Stimulation parameter optimization method and device based on electroencephalogram signals acquired by mechanical arm

Abstract

The invention provides a stimulation parameter optimization method and device based on electroencephalogram signals acquired by a mechanical arm. The method comprises the following steps: acquiring electroencephalogram signals corresponding to a plurality of preset stimulation parameter combinations of at least one target spot; performing multi-stage denoising on the electroencephalogram signal corresponding to each preset stimulation parameter combination of each target point to obtain a corresponding effective electroencephalogram signal; performing feature extraction on the effective electroencephalogram signal corresponding to each preset stimulation parameter combination of each target spot to obtain an electroencephalogram feature index corresponding to each preset stimulation parameter combination of each target spot; and obtaining an optimal target combination and an optimal stimulation parameter combination of each target according to the electroencephalogram characteristic index corresponding to each preset stimulation parameter combination of each target and the parameter screening condition. The device is used for executing the method. According to the stimulation parameter optimization method and device based on the electroencephalogram signal collected by the mechanical arm, the reliability of obtaining the combination of the target spot and the optimal stimulation parameter is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image processing, and in particular to a method and device for optimizing stimulation parameters for collecting electroencephalogram signals based on a mechanical arm. Background Art

[0002] EEG signals can be detected on the surface of the scalp. They reflect the functional status and information processing process of different brain regions and can be applied in many fields such as clinical diagnosis, neuroscience research, psychology and cognitive science research.

[0003] EEG signals can be collected through specific equipment and methods. EEG signal collection equipment of appropriate size, such as EEG caps and EEG brackets, can be worn by the subject, and the sites in the cerebral cortex that need to be stimulated can be selected. Through transcranial magnetic stimulation (TMS) technology, the pulsed magnetic field generated by the TMS coil is used to generate local currents in specific areas to stimulate brain activity, and then the electrical activity induced by the magnetic field is collected to obtain EEG signals. In order to collect EEG signals, it is necessary to set stimulation parameters such as target position, stimulation intensity, and coil lifting height. The setting of the above stimulation parameters will affect the accuracy of the target EEG signal collection. Therefore, how to optimize the stimulation parameters for collecting EEG signals to obtain more accurate EEG signals has become an important issue that needs to be solved in this field. Summary of the invention

[0004] In view of the problems in the prior art, the embodiments of the present invention provide a method and device for optimizing stimulation parameters based on collecting EEG signals by a robotic arm, which can at least partially solve the above problems.

[0005] In a first aspect, a method for optimizing stimulation parameters based on collecting electroencephalogram signals by a robotic arm comprises:

[0006] Acquire EEG signals corresponding to multiple preset stimulation parameter combinations of at least one target point; wherein the EEG signals corresponding to each stimulation parameter combination of each target point are acquired by stimulating the brain of the subject based on each stimulation parameter combination of each target point by carrying a stimulation device on a robotic arm;

[0007] Perform multi-stage denoising on the EEG signals corresponding to each preset stimulation parameter combination of each target point, and obtain the effective EEG signals corresponding to each preset stimulation parameter combination of each target point;

[0008] Extract features of effective EEG signals corresponding to each preset stimulation parameter combination of each target point, and obtain EEG feature indicators corresponding to each preset stimulation parameter combination of each target point;

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

[0010] In a second aspect, the present invention further proposes a method for optimizing stimulation parameters based on collecting EEG signals by a robotic arm, comprising:

[0011] Obtaining an electroencephalogram signal corresponding to a current preset stimulation parameter combination of a current target; wherein the electroencephalogram signal corresponding to the current stimulation parameter combination of the current target is obtained by stimulating the brain of a subject based on the current stimulation parameter combination of the current target by a stimulation device carried by a robotic arm; there are multiple preset stimulation parameter combinations of the current target;

[0012] Perform 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;

[0013] Extract features of effective EEG signals corresponding to the current preset stimulation parameter combination of the current target, and obtain EEG feature indicators corresponding to the current preset stimulation parameter combination of the current target;

[0014] Traversing the preset stimulation parameter combinations of the current target until a suitable stimulation parameter combination of the current target is obtained based on the EEG characteristic index corresponding to the current preset stimulation parameter combination of the current target and the parameter screening condition; or traversing all the preset stimulation parameter combinations of the current target and failing to obtain a suitable stimulation parameter combination of the current target;

[0015] If a suitable stimulation parameter combination for the current target is obtained or all preset stimulation parameter combinations for the current target are traversed and no suitable stimulation parameter combination for the current target is obtained, the next target is obtained as the current target to obtain a suitable stimulation parameter combination for the current target until all targets are traversed.

[0016] In a third aspect, the present invention provides a device for optimizing stimulation parameters of electroencephalogram signals collected by a mechanical arm, comprising:

[0017] A first acquisition module is used to acquire EEG signals corresponding to a plurality of preset stimulation parameter combinations of at least one target point; wherein the EEG signal corresponding to each stimulation parameter combination of each target point is acquired by stimulating the brain of the subject based on each stimulation parameter combination of each target point by carrying a stimulation device on a robotic arm;

[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, so as to obtain the effective EEG signals corresponding to each preset stimulation parameter combination of each target point;

[0019] A first feature extraction module is used to extract features of effective EEG signals corresponding to each preset stimulation parameter combination of each target point, and obtain EEG feature indicators 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 and parameter screening conditions corresponding to each preset stimulation parameter combination of each target.

[0021] In a fourth aspect, the present invention provides a device for optimizing stimulation parameters based on collecting electroencephalogram signals by a mechanical 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; wherein the EEG signal corresponding to the current stimulation parameter combination of the current target is acquired by stimulating the brain of the subject based on the current stimulation parameter combination of the current target by a stimulation device carried by a robotic arm; there are multiple preset stimulation parameter combinations of the current target;

[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, so as to obtain the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target;

[0024] The second feature extraction module is used to extract features of 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 of the current target is obtained based on the EEG characteristic index corresponding to the current preset stimulation parameter combination of the current target and the parameter screening condition; or all preset stimulation parameter combinations of the current target are traversed and no suitable stimulation parameter combination of the current target is obtained;

[0026] The second traversal module is used to obtain the next target as the current target to obtain the appropriate stimulation parameter combination for the current target if a suitable stimulation parameter combination for the current target is obtained or all preset stimulation parameter combinations for the current target are traversed and no suitable stimulation parameter combination for the current target is obtained, until all targets are traversed.

[0027] In a fifth aspect, the present invention provides a computer device comprising 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 collecting electroencephalographic signals by a robotic arm as described in any of the above embodiments.

[0028] The embodiment of the present invention provides a method and device for optimizing stimulation parameters based on collecting EEG signals by a robotic arm, and obtains EEG signals corresponding to multiple preset stimulation parameter combinations of at least one target; wherein the EEG signal corresponding to each stimulation parameter combination of each target is obtained by stimulating and collecting the brain of a subject based on each stimulation parameter combination of each target by carrying a stimulation device on a robotic arm; the EEG signals corresponding to each preset stimulation parameter combination of each target are denoised respectively to obtain effective EEG signals corresponding to each preset stimulation parameter combination of each target; feature extraction is performed on the effective EEG signals corresponding to each preset stimulation parameter combination of each target to obtain EEG feature indicators corresponding to each preset stimulation parameter combination of each target; based on the EEG feature indicators corresponding to each preset stimulation parameter combination of each target and parameter screening conditions, the optimal target combination and the optimal stimulation parameter combination of each target are obtained, thereby improving the reliability of obtaining the target and the optimal stimulation parameter combination. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0030] Figure 1 It is a structural schematic diagram of the EEG signal acquisition system provided by the first embodiment of the present invention.

[0031] Figure 2 It is a flowchart of a method for optimizing stimulation parameters based on collecting EEG signals by a robotic arm provided in the second embodiment of the present invention.

[0032] Figure 3 It is a flowchart of a method for optimizing stimulation parameters based on collecting EEG signals by a robotic arm provided in the third embodiment of the present invention.

[0033] Figure 4 It is a flowchart of a method for optimizing stimulation parameters based on collecting EEG signals by a robotic arm provided in the fourth embodiment of the present invention.

[0034] Figure 5 It is a flowchart of a method for optimizing stimulation parameters based on collecting EEG signals by a robotic arm provided in the fifth embodiment of the present invention.

[0035] Figure 6 It is a flowchart of a method for optimizing stimulation parameters based on collecting EEG signals by a robotic arm provided in the sixth embodiment of the present invention.

[0036] Figure 7It is a flowchart of a method for optimizing stimulation parameters based on collecting EEG signals by a robotic arm provided in the seventh embodiment of the present invention.

[0037] Figure 8 It is a flowchart of a method for optimizing stimulation parameters based on collecting EEG signals by a robotic arm provided in the eighth embodiment of the present invention.

[0038] Fig. 9 It is a schematic diagram of the structure of a device for optimizing stimulation parameters based on collecting electroencephalographic signals by a robotic arm provided in the ninth embodiment of the present invention.

[0039] Fig.10 It is a structural schematic diagram of a device for optimizing stimulation parameters based on collecting electroencephalogram signals by a robotic arm provided in the tenth embodiment of the present invention.

[0040] Fig.11 It is a structural schematic diagram of a device for optimizing stimulation parameters based on collecting electroencephalogram signals by a robotic arm provided in the eleventh embodiment of the present invention.

