Inter-brain synchronization verification method and device, storage medium and electronic equipment
By acquiring and verifying the EEG data in the training sample set, using T test and Granger causality verification, the problem that the existing technology cannot evaluate the dynamic interaction of multiple brains is solved, and effective verification and synchronization of interbrain synchronization is achieved. Identification of direction.
Patent Information
- Application Number
- CN202310730215.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-06-19
- Publication Date
- 2024-12-20
AI Technical Summary
The prior art is difficult to directly evaluate dynamic interactions between two or more brains, and it is impossible to verify interbrain synchronization.
By obtaining training sample sets, including brain synchronization data with high time stress and brain synchronization data with low time stress, the interbrain synchronization between EEG data under different time stress was determined using T test and Granger causality. Coherence and synchronization direction of task association.
Effective verification of interbrain synchronization between two or more brains is achieved, which can identify the impact of time pressure on EEG data and determine the direction of synchronization between EEG data.
Smart Images
Figure CN119157552A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of EEG synchronization technology, and in particular to a method, device, storage medium and electronic device for verifying inter-brain synchronization. Background Art
[0002] Social behavior usually involves two or more people, but most social cognition studies focus only on the brain activity of one person. Although these single-person studies are very effective in observing brain activation in specific social paradigms, they cannot directly assess the dynamic interactions between two (or more) brains.
[0003] Therefore, how to achieve inter-brain synchronization verification between two or more brains is an urgent problem that needs to be solved. Summary of the Invention
[0004] In view of the above problems, the present application provides a method, device, storage medium and electronic device for verifying inter-brain synchronization, which solves the problem of how to achieve inter-brain synchronization verification between two or more brains.
[0005] To solve the above technical problems, this application proposes the following solutions:
[0006] In the first aspect, the present application provides a method for verifying inter-brain synchronization, the method comprising: obtaining a training sample set, the training sample set comprising multiple sets of brain synchronization data with high time pressure and multiple sets of brain synchronization data with low time pressure, the brain synchronization data comprising first EEG data and second EEG data; and verifying the inter-brain synchronization between the first EEG data and the second EEG data under different time pressures based on a T-test.
[0007] In combination with the first aspect, in a possible implementation method, a first inter-brain synchronization value of the first EEG data and the second EEG data in the brain synchronization data is calculated, and the first inter-brain synchronization value is the inter-brain synchronization value under the task state; the task-associated coherence is determined based on the first inter-brain synchronization value and the second inter-brain synchronization value, and the second inter-brain synchronization value is the inter-brain synchronization value under the resting state, and the task-associated coherence is used to indicate the degree of correlation between the inter-brain synchronization value and the task state; the coherence of the brain synchronization data in the training sample set is subjected to a full-channel single-sample T test to obtain a first test result, and the first test result is corrected, and the first test result is used to determine the channel with inter-brain synchronization in the training sample set.
[0008] In combination with the first aspect, in another possible implementation method, the frequency points in the first test result whose P values are less than the first threshold are obtained; the number of consecutive points that meet the preset standards within the preset range of the frequency points is determined; and the target frequency band is determined based on the number of consecutive points.
[0009] In combination with the first aspect, in another possible implementation method, for the channels in the target frequency band and with inter-brain synchronization, an independent sample T test is performed on the coherence of the brain synchronization data with high time pressure and the brain synchronization data with low time pressure to obtain a second test result, and the second test result is corrected; when the P value of the second test result is less than the second threshold and the T value is less than the third threshold, it indicates that there is a difference between the brain synchronization data with high time pressure and the brain synchronization data with low time pressure, and the inter-brain synchronization of the brain synchronization data with low time pressure is stronger than that of the brain synchronization data with high time pressure.
[0010] In combination with the first aspect, in another possible implementation, the inter-brain synchronization direction of the first EEG data and the second EEG data is verified based on Granger causality.
[0011] In combination with the first aspect, in another possible implementation method, for a channel with inter-brain synchronization, the first EEG data and the second EEG data of the same period are extracted from the entire time series; the first Granger causal value between the first EEG data and the second EEG data in a first direction is calculated, and the second Granger causal value between the first EEG data and the second EEG data in a second direction is calculated, the first direction is used to indicate that the subject of the first EEG data points to the subject of the second EEG data, and the second direction is used to indicate that the subject of the second EEG data points to the subject of the first EEG data; a single-sample T test is performed on the first Granger causal value and the second Granger causal value to determine the direction of inter-brain synchronization.
