Template-based near-infrared channel brain region automatic mapping and display method, system and device and storage medium

Abstract

The invention discloses a template-based near-infrared channel brain region automatic mapping and display method, system and device and a storage medium, and the method comprises the steps: taking each detector as a collection channel, correspondingly collecting near-infrared signals, and carrying out the interference removal of each near-infrared signal, so as to calculate the blood oxygen concentration change value of each collection channel; the method comprises the following steps: establishing a corresponding relation between signal intensity of near-infrared signals and activation degrees of brain regions, calculating the activation degree of each brain region, drawing a boundary of a functional region on a preset three-dimensional brain map, marking activation hot spots in the functional region to form an activation map, and marking significant points of blood oxygen concentration change on a preset blood oxygen response curve. The blood oxygen distribution curve graph is formed, automatic positioning of the position of the collection channel is achieved, the manual comparison workload during data analysis is greatly reduced, the analysis efficiency is improved, subjective errors are eliminated through an automatic mapping method based on the template, and the corresponding judgment accuracy of the channel and the anatomical or functional brain area is improved.

Description

[0001] Technology Neighborhood

[0002] This invention relates to the field of brain functional imaging technology, and in particular to a template-based method, system, device, and storage medium for automatic mapping and display of brain regions in the near-infrared channel. Background Technology

[0003] Near-infrared spectroscopy functional brain imaging (fNIRS) is a novel brain imaging technique. Utilizing near-infrared light and a multi-channel sensor consisting of a transmitter and receiver probe, and based on the neural-blood oxygen coupling mechanism, it can penetrate the skull to detect and image changes in brain activity activation at high temporal resolution, effectively visualizing and quantitatively assessing brain function. In recent years, fNIRS technology has been widely used in stroke rehabilitation assessment, brain-computer interfaces, and sleep quality monitoring.

[0004] However, most current fNIRS devices and software still use a generic layout for placing the light source and detectors, such as based on 10-20 EEG coordinates or custom helmet templates. During later data analysis, researchers often need to manually compare the data with standard brain maps to determine the anatomical or functional brain regions corresponding to the acquired channels. This operation is time-consuming, labor-intensive, and prone to subjective errors, hindering multi-center sharing and automated modeling. Furthermore, existing localization software has limited functionality, is cumbersome to operate, lacks user-friendliness, and provides a poor user experience. Summary of the Invention

[0005] The purpose of this invention is to address the technical problems existing in the background art by proposing a template-based automatic mapping and display method, system, device, and storage medium for near-infrared channel brain regions.

[0006] To achieve the above-mentioned technical objectives, the technical solution adopted by the present invention is as follows:

[0007] A first implementation of the first aspect of the present invention provides a template-based automatic mapping and display method for near-infrared channel brain regions, comprising:

[0008] S101. Select any template, set the layout positions of multiple near-infrared light sources and multiple detectors, record the layout positions, and load the standard brain spatial model.

[0009] S102. Using each detector as a data acquisition channel, near-infrared signals are acquired accordingly, and interference is removed from each near-infrared signal to calculate the change in blood oxygen concentration for each data acquisition channel.

[0010] S103. Establish the correspondence between the signal intensity of near-infrared signals and the activation level of brain regions, and calculate the activation level of each brain region based on the signal intensity of each acquisition channel.

[0011] S104. Based on each activation level, draw the boundaries of functional regions on the preset three-dimensional brain map, mark activation hotspots in the functional regions to form an activation map, and mark significant points of blood oxygen concentration change on the preset blood oxygen response curve to form a blood oxygen distribution curve.

[0012] Optionally, in a second implementation of the first aspect of the present invention, the interference removal processing of each near-infrared signal includes:

[0013] S1021. Perform wavelet multi-resolution decomposition on each near-infrared signal and use an adaptive threshold strategy based on signal local energy to remove high-frequency noise.

[0014] S1022. After denoising is completed, adaptive sliding window regression is performed to eliminate low-frequency drift.

[0015] S1023. The transient artifacts of each near-infrared signal are detected by using a preset time derivative distribution repair method, and repaired by combining multi-channel consistency judgment to obtain the repaired near-infrared signal.

