Method and device for optimizing stimulation parameters based on mechanical arm collected electroencephalogram signals

Abstract

The application provides an optimization method and device for stimulation parameters based on mechanical arm acquisition of electroencephalogram signals, the method comprising: obtaining electroencephalogram signals corresponding to a plurality of preset stimulation parameter combinations of at least one target point; performing multi-stage denoising on the electroencephalogram signals corresponding to each preset stimulation parameter combination of each target point respectively to obtain corresponding effective electroencephalogram signals; performing feature extraction on the effective electroencephalogram signals corresponding to each preset stimulation parameter combination of each target point to obtain electroencephalogram feature indexes corresponding to each preset stimulation parameter combination of each target point; and obtaining an optimal target point combination and an optimal stimulation parameter combination of each target point according to the electroencephalogram feature indexes corresponding to each preset stimulation parameter combination of each target point and parameter screening conditions. The device is used to execute the above method. The optimization method and device for stimulation parameters based on mechanical arm acquisition of electroencephalogram signals provided by the embodiment of the application improve the reliability of target points and optimal stimulation parameter combinations.

Description

Technical Field

[0001] This invention relates to the field of medical image processing technology, specifically to a method and apparatus for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm. Background Technology

[0002] Electroencephalogram (EEG) signals can be detected on the surface of the scalp. These signals reflect the functional state and information processing of different areas of the brain and can be applied in various fields such as clinical diagnosis, neuroscience research, psychology, and cognitive science research.

[0003] Electroencephalogram (EEG) signals can be acquired using specific equipment and methods. Appropriately sized EEG acquisition devices, such as EEG caps or EEG stents, can be fitted to the subject. Sites in the cerebral cortex requiring stimulation can be selected, and transcranial magnetic stimulation (TMS) technology can be used. The pulsed magnetic field generated by the TMS coil induces a localized current in a specific area to stimulate brain activity, and the electrical activity induced by the magnetic field is then collected to obtain the EEG signal. To acquire EEG signals, stimulation parameters such as target location, stimulation intensity, and coil lifting height need to be set. These parameters affect the accuracy of the acquired EEG signal. Therefore, optimizing the stimulation parameters for acquiring EEG signals to obtain more accurate signals is a crucial issue that urgently needs to be addressed in this field. Summary of the Invention

[0004] To address the problems in the prior art, embodiments of the present invention provide a method and apparatus for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, which can at least partially solve the above-mentioned problems.

[0005] Firstly, a method for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm includes:

[0006] The brain signals corresponding to multiple preset stimulation parameter combinations for at least one target point are acquired; wherein, the brain signals corresponding to each stimulation parameter combination for each target point are acquired by stimulating the subject's brain with a stimulation device carried by a robotic arm based on each stimulation parameter combination for each target point.

[0007] Multi-stage denoising was performed on the EEG signals corresponding to each preset stimulation parameter combination for each target point to obtain the effective EEG signal corresponding to each preset stimulation parameter combination for each target point.

[0008] Feature extraction is performed on the effective EEG signal corresponding to each preset stimulation parameter combination for each target point to obtain the EEG feature index corresponding to each preset stimulation parameter combination for each target point.

[0009] Based on the EEG characteristic indicators and parameter screening conditions corresponding to each preset stimulation parameter combination for each target, the optimal target combination and the optimal stimulation parameter combination for each target are obtained.

[0010] Secondly, this invention proposes a method for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, comprising:

[0011] The EEG signal corresponding to the current preset stimulation parameter combination of the current target is acquired; wherein, the EEG signal corresponding to the current stimulation parameter combination of the current target is acquired by a robotic arm carrying a stimulation device to stimulate the subject's brain based on the current stimulation parameter combination of the current target; there are multiple preset stimulation parameter combinations for the current target;

[0012] Multi-stage denoising is performed on the EEG signal corresponding to the current combination of stimulation parameters for the current target to obtain the effective EEG signal corresponding to the current preset combination of stimulation parameters for the current target.

[0013] Feature extraction is performed on the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target to obtain the EEG feature index corresponding to the current preset stimulation parameter combination of the current target.

[0014] Iterate through the preset stimulation parameter combinations of the current target until a suitable stimulation parameter combination for the current target is obtained based on the EEG feature indicators corresponding to the current preset stimulation parameter combination and the parameter screening conditions; or iterate through all preset stimulation parameter combinations of the current target without obtaining a suitable stimulation parameter combination for the current target.

[0015] If a suitable combination of stimulation parameters for the current target is obtained, or if all preset combinations of stimulation parameters for the current target have been traversed but no suitable combination of stimulation parameters for the current target has been obtained, then the next target is obtained as the current target and the suitable combination of stimulation parameters for the current target is obtained, until all targets have been traversed.

[0016] Thirdly, the present invention provides an optimization device for stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, comprising:

[0017] The first acquisition module is used to acquire EEG signals corresponding to multiple preset stimulation parameter combinations for at least one target point; wherein, the EEG signal corresponding to each stimulation parameter combination for each target point is acquired by a robotic arm carrying a stimulation device to stimulate the subject's brain based on each stimulation parameter combination for each target point.

[0018] The first denoising module is used to perform multi-stage denoising on the EEG signals corresponding to each preset stimulation parameter combination of each target point to obtain the effective EEG signal corresponding to each preset stimulation parameter combination of each target point.

[0019] The first feature extraction module is used to extract features from the effective EEG signal corresponding to each preset stimulation parameter combination of each target point, and obtain the EEG feature index corresponding to each preset stimulation parameter combination of each target point.

[0020] The screening module is used to obtain the optimal target combination and the optimal stimulation parameter combination for each target based on the EEG characteristic indicators corresponding to each preset stimulation parameter combination for each target and the parameter screening conditions.

[0021] Fourthly, the present invention provides an optimization device for stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, comprising:

[0022] The second acquisition module is used to acquire the EEG signal corresponding to the current preset stimulation parameter combination of the current target point; wherein, the EEG signal corresponding to the current stimulation parameter combination of the current target point is acquired by a robotic arm carrying a stimulation device to stimulate the subject's brain based on the current stimulation parameter combination of the current target point; there are multiple preset stimulation parameter combinations for the current target point;

[0023] The second denoising module is used to perform multi-stage denoising on the EEG signal corresponding to the current stimulation parameter combination of the current target point to obtain the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target point.

[0024] The second feature extraction module is used to extract features from the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target point, and obtain the EEG feature index corresponding to the current preset stimulation parameter combination of the current target point.

[0025] The first traversal module is used to traverse the preset stimulation parameter combinations of the current target until a suitable stimulation parameter combination for the current target is obtained based on the EEG feature indicators and parameter screening conditions corresponding to the current preset stimulation parameter combination; or after traversing all preset stimulation parameter combinations for the current target without obtaining a suitable stimulation parameter combination for the current target.

[0026] The second traversal module is used to obtain the next target as the current target if a suitable combination of stimulation parameters for the current target is obtained, or if all preset combinations of stimulation parameters for the current target have been traversed but no suitable combination of stimulation parameters for the current target has been obtained, until all targets have been traversed.

[0027] Fifthly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the program to implement the method for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm as described in any of the above embodiments.

[0028] The present invention provides a method and apparatus for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm. This method acquires EEG signals corresponding to multiple preset stimulation parameter combinations for at least one target point. The EEG signal corresponding to each stimulation parameter combination for each target point is acquired by stimulating the subject's brain using a stimulation device carried by a robotic arm based on each stimulation parameter combination for each target point. Noise is removed from the EEG signals corresponding to each preset stimulation parameter combination for each target point to obtain effective EEG signals. Feature extraction is performed on the effective EEG signals corresponding to each preset stimulation parameter combination for each target point to obtain EEG feature indicators. Based on the EEG feature indicators corresponding to each preset stimulation parameter combination for each target point and parameter selection conditions, the optimal target point combination and the optimal stimulation parameter combination for each target point are obtained, improving the reliability of obtaining the target point and the optimal stimulation parameter combination. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0030] Figure 1 This is a schematic diagram of the electroencephalogram (EEG) signal acquisition system provided in the first embodiment of the present invention.

[0031] Figure 2 This is a flowchart illustrating the optimization method for stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, provided in the second embodiment of the present invention.

[0032] Figure 3 This is a flowchart illustrating the optimization method for stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, provided in the third embodiment of the present invention.

[0033] Figure 4 This is a flowchart illustrating the optimization method for stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, provided in the fourth embodiment of the present invention.

[0034] Figure 5 This is a flowchart illustrating the optimization method for stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, provided in the fifth embodiment of the present invention.

[0035] Figure 6 This is a flowchart illustrating the method for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, as provided in the sixth embodiment of the present invention.

[0036] Figure 7This is a flowchart illustrating the optimization method for stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, provided in the seventh embodiment of the present invention.

[0037] Figure 8 This is a flowchart illustrating the method for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, provided in the eighth embodiment of the present invention.

[0038] Figure 9 This is a schematic diagram of the structure of the stimulation parameter optimization device based on the acquisition of EEG signals by a robotic arm, provided in the ninth embodiment of the present invention.

[0039] Figure 10 This is a schematic diagram of the structure of the device for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, provided in the tenth embodiment of the present invention.

[0040] Figure 11 This is a schematic diagram of the structure of the device for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, provided in the eleventh embodiment of the present invention.

[0041] Figure 12 This is a schematic diagram of the structure of the device for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, provided in the twelfth embodiment of the present invention.

[0042] Figure 13 This is a schematic diagram of the structure of the stimulation parameter optimization device based on the acquisition of EEG signals by a robotic arm, provided in the thirteenth embodiment of the present invention.

[0043] Figure 14 This is a schematic diagram of the structure of the device for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, provided in the fourteenth embodiment of the present invention.

[0044] Figure 15 This is a schematic diagram of the structure of the device for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, provided in the fifteenth embodiment of the present invention.

[0045] Figure 16 This is a schematic diagram of the structure of the device for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, provided in the sixteenth embodiment of the present invention.

[0046] Figure 17 This is a schematic diagram of the structure of the stimulation parameter optimization device based on the acquisition of EEG signals by a robotic arm, provided in the seventeenth embodiment of the present invention.

[0047] Figure 18 This is a schematic diagram of the structure of the device for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, provided in the eighteenth embodiment of the present invention.

[0048] Figure 19 This is a schematic diagram of the structure of the device for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, provided in the nineteenth embodiment of the present invention.

[0049] Figure 20 This is a schematic diagram of the structure of the stimulation parameter optimization device based on the acquisition of EEG signals by a robotic arm, provided in the twentieth embodiment of the present invention.

[0050] Figure 21 This is a schematic diagram of the physical structure of a computer device provided in the twenty-first embodiment of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments and descriptions of the present invention are used to explain the present invention, but are not intended to limit the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0052] The information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties. The collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation portals are provided for users to choose to authorize or refuse.

[0053] To facilitate understanding of the technical solution provided in this application, the relevant content of the technical solution in this application will be explained below.

[0054] Electroencephalography (EEG) is a technique used to record electrical activity on the surface of the brain. When neurons in the brain are active, they generate tiny electrical currents that create subtle changes in electrical potential on the scalp. EEG captures these potential changes by placing multiple electrodes on the scalp and converts them into visual graphs or data, thus reflecting the functional state of the brain.

[0055] Transcranial magnetic stimulation (TMS) is a non-invasive and painless green treatment technology. Magnetic signals can penetrate the skull without attenuation and directly act on the cerebral cortex. In practical applications, TMS is not limited to brain stimulation; it can also stimulate peripheral nerves and muscles, providing an effective tool for the diagnosis and treatment of various neurological diseases.

