A method, system, device and medium for photoelectro-induced processing of the visual cortex

Abstract

This disclosure relates to a method, system, device, and medium for processing visual cortex photoelectrostimulation. The method includes: acquiring a visual stimulus pattern; determining a mathematical model based on the visual stimulus pattern; generating a visual stimulus signal of a target paradigm according to the mathematical model; acquiring the user's electroencephalogram (EEG) signal and eye and / or pupil response signal corresponding to the visual stimulus signal; processing the response signal to obtain a visual cortex response signal of a target format; acquiring the EEG signal and visual cortex response signal under multiple different target paradigms; selecting different processing methods to extract features from the EEG signal and visual cortex response signal according to different target paradigms to obtain multiple target features; evaluating based on the multiple target features to obtain an evaluation result; and displaying the evaluation result in the form of charts and numerical values ​​on the user terminal interface. This technical solution combines multiple advanced stimulus generation, recording, and analysis methods, making it suitable for various scenarios.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a method, system, device and medium for processing photoelectro-induced visual cortex. Background Technology

[0002] Evoked potentials (EPs) are unique patterns of positive and negative voltage deflection that are time-locked to a specific sensory stimulus or event. Visual evoked potentials (VEPs) are a cluster of electrical signals in response to visual stimuli in the cerebral cortex.

[0003] While visual pathway intervention (VEP) technology is widely used in neurophysiology and clinical practice, existing VEP systems have several limitations. For example, the stimulation modes of the stimulation generator are relatively limited, failing to meet the needs of different patients; the signal noise of the EEG (electroencephalogram) recording system is relatively high, affecting the accuracy of disease diagnosis; existing analysis software cannot fully extract valuable features from VEP; and there is a lack of targeted interventions within the same system framework. These problems not only reduce the efficacy and flexibility of VEP technology in diagnosing and treating visual pathway disorders but also limit its application in other neurological diseases. Summary of the Invention

[0004] To solve the above-mentioned technical problems, or at least partially solve them, this disclosure provides a method, system, device, and medium for photoelectro-induced processing of the visual cortex.

[0005] This disclosure provides a method for photoelectro-induced processing of the visual cortex, the method comprising:

[0006] Obtain visual stimulus patterns, obtain model parameters based on the visual stimulus patterns, determine a mathematical model based on the model parameters, and generate visual stimulus signals of the target paradigm according to the mathematical model.

[0007] Acquire the user's electroencephalogram (EEG) signal corresponding to the visual stimulus signal, acquire the user's eyeball and / or pupil response signal corresponding to the visual stimulus signal, and process the response signal to obtain a visual cortical response signal in the target format;

[0008] The process involves acquiring EEG signals and visual cortex response signals under multiple different target paradigms, and performing feature extraction on the EEG signals and visual cortex response signals according to different processing methods based on the different target paradigms to obtain multiple target features; wherein, all EEG signals and visual cortex response signals under the same target paradigm are processed in batches according to fixed parameters.

[0009] The evaluation is performed based on the multiple target features to obtain the evaluation results, and the evaluation results are displayed on the user terminal interface in the form of charts and numerical values.

[0010] This disclosure also provides a visual cortex photoelectrostimulation system, the system comprising:

[0011] User terminal, used to acquire visual stimulus patterns;

[0012] A stimulus generator is used to acquire model parameters based on the visual stimulus pattern, determine a mathematical model based on the model parameters, and generate a visual stimulus signal of the target paradigm according to the mathematical model.

[0013] EEG recording system, used to acquire the user's electroencephalogram (EEG) signals based on the visual stimulus signals;

[0014] A photoelectric sensor is used to acquire the user's eyeball and / or pupil response signals based on the visual stimulus signal;

[0015] A signal processing unit is used to process the response signal to obtain a visual cortical response signal in a target format;

[0016] Analysis software is used to acquire EEG signals and visual cortex response signals under multiple different target paradigms, and to perform feature extraction on the EEG signals and visual cortex response signals according to different processing methods according to different target paradigms to obtain multiple target features; wherein, all EEG signals and visual cortex response signals under the same target paradigm are processed in batches according to fixed parameters;

[0017] The analysis software is also used to evaluate based on the multiple target features, obtain evaluation results, and display the evaluation results in the form of charts and numerical values ​​on the user terminal interface.

[0018] This disclosure also provides an electronic device, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the visual cortex photoelectrostimulation method provided in this disclosure.

