A thalamus-cortex connectivity detection method and related device

Abstract

The present application discloses a thalamus-cortex connectivity detection method and related devices. By implementing transcranial direct current stimulation on the target detection object and driving the target detection object to perform preset passive movements, the EEG signals of the thalamus and cortex corresponding to the stimulation electrode positions are collected, and the collected EEG signals are analyzed for coherence to obtain the target coherence degree, and the degree of connectivity between the thalamus and the cortex can be determined according to the target coherence degree. By using the embodiments of the present application, accurate detection of thalamus-cortex connectivity is achieved.

Description

Technical Field

[0001] The present application relates to the field of biomedical engineering, and in particular to a thalamus-cortex connectivity detection method and related devices. Background Art

[0002] Studies have shown that abnormal connectivity between the thalamus and cortex is associated with many neuropsychiatric diseases, such as anxiety, insomnia, depression, etc. By testing the connectivity between the thalamus and cortex, connectivity disorders between the thalamus and cortex can be discovered and quantified in a timely manner, which is of great significance for the early diagnosis and treatment of diseases and a deep understanding of brain mechanisms.

[0003] Existing technologies usually use electroencephalogram (EEG) and magnetic resonance imaging (MRI) to detect the connectivity between the thalamus and cortex, but this method requires complex data analysis and is easily limited by multiple interference factors, resulting in low reliability of the final thalamus-cortex connectivity results.

[0004] Therefore, how to accurately detect thalamus-cortex connectivity becomes an urgent problem to be solved. Summary of the invention

[0005] The embodiments of the present application provide a thalamus-cortex connectivity detection method and related devices, which can accurately detect thalamus-cortex connectivity.

[0006] In a first aspect, an embodiment of the present application provides a thalamus-cortex connectivity detection method, which is applied to a controller of a neural connectivity detection system, wherein the neural connectivity detection system further includes a transcranial direct current stimulation device, a manipulation device, and an electroencephalogram acquisition device; the manipulation device includes a piston, an air compressor, and a rubber band, wherein the piston is disposed in an internal hole of the air compressor, and the air compressor is used to drive the piston to move; the rubber band is used to connect the piston and a preset position of a target detection object; the method includes:

[0007] Determine target stimulation parameters for the target detection object; the target stimulation parameters include: target stimulation intensity, first stimulation electrode position, second stimulation electrode position, stimulation duration; the first stimulation electrode position is the electrode position corresponding to the thalamus of the target detection object; the second stimulation electrode position is the electrode position corresponding to the cerebral cortex of the target detection object;

[0008] Implementing transcranial direct current stimulation of the stimulation duration on the target detection object by the transcranial direct current stimulation device according to the target stimulation parameters, and driving the preset part of the target detection object to perform preset passive movement by the manipulation device;

[0009] The EEG acquisition device acquires EEG signals of the target detection object at the first stimulation electrode position and the second stimulation electrode position respectively to obtain a first EEG signal and a second EEG signal;

[0010] Performing coherence analysis on the first EEG signal and the second EEG signal to obtain multiple target coherences;

[0011] The degree of connectivity between the thalamus and the cortex is determined based on the multiple target coherences.

[0012] In a second aspect, an embodiment of the present application provides a thalamus-cortex connectivity detection device, which is applied to a controller of a neural connectivity detection system, wherein the neural connectivity detection system also includes a transcranial direct current stimulation device, a manipulation device, and an electroencephalogram acquisition device; the manipulation device includes a piston, an air compressor, and a rubber band, wherein the piston is arranged in an internal hole of the air compressor, and the air compressor is used to drive the piston to move; the rubber band is used to connect the piston and a preset position of a target detection object; the thalamus-cortex connectivity detection device includes: a determination unit, a coherence analysis unit, wherein,

[0013] The determination unit is used to determine target stimulation parameters for the target detection object; the target stimulation parameters include: target stimulation intensity, first stimulation electrode position, second stimulation electrode position, and stimulation duration; the first stimulation electrode position is the electrode position corresponding to the thalamus of the target detection object; the second stimulation electrode position is the electrode position corresponding to the cerebral cortex of the target detection object;

[0014] Implementing transcranial direct current stimulation of the stimulation duration on the target detection object by the transcranial direct current stimulation device according to the target stimulation parameters, and driving the preset part of the target detection object to perform preset passive movement by the manipulation device;

[0015] The EEG acquisition device acquires EEG signals of the target detection object at the first stimulation electrode position and the second stimulation electrode position respectively to obtain a first EEG signal and a second EEG signal;

[0016] The coherence analysis unit is used to perform coherence analysis on the first EEG signal and the second EEG signal to obtain multiple target coherences;

[0017] The determination unit is further used to determine the degree of connectivity between the thalamus and the cortex according to the multiple target coherences. In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.

[0018] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the embodiment of the present application.

[0019] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0020] By implementing the embodiments of the present application, transcranial direct current stimulation can be implemented on the target detection object through a transcranial direct current stimulation device, and a preset part of the target detection object can be driven to perform a preset passive movement through a manipulation device, and the EEG signals of the target detection object at the stimulation electrode position corresponding to the thalamus and the second stimulation electrode position corresponding to the cortex are respectively collected through an EEG acquisition device to obtain a first EEG signal and a second EEG signal, and coherence analysis is performed on the first EEG signal and the second EEG signal to obtain multiple target coherences, and the degree of connectivity between the thalamus and the cortex is determined based on the multiple target coherences, thereby achieving accurate detection of thalamus-cortex connectivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0022] Figure 1 is a structural schematic diagram of a neural connectivity detection system provided in an embodiment of the present application;

[0023] Figure 2 It is a functional block diagram of a transcranial direct current stimulation device provided in an embodiment of the present application;

[0024] Figure 3 is a functional block diagram of a control device provided in an embodiment of the present application;

[0025] Figure 4It is a flowchart of a thalamus-cortex connectivity detection method provided in an embodiment of the present application;

[0026] Figure 5 is a schematic diagram of an application scenario of a control device provided in an embodiment of the present application;

[0027] Figure 6 It is a block diagram of the functional units of a thalamus-cortex connectivity detection device provided in an embodiment of the present application;

[0028] Figure 7 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0031] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0032] The following is an explanation of the relevant contents, concepts, meanings, technical issues, technical solutions, beneficial effects, etc. involved in the embodiments of the present application.

[0033] First, some terms involved in this application are explained:

[0034] Thalamus: The thalamus is a relay station for the transmission of sensory information. Almost all sensory signals (except for smell) pass through the thalamus before reaching the cerebral cortex. The thalamus performs preliminary processing and integration on these signals and then transmits them to the corresponding areas of the cerebral cortex.

[0035] Cortex: The cerebral cortex is responsible for processing higher-level neural functions, including thinking, consciousness, memory, language, learning, vision, hearing, decision-making, and motor control.

[0036] Transcranial direct current stimulation: Transcranial direct current stimulation (tDCS) is a non-invasive neuroelectric stimulation technology that applies weak current to the scalp through positive and negative electrodes to stimulate specific brain areas and regulate the neural activity and / or excitability of the cerebral cortex. Generally, anode (positive) stimulation increases neuronal excitability, while cathode (negative) stimulation inhibits neuronal excitability.

[0037] 10-20 standard lead system: The 10-20 standard lead system is a naming and positioning system used to standardize electrode placement in electroencephalographic recordings. It records electrical activity in different areas of the brain by placing electrodes at specific percentages on the scalp. "10" and "20" represent the percentage of the distance between electrodes to the widest part of the head and the front-to-back distance of the head, respectively.

[0038] Anti-aliasing filter: An anti-aliasing filter is a low-pass filter whose main function is to filter out frequency components above the Nyquist frequency in a signal before the signal is sampled. In digital signal processing, according to the Nyquist sampling theorem, when digitizing an analog signal, the sampling frequency must be at least twice the highest frequency of the signal to avoid aliasing. Aliasing means that if the sampling frequency is not sufficient to capture all the frequency components of the signal, then the frequency components above half the sampling frequency will mistakenly appear in the low-frequency part of the sampled signal, resulting in loss or distortion of the original signal information.

[0039] Nyquist frequency: Nyquist frequency is the minimum sampling frequency required to prevent signal aliasing.

[0040] Butterworth filter: A Butterworth filter is a type of electronic filter that has a flat frequency response at the pass frequency (i.e., the cutoff frequency of the filter) so that it does not cause phase distortion or amplitude change in the signal at this frequency.

[0041] Hanning Window: Hanning Window is a window function commonly used in signal processing, especially in frequency domain analysis. It is a weighted function used to reduce the mutation effect at both ends of the sample, thereby reducing spectral leakage.

[0042] See also Figure 1 , Figure 1 This is a structural schematic diagram of a neural connectivity detection system provided in an embodiment of the present application. As shown in the figure, the neural connectivity detection system 10 includes a transcranial direct current stimulation device 101, a manipulation device 102, an electroencephalogram acquisition device 103 and a controller 104.