[0056] When using electroencephalography (EEG), electrode placement typically relies on internationally recognized standards to ensure comparability of results across different studies and clinical trials. A commonly used standard is the 10-20 system, which uses specific markers to determine electrode locations. The basic steps for electrode placement are as follows: First, establish reference points on the head; second, measure head dimensions to ensure the appropriate size of the EEG acquisition equipment, such as an EEG cap, electrode patches, or EEG support; then, use the 10-20 system or other standardized systems to determine the location of each electrode; finally, fine-tune the electrodes as needed. In current practice, the placement of EEG caps often relies on the operator's experience and intuition, which limits the ability to accurately match electrodes to specific areas of the cerebral cortex.

[0057] Figure 1 This is a schematic diagram of the electroencephalogram (EEG) signal acquisition system provided in the first embodiment of the present invention, as shown below. Figure 1 As shown, the EEG signal acquisition system provided in this embodiment of the invention includes a computer 1, a robot 2, a stimulation device 3, multiple acquisition electrodes 4, and a navigation device 5, wherein:

[0058] Computer 1 is connected to robot 2, stimulation device 3, multiple acquisition electrodes 4 and navigation device 5 respectively;

[0059] Multiple acquisition electrodes 4 are placed on the subject's head, and can be placed inside an EEG cap for easy wearing. Robot 2 includes a control device and a robotic arm. The control device controls the robotic arm to carry a stimulation device 3 to stimulate target points in the subject's brain. The stimulation device 3 can employ a TMS coil. Navigation device 5 is used to acquire the positional information of each electrode and the stimulation device. Computer 1 constructs a 3D model of the subject's head based on image data of the subject's brain and acquires the subject's EEG signals. Navigation device 5 includes infrared cameras, color cameras, structured light cameras, etc.

[0060] Based on the above-mentioned EEG signal acquisition system, the EEG signal acquisition process is as follows:

[0061] (1) Select the target point to be stimulated for the subject. You can click to select it on the 3D model or input the MRI coordinates of the target point. Save the coordinates after selection. (2) The subject wears the EEG cap with the assistance of the tester and sits within the field of view of the navigation device. (3) Set the parameters of the robotic arm in the controller. Input the height of the EEG cap, the intensity of TMS stimulation, the interval of TMS stimulation, and the number of TMS stimulations. (4) Turn on the robotic arm follow mode and wait 2-3 seconds for the robotic arm to reach the set target point position. (5) Turn on the TMS polling stimulation mode, which will stimulate each target point in turn. (6) Wait for the stimulation to end and collect the EEG signal.

[0062] This application aims to accurately select the optimal TMS stimulation target and the best combination of stimulation parameters using EEG signals. The advantages include: (1) using a robotic arm to collect EEG signals increases operational stability; (2) real-time analysis of EEG signals allows for real-time adjustment of the target and parameters, achieving dynamic optimization of the stimulation effect; and (3) by comparing EEG characteristic indicators under different target and stimulation parameter combinations, the target and stimulation parameter combinations that produce the most significant stimulation effect can be identified. Stimulation parameters include, but are not limited to, stimulation intensity, stimulation frequency, horizontal rotation angle, coil lifting height, normal angle, and target distance.

[0063] Figure 2 This is a flowchart illustrating the optimization method for stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, as provided in the second embodiment of the present invention. Figure 2 As shown in the embodiment of the present invention, the method for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm includes:

[0064] S201. Obtain the EEG signal corresponding to multiple preset stimulation parameter combinations for at least one target point; wherein, the EEG signal corresponding to each stimulation parameter combination for each target point is obtained by stimulating the subject's brain with a stimulation device carried by a robotic arm based on each stimulation parameter combination for each target point.

[0065] Specifically, by using a robotic arm carrying a stimulation device to stimulate each target point in the subject's brain based on each preset stimulation parameter combination for each target point, the electroencephalogram (EEG) signal corresponding to each preset stimulation parameter combination for each target point can be obtained. Multiple preset stimulation parameter combinations for each target point can be acquired. In practical applications, there can be one or multiple target points, selected according to actual needs; this embodiment of the invention does not impose limitations.

[0066] The target points are selected based on practical experience or generated according to existing algorithms, and this embodiment of the invention does not impose any limitations. Multiple target points can be selected for each target region to evaluate the impact of different stimulation points on brain activity in the target region. Target regions include, but are not limited to, M1 and DLPFC. The distance between any two target points must be greater than a set threshold.

[0067] The preset stimulation parameter combinations include, but are not limited to, combinations of stimulation parameters such as stimulation intensity, stimulation frequency, horizontal rotation angle, coil lift height, normal angle, and target distance. These can be selected as needed, and the embodiments of this invention do not limit the selection. The stimulation intensity can be set to multiple different values, such as 80% of the resting motor threshold (RMT), 100% of the RMT, and 120% of the RMT, to explore the effects of different stimulation intensities on EEG activity. The horizontal rotation angle refers to the horizontal angle of the tangent plane to the target normal. The horizontal rotation angle can be set at equal intervals between 0 and 360 degrees to evaluate the differences in the stimulation effect of different angles. For example, an angle can be selected every 30 degrees, for a total of 12 horizontal rotation angles. The coil lift height refers to the distance between the TMS coil and the electrode. The coil lift height affects both artifact generation and electric field density. Multiple different coil lift heights can be set, such as 9cm, 10cm, and 11cm.

[0068] Understandably, the acquisition of EEG signals corresponding to multiple preset stimulation parameter combinations for each target point can be performed either by acquiring the EEG signals corresponding to each preset stimulation parameter combination for each target point one by one for parameter optimization analysis, or by acquiring the EEG signals corresponding to multiple preset stimulation parameter combinations for each target point in real time for parameter optimization analysis.

[0069] S202. Perform multi-stage noise reduction on the EEG signals corresponding to each preset stimulation parameter combination of each target point to obtain the effective EEG signal corresponding to each preset stimulation parameter combination of each target point.

[0070] Specifically, for each target point, the EEG signal corresponding to each preset stimulation parameter combination for that target point is denoised in stages to reduce interference such as artifacts, low-frequency noise, and high-frequency noise in the EEG signal, thereby obtaining the effective EEG signal corresponding to each preset stimulation parameter combination for the target point. Because multi-stage denoising of the EEG signal yields more accurate EEG signals, it is beneficial to improve the accuracy of subsequent preset stimulation parameter combination selection.

[0071] S203. Extract features from the effective EEG signals corresponding to each preset stimulation parameter combination of each target point to obtain the EEG feature indexes corresponding to each preset stimulation parameter combination of each target point.

[0072] Specifically, for each target point, features can be extracted from the effective EEG signals corresponding to each preset stimulation parameter combination of the target point to obtain EEG feature indicators corresponding to each preset stimulation parameter combination of the target point. These EEG feature indicators include, but are not limited to, time-domain features, time-frequency domain features, and source-level features. Time-domain features include, for example, TMS evoked potentials (TEPs), local and global average field power; time-frequency domain features include, for example, event-related spectral perturbation (ERSP), and inter-trial coherence; source-level features include, for example, significant current density and phase synchronization factor. The EEG feature indicators are selected according to actual needs, and this embodiment of the invention does not impose limitations.

[0073] For example, the TEP feature extraction process is as follows: TMS data from multiple trials of effective EEG signals corresponding to preset stimulation parameter combinations for the target are superimposed and averaged; positive and negative peaks in the TEP waveform are identified from the superimposed and averaged data, and the latency and amplitude of the positive and negative peaks are recorded. Superimposed averaging enhances the identifiability of evoked potentials in the signal. Latency refers to the time from the start of stimulation to the appearance of a specific peak, and amplitude refers to the voltage difference between a specific peak and the baseline. Specific peaks are positive and negative peaks.

[0074] For example, the ERSP feature extraction process is as follows: For each trial of the effective EEG signal corresponding to the preset stimulation parameter combination of the target, a short-time Fourier analysis is performed within a defined time window to obtain the time-frequency representation of the current window; the baseline level of the power spectrum is calculated during the baseline period before stimulation, and the power spectrum after stimulation is compared with the baseline power spectrum to eliminate the influence of the baseline power; for each time-frequency point, the difference between the power spectrum after stimulation and the baseline power spectrum is calculated to obtain the ERSP map; the time-frequency variation features are extracted from the ERSP map, such as the frequency range of power increase or decrease, and the changes of time windows or specific frequency bands at different time points.

[0075] S204. Based on the EEG characteristic indicators and parameter screening conditions corresponding to each preset stimulation parameter combination for each target point, obtain the optimal target point combination and the optimal stimulation parameter combination for each target point.

[0076] Specifically, for each target, the EEG characteristic indicators corresponding to each preset stimulation parameter combination of the target are determined to meet the parameter selection criteria. If any one of the preset stimulation parameter combinations of the target meets the parameter selection criteria, the target is retained; if none of the preset stimulation parameter combinations of the target meet the parameter selection criteria, the target is discarded. If only one preset stimulation parameter combination of the target meets the parameter selection criteria, that preset stimulation parameter combination is taken as the optimal stimulation parameter combination for that target. If multiple preset stimulation parameter combinations of the target meet the parameter selection criteria, the EEG characteristic indicators corresponding to these multiple preset stimulation parameter combinations are compared, and the preset stimulation parameter combination with the most significant stimulation effect is selected as the optimal stimulation parameter combination for the target. The larger the value of the EEG characteristic indicator, the more significant the stimulation effect.

[0077] After all targets have been screened, the remaining targets constitute the optimal target combination. Each target has a set of optimal stimulation parameter combinations. The parameter screening conditions are preset and set based on practical experience; this embodiment of the invention does not impose limitations on them.

[0078] For example, if there is only one EEG characteristic indicator, then the parameter selection condition is that the EEG characteristic indicator is greater than the set threshold.

[0079] For example, if there are multiple EEG characteristic indicators, then the parameter selection condition is that each EEG characteristic indicator is greater than the corresponding set threshold.

[0080] The stimulation parameter optimization method for EEG signal acquisition provided in this invention obtains EEG signals corresponding to multiple preset stimulation parameter combinations for at least one target point. The EEG signal corresponding to each stimulation parameter combination for each target point is obtained by stimulating the subject's brain with a stimulation device carried by a robotic arm based on each stimulation parameter combination for each target point. Noise is removed from the EEG signals corresponding to each preset stimulation parameter combination for each target point to obtain the effective EEG signal for each preset stimulation parameter combination for each target point. Feature extraction is performed on the effective EEG signals corresponding to each preset stimulation parameter combination for each target point to obtain EEG feature indicators for each preset stimulation parameter combination for each target point. Based on the EEG feature indicators corresponding to each preset stimulation parameter combination for each target point and parameter selection conditions, the optimal target point combination and the optimal stimulation parameter combination for each target point are obtained, improving the reliability of obtaining the target point and the optimal stimulation parameter combination.

[0081] Figure 3 This is a flowchart illustrating the method for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, as provided in the third embodiment of the present invention. Figure 3 As shown, based on the above embodiments, further, the step of performing multi-stage denoising on the EEG signals corresponding to each preset stimulation parameter combination for each target point to obtain the effective EEG signal corresponding to each preset stimulation parameter combination for each target point includes:

[0082] S301. Based on the EEG signal corresponding to the preset stimulation parameter combination of the target, obtain the TMS pulse time of the current trial corresponding to the preset stimulation parameter combination.

[0083] Specifically, pulse detection is performed on the EEG signal corresponding to the preset stimulation parameter combination of the target point to obtain the TMS pulse time of the current trial corresponding to the preset stimulation parameter combination.