[0041] Fig.12 It is a structural schematic diagram of a device for optimizing stimulation parameters based on collecting electroencephalogram signals by a robotic arm provided in the twelfth embodiment of the present invention.

[0042] Fig.13 It is a structural schematic diagram of a device for optimizing stimulation parameters based on collecting electroencephalogram signals by a robotic arm provided in the thirteenth embodiment of the present invention.

[0043] Fig.14 It is a structural schematic diagram of a device for optimizing stimulation parameters based on collecting electroencephalogram signals by a robotic arm provided in the fourteenth embodiment of the present invention.

[0044] Fig.15 It is a schematic diagram of the structure of a device for optimizing stimulation parameters based on collecting electroencephalographic signals by a robotic arm provided in the fifteenth embodiment of the present invention.

[0045] Fig.16 It is a structural schematic diagram of a device for optimizing stimulation parameters based on collecting electroencephalogram signals by a robotic arm provided in the sixteenth embodiment of the present invention.

[0046] Fig.17 It is a structural schematic diagram of a device for optimizing stimulation parameters based on collecting electroencephalographic signals by a robotic arm provided in the seventeenth embodiment of the present invention.

[0047] Fig.18 It is a schematic diagram of the structure of a device for optimizing stimulation parameters based on collecting electroencephalogram signals by a robotic arm provided in the eighteenth embodiment of the present invention.

[0048] Fig.19 It is a structural schematic diagram of a device for optimizing stimulation parameters based on collecting electroencephalographic signals by a robotic arm provided in the nineteenth embodiment of the present invention.

[0049] Fig. 20 It is a structural schematic diagram of a device for optimizing stimulation parameters based on collecting electroencephalographic signals by a robotic arm provided in the twentieth embodiment of the present invention.

[0050] Fig.21 It is a schematic diagram of the physical structure of a computer device provided in the twenty-first embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention. It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other arbitrarily.

[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, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0053] In order to facilitate understanding of the technical solution provided by this application, the relevant contents of the technical solution of this application are first explained below.

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

[0055] Transcranial Magnetic Stimulation (TMS) is a non-invasive and painless green treatment technology. The magnetic signal can penetrate the skull without attenuation and directly act on the cerebral cortex. In practical applications, the application scope of 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 EEG, the determination of electrode position usually relies on an internationally recognized standard system, which can ensure the comparability of results between different studies and clinical experiments. The commonly used standard system is the 10-20 system, which determines the position of the electrode through specific marking points. The following are the basic steps to determine the position of the electrode: first, calibrate the reference point of the head; second, measure the size of the head to ensure that the appropriate size of EEG acquisition equipment such as EEG caps, electrode patches, EEG brackets, etc. are worn; then use the 10-20 system or other standardized systems to determine the position of each electrode; finally, fine-tune the electrodes as needed. In current technical practices, the wearing 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 is a schematic diagram of the structure of the EEG signal acquisition system provided by the first embodiment of the present invention. Figure 1 As shown, the EEG signal acquisition system provided by the embodiment of the present invention includes a computer 1, a robot 2, a stimulation device 3, a plurality of acquisition electrodes 4 and a navigation device 5, wherein:

[0058] The computer 1 is respectively connected with the robot 2, the stimulation device 3, the plurality of collection electrodes 4 and the navigation device 5;

[0059] A plurality of acquisition electrodes 4 are arranged on the subject's head, and the plurality of acquisition electrodes 4 can be arranged in the EEG cap to facilitate the subject to wear. The robot 2 includes a control device and a mechanical arm, and the control device controls the mechanical arm to carry the stimulation device 3 to stimulate the target point of the subject's brain; the stimulation device 3 can use a TMS coil, and the navigation device 5 is used to obtain the position information of each electrode and the position information of the stimulation device. The computer 1 constructs a 3D model of the subject's head based on the image data of the subject's brain, and obtains the subject's EEG signal. The navigation device 5 includes infrared cameras, color cameras, structured light cameras and other equipment.

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

[0061] (1) Select the target point that the subject wants to stimulate. You can click on the 3D model to select it, or you can enter the MRI coordinates of the target point and save them after selecting the coordinates; (2) The subject wears the EEG cap with the assistance of the tester and then sits within the field of view of the navigation device; (3) Set the robot arm parameters in the controller, enter the EEG cap height, enter the TMS stimulation intensity, enter the TMS stimulation interval, and enter the number of TMS stimulations; (4) Turn on the robot arm following mode and wait for 2-3 seconds for the robot arm to reach the set target position; (5) Turn on the TMS polling stimulation mode to stimulate each target point in turn; (6) Wait for the stimulation to end and collect EEG signals.

[0062] This application aims to accurately select the best TMS stimulation target and the optimal stimulation parameter combination through EEG signals. The advantages include: (1) Using a robotic arm to collect EEG signals can increase the stability of the operation; (2) By analyzing EEG signals in real time, the target and parameters can be adjusted in real time to achieve dynamic optimization of the stimulation effect; (3) By comparing the EEG characteristic indicators under different targets and different stimulation parameter combinations, the target and stimulation parameter combination that produces 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, target distance, etc.

[0063] Figure 2 is a flow chart of a method for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm provided in a second embodiment of the present invention, such as Figure 2 As shown, the method for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm provided in an embodiment of the present invention includes:

[0064] S201, obtaining EEG signals corresponding to a plurality of preset stimulation parameter combinations of at least one target; wherein the EEG signals corresponding to each stimulation parameter combination of each target are obtained by stimulating the brain of the subject based on each stimulation parameter combination of each target by carrying a stimulation device on a robotic arm;

[0065] Specifically, by stimulating each target point in the subject's brain based on each preset stimulation parameter combination of each target point through the stimulation device carried by the robotic arm, an electroencephalogram signal corresponding to each preset stimulation parameter combination of each target point can be obtained. Electroencephalogram signals corresponding to multiple preset stimulation parameter combinations of each target point can be obtained. In actual applications, the target point can be one or more, and the selection is made according to actual needs, and the embodiment of the present invention does not limit this.

[0066] Among them, each target point is selected according to actual experience or generated according to an existing algorithm, which is not limited in the embodiment of the present invention. Multiple target points can be selected for each target area to evaluate the effects of different stimulation points on brain activity in the target area. The target area includes but is not limited to M1, DLPFC, etc. The distance between any two target points is greater than the set threshold.

[0067] Among them, the preset stimulation parameter combination includes but is not limited to the combination of stimulation parameters such as stimulation intensity, stimulation frequency, horizontal rotation angle, coil lifting height, normal angle, target distance, etc., which are selected according to needs and are not limited in the embodiments of the present invention. The stimulation intensity can be 80% of the resting motor threshold (RMT), 100% RMT, 120% RMT and other different values ​​to explore the effects of different stimulation intensities on EEG activity; the horizontal rotation angle refers to the horizontal angle of the target normal tangent plane, and the horizontal rotation angle can be set at equal intervals between 0-360 degrees to evaluate the difference in stimulation effects at different angles, such as selecting an angle every 30 degrees, for a total of 12 horizontal rotation angles; the coil lifting height refers to the distance between the TMS coil and the electrode. The coil lifting height will affect both the artifact generation and the electric field density. Multiple different coil lifting heights can be set, such as 9cm, 10cm and 11cm.

[0068] It can be understood that the collection of EEG signals corresponding to multiple preset stimulation parameter combinations of each target point can be to collect the EEG signals corresponding to each preset stimulation parameter combination of each target point one by one for parameter optimization analysis, or to collect the EEG signals corresponding to multiple preset stimulation parameter combinations of each target point in real time and perform parameter optimization analysis in real time.

[0069] S202, performing multi-stage denoising on the EEG signals corresponding to each preset stimulation parameter combination of each target point, to obtain a valid 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 of the target point is denoised in stages 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 each preset stimulation parameter combination of the target point. Since the EEG signal is denoised at multiple stages, a more accurate EEG signal can be obtained, which is conducive to improving the accuracy of subsequent screening of preset stimulation parameter combinations.

[0071] S203, extracting features of effective EEG signals corresponding to each preset stimulation parameter combination of each target point, and obtaining EEG feature indicators corresponding to each preset stimulation parameter combination of each target point;

[0072] Specifically, for each target point, the effective EEG signal corresponding to each preset stimulation parameter combination of the target point can be feature extracted to obtain the EEG characteristic index corresponding to each preset stimulation parameter combination of the target point. The EEG characteristic index includes but is not limited to time domain features, time-frequency domain features, source level features, etc. Time domain features such as TMS evoked potentials (TEP for short), local and global average field power; time-frequency domain features such as event-related spectral perturbations (ERSP for short), inter-trial coherence; source level features such as significant current density and phase synchronization factor. The EEG characteristic index is selected according to actual needs, and the embodiment of the present invention does not limit it.

[0073] For example, the TEP feature extraction process is as follows: the TMS data of multiple trials of the effective EEG signal corresponding to the preset stimulation parameter combination of the target point are superimposed and averaged; the 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. Among them, superimposed averaging can enhance the identifiability of evoked potentials in the signal. The latency refers to the time from the start of stimulation to the appearance of a specific peak, and the amplitude refers to the voltage difference between a specific peak and the baseline. The specific peak is the positive and negative peak.

