to extract multi-point steady-state visual evoked potential data associated with the presented brain electrical signal response of the visually stimulated subject;
the electroencephalogram processing unit extracts the signal frequency deviation characteristics of the whole area of the occipital region 4 of the head due to the optical flicker effect, compares the signal frequency deviation characteristics with the normal existing data, and outputs the comparison result to an evaluation standard as one of evaluation indexes;
and generating a quantitative assessment of the visual field of the subject based on the multi-point steady-state visual evoked potential data.
A writing system based on the detection device of claim 1, further comprising an artificial neural network, wherein the visual field assessment of the user is input into the artificial neural network as a nonlinear variable, and text information of a region to be seen is output after being calculated by an algorithm through the artificial neural network.
A writing method based on the detection device of claim 1, comprising the steps of:
s1, collecting visual field evaluations of a large number of users as nonlinear variables to obtain a large number of input/output sample pair clusters, and establishing an artificial neural network through a BP algorithm;
s2, the operation of the step S1 is carried out again, the artificial neural network analyzes the visual field assessment by means of the calculation processing module, and character information corresponding to the visual field assessment is obtained;
and S3, the calculation processing module reports the obtained result to the upper computer.
Preferably, after step S3, the method further includes the operation of reducing the error of the artificial neural network:
and S4, uploading the obtained results to a cloud server by a plurality of upper computers.
And S5, performing matching judgment on the result obtained by the collection in the cloud server.
Preferably, the step S5 specifically includes the following sub-steps:
s501, matching an input sample a and an input sample corresponding to the result with one input/output sample pair in the input/output sample pair cluster, and reserving the input/output sample pair;
s502, the input sample a corresponding to the result matches the input sample of one input/output sample pair in the input/output sample pair cluster and the output sample does not match, and the process proceeds to step S6; or,
s503, the input sample a corresponding to the result is not matched with the input samples of all the input/output sample pairs in the input/output sample pair cluster, and the process proceeds to step S7.
S6, performing statistical analysis on the input samples stored in the cloud server and the same as the input sample a in step S502, to obtain a matching rate between the output sample corresponding to the input sample a and the output sample in the input/output sample pair cluster corresponding to the input sample a, and if the matching rate is 60% to 85%, adjusting weights of each layer in the artificial neural network, so that the matching rate between the input sample a and its corresponding output sample and the input/output sample pair in the input/output sample pair cluster is greater than 85%; eliminating input/output sample pairs with the actual matching rate not higher than 60% in the artificial neural network; or,
s7, performing statistical analysis on the same input samples as the input sample a obtained in step S603 stored in the cloud server, to obtain a proportion of output samples corresponding to the input sample a, and if the proportion is not less than 85%, adding the input sample a and the corresponding output samples with a proportion higher than 85% as new input/output sample pairs into the input/output sample pair cluster.
Preferably, the operation of reducing the error of the artificial neural network also comprises a training and monitoring operation: and learning, predicting and correcting the visual frequency peripheral shift characteristic model caused by comparing the optical flicker effect for multiple times, and training and detecting the visual region information at the same time.
Compared with the prior art, the embodiment of the invention at least has the following advantages:
the wearable platform uses high density EEG recording and multi-point steady-state visual evoked potentials, which can provide improved signal-to-noise ratio, increased reproducibility and diagnostic accuracy over existing EEG-based methods for the periphery of a target (e.g., mfVEP).
As a portable platform that can be used for testing without constraints, it can allow for more extensive and frequent testing of patients than existing methods, and can also reduce the number of hospital visits for patients at risk or diagnosed with ophthalmic disease, significantly reducing the economic burden of the disease.
Furthermore, by allowing more frequent testing, this approach can help to distinguish true exacerbations from testing, retesting variability, such as diagnosing early stage disease and detecting recovery effects.
The portable, implemented and objective visual field assessment method may also allow a service population of visual defects to be screened to determine if the person is competent for the task.
Non-invasive detection techniques may not make direct physical contact with the eye of the individual being evaluated, thereby avoiding inadvertent application of force or harmful chemical biological materials to the eye.
Drawings
FIG. 1 is a system connection block diagram of a visual error detection device based on an electroencephalogram wearable device according to the present invention;
fig. 2 is a schematic diagram of a brain region corresponding to an electroencephalogram processing device with electrodes when the electroencephalogram wearable device-based visual error detection device of the present invention is worn;
fig. 3 is a schematic diagram of a visual error detection device based on an electroencephalogram wearable device, wherein the visual stimulation device is provided with a screen and corresponds to a brain region when the visual error detection device is worn.
Detailed Description
The invention will be described in more detail below with reference to the following examples and the accompanying figures 1-3.