[0043] Among them, the neural connectivity detection system 10 is a system for detecting the connectivity between the thalamus and cortex of the subject, which applies weak direct current to the subject's scalp through a transcranial direct current stimulation device 101 to adjust the excitability of the cerebral cortex, and performs passive movements on the subject in combination with a manipulation device 102, and records the electrical activity generated by the subject's brain during stimulation and movement through an electroencephalogram acquisition device 103 and collects electroencephalogram signals for evaluation and analysis to determine the degree of connectivity between the subject's thalamus and cortex.

[0044] The controller 104 is the core control unit of the neural connectivity detection system 10, which is mainly responsible for determining the stimulation parameters for the subject, including: stimulation intensity, first stimulation electrode position, second stimulation electrode position and stimulation duration. Then, the subject is subjected to a pre-stimulation test to ensure that the subject can tolerate the stimulation intensity. After the pre-stimulation test, the controller 104 controls the transcranial direct current stimulation device 101 to apply a weak direct current to the subject's scalp for stimulation, and controls the manipulation device 102 to perform passive movements on the subject, while controlling the EEG acquisition device 103 to record and collect the EEG signals of the subject's brain. The degree of connectivity between the thalamus and the cortex is determined by collecting the EEG signals of the thalamus and the cortex and evaluating and analyzing them, and determining the synchronization of the EEG signals between the thalamus and the cortex.

[0045] In an embodiment of the present application, the transcranial direct current stimulation device 101 can apply a weak direct current to the subject's scalp to regulate the excitability of the cerebral cortex. This transcranial direct current stimulation can change the resting membrane potential of neurons, thereby affecting the activity and connectivity of brain regions. The manipulator 102 is mainly composed of a piston, an air compressor and a rubber band, which is responsible for controlling the subject's fingers or limbs, wherein the piston inside the air compressor moves under the action of air pressure, and the preset part of the subject is driven to perform passive movement through the connected rubber band, thereby simulating the subject's natural movement. The electroencephalogram acquisition device 103 is mainly composed of electroencephalogram electrodes, wires, signal amplifiers, analog-to-digital converters, and data recording and analysis software. It provides data support for analyzing the connectivity between the thalamus and the cortex by placing electrodes in specific brain areas and capturing changes in electroencephalogram signals.

[0046] See also Figure 2 , Figure 2 is a functional block diagram of a transcranial direct current stimulation device provided in an embodiment of the present application, such as Figure 2 As shown, the transcranial direct current stimulation device 101 includes a processing device 201 , a storage device 202 , an interface device 203 , a communication device 204 , a display device 205 , an input device 206 , a speaker 207 and a stimulation device 208 .

[0047] Among them, the transcranial direct current stimulation device 101 is mainly used to apply transcranial direct current stimulation to the scalp of the subject. In the transcranial direct current stimulation device 101, the processing device 201 can be a central processing unit (CPU) or a microcontroller (MCU), etc., which is mainly responsible for executing preset program instructions, processing data, and controlling the overall operation of the transcranial direct current stimulation device 101. The storage device 202 is used to store the operating software, preset programs and user data of the device, which can be a read-only memory (ROM), a random access memory (RAM), a hard disk, etc. The interface device 203 provides a physical connection, such as a USB interface and a headphone interface, allowing the user to connect an external device, such as a computer or a data recorder. The communication device 204 is responsible for wired or wireless data transmission, including Wi-Fi and Bluetooth communication. These communication methods enable the device to exchange data with an external system, which is convenient for remote monitoring and program updates. The display device 205 can be a liquid crystal display or a touch display, etc., which is used to provide the user with visual information of the device status, stimulation parameters and operation feedback. The input device 206 may be a touch screen and a keyboard, which is used to provide an intuitive way for the operator to input commands and adjust settings, so that the operator can conveniently control the stimulation process. The speaker 207 may be used to provide voice feedback for operation confirmation, such as a prompt tone when the user inputs a command or adjusts a setting through the input device, or when an abnormal situation or a matter requiring the user's attention is detected, the speaker may be used to issue an alarm or notification sound, etc. The stimulation device 208 is the core component of the transcranial direct current stimulation device 101, which is responsible for generating and delivering electrical stimulation to the target individual. By precisely controlling the intensity, frequency, and duration of the current, the stimulation device 208 can regulate specific neural pathways.

[0048] See also Figure 3 , Figure 3 is a functional block diagram of a control device provided in an embodiment of the present application, such as Figure 3 As shown, the operating device 102 includes a piston 301 , an air compressor 302 and a rubber band 303 .

[0049] The manipulation device 102 is used to control the subject's limb movements to assist in the collection and analysis of EEG signals. The manipulation device 102 is mainly composed of three parts: a piston 301, an air compressor 302, and a rubber band 303. These three parts work together to achieve manipulation and control of the subject's fingers or limbs.

[0050] The piston 301 is a component for transmitting power in the operating device 102, and can be moved by the pressure provided by the air compressor 302, thereby simulating natural movement. The piston 301 is connected to the air compressor 302 through a plastic tube, and can be extended and retracted when the air compressor 302 provides gas pressure.

[0051] The air compressor 302 is the power source of the manipulator 102, which can adjust the switch of the airflow through a built-in controllable electric valve, and the opening and closing of the electric valve controls the movement of the piston 301, thereby adjusting the movement of the subject's fingers or limbs.

[0052] The rubber band 303 plays the role of connecting and transmitting movement in the operating device 102. One end of the rubber band is connected to the piston 301, and the other end can be fixed on the thumb of the subject. When the piston 301 moves, the piston 301 can drive the thumb to perform corresponding bending or straightening movements, simulating real hand movements.

[0053] In the embodiment of the present application, when using the manipulation device 102, firstly, the rubber band 303 is fixed on the thumb of the subject to ensure a firm connection, and then the air compressor 302 is started to control the movement of the piston 301 by adjusting the opening and closing of the electric valve, thereby driving the thumb to perform a preset passive movement. In the entire passive movement process, the movement state and EEG signals of the subject can be monitored in real time.

[0054] See also Figure 4 , Figure 4 1 is a flow chart of a thalamus-cortex connectivity detection method provided in an embodiment of the present application, the method is applied to a controller of a neural connectivity detection system, the neural connectivity detection system also includes a transcranial direct current stimulation device, a manipulation device, and an electroencephalogram acquisition device; the manipulation device includes a piston, an air compressor, and a rubber band, the piston is arranged in an internal hole of the air compressor, the air compressor is used to drive the piston to move; the rubber band is used to connect the piston and a preset position of a target detection object; the method includes but is not limited to the following steps:

[0055] S401. Determine target stimulation parameters for the target detection object; the target stimulation parameters include: target stimulation intensity, first stimulation electrode position, second stimulation electrode position, and stimulation duration; the first stimulation electrode position is the electrode position corresponding to the thalamus of the target detection object; the second stimulation electrode position is the electrode position corresponding to the cerebral cortex of the target detection object.

[0056] Among them, the target detection object refers to the subject whose thalamus-cortex connectivity needs to be detected. Before stimulating the target detection object, it is necessary to determine the stimulation parameters of the transcranial direct current stimulation device and set the stimulation parameters for the transcranial direct current stimulation device so that the transcranial direct current stimulation device can implement transcranial direct current stimulation for the target detection object. The stimulation parameters include target stimulation intensity, first stimulation electrode position, second stimulation electrode position, and stimulation duration.

[0057] In a specific embodiment, the electrode positions can be marked on the scalp of the target detection object according to the 10-20 standard lead system, and C 3 The position is taken as the corresponding electrode position of the target thalamus, and the position of the first stimulation electrode is C 3 Location, select F p2 The position is the electrode position corresponding to the cerebral cortex of the target detection object, and the position of the second stimulation electrode is F p2 position, and further set the target stimulation intensity to 1 mA and the stimulation duration to 20 minutes.

[0058] Furthermore, an EEG cap is mounted on the head of the target detection object, wherein the EEG cap marks the specific positions of the electrodes according to the 10-20 standard lead system, and then the anode electrode of the EEG cap is placed at the first stimulation electrode position, and the cathode electrode of the EEG cap is placed at the second stimulation electrode position. In order to optimize the transmission efficiency of the stimulation current and reduce the impedance between the electrode and the scalp of the target detection object, a conductive colloid can be applied between the electrode and the scalp of the target detection object to reduce the resistance and improve the stimulation effect.

[0059] S402, performing transcranial direct current stimulation for the stimulation duration on the target detection object according to the target stimulation parameters by means of the transcranial direct current stimulation device, and driving the preset part of the target detection object to perform preset passive movement by means of the operating device.

[0060] The preset part is a specific moving part of the target detection object. When the target detection object actively activates the preset part, the thalamus can receive and process the nerve signals from the moving part, and transmit the nerve signals to the cerebral cortex for further processing. The preset passive movement is a pre-set passive movement, which drives the preset part of the target detection object to perform a pre-set movement through an additional device to induce the exchange of nerve signals between the thalamus and the cerebral cortex of the target detection object.