[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 point, short-time Fourier analysis is performed within the 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 baseline power; for each time-frequency point, the difference between the power spectrum after stimulation and the baseline power spectrum is calculated to obtain an ERSP graph; the characteristics of time-frequency changes are extracted from the ERSP graph, such as the frequency range of power increase or decrease, the time window or the changes in specific frequency bands at different time points.

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

[0076] Specifically, for each target, it is determined whether the EEG characteristic indicators corresponding to each preset stimulation parameter combination of the target meet the parameter screening conditions. If any of the EEG characteristic indicators corresponding to each preset stimulation parameter combination of the target meets the parameter screening conditions, the target will be retained; if none of the EEG characteristic indicators corresponding to each preset stimulation parameter combination of the target meets the parameter screening conditions, the target will be discarded. If only one of the EEG characteristic indicators corresponding to each preset stimulation parameter combination of the target meets the parameter screening conditions, then the preset stimulation parameter combination is used as the optimal stimulation parameter combination for the target. If there are multiple EEG characteristic indicators corresponding to multiple preset stimulation parameter combinations that meet the parameter screening conditions, then the EEG characteristic indicators corresponding to the multiple preset stimulation parameter combinations that meet the parameter screening conditions can be compared, and a 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.

[0077] After all the targets are screened, the remaining targets constitute the optimal target combination. Each target obtains a set of optimal stimulation parameter combinations. The parameter screening conditions are preset and set according to actual experience, and are not limited in the embodiments of the present invention.

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

[0079] For example, if there are multiple EEG characteristic indicators, then the parameter screening 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 by the embodiment of the present invention obtains EEG signals corresponding to multiple preset stimulation parameter combinations of at least one target; wherein the EEG signal corresponding to each stimulation parameter combination of each target is obtained by stimulating and acquiring the brain of a subject based on each stimulation parameter combination of each target by a robotic arm carrying a stimulation device; the EEG signals corresponding to each preset stimulation parameter combination of each target are denoised respectively to obtain effective EEG signals corresponding to each preset stimulation parameter combination of each target; feature extraction is performed on the effective EEG signals corresponding to each preset stimulation parameter combination of each target to obtain EEG feature indicators corresponding to each preset stimulation parameter combination of each target; based on the EEG feature indicators corresponding to each preset stimulation parameter combination of each target and the parameter screening conditions, the optimal target combination and the optimal stimulation parameter combination of each target are obtained, thereby improving the reliability of obtaining the target and the optimal stimulation parameter combination.

[0081] Figure 3 is a flow chart of a method for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm provided in a third embodiment of the present invention, such as Figure 3 As shown, on the basis of the above embodiments, further, the EEG signals corresponding to each preset stimulation parameter combination of each target point are subjected to multi-stage denoising respectively, and the effective EEG signals corresponding to each preset stimulation parameter combination of each target point are obtained, including:

[0082] S301, based on the EEG signal corresponding to the preset stimulation parameter combination of the target point, obtaining the TMS pulse time of the current trial corresponding to the preset stimulation parameter combination;

[0083] Specifically, by performing pulse detection on the EEG signal corresponding to the preset stimulation parameter combination of the target point, the TMS pulse time of the current trial corresponding to the preset stimulation parameter combination can be obtained.

[0084] For example, the TMS pulse of the current trial can be identified from the EEG signal corresponding to the preset stimulation parameter combination of the target by using the Findpulse algorithm, and the time point of the TMS pulse of the current trial can be determined as the TMS pulse time of the current trial. Based on the Findpulse algorithm, let P be a single channel (such as C3) extracted from the EEG signal, and P(t) be the signal value of the channel at time point t. Calculate the first-order derivative of the channel: P'(t)=dP(t) / dt, P'(t) represents the instantaneous rate of change of the signal at time point t. For all time points t, compare P'(t) with the threshold θ. If P'(t) > θ, then mark the time point corresponding to P'(t) greater than θ as the time t when the TMS pulse is emitted. TMS , that is, the TMS pulse of the current trial is obtained. The time t at which a TMS pulse is detected TMS After that, a refractural period will be introduced, that is, a custom time trefract will be 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 identified as a new TMS pulse. Only after this refractural period, if the rate of change of the signal P'(t) exceeds θ again, the next TMS pulse will be detected and marked. The above refractory period mechanism can avoid misidentifying some false pulses caused by signal fluctuations as real 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, extracting the EEG segmentation of the EEG signal corresponding to the preset stimulation parameter combination of the target point according to the determined time window, and then performing baseline correction on each EEG segment, the EEG change signal corresponding to the preset stimulation parameter combination can be obtained. Among them, the specific process of extracting the segmentation and baseline correction is the existing technology and will not be repeated here.

[0087] S303, performing first-stage artifact removal and data filling on the EEG change signal corresponding to the preset stimulation parameter combination to obtain 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, the first stage artifact removal is performed on the EEG change signal corresponding to the preset stimulation parameter combination of the target, and then data filling is performed on the data after the artifact removal, so as to obtain the first intermediate data corresponding to the preset stimulation parameter combination of the target. The first stage artifact removal can remove the TMS pulse artifacts included in the EEG change signal and the muscle artifacts caused by it.

[0089] The electromagnetic pulses of TMS will produce short but huge artifacts in the EEG signal, including TMS pulse artifacts and TMS-induced muscle artifacts. The amplitude of TMS pulse artifacts is usually hundreds of times higher than that of normal EEG signals, while the amplitude of TMS-induced muscle artifacts is dozens of times higher than that of normal EEG signals. These artifacts cannot usually be removed by conventional artifact removal algorithms. Therefore, this application selects data within a period of time before and after the stimulation and directly discards it to reduce the impact of the above artifacts. For example, the data segment from 2ms before the stimulation to 12ms after the stimulation is directly discarded.

[0090] For example, the Removedata algorithm can be used to discard the data of the preset time period to achieve the first stage of artifact removal. The preset time period is set according to actual experience, such as 2ms before stimulation to 12ms after stimulation, which is not limited in the embodiment of the present invention. i , and t i After a preset time period, the time point t i+1 , the time point t i to i+1 The data between is replaced by 0, which can be expressed as EEG(t)=0, t=[t i ,t i+1 Then, the cubic interpolation algorithm is used to calculate the time point t i to i+1The cubic interpolation algorithm assumes that there is a more complex nonlinear relationship between the missing data points and the previous and next data points, and usually uses a cubic polynomial to fit the data. It uses the information of the previous and next data points and fits the curvature of the data to achieve a smoother interpolation effect. Assuming four known data points (t1, y1), (t2, y2), (t3, y3), (t4, y4), a cubic polynomial P(t) can be found such that P(t i )=y i This is 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, d are coefficients obtained by using the least squares method on (t1, y1), (t2, y2), (t3, y3), (t4, y4).

[0091] S304, performing second-stage artifact removal and data filling on the first intermediate data corresponding to the preset stimulation parameter combination to obtain second intermediate data corresponding to the preset stimulation parameter combination;

[0092] Specifically, the second stage artifact removal is performed on the first intermediate data corresponding to the preset stimulation parameter combination of the target point, and then data filling is performed on the data after the artifact removal, so as to obtain the second intermediate data corresponding to the preset stimulation parameter combination of the target point.

[0093] For example, the Removedata algorithm can be used to discard the data in the preset time period to avoid artifact interference caused by the data filled 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 stimulation parameter combination of the target to obtain multiple independent source signals. The Componentselect detection algorithm is used to remove artifacts from each independent source signal to obtain second temporary data. The cubic interpolation algorithm is then used to interpolate the second temporary data to obtain the second intermediate data corresponding to the preset stimulation parameter combination of the target. Based on the Componentselect detection algorithm: Assume that IC i For the i-th independent component obtained by fastICA analysis, first calculate each electrode in each IC i The z-score on the image can help identify abnormal 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, electromyographic artifacts, etc.

[0094] For IC iThe detection of TMS-induced muscle artifacts is performed by comparing the average absolute amplitude within the target window mean_ab_muscle_target with the average absolute amplitude of the entire time window mean_ab_muscle_entire, and setting the threshold muscle_thresh to detect. If mean_ab_muscle_target / mean_ab_muscle_entire > muscle_thresh, IC is considered i is TMS-induced muscle artifact.

[0095] For IC i Detection of electromyographic artifacts: First, take the natural logarithm of the frequency f and power P(f) of the power spectrum to obtain the logarithmic frequency log(f) and the logarithmic power log(P(f)). Use the 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, it is considered that IC i It is an electromyographic artifact.

[0096] After the first stage artifact removal in step 303, some TMS-induced muscle artifacts will still remain, and their amplitudes are still much larger than the electrooculogram, electrocardiogram, and normal EEG components. In fastICA, high-amplitude muscle artifacts may dominate the decomposition effect of ICA, resulting in poor separation of other artifacts (such as electrooculogram, electrocardiogram, etc.). In step S304, the second stage artifact removal can first remove artifacts with larger amplitudes such as TMS-induced muscle artifacts, thereby ensuring that other artifacts can be separated more effectively and removed more reliably in step S307.

[0097] S305, performing bandpass filtering and notch filtering on the second intermediate data corresponding to the preset stimulation parameter combination to obtain third intermediate data corresponding to the preset stimulation parameter combination;

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

[0099] Bandpass filtering and notch filtering should be performed after the second-stage artifact removal. The second-stage artifact removal can remove larger artifacts, and then bandpass filtering and notch 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 TMS-induced muscle artifacts).