[0016] Optionally, in a third implementation of the first aspect of the present invention, the threshold of the adaptive threshold strategy is dynamically adjusted based on the signal variance and decomposition level of the near-infrared signal.

[0017] Optionally, in a fourth implementation of the first aspect of the present invention, the adaptive sliding window is adjusted in real time according to the stability of the near-infrared signal, wherein when the signal variance increases, the window is shortened to improve the response speed; when the near-infrared signal is stable, the window is extended to reduce the risk of overfitting.

[0018] Optionally, in a fifth implementation of the first aspect of the present invention, after step S1023, the method further includes:

[0019] S1024. Perform a comprehensive signal quality score on each repaired near-infrared signal to obtain multiple signal scores;

[0020] S1025. Determine whether the signal score meets the standard threshold.

[0021] S1026. If it does not meet the requirements, return to step S1021 and reprocess the near-infrared signal to remove interference.

[0022] S1027. If the condition is met, output the repaired near-infrared signal.

[0023] Optionally, in a sixth implementation of the first aspect of the present invention, step 103 further includes:

[0024] S1031. Map the position of each acquisition channel onto a preset standard brain template, and use a preset iterative registration algorithm based on minimum distance and combine the probe spacing parameter to correct the deviation, so that each acquisition channel corresponds to each brain region.

[0025] S1032. The near-infrared signals in each acquisition channel are modeled using a pre-set generalized linear model combined with the standard blood oxygen response function to calculate the regression coefficient β value, where the β value is used to characterize the activation degree of the brain region.

[0026] Optionally, in a seventh implementation of the first aspect of the present invention, drawing the boundaries of functional regions on a preset three-dimensional mind map according to each activation level includes:

[0027] A significance test was performed on the β value to obtain the test value;

[0028] When the β value is greater than 0.2 and the test value is less than 0.05, the corresponding brain region is automatically marked as an activated region, and the boundaries of the functional region are drawn on a preset three-dimensional brain map.

[0029] A first implementation of the second aspect of the present invention provides a template-based near-infrared channel brain region automatic mapping and display system, comprising:

[0030] The configuration module is used to select any template, set the layout positions of multiple near-infrared light sources and multiple detectors, record the layout positions, and load a standard brain spatial model.

[0031] The acquisition module is used to use each detector as an acquisition channel to acquire near-infrared signals and to perform interference removal processing on each near-infrared signal in order to calculate the blood oxygen concentration change value of each acquisition channel.

[0032] The calculation module is used to establish the correspondence between the signal intensity of near-infrared signals and the activation level of brain regions, and to calculate the activation level of each brain region based on the signal intensity of each acquisition channel.

[0033] The output module is used to draw the boundaries of functional regions on a preset 3D brain map according to each activation level, mark activation hotspots in the functional regions to form an activation map, and mark significant points of blood oxygen concentration change on a preset blood oxygen response curve to form a blood oxygen distribution curve.

[0034] A first implementation of the third aspect of the present invention provides a template-based near-infrared channel brain region automatic mapping and display device, the template-based near-infrared channel brain region automatic mapping and display device comprising: a memory and at least one processor, the memory storing instructions, the memory and the at least one processor being interconnected via a circuit;

[0035] The at least one processor invokes the instructions in the memory to cause the template-based near-infrared channel brain region automapping and display device to perform the template-based near-infrared channel brain region automapping and display method as described in any one of the first aspects of the present invention.

[0036] A first implementation of the fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the template-based automatic mapping and display method for near-infrared channel brain regions as described in any one of the first aspects of the present invention.

[0037] Compared with existing technologies, this invention has the following beneficial technical effects: By selecting any template, setting the layout positions of multiple near-infrared light sources and multiple detectors, recording the layout positions and loading a standard brain spatial model, using each detector as a collection channel, and correspondingly collecting near-infrared signals, and performing interference removal processing on each near-infrared signal to calculate the blood oxygen concentration change value of each collection channel, establishing the correspondence between the signal intensity of the near-infrared signal and the activation degree of the brain region, and calculating the activation degree of each brain region based on the signal intensity of each collection channel, drawing the boundaries of functional regions on a preset three-dimensional brain map according to each activation degree, marking activation hotspots in the functional regions to form an activation atlas, and marking significant points of blood oxygen concentration change on a preset blood oxygen response curve to form a blood oxygen distribution curve, this invention achieves automatic positioning of the collection channel positions, greatly reducing the manual comparison workload during data analysis, improving analysis efficiency, and the template-based automatic mapping method eliminates subjective errors, improves the accuracy of the correspondence judgment between channels and anatomical or functional brain regions, and ensures the objectivity and reliability of data analysis results. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the first embodiment of the template-based near-infrared channel brain region automatic mapping and display method in this invention;

[0039] Figure 2 This is a schematic diagram of the second embodiment of the template-based near-infrared channel brain region automatic mapping and display method in this invention.

