Acoustic-electric multi-modal neuromodulation method and system for improving language reading ability

Abstract

The application provides a sound and electricity multi-modal neural regulation method and system for improving language reading ability, which comprises the following steps: obtaining brain function activity baseline data based on the abnormal link of the pupil in language reading ability and the resting state and task state electroencephalogram; determining the tES target point and the tFUS target area based on the abnormal link and the brain function activity baseline data; starting the tES module based on the tES target point and starting the tFUS module based on the tFUS target area; starting the tUS module to stimulate and intervene the tES target point and the tFUS target area by focused ultrasound and prompting the pupil to start the training task, obtaining the real-time electroencephalogram signal of the pupil in the intervention process and the behavior performance data of the pupil in the training task process; and adjusting the stimulation intensity of the tUS module to the tES target point and the tFUS target area based on the real-time electroencephalogram signal and the behavior performance data. The application improves the synergistic effect and intervention accuracy.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method and system for improving language reading ability using acoustic and electro-electrical multimodal neural modulation. Background Technology

[0002] Language and reading skills are core elements of children's cognitive, social, and academic development, significantly impacting their academic achievement, social integration, and logical thinking. However, the prevalence of language-related problems, such as developmental dyslexia, is as high as 5%-10%, posing numerous challenges to families, schools, and society.

[0003] In recent years, non-invasive neuromodulation techniques (such as transcranial electrical stimulation (tES) and transcranial focused ultrasound stimulation (tFUS)) have become a research hotspot for improving children's language and reading abilities, taking advantage of the high neuroplasticity of the brains of school-aged children (6-12 years old). Existing electroacoustic multimodal neuromodulation techniques enhance neuroplasticity by combining tES and tFUS. tES covers language-related networks such as the frontal-temporal-parietal axis to regulate cortical neuronal activity, while tFUS focuses on deep structures such as the hippocampus or thalamus to regulate vocabulary memory or speech decoding functions, showing preliminary effectiveness in the intervention of language and reading disorders in children. However, existing methods have several drawbacks. First, they often directly transplant adult neuromodulation parameters, neglecting the dynamic characteristics of children's brain development, resulting in insufficient synergistic effects. Second, they use uniform parameters without dynamically adjusting them according to individual differences in children, and fail to effectively integrate high-resolution brain imaging data and computational resources for personalized modeling, leading to insufficient precision in intervention. Summary of the Invention

[0004] This invention provides a method and system for multimodal neural modulation of sound and electricity to improve language reading ability, thereby enhancing synergistic effects and intervention accuracy.

[0005] In a first aspect, the present invention provides a multimodal neuromodulation method for improving language reading ability, applied to a head-mounted device; the head-mounted device includes a transcranial electrical stimulation (tES) module, a transcranial focused ultrasound (tFUS) module, and a transcranial ultrasound stimulation (tUS) module, and is worn on the head of a primary school student; the method includes:

[0006] Based on the identified abnormalities in primary school students' language reading abilities, combined with resting-state and task-oriented EEG data, baseline data of brain functional activities related to language reading abilities were obtained.

[0007] Based on the abnormal link and the baseline data of brain functional activity, the tES target point and tFUS target region associated with the abnormal link are determined.

[0008] The tES module is activated based on the tES target point and the tFUS module is activated based on the tFUS target region to enhance the excitation of the target network and achieve intervention in the target network; the target network is a neural network composed of multiple brain regions related to language reading ability.

[0009] The tUS module is activated to perform focused ultrasound stimulation intervention on the tES target point and the tFUS target area, and to prompt the primary school students to start the training task. Real-time EEG signals of the primary school students during the intervention process and behavioral performance data of the primary school students during the training task are obtained.

[0010] Based on the real-time EEG signals and the behavioral performance data, the stimulation intensity of the tUS module on the tES target point and the tFUS target area is adjusted.

[0011] Secondly, the present invention also provides an electroacoustic multimodal neuromodulation system for improving language reading ability, applied to a head-mounted device; the head-mounted device includes a transcranial electrical stimulation (tES) module, a transcranial focused ultrasound (tFUS) module, and a transcranial ultrasound stimulation (tUS) module, worn on the head of a primary school student; used to implement the electroacoustic multimodal neuromodulation method described in the first aspect; the system includes:

[0012] The data acquisition module is used to acquire baseline data of brain functional activity related to language reading ability by combining resting-state and task-state EEG based on the abnormal links in the language reading ability of primary school students.

[0013] The stimulation point planning module is used to determine the tES target point and tFUS target region associated with the abnormal link based on the abnormal link and the baseline data of brain functional activity.

[0014] The network intervention module is used to activate the tES module based on the tES target point and the tFUS module based on the tFUS target region to enhance the excitation of the target network and achieve intervention in the target network; the target network is a neural network composed of multiple brain regions related to language reading ability.

[0015] The intervention implementation module is used to activate the tUS module to perform focused ultrasound stimulation intervention on the tES target point and the tFUS target area, and to prompt the primary school students to start the training task, and to obtain the real-time EEG signals of the primary school students during the intervention process and the behavioral performance data of the primary school students during the training task.

[0016] The neuromodulation module is used to adjust the stimulation intensity of the tUS module on the tES target point and the tFUS target area based on the real-time EEG signal and the behavioral performance data.

[0017] Thirdly, the present invention also provides an electronic device, comprising: a memory for storing computer software programs; and a processor for reading and executing the computer software programs, thereby realizing the acoustic-electrical multimodal neural modulation method for improving language reading ability as described above.

[0018] Fourthly, the present invention also provides a non-transitory computer-readable storage medium storing a computer software program, which, when executed by a processor, implements the acoustic-electrical multimodal neural modulation method described above for improving language reading ability.

[0019] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the electro-acoustic multimodal neural modulation method described above for improving language reading ability.

[0020] The electroacoustic multimodal neuromodulation method for improving language reading ability provided in this invention combines three non-invasive neuromodulation techniques: tES, tFUS, and TMS (transcranial magnetic stimulation). It uses a time-sharing or synchronous strategy to jointly stimulate language-related brain regions. Since TMS can modulate the superficial cortex through electromagnetic induction, tFUS targets deep brain regions, and tES covers a wide network, this sequential electroacoustic synergistic stimulation approach of "network preprocessing - node precision focusing" leverages the complementary advantages of tES covering a wide cortical network and tFUS precisely targeting deep brain regions to form a multi-level neural network synergy. This enhances the neural plasticity of the language network, thereby improving synergistic effects. Furthermore, during the intervention, the stimulation intensity of tES and / or tUS is dynamically adjusted based on the child's real-time physiological signals and behavioral data. Therefore, through a multi-dimensional real-time feedback adaptive adjustment mechanism, the intervention plan can be matched to the child's state changes in real time, achieving personalized intervention and improving intervention accuracy. Attached Figure Description

[0021] Figure 1 This is a schematic flowchart of the acoustic-electric multimodal neural modulation method for improving language reading ability provided in an embodiment of the present invention;

[0022] Figure 2 This is one of the structural schematic diagrams of the acoustic-electric multimodal neural modulation system for improving language reading ability provided in the embodiments of the present invention;

[0023] Figure 3 This is a stimulus timing coupling strategy diagram provided in an embodiment of the present invention;

[0024] Figure 4 This is the second schematic diagram of the acoustic-electric multimodal neural modulation system for improving language reading ability provided in the embodiments of the present invention;

[0025] Figure 5 An embodiment diagram of the electronic device provided in this invention;

[0026] Figure 6 An embodiment diagram of a computer-readable storage medium provided in accordance with the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0029] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0030] Optional, see below Figure 1 , Figure 1 This is a flowchart illustrating the acoustic-electrical multimodal neural modulation method for improving language reading ability provided by the present invention. In this embodiment, the executing entity of the acoustic-electrical multimodal neural modulation method for improving language reading ability is a neural modulation system. (See also...) Figure 2 The neuromodulation system is applied to an integrated multimodal head-mounted device; the head-mounted device includes a transcranial electrical stimulation (tES) module, a transcranial focused ultrasound (tFUS) module, and a transcranial ultrasound stimulation (tUS) module, and is worn on the head of elementary school students.