[0100] Bandpass filtering is to filter the data to the frequency range of interest. The commonly used filtering range of scalp EEG is 0.01Hz to 50Hz, which helps to remove some low-frequency and high-frequency noise interference. Notch filtering is used to remove the power frequency interference of the mains, which is usually 50Hz in China and 60Hz abroad.

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

[0102] Specifically, the third stage artifact removal is performed on the third intermediate data corresponding to the preset stimulation parameter combination of the target point, and then data filling is performed on the data after the artifact removal, so as to obtain the effective EEG signal corresponding to the preset stimulation parameter combination of the target point.

[0103] For example, the Removedata algorithm can be used to discard the data of the preset time period after the start of the third intermediate data pulse to achieve the removal of 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 point to obtain multiple independent source signals. The Componentselect detection algorithm is used to remove artifacts from each independent source signal to obtain the third temporary data. The cubic interpolation algorithm is then used to interpolate the third temporary data to obtain the effective EEG signal corresponding to the preset stimulation parameter combination of the target point.

[0104] For IC i Detection of electrooculographic artifacts: By comparing the average absolute Z value mean_z_score = (ZFP1+ ZFP2) / 2 of two electrodes close to the eyes (such as Fp1 and Fp2), and setting the threshold blink_thresh for detection, if mean_z_score is greater than blink_thresh, it is considered that IC i It's an electrooculogram artifact.

[0105] The specific implementation process of step S306 is similar to that of step S305, except that step S306 removes smaller artifacts, such as electrooculographic artifacts, electrocardiographic artifacts, etc.

[0106] On the basis of the above-mentioned embodiments, further, the method for optimizing stimulation parameters based on collecting EEG signals by a robotic arm provided in an embodiment of the present invention also includes downsampling the first intermediate data corresponding to the preset stimulation parameter combination; accordingly, performing a second stage of artifact removal and data filling on the downsampled first intermediate data 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 is downsampled to reduce the amount of data. After downsampling, the first intermediate data after downsampling can be obtained. The second stage artifact removal and data filling are performed on the first intermediate data after downsampling to obtain the second intermediate data corresponding to the preset stimulation parameter combination. Among them, the downsampling method used for downsampling is selected according to actual needs, and the embodiment of the present invention does not limit it.

[0108] The TMS-EEG system usually requires a sampling rate of more than 5kHz for EEG signals to minimize artifacts caused by TMS pulses. In signal processing, in order to speed up data processing, it is necessary to downsample to 1kHz to reduce the amount of data.

[0109] On the basis of the above embodiments, further, after performing the third stage artifact removal and data filling on the third intermediate data corresponding to the preset stimulation parameter combination, the method further includes:

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

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

[0112] Figure 4 is a schematic flow chart of the optimization of stimulation parameters based on the collection of EEG signals by a mechanical arm provided in the fourth embodiment of the present invention, such as Figure 4 As shown, based on the above embodiments, further, performing a second artifact removal on the first intermediate data corresponding to the preset stimulation parameter combination of the target includes:

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

[0114] Specifically, the Removedata algorithm can be used to remove the data of the preset time period in the first intermediate data corresponding to the preset stimulation parameter combination of the target point, and obtain the first temporary data corresponding to the preset stimulation parameter combination of the target point. The data of the preset time period removed is the data filled in step S303, and the purpose of removing the data is to avoid artifact interference caused by the filled data, which affects the subsequent fast independent component analysis.

[0115] S402, performing a fast independent component analysis on the first temporary data corresponding to the preset stimulation parameter combination to obtain a plurality of independent source signals;

[0116] Specifically, by using fast independent component analysis to perform blind source separation on the first temporary data corresponding to the preset stimulation parameter combination, multiple independent source signals corresponding to the preset stimulation parameter combination can be obtained. The original, mutually independent source signals can be restored from the first temporary data corresponding to the preset stimulation parameter combination through fast independent component analysis.

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

[0118] Specifically, the Componentselect detection algorithm is used to remove artifacts from each independent source signal corresponding to the preset stimulation parameter combination to obtain second temporary data corresponding to the preset stimulation parameter combination. The second temporary data is data filled to obtain second intermediate data corresponding to the preset stimulation parameter combination.

[0119] For example, based on the Componentselect detection algorithm: Let IC i For the i-th independent component obtained by fastICA analysis, first calculate each electrode in each IC i The z-score on the image can help identify abnormal 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.

[0120] Figure 5 is a schematic flow chart of the optimization of stimulation parameters based on collecting EEG signals by a mechanical arm provided in the fifth embodiment of the present invention, such as Figure 5As shown, based on the above embodiments, further, the EEG signal corresponding to the preset stimulation parameter combination of the target point includes multiple TMS pulse trials; accordingly, the method further includes:

[0121] S501, obtaining downsampled first intermediate data corresponding to a preset stimulation parameter combination of a target point in each trial;

[0122] Specifically, the EEG signal corresponding to the preset stimulation parameter combination of the target includes multiple TMS pulse trials. The first intermediate data corresponding to the preset stimulation parameter combination of the target for each trial is downsampled, and the downsampled first intermediate data corresponding to the preset stimulation parameter combination of the target for each trial can be obtained.

[0123] S502, based on the downsampled first intermediate data corresponding to the preset stimulation parameter combination of the target point of each trial, calculating the root mean square of each channel of the downsampled first intermediate data corresponding to the preset stimulation parameter combination of the target point of each trial;

[0124] Specifically, according to the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point of 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 of each trial can be calculated.

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

[0126] S503, calculating 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 of each trial, and obtaining 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 of 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 of 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 kth trial k , M represents the total number of channels, R k,jThe first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point of the kth trial includes the root mean square of the jth channel, 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 a threshold, 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 the comprehensive average value corresponding to each trial, the comprehensive average value corresponding to the trial is compared with the threshold value. If the comprehensive average value corresponding to the trial is greater than the threshold value, the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point of the trial is discarded. If the comprehensive average value corresponding to the trial is less than or equal to the threshold value, the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point of the trial is retained. The threshold value is preset.

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

[0132] When processing data from multiple trials, artifacts caused by large-scale head movements and poor electrode contact usually last for a long time and are 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] On the basis of the above embodiments, further, the spacing between adjacent target points is greater than the spacing of an electrode channel. By reasonably limiting the distance between adjacent target points, the spatial distribution law of the collected EEG signal is clearer, which is convenient for analyzing the source, propagation path and mutual relationship of the signal, reducing the complexity of data analysis and improving the accuracy of analyzing brain activity patterns.

[0134] Figure 6 is a flow chart of a method for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm provided in a sixth embodiment of the present invention, such as Figure 6 As shown, the method for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm provided in an embodiment of the present invention includes:

[0135] S601, obtaining an electroencephalogram signal corresponding to a current preset stimulation parameter combination of a current target; wherein the electroencephalogram signal corresponding to the current stimulation parameter combination of the current target is obtained by stimulating the brain of a subject using 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, by stimulating the current target point in the subject's brain based on the current preset stimulation parameter combination of the current target point through the stimulation device carried by the robotic arm, an electroencephalogram signal corresponding to the current preset stimulation parameter combination of the current target point can be obtained. The current target point is the target point that is being processed. Multiple preset stimulation parameter combinations are set for each target point so that a suitable preset stimulation parameter combination can be obtained therefrom. Multiple preset stimulation parameter combinations are set for each target point based on actual experience, and the embodiments of the present invention do not limit this.

[0137] S602, performing multi-stage denoising on the EEG signal corresponding to the current stimulation parameter combination of the current target to obtain a valid 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 of the current target is denoised in stages 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 of the current target. Since the EEG signal is denoised in stages, a more accurate EEG signal can be obtained, which is conducive to improving the accuracy of subsequent screening of appropriate stimulation parameter combinations.

[0139] S603, extracting features of effective EEG signals corresponding to the current preset stimulation parameter combination of the current target, and obtaining EEG feature indicators corresponding to the current preset stimulation parameter combination of the current target;

[0140] Specifically, feature extraction is performed on the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target, and the EEG feature index corresponding to the current preset stimulation parameter combination of the current target can be obtained. The EEG feature index is selected according to actual needs and is not limited in the embodiment of the present invention.

[0141] S604, traversing preset stimulation parameter combinations of the current target until a suitable stimulation parameter combination of the current target is obtained based on the EEG characteristic index corresponding to the current preset stimulation parameter combination of the current target and the parameter screening condition; or traversing all preset stimulation parameter combinations of the current target and failing to obtain a suitable stimulation parameter combination of the current target;

[0142] Specifically, it is determined whether the EEG characteristic index corresponding to the current preset stimulation parameter combination of the current target satisfies the parameter screening condition. If the EEG characteristic index corresponding to the current preset stimulation parameter combination of the current target satisfies the parameter screening condition, then the current preset stimulation parameter combination of the current target is used as the appropriate stimulation parameter combination of the current target. If the EEG characteristic index corresponding to the current preset stimulation parameter combination of the current target does not satisfy the parameter screening condition, then the current preset stimulation parameter combination of the current target is not the appropriate stimulation parameter combination of the current target. If it is determined that the current preset stimulation parameter combination of the current target is the appropriate stimulation parameter combination of the current target, it means that an EEG signal that meets the requirements can be obtained through the appropriate stimulation parameter combination of the current target. In order to save the screening time of the preset stimulation parameter combination of the current target, there is no need to screen the remaining preset stimulation parameter combinations of the current target. The parameter screening condition is preset and is set according to actual needs, and the embodiment of the present invention does not limit it.