[0012] In combination with the first aspect, in another possible implementation method, unqualified EEG data in the training sample set is filtered out, and the unqualified EEG data is the EEG data in which the number of unqualified channels is greater than a fourth threshold; the noise and drift components in the EEG data are removed; and the unqualified channels of the EEG data are filled with data from other adjacent channels in the same functional area.
[0013] In a second aspect, the present application provides a device for verifying inter-brain synchronization, which includes: an acquisition module and a verification module.
[0014] The acquisition module is used to acquire a training sample set, which includes multiple groups of brain synchronization data with high time pressure and multiple groups of brain synchronization data with low time pressure, and the brain synchronization data includes first EEG data and second EEG data.
[0015] The verification module is used to verify the inter-brain synchronization between the first EEG data and the second EEG data under different time pressures based on the T test.
[0016] In combination with the second aspect, in a possible implementation method, the verification module is specifically used to calculate the first inter-brain synchronization value of the first EEG data and the second EEG data in the brain synchronization data, the first inter-brain synchronization value is the inter-brain synchronization value under the task state; the task-associated coherence is determined based on the first inter-brain synchronization value and the second inter-brain synchronization value, the second inter-brain synchronization value is the inter-brain synchronization value under the resting state, and the task-associated coherence is used to indicate the degree of correlation between the inter-brain synchronization value and the task state; the coherence of the brain synchronization data in the training sample set is subjected to a full-channel single-sample T test to obtain a first test result, and the first test result is corrected, and the first test result is used to determine the channel with inter-brain synchronization in the training sample set.
[0017] In combination with the second aspect, in another possible implementation method, the verification module is specifically used to obtain the frequency points in the first test result whose P value is less than the first threshold; determine the number of consecutive points that meet the preset standards within a preset range of frequency points; and determine the target frequency band based on the number of consecutive points.
[0018] In combination with the second aspect, in another possible implementation method, the verification module is specifically used to perform an independent sample T test on the coherence of brain synchronization data with high time pressure and brain synchronization data with low time pressure for the channel in the target frequency band and with inter-brain synchronization, obtain a second test result, and correct the second test result; when the P value of the second test result is less than the second threshold and the T value is less than the third threshold, it indicates that there is a difference between the brain synchronization data with high time pressure and the brain synchronization data with low time pressure, and the inter-brain synchronization of the brain synchronization data with low time pressure is stronger than that of the brain synchronization data with high time pressure.
[0019] In combination with the second aspect, in another possible implementation, the verification module is further used to verify the inter-brain synchronization direction of the first EEG data and the second EEG data based on Granger causality.
[0020] In combination with the second aspect, in another possible implementation method, the verification module is specifically used to extract the first EEG data and the second EEG data of the same period from the entire time series for a channel with inter-brain synchronization; calculate the first Granger causal value between the first EEG data and the second EEG data in a first direction, and calculate the second Granger causal value between the first EEG data and the second EEG data in a second direction, the first direction is used to indicate that the subject of the first EEG data points to the subject of the second EEG data, and the second direction is used to indicate that the subject of the second EEG data points to the subject of the first EEG data; perform a single-sample T test on the first Granger causal value and the second Granger causal value to determine the direction of inter-brain synchronization.
[0021] In combination with the second aspect, in another possible implementation method, the acquisition module is also used to filter out unqualified EEG data in the training sample set, where the unqualified EEG data is EEG data in which the number of unqualified channels is greater than a fourth threshold; remove noise and drift components in the EEG data; and fill the unqualified channels of the EEG data with data from other adjacent channels in the same functional area.
[0022] In order to achieve the above-mentioned purpose, according to the third aspect of the present application, a storage medium is provided, which includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the above-mentioned first aspect of the inter-brain synchronization verification method.
[0023] In order to achieve the above-mentioned purpose, according to the fourth aspect of the present application, an electronic device is provided, which includes at least one processor, and at least one memory and a bus connected to the processor; wherein the processor and the memory communicate with each other through the bus; the processor is used to call the program instructions in the memory to execute the brain-to-brain synchronization verification method of the above-mentioned first aspect.
[0024] By means of the above technical solution, the technical solution provided by this application has at least the following advantages:
[0025] The present application provides a method, device, storage medium, and electronic device for verifying inter-brain synchronization. The present application performs an independent sample T test and a single sample T test on a first EEG data set and a second EEG data set, respectively, to verify the inter-brain synchronization between the first EEG data set and the second EEG data set, as well as the effect of time pressure on the inter-brain synchronization between the first EEG data set and the second EEG data set. The directionality of the inter-brain synchronization between the first EEG data set and the second EEG data set with inter-brain synchronization can also be verified through Granger causality.