[0031] It should be noted that the neuromodulation system also includes a central control and processing unit and a physiological signal acquisition module. The central control and processing unit, consisting of a high-performance computer, runs the control program of this embodiment, responsible for user interaction, subject information management, personalized assessment, generation and optimization of intervention plans, real-time control of hardware modules, and data acquisition, analysis, and storage. The transcranial electrical stimulation (tES) module is a multi-channel precision current stimulation unit that receives instructions from the central control and processing unit to precisely generate and output currents with specific waveforms. The transcranial ultrasound stimulation (tUS) module is a high-precision ultrasound generator and power drive unit that also receives instructions from the central control unit to drive the ultrasound transducer to generate focused or unfocused ultrasound waves with specific acoustic parameters (frequency, power, pulse sequence, etc.). The integrated multimodal head-mounted device is an ergonomically designed head-mounted device specifically for children. It integrates fixed interfaces for 16 tES electrodes and a precisely positioned, angle-adjustable tUS transducer. This device ensures that both types of stimulation can be accurately and stably applied to preset scalp locations. Physiological signal acquisition module: A low-noise electroencephalogram (EEG) acquisition unit synchronized with the system. The real-time EEG data acquired by this module can be sent back to the central control unit for adaptive closed-loop regulation.

[0032] Therefore, the electro-acoustic multimodal neural modulation methods used to improve language reading ability include:

[0033] Step 10: Based on the identified abnormalities in the primary school students' language reading ability, combined with resting-state and task-state EEG data, baseline data of brain functional activity related to language reading ability are obtained.

[0034] Optionally, the neural modulation system uses the Chinese version of the CELF-5 assessment to conduct standardized language ability tests on primary school students aged 6-12, comprehensively evaluating their language comprehension, vocabulary, reading fluency, speech decoding and other dimensions, and accurately locating abnormal links (such as weak vocabulary memory and low reading fluency).

[0035] Furthermore, the neuromodulation system collects resting-state EEG signals (brain activity without a specific task) and task-oriented EEG signals (brain activity when performing tasks such as reading and word matching) of primary school students through the physiological signal acquisition module. It analyzes the neural activity patterns of brain regions related to language reading (such as the left temporoparietal region, hippocampus, and inferior frontal gyrus) and obtains baseline data of brain functional activities related to language reading ability (including indicators such as Gamma wave power and brain region connectivity strength).

[0036] In one embodiment, an 8-year-old primary school student with poor reading fluency is used as an example:

[0037] The neural modulation system was found to have frequent sentence breaks and slow reading speed through the CELF-5 test (the abnormality was a reading fluency defect).

[0038] During resting-state EEG recording, the gamma wave power in the left temporoparietal region was found to be lower than the average for children of the same age (abnormal baseline data). Task-oriented EEG recording (during a paragraph reading task) showed weak neural synchrony between the left temporoparietal region and the inferior frontal gyrus (abnormal baseline data). The neuromodulation system integrated the above data into the student's baseline brain functional activity.

[0039] Step 20: Based on the abnormal links and baseline data of brain functional activity, identify the tES target points and tFUS target regions associated with the abnormal links.

[0040] Furthermore, the neuromodulation system, based on abnormal pathways and baseline data of brain functional activity, and in conjunction with a pediatric neuroanatomy database, matches corresponding brain regions related to language and reading:

[0041] For vocabulary memory deficits (abnormal links), the hippocampus (responsible for memory encoding) is the target area for transcranial focused ultrasound stimulation;

[0042] For language processing defects (abnormal links), the left temporoparietal region (responsible for language integration) is identified as a transcranial electrical stimulation target.

[0043] For speech decoding defects (abnormal links), the associated temporoparietal junction is the target area for transcranial focused ultrasound stimulation, and the left auditory cortex is the target point for transcranial electrical stimulation.

[0044] Furthermore, the neuromodulation system optimizes the spatial localization of target points / areas through algorithms to ensure that it is adapted to the size of the child's brain region and the thickness of the skull.

[0045] Continuing with step 10, the "reading fluency deficit" of the 8-year-old elementary school student:

[0046] Based on the baseline data showing weak activity and poor connectivity with the inferior frontal gyrus in the left temporoparietal region, the system determined the left temporoparietal region as the target area for transcranial electrical stimulation (TES) (to enhance cortical language processing). Simultaneously, considering the correlation between reading fluency and deep memory retrieval, the hippocampus was identified as the target area for transcranial focused ultrasound stimulation (TUS) (to enhance the neural basis of vocabulary memory). Therefore, the neuromodulation system, through the positioning and calibration function of the integrated multimodal headband, precisely fixed the TES electrodes to the scalp projection position in the left temporoparietal region, while the TUS transducer aimed at the intracranial coordinates corresponding to the hippocampus.

[0047] Step 30 involves activating the tES module based on the tES target point and the tFUS module based on the tFUS target region to enhance the excitability of the target network and thus intervene in the target network. The target network is a neural network composed of multiple brain regions related to language reading ability.

[0048] Furthermore, the neuromodulation system activates the transcranial electrical stimulation (TCS) module and the transcranial focused ultrasound (TUS) module to perform network preprocessing: the TCS module outputs 0.8 mA of anodic transcranial direct current stimulation, acting on the TCS target point to cover a wide area of ​​cortical networks (such as the frontotemporal-parietal language network) and enhance overall cortical excitability. The TUS module applies short-duration (5 minutes), low-intensity focused ultrasound waves to the TCS target area to activate deep brain regions (such as the hippocampus). Both modules synergistically regulate the excitability and synchronicity of the target network (the language reading-related neural network composed of cortical and deep brain regions).

[0049] Continuing with the 8-year-old primary school student from step 20: The transcranial electrical stimulation module, through electrodes of an integrated headband device, applied 0.8 mA anodic transcranial direct current stimulation to the left temporoparietal region for 10 minutes, increasing cortical excitability in this area (target network preprocessing). The transcranial focused ultrasound stimulation module applied a frequency of 0.5 MHz and an intensity of 0.3 W / cm² to the hippocampus. 2 Ultrasound was applied for 5 minutes to enhance neural activity (target network preprocessing). After the intervention, EEG monitoring showed a 15% increase in Gamma wave power in the left temporoparietal region and an increase in the connection strength between the hippocampus and the temporoparietal region (target network excitability met the target).

[0050] Step 40: Activate the tUS module to perform focused ultrasound stimulation intervention on the tES target point and tFUS target area, and prompt the primary school students to start the training task. Obtain the real-time EEG signals of the primary school students during the intervention and the behavioral performance data of the primary school students during the training task.