A visual error detection device based on an electroencephalogram wearable device comprises a visual stimulation device 2 with a screen and an electroencephalogram processing device 1 with an electrode 3;
the visual stimulation device with screen 2 comprises:
a visual stimulus unit comprising a display screen to present visual stimuli to a user in a plurality of sectors of a field of view, wherein the presented visual stimuli comprise optical flicker effects at selected frequencies mapped to each sector of the field of view, stimuli configured to excite multi-point steady-state visual evoked potentials in brain electrical signals presented by the user acquired by the sensor unit;
the display synchronization unit is used for synchronously displaying signals and matching with the electroencephalogram signals to obtain accurate frequency results;
the electroencephalogram processing apparatus 1 with the electrodes 3 includes:
the electrode 3 acquires an electroencephalogram signal;
a brain electrical acquisition unit for acquiring brain electrical signals including one or more electrodes 3 attached to a head-worn wearable on a user's head;
an electroencephalogram processing unit in communication with the electroencephalogram acquisition unit and the visual stimulation unit to analyze the acquired electroencephalogram signals and produce an assessment of the user's field of view.
A visual error detection method based on electroencephalogram wearable equipment comprises the following steps:
presenting visual stimuli in a plurality of sectors of a field of view to a subject, wherein for each sector the presented visual stimuli comprises an optical flicker effect;
the display synchronization unit receives the display signal of the display and synchronously sends the display signal to the brain electrical signal processing unit for data processing;
the electroencephalogram data processing device with electrodes 3 acquires electroencephalogram signals from one or more electrodes 3 in contact with the head of the subject;
processing the acquired brain electrical signals to extract multi-point steady-state visual evoked potential data associated with the presented brain electrical signal response of the visually stimulated subject;
the electroencephalogram processing unit extracts the signal frequency deviation characteristics of the whole area of the occipital region 4 of the head due to the optical flicker effect, compares the signal frequency deviation characteristics with the normal existing data, and outputs the comparison result to an evaluation standard as one of evaluation indexes;
and generating a quantitative assessment of the visual field of the subject based on the multi-point steady-state visual evoked potential data.
A writing system based on the detection device of claim 1, further comprising an artificial neural network, wherein the visual field assessment of the user is input into the artificial neural network as a nonlinear variable, and text information of a region to be seen is output after being calculated by an algorithm through the artificial neural network.
A writing method based on the detection device of claim 1, comprising the steps of:
s1, collecting visual field evaluations of a large number of users as nonlinear variables to obtain a large number of input/output sample pair clusters, and establishing an artificial neural network through a BP algorithm;
s2, the operation of the step S1 is carried out again, the artificial neural network analyzes the visual field assessment by means of the calculation processing module, and character information corresponding to the visual field assessment is obtained;
and S3, the calculation processing module reports the obtained result to the upper computer.
Preferably, after step S3, the method further includes the operation of reducing the error of the artificial neural network:
and S4, uploading the obtained results to a cloud server by a plurality of upper computers.
And S5, performing matching judgment on the result obtained by the collection in the cloud server.
The step S5 specifically includes the following sub-steps:
s501, matching an input sample a and an input sample corresponding to the result with one input/output sample pair in the input/output sample pair cluster, and reserving the input/output sample pair;
s502, the input sample a corresponding to the result matches the input sample of one input/output sample pair in the input/output sample pair cluster and the output sample does not match, and the process proceeds to step S6; or,
s503, the input sample a corresponding to the result is not matched with the input samples of all the input/output sample pairs in the input/output sample pair cluster, and the process proceeds to step S7.
S6, performing statistical analysis on the input samples stored in the cloud server and the same as the input sample a in step S502, to obtain a matching rate between the output sample corresponding to the input sample a and the output sample in the input/output sample pair cluster corresponding to the input sample a, and if the matching rate is 60% to 85%, adjusting weights of each layer in the artificial neural network, so that the matching rate between the input sample a and its corresponding output sample and the input/output sample pair in the input/output sample pair cluster is greater than 85%; eliminating input/output sample pairs with the actual matching rate not higher than 60% in the artificial neural network; or,
s7, performing statistical analysis on the same input samples as the input sample a obtained in step S603 stored in the cloud server, to obtain a proportion of output samples corresponding to the input sample a, and if the proportion is not less than 85%, adding the input sample a and the corresponding output samples with a proportion higher than 85% as new input/output sample pairs into the input/output sample pair cluster.
The method also comprises training and monitoring operations while reducing the artificial neural network error: and learning, predicting and correcting the visual frequency peripheral shift characteristic model caused by comparing the optical flicker effect for multiple times, and training and detecting the visual region information at the same time.
The present technology includes methods, systems and apparatus for acquiring, processing and utilizing steady-state visual evoked potentials (SSVEPs) to monitor, track and diagnose visual function; to monitor, follow up and diagnose clinical neuroscience (e.g., aging, neurodegenerative diseases, schizophrenia, ophthalmic disorders, migraine, autism, depression, anxiety, stress and epilepsy), particularly for SSVEP produced by light stimulation.