[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS
[0027] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:
[0028] Figure 1 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown;
[0029] Figure 2 A schematic diagram of a flow chart of a method for verifying inter-brain synchronization provided by an embodiment of the present application is shown;
[0030] Figure 3 A schematic diagram showing the relationship between the channel position and the photoelectrode position provided in an embodiment of the present application is shown;
[0031] Figure 4 A schematic flow chart of another method for verifying inter-brain synchronization provided by an embodiment of the present application is shown;
[0032] Figure 5 A schematic structural diagram of a device for verifying inter-brain synchronization provided in an embodiment of the present application is shown. DETAILED DESCRIPTION
[0033] The following describes exemplary embodiments of the present application in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.
[0034] In the embodiments of the present application, the terms "first" and "second" do not have a logical or temporal dependency, nor do they limit the quantity or execution order. It should also be understood that although the following description uses the terms "first", "second", etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another.
[0035] In the embodiments of the present application, the term "at least one" means one or more, and in the embodiments of the present application, the term "plurality" means two or more.
[0036] It should also be understood that the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting." Similarly, the phrase "if it is determined that..." or "if [stated condition or event] is detected" may be interpreted as "upon determining that..." or "in response to determining that..." or "upon detecting [stated condition or event]" or "in response to detecting [stated condition or event]," depending on the context.
[0037] To facilitate understanding of the solutions of this application, a brief introduction to the relevant concepts is first given as follows.
[0038] Functional near-infrared spectroscopy (fNIRS) utilizes the excellent scattering properties of blood's main components for near-infrared light (600-900 nm) to measure changes in oxyhemoglobin and deoxyhemoglobin during brain activity. Portable / wireless commercial fNIRS systems have a temporal resolution of 1-10 Hz, a depth sensitivity of approximately 1.5 cm, and a spatial resolution of up to 1 cm.
[0039] The above is an introduction to the technical terms involved in the embodiments of this application, which will not be repeated below.
[0040] As described in the background art, social behavior usually involves two or more people, but most social cognition studies focus only on the brain activity of one person. Although these single-person studies are very effective in observing brain activation in specific social paradigms, they cannot directly evaluate the dynamic interactions between two (or more) brains.
[0041] In view of this, an embodiment of the present application provides a method for verifying inter-brain synchronization, and the specific method includes: obtaining a training sample set, the training sample set includes multiple groups of brain synchronization data with high time pressure and multiple groups of brain synchronization data with low time pressure, and the brain synchronization data includes the first EEG data and the second EEG data; verifying the inter-brain synchronization between the first EEG data and the second EEG data under different time pressures based on the T test. By performing an independent sample T test and a single sample T test on the first EEG data and the second EEG data, respectively, the inter-brain synchronization between the first EEG data and the second EEG data, and the effect of time pressure on the inter-brain synchronization between the first EEG data and the second EEG data can be verified. The directionality of the inter-brain synchronization between the first EEG data and the second EEG data with inter-brain synchronization can also be verified by Granger causality.
[0042] The present application also provides an apparatus for verifying inter-brain synchronization, which can be used to perform the above-described method for verifying inter-brain synchronization. Alternatively, the apparatus can be an electronic device with data processing capabilities, or a functional module within such an electronic device, without limitation.
[0043] For example, the electronic device may be a server, which may be a single server, or a server cluster composed of multiple servers. For another example, the electronic device may be a mobile phone, a tablet computer, a desktop, a laptop, a handheld computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, as well as a cellular phone, a personal digital assistant (PDA), an augmented reality (AR), a virtual reality (VR) device and other terminal devices. For another example, the electronic device may also be a recording device, a video surveillance device and other devices. This application does not impose any special restrictions on the specific form of the electronic device.
[0044] The following example uses an electronic device as an example to verify brain synchronization. Figure 1 As shown, Figure 1 This application provides a hardware structure of an electronic device 100.
[0045] like Figure 1 As shown, the electronic device 100 includes a processor 110 , a communication circuit 120 and a communication interface 130 .
[0046] Optionally, the electronic device 100 may further include a memory 140 , wherein the processor 110 , the memory 140 and the communication interface 130 may be connected via a communication line 120 .
[0047] The processor 110 may be a central processing unit (CPU), a general-purpose processor, a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof. The processor 101 may also be any other device with processing capabilities, such as a circuit, a device, or a software module, without limitation.
[0048] In one example, the processor 110 may include one or more CPUs, such as Figure 1 CPU0 and CPU1 in.