[0051] Furthermore, based on the network preprocessing in step 30, the neural modulation system activates the transcranial ultrasound stimulation module to perform high-precision focused ultrasound stimulation (i.e., "node-level fine focusing") on the transcranial electrical stimulation target point and the transcranial focused ultrasound stimulation target area. The stimulation timing coupling strategy is as follows: Figure 3 As shown. Simultaneously, standardized cognitive training tasks (such as paragraph reading and vocabulary matching) are pushed through the central control and processing unit. The physiological signal acquisition module collects the elementary school students' EEG signals in real time (focusing on monitoring the Gamma wave power of the target brain region), and the system synchronously records behavioral data (such as reading speed, accuracy, and reaction time) during the training tasks.

[0052] Continuing with the embodiment of step 30: The transcranial ultrasound stimulation module applies focused ultrasound with a frequency of 1MHz and a pulse sequence of 50ms to the left temporoparietal region (transcranial electrical stimulation target point) and hippocampus (transcranial focused ultrasound stimulation target area), cycling once every 200ms (compliant with...). Figure 3(A temporal coupling strategy). The neuromodulation system pushes standardized paragraph reading tasks suitable for 8-year-old children (such as excerpts from Andersen's fairy tales), requiring students to read aloud. The physiological signal acquisition module monitors in real time that the Gamma wave power in the left temporoparietal region has increased to the preset "optimal" range (a 30% increase from the baseline). Behavioral data shows that their reading speed has increased from the initial 100 words / minute to 120 words / minute, while the accuracy rate remains at 90% (behavioral performance improvement).

[0053] Step 50: Based on real-time EEG signals and behavioral performance data, adjust the stimulation intensity of the tUS module on the tES target and tFUS target area.

[0054] Furthermore, the neuromodulation system adjusts the stimulation intensity of the tUS module on the tES target point and tFUS target area based on real-time EEG signals and behavioral performance data, as described in steps 501 to 510.

[0055] Optionally, embodiments of the present invention also provide a safety protection strategy, namely, during stimulation, if the real-time contact impedance of the tES electrode is detected to be >5kΩ, the neuromodulation system will automatically cut off the current; if the local temperature rise of the tFUS site is >1.5℃, the neuromodulation system will reduce the stimulation power by 50%.

[0056] This invention combines three non-invasive neuromodulation techniques: tES, tFUS, and TMS (transcranial magnetic stimulation). It uses a time-sharing or synchronous strategy to jointly stimulate language-related brain regions. Because TMS can modulate the superficial cortex through electromagnetic induction, tFUS targets deep brain regions, and tES covers a broad network, a sequential electro-acoustic stimulation approach of "network preprocessing - node precision focusing" leverages the complementary advantages of tES's broad cortical network coverage and tFUS's precise targeting of deep brain regions to form a multi-layered neural network synergy. This enhances the neural plasticity of the language network, thereby improving synergistic effects. During the intervention, the stimulation intensity of tES and / or tUS is dynamically adjusted based on the child's real-time physiological signals and behavioral data. Therefore, through a multi-dimensional real-time feedback adaptive adjustment mechanism, the intervention plan can be matched to the child's state changes in real time, achieving personalized intervention and improving intervention precision.

[0057] In one embodiment, steps 501 to 503 include:

[0058] Step 501: If the real-time EEG signal meets the preset requirements, but the behavioral performance data does not meet the preset requirements, then the degree of difference in reaction time is determined based on the actual language reading reaction time in the behavioral performance data and the preset language reading reaction time, and the degree of difference in accuracy is determined based on the actual language reading accuracy rate in the behavioral performance data and the preset language reading accuracy rate.

[0059] Optionally, when the neuromodulation system detects that the real-time EEG signal (such as the Gamma wave power of the target brain region is in the preset "optimal" range) meets the preset requirements, but the behavioral performance data (such as reading task performance) does not meet the preset requirements, it initiates a difference analysis.

[0060] For calculating the degree of difference in reaction time: the actual language reading reaction time T for children to complete language reading-related operations (such as word matching and sentence comprehension) in the training task is extracted by the neuromodulation system. 实际 Compared with the preset language reading reaction time (T is set based on the average level of children of the same age), 预设 By comparison, through formula D T =|T 实际 -T 预设 | / T 预设 *100% Calculation of Difference Level D T (Value range: 0-100%), the larger the value, the more significant the deviation of the reaction rate from the normal level.

[0061] For calculating the degree of difference in accuracy: The neuromodulation system statistically analyzes the actual language reading accuracy rate (C) of children correctly completing language reading operations in the training task. 实际 The preset language reading accuracy rate (based on the average level of children of the same age, C) is compared with the preset language reading accuracy rate. 预设 By comparison, through formula D C =|C 实际 -C 预设 | / C 预设 *100% Calculation of Difference Level D C (Value range: 0-100%), the larger the value, the more significantly the accuracy deviates from the normal level.

[0062] Continuing, taking the example of an 8-year-old primary school student with poor reading fluency in step 40: real-time EEG signals showed that the Gamma wave power in the left temporoparietal region was within the preset "optimal" range (meeting the preset requirements). The training task was "reading aloud a sentence and judging its semantic rationality," with a preset language reading reaction time T. 预设 = 3 seconds / sentence, the student's actual language reading reaction time T 实际 = 4.5 seconds / sentence, D T =50%. Preset language reading accuracy rate C 预设 =85%, the student's actual language reading accuracy rate C 实际 =68%, then D C ≈20%.

[0063] Step 502: Compare the magnitudes of the differences in reaction time and accuracy, and use the index corresponding to the larger difference value as the dominant anomaly index.

[0064] Furthermore, the neural regulation system will adjust the degree of difference in reaction time D. T The degree of difference between accuracy and D C Perform a numerical comparison: If D T >D C If reaction time is the primary cause of substandard behavioral performance, then reaction time will be considered the dominant abnormality indicator. If D C >D T If the accuracy rate is determined to be the main reason for the failure to meet behavioral performance standards, then the accuracy rate will be used as the dominant abnormality indicator. If D T =D C Then, the dominant anomaly indicator is determined by combining the nature of the task (such as reaction time for reading fluency tasks and accuracy for reading comprehension tasks).

[0065] Continuing with the above embodiments, the degree of difference in reaction time D T =50%, accuracy rate difference level D C =20%, and reaction time is the dominant anomaly indicator in the "sentence reading and semantic rationality judgment" task.

[0066] Step 503: Based on the standard deviation and mean of the Gamma wave power of the real-time EEG signal, determine the EEG stability constraint coefficient, and based on the dominant abnormality index and the EEG stability constraint coefficient, enhance the stimulation intensity of the tUS module on the tES target point and tFUS target area.

[0067] Furthermore, the neuromodulation system collects the standard deviation σ and mean μ of the Gamma wave power in the target brain region (such as the left temporoparietal region) from real-time EEG signals.

[0068] Furthermore, the neuromodulation system calculates the EEG stability constraint coefficient K (range 0-1) based on the standard deviation and mean of the Gamma wave power using the formula K = 1 - σ / (σ + μ). The closer the value is to 1, the more stable the EEG signal is, and the stronger the constraint on the adjustment of stimulus intensity.