[0084] For example, the TMS pulse for the current trial can be identified from the EEG signal corresponding to the preset stimulation parameter combination of the target point using the Findpulse algorithm, and the time point of the TMS pulse for the current trial can be determined as the TMS pulse time for the current trial. Based on the Findpulse algorithm, let P be a single channel (e.g., C3) extracted from the EEG signal, and P(t) be the signal value of that channel at time point t. Calculate the first derivative of that channel: P'(t) = dP(t) / dt, where P'(t) represents the instantaneous rate of change of the signal at time point t. For all time points t, compare P'(t) with a threshold θ. If P'(t) > θ, then mark the time point corresponding to P'(t) greater than θ as the TMS pulse emission time t. TMS This means obtaining the TMS pulse for the current trial. A TMS pulse is detected at time t. TMS Subsequently, a refractory period is introduced, during which a custom time interval (trefract) is waited. During this period, even if the rate of change of the EEG signal P'(t) exceeds the threshold θ again, it will not be recognized as a new TMS pulse emission moment. Only after this refractory period has elapsed, if the rate of change of the signal P'(t) exceeds θ again, will the next TMS pulse emission moment be detected and marked. This refractory period mechanism avoids misidentifying some spurious pulses caused by signal fluctuations as genuine TMS pulses, thereby improving the accuracy and reliability of pulse detection.

[0085] S302. Based on the TMS pulse time of the current trial corresponding to the preset stimulation parameter combination, extract and segment the EEG signal corresponding to the preset stimulation parameter combination of the target point and perform baseline correction to obtain the EEG change signal corresponding to the preset stimulation parameter combination.

[0086] Specifically, taking the TMS pulse time of the current trial corresponding to the preset stimulation parameter combination as the center, the EEG segments of the EEG signal corresponding to the preset stimulation parameter combination of the target point are extracted according to a determined time window. Then, baseline correction is performed on each EEG segment to obtain the EEG change signal corresponding to the preset stimulation parameter combination. The specific processes of segment extraction and baseline correction are existing technologies and will not be described in detail here.

[0087] S303. Perform a first-stage artifact removal and data filling on the EEG change signal corresponding to the preset stimulation parameter combination to obtain the first intermediate data corresponding to the preset stimulation parameter combination.

[0088] Specifically, after obtaining the EEG change signal corresponding to the preset stimulation parameter combination of the target point, a first-stage artifact removal is performed on the EEG change signal corresponding to the preset stimulation parameter combination of the target point. Then, the data after artifact removal is filled to obtain the first intermediate data corresponding to the preset stimulation parameter combination of the target point. The first-stage artifact removal can remove TMS pulse artifacts and the muscle artifacts they cause from the EEG change signal.

[0089] The electromagnetic pulses from TMS can produce brief but significant artifacts in EEG signals, including TMS pulse artifacts and TMS-induced muscle artifacts. The amplitude of TMS pulse artifacts is typically hundreds of times higher than that of normal EEG signals, while the amplitude of TMS-induced muscle artifacts is tens of times higher. These artifacts are usually impossible to remove using conventional artifact removal algorithms. Therefore, this application directly discards data within a certain period before and after stimulation to reduce the impact of these artifacts. For example, the data segment from 2 ms before stimulation to 12 ms after stimulation is directly discarded.

[0090] For example, the Removedata algorithm can be used to discard data within a preset time period to achieve the first stage of artifact removal. The preset time period is set based on practical experience, such as 2ms before stimulation to 12ms after stimulation; this embodiment of the invention does not impose a limitation. The time point t before the pulse begins is obtained. i , and t i After a preset time period, at point t i+1 , time point t i To t i+1 The data between them is replaced with 0, which can be represented as EEG(t)=0, t=[t i ,t i+1 Then, a cubic interpolation algorithm is used to interpolate the time point t. i To t i+1The data between the missing data points is filled in. Cubic interpolation algorithms assume a more complex nonlinear relationship between the missing data points and the preceding and following data points, and typically use a cubic polynomial to fit the data. By utilizing information from the preceding and following data points and fitting the curvature of the data, a smoother interpolation effect is achieved. Assuming four known data points (t1, y1), (t2, y2), (t3, y3), (t4, y4), can a cubic polynomial P(t) be found such that P(t) = ... i )=y i This holds true for i = 1, 2, 3, 4. This cubic polynomial can be expressed as P(t) = at. 3 +bt 2 +ct+d, where a, b, c, and d are coefficients obtained by using the least squares method on (t1, y1), (t2, y2), (t3, y3), and (t4, y4).

[0091] S304. Perform a second-stage artifact removal and data filling on the first intermediate data corresponding to the preset stimulus parameter combination to obtain the second intermediate data corresponding to the preset stimulus parameter combination.

[0092] Specifically, by performing a second-stage artifact removal on the first intermediate data corresponding to the preset stimulation parameter combination of the target, and then filling the data after artifact removal, the second intermediate data corresponding to the preset stimulation parameter combination of the target can be obtained.

[0093] For example, the Removedata algorithm can be used to discard data within a preset time period to avoid artifact interference caused by data filling in step S303. Then, Fast Independent Component Analysis (fastICA) is used to perform blind source separation on the first intermediate data corresponding to the preset stimulus parameter combination of the target, obtaining multiple independent source signals. The Componentselect detection algorithm is used to remove artifacts from each independent source signal to obtain second temporary data. Then, a cubic interpolation algorithm is used to interpolate the second temporary data to obtain the second intermediate data corresponding to the preset stimulus parameter combination of the target. Based on the Componentselect detection algorithm: Let IC i To obtain the i-th independent component through fastICA analysis, first calculate the value of each electrode at each IC. i The z-score helps identify anomalous noise: z=(x-μ) / σ, where x is the observed value, μ is the mean, and σ is the standard deviation. By setting different detection thresholds, different artifacts can be detected, including but not limited to TMS-induced muscle artifacts and electromyography artifacts.

[0094] For IC iThe detection of TMS-induced muscle artifacts is achieved by comparing the mean absolute amplitude within the target window (mean_ab_muscle_target) with the mean absolute amplitude (mean_ab_muscle_entire) over the entire time window, with a threshold (muscle_thresh) set. If mean_ab_muscle_target / mean_ab_muscle_entire > muscle_thresh, then the artifact is considered to be IC (intracytoplasmic muscle activity). i It is TMS that induces muscle artifacts.

[0095] For IC i Detection of electromyography artifacts: First, take the natural logarithm of the power spectrum frequency f and power P(f) to obtain the logarithmic frequency log(f) and logarithmic power log(P(f)). Use a linear fitting method to fit the relationship between the logarithmic frequency and the logarithmic power. The fitted linear model is: log(P(f)) = mlog(f) + u, where m is the slope and u is the intercept. If the calculated slope m is greater than the preset threshold muscle_thresh, then IC is considered to be present. i It is an electromyography artifact.

[0096] Even after the first-stage artifact removal in step 303, some TMS-induced muscle artifacts remain, and their amplitudes are still much larger than those of EEG, ECG, and normal EEG components. In fastICA, high-amplitude muscle artifacts may dominate the ICA's decomposition effect, leading to poor separation of other artifacts (such as EEG and ECG). The second-stage artifact removal in step S304 removes larger-amplitude artifacts, such as TMS-induced muscle artifacts, first, thus ensuring more effective separation and reliable removal of other artifacts in step S307.

[0097] S305. Perform bandpass filtering and concave filtering on the second intermediate data corresponding to the preset stimulus parameter combination to obtain the third intermediate data corresponding to the preset stimulus parameter combination.

[0098] Specifically, after obtaining the second intermediate data corresponding to the preset stimulation parameter combination of the target point, bandpass filtering and concave filtering can be applied to the second intermediate data to obtain the third intermediate data corresponding to the preset stimulation parameter combination of the target point. The frequency range of the bandpass filter is 0.01Hz-50Hz. The concave filtering can remove power frequency interference of 50Hz or 60Hz.

[0099] Bandpass filtering and concave filtering should be performed after the second-stage artifact removal. The second-stage artifact removal can remove larger artifacts. Then, bandpass filtering and concave filtering can avoid the generation of new artifacts, that is, avoid introducing new artifacts when filtering high-amplitude data (such as TMS pulse artifacts and muscle artifacts caused by TMS).

[0100] Bandpass filtering filters data to the frequency range of interest. The commonly used filtering range for scalp EEG is 0.01Hz to 50Hz, which helps remove some low-frequency and high-frequency noise interference. Concave filtering is used to remove mains frequency interference; domestically, it's typically 50Hz, while internationally it's usually 60Hz.

[0101] S306. Perform third-stage artifact removal and data filling on the third intermediate data corresponding to the preset stimulation parameter combination to obtain the effective EEG signal corresponding to the preset stimulation parameter combination.

[0102] Specifically, by performing third-stage artifact removal on the third intermediate data corresponding to the preset stimulation parameter combination of the target, and then filling the data after artifact removal, the effective EEG signal corresponding to the preset stimulation parameter combination of the target can be obtained.

[0103] For example, the Removedata algorithm can be used to discard data within a preset time period after the start of the third intermediate data pulse, thus removing some artifacts. Then, Fast Independent Component Analysis (fastICA) is used to perform blind source separation on the third intermediate data corresponding to the preset stimulation parameter combination of the target, obtaining multiple independent source signals. The Componentselect detection algorithm is used to remove artifacts from each independent source signal, obtaining third temporary data. Finally, a cubic interpolation algorithm is used to interpolate the third temporary data, thereby obtaining the effective EEG signal corresponding to the preset stimulation parameter combination of the target.

[0104] For IC i Detection of electrooculography artifacts: This is achieved by comparing the average absolute Z-score (mean_z_score = (ZFP1 + ZFP2) / 2) of two electrodes close to the eye (e.g., Fp1 and Fp2), with a threshold (blink_thresh) set. If the mean_z_score is greater than the blink_thresh, then the electrooculography artifact is considered to be IC. i It is an artifact of electrooculography.

[0105] The specific implementation process of step S306 is similar to that of step S305, except that step S306 will remove smaller artifacts, such as electrooculogram artifacts and electrocardiogram artifacts.

[0106] Based on the above embodiments, the method for optimizing stimulation parameters based on EEG signals acquired by a robotic arm provided in this embodiment of the invention further includes downsampling the first intermediate data corresponding to the preset stimulation parameter combination; correspondingly, the downsampled first intermediate data is subjected to a second stage of artifact removal and data filling to obtain the second intermediate data corresponding to the preset stimulation parameter combination.

[0107] Specifically, the first intermediate data corresponding to the preset stimulation parameter combination of the target point is downsampled to reduce the amount of data. Downsampling yields the downsampled first intermediate data. A second-stage artifact removal and data filling process is then performed on the downsampled first intermediate data to obtain the second intermediate data corresponding to the preset stimulation parameter combination. The downsampling method used is selected according to actual needs, and this embodiment of the invention does not impose any limitations.

[0108] TMS-EEG systems typically require a sampling rate of 5kHz or higher for EEG signals to minimize artifacts caused by TMS pulses. In signal processing, to accelerate data processing, downsampling to 1kHz is necessary to reduce the amount of data.

[0109] Based on the above embodiments, further comprising, after performing third-stage artifact removal and data filling on the third intermediate data corresponding to the preset stimulus parameter combination, the method further includes:

[0110] The first data obtained after the third stage artifact removal and data filling is subjected to whole-brain average rereference, and the second data after whole-brain average rereference is used as the effective EEG signal corresponding to the preset stimulation parameter combination of the target.