[0143] If it is determined that the current preset stimulation parameter combination of the current target is not a suitable stimulation parameter combination for the current target, then the next preset stimulation parameter combination of the current target is obtained as the current preset stimulation parameter combination to repeat the judgment of whether the EEG characteristic indicators corresponding to the current set stimulation parameter combination of the current target meet the parameter screening conditions; if the suitable stimulation parameter combination for the current target is still not obtained, then the next preset stimulation parameter combination of the current target is obtained as the current preset stimulation parameter combination to continue to judge whether the EEG characteristic indicators corresponding to the current set stimulation parameter combination of the current target meet the parameter screening conditions, and so on; if a suitable stimulation parameter combination for the current target is obtained or all preset stimulation parameter combinations for the current target are traversed and no suitable stimulation parameter combination for the current target is obtained, then the optimization process of the preset stimulation parameter combination for the current target is stopped.

[0144] S605. If a suitable stimulation parameter combination for the current target is obtained or all preset stimulation parameter combinations for the current target are traversed and no suitable stimulation parameter combination for the current target is obtained, the next target is obtained as the current target to obtain a suitable stimulation parameter combination for the current target until all targets are traversed.

[0145] Specifically, after obtaining a suitable stimulation parameter combination for the current target or traversing all preset stimulation parameter combinations for the current target, the next target is obtained as the current target to obtain a suitable stimulation parameter combination for the current target. When all targets are traversed, any target either obtains a suitable stimulation parameter combination, or no suitable stimulation parameter combination is obtained after traversing all preset stimulation parameter combinations for the target, and the process ends. Acquiring a suitable stimulation parameter combination for the current target is the process of steps S601 to S604 above.

[0146] The embodiment of the present invention provides a method for optimizing stimulation parameters based on collecting EEG signals by a robotic arm, which obtains the EEG signal corresponding to the current preset stimulation parameter combination of the current target, performs multi-stage denoising on the EEG signal corresponding to the current stimulation parameter combination of the current target, and obtains the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target; performs feature extraction on the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target, and obtains the EEG feature index corresponding to the current preset stimulation parameter combination of the current target; traverses the preset stimulation parameter combination of the current target until a suitable stimulation parameter combination for the current target is obtained based on the EEG feature index corresponding to the current preset stimulation parameter combination of the current target and parameter screening conditions; or traverses all preset stimulation parameter combinations of the current target and fails to obtain a suitable stimulation parameter combination for the current target; if a suitable stimulation parameter combination for the current target is obtained or all preset stimulation parameter combinations for the current target are traversed and fail to obtain a suitable stimulation parameter combination for the current target, then obtain the next target as the current target to obtain a suitable stimulation parameter combination for the current target until all targets are traversed, so that a suitable stimulation parameter combination for the target can be obtained as soon as possible, thereby improving the parameter optimization efficiency.

[0147] Figure 7 is a flow chart of a method for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm provided in a seventh embodiment of the present invention, such as Figure 7 As shown, on the basis of the above embodiments, further, the multi-stage denoising is performed 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, including:

[0148] S701, based on the EEG signal corresponding to the current preset stimulation parameter combination of the current target point, obtaining the TMS pulse time of the current trial corresponding to the current preset stimulation parameter combination;

[0149] Specifically, the EEG signal corresponding to the current preset stimulation parameter combination of the current target is pulse detected to obtain the TMS pulse time of the current trial corresponding to the current preset stimulation parameter combination of the current target. The specific implementation process of this step is similar to step S301 and will not be repeated 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, extracting the EEG segment of the EEG signal corresponding to the current preset stimulation parameter combination of the target point according to the determined time window, and then performing baseline correction on each EEG segment, the EEG change signal corresponding to the current preset stimulation parameter combination can be obtained. The specific implementation process of this step is similar to step S302, and will not be repeated here.

[0152] S703, performing first-stage artifact removal and data filling on the EEG change signal corresponding to the current preset stimulation parameter combination to obtain 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 of the current target, the first stage artifact removal is performed on the EEG change signal corresponding to the current preset stimulation parameter combination of the current target, and then data filling is performed on the data after the artifact removal, so as to obtain the first intermediate data corresponding to the current preset stimulation parameter combination of the current target. Among them, the first stage artifact removal can remove the TMS pulse artifacts included in the EEG change signal and the muscle artifacts caused by it. The specific implementation process of this step is similar to step S303, which will not be repeated here.

[0154] S704, performing second-stage artifact removal and data filling on the first intermediate data corresponding to the current preset stimulation parameter combination to obtain second intermediate data corresponding to the current preset stimulation parameter combination;

[0155] Specifically, the second stage artifact removal is performed on the first intermediate data corresponding to the current preset stimulation parameter combination of the current target, and then data filling is performed on the data after the artifact removal, so as to obtain the second intermediate data corresponding to the current preset stimulation parameter combination of the current target. The specific implementation process of this step is similar to step S304, and will not be repeated here.

[0156] S705, performing bandpass filtering and notch filtering on the second intermediate data corresponding to the current preset stimulation parameter combination to obtain third intermediate data corresponding to the current preset stimulation parameter combination;

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

[0158] S706, performing third-stage artifact removal and data filling on the third intermediate data corresponding to the current preset stimulation parameter combination to obtain a valid EEG signal corresponding to the current preset stimulation parameter combination of the current target point;

[0159] Specifically, the third stage artifact removal is performed on the third intermediate data corresponding to the current preset stimulation parameter combination of the current target, and then data filling is performed on the data after the artifact removal, so that the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target can be obtained. The specific implementation process of this step is similar to step S306, which will not be repeated here.

[0160] On the basis of the above-mentioned embodiments, further, the method for optimizing stimulation parameters based on collecting EEG signals by a robotic arm provided in an embodiment of the present invention also includes downsampling the first intermediate data corresponding to the current preset stimulation parameter combination; accordingly, the downsampled first intermediate data is subjected to a second-stage 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 of the current target is downsampled to reduce the amount of data. After downsampling, the first intermediate data after downsampling can be obtained. The second stage artifact removal and data filling are performed on the first intermediate data after downsampling to obtain the second intermediate data corresponding to the current preset stimulation parameter combination. Among them, the downsampling method used for downsampling is selected according to actual needs, and the embodiment of the present invention does not limit it.

[0162] The sampling rate requirement of the TMS-EEG system for EEG signals is usually above 5kHz to minimize the artifacts caused by TMS pulses. In signal processing, in order to speed up data processing, it is necessary to downsample to 1kHz to reduce the amount of data. On the basis of the above embodiments, further, after performing the third stage artifact removal and data filling on the third intermediate data corresponding to the current preset stimulation parameter combination, it also includes:

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

[0164] Specifically, after performing the third stage artifact removal and data filling on the third intermediate data corresponding to the current preset stimulation parameter combination, the third data is obtained; the third data is subjected to whole-brain average re-reference to obtain fourth data, and the fourth data is used as the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target. Whole-brain average re-reference is to use the average value of the signals of all electrode channels as the reference signal, and subtract the above average value from the signal of each channel. Whole-brain average re-reference can enhance the contrast of local EEG activity.

[0165] Figure 8 is a flow chart of a method for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm provided in an eighth embodiment of the present invention, such as Figure 8 As shown, based on the above embodiments, further, performing the second artifact removal on the first intermediate data corresponding to the current preset stimulation parameter combination includes:

[0166] S801, removing data of a preset time period from first intermediate data corresponding to a current preset stimulation parameter combination, to obtain first temporary data corresponding to the current preset stimulation parameter combination;

[0167] Specifically, the Removedata algorithm can be used to remove the data of the preset time period in the first intermediate data corresponding to the current preset stimulation parameter combination of the current target, and obtain the first temporary data corresponding to the current preset stimulation parameter combination of the current target. The data of the preset time period removed is the data filled in step S703, and the purpose of removing the data is to avoid artifact interference caused by the filled data, which affects the subsequent fast independent component analysis.

[0168] S802, performing a fast independent component analysis on the first temporary data corresponding to the current preset stimulation parameter combination to obtain a plurality of independent source signals corresponding to the current preset stimulation parameter combination;

[0169] Specifically, by using fast independent component analysis to perform blind source separation on the first temporary data corresponding to the current preset stimulation parameter combination, multiple independent source signals corresponding to the current preset stimulation parameter combination can be obtained. The original, mutually independent source signals can be restored from the first temporary data corresponding to the current preset stimulation parameter combination through fast independent component analysis.

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

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

[0172] On the basis of the above embodiments, further, the optimization of stimulation parameters based on the collection of EEG signals by the mechanical arm provided in the embodiments of the present invention also includes:

[0173] After all preset stimulation parameter combinations of the current target are traversed and no suitable stimulation parameter combination of the current target is obtained, a prompt message is issued that no suitable stimulation parameter combination of the current target is found.

[0174] Specifically, if all preset stimulation parameter combinations for the current target are traversed but no suitable stimulation parameter combination for the current target is obtained, it means that there is no suitable stimulation parameter combination among all preset stimulation parameter combinations. A prompt message can be issued that no suitable stimulation parameter combination is found for the current target to facilitate relevant personnel to adjust the preset stimulation parameter combination.

