Transcranial direct current stimulation effectiveness prediction method

Abstract

The invention relates to a transcranial direct current stimulation effectiveness prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring baseline cognitive competence test and resting state electroencephalogram data of a preset number of testees; performing transcranial direct current stimulation nerve regulation and control on the testee based on a preset transcranial direct current stimulation nerve regulation and control scheme; based on a preset experimental normal form, performing cognitive ability test on the testee after transcranial direct electrical stimulation to generate cognitive ability test results, and classifying the cognitive ability test results according to regulation and control effects; performing preprocessing and function connection index calculation on the electroencephalogram signals to obtain a training data set; based on a preset feature training classifier, performing model training on the training data set by using a machine learning algorithm; and completing transcranial direct current stimulation effectiveness prediction based on the trained model. According to the invention, the effectiveness of the transcranial direct current stimulation can be predicted more accurately, and an important basis can be provided for personalized nerve regulation of the transcranial direct current stimulation.

Description

Background Art

[0002] Transcranial electrical stimulation technology, especially transcranial direct current stimulation, has attracted widespread attention in the field of neuroscience in recent years. As a non-invasive neuromodulation method, this technology has been applied to neurorehabilitation, cognitive enhancement, emotion regulation and many other aspects. However, in actual applications, there are significant differences in the responses of different subjects to transcranial direct current stimulation. Some subjects can produce obvious effects after electrical stimulation, while others have almost no effect.

[0003] Before TDCS, different individuals may have different brain functional connectivity patterns, and these differences may affect the effectiveness of TDCS. For example, some individuals may have specific functional connectivity patterns that make them more sensitive to TDCS, while other individuals may respond less to stimulation due to different functional connectivity patterns. Previous studies have found that there is a correlation between specific functional connectivity indicators and the effectiveness of TDCS. This correlation can provide a basis for predicting the effectiveness of TDCS.

[0004] At present, the prediction of the effectiveness of transcranial direct current stimulation mainly relies on clinical experience, traditional neurophysiological indicators, etc. However, these methods often have problems such as low accuracy and low specificity. In the field of EEG research, resting-state EEG functional connectivity, as an indicator that can reflect the state of intrinsic network connectivity in the brain, has gradually become a research hotspot. Existing studies have shown that resting-state EEG functional connectivity is closely related to the brain's cognitive function, neuropsychiatric diseases, etc. However, there are relatively few studies that apply resting-state EEG functional connectivity to the prediction of the effectiveness of transcranial direct current stimulation.

[0005] Therefore, one or more methods are needed to solve the above problems.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0007] The purpose of the present disclosure is to provide a method, device, electronic device and computer-readable storage medium for predicting the effectiveness of transcranial direct current stimulation, thereby overcoming one or more problems caused by the limitations and defects of related technologies at least to a certain extent.

[0008] According to one aspect of the present disclosure, a method for predicting the effectiveness of transcranial direct current stimulation is provided, comprising:

[0009] Based on the preset method, baseline cognitive ability tests and resting-state EEG data were collected from a preset number of subjects before transcranial direct current stimulation;

[0010] After completing the baseline cognitive ability test and resting-state EEG data collection before transcranial direct current stimulation, the subject is subjected to transcranial direct current stimulation neuromodulation based on a preset transcranial direct current stimulation neuromodulation scheme;

[0011] After the subject is subjected to transcranial direct current stimulation neural regulation, based on a preset experimental paradigm, a cognitive ability test is performed on the subject after transcranial direct current stimulation, a cognitive ability test result is generated, and the cognitive ability test result is classified according to the regulation effect;

[0012] Preprocess the EEG signals and calculate the functional connectivity index to obtain the training data set;

[0013] Train the classifier based on preset features and use machine learning algorithms to train the model on the training data set;

[0014] Complete the prediction of transcranial direct current stimulation effectiveness based on the trained model.

[0015] In an exemplary embodiment of the present disclosure, the method further includes:

[0016] Based on the preset method, baseline cognitive ability tests and resting-state EEG data of 40 subjects were collected based on the 2-back experimental paradigm before transcranial direct current stimulation.

[0017] In an exemplary embodiment of the present disclosure, the preset transcranial direct current stimulation neural regulation scheme of the method further includes:

[0018] The stimulated brain area was the left dorsolateral prefrontal cortex, and the stimulating electrodes were arranged in a manner that the anode F3 was the center and the cathode electrodes FP1, Fz, C3, and FT7 were surrounded;

[0019] During stimulation, the current increased from 0 to 1.5 mA with a rise time of 30 s, then remained at 1.5 mA for 25 min, and finally gradually decreased to 0 within 30 s.