[0111] Specifically, after performing third-stage artifact removal and data filling on the third intermediate data corresponding to the preset stimulation parameter combination, first data is obtained; the first data is then subjected to whole-brain average rereference to obtain second data, which is used as the effective EEG signal corresponding to the preset stimulation parameter combination of the target point. Whole-brain average rereference uses the average value of the signals from all electrode channels as a reference signal, subtracting the average value from the signal of each channel. Whole-brain average rereference can enhance the contrast of local EEG activity.

[0112] Figure 4 This is a flowchart illustrating the optimization of stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, as provided in the fourth embodiment of the present invention. Figure 4 As shown, based on the above embodiments, the second artifact removal of the first intermediate data corresponding to the preset stimulation parameter combination of the target point further includes:

[0113] S401. Remove the data of a preset time period from the first intermediate data corresponding to the preset stimulation parameter combination of the target point to obtain the first temporary data corresponding to the preset stimulation parameter combination.

[0114] Specifically, the Removedata algorithm can be used to remove data within a preset time period from the first intermediate data corresponding to the preset stimulation parameter combination of the target, thereby obtaining the first temporary data corresponding to the preset stimulation parameter combination of the target. The data removed within the preset time period is the data filled in step S303. The purpose of removing the data is to avoid artifact interference from the filled data, which would affect the subsequent rapid independent component analysis.

[0115] S402. Perform rapid independent component analysis on the first temporary data corresponding to the preset stimulus parameter combination to obtain multiple independent source signals;

[0116] Specifically, by employing rapid independent component analysis (XICA) to perform blind source separation on the first temporary data corresponding to the preset stimulus parameter combination, multiple independent source signals corresponding to the preset stimulus parameter combination can be obtained. Through XICA, the original, mutually independent source signals can be recovered from the first temporary data corresponding to the preset stimulus parameter combination.

[0117] S403. Remove artifacts from each independent source signal corresponding to the preset stimulus parameter combination to obtain the second temporary data corresponding to the preset stimulus parameter combination.

[0118] Specifically, the Componentselect detection algorithm is used to remove artifacts from each independent source signal corresponding to the preset stimulus parameter combination to obtain the second temporary data corresponding to the preset stimulus parameter combination. Data filling is then performed on the second temporary data to obtain the second intermediate data corresponding to the preset stimulus parameter combination.

[0119] For example, based on the Componentselect detection algorithm: Let IC i To obtain the i-th independent component through fastICA analysis, first calculate the value of each electrode at each IC. i The z-score helps identify anomalous noise: z=(x-μ) / σ, where x is the observed value, μ is the mean, and σ is the standard deviation; by setting different detection thresholds, different artifacts can be detected, including but not limited to electrooculogram and electrocardiogram artifacts.

[0120] Figure 5 This is a flowchart illustrating the optimization of stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, as provided in the fifth embodiment of the present invention. Figure 5As shown, based on the above embodiments, the EEG signal corresponding to the preset stimulation parameter combination of the target point further includes multiple trials of TMS pulses; correspondingly, the method further includes:

[0121] S501. Obtain the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point for each trial.

[0122] Specifically, the EEG signal corresponding to the preset stimulation parameter combination of the target point includes multiple trials of TMS pulses. By downsampling the first intermediate data corresponding to the preset stimulation parameter combination of the target point in each trial, the downsampled first intermediate data corresponding to the preset stimulation parameter combination of the target point in each trial can be obtained.

[0123] S502. Based on the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point in each trial, calculate the root mean square of each channel of the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point in each trial.

[0124] Specifically, based on the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point in each trial, the root mean square of each channel of the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point in each trial can be calculated.

[0125] For example, according to the formula Calculate the root mean square of the j-th channel in the first intermediate data after downsampling, corresponding to the preset stimulation parameter combination of the target point in the k-th trial. k,j,i The value of the i-th sampling point in the j-th channel is included in the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point in the k-th trial. N represents the sampling rate of the signal, and i, j, and k are positive integers, with i less than or equal to N.

[0126] S503. Calculate the root mean square average of each channel of the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point for each trial, and obtain the comprehensive average value corresponding to each trial.

[0127] Specifically, for the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point in each trial, the root mean square average of each channel of the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point in the trial is calculated, and the calculation result of the above average value is used as the comprehensive average value corresponding to the trial.

[0128] For example, according to the formula Calculate the comprehensive average value R corresponding to the k-th trial. k M represents the total number of channels, R k,jThe root mean square of the j-th channel is included in the first intermediate data after downsampling, corresponding to the preset stimulation parameter combination of the target point in the k-th trial, where j is a positive integer and j is less than or equal to M.

[0129] S504. If it is determined that the comprehensive average value corresponding to the trial is greater than the threshold, then the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point of the trial is discarded; wherein, the threshold is preset.

[0130] Specifically, for each trial's overall average value, the overall average value is compared with a threshold. If the overall average value is greater than the threshold, the downsampled first intermediate data corresponding to the preset stimulus parameter combination for the target point in that trial is discarded. If the overall average value is less than or equal to the threshold, the downsampled first intermediate data corresponding to the preset stimulus parameter combination for the target point in that trial is retained. The threshold is preset.

[0131] For example, a threshold T = ω ± 2β can be set for the statistical distribution of the composite average corresponding to each trial, where ω represents the mean of the composite average corresponding to all trials and β represents the standard deviation of the composite average corresponding to all trials.

[0132] When processing data from multiple trials, artifacts caused by large-scale head movements and poor electrode contact are often long-lasting and difficult to predict. Trials containing such artifacts can be discarded using an automatic threshold detection algorithm based on statistical distribution to improve the reliability of parameter optimization.

[0133] Building upon the aforementioned embodiments, the spacing between adjacent target points is further increased to the interval of one electrode channel. By reasonably limiting the distance between adjacent target points, the spatial distribution pattern of the acquired EEG signals becomes clearer, facilitating the analysis of signal sources, propagation paths, and interrelationships, reducing data analysis complexity, and improving the accuracy of interpreting brain activity patterns.

[0134] Figure 6 This is a flowchart illustrating the optimization method for stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, as provided in the sixth embodiment of the present invention. Figure 6 As shown in the embodiment of the present invention, the method for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm includes:

[0135] S601. Obtain the EEG signal corresponding to the current preset stimulation parameter combination of the current target; wherein, the EEG signal corresponding to the current stimulation parameter combination of the current target is obtained by stimulating the subject's brain with a stimulation device carried by a robotic arm based on the current stimulation parameter combination of the current target; there are multiple preset stimulation parameter combinations for the current target;

[0136] Specifically, a robotic arm carrying a stimulation device stimulates the target point in the subject's brain based on a current preset stimulation parameter combination, thereby obtaining the electroencephalogram (EEG) signal corresponding to the current preset stimulation parameter combination for that target point. The current target point is the target point currently undergoing data processing. Multiple preset stimulation parameter combinations are set for each target point to obtain a suitable one. The number of preset stimulation parameter combinations for each target point is set based on practical experience, and this embodiment of the invention does not impose limitations.

[0137] S602. Perform multi-stage noise reduction on the EEG signal corresponding to the current stimulation parameter combination of the current target to obtain the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target.

[0138] Specifically, the EEG signal corresponding to the current preset stimulation parameter combination for the current target is denoised in stages to reduce interference such as artifacts, low-frequency noise, and high-frequency noise in the EEG signal, thereby obtaining the effective EEG signal corresponding to the current preset stimulation parameter combination for the current target. Because the EEG signal is denoised in stages, a more accurate EEG signal can be obtained, which is beneficial to improving the accuracy of subsequent selection of suitable stimulation parameter combinations.

[0139] S603. Extract features from the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target point to obtain the EEG feature index corresponding to the current preset stimulation parameter combination of the current target point.

[0140] Specifically, feature extraction is performed on the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target to obtain the EEG feature index corresponding to the current preset stimulation parameter combination of the current target. The EEG feature index is selected according to actual needs, and this embodiment of the invention does not impose any limitations.

[0141] S604. Traverse the preset stimulation parameter combinations of the current target until a suitable stimulation parameter combination for the current target is obtained based on the EEG characteristic indicators and parameter screening conditions corresponding to the current preset stimulation parameter combination of the current target; or traverse all preset stimulation parameter combinations of the current target without obtaining a suitable stimulation parameter combination for the current target.

[0142] Specifically, it is determined whether the EEG characteristic indicators corresponding to the current preset stimulation parameter combination of the current target meet the parameter screening conditions. If the EEG characteristic indicators corresponding to the current preset stimulation parameter combination of the current target meet the parameter screening conditions, then the current preset stimulation parameter combination of the current target is considered a suitable stimulation parameter combination for the current target. If the EEG characteristic indicators corresponding to the current preset stimulation parameter combination of the current target do not meet the parameter screening conditions, then the current preset stimulation parameter combination of the current target is not a suitable stimulation parameter combination for the current target. If it is determined that the current preset stimulation parameter combination of the current target is a suitable stimulation parameter combination for the current target, it means that a satisfactory EEG signal can be obtained through the suitable stimulation parameter combination of the current target. In order to save the screening time of the current preset stimulation parameter combination, it is not necessary to screen the remaining preset stimulation parameter combinations of the current target. The parameter screening conditions are preset and can be set according to actual needs. This embodiment of the invention does not limit the settings.

[0143] If it is determined that the current preset stimulation parameter combination for the current target is not a suitable stimulation parameter combination for the current target, then the next preset stimulation parameter combination for the current target is obtained as the current preset stimulation parameter combination, and the judgment on whether the EEG feature indicators corresponding to the current preset stimulation parameter combination for the current target meet the parameter screening conditions is repeated. If a suitable stimulation parameter combination for the current target is still not obtained, then the next preset stimulation parameter combination for the current target will be obtained as the current preset stimulation parameter combination, and the judgment on whether the EEG feature indicators corresponding to the current preset stimulation parameter combination for the current target meet the parameter screening conditions will continue, and so on. If a suitable stimulation parameter combination for the current target is obtained, or if all preset stimulation parameter combinations for the current target have been traversed and no suitable stimulation parameter combination for the current target has been obtained, then the optimization process of the preset stimulation parameter combination for the current target will stop.

[0144] S605. If a suitable combination of stimulation parameters for the current target is obtained, or if all preset combinations of stimulation parameters for the current target have been traversed and no suitable combination of stimulation parameters for the current target has been obtained, then the next target is obtained as the current target and the suitable combination of stimulation parameters for the current target is obtained, until all targets have been traversed.

[0145] Specifically, after obtaining a suitable combination of stimulation parameters for the current target or after iterating through all preset combinations of stimulation parameters for the current target, the next target is selected as the current target, and a suitable combination of stimulation parameters for the current target is obtained. When all targets have been traversed, for any given target, either a suitable combination of stimulation parameters is obtained, or no suitable combination of stimulation parameters is obtained after iterating through all preset combinations of stimulation parameters for the target, the process ends. Obtaining a suitable combination of stimulation parameters for the current target is the process described in steps S601 to S604 above.