[0049] As an optional implementation, the electronic device 100 includes multiple processors. For example, in addition to the processor 110, it may also include a processor 170. The communication line 120 is used to transmit information between the various components included in the electronic device 100.
[0050] Communication interface 130 is used to communicate with other devices or other communication networks. Such other communication networks may be Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc. Communication interface 130 may be a module, circuit, transceiver, or any other device capable of communication.
[0051] The memory 140 is used to store instructions, where the instructions may be computer programs.
[0052] Among them, the memory 140 can be a read-only memory (ROM) or other types of static storage devices that can store static information and / or instructions, or a random access memory (RAM) or other types of dynamic storage devices that can store information and / or instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, etc., without limitation.
[0053] It should be noted that the memory 140 can exist independently of the processor 110 or can be integrated with the processor 110. The memory 140 can be used to store instructions, program codes, or some data. The memory 140 can be located inside the electronic device 100 or outside the electronic device 100, without limitation.
[0054] The processor 110 is configured to execute instructions stored in the memory 140 to implement the communication method provided in the following embodiments of the present application. For example, when the electronic device 100 is a terminal or a chip in a terminal, the processor 110 may execute instructions stored in the memory 140 to implement the steps performed by the transmitting end in the following embodiments of the present application.
[0055] As an optional implementation, the electronic device 100 further includes an output device 150 and an input device 160. The output device 150 may be a device such as a display screen or a speaker that can output data from the electronic device 100 to a user. The input device 160 may be a device such as a keyboard, a mouse, a microphone, or a joystick that can input data to the electronic device 100.
[0056] It should be pointed out that Figure 1 The structure shown in the figure does not constitute a limitation on the computing device, except Figure 1 In addition to the components shown, the computing device may include more or fewer components than shown, or combine certain components, or arrange the components differently.
[0057] The verification device and application scenario of inter-brain synchronization described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Ordinary technicians in this field can know that with the evolution of the verification device of inter-brain synchronization and the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.
[0058] Next, the verification method of inter-brain synchronization is described in detail with reference to the accompanying drawings. Figure 2 This is a flow chart of a method for verifying brain synchronization provided by this application. Figure 1 The device for verifying brain synchronization with the hardware structure shown specifically includes the following steps:
[0059] Step 210: Obtain a training sample set.
[0060] The training sample set includes multiple sets of inter-brain synchronization data with high time pressure and multiple sets of inter-brain synchronization data with low time pressure, and each set of inter-brain synchronization data includes first EEG data and second EEG data.
[0061] For example, the first EEG data is obtained using fNIRS technology for the judge, and the second EEG data is obtained using fNIRS technology for the client. In this embodiment, the effect of time pressure on brain synchronization between the judge and the client is verified based on the EEG data of the judge and the client.
[0062] The human brain includes multiple brain regions, such as the frontal lobe, parietal lobe, temporal lobe and occipital lobe. The frontal lobe is a brain region related to functions such as cognition, emotion, pain and behavior management. The right temporoparietal junction is a brain region related to social connection, cognitive empathy and psychological theory. Therefore, this application only focuses on the frontal lobe and the right temporoparietal junction. In the embodiment of the present application, the functional near-infrared spectroscopy imaging system uses 2 photoelectrodes to collect EEG data. Each photoelectrode consists of 5 transmitters and 4 receivers, forming a 4*2+1 multi-channel probe rack, with a total of 11 channels. The wavelength of the near-infrared light emitted by the transmitter is between 755nm and 852nm, the sampling rate is 10Hz, and the spacing between each probe is 3cm. The frontal lobe and the right temporoparietal junction are measured separately, that is, 11 channels for each brain region, for a total of 22 channels. Figure 3A schematic diagram of the relationship between the channel position and the photoelectrode position provided in an embodiment of the present application.
[0063] Furthermore, compared with deoxyhemoglobin and total hemoglobin, the oxyhemoglobin (Oxy-Hb, HbO) indicator is more sensitive to changes in brain activity caused by task stimulation, and oxyhemoglobin is closer to the fNIRS signal. Therefore, this application judges the inter-brain synchronization between different subjects based on the changes in the concentration of oxyhemoglobin in each brain region.
[0064] In one implementation, the EEG data (oxyhemoglobin data) from the training sample set, both in task state and resting state, must first be preprocessed to remove inaccurate data and improve the accuracy of subsequent inter-brain synchronization verification. Task state indicates the state of the brain when performing specific tasks such as memory, recognition, and movement. Resting state indicates the state of the brain when not performing specific cognitive tasks, remaining quiet and relaxed.