[0020] In an exemplary embodiment of the present disclosure, the method further includes:

[0021] The cognitive ability test results were analyzed, compared and evaluated by experts, and the subjects corresponding to the cognitive ability test results were divided into two groups: stimulation-effective and stimulation-ineffective.

[0022] In an exemplary embodiment of the present disclosure, in the method, preprocessing the EEG signal further comprises:

[0023] The EEG signals are preprocessed by re-referencing, filtering, segmenting, interpolating bad electrodes, removing bad segments, and removing artifacts using independent component analysis.

[0024] In an exemplary embodiment of the present disclosure, in the method, calculating the functional connectivity index of the EEG signal further comprises:

[0025] The preprocessed EEG signals are used to calculate the functional connectivity within the segment, feature average, construct feature matrix, and calculate the functional connectivity index of feature dimension reduction.

[0026] In an exemplary embodiment of the present disclosure, the model training of the method further includes:

[0027] Use effective stimulation and ineffective stimulation as two types of labels, train the classifier based on the preset features, and use the reduced-dimensional features for machine learning modeling training;

[0028] Optimize model parameters by adjusting model parameters and cross-validation methods;

[0029] After a preset number of iterative training, the model training is completed.

[0030] In one aspect of the present disclosure, a device for predicting the effectiveness of transcranial direct current stimulation is provided, comprising:

[0031] A static data acquisition module, used to collect baseline cognitive ability test and resting EEG data of a preset number of subjects before transcranial direct current stimulation based on a preset method;

[0032] A direct current stimulation module, used for performing transcranial direct current stimulation neural regulation on the subject based on a preset transcranial direct current stimulation neural regulation scheme after completing a baseline cognitive ability test and resting-state electroencephalogram data collection before transcranial direct current stimulation;

[0033] A cognitive ability test module, for performing a cognitive ability test on the subject after transcranial direct current stimulation neural regulation based on a preset experimental paradigm, generating cognitive ability test results, and classifying the cognitive ability test results according to regulation effects;

[0034] A training data set generation module is used to preprocess the EEG signals and calculate the functional connectivity index to obtain the training data set;

[0035] Model training module, used to train classifiers based on preset features and use machine learning algorithms to train models on training data sets;

[0036] The effectiveness prediction module is used to complete the effectiveness prediction of transcranial direct current stimulation based on the trained model.

[0037] In one aspect of the present disclosure, there is provided an electronic device, comprising:

[0038] Processor; and

[0039] A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method according to any one of the above items.

[0040] In one aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method according to any one of the above items is implemented.

[0041] A method for predicting the effectiveness of transcranial direct current stimulation in an exemplary embodiment of the present disclosure, wherein the method includes: collecting baseline cognitive ability tests and resting-state EEG data of a preset number of subjects; performing transcranial direct current stimulation neural regulation on the subjects based on a preset transcranial direct current stimulation neural regulation scheme; performing cognitive ability tests on the subjects after transcranial direct current stimulation based on a preset experimental paradigm, generating cognitive ability test results, and classifying the cognitive ability test results according to the regulation effect; preprocessing the EEG signals and calculating functional connectivity indicators to obtain a training data set; training a classifier based on preset features, and using a machine learning algorithm to perform model training on the training data set; and completing the prediction of the effectiveness of transcranial direct current stimulation based on the trained model. The present disclosure more accurately predicts the effectiveness of transcranial direct current stimulation, and can provide an important basis for personalized neural regulation of transcranial direct current stimulation.

[0042] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and other features and advantages of the present disclosure will become more apparent by describing in detail example embodiments thereof with reference to the attached drawings.

[0044] Figure 1 A flowchart of a method for predicting the effectiveness of transcranial direct current stimulation according to an exemplary embodiment of the present disclosure is shown;

[0045] Figure 2 A schematic diagram showing an application scenario of a method for predicting the effectiveness of transcranial direct current stimulation according to an exemplary embodiment of the present disclosure is shown;

[0046] Figure 3 A structural block diagram of a device for predicting the effectiveness of transcranial direct current stimulation according to an exemplary embodiment of the present disclosure is shown;

[0047] Figure 4 A block diagram schematically shows an electronic device according to an exemplary embodiment of the present disclosure;

[0048] Figure 5 A schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0049] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.