[0175] Fig. 9 is a schematic diagram of the structure of a device for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm, provided in the ninth embodiment of the present invention. Fig. 9 As shown, the device for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm provided in an embodiment of the present 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 of at least one target; wherein, the EEG signals corresponding to each stimulation parameter combination of each target are acquired by stimulating the brain of the subject based on each stimulation parameter combination of each target by carrying a stimulation device on a robotic arm; the first denoising module 902 is used to perform multi-stage denoising on the EEG signals corresponding to each preset stimulation parameter combination of each target, respectively, to obtain the effective EEG signals corresponding to each preset stimulation parameter combination of each target; the first feature extraction module 903 is used to perform feature extraction on the effective EEG signals corresponding to each preset stimulation parameter combination of each target, to obtain the EEG feature index corresponding to each preset stimulation parameter combination of each target; the screening module 904 is used to obtain the optimal target combination and the optimal stimulation parameter combination for each target according to the EEG feature index corresponding to each preset stimulation parameter combination of each target and the parameter screening conditions.

[0177] Specifically, by stimulating each target point in the subject's brain based on each preset stimulation parameter combination of each target point through the stimulation device carried by the robotic arm, an electroencephalogram signal corresponding to each preset stimulation parameter combination of each target point can be obtained. The first acquisition module 901 can obtain electroencephalogram signals corresponding to multiple preset stimulation parameter combinations of each target point. In actual applications, the target point can be one or more, and the selection is made according to actual needs, and the embodiment of the present invention does not limit it.

[0178] For each target point, the first denoising module 902 denoises the EEG signal corresponding to each preset stimulation parameter combination of the target point in stages to reduce interference such as artifacts, low-frequency noise, and high-frequency noise in the EEG signal, and obtains a valid EEG signal corresponding to each preset stimulation parameter combination of the target point. Since the EEG signal is denoised at multiple stages, a more accurate EEG signal can be obtained, which is conducive to improving the accuracy of subsequent screening of preset stimulation parameter combinations.

[0179] For each target point, the first feature extraction module 903 can extract features from the effective EEG signals corresponding to each preset stimulation parameter combination of the target point, and obtain 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, source level features, etc. The EEG feature indicators are selected according to actual needs and are not limited in the embodiments of the present invention.

[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 of the EEG characteristic indicators corresponding to each preset stimulation parameter combination of the target meets the parameter screening conditions, the target will be retained; if none of the EEG characteristic indicators corresponding to each preset stimulation parameter combination of the target meets the parameter screening conditions, the target will be discarded. If only one of the EEG characteristic indicators corresponding to each preset stimulation parameter combination of the target meets the parameter screening conditions, the preset stimulation parameter combination is used as the optimal stimulation parameter combination of the target. If there are multiple EEG characteristic indicators corresponding to multiple preset stimulation parameter combinations that meet the parameter screening conditions among the EEG characteristic indicators corresponding to each preset stimulation parameter combination of the target, the EEG characteristic indicators corresponding to the multiple preset stimulation parameter combinations that meet the parameter screening conditions can be compared, and a preset stimulation parameter combination with the most significant stimulation effect can be selected as the optimal stimulation parameter combination of the target. The larger the value of the EEG characteristic indicator, the more significant the stimulation effect.

[0181] The embodiment of the present invention provides a device for optimizing stimulation parameters based on collecting EEG signals by a robotic arm, which obtains EEG signals corresponding to multiple preset stimulation parameter combinations of at least one target; wherein the EEG signal corresponding to each stimulation parameter combination of each target is obtained by stimulating and collecting the brain of a subject based on each stimulation parameter combination of each target by carrying a stimulation device on a robotic arm; the EEG signals corresponding to each preset stimulation parameter combination of each target are denoised respectively to obtain effective EEG signals corresponding to each preset stimulation parameter combination of each target; feature extraction is performed on the effective EEG signals corresponding to each preset stimulation parameter combination of each target to obtain EEG feature indicators corresponding to each preset stimulation parameter combination of each target; based on the EEG feature indicators corresponding to each preset stimulation parameter combination of each target and parameter screening conditions, the optimal target combination and the optimal stimulation parameter combination of each target are obtained, thereby improving the reliability of obtaining the target and the optimal stimulation parameter combination.

[0182] Fig.10 is a schematic diagram of the structure of a device for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm, provided in the tenth embodiment of the present invention. Fig.10 As shown, on the basis of the above embodiments, further, the first denoising module 902 includes a first obtaining unit 90201, a second obtaining 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 obtaining unit 90201 is used to obtain the TMS pulse time 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 90202 is used to extract and segment the EEG signal corresponding to the preset stimulation parameter combination of the target 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 90203 is used to perform the first stage artifact removal and data filling on the EEG change signal corresponding to the preset stimulation parameter combination, so as to obtain the preset stimulation parameter combination. The first intermediate data corresponding to the preset stimulation parameter combination; the second artifact removal unit 90204 is used to perform the second stage artifact removal and data filling on the first intermediate data corresponding to the preset stimulation parameter combination, so as to obtain the second intermediate data corresponding to the preset stimulation parameter combination; the first filtering unit 90205 is used to perform bandpass filtering and notch filtering on the second intermediate data corresponding to the preset stimulation parameter combination, so as to obtain the third intermediate data corresponding to the preset stimulation parameter combination; the third artifact removal unit 90206 is used to perform the third stage artifact removal and data filling on the third intermediate data corresponding to the preset stimulation parameter combination, so as to obtain the effective EEG signal corresponding to the preset stimulation parameter combination.

[0184] Fig.11 is a schematic diagram of the structure of a device for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm according to the eleventh embodiment of the present invention. Fig.11 As shown, based on the above embodiments, further, the second artifact removal unit 90204 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 the data of the preset time period in the first intermediate data corresponding to the preset stimulation parameter combination of the target point, so as to obtain the first temporary data corresponding to the preset stimulation parameter combination; the first analysis subunit 902042 is used to perform a fast independent component analysis on the first temporary data corresponding to the preset stimulation parameter combination, so as to obtain multiple independent source signals corresponding to the preset stimulation parameter combination; the first removal subunit 902043 is used to remove the artifacts of each independent source signal corresponding to the preset stimulation parameter combination, so as to obtain the second temporary data corresponding to the preset stimulation parameter combination.

[0186] Fig.12 is a schematic diagram of the structure of a device for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm, provided in the twelfth embodiment of the present invention. Fig.12 As shown, based on the above embodiments, the first denoising module 902 further includes a whole-brain average re-reference unit 90207, wherein:

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

[0188] Fig.13 is a schematic diagram of the structure of a device for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm, provided in the thirteenth embodiment of the present invention. Fig.13 As shown, based on the above embodiments, the first denoising 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 stimulation parameter combination; accordingly, 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 stimulation parameter combination.

[0190] On the basis of the above embodiments, further, the EEG signal corresponding to the preset stimulation parameter combination of the target point includes multiple TMS pulse trials; accordingly, Fig.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 first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point of each trial; the first calculation unit 90210 is used to 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 of each trial based on the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point of each trial; the second calculation unit 90211 is used to calculate the average value of 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 of each trial, and obtain the comprehensive average value corresponding to each trial; the judgment unit 90212 is used to discard the first intermediate data after downsampling corresponding to the preset stimulation parameter combination of the target point of the trial if it is judged that the comprehensive average value corresponding to the trial is greater than a threshold value; wherein the threshold value is pre-set.

[0192] On the basis of the above embodiments, further, the distance between adjacent target points is greater than the interval of one electrode channel.

[0193] Fig.15is a schematic diagram of the structure of a device for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm, provided in the fifteenth embodiment of the present invention. Fig.15 As shown, the device for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm provided in an embodiment of the present 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 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 based on the current stimulation parameter combination of the current target by carrying a stimulation device on a robotic arm; there are multiple preset stimulation parameter combinations for the current target; 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, and obtain the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target; the second feature extraction module 1503 is used to perform feature extraction on the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target, and obtain the current preset stimulation parameter combination of the current target. Assume that the stimulation parameter combination corresponds to the EEG characteristic index; the first traversal module 1504 is used to traverse the preset stimulation parameter combination of the current target until the appropriate stimulation parameter combination of the current target is obtained based on the EEG characteristic index corresponding to the current preset stimulation parameter combination of the current target and the parameter screening condition; or all the preset stimulation parameter combinations of the current target are traversed and no appropriate stimulation parameter combination of the current target is obtained; the second traversal module 1505 is used to obtain the next target as the current target to obtain the appropriate stimulation parameter combination of the current target if the appropriate stimulation parameter combination of the current target is obtained or all the preset stimulation parameter combinations of the current target are traversed and no appropriate stimulation parameter combination of the current target is obtained, until all targets are traversed.

[0195] Specifically, by stimulating the current target of the subject's brain based on the current preset stimulation parameter combination of the current target through the stimulation device carried by the robotic arm, the EEG signal corresponding to the current preset stimulation parameter combination of the current target can be obtained. The second acquisition module 1501 will obtain the EEG signal corresponding to the current preset stimulation parameter combination of the current target. The current target is the target that is undergoing data processing. Multiple preset stimulation parameter combinations will be set for each target so that a suitable preset stimulation parameter combination can be obtained therefrom. Multiple preset stimulation parameter combinations are set for each target based on actual experience, and the embodiments of the present invention are not limited thereto.