[0069] Furthermore, the neural modulation system enhances the stimulation intensity of the tUS module on the tES target point and tFUS target area based on the dominant abnormal indicators and the EEG stability constraint coefficient, as specifically in steps 5031 to 5034.

[0070] This invention, while ensuring that the neural activity in the target brain region is within a safe and stable range, prioritizes addressing the key issues leading to poor behavioral performance. By adjusting the intensity of stimulation through quantitative calculations, it can precisely target the core deficiencies in behavioral performance to enhance stimulation, thereby pushing primary school students' language and reading behavior closer to the preset standards. At the same time, it avoids EEG fluctuations caused by overstimulation, thus balancing the effectiveness and safety of the intervention.

[0071] In one embodiment, steps 5031 to 5034 include:

[0072] Step 5031: Determine the first adjustment magnitude value based on the degree of difference of the dominant abnormal indicators and the current stimulation intensity of the tUS module on the tES target and tFUS target area.

[0073] Optionally, in this embodiment of the invention, the greater the degree of difference and the lower the current intensity, the greater the adjustment range. The neural modulation system is based on the degree of difference D of the dominant abnormal indicator. 主导 The transcranial ultrasound stimulation module controls the current stimulation intensity I of the transcranial electrical stimulation target point and the transcranial focused ultrasound stimulation target area. 当前 The specific formula for calculating the first adjustment amplitude value A1 is: A1 = γ * D 主导 *(1-I 当前 / I 安全阈值 Where γ is the scene adaptation coefficient (preset to 0.003 W·cm for language reading tasks). -2 / %), I 安全阈值 The maximum safe intensity for transcranial ultrasound stimulation (preset to 1.0 W·cm) -2 ).

[0074] Continuing with the above embodiments, the dominant anomaly indicator is reaction time, D 主导 =50%, current intensity I of transcranial ultrasound stimulation 当前 =0.29W·cm -2 Therefore, the first adjustment amplitude value A1 = 0.1065 W·cm -2 .

[0075] Step 5032: Determine the first preliminary adjustment value based on the EEG stability constraint coefficient and the first adjustment amplitude value.

[0076] Furthermore, the neural modulation system calculates the first adjustment amplitude value A1 and the EEG stability constraint coefficient K using an exponential decay formula to obtain the first preliminary adjustment value V1. In this embodiment of the invention, the exponential decay formula is: V1 = A1 * K β β represents the constraint sensitivity (the weight of the effect of enhanced EEG stability on adjustment). Therefore, the adjustment range is limited by the EEG stability constraint coefficient to avoid excessive fluctuations in EEG signals.

[0077] Continuing with the above embodiments, A1 = 0.1065 W·cm -2 Since K = 0.83, the calculated first target adjustment value V1 = 0.0841 W·cm -2 .

[0078] Step 5033: Summing the first preliminary adjustment value and the current stimulus intensity yields the first target adjustment value.

[0079] Furthermore, the neural modulation system modulates the initial adjustment value V1 with the current stimulation intensity I of the transcranial ultrasound stimulation module. 当前 Summing yields the first target adjustment value I. 目标1 It should be noted that the neural modulation system needs to verify whether this value exceeds a preset safety threshold (1.0 W·cm). -2 If the value exceeds the limit, a safety threshold will be forcibly applied.

[0080] Continuing with the above embodiments, I 当前 =0.29W·cm -2 V1 = 0.08410.29 W·cm -2 First target adjustment value I 目标1 =0.29 + 0.0841 = 0.3741 W·cm -2 It did not exceed the safety threshold (1.0 W·cm). -2 ),efficient.

[0081] Step 5034: Increase the stimulation intensity of the tUS module on the tES target and tFUS target area based on the first target adjustment value.

[0082] Furthermore, the neuromodulation system sends instructions to the transcranial ultrasound stimulation module via the central control and processing unit, increasing the stimulation intensity of the transcranial electrical stimulation target (such as the left temporoparietal region) and the transcranial focused ultrasound stimulation target area (such as the hippocampus) from the current value I. 当前 Adjust to the first target adjustment value I 目标1 During the adjustment process, the neuromodulation system monitors the impedance and temperature of the stimulation site in real time through sensors in the integrated multimodal headband device to ensure compliance with safety standards (impedance ≤ 5kΩ, temperature rise ≤ 1.5℃).

[0083] This invention provides precise stimulation enhancement based on the degree of behavioral deficit and EEG stability. While ensuring stable EEG signals, it dynamically increases stimulation intensity for dominant abnormal indicators (such as slow reaction time). This avoids safety risks caused by overstimulation and can target and improve weaknesses in language reading behavior. For example, after increasing stimulation intensity, students' reaction time in the "sentence reading and semantic rationality judgment" task decreased from 4.5 seconds / sentence to 3.8 seconds / sentence, approaching the preset standard (3 seconds / sentence), thus verifying the effectiveness of the approach.

[0084] In one embodiment, steps 504 to 506 include:

[0085] Step 504: If the real-time EEG signal does not meet the preset requirements, but the behavioral performance data meets the preset requirements, then the degree of difference in Gamma wave power is determined based on the actual value of Gamma wave power of the real-time EEG signal and the preset value of Gamma wave power.

[0086] Optionally, the neuromodulation system may calculate the degree of difference in Gamma wave power D when the real-time EEG signal does not meet the preset requirements (e.g., the Gamma wave power in the target brain region is below the "optimal" range) but the behavioral performance data meets the requirements. γ Specifically, the actual value P of the Gamma wave power in the target brain region (such as the left temporoparietal region) is obtained through the physiological signal acquisition module. γ,实际 , compared with the preset optimal value of Gamma wave power P γ,预设 In comparison, the normalization formula is used: D γ =(P γ,预设 -P γ,实际 ) / P γ,预设 *100%, Gamma wave power difference degree D γ It reflects the gap between brain electrical activity and the ideal state; the larger the value, the more significant the insufficient neural activation.

[0087] Continuing with the example of an 8-year-old primary school student (in reading fluency training): Real-time EEG signal shows the actual value P of the Gamma wave power in the left temporoparietal region. γ,实际 =2.8μV, preset optimal value P γ,预设 = 4.0μV (not met). Behavioral performance data: Actual language reading reaction time 3.2 seconds / sentence (preset 3 seconds / sentence), actual language reading accuracy 88% (preset 85%), both met, therefore D was calculated. γ =30%.

[0088] Step 505: Determine the behavioral stability constraint coefficient based on the joint standard deviation between actual language reading reaction time and actual language reading accuracy in the behavioral performance data, and the joint mean between actual language reading reaction time and actual language reading accuracy.

[0089] Furthermore, the neural modulation system is based on the actual language reading reaction time T in behavioral performance data. 实际 And the accuracy rate of actual language reading C 实际 The joint statistical characteristics of the two factors are used to calculate the behavioral stability constraint coefficient L. The specific steps are: calculate the joint standard deviation σ of the two factors. 联合 , Where, σ T σ is the standard deviation of the actual reaction time (last 5 tasks). C Let be the standard deviation of the actual accuracy (from the last 5 tasks). Calculate the joint mean μ of the two. 联合 μ 联合 =(μ T +μ C ) / 2, where μ T The mean of the actual reaction time, μ CThis represents the average actual accuracy rate (converted to a value on the same order of magnitude as reaction time, e.g., 88% → 0.88). Therefore, the behavioral stability constraint coefficient formula is: L = μ 联合 / (μ 联合 +σ 联合 L ranges from 0 to 1. The closer it is to 1, the more stable the behavior and the stronger the constraint on the adjustment of brain electrical stimulation.