Electroencephalography (EEG) is the recording of electrical activity of the brain using electrodes 3 located on the scalp of a subject, forming signal oscillations of neurons, which comprise an EEG data set. In some literature, EEG refers to the recording of the activity of spontaneous brain electrical activity over a short period of time. EEG data is collected for clinical diagnostic applications including the study of epilepsy, coma, brain death and other diseases and deficiencies, as well as sleep and sleep disorders. In some cases, EEG data has been acquired for diagnosis of tumors, stroke, and other focal brain diseases.
Electroencephalogram (EEG) based brain acquisition measurement methods, systems, and apparatuses for visual field inspection by associating multi-point steady-state visual evoked potentials with visual field defects using high density EEG. In one aspect, the technology integrates multipoint steady-state visual evoked potentials into a portable platform using wireless EEG and a head mounted display capable of assessing potential visual field defects. In some cases, the techniques may be applied to diagnose and track ocular neuropathy, such as macular degeneration, diabetic retinopathy, optic neuritis, papilloma edema, anterior ischemic optic neuropathy, and/or tumors.
For example, in contrast to event-related potentials that arise during conventional VEP examinations, the present technique utilizes a fast blinking stimulus to produce a brain response of quasi-sinusoidal waveform with frequency components that are constant in amplitude, as well as phase, etc., such as the so-called steady-state response. In some cases, for example, the portable platform integrates a wearable wireless high density dry electrode 3EEG system and a head mounted display that allows the user to routinely monitor brain electrical activity associated with visual field stimulation. The technology includes the use of an EEG dry sensor array, wireless wearable data acquisition and signal processing hardware, and brain-computer interface software. These interfaces can monitor and record non-invasive, high spatiotemporal resolution brain activity of unconstrained, actively involved human subjects.
For example, the technique may be applied to the ophthalmic diagnosis of neurological complications, including especially glaucoma, retinal abnormalities and vision, retinal degeneration of retinal structure and macular degeneration, diabetic retinopathy, ocular inflammation, optic neuroma, or degenerative diseases (e.g., parkinson's disease, alzheimer's disease, non-alzheimer's dementia, multiple sclerosis, ALS, head trauma), diabetes or other cognitive disorders such as dyslexia, and the like. More broadly, the present techniques can be used to characterize inappropriate responses to contrast sensitivity patterns and conditions affecting the optic nerve and visual cortex.
In a first aspect, a system for monitoring brain activity associated with a field of view of a user comprises: a sensor unit for acquiring brain electrical signals including one or more electrodes 3 attached to a head-worn wearable on a user's head; a visual display unit comprising a display screen to present visual stimuli to a user in a plurality of sectors of a field of view, wherein the presented visual stimuli comprise optical flicker effects at selected frequencies mapped to each sector of the field of view, configured to excite multi-point steady-state visual evoked potential stimuli in an EEG signal presented by the user acquired by the sensor unit; and a data processing unit in communication with the sensor unit and the visual display unit to analyze the acquired EEG signals and generate an assessment of the user's field of view.
In one aspect, a method for inspecting a field of view of an object includes: presenting visual stimuli in a plurality of sectors of a field of view to a subject, wherein for each sector the presented visual stimuli comprises an optical flicker effect; acquiring brain electrical signals from one or more electrodes 3 in contact with the subject's head; processing the acquired EEG signals to extract multipoint steady-state visual evoked potential data associated with the presented EEG signal responses of the visually stimulated subject; and generating a quantitative assessment of the visual field of the subject based on the multi-point steady-state visual evoked potential data.