[0061] In a specific embodiment, the stimulation parameters are input into the transcranial direct current stimulation device so that the transcranial direct current stimulation device can perform transcranial direct current stimulation of a desired detection object for a desired duration. At the same time, by controlling the air compressor, the piston inside the air compressor is driven to regularly expand and contract, so as to drive the preset part of the target detection object to perform a preset passive movement.

[0062] In a possible embodiment, the preset position is the right thumb of the target detection object, and the right thumb is connected to the piston of the operating device through a rubber band. With the help of the piston, the piston can make the right thumb of the target detection object perform full-range passive movements in all directions, such as straightening, bending, rotating inward or outward, etc.

[0063] Furthermore, within 20 minutes of stimulation, preset passive movements are performed on the right thumb of the target detection object, wherein each group of movement cycles is 20 seconds, 7 passive movements are performed within 20 seconds, and the duration of each passive movement can be 2-3 seconds. After each group of 7 passive movements, 20 seconds of rest are performed. During the total 20 minutes of stimulation, 30 groups of passive movements are performed, and the total number of passive movements is 210 times, which specifically includes 30 passive movements and 30 rests.

[0064] For easier understanding, see Figure 5 , Figure 5 This is a schematic diagram of an application scenario of a manipulation device provided in an embodiment of the present application. As shown in the figure, the manipulation device includes a piston, an air compressor, and a rubber band. The rubber band is used to connect the right thumb of the target detection object to the piston. At the same time, the piston is a movable component inside the air compressor, which is connected to the right thumb of the target detection object through a rubber band. When the manipulation device is started, the air compressor starts to work, and the inside is inflated, so that the piston moves outward, thereby driving the right thumb of the target detection object to bend. When the air compressor releases gas, the piston is driven to move inside the air compressor, driving the right thumb of the target detection object to straighten, and by driving the piston inside the air compressor to regularly expand and contract, the preset part of the target detection object performs a preset passive movement.

[0065] Optionally, before the above step S402, in which the transcranial direct current stimulation device performs the transcranial direct current stimulation of the stimulation duration on the target detection object according to the target stimulation parameters, the following steps may also be included:

[0066] A21, obtaining the current stimulation intensity of the transcranial direct current stimulation device at the current moment;

[0067] A22. Increasing the current stimulation intensity to a preset stimulation intensity within a first preset time period;

[0068] A23, continuously stimulating the target detection object according to the preset stimulation intensity within a second preset time period, monitoring the physiological signal of the target detection object, and obtaining a target physiological signal; the start time of the second preset time period is later than the end time of the first preset time period;

[0069] A24. When the target physiological signal satisfies a first preset condition, the preset stimulation intensity is determined as the target stimulation intensity; the first preset condition is that the target physiological signal is within a preset safety range;

[0070] A25. When the target physiological signal does not satisfy the first preset condition, adjusting the preset stimulation intensity based on the target physiological signal to obtain a standard stimulation intensity;

[0071] A26. Determine the standard stimulation intensity as the target stimulation intensity.

[0072] It should be noted that before implementing transcranial direct current stimulation of a certain duration on the target detection object through a transcranial direct current stimulation device according to the target stimulation parameters, a pre-stimulation test is required on the target detection object to ensure that the target detection object can tolerate the stimulation.

[0073] Among them, the first preset time period is pre-set, indicating that the transcranial direct current stimulation device slowly increases the stimulation intensity within the first preset time period. The second preset time period is pre-set, indicating that the transcranial direct current stimulation device continues to stimulate the target detection object within the second preset time period, and the start time of the second preset time period is later than the end time of the first preset time period. The preset condition is that the physiological signal of the target detection object is within the preset safety interval, indicating that the target detection object can withstand the stimulation of the transcranial direct current stimulation device. The preset safety interval is an acceptable range of physiological parameters of the preset target detection object. According to the possible impact of neural connectivity detection, the preset safety interval can be adaptively adjusted based on the standard safety interval. The preset stimulation intensity can be a preset target stimulation intensity, or it can be a stimulation intensity preset according to the condition of the subject.

[0074] In a specific embodiment, by performing a pre-stimulation test on the target detection object through a transcranial direct current stimulation device, the current stimulation intensity of the transcranial direct current stimulation device at the current moment can be obtained, and the current stimulation intensity is less than the preset stimulation intensity, wherein the preset stimulation intensity can be the stimulation intensity pre-set for the transcranial direct current stimulation device, the current stimulation intensity is increased to the preset stimulation intensity within a first preset time period, and the target detection object is continuously stimulated according to the preset stimulation intensity within a second preset time period, and the physiological signals of the target detection object are monitored at the same time, and the target physiological signals can be obtained, wherein the target physiological signals include but are not limited to heart rate, blood pressure, respiratory rate, body temperature, skin electrodermal activity, electroencephalogram signals, etc., and the target physiological signals are analyzed to determine whether the target detection object can withstand the preset stimulation intensity.

[0075] Furthermore, when the target physiological signal satisfies the first preset condition, that is, the target physiological signal is in a preset safety interval, the preset stimulation intensity is determined as the target stimulation intensity, and the transcranial direct current stimulation device can implement stimulation of the target stimulation intensity on the target detection object. When the target physiological signal does not meet the first preset condition, it means that the target detection object is intolerant to the stimulation of the preset stimulation intensity during the pre-test process, and it is necessary to adjust the preset stimulation intensity based on the target physiological signal to obtain the standard stimulation intensity, and determine the adjusted standard stimulation intensity as the target stimulation intensity. It can be seen that by performing a pre-stimulation test on the target detection object, it can be ensured that the target detection object can withstand the target stimulation intensity, so as to reduce the interference of the connectivity detection between the thalamus and the cortex, and ensure the accuracy of the connectivity detection and the safety of the target detection object.

[0076] In one possible embodiment, the current stimulation intensity of the transcranial direct current stimulation device at the current moment is 0mA. After the transcranial direct current stimulation device is turned on, the stimulation intensity is slowly increased to 1mA within 15s, and the target detection object is stimulated for 10s based on the stimulation intensity of 1mA, while the heart rate change of the target detection object is monitored. If it is determined that the heart rate change is always maintained within 70-110 times / minute, it means that the target detection object can withstand the stimulation intensity of 1mA. Otherwise, the current stimulation intensity is adjusted so that the target detection object can withstand the adjusted stimulation intensity. After determining the stimulation intensity that the target detection object can withstand, the stimulation of the transcranial direct current stimulation device can be slowly reduced to 0 within 15s to prepare for neural connectivity detection.

[0077] Optionally, when the target physiological signal includes a heart rate, the above step A25, when the target physiological signal does not meet the first preset condition, adjusting the preset stimulation intensity based on the target physiological signal to obtain a standard stimulation intensity, may include the following steps:

[0078] B21, obtaining the target safety interval and standard heart rate value of the heart rate;

[0079] B22, obtaining a difference between the heart rate and the standard heart rate value to obtain a heart rate difference;

[0080] B23, determining an adjustment parameter of the preset stimulation intensity according to the heart rate difference to obtain a target adjustment parameter;

[0081] B24. When the heart rate is not within the target safety interval, determining a deviation of the heart rate based on the target safety interval to obtain a target deviation;

[0082] B25, determining a target adjustment factor corresponding to the target deviation;

[0083] B26. Adjust the preset stimulation intensity according to the target adjustment factor and the target adjustment parameter to obtain the standard stimulation intensity.

[0084] Among them, when the target physiological signal to be detected and analyzed is the heart rate, a target safety interval is set, which represents the safety interval of the heart rate change of the target detection object when receiving stimulation, and a standard heart rate value can be set, which represents the expected heart rate level of the target detection object after stimulation. Then, the adjustment parameters and adjustment factors of the preset stimulation intensity can be determined according to the deviation between the heart rate change data and the preset standard to adjust the preset stimulation intensity.

[0085] In a specific embodiment, the average value of the heart rate of the target detection object detected within the second preset time period can be used as the evaluated heart rate, and then the target safety interval and the standard heart rate value of the heart rate can be obtained. The difference between the heart rate and the standard heart rate value is obtained to obtain the heart rate difference, and the adjustment parameter of the preset stimulation intensity can be determined according to the heart rate difference to obtain the target adjustment parameter. When the heart rate is not in the target safety interval, the deviation of the heart rate based on the target safety interval is determined to obtain the target deviation, and the target adjustment factor corresponding to the target deviation is determined. The preset stimulation intensity is adjusted according to the target adjustment factor and the target adjustment parameter to obtain the standard stimulation intensity.