[0019] This disclosure also provides a computer-readable storage medium storing a computer program for executing the visual cortex photoelectro-induced processing method provided in this disclosure.

[0020] Compared with the prior art, the technical solution provided in this disclosure has the following advantages: The visual cortex photoelectro-evoked processing scheme provided in this disclosure acquires a visual stimulation pattern, acquires model parameters based on the visual stimulation pattern, determines a mathematical model based on the model parameters, generates a visual stimulation signal of the target paradigm according to the mathematical model, acquires the user's EEG signal corresponding to the visual stimulation signal, acquires the user's eyeball and / or pupil response signal corresponding to the visual stimulation signal, processes the response signal to obtain the visual cortex response signal of the target format, acquires EEG signals and visual cortex response signals under multiple different target paradigms, selects different processing methods to extract features from the EEG signals and visual cortex response signals according to different target paradigms, and acquires multiple target features; wherein, all EEG signals and visual cortex response signals under the same target paradigm are batch processed according to fixed parameters, evaluated according to multiple target features, and the evaluation results are displayed on the user terminal interface in the form of charts and values. By employing the above technical solution, a variety of visual stimuli, such as flashes of light or the appearance of images, sudden changes in color or pattern, and other diverse visual stimulation modes, are used to improve the efficiency of eliciting electrical signal responses in the cerebral cortex. High-sensitivity photoelectric sensors are used to acquire response signals from the eyeball and / or pupil, significantly reducing testing time. Based on target paradigms such as SSVEP, stable oscillations of the evoked voltage through rapid, repetitive stimulation provide a stable, instruction-rich, and high-speed solution for complex visual pathway analysis. This greatly facilitates users in comprehensively and efficiently processing large volumes of EEG data, aiding in disease diagnosis and treatment efficacy assessment. Furthermore, fixed parameters are set for the target paradigm, avoiding repetitive parameter settings and facilitating clinical EEG examinations, helping users comprehensively, quickly, and efficiently process large volumes of EEG data. Attached Figure Description

[0021] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0022] Figure 1 A schematic flowchart of a method for photoelectro-induced processing of the visual cortex provided in an embodiment of this disclosure;

[0023] Figure 2 This is a schematic diagram of the structure of a visual cortex photoelectrostimulation system provided in an embodiment of the present disclosure;

[0024] Figure 3 A structural example diagram of a visual cortex photoelectrostimulation processing system provided in this embodiment of the disclosure;

[0025] Figure 4This is a flowchart illustrating a method for photoelectro-induced processing of the visual cortex, provided in an embodiment of this disclosure. Detailed Implementation

[0026] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0027] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0028] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0029] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0030] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0031] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0032] Visual evoked potentials (VEPs), as a neurophysiological assessment technique, are primarily used to detect the functional integrity of the visual pathway from the retina (eyeball) to the occipital cortex (brain). Prominent VEP deflections or components include N75 and P100, which originate in or near the primary visual cortex. Different brain-computer interface paradigms exist for different types of EEG signals. Steady-State Visual Evoked Potentials (SSVEPs) are stable voltage oscillations evoked by rapid, repetitive stimuli, such as the flashing of a flashlight or the brightness flickering of a light-emitting diode. Continuous stimulation elicits similar responses, and the overlap of these responses produces steady-state oscillations. The SSVEP paradigm has advantages such as stability, rich instruction sets, and high information transmission rates.

[0033] Visual motor evoked potentials (VEMPs) are electrical muscle responses induced by visual stimuli. By analyzing the electrical responses of muscles to visual stimuli, VEMPs can assess the interaction between the visual and motor systems. VEMPs can be used to diagnose balance disorders related to the visual and vestibular systems. VEMPs can also assess the functional state of the neuromuscular system by analyzing muscle responses to visual stimuli.

[0034] Visual evoked magnetic field (VFM) is a technique for assessing visual system function by measuring the brain's magnetic field response to visual stimuli. When visual stimuli (such as flashes of light or pattern changes) are presented to an observer, the visual cortex generates specific electrical activity, producing a measurable magnetic field. VFM is primarily used to assess visual abnormalities such as epilepsy and visual field defects. Furthermore, VFM can locate areas of activity in the visual cortex, providing a reference for surgical and treatment planning.

[0035] Electroretinography (ERG) is a technique that measures the electrical response of retinal cells to light stimulation. By placing electrodes on the surface of the eye, ERG records the electrical response of retinal cells (especially rod and cone cells) to light stimulation. ERG is widely used in the diagnosis of various retinal diseases, such as retinitis pigmentosa and macular degeneration. Furthermore, ERG can be used to monitor the potential effects of certain drugs on the retina, as some medications may have toxic effects on the retina.