[0196] The second denoising module 1502 denoises the EEG signal corresponding to the current preset stimulation parameter combination of the current target in stages to reduce interference such as artifacts, low-frequency noise, and high-frequency noise in the EEG signal, and obtains a valid EEG signal corresponding to the current preset stimulation parameter combination of the current target. Since the EEG signal is denoised in stages, a more accurate EEG signal can be obtained, which is conducive to improving the accuracy of subsequent screening of appropriate 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, and obtains 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 is not limited in the embodiment of the present invention.

[0198] The first traversal module 1504 determines whether the EEG characteristic index corresponding to the current preset stimulation parameter combination of the current target satisfies the parameter screening condition. If the EEG characteristic index corresponding to the current preset stimulation parameter combination of the current target satisfies the parameter screening condition, then the current preset stimulation parameter combination of the current target is used as the appropriate stimulation parameter combination of the current target. If the EEG characteristic index corresponding to the current preset stimulation parameter combination of the current target does not satisfy the parameter screening condition, then the current preset stimulation parameter combination of the current target is not the appropriate stimulation parameter combination of the current target. If it is determined that the current preset stimulation parameter combination of the current target is the appropriate stimulation parameter combination of the current target, it means that an EEG signal that meets the requirements can be obtained through the appropriate stimulation parameter combination of the current target. In order to save the screening time of the preset stimulation parameter combination of the current target, there is no need to screen the remaining preset stimulation parameter combinations of the current target. The parameter screening condition is preset and is set according to actual needs, and the embodiment of the present invention does not limit it.

[0199] If the first traversal module 1504 determines that the current preset stimulation parameter combination of the current target is not a suitable stimulation parameter combination for the current target, then the next preset stimulation parameter combination of the current target is obtained as the current preset stimulation parameter combination to repeat the judgment of whether the EEG characteristic indicators corresponding to the current set stimulation parameter combination of the current target meet the parameter screening conditions; if the suitable stimulation parameter combination for the current target is still not obtained, the next preset stimulation parameter combination of the current target is obtained as the current preset stimulation parameter combination to continue to judge whether the EEG characteristic indicators corresponding to the current set stimulation parameter combination of the current target meet the parameter screening conditions, and so on; if the suitable stimulation parameter combination for the current target is obtained or all the preset stimulation parameter combinations for the current target are traversed and the suitable stimulation parameter combination for the current target is not obtained, the optimization process of the preset stimulation parameter combination for the current target is stopped.

[0200] After obtaining the appropriate stimulation parameter combination for the current target or traversing all preset stimulation parameter combinations for the current target, the second traversal module 1505 will obtain the next target as the current target and obtain the appropriate stimulation parameter combination for the current target. When all targets are traversed, any target either obtains the appropriate stimulation parameter combination, or no appropriate stimulation parameter combination is obtained after traversing all preset stimulation parameter combinations for the target, the process ends.

[0201] The device for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm provided in an embodiment of the present invention obtains the EEG signal corresponding to the current preset stimulation parameter combination of the current target, performs multi-stage denoising on the EEG signal corresponding to the current stimulation parameter combination of the current target, and obtains the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target; performs feature extraction on the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target, and obtains the EEG characteristic index corresponding to the current preset stimulation parameter combination of the current target; judges whether the current preset stimulation parameter combination of the current target is a suitable stimulation parameter for the target based on the EEG characteristic index corresponding to the current preset stimulation parameter combination of the current target and the parameter screening condition. combination; 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 traverse all the preset stimulation parameter combinations of the current target and fail to obtain a suitable stimulation parameter combination for the current target; if a suitable stimulation parameter combination for the current target is obtained or all the preset stimulation parameter combinations of the current target are traversed and fail to obtain a suitable stimulation parameter combination for the current target, then obtain the next target as the current target to obtain a suitable stimulation parameter combination for the current target, until all targets are traversed, so that a suitable stimulation parameter combination for the target can be obtained as soon as possible, thereby improving the efficiency of parameter optimization.

[0202] Fig.16 is a schematic diagram of the structure of a device for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm, provided in the sixteenth embodiment of the present invention. Fig.16 As shown, on the basis of the above embodiments, further, the second denoising module 1502 includes a third obtaining unit 150201, a fourth obtaining 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 time of the current trial corresponding to the current preset stimulation parameter combination based on the EEG signal corresponding to the current preset stimulation parameter combination of the current target; 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 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 150203 is used to perform the first stage artifact removal and data filling on the EEG change signal corresponding to the current preset stimulation parameter combination, so as to obtain the current preset stimulation parameter combination. The corresponding first intermediate data; the fifth artifact removal unit 150204 is used to perform the second stage artifact removal and data filling on the first intermediate data corresponding to the current preset stimulation parameter combination, so as 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 notch filtering on the second intermediate data corresponding to the current preset stimulation parameter combination, so as to obtain the third intermediate data corresponding to the current preset stimulation parameter combination; the sixth artifact removal unit 150206 is used to perform the third stage artifact removal and data filling on the third intermediate data corresponding to the current preset stimulation parameter combination, so as to obtain the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target.

[0204] Fig.17 is a schematic diagram of the structure of a device for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm, provided in the seventeenth embodiment of the present invention. Fig.17 As shown, based on the above embodiments, further, the fifth artifact removal unit 150204 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 the data of the preset time period in the first intermediate data corresponding to the current preset stimulation parameter combination, and obtain the first temporary data corresponding to the current preset stimulation parameter combination; the second analysis subunit 1502042 is used to perform a fast independent component analysis on the first temporary data corresponding to the current preset stimulation parameter combination, and obtain multiple independent source signals corresponding to the current preset stimulation parameter combination; the second removal subunit 1502043 is used to remove the artifacts of each independent source signal corresponding to the current preset stimulation parameter combination, and obtain the second temporary data corresponding to the current preset stimulation parameter combination.

[0206] Fig.18 is a schematic diagram of the structure of a device for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm, provided in the eighteenth embodiment of the present invention. Fig.18As shown, based on the above embodiments, the second denoising 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 stimulation parameter combination; accordingly, the fifth artifact removal unit 150204 is 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 current preset stimulation parameter combination.

[0208] Fig.19 is a schematic diagram of the structure of a device for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm, provided in the nineteenth embodiment of the present invention. Fig.19 As shown, based on the above embodiments, the second denoising module 1502 further includes a second whole-brain average re-reference unit 150208, wherein:

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

[0210] Fig. 20 is a schematic diagram of the structure of a device for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm, provided in the twentieth embodiment of the present invention. Fig. 20 As shown, on the basis of the above embodiments, further, the device for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm provided in an embodiment of the present invention further includes an issuing module 1506, wherein:

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

[0212] The embodiments of the apparatus provided in the embodiments of the present invention can be specifically used to execute the processing flow of the above-mentioned method embodiments, and its functions are not described in detail here, and reference can be made to the detailed description of the above-mentioned method embodiments.

[0213] Fig.21 is a schematic diagram of the physical structure of a computer device provided by the seventeenth embodiment of the present invention, such as Fig.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 call the logic instructions in the memory 140 to execute the methods provided by the above-mentioned various 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 can execute the methods provided by the above-mentioned method embodiments.

[0215] This embodiment provides a computer-readable storage medium, which stores a computer program / instruction. When the computer program / instruction is executed by a processor, the computer executes the method provided by the above-mentioned method embodiments.

[0216] like Fig.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 have to include Fig.21 In addition, the computer device 600 may also include Fig.21 For components not shown in the figure, reference may be made to the prior art. It should be noted that the figure is exemplary; other types of structures may also be used to supplement or replace the structure to achieve telecommunication functions or other functions.

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

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

[0219] The input unit 120 provides input to the processor 100. The input unit 120 is, for example, a key or touch input device. The power supply 170 is used to provide power to the computer device 600. The display 160 is used to display display objects such as images and text. The display 160 can be, for example, an LCD display, but is not limited thereto.

[0220] The memory 140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, and examples of the memory 140 are sometimes referred to as EPROMs, etc. The memory 140 may also be some other type of device. The memory 140 includes a buffer 141 (sometimes referred to as a buffer memory). The memory 140 may include an application / function storage unit 142, which is used to store application programs and function programs or processes for executing the operation of the computer device 600 through 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 communication function of the computer device and / or for executing other functions of the computer device (such as a messaging application, a contact book application, etc.).

[0222] The communication module 110 includes a transmitter / receiver for transmitting and receiving signals via an antenna 111. The communication module 110 is coupled to the processor 100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.

[0223] Based on different communication technologies, multiple communication modules 110 may be provided in the same computer device, such as a cellular network module, a Bluetooth module and / or a wireless local area network module. The communication module 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide an audio output via the speaker 131 and receive an audio input from the microphone 132, thereby realizing a common telecommunication function. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 130 is also coupled to the processor 100, so that recording can be performed on the local machine through the microphone 132, and the sound stored on the local machine can be played through the speaker 131.

[0224] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented 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] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0226] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0227] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0228] In the description of this specification, the description with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0229] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is 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 in the scope of protection of the present invention.