[0086] In one possible embodiment, the preset stimulation intensity is set to 1mA, the target safety interval of the heart rate of the target detection object is set to 70-110 beats / minute, the standard heart rate value is set to 90 beats / minute, and during the continuous stimulation period of 10s, the average heart rate of the target detection object is detected to be 60 beats / minute, then the heart rate difference is 30 beats / minute, and the target adjustment parameter is 6, that is, the heart rate difference is set to be 5 beats / minute, and the target adjustment parameter is the ratio of the heart rate difference to 5. The adjustment amplitude of the stimulation intensity of each unit of the target adjustment parameter is set to 0.05mA. When the target adjustment parameter is 6 this time, the adjustment amplitude is 0.3mA. At the same time, it is detected that the heart rate of the target detection object does not exceed the lower limit of the target safety interval, then the difference between the heart rate and the lower limit of the target safety interval is determined to be 10 according to the heart rate and the lower limit of the target safety interval, and the interval length of the target safety interval is 40, then the target deviation is 1 / 4, and the target adjustment factor is 1 / 4. The preset stimulation intensity is adjusted based on the following formula: standard stimulation intensity = preset stimulation intensity + (0.05 × target adjustment parameter × (1 + target adjustment factor)). Then the standard stimulation intensity is obtained to be 1.375 mA.

[0087] In one possible embodiment, the preset stimulation intensity is set to 1mA, the target safety interval of the heart rate of the target detection object is set to 70-110 beats / minute, the standard heart rate value is set to 90 beats / minute, and during the continuous stimulation period of 10s, the average heart rate of the target detection object is detected to be 115 beats / minute, then the heart rate difference is 25 beats / minute, and the target adjustment parameter is 5, that is, the heart rate difference is set to be 5 beats / minute, and the target adjustment parameter is the ratio of the heart rate difference to 5. The adjustment amplitude of the stimulation intensity of each unit of the target adjustment parameter is set to 0.05mA. When the target adjustment parameter is 5 this time, the adjustment amplitude is 0.25mA. At the same time, it is detected that the heart rate of the target detection object exceeds the upper limit of the target safety interval, then the difference between the heart rate and the upper limit of the target safety interval is determined to be 5 according to the heart rate and the upper limit of the target safety interval, and the interval length of the target safety interval is 40, then the target deviation is 1 / 8, and the target adjustment factor is 1 / 8. The preset stimulation intensity was adjusted based on the following formula: standard stimulation intensity = preset stimulation intensity - (0.05 × target adjustment parameter × (1 + target adjustment factor)). Then the standard stimulation intensity was obtained to be 0.71875 mA.

[0088] S403 , using the EEG acquisition device to respectively acquire EEG signals of the target detection object at the first stimulation electrode position and the second stimulation electrode position to obtain a first EEG signal and a second EEG signal.

[0089] Among them, the EEG acquisition device may include EEG electrodes, wires, signal amplifiers, analog-to-digital converters, and data recording and analysis software. The EEG acquisition device is connected to the stimulation electrodes of the EEG cap, and can collect and record the EEG signals of the target detection object at the specific stimulation electrode position.

[0090] In a specific embodiment, the first stimulation electrode position and the second stimulation electrode position are connected to an EEG acquisition device. During the period when the transcranial direct current stimulation device stimulates the target detection object, the weak EEG signal is amplified by the signal amplifier of the EEG acquisition device, and the analog EEG signal is converted into a digital signal by an analog-to-digital converter. Before the signal is converted into a digital signal by the analog-to-digital converter, an anti-aliasing filter can be applied to remove signal components higher than the Nyquist frequency to reduce aliasing. The sampled digital signal includes EEG activity signals of multiple frequency bands, including the Theta band, the Alpha band, the Beta band, etc., which are not limited here. By recording these EEG activity signals through data recording and analysis software, the first EEG signal and the second EEG signal can be obtained.

[0091] S404: Perform coherence analysis on the first EEG signal and the second EEG signal to obtain multiple target coherences.

[0092] Among them, the multiple target coherences obtained by performing coherence analysis on the first EEG signal and the second EEG signal can reflect the synchronization of the activities of the two EEG signals in the same frequency band.

[0093] In a specific embodiment, the EEG signal at the first stimulation electrode position is collected to obtain a first EEG signal, and the EEG signal at the second collection electrode position is collected to obtain a second EEG signal. Coherence analysis is performed on the first EEG signal and the second EEG signal to evaluate the functional connection strength between the thalamus and the cortex.

[0094] In an embodiment of the present application, the collected EEG signal includes signals of multiple frequency bands, and the signal of a specific frequency band can be extracted for coherence analysis. Specifically, the first EEG signal and the second EEG signal can be preprocessed using a bandpass filter to extract the signal of the specific frequency band, and the filtered signal can be Fourier transformed to convert it from a time domain signal to a frequency domain signal, and the amplitude and phase information at each frequency point can be calculated. By analyzing the coherence of the first EEG signal and the second EEG signal in a specific frequency band, that is, the synchronization of the amplitude spectra of the first EEG signal and the second EEG signal in the specific frequency band, multiple target coherences can be obtained. The coherence can reflect the degree of correlation between the thalamus and the cortex in a specific frequency band. A high coherence indicates that the activities of the thalamus and the cortex in the specific frequency band are highly synchronized, that is, the connectivity between the thalamus and the cortex is strong.

[0095] Optionally, the above step S404, performing coherence analysis on the first EEG signal and the second EEG signal to obtain multiple target coherences, may include the following steps:

[0096] A41. Filter the first EEG signal and the second EEG signal based on a preset filter to obtain a third EEG signal and a fourth EEG signal;

[0097] A42. Discretize the third EEG signal and the fourth EEG signal respectively by using a preset window function to obtain a first time domain signal and a second time domain signal;

[0098] A43. Perform Fourier transform on the first time domain signal and the second time domain signal respectively to obtain a first frequency domain signal and a second frequency domain signal;

[0099] A44, determining frequency points at the same frequency in the first frequency domain signal and the second frequency domain signal to obtain multiple frequency point groups; each frequency point group includes a first frequency point set and a second frequency point set; each frequency point set includes multiple frequency points; the first frequency point set is the frequency point set of the first frequency domain signal; the second frequency point set is the frequency point set of the second frequency domain signal;

[0100] A45. Determine the coherence of the first frequency domain signal and the second frequency domain signal according to the multiple frequency point groups to obtain multiple target coherences.

[0101] Specifically, a preset filter is used to preprocess the first EEG signal and the second EEG signal to extract signals in a specific frequency band, wherein the specific frequency band may be a signal in the Alpha band, and the preset filter may be a Butterworth 2nd-order filter. The first EEG signal and the second EEG signal are filtered using a Butterworth 2nd-order filter, and the bandpass filter range is 8-13Hz. Based on this, the Alpha band EEG signal can be extracted from the first EEG signal and the second EEG signal to obtain the third EEG signal and the fourth EEG signal, respectively.

[0102] Further, the third EEG signal and the fourth EEG signal are discretized respectively by using a preset window function to convert them into time domain signals suitable for Fourier transform, wherein the preset window function may be a Hanning window function, and the third EEG signal and the fourth EEG signal are discretized respectively by the Hanning window function to obtain a first time domain signal and a second time domain signal, wherein the calculation formula for the discretization processing is as follows:

[0103]

[0104] In the above formula, ω(n) is the value of the window function at discrete time point n; n is the index of the discrete time point, n=0,1,2,...,N-1, where N is the total number of signal samples; N is the length of the window function, that is, the total number of signal samples.

[0105] Further, the first time domain signal and the second time domain signal are respectively subjected to Fourier transform to obtain a first frequency domain signal and a second frequency domain signal, wherein the Fourier transform may use a fast Fourier transform (FFT) to obtain a first frequency domain signal and a second frequency domain signal, wherein the Fourier transform formula is as follows:

[0106]

[0107] In the above formula, F(k) is the value of the frequency domain signal at the kth frequency point, k=n=0,1,2,...,N-1, where N is the total number of signal samples; ω(n) is the value of the time domain signal at the nth time point after window function processing, n=0,1,2,...,N-1; i is the imaginary unit.

[0108] Further, the frequency points at the same frequency in the first frequency domain signal and the second frequency domain signal are determined to obtain multiple frequency point groups, each frequency point group includes a first frequency point set and a second frequency point set, and each frequency point set includes multiple frequency points, wherein the first frequency point set is the frequency point set of the first frequency domain signal, and the second frequency point set is the frequency point set of the second frequency domain signal.

[0109] It should be noted that since the filtering range of the preset filter is 8-13 Hz, if the coherence of the third EEG signal and the fourth EEG signal is calculated directly within this frequency range, it may not be possible to accurately reflect the coherence of the signals due to insufficient frequency resolution. Therefore, 8-13 Hz can be divided into multiple sub-frequency bands, such as 8-8.5 Hz, 8.5-9 Hz, etc., and the frequency points in the sub-frequency bands are sampled as the same frequency points to obtain a set of frequency points, and multiple frequency point groups can be obtained based on multiple sub-frequency bands.