[0036] The relevant method utilizes a full-view, pattern-flipping approach, typically employing alternating black and white checkerboard patterns at a fixed frequency. The system includes standard components: a stimulus generator, an EEG recording system, and analysis software. Electrodes are placed at specific locations on the patient's scalp according to the international 10-20 system. After presenting black and white visual stimuli to the patient, the EEG recording system records the brain's electrical response. Subsequently, the built-in software analyzes the visual pathway response (VEP) and examines parameters such as the latency and amplitude of N75, P100, and N135. These parameters can improve the sensitivity and specificity of diagnosing visual pathway disorders, such as prolonged latency or reduced amplitude.

[0037] This invention incorporates advanced photoelectric stimulation, an optimized EEG recording system, advanced signal processing algorithms, and intervention and treatment equipment. It can not only detect visual pathways and cortical function more accurately, flexibly, and efficiently, but also enable the intervention and treatment of neuropsychiatric diseases such as epilepsy and Alzheimer's disease.

[0038] Specifically, Figure 1 This is a flowchart illustrating a method for visual cortex photoelectrostimulation processing according to an embodiment of this disclosure. This method can be executed by a visual cortex photoelectrostimulation processing system, which can be implemented using software and / or hardware, and is generally integrated into an electronic device. For example... Figure 1 As shown, the method includes:

[0039] Step 101: Obtain the visual stimulus pattern, obtain the model parameters based on the visual stimulus pattern, determine the mathematical model based on the model parameters, and generate the visual stimulus signal of the target paradigm according to the mathematical model.

[0040] In this embodiment of the disclosure, acquiring visual stimulation patterns includes: acquiring a flickering stimulation pattern that induces flickering visual evoked potentials, a grid stimulation pattern for detecting visual contrast and spatial frequency sensitivity, a single image stimulation pattern for brain responses to characteristic stimuli, a detection change stimulation pattern that displays sudden appearances or changes, an image detection task stimulation pattern, and a color stimulation pattern.

[0041] Specifically, diverse visual stimuli generated by a stimulator induce electrical signal responses in the cerebral cortex, which are then captured and recorded by an EEG recording system.

[0042] For example, after turning on the device and performing a self-test and initialization, you can select the desired visual stimulation mode through the user interface, such as: Flickering stimulation mode: switching between light and dark on the screen or frequent image flickering, used to induce flickering VEP, typically used to study visual responses to light and dark depths; Grid stimulation: displaying regularly arranged black and white grids, used to detect visual contrast and spatial frequency sensitivity; Single image stimulation: a picture or graphic appears on the screen, used to study brain responses to specific stimuli; Detecting change stimuli: studying changes in attention and perception by displaying suddenly appearing or changing stimuli; Image detection tasks; Color stimulation, etc.

[0043] In this embodiment of the disclosure, the mathematical model is: I(t)=Asin(2πft+φ); where I(t) is the light intensity, A is the amplitude, f is the frequency, φ is the phase, the adjustable flash frequency is 0 to 100 steps with a step of 1; the presentation time is 1 to 100 steps with a step of 1; and the brightness is 0.001 in steps from 0.1 to 9.999.

[0044] Specifically, the stimulus generator uses the above mathematical model to generate visual stimuli, that is, by modifying different parameters under the mathematical model and adjusting the parameter combination, multiple different types of stimuli can be created.

[0045] In this embodiment, the target paradigm is the SSVEP paradigm, which provides a stable, instruction-rich, and high-speed information transmission solution for complex visual pathway analysis by inducing stable voltage oscillations through rapid repetitive stimulation. In other words, the user's passive stimulation is not randomly generated; it must follow the guidelines of the SSVEP paradigm, a brain-computer interface research method, using stable and repetitive visual stimuli to elicit a specific frequency of electrophysiological response in the brain. Only this specific frequency signal can be captured by the scalp electrode sensors. The SSVEP paradigm requires the generation of visual stimuli according to a fixed mathematical model.

[0046] Step 102: Obtain the EEG signal corresponding to the visual stimulus signal from the user, and obtain the response signal of the eyeball and / or pupil corresponding to the visual stimulus signal from the user, and process the response signal to obtain the visual cortex response signal in the target format.