[0090] Continuing with the above embodiments, data from the last 5 tasks: Actual response time:

[0091] 3.1, 3.3, 3.2, 3.0, 3.4 seconds, therefore σ T =0.14μ T =3.2. Actual accuracy rates: 89%, 87%, 88%, 86%, 90%, which translates to 0.89, 0.87, 0.88, 0.86, 0.90, therefore σ C =0.014, μ C =0.88. Calculation yields L≈0.95.

[0092] Step 506: Based on the degree of difference in Gamma wave power and the behavioral stability constraint coefficient, enhance the stimulation intensity of the tUS module on the tES target and the tFUS target region.

[0093] Furthermore, the neural modulation system enhances the stimulation intensity of the tUS module on the tES target and tFUS target area based on the degree of difference in Gamma wave power and the behavioral stability constraint coefficient, as described in steps 5061 to 5065.

[0094] Under the premise that behavioral data meet the standards, this invention identifies insufficient brain electrical activity by the difference in Gamma wave power, and dynamically adjusts the stimulation intensity in combination with behavioral stability. This avoids overstimulation from affecting the already achieved behavioral performance, and can also specifically improve the level of neural activation.

[0095] In one embodiment, steps 5061 to 5065 include:

[0096] Step 5061: Determine the second adjustment amplitude value based on the degree of difference in Gamma wave power and the current stimulation intensity of the tUS module on the tES target and tFUS target area.

[0097] Optionally, in this embodiment of the invention, the greater the difference in Gamma wave power and the lower the current intensity, the greater the adjustment range. Therefore, the neural modulation system adjusts the Gamma wave power based on the degree of difference D. γ The transcranial ultrasound stimulation module controls the current stimulation intensity I of the transcranial electrical stimulation target point and the transcranial focused ultrasound stimulation target area. 当前 The second adjustment magnitude value A2 is calculated using a two-factor formula, specifically: A2 = θ * Dγ *exp(-I 当前 / I 安全阈值 ), where θ is the Gamma wave sensitivity coefficient (preset to 0.004 W·cm for language reading tasks). -2 / %), I 安全阈值 The maximum safe intensity for transcranial ultrasound stimulation is 1.0 W·cm. -2 ).

[0098] Continuing with the above example, an 8-year-old primary school student, D γ =30%, current intensity I of transcranial ultrasound stimulation 当前

[0099] =0.37W·cm -2 Therefore, the calculated second adjustment amplitude value A2 = 0.0829 W·cm -2 .

[0100] Step 5062: Determine the second adjustment magnitude value based on the second adjustment magnitude value and the behavior stability constraint coefficient.

[0101] Furthermore, the neural modulation system calculates the second adjustment amplitude value A2 and the behavioral stability constraint coefficient L using a power function coupling formula to obtain the second preliminary adjustment value V2. In this embodiment of the invention, the power function coupling formula is: V2 = A2 * (1 + L). η Where η is the behavioral constraint weight (preset to 0.8, to strengthen the positive impact of behavioral stability on adjustment), thus ensuring that the more stable the behavioral performance (the closer L is to 1), the greater the adjustment range (because when the behavior is stable, it can better withstand the stimulation enhancement).

[0102] Continuing with the above embodiment, A2 = 0.0829 W·cm -2 Since L = 0.95, the second preliminary adjustment value V2 is calculated to be approximately 0.145 W·cm. -2 .

[0103] Step 5063: Summing the second adjustment amplitude value and the current stimulus intensity yields the second target adjustment value.

[0104] Furthermore, the neural modulation system modulates the second preliminary adjustment value V2 with the current stimulation intensity I of the transcranial ultrasound stimulation module. 当前 Summing yields the second target adjustment value I. 目标2 .

[0105] Continuing with the above embodiments, I 当前 =0.37W·cm -2 V2 = 0.145 W·cm -2 Therefore, the second target adjustment value I is calculated. 目标2=0.37 + 0.145 = 0.515 W·cm -2 .

[0106] Step 5064: If the second target adjustment value is greater than the maximum stimulus intensity that stabilizes behavioral performance, then the stimulus intensity of the tUS module on the tES target and tFUS target area is increased based on the maximum stimulus intensity.

[0107] Optionally, in this embodiment of the invention, a maximum stimulus intensity I for stable behavioral performance is set. 行为稳定阈值 If the preset value is 0.6 W·cm -2 Based on experimental data on children's behavioral stability, exceeding this value may lead to fluctuations in behavioral performance. Therefore, the neural regulation system is compared with the second target adjustment value I. 目标2 Maximum stimulus intensity I with stable behavioral performance 行为稳定阈值 =0.6W·cm -2 , if I 目标2 >I 行为稳定阈值 Then adjust the stimulation intensity of the transcranial ultrasound stimulation module to I. 行为稳定阈值 This ensures that while enhancing brain activity, the stability of already achieved behavioral performance is not compromised.

[0108] Continuing with the above embodiments, I 目标2 =0.65W·cm -2 (greater than 0.6 W·cm) -2 Therefore, the transcranial ultrasound stimulation intensity was adjusted to 0.6 W·cm. -2 And record the adjustment as "maximum enhancement under behavioral constraints".

[0109] Step 5065: If the second target adjustment value is less than or equal to the maximum stimulation intensity, then the stimulation intensity of the tUS module on the tES target and tFUS target area is increased based on the second target adjustment value.

[0110] Furthermore, if the neural regulation system determines I 目标2 ≤I 行为稳定阈值 Then directly adjust the stimulation intensity of the transcranial ultrasound stimulation module to I. 目标2 During the adjustment process, the neuromodulation system monitors the Gamma wave power and behavioral performance data of the target brain region in real time through the physiological signal acquisition module, ensuring that while brain electrical activity is enhanced, behavioral performance remains stable (e.g., reaction time fluctuation ≤ 0.3 seconds / sentence, accuracy fluctuation ≤ 5%).

[0111] Continuing with the above embodiments, I 目标2 =0.515W·cm -2 <0.6W·cm -2 Therefore, the neuromodulation system controls the transcranial ultrasound stimulation module to increase the stimulation intensity on the left temporoparietal region and hippocampus from 0.37 W·cm.-2 Enhanced to 0.515 W·cm -2 Monitoring showed that the Gamma wave power in the left temporoparietal region increased to 3.6 μV (close to the preset 4.0 μV), and the behavioral performance was stable (reaction time 3.2 seconds / sentence, accuracy 88%).

[0112] This invention determines the need for stimulus enhancement by directing the degree of difference in Gamma wave power, and controls the enhancement amplitude by combining it with behavioral stability constraints. This ensures that brain electrical activity approaches the optimal state while avoiding overstimulation that could disrupt already achieved behavioral performance. For example, after adjustment, a child's Gamma wave power increased by 28.6% (from 2.8μV to 3.6μV), with no significant fluctuations in behavioral performance. This achieves synergistic optimization of neural activity and behavioral ability, laying a neural foundation for long-term improvement in language reading ability.

[0113] In one embodiment, steps 507 to 510 include:

[0114] Step 507: If the real-time EEG signal meets the preset requirements and the behavioral performance data meets the preset requirements, then the redundancy of the EEG signal is determined based on the mean value of the Gamma wave power of the real-time EEG signal and the preset value of the Gamma wave power.