[0110] Specifically, the coherence of the two frequency domain signals at each frequency point can be calculated based on the first frequency point set and the second frequency point set, and the correlation analysis of the amplitude spectra of the first EEG signal and the second EEG signal at a specific frequency can be determined to obtain multiple target coherences. Analysis of the multiple target coherences can accurately reflect the synchronization of the first EEG signal and the second EEG signal in the alpha frequency band, thereby providing connectivity information between the thalamus and the cortex.

[0111] Optionally, the above step A45, determining the coherence of the first frequency domain signal and the second frequency domain signal according to the multiple frequency point groups to obtain multiple target coherences, may include the following steps:

[0112] B41. Determine a target frequency point group; the target frequency point group is any frequency point group among the multiple frequency point groups;

[0113] B42. Determine the autopower spectral density of the first frequency domain signal according to the first frequency point set in the target frequency point group to obtain a first power spectral density;

[0114] B43, determining the autopower spectral density of the second frequency domain signal according to the second frequency point set in the target frequency point group to obtain a second power spectral density;

[0115] B44. Determine the cross power spectrum density of the first frequency domain signal and the second frequency domain signal according to the target frequency point group to obtain a third power spectrum density;

[0116] B45. Determine a target coherence based on the first power spectral density, the second power spectral density and the third power spectral density.

[0117] Specifically, any frequency point group is selected from multiple frequency point groups as the target frequency point group, and the power spectral density of the first frequency domain signal and the second frequency domain signal is calculated according to the target frequency point group. The auto-power spectral density of the first frequency domain signal is determined according to the first frequency point set in the target frequency point group to obtain the first power spectral density, and the auto-power spectral density of the second frequency domain signal is determined according to the second frequency point set in the target frequency point group to obtain the second power spectral density, wherein the calculation formula of the auto-power spectral density is as follows:

[0118]

[0119] In the above formula, P(k) is the power spectrum density at the kth frequency point; f 2 is the upper limit of the sub-band of interest; f 1 is the lower limit of the sub-band of interest; F(k) is the value of the frequency domain signal at the kth frequency point, k=n=0,1,2,...,N-1, where N is the total number of signal samples.

[0120] Further, the cross power spectrum density of the first frequency domain signal and the second frequency domain signal is determined according to the target frequency point group to obtain a third power spectrum density, wherein the calculation formula of the cross power spectrum density is as follows:

[0121]

[0122] In the above formula, P xy (k) is the cross power spectral density of the two signals x and y at the frequency point k, where is the cross power spectral density of the first frequency domain signal and the second frequency domain signal at the frequency point k; f 2 is the upper limit of the sub-band of interest; f 1 is the lower limit of the sub-band of interest; F x (k) is the value of the first frequency domain signal at the kth frequency point, k=n=0,1,2,...,N-1, where N is the total number of signal samples; F y (k) is the value of the second frequency domain signal at the kth frequency point.

[0123] Next, the target coherence is determined according to the first power spectral density, the second power spectral density, and the third power spectral density, wherein the calculation formula of the coherence is as follows:

[0124]

[0125] In the above formula, C xy (k) is the coherence of the first frequency domain signal and the second frequency domain signal at the frequency point k, and its value range is between 0 and 1. When the coherence is close to 1, it means that the two signals are highly correlated, and when it is close to 0, it means that the two signals are basically unrelated; P xy(k) is the cross power spectrum density of the first frequency domain signal and the second frequency domain signal, that is, the third power spectrum density; P x (k) is the auto-power spectral density of the first frequency domain signal, that is, the first power spectral density; P y (k) is the autopower spectral density of the second frequency domain signal, that is, the second power spectral density.

[0126] In a specific embodiment, coherence analysis is performed on the first EEG signal and the second EEG signal to determine whether the thalamus and the cortex are synchronously active at a specific frequency, and the strength of connectivity between the thalamus and the cortex can be evaluated based on the multiple target coherences obtained.

[0127] S405: Determine the degree of connectivity between the thalamus and the cortex according to the multiple target coherences.

[0128] In a specific embodiment, the overall coherence of the first EEG signal and the second EEG signal is determined by multiple target coherences, and the degree of connectivity between the thalamus and the cortex is analyzed based on the overall coherence. If the overall coherence is higher than or equal to the preset first threshold, it can be determined that there is a strong connectivity between the thalamus and the cerebral cortex. If the overall coherence is lower than or equal to the preset second threshold, it can be determined that there is a weak connectivity between the thalamus and the cerebral cortex. If the overall coherence is between the preset second threshold and the preset first threshold, it is necessary to further determine the connectivity between the thalamus and the cerebral cortex based on the degree of proximity of the overall coherence to the two thresholds. Among them, the preset first threshold and the preset second threshold are pre-set thresholds, which can be set according to the conditions of different populations, such as different ages, genders, health conditions, etc.

[0129] Optionally, the above step S405, determining the degree of connectivity between the thalamus and the cortex according to the multiple target coherences, may include the following steps:

[0130] A501, determining an average value of the plurality of target coherences to obtain a target average value;

[0131] A502, when the target average value is greater than or equal to a preset first coherence degree, determining that the connectivity between the thalamus and the cortex is in a strong connection state;

[0132] A503, when the target average value is less than or equal to the preset second coherence degree, determining that the connectivity between the thalamus and the cortex is in a weak connection state;

[0133] A504, when the target average value is greater than the preset second coherence and the target average value is less than the preset first coherence, fitting is performed according to the multiple target coherences to obtain a target coherence curve; the horizontal axis of the target coherence curve is frequency and the vertical axis is coherence;

[0134] A505, determining a target weighted coherence according to the multiple target coherences and the target average value;

[0135] A506, determining a segmentation line according to the target weighted coherence; the segmentation line is parallel to the horizontal axis;

[0136] A507, determining a first area value of a region above the dividing line and a second area value of a region below the dividing line in the target coherence curve;

[0137] A508. Determine a ratio between the first area value and the second area value to obtain a target ratio;

[0138] A509, obtaining a preset ratio threshold;

[0139] A510, when the target ratio is greater than or equal to the preset ratio threshold, determining that the connectivity between the thalamus and the cortex is in a strong connection state;

[0140] A511. When the target ratio is less than the preset ratio threshold, it is determined that the connectivity between the thalamus and the cortex is in a weak connection state.

[0141] Among them, the preset first coherence is pre-set and used to determine that the connectivity between the thalamus and the cortex is a strong connection coherence, the preset second coherence is pre-set and used to determine that the connectivity between the thalamus and the cortex is a weak connection coherence, and the preset ratio threshold is a pre-set ratio, which represents the standard value of the ratio of the number of target coherences greater than the target weighted coherence to the number of target coherences less than the target weighted coherence among multiple target coherences.

[0142] In a specific embodiment, an average value of multiple target coherences is determined to obtain a target average value. When the target average value is greater than or equal to a preset first coherence, it can be determined that the connectivity between the thalamus and the cortex is in a strongly connected state. When the target average value is less than or equal to a preset second coherence, it can be determined that the connectivity between the thalamus and the cortex is in a weakly connected state. When the target average value is greater than the preset second coherence and the target average value is less than the preset first coherence, it can be considered that the current multiple target coherences are unstable coherences, and it is necessary to perform an overall trend analysis on the multiple target coherences to determine whether the multiple target coherences tend to the preset first coherence. If the multiple target coherences tend to the preset first coherence, it can be determined that the connectivity between the thalamus and the cortex is in a strongly connected state. Otherwise, if the multiple target coherences tend to the preset second coherence, it can be determined that the connectivity between the thalamus and the cortex is in a weakly connected state.

[0143] Furthermore, fitting is performed based on multiple target coherences to obtain a target coherence curve, wherein the horizontal axis of the target coherence curve is frequency and the vertical axis is coherence, and a target weighted coherence is determined based on multiple target coherences and a target average value, wherein the target weighted coherence represents a reference coherence among multiple target coherences. A dividing line is determined based on the target weighted coherence, wherein the dividing line is parallel to the horizontal axis, and a first area value of the area above the dividing line in the target coherence curve and a second area value of the area below the dividing line are calculated, and then the ratio between the first area value and the second area value is determined to obtain a target ratio. When the target ratio is greater than or equal to a preset ratio threshold, it can be determined that the connectivity between the thalamus and the cortex is in a strongly connected state, and when the target ratio is less than the preset ratio threshold, it can be determined that the connectivity between the thalamus and the cortex is in a weakly connected state.

[0144] Optionally, the above step A505, determining a target weighted coherence according to the multiple target coherences and the target average value, may include the following steps:

[0145] B501, determining the median, maximum and minimum values ​​of the multiple target coherences;

[0146] B502. Determine a difference between the maximum value and the minimum value to obtain a first difference;

[0147] B503. Determine the difference between the median and the minimum value to obtain a second difference;

[0148] B504. Determine the difference between the target average value and the minimum value to obtain a third difference;

[0149] B505. Determine a ratio of the second difference to the first difference to obtain a first ratio;

[0150] B506. Determine a ratio of the third difference to the first difference to obtain a second ratio;

[0151] B507. Normalize the first ratio and the second ratio respectively to obtain a first weight and a second weight; the first weight corresponds to the median; and the second weight corresponds to the target average value;

[0152] B508. Perform weighted calculation on the median and the target average value according to the first weight and the second weight to obtain a target weighted coherence.