[0047] In this embodiment of the disclosure, the response signal is processed to obtain a visual cortical response signal in the target format, including: upsampling the response signal and performing a Fourier transform, performing bandpass filtering in the frequency domain, adjusting the frequency spectrum to a high frequency for quadrature demodulation to obtain a sinusoidal signal and a DC signal, generating a finite response signal sequence that fluctuates around the baseline using the DC signal as the baseline, converting the finite response signal sequence into an intermediate format, and calling a matrix format conversion function to convert the intermediate format into the target format to obtain the visual cortical response signal in the target format.

[0048] Specifically, photoelectric sensors can detect the response signals of the eyeball and / or pupil (including the number of pupil dilations and constrictions, the rate of eye movement, the tremor period, etc.), providing data for assessing the structural integrity of the visual pathway from the retina to the occipital cortex and changes in cortical function.

[0049] Specifically, the signal captured by the photoelectric sensor first undergoes a Fourier transform: Where F(ω) is the spectrum, f(t) is the time-domain signal, and ω is the angular frequency.

[0050] Specifically, the received response signal is upsampled and then Fourier transform is introduced. Bandpass filtering is performed in the frequency domain to remove unanalyzable Gaussian white noise and narrowband noise. The frequency spectrum is then shifted to a higher frequency and orthogonally demodulated to separate a relatively regular sinusoidal signal and a DC signal. Using this DC component as a baseline, a finite response signal sequence that fluctuates around the baseline is generated for subsequent extraction of the visual cortex response features.

[0051] Specifically, first export the file corresponding to the finite response signal sequence as an Excel file with the .xlsx extension, then convert it to .txt format, and finally call the matrix format conversion function in the Matlab command line to output a .mat file and save it. This data file can be processed and run by Matlab.

[0052] Specifically, the EEG recording system captures electrical signal responses and transmits the data to MATLAB and related analysis tools for processing and analysis. The analyzed data is then displayed in the form of charts and numerical values ​​through a user interface. In other words, raw data can be analyzed and processed to obtain the desired indicators and results, which can be presented through charts and numerical values. Compared to existing technologies, this method is simpler and more convenient, allowing users to customize chart layouts and easily export results.

[0053] Step 103: Obtain EEG signals and visual cortex response signals under multiple different target paradigms, and select different processing methods to extract features from the EEG signals and visual cortex response signals according to different target paradigms to obtain multiple target features; among them, batch processing is performed on all EEG signals and visual cortex response signals under the same target paradigm according to fixed parameters.

[0054] In this embodiment of the disclosure, different tests corresponding to different triggers are set according to different target paradigms, and the amplitude, latency, synchronization and time-frequency characteristics of a specific waveform are obtained by superimposing and averaging.

[0055] Specifically, the data obtained from the EEG recording system and photoelectric sensors (i.e., the EEG data recorded during the test) is processed and analyzed to extract useful features. (Here, useful features refer to the different features that need to be extracted based on the recorded EEG data for different task paradigms. For example, in the VEP paradigm, different triggers set under the paradigm need to be extracted for the corresponding test trials, and the features are superimposed and averaged to form the final waveform. This process is implemented in the data processing.)

[0056] Further optimization analysis is conducted, which includes many aspects, such as batch data processing. This involves setting fixed parameters for the inherent paradigm, as mentioned above, sharing the same parameter settings under the same task, and performing batch processing and analysis according to a certain process. No code is required; it can be achieved simply by clicking the corresponding buttons in the GUI interface, which facilitates disease diagnosis and evaluation of treatment effects.

[0057] Step 104: Evaluate based on multiple target features, obtain evaluation results, and display the evaluation results in the form of charts and numerical values ​​on the user terminal interface.

[0058] In this embodiment of the disclosure, a set of parameters for downsampling, rereference, filtering, and determining the electrode point position corresponding to each paradigm is determined. The parameter set is saved according to a preset format to obtain a parameter file, and the parameter file is associated with and saved with the corresponding paradigm.

[0059] Specifically, it features highly customizable parameter settings: users can set fixed parameters for specific experimental paradigms, avoiding repetitive settings of the same parameters, facilitating clinical EEG examinations, and helping users comprehensively, quickly, and efficiently process large volumes of EEG data. The fixed parameters for specific experimental paradigms include parameters such as downsampling, rereference, filtering, and electrode location determination, all of which can be implemented in the same step. All parameter settings are saved as a single parameter file, and subsequent data processing runs on top of this parameter setting file. Therefore, for a fixed paradigm, all parameters can be set once and saved as a parameter file. Subsequent processing of data under this paradigm can then directly call the parameter file without resetting them.