[0065] Specifically, the training sample set is first filtered for unqualified EEG data. Unqualified EEG data refers to EEG data where the number of unqualified channels exceeds a fourth threshold. For example, the fourth threshold could be 30%, meaning unqualified EEG data refers to EEG data where the number of unqualified channels exceeds 30% of the total number of channels. Unqualified channels refer to EEG data where artifacts exceed a fifth threshold, which could be 5%. Artifacts are irrelevant data that appear in the sample but are not part of the original data being collected.
[0066] Secondly, discrete wavelet transform (DWT) and principal component analysis (PCA) were used to remove noise and drift from the EEG data. This removes systematic errors. Finally, data from adjacent channels in the same functional area were used to fill in missing EEG channels, thereby reducing the proportion of missing data.
[0067] Step 220: Verify the inter-brain synchronization between the first EEG data and the second EEG data under different time pressures based on a T test.
[0068] For the above-mentioned preprocessed EEG data, wavelet transform coherence analysis (WTC) is used to obtain the inter-brain synchronization between the first EEG data and the second EEG data. Compared with other methods, such as inter-subject correlation analysis (ISC) and general linear model (GLM), wavelet transform coherence analysis has many advantages. For example, wavelet transform coherence analysis can split the signal into frequency domain calculation, which can avoid the influence of global drift or local fluctuation on the data results; while splitting the frequency domain calculation, it retains the time domain information and reflects the behavioral response more accurately; wavelet transform coherence analysis is more suitable for non-periodic non-stationary signals, which is consistent with the characteristics of EEG data. Therefore, in an embodiment of the present application, wavelet transform coherence analysis is used to obtain the first inter-brain synchronization value between the first EEG data and the second EEG data. The first inter-brain synchronization value is the inter-brain synchronization value under the task state.
[0069] To facilitate subsequent statistical analysis, after calculating the first inter-brain synchronization value, the first inter-brain synchronization value was subjected to Fisher's Z transformation (Fisher-z). The Fisher-z transformation indicates the process of converting the correlation coefficient R values of several samples taken from the same population into Fisher Z scores with equally spaced units when combining the correlation coefficients. The task-related coherence was calculated by subtracting the second inter-brain synchronization value from the first inter-brain synchronization value after the Fisher-z transformation. The second inter-brain synchronization value is the inter-brain synchronization value in the resting state. The task-related coherence is used to indicate the degree of correlation between the inter-brain synchronization value and the task state.
[0070] Furthermore, a single-sample T-test was performed on all channels of the coherence data for all brain synchronization data in the training sample set to obtain a first test result, which was then corrected for the false discovery rate (FDR). This first test result was used to identify channels in the training sample set that exhibited inter-brain synchronization.
[0071] For example, when the T value of a certain channel of the first EEG data and the second EEG data is greater than a preset value, it is considered that the channel of the first EEG data and the second EEG data has inter-brain synchronization; otherwise, the channel does not have inter-brain synchronization.
[0072] In one embodiment, frequency points in the corrected first test result with a P value less than a first threshold are obtained. For example, the first threshold may be 0.0005, i.e., frequency points in the corrected first test result with a P value less than 0.0005 are obtained. The number of consecutive points within a preset range of frequency points that meet a preset criterion is determined; a target frequency band is determined based on the number of consecutive points. This target frequency band is the frequency band most sensitive to inter-brain synchronization.
[0073] Furthermore, for the channels in the target frequency band with inter-brain synchronization, an independent sample T-test was performed on the coherence of the brain synchronization data with high time pressure and the brain synchronization data with low time pressure (to verify the difference between the two groups of data), and a second test result was obtained, and the second test result was corrected.
[0074] When the P value of the second test result is less than the second threshold and the T value is less than the third threshold, it indicates that there is a difference between the brain synchronization data with high time pressure and the brain synchronization data with low time pressure, and the inter-brain synchronization of the brain synchronization data with low time pressure is stronger than that of the brain synchronization data with high time pressure.
[0075] In addition, the nirsimg function in the xjview toolkit can be used to convert the T values of the 22 channels into image files for visualization.
[0076] Figure 4 The present invention provides another flow chart of a verification method for brain synchronization. Figure 2 The difference of the method shown is that while verifying the inter-brain synchronization between the first EEG data and the second EEG data under different time pressures, it is also necessary to verify the inter-brain synchronization direction of the first EEG data and the second EEG data.
[0077] Step 410: Verify the inter-brain synchronization direction between the first EEG data and the second EEG data based on Granger causality.