[0153] In a specific embodiment, the median, maximum value and minimum value of multiple target coherences are determined, the difference between the maximum value and the minimum value is determined to obtain a first difference, the difference between the median and the minimum value is determined to obtain a second difference, the difference between the target average value and the minimum value is determined to obtain a third difference, the ratio of the second difference to the first difference is determined to obtain a first ratio, the ratio of the third difference to the first difference is determined to obtain a second ratio, and the first ratio and the second ratio are normalized respectively to obtain a first weight and a second weight, wherein the first weight corresponds to the median and the second weight corresponds to the average. The target weighted coherence can be obtained by weighting the median and the average according to the first weight and the second weight.

[0154] To give an example, the preset first coherence is 0.7, the preset second coherence is 0.5, and multiple target coherences are {0.6, 0.5, 0.5, 0.6, 0.6}, then the target average is 0.56, and the target average is between the preset second coherence and the preset first coherence. Then the median of the multiple target coherences is determined to be 0.6, the maximum value is 0.6, the minimum value is 0.5, the first difference is 0.1, the second difference is 0.1, the third difference is 0.06, the first ratio is 1, and the second ratio is 0.6. After normalizing the first ratio and the second ratio, the corresponding value of the normalized first ratio is the first weight, i.e. 0.625, and the corresponding value of the normalized second ratio is the second weight, i.e. 0.375. Then, weighted calculation of the median and the average according to the first weight and the second weight can obtain the target weighted coherence of 0.585.

[0155] In one possible embodiment, the fitted target coherence curve is segmented based on the target weighted coherence, and a first area value of the area above the segmentation line in the target coherence curve and a second area value of the area below the segmentation line are determined. If the target ratio between the first area value and the second area value is greater than a preset ratio threshold, it is determined that the connectivity between the thalamus and the cortex is in a strongly connected state.

[0156] To summarize, by implementing the embodiments of the present application, transcranial direct current stimulation can be implemented on the target detection object through a transcranial direct current stimulation device, and a preset part of the target detection object can be driven to perform a preset passive movement through a manipulation device, and the EEG signals of the target detection object at the stimulation electrode position corresponding to the thalamus and the second stimulation electrode position corresponding to the cortex are respectively collected through an EEG acquisition device to obtain a first EEG signal and a second EEG signal, and coherence analysis is performed on the first EEG signal and the second EEG signal to obtain multiple target coherences, and the degree of connectivity between the thalamus and the cortex is determined based on the multiple target coherences, thereby achieving accurate detection of thalamus-cortex connectivity.

[0157] See also Figure 6 , Figure 6It is a functional unit composition block diagram of a thalamus-cortex connectivity detection device provided in an embodiment of the present application. The thalamus-cortex connectivity detection device 60 is applied to the controller of a neural connectivity detection system. The neural connectivity detection system also includes a transcranial direct current stimulation device, a manipulation device, and an electroencephalogram acquisition device; the manipulation device includes a piston, an air compressor, and a rubber band. The piston is arranged in an internal hole of the air compressor, and the air compressor is used to drive the piston to move; the rubber band is used to connect the piston and a preset position of a target detection object; the thalamus-cortex connectivity detection device 60 includes: a determination unit 601, a coherence analysis unit 602, wherein,

[0158] The determination unit 601 is used to determine target stimulation parameters for the target detection object; the target stimulation parameters include: target stimulation intensity, first stimulation electrode position, second stimulation electrode position, stimulation duration; the first stimulation electrode position is the electrode position corresponding to the thalamus of the target detection object; the second stimulation electrode position is the electrode position corresponding to the cerebral cortex of the target detection object;

[0159] Implementing transcranial direct current stimulation of the stimulation duration on the target detection object by the transcranial direct current stimulation device according to the target stimulation parameters, and driving the preset part of the target detection object to perform preset passive movement by the manipulation device;

[0160] The EEG acquisition device acquires EEG signals of the target detection object at the first stimulation electrode position and the second stimulation electrode position respectively to obtain a first EEG signal and a second EEG signal;

[0161] The coherence analysis unit 602 is used to perform coherence analysis on the first EEG signal and the second EEG signal to obtain multiple target coherences;

[0162] The determining unit 601 is further configured to determine the degree of connectivity between the thalamus and the cortex according to the multiple target coherences.

[0163] Optionally, before the transcranial direct current stimulation device performs the transcranial direct current stimulation of the stimulation duration on the target detection object according to the target stimulation parameters, the thalamus-cortex connectivity detection device 60 is further specifically used to:

[0164] Obtaining the current stimulation intensity of the transcranial direct current stimulation device at the current moment;

[0165] Increasing the current stimulation intensity to a preset stimulation intensity within a first preset time period;

[0166] Continuously stimulating the target detection object according to the preset stimulation intensity within a second preset time period, monitoring the physiological signal of the target detection object, and obtaining a target physiological signal; the start time of the second preset time period is later than the end time of the first preset time period;

[0167] When the target physiological signal satisfies a first preset condition, the preset stimulation intensity is determined as the target stimulation intensity; the first preset condition is that the target physiological signal is within a preset safety range;

[0168] When the target physiological signal does not meet the first preset condition, adjusting the preset stimulation intensity based on the target physiological signal to obtain a standard stimulation intensity;

[0169] The standard stimulation intensity is determined as the target stimulation intensity.

[0170] Optionally, when the target physiological signal includes a heart rate, in terms of adjusting the preset stimulation intensity based on the target physiological signal to obtain a standard stimulation intensity, the thalamus-cortex connectivity detection device 60 is further specifically used for:

[0171] Obtaining a target safety interval and a standard heart rate value of the heart rate;

[0172] Obtaining a difference between the heart rate and the standard heart rate value to obtain a heart rate difference;

[0173] Determine the adjustment parameter of the preset stimulation intensity according to the heart rate difference to obtain a target adjustment parameter;

[0174] When the heart rate is not within the target safety interval, determining a deviation of the heart rate based on the target safety interval to obtain a target deviation;

[0175] Determining a target adjustment factor corresponding to the target deviation;

[0176] The preset stimulation intensity is adjusted according to the target adjustment factor and the target adjustment parameter to obtain the standard stimulation intensity.

[0177] Optionally, in performing coherence analysis on the first EEG signal and the second EEG signal to obtain a plurality of target coherences, the coherence analysis unit 602 is further specifically used to:

[0178] Based on a preset filter, the first EEG signal and the second EEG signal are filtered to obtain a third EEG signal and a fourth EEG signal;

[0179] Discretizing the third EEG signal and the fourth EEG signal respectively by using a preset window function to obtain a first time domain signal and a second time domain signal;

[0180] Performing Fourier transform on the first time domain signal and the second time domain signal respectively to obtain a first frequency domain signal and a second frequency domain signal;

[0181] Determine frequency points at the same frequency in the first frequency domain signal and the second frequency domain signal to obtain multiple frequency point groups; each frequency point group includes a first frequency point set and a second frequency point set; each frequency point set includes multiple frequency points; the first frequency point set is the frequency point set of the first frequency domain signal; the second frequency point set is the frequency point set of the second frequency domain signal;

[0182] The coherence of the first frequency domain signal and the second frequency domain signal is determined according to the multiple frequency point groups to obtain multiple target coherence degrees.

[0183] Optionally, in determining the coherence of the first frequency domain signal and the second frequency domain signal according to the multiple frequency point groups to obtain multiple target coherences, the coherence analysis unit 602 is further specifically used to:

[0184] Determine a target frequency point group; the target frequency point group is any frequency point group among the multiple frequency point groups;

[0185] Determine the autopower spectral density of the first frequency domain signal according to the first frequency point set in the target frequency point group to obtain a first power spectral density;

[0186] Determine the autopower spectral density of the second frequency domain signal according to the second frequency point set in the target frequency point group to obtain a second power spectral density;

[0187] Determine the cross power spectrum density of the first frequency domain signal and the second frequency domain signal according to the target frequency point group to obtain a third power spectrum density;

[0188] A target coherence degree is determined according to the first power spectral density, the second power spectral density, and the third power spectral density.