[0040] Figure 3 This is a schematic diagram of the fifth embodiment of the template-based near-infrared channel brain region automatic mapping and display method in this invention.

[0041] Figure 4 This is a schematic diagram of the sixth embodiment of the template-based near-infrared channel brain region automatic mapping and display method in this invention;

[0042] Figure 5This is a flowchart illustrating the template-based automatic mapping and display method for near-infrared channel brain regions in an embodiment of the present invention.

[0043] Figure 6 This is a schematic diagram of an embodiment of the template-based near-infrared channel brain region automatic mapping and display system of the present invention;

[0044] Figure 7 This is a schematic diagram of an embodiment of the template-based near-infrared channel brain region automatic mapping and display device of the present invention. Detailed Implementation

[0045] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0046] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figures 1-5 The template-based automatic mapping and display method for near-infrared channel brain regions in this embodiment of the invention includes:

[0047] S101. Select any template, set the layout positions of multiple near-infrared light sources and multiple detectors, record the layout positions, and load the standard brain spatial model.

[0048] In this embodiment, the layout positions of near-infrared light sources and detectors are set according to the selected template type, including the number of light sources, the number of detectors, and the position coordinates of each channel. For example, a general layout method based on 10-20 EEG coordinates is adopted, and the number and position of light sources and detectors are automatically set according to the template type. For example, 64 light sources and 64 detectors are set, and the position coordinates of the light sources and detectors are respectively: Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, etc.

[0049] S102. Using each detector as a data acquisition channel, near-infrared signals are acquired accordingly, and interference is removed from each near-infrared signal to calculate the change in blood oxygen concentration for each data acquisition channel.

[0050] In this embodiment, various interference factors are inevitably present during the actual signal acquisition process. These include stray light from other light sources in the environment, electrical noise from the detector itself, and electromagnetic interference from surrounding electronic devices. These interferences make the acquired near-infrared signals "impure," affecting subsequent accurate analysis. Therefore, a series of technical means are needed, such as filtering (which can be high-pass or low-pass filtering in electronic circuits to remove interference signals outside a specific frequency range), signal averaging (averaging multiple acquisitions to reduce the influence of random noise), and shielding measures (to reduce the influence of electromagnetic interference), to remove these interference factors and ensure that the near-infrared signals reflect the characteristics of the object we want to study as accurately as possible.

[0051] Furthermore, the interference removal processing for each near-infrared signal includes:

[0052] S1021. Perform wavelet multi-resolution decomposition on each near-infrared signal and use an adaptive threshold strategy based on signal local energy to remove high-frequency noise.

[0053] S1022. After denoising is completed, adaptive sliding window regression is performed to eliminate low-frequency drift.

[0054] S1023. The transient artifacts of each near-infrared signal are detected by using a preset time derivative distribution repair method, and repaired by combining multi-channel consistency judgment to obtain the repaired near-infrared signal.

[0055] In this embodiment, the threshold of the adaptive threshold strategy is dynamically adjusted based on the signal variance and decomposition level of the near-infrared signal; the adaptive sliding window is adjusted in real time according to the stability of the near-infrared signal, wherein when the signal variance increases, the window is shortened to improve the response speed; when the near-infrared signal is stable, the window is extended to reduce the risk of overfitting.

[0056] Wavelet transform is a time-frequency analysis method that decomposes a signal into components of different frequencies, much like breaking down a complex sound into different frequency bands such as high, mid, and low. Wavelet multi-resolution decomposition performs this decomposition at different scales, breaking down near-infrared signals into multiple sub-bands, each corresponding to a different frequency range, gradually presenting from high to low frequencies. This allows for more detailed analysis of the signal's characteristics in different frequency bands.