[0078] In neuroimaging research, many methods can be used to test the directional directionality between a pair of neural signals, such as Granger causality analysis (GCA), phase transfer entropy (PTE), permutation conditional mutual information (PCMI), etc.
[0079] Granger causality verification has been widely used in previous near-infrared studies to analyze the causal relationship of near-infrared time series data. Granger causality verification is a test of whether there is a predictive relationship between two stationary time series, and can be used to examine the directionality of brain signals between individuals during interaction. Therefore, in this embodiment of the present application, the inter-brain synchronization direction of the first EEG data and the second EEG data is verified based on Granger causality.
[0080] Granger causality analysis was performed on the channels with significant inter-brain synchronization to test the direction of inter-brain synchronization. In the embodiment of the present application, the data used was the EEG data after the above preprocessing, because after removing the global component through the above principal component analysis, the EEG data is relatively stable.
[0081] Specifically, first, for channels with inter-brain synchronization, the first EEG data and the second EEG data of the same period are extracted from the entire time series. Secondly, the first Granger causality value between the first EEG data and the second EEG data in the first direction is calculated, and the second Granger causality value between the first EEG data and the second EEG data in the second direction is calculated. The first direction is used to indicate that the subject of the first EEG data points to the subject of the second EEG data, and the second direction is used to indicate that the subject of the second EEG data points to the subject of the first EEG data. Finally, a single-sample T-test is performed on the first and second Granger causality values to determine the direction of inter-brain synchronization, and a paired-sample T-test is used to test the magnitude of the first and second directions.
[0082] In summary, by performing independent sample T-test and one-sample T-test on the first EEG data and the second EEG data, we can verify the inter-brain synchronization between the first EEG data and the second EEG data, as well as the effect of time pressure on the inter-brain synchronization between the first EEG data and the second EEG data. We can also verify the directionality of the inter-brain synchronization between the first EEG data and the second EEG data with inter-brain synchronization through Granger causality.
[0083] It is understood that in order to implement the functions in the above embodiments, the computer device includes hardware structures and / or software modules corresponding to the execution of each function. It should be readily apparent to those skilled in the art that, in combination with the units and method steps of each example described in the embodiments disclosed in this application, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware-driven manner or in a computer software-driven hardware manner depends on the specific application scenario and design constraints of the technical solution.
[0084] Furthermore, as a response to the above Figure 2 or Figure 4The embodiment of the method shown in the figure is implemented. The embodiment of the present application provides a device for verifying inter-brain synchronization, which is used to verify the inter-brain synchronization between the first EEG data and the second EEG data. The embodiment of the device corresponds to the aforementioned method embodiment. For ease of reading, this embodiment will no longer repeat the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can correspond to all the contents of the aforementioned method embodiment. Specifically, Figure 5 As shown, the inter-brain synchronization verification device 500 includes: an acquisition module 510 and a verification module 520.
[0085] The acquisition module 510 is used to acquire a training sample set, which includes multiple groups of brain synchronization data with high time pressure and multiple groups of brain synchronization data with low time pressure, and the brain synchronization data includes first EEG data and second EEG data.
[0086] The verification module 520 is used to verify the inter-brain synchronization between the first EEG data and the second EEG data under different time pressures based on a T test.
[0087] Further, such as Figure 5 As shown, the verification module 520 is specifically used to calculate the first inter-brain synchronization value of the first EEG data and the second EEG data in the brain synchronization data, the first inter-brain synchronization value is the inter-brain synchronization value under the task state; determine the coherence of the task association according to the first inter-brain synchronization value and the second inter-brain synchronization value, the second inter-brain synchronization value is the inter-brain synchronization value under the resting state, and the coherence of the task association is used to indicate the degree of correlation between the inter-brain synchronization value and the task state; perform a full-channel single-sample T test on the coherence of the brain synchronization data in the training sample set to obtain a first test result, and correct the first test result, and the first test result is used to determine the channel with inter-brain synchronization in the training sample set.
[0088] Further, such as Figure 5 As shown, the verification module 520 is specifically used to obtain the frequency points whose P values are less than the first threshold in the first test result; determine the number of consecutive points that meet the preset standards within the preset range of frequency points; and determine the target frequency band according to the number of consecutive points.