[0189] Optionally, in determining the degree of connectivity between the thalamus and the cortex according to the multiple target coherences, the determining unit 601 is further specifically configured to:

[0190] Determining an average value of the multiple target coherences to obtain a target average value;

[0191] When the target average value is greater than or equal to a preset first coherence degree, determining that the connectivity between the thalamus and the cortex is in a strong connection state;

[0192] When the target average value is less than or equal to a preset second coherence degree, determining that the connectivity between the thalamus and the cortex is in a weak connection state;

[0193] When the target average value is greater than the preset second coherence and the target average value is less than the preset first coherence, fitting is performed according to the multiple target coherences to obtain a target coherence curve; the horizontal axis of the target coherence curve is frequency and the vertical axis is coherence;

[0194] Determining a target weighted coherence according to the multiple target coherences and the target average value;

[0195] Determining a segmentation line according to the target weighted coherence; the segmentation line is parallel to the horizontal axis;

[0196] Determine a first area value of a region above the dividing line and a second area value of a region below the dividing line in the target coherence curve;

[0197] Determine a ratio between the first area value and the second area value to obtain a target ratio;

[0198] Obtaining a preset ratio threshold;

[0199] When the target ratio is greater than or equal to the preset ratio threshold, determining that the connectivity between the thalamus and the cortex is in a strong connection state;

[0200] When the target ratio is less than the preset ratio threshold, it is determined that the connectivity between the thalamus and the cortex is in a weak connection state.

[0201] Optionally, in determining the target weighted coherence according to the multiple target coherences and the target average value, the determining unit 601 is further specifically configured to:

[0202] determining a median, a maximum, and a minimum of the plurality of target coherences;

[0203] Determine a difference between the maximum value and the minimum value to obtain a first difference;

[0204] Determine the difference between the median and the minimum value to obtain a second difference;

[0205] Determine a difference between the target average value and the minimum value to obtain a third difference value;

[0206] determining a ratio of the second difference to the first difference to obtain a first ratio;

[0207] Determine a ratio of the third difference to the first difference to obtain a second ratio;

[0208] Normalizing the first ratio and the second ratio respectively to obtain a first weight and a second weight; the first weight corresponds to the median; and the second weight corresponds to the target average;

[0209] The median and the target average are weightedly calculated according to the first weight and the second weight to obtain a target weighted coherence.

[0210] The thalamus-cortex connectivity detection device 60 described in the present application can implement transcranial direct current stimulation on the target detection object through a transcranial direct current stimulation device, and drive a preset part of the target detection object to perform a preset passive movement through a manipulation device, and respectively collect the EEG signals of the target detection object at the stimulation electrode position corresponding to the thalamus and the second stimulation electrode position corresponding to the cortex through an EEG acquisition device to obtain a first EEG signal and a second EEG signal, perform coherence analysis on the first EEG signal and the second EEG signal to obtain multiple target coherences, and determine the degree of connectivity between the thalamus and the cortex based on the multiple target coherences, thereby achieving accurate detection of thalamus-cortex connectivity.

[0211] See also Figure 7 , Figure 7 : is a structural schematic diagram of an electronic device provided in an embodiment of the present application, the electronic device may include a processor, a memory, a communication interface and one or more programs, the processor, the memory and the communication interface may be interconnected through a bus; the one or more programs are stored in the memory and are configured to be executed by the processor; the electronic device includes a controller of a neural connectivity detection system, the neural connectivity detection system also includes a transcranial direct current stimulation device, a manipulation device, and an electroencephalogram acquisition device; the manipulation device includes a piston, an air compressor, and a rubber band, the piston is arranged in an internal hole of the air compressor, the air compressor is used to drive the piston to move; the rubber band is used to connect the piston and a preset position of a target detection object; in the embodiment of the present application, the program includes instructions for executing the following steps:

[0212] Determine target stimulation parameters for the target detection object; the target stimulation parameters include: target stimulation intensity, first stimulation electrode position, second stimulation electrode position, stimulation duration; the first stimulation electrode position is the electrode position corresponding to the thalamus of the target detection object; the second stimulation electrode position is the electrode position corresponding to the cerebral cortex of the target detection object;

[0213] Implementing transcranial direct current stimulation of the stimulation duration on the target detection object by the transcranial direct current stimulation device according to the target stimulation parameters, and driving the preset part of the target detection object to perform preset passive movement by the manipulation device;

[0214] The EEG acquisition device acquires EEG signals of the target detection object at the first stimulation electrode position and the second stimulation electrode position respectively to obtain a first EEG signal and a second EEG signal;

[0215] Performing coherence analysis on the first EEG signal and the second EEG signal to obtain multiple target coherences;

[0216] The degree of connectivity between the thalamus and the cortex is determined based on the multiple target coherences.

[0217] The electronic device described in the present application can implement transcranial direct current stimulation on a target detection object through a transcranial direct current stimulation device, and drive a preset part of the target detection object to perform a preset passive movement through a manipulation device, and respectively collect the EEG signals of the target detection object at the stimulation electrode position corresponding to the thalamus and the second stimulation electrode position corresponding to the cortex through an EEG acquisition device to obtain a first EEG signal and a second EEG signal, perform coherence analysis on the first EEG signal and the second EEG signal to obtain multiple target coherences, determine the degree of connectivity between the thalamus and the cortex based on the multiple target coherences, and thereby achieve accurate detection of thalamus-cortex connectivity.

[0218] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0219] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0220] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

[0221] The steps of the method or algorithm described in the embodiments of the present application can be implemented in hardware or by executing software instructions by a processor. The software instructions can be composed of corresponding software modules, and the software modules can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (electrically EPROM, EEPROM), registers, hard disks, mobile hard disks, read-only compact disks (CD-ROMs) or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and the storage medium can also exist in a terminal device or a management device as discrete components.

[0222] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0223] The modules / units included in the devices and products described in the above embodiments may be software modules / units or hardware modules / units, or may be partially software modules / units and partially hardware modules / units. For example, for the devices and products applied to or integrated in the chip, the modules / units included therein may all be implemented in the form of hardware such as circuits, or at least some of the modules / units may be implemented in the form of software programs, which run on the processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for the devices and products applied to or integrated in the chip module, the modules / units included therein may all be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as chip, circuit module, etc.) or different components of the chip module, or at least some of the modules / units may be implemented in the form of software programs. It is implemented in the form of a software program that runs on a processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in hardware such as circuits; for various devices and products applied to or integrated in the terminal equipment, the various modules / units contained therein can be implemented in hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or different components in the terminal equipment, or at least some of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the terminal equipment, and the remaining (if any) modules / units can be implemented in hardware such as circuits.

[0224] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above description is only the specific implementation method of the embodiments of the present application and is not intended to limit the protection scope of the embodiments of the present application. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the protection scope of the embodiments of the present application.

Claims

1. A method for detecting thalamus-cortex connectivity, characterized in that: A controller applied to a neural connectivity detection system, wherein the neural connectivity detection system further comprises a transcranial direct current stimulation device, a manipulation device, and an electroencephalogram acquisition device; the manipulation device comprises a piston, an air compressor, and a rubber band, wherein the piston is disposed in an internal hole of the air compressor, and the air compressor is used to drive the piston to move; The rubber band is used to connect the piston and a preset position of the target detection object; the method comprises: Determine target stimulation parameters for the target detection object; the target stimulation parameters include: target stimulation intensity, first stimulation electrode position, second stimulation electrode position, stimulation duration; the first stimulation electrode position is the electrode position corresponding to the thalamus of the target detection object; the second stimulation electrode position is the electrode position corresponding to the cerebral cortex of the target detection object; The transcranial direct current stimulation device performs transcranial direct current stimulation of the target detection object for the stimulation duration according to the target stimulation parameters, and the operating device drives the preset part of the target detection object to perform a preset passive movement; the preset passive movement includes one of the following: straightening, bending, inward rotation, and outward rotation; The EEG acquisition device acquires EEG signals of the target detection object at the first stimulation electrode position and the second stimulation electrode position respectively to obtain a first EEG signal and a second EEG signal; Performing coherence analysis on the first EEG signal and the second EEG signal to obtain multiple target coherences; determining the degree of connectivity between the thalamus and the cortex according to the multiple target coherences, where the degree of connectivity is used to evaluate the strength of the functional connection between the thalamus and the cortex; The coherence analysis of the first EEG signal and the second EEG signal is performed to obtain multiple target coherences, including: Based on a preset filter, the first EEG signal and the second EEG signal are filtered to obtain a third EEG signal and a fourth EEG signal; Discretizing the third EEG signal and the fourth EEG signal respectively by using a preset window function to obtain a first time domain signal and a second time domain signal; Performing Fourier transform on the first time domain signal and the second time domain signal respectively to obtain a first frequency domain signal and a second frequency domain signal; Determine frequency points at the same frequency in the first frequency domain signal and the second frequency domain signal to obtain multiple frequency point groups; each frequency point group includes a first frequency point set and a second frequency point set; each frequency point set includes multiple frequency points; the first frequency point set is the frequency point set of the first frequency domain signal; the second frequency point set is the frequency point set of the second frequency domain signal; Determining the coherence of the first frequency domain signal and the second frequency domain signal according to the multiple frequency point groups to obtain multiple target coherences; The determining the coherence of the first frequency domain signal and the second frequency domain signal according to the multiple frequency point groups to obtain multiple target coherences includes: Determining an average value of the multiple target coherences to obtain a target average value; When the target average value is greater than or equal to a preset first coherence degree, determining that the connectivity between the thalamus and the cortex is in a strong connection state; When the target average value is less than or equal to a preset second coherence degree, determining that the connectivity between the thalamus and the cortex is in a weak connection state; When the target average value is greater than the preset second coherence and the target average value is less than the preset first coherence, fitting is performed according to the multiple target coherences to obtain a target coherence curve; the horizontal axis of the target coherence curve is frequency and the vertical axis is coherence; Determining a target weighted coherence according to the multiple target coherences and the target average value; Determining a segmentation line according to the target weighted coherence; the segmentation line is parallel to the horizontal axis; Determine a first area value of a region above the dividing line and a second area value of a region below the dividing line in the target coherence curve; Determine a ratio between the first area value and the second area value to obtain a target ratio; Obtaining a preset ratio threshold; When the target ratio is greater than or equal to the preset ratio threshold, determining that the connectivity between the thalamus and the cortex is in a strong connection state; When the target ratio is less than the preset ratio threshold, determining that the connectivity between the thalamus and the cortex is in a weak connection state; Wherein, determining the degree of connectivity between the thalamus and the cortex according to the multiple target coherences includes: determining a median, a maximum, and a minimum of the plurality of target coherences; Determine a difference between the maximum value and the minimum value to obtain a first difference; Determine the difference between the median and the minimum value to obtain a second difference; Determine a difference between the target average value and the minimum value to obtain a third difference value; determining a ratio of the second difference to the first difference to obtain a first ratio; Determine a ratio of the third difference to the first difference to obtain a second ratio; Normalizing the first ratio and the second ratio respectively to obtain a first weight and a second weight; the first weight corresponds to the median; and the second weight corresponds to the target average; The median and the target average are weightedly calculated according to the first weight and the second weight to obtain a target weighted coherence.