[0115] Optionally, the neuromodulation system calculates the redundancy R of the EEG signal when the real-time EEG signal meets the preset requirements (the Gamma wave power in the target brain region is in the "optimal" range) and the behavioral performance data meets the standards. γ EEG signal redundancy R γ The redundancy of brain electrical activity beyond the necessary level is reflected by the real-time average Gamma wave power μ. γ,实际 Compared with the preset optimal value P γ,预设 The ratio is calculated using the following formula: R γ =(μ γ,实际 -P γ,预设 ) / P γ,预设 *100% (if μ) γ,实际 ≤P γ,预设 Then R γ =0%). The higher the redundancy, the more significant the redundancy of over-activation of brain electrical activity, and the more necessary it is to reduce the stimulation intensity.

[0116] Continuing with the example of the cedar tree, taking an 8-year-old primary school student as an example (in reading training): Real-time EEG signal display shows the average power μ of the Gamma wave in the left temporoparietal region. γ,实际 =4.5μV, preset optimal value P γ,预设 =4.0μV (meets preset requirements). The calculated EEG signal redundancy R is obtained. γ =12.5%.

[0117] Step 508: Based on the actual language reading reaction time and the preset language reading reaction time in the behavioral performance data, determine the reaction time redundancy, and based on the actual language reading accuracy rate and the preset language reading accuracy rate in the behavioral performance data, determine the accuracy rate redundancy.

[0118] Furthermore, the neural modulation system determines the reaction time redundancy based on the actual language reading reaction time and the preset language reading reaction time in the behavioral performance data, and determines the accuracy redundancy based on the actual language reading accuracy and the preset language reading accuracy in the behavioral performance data. The specific calculation process of EEG signal redundancy in step 507 will not be repeated here.

[0119] Step 509: Compare the redundancy of EEG signals, reaction time redundancy, and accuracy redundancy, and identify the dominant redundancy index corresponding to the largest redundancy.

[0120] Furthermore, the neuromodulation system compared the redundancy R of EEG signals. γ Reaction time redundancy R T And accuracy redundancy R C The indicator with the largest value is selected as the dominant redundancy indicator: if R γ >R T And R γ >R C Then the dominant redundancy indicator is "EEG signal". If R T >R γ And R T >R C Then the dominant redundancy indicator is "response time". If R C >R γ And R C >R T The dominant redundancy indicator is "accuracy rate," ensuring that the stimuli that cause the most significant redundancy are reduced first, and avoiding over-regulation.

[0121] Continuing with the above embodiment, the redundancy value is: R γ =12.5%, R T =10%, R C ≈8.24%. Since 12.5% ​​> 10% > 8.24%, the dominant redundancy indicator is determined to be "EEG signal".

[0122] Step 510: Reduce the stimulation intensity of the tUS module on the tES target and tFUS target region based on the dominant redundancy index.

[0123] Furthermore, the neural modulation system reduces the stimulation intensity of the tUS module on the tES target and tFUS target area according to the dominant redundancy index, as specifically in steps 5101 to 5105.

[0124] In this invention, when both EEG signals and behavioral performance meet the standards, redundancy is used to identify overactivated pathways and target the intensity of stimulation. This avoids resource waste and potential overstimulation risks while promoting the autonomous consolidation of children's neural functions. For example, after the stimulation is reduced, the power of the Gamma wave in the left temporoparietal region drops to 4.1 μV (still within the "optimal" range), and behavioral performance remains stable (reaction time 2.8 seconds / sentence, accuracy 91%), achieving a balance between energy conservation and safety through "on-demand regulation".

[0125] In one embodiment, steps 5101 to 5105 include:

[0126] Step 5101: Determine the base reduction ratio based on the redundancy of the dominant redundancy index, and determine the collaborative stability coefficient based on the redundancy of EEG signals, reaction time redundancy, and accuracy redundancy.

[0127] Optionally, in this embodiment of the invention, the higher the redundancy, the greater the reduction ratio. Therefore, the neural modulation system uses an exponentially increasing formula combined with the redundancy R of the dominant redundancy index. 主导 The calculation of the base reduction ratio, where the exponential increase formula is: r 基础 =1-exp(-α) 主导 *R 主导 ), where α 主导 The specific coefficients are as follows: (EEG signal redundancy is 0.05 / %, reaction time redundancy is 0.04 / %, and accuracy redundancy is 0.03 / %).

[0128] Furthermore, the neural modulation system discretizes the redundancy of EEG signals, reaction time redundancy, and accuracy redundancy to obtain a co-stability coefficient. This co-stability coefficient reflects the co-stability of multiple indicators, and its specific calculation formula is as follows: in, For average redundancy (if Then S = 1). S takes values ​​from 0 to 1. The closer to 1, the more consistent the redundancy of the multiple indicators and the better the synergy.

[0129] Continuing with the above embodiments, R γ =12.5%, R T =10%, R C ≈8.24%, therefore the base ratio r is lowered. 基础 ≈1-0.5353=0.4647 (i.e. 46.47%), co-stability coefficient S≈0.901.

[0130] Step 5102: Determine the actual reduction ratio based on the base reduction ratio and the collaborative stability coefficient.

[0131] Furthermore, the neural regulation system will downregulate the baseline ratio r 基础 The actual down-adjustment ratio r is obtained by coupling the co-stability coefficient S with a power function. 实际 In this embodiment of the invention, the formula for power function coupling is: r 实际 =r 基础 *S β β is the synergy weighting coefficient (preset to 0.6), which ensures that when the synergy is high (S is close to 1), the actual reduction ratio is closer to the base ratio; when the synergy is poor (S is low), the reduction ratio is suppressed to avoid over-adjustment.

[0132] Continuing with the above embodiments, the base ratio r is lowered. 基础 =0.4647, the collaborative stability coefficient S≈0.901, therefore the actual downward adjustment ratio r is calculated. 实际 ≈0.434 (i.e., 43.4%).

[0133] Step 5103: Determine the magnitude of stimulus intensity reduction based on the actual down-adjustment ratio and the current stimulation intensity of the tES target and tFUS target area by the tUS module.

[0134] Furthermore, the neural regulation system adjusts the rate of downregulation r based on the actual downregulation ratio. 实际 The current stimulation intensity I of the transcranial ultrasound stimulation module 当前 The downward adjustment magnitude ΔI is calculated using the following formula: ΔI = I 当前 *r 实际 *(1-I 下限 / I 当前 ), where I 下限 The lower limit of safety for stimulation intensity (0.2 W·cm) -2 To ensure that the current intensity is as close to the lower limit as possible, the downward adjustment should be smaller to avoid falling below the safe value.

[0135] Continuing with the above embodiments, I 当前 =0.427 W·cm -2 r 实际 =0.434, therefore, the calculated downward adjustment is approximately 0.098 W·cm -2 .

[0136] Step 5104: Calculate the difference between the current stimulus intensity and the magnitude of the stimulus intensity reduction to obtain the third target adjustment value.

[0137] Furthermore, the neural modulation system will adjust the current stimulus intensity I 当前 The difference between the adjustment value and the downward adjustment ΔI is used to obtain the third target adjustment value I. 目标3 If the third target adjustment value I 目标3 Below the safety limit (0.2 W·cm) -2 If I is forced to be taken, then I will be forced to take the first one.目标3 =I 下限 Ensure that the intensity of stimulation is within a safe range.