[0146] The present invention provides a method for optimizing stimulation parameters based on EEG signals acquired by a robotic arm. This method acquires the EEG signal corresponding to the current preset stimulation parameter combination for the current target point; performs multi-stage denoising on the EEG signal corresponding to the current preset stimulation parameter combination for the current target point to obtain the effective EEG signal corresponding to the current preset stimulation parameter combination for the current target point; extracts features from the effective EEG signal corresponding to the current preset stimulation parameter combination for the current target point to obtain EEG feature indicators corresponding to the current preset stimulation parameter combination for the current target point; it iterates through the preset stimulation parameter combinations for the current target point until a suitable stimulation parameter combination for the current target point is obtained based on the EEG feature indicators corresponding to the current preset stimulation parameter combination for the current target point and parameter selection conditions; or it iterates through all preset stimulation parameter combinations for the current target point without obtaining a suitable stimulation parameter combination for the current target point; if a suitable stimulation parameter combination for the current target point is obtained, or if all preset stimulation parameter combinations for the current target point are iterated through without obtaining a suitable stimulation parameter combination for the current target point, then the next target point is acquired as the current target point to obtain a suitable stimulation parameter combination for the current target point, until all targets are traversed. This method can quickly obtain a suitable stimulation parameter combination for the target point and improve parameter optimization efficiency.

[0147] Figure 7 This is a flowchart illustrating the optimization method for stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, as provided in the seventh embodiment of the present invention. Figure 7 As shown, based on the above embodiments, further, the step of performing multi-stage denoising on the EEG signal corresponding to the current stimulation parameter combination of the current target to obtain the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target includes:

[0148] S701. Based on the EEG signal corresponding to the current preset stimulation parameter combination of the current target, obtain the TMS pulse time of the current trial corresponding to the current preset stimulation parameter combination.

[0149] Specifically, pulse detection is performed on the EEG signal corresponding to the current preset stimulation parameter combination for the current target, which can obtain the TMS pulse duration for the current trial corresponding to the current preset stimulation parameter combination for the current target. The specific implementation process of this step is similar to that of step S301, and will not be described in detail here.

[0150] S702. Based on the TMS pulse time of the current trial corresponding to the current preset stimulation parameter combination, extract and segment the EEG signal corresponding to the current preset stimulation parameter combination of the target point and perform baseline correction to obtain the EEG change signal corresponding to the current preset stimulation parameter combination.

[0151] Specifically, taking the TMS pulse time of the current trial corresponding to the current preset stimulation parameter combination as the center, the EEG segments of the EEG signal corresponding to the current preset stimulation parameter combination of the target point are extracted according to a determined time window. Then, baseline correction is performed on each EEG segment to obtain the EEG change signal corresponding to the current preset stimulation parameter combination. The specific implementation process of this step is similar to step S302, and will not be described in detail here.

[0152] S703. Perform first-stage artifact removal and data filling on the EEG change signal corresponding to the current preset stimulation parameter combination to obtain the first intermediate data corresponding to the current preset stimulation parameter combination.

[0153] Specifically, after obtaining the EEG change signal corresponding to the current preset stimulation parameter combination for the current target, a first-stage artifact removal is performed on the EEG change signal corresponding to the current preset stimulation parameter combination for the current target. Then, the data after artifact removal is filled to obtain the first intermediate data corresponding to the current preset stimulation parameter combination for the current target. The first-stage artifact removal removes TMS pulse artifacts and the muscle artifacts they cause from the EEG change signal. The specific implementation process of this step is similar to step S303 and will not be repeated here.

[0154] S704. Perform second-stage artifact removal and data filling on the first intermediate data corresponding to the current preset stimulus parameter combination to obtain the second intermediate data corresponding to the current preset stimulus parameter combination.

[0155] Specifically, a second-stage artifact removal process is performed on the first intermediate data corresponding to the current preset stimulation parameter combination for the current target. Then, the data after artifact removal is filled in to obtain the second intermediate data corresponding to the current preset stimulation parameter combination for the current target. The specific implementation process of this step is similar to that of step S304, and will not be described in detail here.

[0156] S705. Perform bandpass filtering and concave filtering on the second intermediate data corresponding to the current preset stimulus parameter combination to obtain the third intermediate data corresponding to the current preset stimulus parameter combination.

[0157] Specifically, after obtaining the second intermediate data corresponding to the current preset stimulation parameter combination for the current target, bandpass filtering and concave filtering can be applied to the second intermediate data to obtain the third intermediate data corresponding to the current preset stimulation parameter combination for the current target. The specific implementation process of this step is similar to that of step S305, and will not be described in detail here.

[0158] S706. Perform third-stage artifact removal and data filling on the third intermediate data corresponding to the current preset stimulation parameter combination to obtain the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target.

[0159] Specifically, a third-stage artifact removal process is performed on the third intermediate data corresponding to the current preset stimulation parameter combination for the current target. Then, the data after artifact removal is filled in to obtain the effective EEG signal corresponding to the current preset stimulation parameter combination for the current target. The specific implementation process of this step is similar to step S306 and will not be described in detail here.

[0160] Based on the above embodiments, the method for optimizing stimulation parameters based on EEG signals acquired by a robotic arm provided in this embodiment of the invention further includes downsampling the first intermediate data corresponding to the current preset stimulation parameter combination; correspondingly, the downsampled first intermediate data is subjected to a second stage of artifact removal and data filling to obtain the second intermediate data corresponding to the current preset stimulation parameter combination.

[0161] Specifically, the first intermediate data corresponding to the current preset stimulation parameter combination for the current target is downsampled to reduce the amount of data. After downsampling, the downsampled first intermediate data is obtained. A second-stage artifact removal and data filling process is then performed on the downsampled first intermediate data to obtain the second intermediate data corresponding to the current preset stimulation parameter combination. The downsampling method used is selected according to actual needs, and this embodiment of the invention does not impose any limitations.

[0162] TMS-EEG systems typically require a sampling rate of 5kHz or higher for EEG signals to minimize artifacts introduced by TMS pulses. In signal processing, to accelerate data processing, downsampling to 1kHz is necessary to reduce the amount of data. Building upon the above embodiments, after performing a third-stage artifact removal and data padding on the third intermediate data corresponding to the current preset stimulus parameter combination, the system further includes:

[0163] The third data obtained after the third stage artifact removal and data filling is subjected to whole-brain average rereference, and the fourth data after whole-brain average rereference is used as the effective EEG signal corresponding to the current preset stimulation parameter combination of the target.

[0164] Specifically, after performing third-stage artifact removal and data filling on the third intermediate data corresponding to the current preset stimulation parameter combination, third data is obtained. Whole-brain average rereference is then applied to the third data to obtain fourth data, which is used as the effective EEG signal corresponding to the current preset stimulation parameter combination for the current target. Whole-brain average rereference uses the average value of the signals from all electrode channels as a reference signal, subtracting this average value from the signal of each channel. Whole-brain average rereference can enhance the contrast of local EEG activity.

[0165] Figure 8 This is a flowchart illustrating the method for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, as provided in the eighth embodiment of the present invention. Figure 8 As shown, based on the above embodiments, the second artifact removal of the first intermediate data corresponding to the current preset stimulus parameter combination further includes:

[0166] S801. Remove the data of the preset time period from the first intermediate data corresponding to the current preset stimulus parameter combination to obtain the first temporary data corresponding to the current preset stimulus parameter combination.

[0167] Specifically, the Removedata algorithm can be used to remove data within a preset time period from the first intermediate data corresponding to the current preset stimulus parameter combination for the current target, thereby obtaining the first temporary data corresponding to the current preset stimulus parameter combination for the current target. The data within the preset time period that is removed is the data filled in step S703. The purpose of removing the data is to avoid artifact interference from the filled data, which would affect the subsequent rapid independent component analysis.

[0168] S802. Perform rapid independent component analysis on the first temporary data corresponding to the current preset stimulus parameter combination to obtain multiple independent source signals corresponding to the current preset stimulus parameter combination.

[0169] Specifically, by employing rapid independent component analysis (RIA) to perform blind source separation on the first temporary data corresponding to the current preset stimulus parameter combination, multiple independent source signals corresponding to the current preset stimulus parameter combination can be obtained. Through RIA, the original, mutually independent source signals can be recovered from the first temporary data corresponding to the current preset stimulus parameter combination.

[0170] S803. Remove artifacts from each independent source signal corresponding to the current preset stimulus parameter combination to obtain the second temporary data corresponding to the current preset stimulus parameter combination.

[0171] Specifically, the Componentselect detection algorithm is used to remove artifacts from each independent source signal corresponding to the current preset stimulus parameter combination, obtaining the second temporary data corresponding to the current preset stimulus parameter combination. Data filling is then performed on the second temporary data corresponding to the current preset stimulus parameter combination to obtain the second intermediate data corresponding to the current preset stimulus parameter combination. The specific implementation process of this step is similar to step S403, and will not be repeated here.

[0172] Based on the above embodiments, the optimization of stimulation parameters for acquiring EEG signals using a robotic arm, as provided in the embodiments of the present invention, further includes:

[0173] After traversing all preset stimulation parameter combinations for the current target and failing to find a suitable stimulation parameter combination for the current target, a prompt message is issued indicating that no suitable stimulation parameter combination was found for the current target.

[0174] Specifically, if all preset stimulus parameter combinations for the current target have been traversed but no suitable stimulus parameter combination for the current target has been found, it means that there is no suitable stimulus parameter combination among all preset stimulus parameter combinations. A prompt message can be issued that no suitable stimulus parameter combination has been found for the current target, so that relevant personnel can adjust the preset stimulus parameter combination.

[0175] Figure 9 This is a schematic diagram of the structure of the stimulation parameter optimization device based on the acquisition of EEG signals by a robotic arm, provided in the ninth embodiment of the present invention. Figure 9 As shown, the optimization device for stimulation parameters based on EEG signals acquired by a robotic arm provided in this embodiment of the invention includes a first acquisition module 901, a first denoising module 902, a first feature extraction module 903, and a screening module 904, wherein:

[0176] The first acquisition module 901 is used to acquire EEG signals corresponding to multiple preset stimulation parameter combinations for at least one target point; wherein, the EEG signal corresponding to each stimulation parameter combination for each target point is acquired by stimulating the subject's brain with a stimulation device carried by a robotic arm based on each stimulation parameter combination for each target point; the first denoising module 902 is used to perform multi-stage denoising on the EEG signals corresponding to each preset stimulation parameter combination for each target point to obtain the effective EEG signal corresponding to each preset stimulation parameter combination for each target point; the first feature extraction module 903 is used to extract features from the effective EEG signals corresponding to each preset stimulation parameter combination for each target point to obtain the EEG feature index corresponding to each preset stimulation parameter combination for each target point; the screening module 904 is used to obtain the optimal target point combination and the optimal stimulation parameter combination for each target point based on the EEG feature index corresponding to each preset stimulation parameter combination for each target point and the parameter screening conditions.

[0177] Specifically, by using a robotic arm carrying a stimulation device to stimulate each target point in the subject's brain based on each preset stimulation parameter combination for each target point, the electroencephalogram (EEG) signal corresponding to each preset stimulation parameter combination for each target point can be obtained. The first acquisition module 901 can acquire the EEG signals corresponding to multiple preset stimulation parameter combinations for each target point. In practical applications, there can be one target point or multiple target points, selected according to actual needs; this embodiment of the invention does not impose any limitations.

[0178] For each target, the first denoising module 902 performs staged denoising on the EEG signal corresponding to each preset stimulation parameter combination for the target to reduce interference such as artifacts, low-frequency noise, and high-frequency noise in the EEG signal, thereby obtaining the effective EEG signal corresponding to each preset stimulation parameter combination for the target. Because multi-stage denoising of the EEG signal can obtain a more accurate EEG signal, it is beneficial to improve the accuracy of subsequent preset stimulation parameter combination selection.

[0179] For each target point, the first feature extraction module 903 can extract features from the effective EEG signal corresponding to each preset stimulation parameter combination of the target point, obtaining EEG feature indicators corresponding to each preset stimulation parameter combination of the target point. The EEG feature indicators include, but are not limited to, time-domain features, time-frequency domain features, and source-level features. The EEG feature indicators are selected according to actual needs, and this embodiment of the invention does not impose any limitations.