2. The method according to claim 1, characterized in that Before performing transcranial direct current stimulation of the stimulation duration on the target detection object by the transcranial direct current stimulation device according to the target stimulation parameters, the method includes: Obtaining the current stimulation intensity of the transcranial direct current stimulation device at the current moment; Increasing the current stimulation intensity to a preset stimulation intensity within a first preset time period; Continuously stimulating the target detection object according to the preset stimulation intensity within a second preset time period, monitoring the physiological signal of the target detection object, and obtaining a target physiological signal; the start time of the second preset time period is later than the end time of the first preset time period; When the target physiological signal satisfies a first preset condition, the preset stimulation intensity is determined as the target stimulation intensity; the first preset condition is that the target physiological signal is within a preset safety range; When the target physiological signal does not meet the first preset condition, adjusting the preset stimulation intensity based on the target physiological signal to obtain a standard stimulation intensity; The standard stimulation intensity is determined as the target stimulation intensity.

3. The method according to claim 2, characterized in that When the target physiological signal includes a heart rate, adjusting the preset stimulation intensity based on the target physiological signal to obtain a standard stimulation intensity includes: Obtaining a target safety interval and a standard heart rate value of the heart rate; Obtaining a difference between the heart rate and the standard heart rate value to obtain a heart rate difference; Determine the adjustment parameter of the preset stimulation intensity according to the heart rate difference to obtain a target adjustment parameter; When the heart rate is not within the target safety interval, determining a deviation of the heart rate based on the target safety interval to obtain a target deviation; Determining a target adjustment factor corresponding to the target deviation; The preset stimulation intensity is adjusted according to the target adjustment factor and the target adjustment parameter to obtain the standard stimulation intensity.

4. The method according to claim 1, characterized in that The determining the coherence of the first frequency domain signal and the second frequency domain signal according to the multiple frequency point groups to obtain multiple target coherences includes: Determine a target frequency point group; the target frequency point group is any frequency point group among the multiple frequency point groups; Determine the autopower spectral density of the first frequency domain signal according to the first frequency point set in the target frequency point group to obtain a first power spectral density; Determine the autopower spectral density of the second frequency domain signal according to the second frequency point set in the target frequency point group to obtain a second power spectral density; Determine the cross power spectrum density of the first frequency domain signal and the second frequency domain signal according to the target frequency point group to obtain a third power spectrum density; A target coherence degree is determined according to the first power spectral density, the second power spectral density, and the third power spectral density.

5. A thalamus-cortex connectivity detection device, characterized in that: A controller applied to a neural connectivity detection system, wherein the neural connectivity detection system further comprises a transcranial direct current stimulation device, a manipulation device, and an electroencephalogram acquisition device; the manipulation device comprises a piston, an air compressor, and a rubber band, wherein the piston is disposed in an internal hole of the air compressor, and the air compressor is used to drive the piston to move; The rubber band is used to connect the piston and a preset position of the target detection object; The thalamus-cortex connectivity detection device comprises: a determination unit and a coherence analysis unit, wherein: The determination unit is used to determine target stimulation parameters for the target detection object; the target stimulation parameters include: target stimulation intensity, first stimulation electrode position, second stimulation electrode position, and stimulation duration; the first stimulation electrode position is the electrode position corresponding to the thalamus of the target detection object; the second stimulation electrode position is the electrode position corresponding to the cerebral cortex of the target detection object; The transcranial direct current stimulation device performs transcranial direct current stimulation of the target detection object for the stimulation duration according to the target stimulation parameters, and the operating device drives the preset part of the target detection object to perform a preset passive movement; the preset passive movement includes one of the following: straightening, bending, inward rotation, and outward rotation; The EEG acquisition device acquires EEG signals of the target detection object at the first stimulation electrode position and the second stimulation electrode position respectively to obtain a first EEG signal and a second EEG signal; The coherence analysis unit is used to perform coherence analysis on the first EEG signal and the second EEG signal to obtain multiple target coherences; The determination unit is further used to determine the degree of connectivity between the thalamus and the cortex according to the multiple target coherences, and the degree of connectivity is used to evaluate the functional connection strength between the thalamus and the cortex; The coherence analysis of the first EEG signal and the second EEG signal is performed to obtain multiple target coherences, including: Based on a preset filter, the first EEG signal and the second EEG signal are filtered to obtain a third EEG signal and a fourth EEG signal; Discretizing the third EEG signal and the fourth EEG signal respectively by using a preset window function to obtain a first time domain signal and a second time domain signal; Performing Fourier transform on the first time domain signal and the second time domain signal respectively to obtain a first frequency domain signal and a second frequency domain signal; Determine frequency points at the same frequency in the first frequency domain signal and the second frequency domain signal to obtain multiple frequency point groups; each frequency point group includes a first frequency point set and a second frequency point set; each frequency point set includes multiple frequency points; the first frequency point set is the frequency point set of the first frequency domain signal; the second frequency point set is the frequency point set of the second frequency domain signal; Determining the coherence of the first frequency domain signal and the second frequency domain signal according to the multiple frequency point groups to obtain multiple target coherences; The determining the coherence of the first frequency domain signal and the second frequency domain signal according to the multiple frequency point groups to obtain multiple target coherences includes: Determining an average value of the multiple target coherences to obtain a target average value; When the target average value is greater than or equal to a preset first coherence degree, determining that the connectivity between the thalamus and the cortex is in a strong connection state; When the target average value is less than or equal to a preset second coherence degree, determining that the connectivity between the thalamus and the cortex is in a weak connection state; When the target average value is greater than the preset second coherence and the target average value is less than the preset first coherence, fitting is performed according to the multiple target coherences to obtain a target coherence curve; the horizontal axis of the target coherence curve is frequency and the vertical axis is coherence; Determining a target weighted coherence according to the multiple target coherences and the target average value; Determining a segmentation line according to the target weighted coherence; the segmentation line is parallel to the horizontal axis; Determine a first area value of a region above the dividing line and a second area value of a region below the dividing line in the target coherence curve; Determine a ratio between the first area value and the second area value to obtain a target ratio; Obtaining a preset ratio threshold; When the target ratio is greater than or equal to the preset ratio threshold, determining that the connectivity between the thalamus and the cortex is in a strong connection state; When the target ratio is less than the preset ratio threshold, determining that the connectivity between the thalamus and the cortex is in a weak connection state; Wherein, determining the degree of connectivity between the thalamus and the cortex according to the multiple target coherences includes: Determining the median, maximum, and minimum values ​​of the plurality of target coherences; Determine a difference between the maximum value and the minimum value to obtain a first difference; Determine the difference between the median and the minimum value to obtain a second difference; Determine a difference between the target average value and the minimum value to obtain a third difference value; determining a ratio of the second difference to the first difference to obtain a first ratio; Determine a ratio of the third difference to the first difference to obtain a second ratio; Normalizing the first ratio and the second ratio respectively to obtain a first weight and a second weight; the first weight corresponds to the median; and the second weight corresponds to the target average; The median and the target average are weightedly calculated according to the first weight and the second weight to obtain a target weighted coherence.

6. An electronic device, characterized in that: include: A processor, a memory, a communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, the programs comprising instructions for executing the steps in the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 4.

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