[0138] Continuing with the above embodiments, I 目标3 =0.427-0.098=0.329W·cm -2 Above 0.2 W·cm -2 ,efficient.

[0139] Step 5105: Reduce the stimulation intensity of the tUS module on the tES target and tFUS target area based on the third target adjustment value.

[0140] Furthermore, the neuromodulation system sends instructions to the transcranial ultrasound stimulation module via the central control and processing unit, increasing the stimulation intensity of the transcranial electrical stimulation target (such as the left temporoparietal region) and the transcranial focused ultrasound stimulation target area (such as the hippocampus) from the current value I. 当前 Adjusted to the third target adjustment value I 目标3 After adjustment, the neuromodulation system continuously monitors real-time EEG signals and behavioral performance data to ensure that EEG activity remains within the "optimal" range and behavioral performance is stable (e.g., reaction time fluctuation ≤ 0.3 seconds / sentence, accuracy fluctuation ≤ 0.3 seconds / sentence).

[0141] ≤5%).

[0142] Continuing with the above embodiments, the neuromodulation system controls the transcranial ultrasound stimulation module to increase the stimulation intensity on the left temporoparietal region and hippocampus from 0.427 W·cm. -2 Reduced to 0.329 W·cm -2 Monitoring showed that the Gamma wave power in the left temporoparietal region decreased to 4.2 μV (still in the "optimal" range), and behavioral performance remained stable (reaction time 2.9 seconds / sentence, accuracy 90%).

[0143] This invention determines the need for downregulation through redundancy analysis and dynamically adjusts the weakening range based on the synergistic stability of multiple indicators. While ensuring that EEG signals and behavioral performance remain within acceptable limits, it precisely reduces the stimulation intensity, avoiding resource waste or decreased neural adaptability caused by over-regulation. For example, after reducing the stimulation intensity by 22.9%, the child's neural activity and behavioral performance remain stable, conserving stimulation resources and creating conditions for the autonomous consolidation of their own neural functions, achieving an intelligent balance of "on-demand regulation."

[0144] The following describes the acoustic-electric multimodal neural modulation system for improving language reading ability provided by the present invention. The acoustic-electric multimodal neural modulation system for improving language reading ability described below can be referred to in correspondence with the acoustic-electric multimodal neural modulation method for improving language reading ability described above.

[0145] Optional, refer to Figure 4 , Figure 4 This is the second schematic diagram of the acoustic-electric multimodal neural modulation system for improving language reading ability provided by the present invention. The acoustic-electric multimodal neural modulation system includes...

[0146] The data acquisition module 410 is used to acquire baseline data of brain functional activity related to language reading ability by combining resting-state and task-state EEG based on the abnormal links in the language reading ability of primary school students located.

[0147] Stimulus point planning module 420 is used to identify tES target points and tFUS target regions associated with abnormal links based on abnormal links and baseline data of brain functional activity.

[0148] The network intervention module 430 is used to activate the tES module based on the tES target point and the tFUS module based on the tFUS target area to enhance the excitation of the target network and realize the intervention of the target network; the target network is a neural network composed of multiple brain regions related to language reading ability.

[0149] Intervention Implementation Module 440 is used to activate the tUS module to perform focused ultrasound stimulation intervention on the tES target point and tFUS target area, and to prompt primary school students to start the training task, and to obtain the real-time EEG signals of primary school students during the intervention process and the behavioral performance data of primary school students during the training task.

[0150] The neuromodulation module 450 is used to adjust the stimulation intensity of the tUS module on the tES target and tFUS target area based on real-time EEG signals and behavioral performance data.

[0151] This invention combines three non-invasive neuromodulation techniques: tES, tFUS, and TMS (transcranial magnetic stimulation). It uses a time-sharing or synchronous strategy to jointly stimulate language-related brain regions. Because TMS can modulate the superficial cortex through electromagnetic induction, tFUS targets deep brain regions, and tES covers a broad network, a sequential electro-acoustic stimulation approach of "network preprocessing - node precision focusing" leverages the complementary advantages of tES's broad cortical network coverage and tFUS's precise targeting of deep brain regions to form a multi-layered neural network synergy. This enhances the neural plasticity of the language network, thereby improving synergistic effects. During the intervention, the stimulation intensity of tES and / or tUS is dynamically adjusted based on the child's real-time physiological signals and behavioral data. Therefore, through a multi-dimensional real-time feedback adaptive adjustment mechanism, the intervention plan can be matched to the child's state changes in real time, achieving personalized intervention and improving intervention precision.

[0152] Please see Figure 5 , Figure 5 An embodiment diagram of an electronic device provided in accordance with the present invention. For example... Figure 5As shown, this embodiment of the invention provides an electronic device 500, including a memory 510, a processor 520, and a computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the computer program 511, it performs the following steps:

[0153] Based on the identified abnormalities in primary school students' language reading abilities, combined with resting-state and task-oriented EEG data, baseline data of brain functional activities related to language reading abilities were obtained.

[0154] Based on the abnormal links and baseline data of brain functional activity, the tES target points and tFUS target regions associated with the abnormal links were identified.

[0155] The tES module is activated based on the tES target point and the tFUS module is activated based on the tFUS target region to enhance the excitability of the target network and achieve intervention in the target network; the target network is a neural network composed of multiple brain regions related to language reading ability.

[0156] The tUS module was activated to perform focused ultrasound stimulation intervention on the tES target point and tFUS target area and to prompt primary school students to start the training task. Real-time EEG signals of primary school students during the intervention and behavioral performance data of primary school students during the training task were obtained.

[0157] Based on real-time EEG signals and behavioral data, the stimulation intensity of the tUS module on the tES target and tFUS target area is adjusted.

[0158] Please see Figure 6 , Figure 6 An embodiment diagram of a computer-readable storage medium provided in accordance with an embodiment of the present invention is shown. Figure 6 As shown, this embodiment provides a computer-readable storage medium 600, on which a computer program 511 is stored. When the computer program 511 is executed by a processor, it performs the following steps:

[0159] Based on the identified abnormalities in primary school students' language reading abilities, combined with resting-state and task-oriented EEG data, baseline data of brain functional activities related to language reading abilities were obtained.

[0160] Based on the abnormal links and baseline data of brain functional activity, the tES target points and tFUS target regions associated with the abnormal links were identified.

[0161] The tES module is activated based on the tES target point and the tFUS module is activated based on the tFUS target region to enhance the excitability of the target network and achieve intervention in the target network; the target network is a neural network composed of multiple brain regions related to language reading ability.

[0162] The tUS module was activated to perform focused ultrasound stimulation intervention on the tES target point and tFUS target area and to prompt primary school students to start the training task. Real-time EEG signals of primary school students during the intervention and behavioral performance data of primary school students during the training task were obtained.

[0163] Based on real-time EEG signals and behavioral data, the stimulation intensity of the tUS module on the tES target and tFUS target area is adjusted.

[0164] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the acoustic-electrical multimodal neural modulation method for improving language reading ability provided by the above methods, the method comprising:

[0165] Based on the identified abnormalities in primary school students' language reading abilities, combined with resting-state and task-oriented EEG data, baseline data of brain functional activities related to language reading abilities were obtained.