[0060] In this embodiment of the disclosure, an evaluation is performed based on multiple target features to obtain an evaluation result, including: acquiring a preset set of standard features; comparing the multiple target features with the set of standard features to obtain a current evaluation index; acquiring the user's historical evaluation index; and determining the visual cortex photoelectro-evoked result as the evaluation result based on the historical evaluation index and the current evaluation index. The set of standard features can be multiple features of a healthy norm, and the comparison between the current and historical features determines whether a certain effect has been achieved.

[0061] Specifically, individual user results can be compared with those of healthy norms / previous individuals to identify differences. Using electrophysiological indicators for evaluation provides a more objective assessment.

[0062] Specifically, the Adam optimizer was used, with parameters β1 = 0.5, β2 = 0.999, and a fixed learning rate of 0.0005. `self.optim = torch.optim.Adam(self.model.parameters(), Lr = 5e-4, betas = (0.5, 0.999))`. This optimizer helps the algorithm converge faster and improves its fault tolerance and generalization ability. Valuable data refers to the portion of the data that has not been significantly distorted during processing; features of this portion are more valuable for disease diagnosis and treatment efficacy evaluation. Therefore, processing data to extract valuable features provides a basis for disease diagnosis and treatment effect evaluation.

[0063] The visual cortex photoelectro-evoked processing scheme provided in this embodiment acquires a visual stimulation pattern, obtains model parameters based on the visual stimulation pattern, determines a mathematical model based on the model parameters, generates a visual stimulation signal of a target paradigm according to the mathematical model, acquires the user's EEG signal corresponding to the visual stimulation signal, acquires the user's eyeball and / or pupil response signal corresponding to the visual stimulation signal, processes the response signal to obtain a visual cortex response signal of the target format, acquires EEG signals and visual cortex response signals under multiple different target paradigms, selects different processing methods to extract features from the EEG signals and visual cortex response signals according to different target paradigms, and acquires multiple target features; wherein, all EEG signals and visual cortex response signals under the same target paradigm are batch processed according to fixed parameters, evaluated according to multiple target features, and the evaluation results are displayed on the user terminal interface in the form of charts and values. By employing the above technical solution, a variety of visual stimuli, such as flashes of light or the appearance of images, sudden changes in color or pattern, and other diverse visual stimulation modes, are used to improve the efficiency of eliciting electrical signal responses in the cerebral cortex. High-sensitivity photoelectric sensors are used to acquire response signals from the eyeball and / or pupil, significantly reducing testing time. Based on target paradigms such as SSVEP, stable oscillations of the evoked voltage through rapid, repetitive stimulation provide a stable, instruction-rich, and high-speed solution for complex visual pathway analysis. This greatly facilitates users in comprehensively and efficiently processing large volumes of EEG data, aiding in disease diagnosis and treatment efficacy assessment. Furthermore, fixed parameters are set for the target paradigm, avoiding repetitive parameter settings and facilitating clinical EEG examinations, helping users comprehensively, quickly, and efficiently process large volumes of EEG data.

[0064] A typical VEP system typically includes the following components: Stimulus generator: A computer device used to present visual stimuli, usually displaying a series of patterns that change over time. The most common stimulus patterns are checkerboard patterns, flashing stimuli, or alternating black and white stripes. EEG recording system: Records the electrical signals generated by the brain in response to visual stimuli. This usually includes a set of electrodes placed on the patient's scalp, an amplifier, and a computer-based recording system. Analysis software: The recorded signals are often noisy and require processing and analysis. This type of software is used to improve the signal-to-medium ratio and extract useful features from the VEP, such as the amplitude and latency of specific waveform components.

[0065] Existing stimulus generators primarily use basic visual stimuli such as alternating black and white checkerboard patterns at fixed frequencies, lacking diverse and personalized stimulus options. This disclosure improves the efficiency of evoking electrical signal responses in the cerebral cortex through rich visual stimuli, such as flashes of light or the appearance of images, and sudden changes in color or pattern. In addition to passively receiving visual stimuli, it also supports active control by the test subject (for example, the test subject can autonomously adjust and control the color, intensity, and frequency of change of the stimulus pattern based on their intuitive perception of the visual stimuli on the screen, thereby achieving the optimal stimulation effect). Furthermore, the stimulation parameters and recording system are optimized, and a high-sensitivity photoelectric sensor is incorporated, significantly reducing testing time. This disclosure, with its precise, efficient, and autonomous control over the type and frequency of stimulation, detects the structural integrity of the visual pathway from the retina to the occipital cortex and changes in cortical function, aiding in the diagnosis and monitoring of neuropsychiatric diseases such as visual epilepsy and Alzheimer's disease, as well as optic nerve dysfunction and demyelinating diseases.