[0180] For each target, the screening module 904 determines whether the EEG characteristic indicators corresponding to each preset stimulation parameter combination of the target meet the parameter screening conditions. If any one of the preset stimulation parameter combinations of the target meets the parameter screening conditions, the target is retained; if none of the preset stimulation parameter combinations of the target meet the parameter screening conditions, the target is discarded. If only one preset stimulation parameter combination of the target meets the parameter screening conditions, that preset stimulation parameter combination is taken as the optimal stimulation parameter combination for that target. If multiple preset stimulation parameter combinations of the target meet the parameter screening conditions, the EEG characteristic indicators corresponding to these multiple preset stimulation parameter combinations can be compared, and the preset stimulation parameter combination with the most significant stimulation effect can be selected as the optimal stimulation parameter combination for the target. The larger the value of the EEG characteristic indicator, the more significant the stimulation effect.

[0181] The present invention provides an optimization device for stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm. This device acquires EEG signals corresponding to multiple preset stimulation parameter combinations for at least one target point. The EEG signal corresponding to each stimulation parameter combination for each target point is acquired by stimulating the subject's brain using a stimulation device carried by a robotic arm based on each stimulation parameter combination for each target point. Noise is removed from the EEG signals corresponding to each preset stimulation parameter combination for each target point to obtain the effective EEG signal. Feature extraction is performed on the effective EEG signals corresponding to each preset stimulation parameter combination for each target point to obtain EEG feature indicators. Based on the EEG feature indicators corresponding to each preset stimulation parameter combination for each target point and parameter selection conditions, the optimal target point combination and the optimal stimulation parameter combination for each target point are obtained, improving the reliability of obtaining the target point and the optimal stimulation parameter combination.

[0182] Figure 10 This is a schematic diagram of the structure of the stimulation parameter optimization device based on the acquisition of EEG signals by a robotic arm, as provided in the tenth embodiment of the present invention. Figure 10 As shown, based on the above embodiments, the first noise reduction module 902 further includes a first acquisition unit 90201, a second acquisition unit 90202, a first artifact removal unit 90203, a second artifact removal unit 90204, a first filtering unit 90205, and a third artifact removal unit 90206, wherein:

[0183] The first acquisition unit 90201 is used to obtain the TMS pulse duration of the current trial corresponding to the preset stimulation parameter combination based on the EEG signal corresponding to the preset stimulation parameter combination of the target point; the second acquisition unit 90202 is used to extract and segment the EEG signal corresponding to the preset stimulation parameter combination of the target point and perform baseline correction based on the TMS pulse duration of the current trial corresponding to the preset stimulation parameter combination, to obtain the EEG change signal corresponding to the preset stimulation parameter combination; the first artifact removal unit 90203 is used to perform a first-stage artifact removal and data filling on the EEG change signal corresponding to the preset stimulation parameter combination, to obtain the preset stimulation parameter combination. The first intermediate data is obtained by performing a second-stage artifact removal and data filling on the first intermediate data corresponding to the preset stimulus parameter combination, and obtaining the second intermediate data corresponding to the preset stimulus parameter combination; the first filtering unit 90205 performs bandpass filtering and concave filtering on the second intermediate data corresponding to the preset stimulus parameter combination, and obtaining the third intermediate data corresponding to the preset stimulus parameter combination; the third artifact removal unit 90206 performs a third-stage artifact removal and data filling on the third intermediate data corresponding to the preset stimulus parameter combination, and obtains the effective EEG signal corresponding to the preset stimulus parameter combination.

[0184] Figure 11 This is a schematic diagram of the structure of the stimulation parameter optimization device based on the acquisition of EEG signals by a robotic arm according to the eleventh embodiment of the present invention, as shown below. Figure 11 As shown, based on the above embodiments, the second artifact removal unit 90204 further includes a first removal subunit 902041, a first analysis subunit 902042, and a first removal subunit 902043, wherein:

[0185] The first removal subunit 902041 is used to remove data within a preset time period from the first intermediate data corresponding to the preset stimulation parameter combination of the target to obtain the first temporary data corresponding to the preset stimulation parameter combination; the first analysis subunit 902042 is used to perform rapid independent component analysis on the first temporary data corresponding to the preset stimulation parameter combination to obtain multiple independent source signals corresponding to the preset stimulation parameter combination; the first removal subunit 902043 is used to remove artifacts from each independent source signal corresponding to the preset stimulation parameter combination to obtain the second temporary data corresponding to the preset stimulation parameter combination.

[0186] Figure 12 This is a schematic diagram of the structure of the stimulation parameter optimization device based on the acquisition of EEG signals by a robotic arm, as provided in the twelfth embodiment of the present invention. Figure 12 As shown, based on the above embodiments, the first denoising module 902 further includes a whole-brain average rereference unit 90207, wherein:

[0187] The first whole-brain average rereference unit 90207 is used to perform whole-brain average rereference on the first data obtained after the third-stage artifact removal and data filling, and to use the second data after whole-brain average rereference as the effective EEG signal corresponding to the preset stimulation parameter combination of the target.

[0188] Figure 13 This is a schematic diagram of the structure of the stimulation parameter optimization device based on the acquisition of EEG signals by a robotic arm according to the thirteenth embodiment of the present invention, as shown below. Figure 13 As shown, based on the above embodiments, the first noise reduction module 902 further includes a first downsampling unit 90208, wherein:

[0189] The first downsampling unit 90208 is used to downsample the first intermediate data corresponding to the preset stimulus parameter combination; correspondingly, the second artifact removal unit 90204 is specifically used to perform a second-stage artifact removal and data filling on the downsampled first intermediate data to obtain the second intermediate data corresponding to the preset stimulus parameter combination.

[0190] Based on the above embodiments, further, the EEG signal corresponding to the preset stimulation parameter combination of the target point includes multiple trials of TMS pulses; correspondingly, as Figure 14 As shown, the first denoising module 902 further includes an acquisition unit 90209, a first calculation unit 90210, a second calculation unit 90211, and a judgment unit 90212, wherein:

[0191] The acquisition unit 90209 is used to acquire the downsampled first intermediate data corresponding to the preset stimulation parameter combination of the target point for each trial; the first calculation unit 90210 is used to calculate the root mean square of each channel of the downsampled first intermediate data corresponding to the preset stimulation parameter combination of the target point for each trial based on the downsampled first intermediate data corresponding to the preset stimulation parameter combination of the target point for each trial; the second calculation unit 90211 is used to calculate the average value of the root mean square of each channel of the downsampled first intermediate data corresponding to the preset stimulation parameter combination of the target point for each trial, and obtain the comprehensive average value corresponding to each trial; the judgment unit 90212 is used to discard the downsampled first intermediate data corresponding to the preset stimulation parameter combination of the target point for the trial if it is determined that the comprehensive average value corresponding to the trial is greater than a threshold; wherein, the threshold is preset.

[0192] Based on the above embodiments, the spacing between adjacent target points is further greater than the interval of an electrode channel.

[0193] Figure 15This is a schematic diagram of the structure of the stimulation parameter optimization device based on the acquisition of EEG signals by a robotic arm, as provided in the fifteenth embodiment of the present invention. Figure 15 As shown, the optimization device for stimulation parameters based on EEG signals acquired by a robotic arm provided in this embodiment of the invention includes a second acquisition module 1501, a second denoising module 1502, a second feature extraction module 1503, a first traversal module 1504, and a second traversal module 1505, wherein:

[0194] The second acquisition module 1501 is used to acquire the EEG signal corresponding to the current preset stimulation parameter combination of the current target point; wherein, the EEG signal corresponding to the current stimulation parameter combination of the current target point is acquired by a robotic arm carrying a stimulation device to stimulate the subject's brain based on the current stimulation parameter combination of the current target point; there are multiple preset stimulation parameter combinations for the current target point; the second denoising module 1502 is used to perform multi-stage denoising on the EEG signal corresponding to the current stimulation parameter combination of the current target point to obtain the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target point; the second feature extraction module 1503 is used to extract features from the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target point to obtain the current preset stimulation parameter combination of the current target point. Let there be EEG characteristic indicators corresponding to the stimulation parameter combinations; the first traversal module 1504 is used to traverse the preset stimulation parameter combinations of the current target until a suitable stimulation parameter combination for the current target is obtained based on the EEG characteristic indicators corresponding to the current preset stimulation parameter combination of the current target and the parameter screening conditions; or after traversing all preset stimulation parameter combinations of the current target and no suitable stimulation parameter combination for the current target is obtained; the second traversal module 1505 is used to obtain the next target as the current target if a suitable stimulation parameter combination for the current target is obtained or after traversing all preset stimulation parameter combinations of the current target and no suitable stimulation parameter combination for the current target is obtained, until all targets are traversed.

[0195] Specifically, a robotic arm carrying a stimulation device stimulates the target point in the subject's brain based on a current preset stimulation parameter combination, thereby obtaining the electroencephalogram (EEG) signal corresponding to the current preset stimulation parameter combination for the current target point. The second acquisition module 1501 acquires the EEG signal corresponding to the current preset stimulation parameter combination for the current target point. The current target point is the target point currently undergoing data processing. Multiple preset stimulation parameter combinations are set for each target point to obtain a suitable one. The number of preset stimulation parameter combinations for each target point is set based on practical experience, and this embodiment of the invention does not impose limitations.

[0196] The second denoising module 1502 performs staged denoising on the EEG signal corresponding to the current preset stimulation parameter combination for the current target point, in order to reduce interference such as artifacts, low-frequency noise, and high-frequency noise in the EEG signal, and obtain the effective EEG signal corresponding to the current preset stimulation parameter combination for the current target point. Since the EEG signal is denoised in stages, a more accurate EEG signal can be obtained, which is beneficial to improving the accuracy of subsequent selection of suitable stimulation parameter combinations.

[0197] The second feature extraction module 1503 extracts features from the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target point, thereby obtaining the EEG feature index corresponding to the current preset stimulation parameter combination of the current target point. The EEG feature index is selected according to actual needs, and this embodiment of the invention does not impose any limitations.

[0198] The first iteration module 1504 determines whether the EEG characteristic indicators corresponding to the current preset stimulation parameter combination of the current target point meet the parameter screening conditions. If the EEG characteristic indicators corresponding to the current preset stimulation parameter combination of the current target point meet the parameter screening conditions, then the current preset stimulation parameter combination of the current target point is considered a suitable stimulation parameter combination for the current target point. If the EEG characteristic indicators corresponding to the current preset stimulation parameter combination of the current target point do not meet the parameter screening conditions, then the current preset stimulation parameter combination of the current target point is not a suitable stimulation parameter combination for the current target point. If it is determined that the current preset stimulation parameter combination of the current target point is a suitable stimulation parameter combination for the current target point, it means that a satisfactory EEG signal can be obtained through the suitable stimulation parameter combination of the current target point. In order to save the screening time of the current target point's preset stimulation parameter combination, it is not necessary to screen the remaining preset stimulation parameter combinations of the current target point. The parameter screening conditions are preset and can be set according to actual needs. This embodiment of the invention does not limit the settings.

[0199] In the first iteration of module 1504, if it is determined that the current preset stimulus parameter combination for the current target is not a suitable stimulus parameter combination for the current target, then the next preset stimulus parameter combination for the current target is obtained as the current preset stimulus parameter combination, and the judgment on whether the EEG characteristic indicators corresponding to the current preset stimulus parameter combination for the current target meet the parameter selection conditions is repeated. If a suitable stimulus parameter combination for the current target is still not obtained, then the next preset stimulus parameter combination for the current target will be obtained as the current preset stimulus parameter combination, and the judgment on whether the EEG characteristic indicators corresponding to the current preset stimulus parameter combination for the current target meet the parameter selection conditions will continue, and so on. If a suitable stimulus parameter combination for the current target is obtained, or if all preset stimulus parameter combinations for the current target have been iterated and no suitable stimulus parameter combination for the current target has been obtained, then the optimization process of the preset stimulus parameter combination for the current target will stop.