[0050] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, materials, devices, steps, etc. may be adopted. In other cases, known structures, methods, devices, implementations, materials or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0051] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or these functional entities or parts of functional entities may be implemented in one or more software hardened modules, or these functional entities may be implemented in different networks and / or processor devices and / or microcontroller devices.

[0052] In this exemplary embodiment, a method for predicting the effectiveness of transcranial direct current stimulation is first provided; Figure 1 As shown in , the method for predicting the effectiveness of transcranial direct current stimulation may include the following steps:

[0053] Step S110, based on a preset method, before transcranial direct current stimulation, collecting baseline cognitive ability test and resting-state EEG data of a preset number of subjects;

[0054] Step S120, after completing the baseline cognitive ability test before transcranial direct current stimulation and the collection of resting-state EEG data, performing transcranial direct current stimulation neural regulation on the subject based on a preset transcranial direct current stimulation neural regulation scheme;

[0055] Step S130, after the subject is subjected to transcranial direct current stimulation neural regulation, based on a preset experimental paradigm, a cognitive ability test is performed on the subject after transcranial direct current stimulation, a cognitive ability test result is generated, and the cognitive ability test result is classified according to the regulation effect;

[0056] Step S140, preprocessing the EEG signal and calculating the functional connectivity index to obtain a training data set;

[0057] Step S150, training a classifier based on preset features, and performing model training on the training data set using a machine learning algorithm;

[0058] Step S160, completing the prediction of transcranial direct current stimulation effectiveness based on the trained model.

[0059] A method for predicting the effectiveness of transcranial direct current stimulation in an exemplary embodiment of the present disclosure, wherein the method includes: collecting baseline cognitive ability tests and resting-state EEG data of a preset number of subjects; performing transcranial direct current stimulation neural regulation on the subjects based on a preset transcranial direct current stimulation neural regulation scheme; performing cognitive ability tests on the subjects after transcranial direct current stimulation based on a preset experimental paradigm, generating cognitive ability test results, and classifying the cognitive ability test results according to the regulation effect; preprocessing the EEG signals and calculating functional connectivity indicators to obtain a training data set; training a classifier based on preset features, and using a machine learning algorithm to perform model training on the training data set; and completing the prediction of the effectiveness of transcranial direct current stimulation based on the trained model. The present disclosure more accurately predicts the effectiveness of transcranial direct current stimulation, and can provide an important basis for personalized neural regulation of transcranial direct current stimulation.

[0060] Next, a method for predicting the effectiveness of transcranial direct current stimulation in this exemplary embodiment will be further described.

[0061] Embodiment 1:

[0062] In step S110, based on a preset method, baseline cognitive ability test and resting-state EEG data of a preset number of subjects may be collected before transcranial direct current stimulation.

[0063] In the embodiment of this example, the method further includes:

[0064] Based on the preset method, baseline cognitive ability tests and resting-state EEG data of 40 subjects were collected based on the 2-back experimental paradigm before transcranial direct current stimulation.

[0065] In step S120, after completing the baseline cognitive ability test before transcranial direct current stimulation and the collection of resting-state EEG data, transcranial direct current stimulation neuroregulation may be performed on the subject based on a preset transcranial direct current stimulation neuroregulation scheme.

[0066] In the embodiment of this example, the preset transcranial direct current stimulation neural regulation scheme of the method further includes:

[0067] The stimulated brain area was the left dorsolateral prefrontal cortex, and the stimulating electrodes were arranged in a manner that the anode F3 was the center and the cathode electrodes FP1, Fz, C3, and FT7 were surrounded;

[0068] During stimulation, the current increased from 0 to 1.5 mA with a rise time of 30 s, then remained at 1.5 mA for 25 min, and finally gradually decreased to 0 within 30 s.

[0069] In step S130, after the subject is subjected to transcranial direct current stimulation neural regulation, a cognitive ability test after transcranial direct current stimulation may be performed on the subject based on a preset experimental paradigm to generate cognitive ability test results, and the cognitive ability test results may be classified according to the regulation effect.

[0070] In the embodiment of this example, the method further includes:

[0071] The cognitive ability test results were analyzed, compared and evaluated by experts, and the subjects corresponding to the cognitive ability test results were divided into two groups: stimulation-effective and stimulation-ineffective.

[0072] In step S140, the EEG signal may be preprocessed and the functional connectivity index may be calculated to obtain a training data set.

[0073] In the embodiment of this example, preprocessing the EEG signal further includes:

[0074] The EEG signals are preprocessed by re-referencing, filtering, segmenting, interpolating bad electrodes, removing bad segments, and removing artifacts using independent component analysis.