[0089] Further, such as Figure 5 As shown, the verification module 520 is specifically used to perform an independent sample T test on the coherence of the brain synchronization data with high time pressure and the brain synchronization data with low time pressure for the channel in the target frequency band and with inter-brain synchronization, obtain a second test result, and correct the second test result; when the P value of the second test result is less than the second threshold and the T value is less than the third threshold, it indicates that there is a difference between the brain synchronization data with high time pressure and the brain synchronization data with low time pressure, and the inter-brain synchronization of the brain synchronization data with low time pressure is stronger than that of the brain synchronization data with high time pressure.
[0090] Further, such as Figure 5 As shown, the verification module 520 is further used to verify the inter-brain synchronization direction of the first EEG data and the second EEG data based on Granger causality.
[0091] Further, such as Figure 5 As shown, the verification module 520 is specifically used to extract the first EEG data and the second EEG data of the same period from the entire time series for the channel with inter-brain synchronization; calculate the first Granger causal value between the first EEG data and the second EEG data in the first direction, and calculate the second Granger causal value between the first EEG data and the second EEG data in the second direction, the first direction is used to indicate that the subject of the first EEG data points to the subject of the second EEG data, and the second direction is used to indicate that the subject of the second EEG data points to the subject of the first EEG data; perform a single-sample T test on the first Granger causal value and the second Granger causal value to determine the direction of inter-brain synchronization.
[0092] Further, such as Figure 5 As shown, the acquisition module 510 is also used to filter out unqualified EEG data in the training sample set, where the unqualified EEG data is the EEG data in which the number of unqualified channels is greater than a fourth threshold; remove noise and drift components in the EEG data; and fill the unqualified channels of the EEG data with data from other adjacent channels in the same functional area.
[0093] Furthermore, an embodiment of the present application provides an electronic device, comprising a processor and a memory, wherein the acquisition module 510 and the verification module 520 are stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions. The processor includes a kernel, which retrieves the corresponding program units from the memory.
[0094] An embodiment of the present application provides a storage medium having a program stored thereon, which implements the method for verifying inter-brain synchronization when executed by a processor.
[0095] An embodiment of the present application provides a processor, which is used to run a program, wherein the method for verifying inter-brain synchronization is executed when the program is running.
[0096] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having the following method steps: obtaining a training sample set, the training sample set including multiple groups of brain synchronization data with high time pressure and multiple groups of brain synchronization data with low time pressure, the brain synchronization data including first EEG data and second EEG data; verifying the inter-brain synchronization between the first EEG data and the second EEG data under different time pressures based on a T test.
[0097] Furthermore, the first inter-brain synchronization value of the first EEG data and the second EEG data in the brain synchronization data is calculated, and the first inter-brain synchronization value is the inter-brain synchronization value under the task state; the task-associated coherence is determined according to the first inter-brain synchronization value and the second inter-brain synchronization value, and the second inter-brain synchronization value is the inter-brain synchronization value under the resting state, and the task-associated coherence is used to indicate the degree of correlation between the inter-brain synchronization value and the task state; the coherence of the brain synchronization data in the training sample set is subjected to a full-channel single-sample T test to obtain a first test result, and the first test result is corrected, and the first test result is used to determine the channel with inter-brain synchronization in the training sample set.
[0098] Furthermore, frequency points whose P values are less than a first threshold value in the first test result are obtained; the number of consecutive points that meet a preset standard within a preset range of frequency points is determined; and the target frequency band is determined according to the number of consecutive points.
[0099] Furthermore, for the channels in the target frequency band with inter-brain synchronization, an independent sample T test is performed on the coherence of the brain synchronization data with high time pressure and the brain synchronization data with low time pressure to obtain a second test result, and the second test result is corrected; when the P value of the second test result is less than the second threshold and the T value is less than the third threshold, it indicates that there is a difference between the brain synchronization data with high time pressure and the brain synchronization data with low time pressure, and the inter-brain synchronization of the brain synchronization data with low time pressure is stronger than that of the brain synchronization data with high time pressure.
[0100] Furthermore, the inter-brain synchronization direction of the first EEG data and the second EEG data is verified based on Granger causality.
[0101] Furthermore, for channels with inter-brain synchronization, the first EEG data and the second EEG data of the same period are extracted from the entire time series; the first Granger causal value between the first EEG data and the second EEG data in the first direction is calculated, and the second Granger causal value between the first EEG data and the second EEG data in the second direction is calculated, the first direction is used to indicate that the subject of the first EEG data points to the subject of the second EEG data, and the second direction is used to indicate that the subject of the second EEG data points to the subject of the first EEG data; a single-sample T test is performed on the first Granger causal value and the second Granger causal value to determine the direction of inter-brain synchronization.