[0200] After obtaining a suitable combination of stimulus parameters for the current target or after traversing all preset combination of stimulus parameters for the current target, the second traversal module 1505 will acquire the next target as the current target and proceed to acquire a suitable combination of stimulus parameters for that target. When all targets have been traversed, for any given target, either a suitable combination of stimulus parameters is obtained, or no suitable combination of stimulus parameters is found after traversing all preset combination of stimulus parameters for the target, the process ends.

[0201] The stimulation parameter optimization device based on the acquisition of EEG signals by a robotic arm provided in this embodiment of the invention acquires the EEG signal corresponding to the current preset stimulation parameter combination of the current target point, performs multi-stage denoising on the EEG signal corresponding to the current preset stimulation parameter combination of the current target point to obtain the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target point, performs feature extraction on the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target point to obtain the EEG feature index corresponding to the current preset stimulation parameter combination of the current target point, and determines whether the current preset stimulation parameter combination of the current target point is a suitable stimulation parameter for the target point based on the EEG feature index corresponding to the current preset stimulation parameter combination of the current target point and parameter screening conditions. The algorithm iterates through the preset stimulation parameter combinations for the current target until a suitable stimulation parameter combination for the current target is obtained based on the EEG characteristic indicators and parameter selection conditions corresponding to the current preset stimulation parameter combination; or it iterates through all preset stimulation parameter combinations for the current target without obtaining a suitable stimulation parameter combination for the current target; if a suitable stimulation parameter combination for the current target is obtained or if all preset stimulation parameter combinations for the current target are iterated through without obtaining a suitable stimulation parameter combination for the current target, then the next target is obtained as the current target to obtain a suitable stimulation parameter combination for the current target, until all targets are iterated through, so as to obtain a suitable stimulation parameter combination for the target as quickly as possible and improve the efficiency of parameter optimization.

[0202] Figure 16 This is a schematic diagram of the structure of the stimulation parameter optimization device based on the acquisition of EEG signals by a robotic arm, as provided in the sixteenth embodiment of the present invention. Figure 16 As shown, based on the above embodiments, the second noise reduction module 1502 further includes a third acquisition unit 150201, a fourth acquisition unit 150202, a fourth artifact removal unit 150203, a fifth artifact removal unit 150204, a second filtering unit 150205, and a sixth artifact removal unit 150206, wherein:

[0203] The third acquisition unit 150201 is used to obtain the TMS pulse duration of the current trial corresponding to the current preset stimulation parameter combination based on the EEG signal corresponding to the current target point and the current preset stimulation parameter combination; the fourth acquisition unit 150202 is used to extract and segment the EEG signal corresponding to the current preset stimulation parameter combination of the target point and perform baseline correction based on the TMS pulse duration of the current trial corresponding to the current preset stimulation parameter combination, thereby obtaining the EEG change signal corresponding to the current preset stimulation parameter combination; the fourth artifact removal unit 150203 is used to perform a first-stage artifact removal and data filling on the EEG change signal corresponding to the current preset stimulation parameter combination, thereby obtaining the current preset stimulation parameter combination. The first intermediate data is obtained from the first intermediate data corresponding to the current preset stimulation parameter combination; the fifth artifact removal unit 150204 is used to perform second-stage artifact removal and data filling on the first intermediate data corresponding to the current preset stimulation parameter combination to obtain the second intermediate data corresponding to the current preset stimulation parameter combination; the second filtering unit 150205 is used to perform bandpass filtering and concave filtering on the second intermediate data corresponding to the current preset stimulation parameter combination to obtain the third intermediate data corresponding to the current preset stimulation parameter combination; the sixth artifact removal unit 150206 is used to perform third-stage artifact removal and data filling on the third intermediate data corresponding to the current preset stimulation parameter combination to obtain the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target.

[0204] Figure 17 This is a schematic diagram of the structure of the stimulation parameter optimization device based on the acquisition of EEG signals by a robotic arm, as provided in the seventeenth embodiment of the present invention. Figure 17 As shown, based on the above embodiments, the fifth artifact removal unit 150204 further includes a second removal subunit 1502041, a second analysis subunit 1502042, and a second removal subunit 1502043, wherein:

[0205] The second removal subunit 1502041 is used to remove data of a preset time period from the first intermediate data corresponding to the current preset stimulus parameter combination to obtain the first temporary data corresponding to the current preset stimulus parameter combination; the second analysis subunit 1502042 is used to perform rapid independent component analysis on the first temporary data corresponding to the current preset stimulus parameter combination to obtain multiple independent source signals corresponding to the current preset stimulus parameter combination; the second removal subunit 1502043 is used to remove artifacts from each independent source signal corresponding to the current preset stimulus parameter combination to obtain the second temporary data corresponding to the current preset stimulus parameter combination.

[0206] Figure 18 This is a schematic diagram of the structure of the stimulation parameter optimization device based on the acquisition of EEG signals by a robotic arm, as provided in the eighteenth embodiment of the present invention. Figure 18As shown, based on the above embodiments, the second noise reduction module 1502 further includes a second downsampling unit 150207, wherein:

[0207] The second downsampling unit 150207 is used to downsample the first intermediate data corresponding to the current preset stimulus parameter combination; correspondingly, the fifth artifact removal unit 150204 is used to perform second-stage artifact removal and data filling on the downsampled first intermediate data to obtain the second intermediate data corresponding to the current preset stimulus parameter combination.

[0208] Figure 19 This is a schematic diagram of the structure of the stimulation parameter optimization device based on the acquisition of EEG signals by a robotic arm, as provided in the nineteenth embodiment of the present invention. Figure 19 As shown, based on the above embodiments, the second denoising module 1502 further includes a second whole-brain average rereference unit 150208, wherein:

[0209] The second whole-brain average rereference unit 150208 is used to perform whole-brain average rereference on the third data obtained after the third stage artifact removal and data filling, and to use the fourth data after whole-brain average rereference as the effective EEG signal corresponding to the current preset stimulation parameter combination of the target.

[0210] Figure 20 This is a schematic diagram of the structure of the stimulation parameter optimization device based on the acquisition of EEG signals by a robotic arm according to the twentieth embodiment of the present invention, as shown below. Figure 20 As shown, based on the above embodiments, the device for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, provided in this embodiment of the invention, further includes an output module 1506, wherein:

[0211] The issuing module 1506 is used to issue a prompt message that no suitable stimulus parameter combination was found for the current target after traversing all preset stimulus parameter combinations for the current target and no suitable stimulus parameter combination for the current target has been found.

[0212] The embodiments of the device provided in this invention can be used to execute the processing flow of the above-described method embodiments. Its functions will not be repeated here, but can be referred to the detailed description of the above-described method embodiments.

[0213] Figure 21 This is a schematic diagram of the physical structure of the computer device provided in the seventeenth embodiment of the present invention, as shown below. Figure 21 As shown, the computer device 600 may include a processor 100 and a memory 140. The memory 140 is coupled to the processor 100. The processor 100 may invoke logical instructions in the memory 140 to execute the methods provided in the above-described method embodiments.

[0214] This embodiment discloses a computer program product, which includes a computer program / instructions stored on a computer-readable storage medium. When the computer program / instructions are executed by a computer, the computer is able to perform the methods provided in the above-described method embodiments.

[0215] This embodiment provides a computer-readable storage medium that stores a computer program / instruction. When the computer program / instruction is executed by a processor, it causes the computer to perform the methods provided in the above-described method embodiments.

[0216] like Figure 21 As shown, the computer device 600 may also include: a communication module 110, an input unit 120, an audio processor 130, a display 160, and a power supply 170. It is worth noting that the computer device 600 does not necessarily need to include these components. Figure 21 All components shown; in addition, computer device 600 may also include Figure 21 For components not shown in the figure, refer to existing technologies. It is worth noting that this figure is exemplary; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0217] like Figure 21 As shown, the processor 100, sometimes also referred to as a controller or operation control, may include a microprocessor or other processor device and / or logic device. The processor 100 receives input and controls the operation of various components of the computer device 600.

[0218] The memory 140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The processor 100 may execute the program stored in the memory 140 to perform information storage or processing, etc.

[0219] Input unit 120 provides input to processor 100. Input unit 120 may be, for example, a keypad or touch input device. Power supply 170 provides power to computer device 600. Display 160 displays images and text. Display 160 may be, for example, an LCD display, but is not limited thereto.

[0220] Memory 140 can be solid-state memory, such as read-only memory (ROM), random access memory (RAM), SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of memory 140 are sometimes referred to as EPROM, etc. Memory 140 can also be some other type of device. Memory 140 includes a buffer 141 (sometimes referred to as buffer memory). Memory 140 may include an application / function storage unit 142 for storing application programs and function programs or processes for executing operations of the computer device 600 via the processor 100.

[0221] The memory 140 may also include a data storage unit 143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the computer device. The driver storage unit 144 of the memory 140 may include various drivers for the computer device for communication functions and / or for performing other functions of the computer device (such as messaging applications, address book applications, etc.).

[0222] The communication module 110 includes a transmitter / receiver that transmits and receives signals via antenna 111. The communication module 110 is coupled to processor 100 to provide input signals and receive output signals, which can be the same as in a conventional mobile communication terminal.

[0223] Based on different communication technologies, multiple communication modules 110 can be configured in the same computer device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132, thereby realizing typical telecommunications functions. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 130 is coupled to the processor 100, enabling on-device recording via the microphone 132 and on-device playback of stored sound via the speaker 131.

[0224] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0225] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0226] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0227] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0228] In the description of this specification, the references to terms such as "an embodiment," "a specific embodiment," "some embodiments," "for example," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0229] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, characterized in that, include: The brain signals corresponding to multiple preset stimulation parameter combinations for at least one target point are acquired; wherein, the brain signals corresponding to each stimulation parameter combination for each target point are acquired by stimulating the subject's brain with a stimulation device carried by a robotic arm based on each stimulation parameter combination for each target point. Multi-stage denoising was performed on the EEG signals corresponding to each preset stimulation parameter combination for each target point to obtain the effective EEG signal corresponding to each preset stimulation parameter combination for each target point. Feature extraction is performed on the effective EEG signal corresponding to each preset stimulation parameter combination for each target point to obtain the EEG feature index corresponding to each preset stimulation parameter combination for each target point. Based on the EEG characteristic indicators and parameter screening conditions corresponding to each preset stimulation parameter combination for each target, the optimal target combination and the optimal stimulation parameter combination for each target are obtained. The step of performing multi-stage denoising on the EEG signals corresponding to each preset stimulation parameter combination for each target point to obtain the effective EEG signal corresponding to each preset stimulation parameter combination for each target point includes: Based on the EEG signal corresponding to the preset stimulation parameter combination of the target, the TMS pulse duration of the current trial corresponding to the preset stimulation parameter combination is obtained. Based on the TMS pulse time of the current trial corresponding to the preset stimulation parameter combination, the EEG signal corresponding to the preset stimulation parameter combination of the target point is extracted, segmented and baseline corrected to obtain the EEG change signal corresponding to the preset stimulation parameter combination. The EEG change signals corresponding to the preset stimulation parameter combination are subjected to first-stage artifact removal and data filling to obtain the first intermediate data corresponding to the preset stimulation parameter combination. The first intermediate data corresponding to the preset stimulus parameter combination is subjected to a second stage of artifact removal and data filling to obtain the second intermediate data corresponding to the preset stimulus parameter combination. Bandpass filtering and concave filtering are applied to the second intermediate data corresponding to the preset stimulus parameter combination to obtain the third intermediate data corresponding to the preset stimulus parameter combination. The third intermediate data corresponding to the preset stimulation parameter combination is subjected to third-stage artifact removal and data filling to obtain the effective EEG signal corresponding to the preset stimulation parameter combination.