[0075] In the embodiment of this example, calculating the functional connectivity index of the EEG signal further includes:

[0076] The preprocessed EEG signals are used to calculate the functional connectivity within the segment, feature average, construct feature matrix, and calculate the functional connectivity index of feature dimension reduction.

[0077] In step S150, a classifier may be trained based on preset features, and a machine learning algorithm may be used to perform model training on the training data set.

[0078] In the embodiment of this example, the model training of the method further includes:

[0079] Use effective stimulation and ineffective stimulation as two types of labels, train the classifier based on the preset features, and use the reduced-dimensional features for machine learning modeling training;

[0080] Optimize model parameters by adjusting model parameters and cross-validation methods;

[0081] After a preset number of iterative training, the model training is completed.

[0082] In step S160, the effectiveness prediction of transcranial direct current stimulation is completed based on the trained model.

[0083] In the embodiment of this example, the method for predicting the effectiveness of transcranial direct current stimulation based on resting-state EEG functional connectivity proposed in the present disclosure has the following significant advantages. First, the method uses the resting-state EEG functional connectivity index before stimulation for machine learning modeling training, which can more accurately predict the effectiveness of transcranial direct current stimulation. Second, the method can provide an important basis for personalized neural regulation of transcranial direct current stimulation, improve the regulation effect, and reduce unnecessary waste of resources.

[0084] Embodiment 2:

[0085] In this exemplary embodiment, if Figure 1 A flowchart for implementing a method for predicting the effectiveness of transcranial direct current stimulation based on resting-state EEG functional connectivity provided by an embodiment of the present invention is described in detail as follows:

[0086] (1) Baseline cognitive ability test and resting-state EEG data collection before transcranial direct current stimulation

[0087] 40 subjects were selected. Before transcranial direct current stimulation, the 2-back experimental paradigm was used to test the subjects' baseline cognitive ability, reaction time and accuracy. After the test, a professional 64-channel EEG acquisition device was used to collect the subjects' resting EEG data for about 5 minutes. The EEG electrodes were arranged according to the 10-20 international system, and the impedance of all electrodes was kept below 5kΩ. During the acquisition process, the subjects were required to remain quiet, relaxed, and keep their eyes closed.

[0088] (2) Transcranial direct current stimulation neural regulation

[0089] The stimulated brain area was the left dorsolateral prefrontal cortex, and the stimulation electrodes were arranged in a way that the anode was the center (F3) and the cathode electrodes (FP1, Fz, C3, FT7) were surrounded. During stimulation, the current rose from 0 to 1.5 mA, and the rise time was 30 seconds. After that, it was maintained at 1.5 mA for 25 minutes, and finally gradually decreased to 0 within 30 seconds. At the beginning and end of the stimulation, a fade-in / fade-out design (30 seconds each) was used to reduce the potential sudden changes in skin sensation.

[0090] (3) Cognitive ability test after transcranial direct current stimulation and classification according to the regulation effect

[0091] The 2-back experimental paradigm was used to test the cognitive ability of the subjects after transcranial direct current stimulation, and the reaction time and accuracy were obtained. The reaction time and accuracy obtained in the baseline cognitive ability test before stimulation were analyzed and compared, and the experts evaluated, and the subjects were divided into two groups: "stimulation effective" and "stimulation ineffective".

[0092] (4) Preprocessing of EEG signals and calculation of functional connectivity indicators to obtain training data sets, such as Figure 2 As shown;

[0093] The preprocessing steps are as follows:

[0094] ①Re-reference: use the average reference of the whole brain;

[0095] ②Filtering: The filtering range is 0.5~40Hz;

[0096] ③ Segmentation: Divide the continuous resting-state EEG information into multiple segments of 2 seconds each;

[0097] ④ Interpolation of bad electrodes: Use the spherical curve method to interpolate and replace the electrode data that is obviously different from the EEG data of other electrodes;

[0098] ⑤ Eliminate bad segments: Check the resting EEG data and eliminate segments with large artifacts or abnormal data distribution;

[0099] ⑥ Independent component analysis to remove artifacts: Use independent component analysis to identify and remove non-EEG components such as electrocardiogram, eye movements, blinking, electromyography, head movements, etc. to obtain clean resting EEG data.

[0100] The functional connectivity index is calculated as follows:

[0101] ① Calculate intra-segment functional connectivity: For each EEG segment (2s), calculate the coherence between all electrode pairs to measure the strength of functional connectivity. The formula for calculating the coherence coefficient is:

[0102]

[0103] Where Sxy(f) is the cross power spectral density of the two electrodes, and Sxx(f) and Syy(f) are the autopower spectral densities of the two electrode signals, respectively.