[0066] Brain-computer interface systems based on the spontaneous potential paradigm often require extensive training of subjects, exhibit significant inter-individual pattern differences, and have low recognition rates. This disclosure, based on the SSVEP paradigm, provides a stable, instruction-rich, and high-speed solution for complex visual pathway analysis by rapidly repeating stimulation to induce stable voltage oscillations.

[0067] Specifically, the raw VEP signals often have significant noise, requiring optimization analysis using specialized software. This publication includes the team's self-developed MatLab toolkit, EPAT 4.9, which can extract useful features from imported EEG data (allowing for targeted parameter settings for specific experimental paradigms and extracting valuable features such as amplitude, latency, synchronicity, and time-frequency characteristics of specific waveform components from imported EEG data). It also allows for setting fixed parameters for specific experimental paradigms (EPAT offers highly customizable parameter settings: users can set fixed parameters for specific experimental paradigms, avoiding repetitive settings of the same parameters, facilitating clinical EEG examinations, and helping users comprehensively, quickly, and efficiently process large volumes of EEG data). (Specific experimental paradigms) Paradigm-based parameter setting: In EPAT, parameters such as downsampling, rereference, filtering, and electrode location determination can all be implemented in the same step, and all parameter settings are saved as a single parameter file. Subsequent data processing runs on top of this parameter setting file. Therefore, for a fixed paradigm, all parameters can be set once and the parameter file saved. Each time data under this paradigm is processed, the parameter file can be directly called without resetting, greatly facilitating users to comprehensively and efficiently process large volumes of EEG data. This is helpful for disease diagnosis and treatment effectiveness assessment (such as visual pathway assessment for patients with traumatic brain injury and brain tumors).

[0068] Therefore, this disclosure combines multiple advanced stimulation generation, recording, and analysis technologies, applicable to various clinical and research scenarios, and capable of identifying and monitoring brain function in various neuropsychiatric disease states. Simultaneously, by controlling and modulating neuronal firing, it is also possible to study the function and abnormalities of neural networks, contributing to the development of potential treatments.

[0069] Figure 2 This is a schematic diagram of a visual cortex photoelectro-induced processing system provided in an embodiment of the present disclosure. The system can be implemented by software and / or hardware and is generally integrated into an electronic device.

[0070] like Figure 2 As shown, the system includes:

[0071] User terminal 100 is used to acquire visual stimulus patterns.

[0072] Stimulus generator 200 is used to acquire model parameters based on the visual stimulus pattern, determine a mathematical model based on the model parameters, and generate a visual stimulus signal of the target paradigm according to the mathematical model.

[0073] EEG recording system 300 is used to acquire the electroencephalogram (EEG) signals of the user based on the visual stimulus signals.

[0074] The photoelectric sensor 400 is used to acquire the user's eyeball and / or pupil response signal based on the visual stimulus signal.

[0075] The signal processing unit 500 is used to process the response signal to obtain a visual cortical response signal in the target format.

[0076] Analysis software 600 is used to acquire the electroencephalogram (EEG) signals and visual cortex response signals under multiple different target paradigms, and to select different processing methods to extract features from the EEG signals and visual cortex response signals according to different target paradigms to obtain multiple target features; wherein, all EEG signals and visual cortex response signals under the same target paradigm are processed in batches according to fixed parameters.

[0077] The analysis software 600 is also used to evaluate based on the multiple target features, obtain evaluation results, and display the evaluation results in the form of charts and numerical values ​​on the interface of the user terminal 100.

[0078] The system includes a highly programmable stimulus generator 200 capable of generating various types of visual stimuli (including but not limited to flashes (black and white), image changes, and different colors or patterns), such as flashes (black and white), image changes, and sudden changes in color or pattern (orderly / random changes in different colors or patterns).

[0079] It is equipped with a set of electrodes, an amplifier, and a computer-based EEG recording system 300 placed on the patient's scalp to capture the brain's electrical signal response to visual stimuli (for recording the brain electrical activity induced by visual stimuli).