[0057] In the sub-bands obtained from the decomposition, the high-frequency components often contain a significant amount of noise. Since signal and noise differ in energy distribution, a suitable threshold is determined based on the characteristics of the signal's local energy. This threshold is not fixed but dynamically adjusted according to the energy conditions at different locations and in different parts of the signal. When a wavelet coefficient (a representation of the decomposed signal component) is less than this threshold, it is considered to be primarily contributed by noise, and it is set to zero or subjected to appropriate processing to remove high-frequency noise and retain relatively pure signal components.

[0058] Even after removing high-frequency noise, low-frequency drift may still exist in the signal, meaning the signal as a whole exhibits a slow, fluctuating trend. This could be due to factors such as slow environmental changes or instrument temperature drift. Adaptive sliding window regression addresses this by setting a sliding window and continuously moving it across the signal. Regression analysis is then used to fit a local trend based on the data within the window, and this trend is subtracted from the original signal to eliminate low-frequency drift. This results in a more stable signal that better reflects the true, useful information. After removing low-frequency drift, the signal baseline becomes more stable. Subsequent calculations and analyses of relevant indicators (such as blood oxygen saturation) using this signal reduce errors caused by these slow changes, improving the accuracy of the results.

[0059] During near-infrared signal acquisition, momentary anomalies may occur, such as sudden electromagnetic interference pulses or large, instantaneous movements of the human body. These can cause transient artifacts in the signal, meaning the signal changes abnormally within a short period. The preset time derivative distribution repair method analyzes the derivative of the signal over time, identifying transient artifacts according to pre-defined rules and distribution characteristics. Since multiple acquisition channels often operate simultaneously, when a transient artifact is detected in a channel, it is compared with signals from other channels at the same or similar times. If the signals from other channels are normal or conform to certain reasonable patterns, the channel with the artifact can be repaired by referencing the signals from these normal channels. This can be done by replacing data with corresponding data from other channels or by using weighted averaging or other comprehensive processing methods. The result is a repaired near-infrared signal that is closer to the true and accurate state, facilitating further analysis and application.

[0060] Furthermore, after step S1023, the following steps are also included:

[0061] S1024. Perform a comprehensive signal quality score on each repaired near-infrared signal to obtain multiple signal scores;

[0062] S1025. Determine whether the signal score meets the standard threshold.

[0063] S1026. If it does not meet the requirements, return to step S1021 and reprocess the near-infrared signal to remove interference.

[0064] S1027. If the condition is met, output the repaired near-infrared signal.

[0065] In this embodiment, the overall signal quality score Q is calculated based on the following formula:

[0066] Q = w1*SNR + w2*HRF + w3*Corr;

[0067] Wherein, SNR: signal-to-noise ratio, HRF: goodness of fit of the signal to the standard blood oxygen response function, and Corr: correlation coefficient between the signal and the task design paradigm.

[0068] When Q is lower than the set threshold, the system automatically re-executes step 302 or step 303 to form a closed-loop optimization process, ensuring that the signal quality meets the analysis requirements.

[0069] S103. Establish the correspondence between the signal intensity of near-infrared signals and the activation level of brain regions, and calculate the activation level of each brain region based on the signal intensity of each acquisition channel.

[0070] Furthermore, step 103 also includes:

[0071] S1031. Map the position of each acquisition channel onto a preset standard brain template, and use a preset iterative registration algorithm based on minimum distance and combine the probe spacing parameter to correct the deviation, so that each acquisition channel corresponds to each brain region.

[0072] S1032. The near-infrared signals in each acquisition channel are modeled using a pre-set generalized linear model combined with the standard blood oxygen response function to calculate the regression coefficient β value, where the β value is used to characterize the activation degree of the brain region.

[0073] In this embodiment, when mapping the acquisition channel position to a standard brain template, direct and precise mapping is difficult due to individual differences in brain morphology. This algorithm uses iterative calculations to find the minimum distance between the acquisition channel position and each point on the standard brain template, thus determining the optimal correspondence. This ensures the acquisition channel position matches the appropriate location on the template as accurately as possible, much like continuously adjusting the positions of jigsaw puzzle pieces to find the perfect fit. The spacing between the probes (i.e., the devices corresponding to the acquisition channels) has actual measured values, but during the mapping to the standard brain template, positional deviations may occur due to individual differences. By comparing this probe spacing parameter with the ideal spacing information in the standard brain template, adjustments and corrections are made to correct any deviations, allowing the acquisition channel to more accurately correspond to each brain region, ensuring the accuracy of data in subsequent analyses of brain activity.