[0104] ② Feature averaging: Average the functional connection strength feature vectors of all segments to obtain the average functional connection strength of each electrode pair.

[0105] ③ Construct feature matrix: Use these averaged functional connectivity strength features to construct a functional connectivity feature matrix. For 64 electrodes, there are 64*(64-1) / 2=2016 features (each pair of electrodes needs to be calculated once), such as Figure 3 shown.

[0106] ④ Feature dimensionality reduction

[0107] Principal component analysis (PCA) was used to reduce the dimension of the feature matrix, retaining 90% of the variance, and the most discriminative functional connectivity strength features were selected to reduce the feature dimension while retaining the most information.

[0108] (5) Use machine learning algorithms to train models on training data sets;

[0109] The stimulation is effective and ineffective as two types of labels, and the selected features are used to train classifiers (such as support vector machines, random forests, etc.), and the features after dimensionality reduction are used for machine learning modeling training. By adjusting the model parameters, the model can accurately learn the relationship between functional connection features and stimulation effects. During the training process, the cross-validation method is used to optimize the model parameters and improve the generalization ability of the model. After multiple iterative training, a model with stable performance is obtained.

[0110] (6) Prediction of the effectiveness of transcranial direct current stimulation neural regulation

[0111] Select a new subject, follow the same process to obtain its resting-state EEG data, calculate the functional connectivity index and select features, and then input it into the trained model. The model can output the prediction result of the effectiveness of transcranial direct current stimulation for the subject. If the prediction result is that the stimulation is effective, a personalized transcranial direct current stimulation plan can be formulated for the subject according to the specific situation; if the prediction result is that the stimulation is ineffective, other treatment methods can be considered or the stimulation parameters can be adjusted.

[0112] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0113] In addition, in this exemplary embodiment, a device for predicting the effectiveness of transcranial direct current stimulation is also provided. Figure 3 As shown, the transcranial direct current stimulation effectiveness prediction device 300 may include: a static data acquisition module 310, a direct current stimulation module 320, a cognitive ability test module 330, a training data set generation module 340, a model training module 350 and an effectiveness prediction module 360. Among them:

[0114] The static data collection module 310 is used to collect baseline cognitive ability test and resting EEG data of a preset number of subjects before transcranial direct current stimulation based on a preset method;

[0115] The direct current stimulation module 320 is used to perform transcranial direct current stimulation neural regulation on the subject based on a preset transcranial direct current stimulation neural regulation scheme after completing the baseline cognitive ability test and resting-state EEG data collection before transcranial direct current stimulation;

[0116] A cognitive ability test module 330 is used to perform a cognitive ability test on the subject after the subject is subjected to transcranial direct current stimulation neural regulation based on a preset experimental paradigm, generate cognitive ability test results, and classify the cognitive ability test results according to the regulation effect;

[0117] A training data set generation module 340 is used to pre-process the EEG signal and calculate the functional connectivity index to obtain a training data set;

[0118] A model training module 350 is used to train a classifier based on preset features and perform model training on a training data set using a machine learning algorithm;

[0119] The effectiveness prediction module 360 ​​is used to complete the effectiveness prediction of transcranial direct current stimulation based on the trained model.

[0120] The specific details of each of the above-mentioned transcranial direct current stimulation effectiveness prediction device modules have been described in detail in a corresponding transcranial direct current stimulation effectiveness prediction method, and will not be repeated here.

[0121] It should be noted that, although several modules or units of a transcranial direct current stimulation effectiveness prediction device 300 are mentioned in the above detailed description, such division is not mandatory. In fact, according to an embodiment of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0122] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0123] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware embodiments, complete software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0124] Refer to the following Figure 4 An electronic device 400 according to such an embodiment of the present invention is described. Figure 4 The electronic device 400 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0125] like Figure 4As shown, the electronic device 400 is in the form of a general computing device. The components of the electronic device 400 may include but are not limited to: the at least one processing unit 410, the at least one storage unit 420, a bus 430 connecting different system components (including the storage unit 420 and the processing unit 410), and a display unit 440.

[0126] The storage unit stores program codes, which can be executed by the processing unit 410, so that the processing unit 410 performs the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification. For example, the processing unit 410 can perform the following steps: Figure 1 Steps S110 to S160 shown in FIG.