In another aspect, a portable system for monitoring brain activity associated with a field of view of a user includes: a brain signal sensor means for acquiring EEG signals comprising one or more electrodes 3 attached to a head-worn wearable on the head of a user; a wearable visual display unit for presenting visual stimuli to a user and configured to comprise a display screen and a head-piece securable to the head of the user, wherein the wearable visual display is operable to present the visual stimuli in a plurality of sectors such that for each sector the presented visual stimuli comprise an optical blinking effect at a selected frequency, and wherein the visual stimuli are configured to evoke in the EEG signals a presentation of the user of multi-point steady-state visual evoked potentials acquired by the brain signal sensor apparatus; a data processing unit in communication with the brain signal sensor device and the wearable visual display unit to provide visual stimuli to the wearable visual display unit and to analyze the acquired EEG signals and produce an assessment of the user's field of view; and an electro-oculogram (EOG) unit comprising one or more electrodes 3 placed close to the outer canthus of each user's eye to measure corneal-retinal stance potential (CRSP) signals, wherein the one or more electrodes 3 of the EOG unit are in communication with the data processing unit to process CRSP signals acquired from the one or more electrodes 3 to determine movement of the user's eye.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. Visual error detection equipment based on electroencephalogram wearable equipment is characterized by comprising visual stimulation equipment with a screen and electroencephalogram processing equipment with electrodes;
the visual stimulation device with screen comprises:
a visual stimulus unit comprising a display screen to present visual stimuli to a user in a plurality of sectors of a field of view, wherein the presented visual stimuli comprise optical flicker effects at selected frequencies mapped to each sector of the field of view, stimuli configured to excite multi-point steady-state visual evoked potentials in brain electrical signals presented by the user acquired by the sensor unit;
the display synchronization unit is used for synchronously displaying signals and matching with the electroencephalogram signals to obtain accurate frequency results;
the electroencephalogram processing device with the electrodes comprises:
the electrode acquires an electroencephalogram signal;
a brain electrical acquisition unit for acquiring brain electrical signals comprising one or more electrodes attached to a head wearable on a user's head;
an electroencephalogram processing unit in communication with the electroencephalogram acquisition unit and the visual stimulation unit to analyze the acquired electroencephalogram signals and produce an assessment of the user's field of view.
2. A visual error detection method based on electroencephalogram wearable equipment is characterized by comprising the following steps:
presenting visual stimuli in a plurality of sectors of a field of view to a subject, wherein for each sector the presented visual stimuli comprises an optical flicker effect;
the display synchronization unit receives the display signal of the display and synchronously sends the display signal to the brain electrical signal processing unit for data processing;
an electroencephalographic data processing device with electrodes acquires an electroencephalographic signal from one or more electrodes in contact with the head of the subject;
processing the acquired brain electrical signals to extract multi-point steady-state visual evoked potential data associated with the presented brain electrical signal response of the visually stimulated subject;
the electroencephalogram processing unit extracts the signal frequency deviation characteristics of the whole area of the occipital region of the head, which are caused by the optical flicker effect, compares the signal frequency deviation characteristics with the normal existing data, and outputs the comparison result to an evaluation standard as one of evaluation indexes;
and generating a quantitative assessment of the visual field of the subject based on the multi-point steady-state visual evoked potential data.
3. A writing system based on the detection device of claim 1, further comprising an artificial neural network, wherein the visual field assessment of the user is input into the artificial neural network as a nonlinear variable, and text information of a region to be viewed is output after being calculated by an algorithm through the artificial neural network.
4. A writing method based on the detection device of claim 1, characterized by comprising the following steps:
s1, collecting visual field evaluations of a large number of users as nonlinear variables to obtain a large number of input/output sample pair clusters, and establishing an artificial neural network through a BP algorithm;
s2, the operation of the step S1 is carried out again, the artificial neural network analyzes the visual field assessment by means of the calculation processing module, and character information corresponding to the visual field assessment is obtained;
and S3, the calculation processing module reports the obtained result to the upper computer.
5. The writing method of claim 4, further comprising, after step S3, the operation of reducing the artificial neural network error:
and S4, uploading the obtained results to a cloud server by a plurality of upper computers.
And S5, performing matching judgment on the result obtained by the collection in the cloud server.
6. The writing method according to claim 5, wherein the step S5 comprises the following sub-steps:
s501, matching an input sample a and an input sample corresponding to the result with one input/output sample pair in the input/output sample pair cluster, and reserving the input/output sample pair;
s502, the input sample a corresponding to the result matches the input sample of one input/output sample pair in the input/output sample pair cluster and the output sample does not match, and the process proceeds to step S6; or,
s503, the input sample a corresponding to the result is not matched with the input samples of all the input/output sample pairs in the input/output sample pair cluster, and the process proceeds to step S7.
S6, performing statistical analysis on the input samples stored in the cloud server and the same as the input sample a in step S502, to obtain a matching rate between the output sample corresponding to the input sample a and the output sample in the input/output sample pair cluster corresponding to the input sample a, and if the matching rate is 60% to 85%, adjusting weights of each layer in the artificial neural network, so that the matching rate between the input sample a and its corresponding output sample and the input/output sample pair in the input/output sample pair cluster is greater than 85%; eliminating input/output sample pairs with the actual matching rate not higher than 60% in the artificial neural network; or,
and S7, performing statistical analysis on the input samples stored in the server and the same as the input samples a obtained in the step S603 to obtain the proportion of the output samples corresponding to the input samples a, and if the proportion is not lower than 85%, adding the input samples a and the corresponding output samples with the proportion higher than 85% into an input/output sample pair cluster as a new input/output sample pair.
7. The writing method of claim 5, further comprising training and monitoring operations while reducing the artificial neural network error: and learning, predicting and correcting the visual frequency peripheral shift characteristic model caused by comparing the optical flicker effect for multiple times, and training and detecting the visual region information at the same time.
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