[0080] Among them, the high-sensitivity photoelectric sensor, namely photoelectric sensor 400, employs high-sensitivity sensor technology to accurately detect and capture the electrical activity of the visual cortex (used to capture potential changes, directional or non-directional charge movement, etc., generated in the occipital visual cortex after the retina receives flash or image stimulation). By detecting and recording the responses of the eyeball and / or pupil (specifically including the number of pupil dilation and constriction, eye movement rate, nystagmus period, etc.), the integrity of the visual pathway structure from the retina to the occipital cortex and changes in cortical function are assessed.

[0081] The signal processing unit 500 integrates a high-performance microprocessor and a dedicated algorithm (using an MSP430F5529 microcontroller, integrated with an eight-pin STM32 board as the signal processing unit. Specifically, the algorithm upsamples the received signal, introduces a Fourier transform, performs bandpass filtering in the frequency domain to remove unanalyzable Gaussian white noise and narrowband noise, then shifts the frequency spectrum to a higher frequency, performs quadrature demodulation, separates a relatively regular sinusoidal signal and a DC signal, and then uses this DC component as a baseline to generate a finite response signal sequence that fluctuates around the baseline for software analysis). This unit is used for real-time analysis of signals received from the photoelectric sensor (since EPAT is a dedicated EEG signal analysis toolkit developed for EEG, it does not need to go through the signal processing unit and can directly perform feature optimization analysis).

[0082] The analysis software 600 is used to process and analyze data obtained from the EEG recording system and photoelectric sensors (i.e., the EEG data recorded during the test), extract useful features (useful features refer to different features that need to be extracted based on the recorded EEG data for different task paradigms, such as the VEP paradigm, which requires extracting the trials corresponding to different triggers set under the paradigm, superimposing and averaging them to form the final waveform, a process implemented in data processing) and optimize the analysis (optimization analysis includes many aspects, such as batch processing of data, combining the fixed parameters set for the inherent paradigm mentioned above, sharing the same parameter settings under the same task, and performing batch processing and analysis according to a certain process, without the need for code, only by clicking the corresponding buttons in the GUI interface), in order to facilitate disease diagnosis and evaluation of treatment effects.

[0083] Among them, the user terminal 100 provides an intuitive graphical interface for setting stimulus parameters, displaying real-time data, and analyzing results.

[0084] As an example, such as Figure 3 As shown, the user terminal 100 displays user interface settings parameters. The stimulus generator 200 induces stimulation according to the SSVEP paradigm through a mathematical model. The EEG recording system 300 and the high-sensitivity photoelectric sensor 400 capture the signals. The signal processing unit 500 processes the signals, and the analysis software 600 analyzes the results before displaying the evaluation effect on the user terminal 100.

[0085] As an example of a scenario, such as Figure 4As shown, 1. Turn on the device and perform self-test and initialization; 2. Select the desired visual stimulation mode through the user interface; 3. Press the "Start" button to start the stimulation generator and photoelectric sensor; 4. After receiving the data, the signal processing unit begins to analyze it; 5. The analysis results are displayed on the user interface in real time and saved in the internal memory for subsequent analysis.

[0086] The visual cortex photoelectro-evoked processing system provided in this disclosure can execute the visual cortex photoelectro-evoked processing method provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects of executing the method.

[0087] This disclosure also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implements the visual cortex photoelectro-induced processing method provided in any embodiment of this disclosure.

[0088] According to one or more embodiments of this disclosure, this disclosure provides an electronic device, including:

[0089] processor;

[0090] Memory used to store the processor's executable instructions;

[0091] The processor is configured to read the executable instructions from the memory and execute the instructions to implement any of the visual cortex photoelectrostimulation methods provided in this disclosure.

[0092] According to one or more embodiments of the present disclosure, the present disclosure provides a computer-readable storage medium storing a computer program for performing any of the visual cortex photoelectrostimulation methods provided in the present disclosure.

[0093] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0094] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0095] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A visual cortex optically evoked processing method, characterized by, include: A visual stimulus pattern is acquired, and model parameters are acquired based on the visual stimulus pattern. A mathematical model is determined based on the model parameters, and a visual stimulus signal of the target paradigm is generated according to the mathematical model. The acquisition of the visual stimulus pattern includes: acquiring a flickering stimulus pattern to induce flickering visual evoked potentials, a grid stimulation pattern for detecting visual contrast and spatial frequency sensitivity, a single image stimulation pattern for brain response to specific stimuli, a detection change stimulation pattern to display sudden appearance or change, an image detection task stimulation pattern, and a color stimulation pattern. Acquire the user's electroencephalogram (EEG) signal corresponding to the visual stimulus signal, acquire the user's eyeball and / or pupil response signal corresponding to the visual stimulus signal, and process the response signal to obtain a visual cortical response signal in the target format; The process involves acquiring EEG signals and visual cortex response signals under multiple different target paradigms, and performing feature extraction on the EEG signals and visual cortex response signals according to different processing methods based on the different target paradigms to obtain multiple target features; wherein, all EEG signals and visual cortex response signals under the same target paradigm are processed in batches according to fixed parameters. The evaluation is performed based on the multiple target features to obtain the evaluation results, and the evaluation results are displayed on the user terminal interface in the form of charts and numerical values.