[0102] Furthermore, unqualified EEG data in the training sample set are filtered out, where the number of unqualified EEG data channels in the EEG data is greater than a fourth threshold value; noise and drift components in the EEG data are removed; and unqualified channels of the EEG data are filled with data from other adjacent channels in the same functional area.
[0103] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0104] In a typical configuration, the device includes one or more processors (CPUs), memory, and a bus. The device may also include input / output interfaces, network interfaces, and the like.
[0105] Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip. Memory is an example of a computer-readable medium.
[0106] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.
[0107] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.
[0108] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0109] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.
Claims
1. A method for verifying inter-brain synchronization, characterized in that: The method comprises: Acquire a training sample set, wherein the training sample set includes multiple groups of brain synchronization data with high time pressure and multiple groups of brain synchronization data with low time pressure, wherein the brain synchronization data includes first EEG data and second EEG data; The inter-brain synchronization between the first EEG data and the second EEG data under different time pressures is verified based on the T test.
2. The method according to claim 1, characterized in that: Verifying the inter-brain synchronization between the first EEG data and the second EEG data under different time pressures based on a T test includes: Calculating a first inter-brain synchronization value of the first EEG data and the second EEG data in the brain synchronization data, wherein the first inter-brain synchronization value is an inter-brain synchronization value under a task state; determining task-associated coherence according to the first inter-brain synchronization value and the second inter-brain synchronization value, wherein the second inter-brain synchronization value is an inter-brain synchronization value in a resting state, and the task-associated coherence is used to indicate a degree of correlation between the inter-brain synchronization value and the task state; The coherence of the brain synchronization data in the training sample set is subjected to a single-sample T test of all channels to obtain a first test result, and the first test result is corrected. The first test result is used to determine the channels in the training sample set with brain-to-brain synchronization.
3. The method according to claim 2, characterized in that After obtaining the first test result and correcting the first test result, the method further includes: Obtaining frequency points in the first test result where the P value is less than a first threshold; Determining the number of consecutive points that meet a preset criterion within a preset range of the frequency points; The target frequency band is determined according to the number of consecutive points.
4. The method according to claim 3, characterized in that: The method further comprises: For the channel in the target frequency band and with inter-brain synchronization, an independent sample T test is performed on the coherence of the high time pressure brain synchronization data and the low time pressure brain synchronization data to obtain a second test result, and the second test result is corrected; When the P value of the second test result is less than the second threshold and the T value is less than the third threshold, it indicates that there is a difference between the brain synchronization data with high time pressure and the brain synchronization data with low time pressure, and the brain-to-brain synchronization of the brain synchronization data with low time pressure is stronger than that of the brain synchronization data with high time pressure.
5. The method according to claim 1, characterized in that: The method further comprises: The inter-brain synchronization direction of the first EEG data and the second EEG data is verified based on Granger causality.
6. The method according to claim 5, characterized in that Verifying the brain synchronization direction of the first EEG data and the second EEG data based on Granger causality includes: For channels with inter-brain synchronization, the first EEG data and the second EEG data of the same period are extracted from the entire time series; Calculating a first Granger causal value between the first EEG data and the second EEG data in a first direction, and calculating a second Granger causal value between the first EEG data and the second EEG data in a second direction, wherein the first direction is used to indicate that the subject of the first EEG data points to the subject of the second EEG data, and the second direction is used to indicate that the subject of the second EEG data points to the subject of the first EEG data; A one-sample T-test is performed on the first Granger causal value and the second Granger causal value to determine the direction of inter-brain synchronization.
7. The method according to claim 1, characterized in that After obtaining the training sample set, the method further includes: Filtering unqualified EEG data in the training sample set, wherein the unqualified EEG data is EEG data in which the number of unqualified channels in the EEG data is greater than a fourth threshold; removing noise and drift components in the EEG data; The unqualified channels of the electroencephalogram data are filled with data from other adjacent channels in the same functional area.
8. A device for verifying brain synchronization, characterized in that: The device comprises: An acquisition module, used for acquiring a training sample set, wherein the training sample set includes a plurality of groups of brain synchronization data with high time pressure and a plurality of groups of brain synchronization data with low time pressure, wherein the brain synchronization data includes a first EEG data and a second EEG data; A verification module is used to verify the inter-brain synchronization between the first EEG data and the second EEG data under different time pressures based on a T test.
9. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the method for verifying brain-to-brain synchronization as described in any one of claims 1-7.
10. An electronic device, characterized in that: The device includes at least one processor, and at least one memory and a bus connected to the processor; wherein the processor and the memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the method for verifying brain-to-brain synchronization as described in any one of claims 1-7.