Claims

1. A method for optimizing stimulation parameters based on collecting EEG signals by a robotic arm, characterized in that: include: Acquire EEG signals corresponding to multiple preset stimulation parameter combinations of at least one target point; wherein the EEG signals corresponding to each stimulation parameter combination of each target point are acquired by stimulating the brain of the subject based on each stimulation parameter combination of each target point by carrying a stimulation device on a robotic arm; Perform multi-stage denoising on the EEG signals corresponding to each preset stimulation parameter combination of each target point, and obtain the effective EEG signals corresponding to each preset stimulation parameter combination of each target point; Extract features of effective EEG signals corresponding to each preset stimulation parameter combination of each target point, and obtain EEG feature indicators corresponding to each preset stimulation parameter combination of each target point; According to the EEG characteristic indicators and parameter screening conditions corresponding to each preset stimulation parameter combination of each target, the optimal target combination and the optimal stimulation parameter combination of each target are obtained.

2. The method according to claim 1, characterized in that The multi-stage denoising is performed on the EEG signals corresponding to each preset stimulation parameter combination of each target point to obtain the effective EEG signals corresponding to each preset stimulation parameter combination of each target point, which includes: Based on the EEG signal corresponding to the preset stimulation parameter combination of the target point, obtaining the TMS pulse time of the current trial corresponding to the preset stimulation parameter combination; 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 and segmented and baseline corrected to obtain the EEG change signal corresponding to the preset stimulation parameter combination; Performing first-stage artifact removal and data filling on the EEG change signal corresponding to the preset stimulation parameter combination to obtain first intermediate data corresponding to the preset stimulation parameter combination; Performing second-stage artifact removal and data filling on the first intermediate data corresponding to the preset stimulation parameter combination to obtain second intermediate data corresponding to the preset stimulation parameter combination; Performing bandpass filtering and notch filtering on the second intermediate data corresponding to the preset stimulation parameter combination to obtain third intermediate data corresponding to the preset stimulation parameter combination; Perform third-stage artifact removal and data filling on the third intermediate data corresponding to the preset stimulation parameter combination to obtain a valid EEG signal corresponding to the preset stimulation parameter combination.

3. The method according to claim 2, characterized in that The performing second-stage artifact removal on the first intermediate data corresponding to the preset stimulation parameter combination comprises: Removing data of a preset time period from first intermediate data corresponding to a preset stimulation parameter combination of the target point, to obtain first temporary data corresponding to the preset stimulation parameter combination; Performing a fast independent component analysis on the first temporary data corresponding to the preset stimulation parameter combination to obtain a plurality of independent source signals corresponding to the preset stimulation parameter combination; The artifact of each independent source signal corresponding to the preset stimulation parameter combination is removed to obtain second temporary data corresponding to the preset stimulation parameter combination.

4. The method according to claim 2, characterized in that: After performing third-stage artifact removal and data filling on the third intermediate data corresponding to the preset stimulation parameter combination, the method further includes: The first data obtained after the third-stage artifact removal and data filling are subjected to whole-brain average re-reference, and the second data after the whole-brain average re-reference is used as the effective EEG signal corresponding to the preset stimulation parameter combination of the target.

5. The method according to claim 2, characterized in that: It also includes downsampling the first intermediate data corresponding to the preset stimulation parameter combination; accordingly, performing second-stage artifact removal and data filling on the downsampled first intermediate data to obtain second intermediate data corresponding to the preset stimulation parameter combination.

6. The method according to any one of claims 2 to 5, characterized in that: The EEG signal corresponding to the preset stimulation parameter combination of the target point includes multiple TMS pulse trials; accordingly, the method further includes: Obtaining downsampled first intermediate data corresponding to a preset stimulation parameter combination of a target point in each trial; Based on the downsampled first intermediate data corresponding to the preset stimulation parameter combination of the target point of each trial, calculating the root mean square of each channel of the downsampled first intermediate data corresponding to the preset stimulation parameter combination of the target point of each trial; 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 of each trial, 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, 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.

7. A method for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm, characterized in that: include: Obtaining an electroencephalogram signal corresponding to a current preset stimulation parameter combination of a current target; wherein the electroencephalogram signal corresponding to the current stimulation parameter combination of the current target is obtained by stimulating the brain of a subject based on the current stimulation parameter combination of the current target by a stimulation device carried by a robotic arm; there are multiple preset stimulation parameter combinations of the current target; Perform 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; Extract features of effective EEG signals corresponding to the current preset stimulation parameter combination of the current target, and obtain EEG feature indicators corresponding to the current preset stimulation parameter combination of the current target; Traversing the preset stimulation parameter combinations of the current target until a suitable stimulation parameter combination of the current target is obtained based on the EEG characteristic index corresponding to the current preset stimulation parameter combination of the current target and the parameter screening condition; or traversing all the preset stimulation parameter combinations of the current target and failing to obtain a suitable stimulation parameter combination of the current target; If a suitable stimulation parameter combination for the current target is obtained or all preset stimulation parameter combinations for the current target are traversed and no suitable stimulation parameter combination for the current target is obtained, the next target is obtained as the current target to obtain a suitable stimulation parameter combination for the current target until all targets are traversed.

8. The method according to claim 7, characterized in that The 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 comprises: Based on the EEG signal corresponding to the current preset stimulation parameter combination of the current target point, obtaining the TMS pulse time of the current trial corresponding to the current preset stimulation parameter combination; 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 and segmented, and baseline correction is performed to obtain the EEG change signal corresponding to the current preset stimulation parameter combination; Performing first-stage artifact removal and data filling on the EEG change signal corresponding to the current preset stimulation parameter combination to obtain first intermediate data corresponding to the current preset stimulation parameter combination; Performing second-stage artifact removal and data filling on the first intermediate data corresponding to the current preset stimulation parameter combination to obtain second intermediate data corresponding to the current preset stimulation parameter combination; Performing bandpass filtering and notch filtering on the second intermediate data corresponding to the current preset stimulation parameter combination to obtain third intermediate data corresponding to the current preset stimulation parameter combination; The third intermediate data corresponding to the current preset stimulation parameter combination is subjected to the third stage artifact removal and data filling to obtain the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target point.

9. The method according to claim 8, characterized in that The performing second artifact removal on the first intermediate data corresponding to the current preset stimulation parameter combination comprises: Removing data of a preset time period from the first intermediate data corresponding to the current preset stimulation parameter combination to obtain first temporary data corresponding to the current preset stimulation parameter combination; Performing a fast independent component analysis on the first temporary data corresponding to the current preset stimulation parameter combination to obtain a plurality of independent source signals corresponding to the current preset stimulation parameter combination; The artifact of each independent source signal corresponding to the current preset stimulation parameter combination is removed to obtain second temporary data corresponding to the current preset stimulation parameter combination.

10. The method according to claim 8, characterized in that It also includes downsampling the first intermediate data corresponding to the current preset stimulation parameter combination; accordingly, performing second-stage artifact removal and data filling on the downsampled first intermediate data to obtain second intermediate data corresponding to the current preset stimulation parameter combination.

11. The method according to claim 8, characterized in that After performing the third stage artifact removal and data filling on the third intermediate data corresponding to the current preset stimulation parameter combination, the method further includes: The third data obtained after the third stage artifact removal and data filling are subjected to whole-brain average re-reference, and the fourth data after the whole-brain average re-reference is used as the effective EEG signal corresponding to the current preset stimulation parameter combination of the target.

12. The method according to claim 7, characterized in that Also includes: After all preset stimulation parameter combinations of the current target are traversed and no suitable stimulation parameter combination of the current target is obtained, a prompt message is issued that no suitable stimulation parameter combination of the current target is found.

13. A device for optimizing stimulation parameters based on collecting EEG signals by a mechanical arm, characterized in that: include: A first acquisition module is used to acquire EEG signals corresponding to a plurality of preset stimulation parameter combinations of at least one target point; wherein the EEG signal corresponding to each stimulation parameter combination of each target point is acquired by stimulating the brain of the subject based on each stimulation parameter combination of each target point by carrying a stimulation device on a robotic arm; 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, so as to obtain the effective EEG signals corresponding to each preset stimulation parameter combination of each target point; A first feature extraction module is used to extract features of effective EEG signals corresponding to each preset stimulation parameter combination of each target point, and obtain EEG feature indicators 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 and parameter screening conditions corresponding to each preset stimulation parameter combination of each target.

14. A device for optimizing stimulation parameters based on collecting electroencephalogram signals by a mechanical 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; wherein the EEG signal corresponding to the current stimulation parameter combination of the current target is acquired by stimulating the brain of the subject based on the current stimulation parameter combination of the current target by a stimulation device carried by a robotic arm; there are multiple preset stimulation parameter combinations of the current target; 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, so as to obtain the effective EEG signal corresponding to the current preset stimulation parameter combination of the current target; The second feature extraction module is used to extract features of 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 of the current target is obtained based on the EEG characteristic index corresponding to the current preset stimulation parameter combination of the current target and the parameter screening condition; or all preset stimulation parameter combinations of the current target are traversed and no suitable stimulation parameter combination of the current target is obtained; The second traversal module is used to obtain the next target as the current target to obtain the appropriate stimulation parameter combination for the current target if a suitable stimulation parameter combination for the current target is obtained or all preset stimulation parameter combinations for the current target are traversed and no suitable stimulation parameter combination for the current target is obtained, until all targets are traversed.

15. 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 12.

Images