[0074] By combining a generalized linear model with the standard blood oxygen response function, and substituting near-infrared signal data from the acquisition channel into this combined model for analysis and calculation, the regression coefficient β value can be obtained through a series of mathematical operations (such as parameter estimation using the least squares method). This β value is of great significance; it can serve as a quantitative indicator to represent the activation level of a brain region. In other words, the level of activity of a brain region under external stimulation or in a certain activity state can be intuitively reflected by this β value, helping researchers better understand the functional state and changes of different brain regions.

[0075] S104. Based on each activation level, draw the boundaries of functional regions on a preset 3D brain map, mark activation hotspots within the functional regions to form an activation atlas, and mark significant points of blood oxygen concentration change on a preset blood oxygen response curve to form a blood oxygen distribution curve.

[0076] Furthermore, based on each activation level, the boundaries of functional regions are drawn on a pre-defined 3D mind map, including:

[0077] A significance test was performed on the β value to obtain the test value;

[0078] When the β value is greater than 0.2 and the test value is less than 0.05, the corresponding brain region is automatically marked as an activated region, and the boundaries of the functional region are drawn on a preset three-dimensional brain map.

[0079] In this embodiment, by selecting any template, the layout positions of multiple near-infrared light sources and multiple detectors are set, and the layout positions and a standard brain spatial model are loaded. Each detector is used as a acquisition channel to acquire near-infrared signals, and interference removal processing is performed on each near-infrared signal to calculate the blood oxygen concentration change value of each acquisition channel. The correspondence between the signal intensity of the near-infrared signal and the activation degree of the brain region is established. Based on the signal intensity of each acquisition channel, the activation degree of each brain region is calculated. According to each activation degree, the boundary of the functional region is drawn on a preset three-dimensional brain map, and activation hotspots are marked in the functional region to form an activation map. Significant points of blood oxygen concentration change are marked on a preset blood oxygen response curve to form a blood oxygen distribution curve. This realizes the automatic positioning of the acquisition channel position, which greatly reduces the manual comparison workload during data analysis, improves the analysis efficiency, and the template-based automatic mapping method eliminates subjective errors, improves the accuracy of the correspondence judgment between the channel and the anatomical or functional brain region, and ensures the objectivity and reliability of the data analysis results.

[0080] The template-based automatic mapping and display method for near-infrared channel brain regions in embodiments of the present invention has been described above. The template-based automatic mapping and display system for near-infrared channel brain regions in embodiments of the present invention will be described below. Please refer to [link to relevant documentation]. Figure 6 The template-based near-infrared channel brain region automapping and display system includes:

[0081] Configuration module 201 is used to select any template, set the layout positions of multiple near-infrared light sources and multiple detectors, record the layout positions, and load a standard brain spatial model.

[0082] The acquisition module 202 is used to use each detector as an acquisition channel to acquire near-infrared signals and to perform interference removal processing on each near-infrared signal in order to calculate the blood oxygen concentration change value of each acquisition channel.

[0083] The calculation module 203 is used to establish the correspondence between the signal intensity of the near-infrared signal and the activation level of the brain region, and to calculate the activation level of each brain region based on the signal intensity of each acquisition channel.

[0084] The output module 204 is used to draw the boundaries of functional regions on a preset three-dimensional brain map according to each activation level, mark activation hotspots in the functional regions to form an activation map, and mark significant points of blood oxygen concentration change on a preset blood oxygen response curve to form a blood oxygen distribution curve.

[0085] The interference removal processing of each near-infrared signal in the acquisition module 202 includes:

[0086] S1021. Perform wavelet multi-resolution decomposition on each near-infrared signal and use an adaptive threshold strategy based on signal local energy to remove high-frequency noise.

[0087] S1022. After denoising is completed, adaptive sliding window regression is performed to eliminate low-frequency drift.

[0088] S1023. The transient artifacts of each near-infrared signal are detected by using a preset time derivative distribution repair method, and repaired by combining multi-channel consistency judgment to obtain the repaired near-infrared signal.