[0166] Based on the abnormal links and baseline data of brain functional activity, the tES target points and tFUS target regions associated with the abnormal links were identified.

[0167] The tES module is activated based on the tES target point and the tFUS module is activated based on the tFUS target region to enhance the excitability of the target network and achieve intervention in the target network; the target network is a neural network composed of multiple brain regions related to language reading ability.

[0168] The tUS module was activated to perform focused ultrasound stimulation intervention on the tES target point and tFUS target area and to prompt primary school students to start the training task. Real-time EEG signals of primary school students during the intervention and behavioral performance data of primary school students during the training task were obtained.

[0169] Based on real-time EEG signals and behavioral data, the stimulation intensity of the tUS module on the tES target and tFUS target area is adjusted.

[0170] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0171] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multimodal acoustic-electrical neural modulation system for improving language reading ability, characterized in that, Application in a head-mounted device; the head-mounted device includes a transcranial electrical stimulation (tES) module, a transcranial focused ultrasound (tFUS) module, and a transcranial ultrasound stimulation (tUS) module, worn on the head of a primary school student; the system includes: The data acquisition module is used to acquire baseline data of brain functional activity related to language reading ability by combining resting-state and task-state EEG based on the abnormal links in the language reading ability of primary school students. The stimulation point planning module is used to determine the tES target point and tFUS target region associated with the abnormal link based on the abnormal link and the baseline data of brain functional activity. The network intervention module is used to activate the tES module based on the tES target point and the tFUS module based on the tFUS target region to enhance the excitation of the target network and achieve intervention in the target network; the target network is a neural network composed of multiple brain regions related to language reading ability. The intervention implementation module is used to activate the tUS module to perform focused ultrasound stimulation intervention on the tES target point and the tFUS target area, and to prompt the primary school students to start the training task, and to obtain the real-time EEG signals of the primary school students during the intervention process and the behavioral performance data of the primary school students during the training task. A neuromodulation module is used to adjust the stimulation intensity of the tUS module on the tES target point and the tFUS target area based on the real-time EEG signal and the behavioral performance data; The step of adjusting the stimulation intensity of the tUS module on the tES target point and the tFUS target area based on the real-time EEG signal and the behavioral performance data includes: If the real-time EEG signal does not meet the preset requirements, but the behavioral performance data meets the preset requirements, then the degree of difference in Gamma wave power is determined based on the actual value of Gamma wave power of the real-time EEG signal and the preset value of Gamma wave power. Based on the joint standard deviation between actual language reading reaction time and actual language reading accuracy in the behavioral performance data, and the joint mean between actual language reading reaction time and actual language reading accuracy, the behavioral stability constraint coefficient is determined. Based on the degree of difference in Gamma wave power and the behavioral stability constraint coefficient, the stimulation intensity of the tUS module on the tES target and the tFUS target region is enhanced. The enhancement of the stimulation intensity of the tUS module on the tES target and the tFUS target region based on the degree of Gamma wave power difference and the behavioral stability constraint coefficient includes: Based on the degree of difference in Gamma wave power and the current stimulation intensity of the tUS module on the tES target and the tFUS target region, a second adjustment amplitude value is determined. The second adjustment range value is determined based on the second adjustment range value and the behavior stability constraint coefficient; The second target adjustment value is obtained by summing the second adjustment amplitude value and the current stimulus intensity; If the second target adjustment value is greater than the maximum stimulus intensity that stabilizes behavioral performance, then the stimulation intensity of the tUS module on the tES target point and the tFUS target area is increased based on the maximum stimulus intensity. If the second target adjustment value is less than or equal to the maximum stimulation intensity, then the stimulation intensity of the tUS module on the tES target point and the tFUS target region is increased based on the second target adjustment value.

2. The acoustic-electric multimodal neural modulation system for improving language reading ability according to claim 1, characterized in that, The step of adjusting the stimulation intensity of the tUS module on the tES target point and the tFUS target area based on the real-time EEG signal and the behavioral performance data includes: If the real-time EEG signal meets the preset requirements, but the behavioral performance data does not meet the preset requirements, then the degree of difference in reaction time is determined based on the actual language reading reaction time in the behavioral performance data and the preset language reading reaction time, and the degree of difference in accuracy is determined based on the actual language reading accuracy rate in the behavioral performance data and the preset language reading accuracy rate. The magnitudes of the differences in reaction time and accuracy are compared, and the index corresponding to the larger difference value is taken as the dominant anomaly index. Based on the standard deviation and mean of the Gamma wave power of the real-time EEG signal, the EEG stability constraint coefficient is determined, and based on the dominant abnormality index and the EEG stability constraint coefficient, the stimulation intensity of the tUS module on the tES target point and the tFUS target area is enhanced.

3. The acoustic-electric multimodal neural modulation system for improving language reading ability according to claim 2, characterized in that, The enhancement of the stimulation intensity of the tUS module on the tES target and the tFUS target region based on the dominant abnormality index and the EEG stability constraint coefficient includes: Based on the degree of difference of the dominant abnormality index and the current stimulation intensity of the tUS module on the tES target and the tFUS target region, a first adjustment amplitude value is determined; A first preliminary adjustment value is determined based on the EEG stability constraint coefficient and the first adjustment amplitude value; The first target adjustment value is obtained by summing the first preliminary adjustment value and the current stimulus intensity; The stimulation intensity of the tUS module on the tES target and the tFUS target region is enhanced based on the first target adjustment value.

4. The acoustic-electric multimodal neural modulation system for improving language reading ability according to claim 1, characterized in that, The step of adjusting the stimulation intensity of the tUS module on the tES target point and the tFUS target area based on the real-time EEG signal and the behavioral performance data includes: If the real-time EEG signal meets the preset requirements and the behavioral performance data meets the preset requirements, then the redundancy of the EEG signal is determined based on the mean value of the Gamma wave power of the real-time EEG signal and the preset value of the Gamma wave power. Based on the actual language reading reaction time and the preset language reading reaction time in the behavioral performance data, the reaction time redundancy is determined, and based on the actual language reading accuracy and the preset language reading accuracy in the behavioral performance data, the accuracy redundancy is determined. The redundancy of the EEG signal, the redundancy of the reaction time, and the redundancy of the accuracy rate are compared, and the dominant redundancy index corresponding to the largest redundancy is selected. The stimulation intensity of the tUS module on the tES target and the tFUS target region is reduced based on the dominant redundancy index.

5. The acoustic-electric multimodal neural modulation system for improving language reading ability according to claim 4, characterized in that, The reduction of the stimulation intensity of the tUS module on the tES target and the tFUS target region based on the dominant redundancy index includes: The base ratio for reduction is determined based on the redundancy of the dominant redundancy index, and the collaborative stability coefficient is determined based on the redundancy of the EEG signal, the redundancy of the reaction time, and the redundancy of the accuracy rate. The actual reduction ratio is determined based on the aforementioned base reduction ratio and the aforementioned collaborative stability coefficient; Based on the actual down-adjustment ratio and the current stimulation intensity of the tUS module for the tES target and the tFUS target region, the magnitude of the stimulation intensity down-adjustment is determined. The third target adjustment value is obtained by calculating the difference between the current stimulus intensity and the reduction range of the stimulus intensity. The stimulation intensity of the tUS module on the tES target and the tFUS target region is reduced based on the third target adjustment value.

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