2. The visual cortex photo-induced treatment method according to claim 1, characterized in that, The mathematical model is as follows: I(t) = Asin(2πft + φ) ; (1) ) ; (2) where I(t) is the light intensity, A is the amplitude, f is the frequency, is the phase, the flash frequency is adjustable from 0 to 100 in steps of 1; the presentation time is adjustable from 1 to 100 in steps of 1; the brightness is adjustable from 0.1 to 9.999 in steps of 0.

001.

3. The method for photoelectro-induced processing of the visual cortex according to claim 1, characterized in that, The process of processing the response signal to obtain the visual cortex response signal in the target format includes: After upsampling the response signal, a Fourier transform is performed, and bandpass filtering is applied in the frequency domain. The frequency spectrum is adjusted to a high frequency for quadrature demodulation to obtain a sinusoidal signal and a DC signal. Using the DC signal as a baseline, a finite response signal sequence fluctuating around the baseline is generated. The finite response signal sequence is converted into an intermediate format, and a matrix format conversion function is called to convert the intermediate format into the target format to obtain the visual cortical response signal in the target format.

4. The method for photoelectro-induced processing of the visual cortex according to claim 1, characterized in that, The process involves selecting different processing methods according to different target paradigms to extract features from the EEG signals and the visual cortex response signals, thereby obtaining multiple target features, including: Based on different target paradigms, different triggers are set up for the corresponding tests, and the amplitude, latency, synchronization and time-frequency characteristics of a specific waveform are obtained by superimposing and averaging.

5. The method for photoelectro-induced processing of the visual cortex according to claim 1, characterized in that, The evaluation based on the multiple target features to obtain the evaluation result includes: Obtain a pre-defined set of standard features; The current evaluation index is obtained by comparing the multiple target features with the standard feature set; Obtain the user's historical evaluation metrics; The visual cortex photoelectro-evoked results are determined as the evaluation results based on the historical evaluation indicators and the current evaluation indicators.

6. The method for photoelectro-induced processing of the visual cortex according to claim 1, characterized in that, Also includes: Determine the set of parameters for downsampling, rereference, filtering, and electrode point location for each paradigm; The parameter set is saved according to a preset format to obtain a parameter file; The parameter file and its corresponding paradigm are associated and saved.

7. A visual cortex photoelectro-induced processing system, characterized in that, include: The user terminal is used to acquire visual stimulation patterns; wherein, the acquisition of visual stimulation patterns includes: acquiring a flickering stimulation pattern to induce flickering visual evoked potentials, a grid stimulation pattern for detecting visual contrast and spatial frequency sensitivity, a single image stimulation pattern for brain responses to specific stimuli, a detection change stimulation pattern that displays sudden appearances or changes, an image detection task stimulation pattern, and a color stimulation pattern. A stimulus generator is used to acquire model parameters based on the visual stimulus pattern, determine a mathematical model based on the model parameters, and generate a visual stimulus signal of the target paradigm according to the mathematical model. EEG recording system, used to acquire the user's electroencephalogram (EEG) signals based on the visual stimulus signals; A photoelectric sensor is used to acquire the user's eyeball and / or pupil response signals based on the visual stimulus signal; A signal processing unit is used to process the response signal to obtain a visual cortical response signal in a target format; Analysis software is used to acquire EEG signals and visual cortex response signals under multiple different target paradigms, and to perform feature extraction on the EEG signals and visual cortex response signals according to different processing methods according to different target paradigms to obtain multiple target features; wherein, all EEG signals and visual cortex response signals under the same target paradigm are processed in batches according to fixed parameters; The analysis software is also used to evaluate based on the multiple target features, obtain evaluation results, and display the evaluation results in the form of charts and numerical values ​​on the user terminal interface.

8. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the visual cortex photoelectro-induced processing method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for executing the visual cortex photoelectro-induced processing method according to any one of claims 1-6.

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