2. The method according to claim 1, characterized in that, The second-stage artifact removal of the first intermediate data corresponding to the preset stimulus parameter combination includes: Remove data within a preset time period from the first intermediate data corresponding to the preset stimulation parameter combination of the target point to obtain the first temporary data corresponding to the preset stimulation parameter combination; Perform rapid independent component analysis on the first temporary data corresponding to the preset stimulus parameter combination to obtain multiple independent source signals corresponding to the preset stimulus parameter combination; Remove artifacts from each independent source signal corresponding to the preset stimulus parameter combination to obtain the second temporary data corresponding to the preset stimulus parameter combination.

3. The method according to claim 1, characterized in that, After performing third-stage artifact removal and data filling on the third intermediate data corresponding to the preset stimulus parameter combination, the method further includes: The first data obtained after the third stage artifact removal and data filling is subjected to whole-brain average rereference, and the second data after whole-brain average rereference is used as the effective EEG signal corresponding to the preset stimulation parameter combination of the target.

4. The method according to claim 1, characterized in that, It also includes downsampling the first intermediate data corresponding to the preset stimulus parameter combination; accordingly, the downsampled first intermediate data is subjected to a second stage of artifact removal and data filling to obtain the second intermediate data corresponding to the preset stimulus parameter combination.

5. The method according to any one of claims 1 to 4, characterized in that, The electroencephalogram (EEG) signal corresponding to the preset stimulation parameter combination of the target point includes multiple trials of TMS pulses; correspondingly, the method further includes: Obtain the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point for each trial; Based on the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point in each trial, calculate the root mean square of each channel of the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point in each trial. Calculate the root mean square average of each channel of the first intermediate data after downsampling for each trial corresponding to the preset stimulation parameter combination of the target point, and obtain the comprehensive average value corresponding to each trial. If it is determined that the comprehensive average value corresponding to the trial is greater than a threshold, then the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point of the trial is discarded; wherein, the threshold is preset.

6. A method for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, characterized in that, include: The EEG signal corresponding to the current preset stimulation parameter combination of the current target is acquired; wherein, the EEG signal corresponding to the current stimulation parameter combination of the current target is acquired by a robotic arm carrying a stimulation device to stimulate the subject's brain based on the current stimulation parameter combination of the current target; there are multiple preset stimulation parameter combinations for the current target; Multi-stage denoising is performed on the EEG signal corresponding to the current combination of stimulation parameters for the current target to obtain the effective EEG signal corresponding to the current preset combination of stimulation parameters for the current target. Feature extraction is performed on the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target to obtain the EEG feature index corresponding to the current preset stimulation parameter combination of the current target. Iterate through the preset stimulation parameter combinations of the current target until a suitable stimulation parameter combination for the current target is obtained based on the EEG feature indicators corresponding to the current preset stimulation parameter combination and the parameter screening conditions; or iterate through all preset stimulation parameter combinations of the current target without obtaining a suitable stimulation parameter combination for the current target. If a suitable combination of stimulation parameters for the current target is obtained, or if all preset combinations of stimulation parameters for the current target have been traversed and no suitable combination of stimulation parameters for the current target has been obtained, then the next target is obtained as the current target and the suitable combination of stimulation parameters for the current target is obtained, until all targets have been traversed. The step of performing multi-stage denoising on the EEG signal corresponding to the current combination of stimulation parameters for the current target to obtain the effective EEG signal corresponding to the current preset combination of stimulation parameters for the current target includes: Based on the EEG signal corresponding to the current preset stimulation parameter combination for the current target, the TMS pulse duration for the current trial corresponding to the current preset stimulation parameter combination is obtained. Based on the TMS pulse time of the current trial corresponding to the current preset stimulation parameter combination, the EEG signal corresponding to the current preset stimulation parameter combination of the target point is extracted, segmented and baseline corrected to obtain the EEG change signal corresponding to the current preset stimulation parameter combination. The first stage artifact removal and data filling are performed on the EEG change signals corresponding to the current preset stimulus parameter combination to obtain the first intermediate data corresponding to the current preset stimulus parameter combination. The second stage artifact removal and data filling are performed on the first intermediate data corresponding to the current preset stimulus parameter combination to obtain the second intermediate data corresponding to the current preset stimulus parameter combination. Bandpass filtering and concave filtering are applied to the second intermediate data corresponding to the current preset stimulus parameter combination to obtain the third intermediate data corresponding to the current preset stimulus parameter combination. The third stage artifact removal and data filling are performed on the third intermediate data corresponding to the current preset stimulation parameter combination to obtain the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target.

7. The method according to claim 6, characterized in that, The second artifact removal process for the first intermediate data corresponding to the current preset stimulus parameter combination includes: Remove the data of the preset time period from the first intermediate data corresponding to the current preset stimulus parameter combination to obtain the first temporary data corresponding to the current preset stimulus parameter combination; Perform rapid independent component analysis on the first temporary data corresponding to the current preset stimulus parameter combination to obtain multiple independent source signals corresponding to the current preset stimulus parameter combination; Remove artifacts from each independent source signal corresponding to the current preset stimulus parameter combination to obtain the second temporary data corresponding to the current preset stimulus parameter combination.

8. The method according to claim 6, characterized in that, It also includes downsampling the first intermediate data corresponding to the current preset stimulus parameter combination; accordingly, the downsampled first intermediate data is subjected to a second stage of artifact removal and data filling to obtain the second intermediate data corresponding to the current preset stimulus parameter combination.

9. The method according to claim 6, characterized in that, After performing third-stage artifact removal and data filling on the third intermediate data corresponding to the current preset stimulus parameter combination, the process also includes: The third data obtained after the third stage artifact removal and data filling is subjected to whole-brain average rereference, and the fourth data after whole-brain average rereference is used as the effective EEG signal corresponding to the current preset stimulation parameter combination of the target.

10. The method according to claim 6, characterized in that, Also includes: After traversing all preset stimulation parameter combinations for the current target and failing to find a suitable stimulation parameter combination for the current target, a prompt message is issued indicating that no suitable stimulation parameter combination was found for the current target.

11. A device for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, characterized in that, include: The first acquisition module is used to acquire EEG signals corresponding to multiple preset stimulation parameter combinations for at least one target point; wherein, the EEG signal corresponding to each stimulation parameter combination for each target point is acquired by a robotic arm carrying a stimulation device to stimulate the subject's brain based on each stimulation parameter combination for each target point. The first denoising module is used to perform multi-stage denoising on the EEG signals corresponding to each preset stimulation parameter combination of each target point to obtain the effective EEG signal corresponding to each preset stimulation parameter combination of each target point. The first feature extraction module is used to extract features from the effective EEG signal corresponding to each preset stimulation parameter combination of each target point, and obtain the EEG feature index corresponding to each preset stimulation parameter combination of each target point. The screening module is used to obtain the optimal target combination and the optimal stimulation parameter combination for each target based on the EEG characteristic indicators corresponding to each preset stimulation parameter combination for each target and the parameter screening conditions. The first noise reduction module includes: The first obtaining unit is used to obtain the TMS pulse duration of the current trial corresponding to the preset stimulation parameter combination based on the EEG signal corresponding to the preset stimulation parameter combination of the target. The second obtaining unit is used to extract and segment the EEG signal corresponding to the preset stimulation parameter combination of the target point and perform baseline correction based on the TMS pulse time of the current trial corresponding to the preset stimulation parameter combination, so as to obtain the EEG change signal corresponding to the preset stimulation parameter combination. The first artifact removal unit is used to perform a first-stage artifact removal and data filling on the EEG change signal corresponding to the preset stimulus parameter combination to obtain the first intermediate data corresponding to the preset stimulus parameter combination. The second artifact removal unit is used to perform a second-stage artifact removal and data filling on the first intermediate data corresponding to the preset stimulus parameter combination to obtain the second intermediate data corresponding to the preset stimulus parameter combination. The first filtering unit is used to perform bandpass filtering and concave filtering on the second intermediate data corresponding to the preset stimulus parameter combination to obtain the third intermediate data corresponding to the preset stimulus parameter combination. The third artifact removal unit is used to perform third-stage artifact removal and data filling on the third intermediate data corresponding to the preset stimulus parameter combination to obtain the effective EEG signal corresponding to the preset stimulus parameter combination.

12. A device for optimizing stimulation parameters based on electroencephalogram (EEG) signals acquired by a robotic arm, characterized in that, include: The second acquisition module is used to acquire the EEG signal corresponding to the current preset stimulation parameter combination of the current target point; wherein, the EEG signal corresponding to the current stimulation parameter combination of the current target point is acquired by a robotic arm carrying a stimulation device to stimulate the subject's brain based on the current stimulation parameter combination of the current target point; there are multiple preset stimulation parameter combinations for the current target point; The second denoising module is used to perform multi-stage denoising on the EEG signal corresponding to the current stimulation parameter combination of the current target point to obtain the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target point. The second feature extraction module is used to extract features from the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target point, and obtain the EEG feature index corresponding to the current preset stimulation parameter combination of the current target point. The first traversal module is used to traverse the preset stimulation parameter combinations of the current target until a suitable stimulation parameter combination for the current target is obtained based on the EEG feature indicators and parameter screening conditions corresponding to the current preset stimulation parameter combination; or after traversing all preset stimulation parameter combinations for the current target without obtaining a suitable stimulation parameter combination for the current target. The second traversal module is used to obtain the next target as the current target if a suitable combination of stimulation parameters for the current target is obtained, or if all preset combinations of stimulation parameters for the current target have been traversed but no suitable combination of stimulation parameters for the current target has been obtained, until all targets have been traversed. The second noise reduction module includes: The third acquisition unit is used to obtain the TMS pulse time of the current trial corresponding to the current preset stimulation parameter combination based on the EEG signal corresponding to the current target point and the current preset stimulation parameter combination. The fourth obtaining unit is used to extract and segment the EEG signal corresponding to the current preset stimulation parameter combination of the target point and perform baseline correction based on the TMS pulse time of the current trial corresponding to the current preset stimulation parameter combination, so as to obtain the EEG change signal corresponding to the current preset stimulation parameter combination. The fourth artifact removal unit is used to perform first-stage artifact removal and data filling on the EEG change signal corresponding to the current preset stimulus parameter combination to obtain the first intermediate data corresponding to the current preset stimulus parameter combination. The fifth artifact removal unit is used to perform second-stage artifact removal and data filling on the first intermediate data corresponding to the current preset stimulus parameter combination to obtain the second intermediate data corresponding to the current preset stimulus parameter combination. The second filtering unit is used to perform bandpass filtering and concave filtering on the second intermediate data corresponding to the current preset stimulus parameter combination to obtain the third intermediate data corresponding to the current preset stimulus parameter combination. The sixth artifact removal unit is used to perform third-stage artifact removal and data filling on the third intermediate data corresponding to the current preset stimulus parameter combination, so as to obtain the effective EEG signal corresponding to the current preset stimulus parameter combination of the current target.

13. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method according to any one of claims 1 to 11.

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