[0127] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 4201 and / or a cache storage unit 4202 , and may further include a read-only storage unit (ROM) 4203 .

[0128] The storage unit 420 may also include a program / utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0129] Bus 430 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0130] The electronic device 400 may also communicate with one or more external devices 470 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or communicate with any device that enables the electronic device 400 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 450. In addition, the electronic device 400 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 460. As shown, the network adapter 460 communicates with other modules of the electronic device 400 via a bus 430. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0131] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0132] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible embodiments, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to perform the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0133] refer to Figure 5 As shown, a program product 500 for implementing the above method according to an embodiment of the present invention is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0134] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0135] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0136] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0137] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0138] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0139] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0140] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for predicting the effectiveness of transcranial direct current stimulation, characterized in that: The method comprises: Based on the preset method, baseline cognitive ability tests and resting-state EEG data were collected from a preset number of subjects before transcranial direct current stimulation; After completing the baseline cognitive ability test and resting-state EEG data collection before transcranial direct current stimulation, the subject is subjected to transcranial direct current stimulation neuromodulation based on a preset transcranial direct current stimulation neuromodulation scheme; After the subject is subjected to transcranial direct current stimulation neural regulation, based on a preset experimental paradigm, a cognitive ability test is performed on the subject after transcranial direct current stimulation, a cognitive ability test result is generated, and the cognitive ability test result is classified according to the regulation effect; Preprocess the EEG signals and calculate the functional connectivity index to obtain the training data set; Train the classifier based on preset features and use machine learning algorithms to train the model on the training data set; Complete the prediction of transcranial direct current stimulation effectiveness based on the trained model.

2. The method according to claim 1, characterized in that The method further comprises: Based on the preset method, baseline cognitive ability tests and resting-state EEG data of 40 subjects were collected based on the 2-back experimental paradigm before transcranial direct current stimulation.

3. The method according to claim 1, characterized in that The preset transcranial direct current stimulation neural regulation scheme of the method also includes: The stimulated brain area was the left dorsolateral prefrontal cortex, and the stimulating electrodes were arranged in a manner that the anode F3 was the center and the cathode electrodes FP1, Fz, C3, and FT7 were surrounded; During stimulation, the current increased from 0 to 1.5 mA with a rise time of 30 s, then remained at 1.5 mA for 25 min, and finally gradually decreased to 0 within 30 s.

4. The method according to claim 1, characterized in that The method further comprises: The cognitive ability test results were analyzed, compared and evaluated by experts, and the subjects corresponding to the cognitive ability test results were divided into two groups: stimulation-effective and stimulation-ineffective.

5. The method according to claim 1, characterized in that In the method, preprocessing the EEG signal further comprises: The EEG signals are preprocessed by re-referencing, filtering, segmenting, interpolating bad electrodes, removing bad segments, and removing artifacts using independent component analysis.

6. The method according to claim 1, characterized in that In the method, calculating the functional connectivity index of the EEG signal also includes: The preprocessed EEG signals are used to calculate the functional connectivity within the segment, feature average, construct feature matrix, and calculate the functional connectivity index of feature dimension reduction.

7. The method according to claim 1, characterized in that The model training of the method further includes: Use effective stimulation and ineffective stimulation as two types of labels, train the classifier based on the preset features, and use the reduced-dimensional features for machine learning modeling training; Optimize model parameters by adjusting model parameters and cross-validation methods; After a preset number of iterative training, the model training is completed.

8. A device for predicting the effectiveness of transcranial direct current stimulation, characterized in that: The device comprises: A static data acquisition module, used to collect baseline cognitive ability test and resting EEG data of a preset number of subjects before transcranial direct current stimulation based on a preset method; A direct current stimulation module, used for performing transcranial direct current stimulation neural regulation on the subject based on a preset transcranial direct current stimulation neural regulation scheme after completing a baseline cognitive ability test and resting-state electroencephalogram data collection before transcranial direct current stimulation; A cognitive ability test module, for performing a cognitive ability test on the subject after transcranial direct current stimulation neural regulation based on a preset experimental paradigm, generating cognitive ability test results, and classifying the cognitive ability test results according to regulation effects; A training data set generation module is used to preprocess the EEG signals and calculate the functional connectivity index to obtain the training data set; Model training module, used to train classifiers based on preset features and use machine learning algorithms to train models on training data sets; The effectiveness prediction module is used to complete the effectiveness prediction of transcranial direct current stimulation based on the trained model.

9. An electronic device, characterized in that: include Processor; and A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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