[0089] S1024. Perform a comprehensive signal quality score on each repaired near-infrared signal to obtain multiple signal scores;

[0090] S1025. Determine whether the signal score meets the standard threshold.

[0091] S1026. If it does not meet the requirements, return to step S1021 and reprocess the near-infrared signal to remove interference.

[0092] S1027. If the condition is met, output the repaired near-infrared signal.

[0093] The threshold of the adaptive threshold strategy is dynamically adjusted based on the signal variance and decomposition level of the near-infrared signal.

[0094] The adaptive sliding window is adjusted in real time based on the stability of the near-infrared signal. When the signal variance increases, the window is shortened to improve the response speed; when the near-infrared signal is stable, the window is extended to reduce the risk of overfitting.

[0095] Specifically, the calculation module 203 also performs the following:

[0096] S1031. Map the position of each acquisition channel onto a preset standard brain template, and use a preset iterative registration algorithm based on minimum distance and combine the probe spacing parameter to correct the deviation, so that each acquisition channel corresponds to each brain region.

[0097] S1032. The near-infrared signals in each acquisition channel are modeled using a pre-set generalized linear model combined with the standard blood oxygen response function to calculate the regression coefficient β value, where the β value is used to characterize the activation degree of the brain region.

[0098] The output module 204, based on each activation level, draws the boundaries of functional regions on a preset 3D mind map, including:

[0099] A significance test was performed on the β value to obtain the test value;

[0100] When the β value is greater than 0.2 and the test value is less than 0.05, the corresponding brain region is automatically labeled as an activated region, and the boundaries of the functional regions are drawn on a preset 3D brain map.

[0101] The above is attached Figure 6 The template-based automatic mapping and display method for near-infrared channel brain regions in this embodiment of the invention is described in detail from the perspective of modular functional entities. The template-based automatic mapping and display device for near-infrared channel brain regions in this embodiment of the invention is described in detail from the perspective of hardware processing.

[0102] Figure 7 This is a schematic diagram of a template-based near-infrared channel brain region automapping and display device 300 provided in an embodiment of the present invention. The template-based near-infrared channel brain region automapping and display device 300 can vary considerably due to different configurations or performance. It may include one or more central processing units (CPUs) 310 (e.g., one or more processors) and a memory 320, and one or more storage media 330 (e.g., one or more mass storage devices) storing application programs 333 or data 332. The memory 320 and storage media 330 can be temporary or persistent storage. The program stored in the storage media 330 may include one or more units (not shown in the diagram), each unit may include a series of instruction operations in the template-based near-infrared channel brain region automapping and display device 300. Furthermore, the processor 310 may be configured to communicate with the storage media 330 and execute the series of instruction operations in the storage media 330 on the template-based near-infrared channel brain region automapping and display device 300.

[0103] The template-based near-infrared channel brain region automapping and display device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 7 The illustrated template-based near-infrared channel brain region automapping and display device structure does not constitute a limitation on communication protocol devices based on local area network projection. It may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0104] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the template-based near-infrared channel brain region automatic mapping and display method.

[0105] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0106] The above describes a template-based automatic mapping and display method for near-infrared channel brain regions, or various implementation methods, in conjunction with specific content. It is not intended that the specific implementation of this invention is limited to these descriptions. Any methods or structures similar to or identical to those of this invention, or any technical deductions or substitutions made based on the concept of this invention, should be considered within the scope of protection of this invention.

Claims

1. A template-based method for automatic mapping and display of near-infrared channel brain regions, characterized in that, include: S101. Select any of the templates, set the layout positions of multiple near-infrared light sources and multiple detectors, and record the layout positions and load the standard brain spatial model. S102. Using each of the detectors as acquisition channels, near-infrared signals are acquired accordingly, and interference removal processing is performed on each of the near-infrared signals to calculate the blood oxygen concentration change value of each acquisition channel. S103. Establish the correspondence between the signal intensity of the near-infrared signal and the activation degree of the brain region, and calculate the activation degree of each brain region based on the signal intensity of each acquisition channel. S104. Based on the activation levels, draw the boundaries of functional regions on a preset three-dimensional brain map, mark activation hotspots in the functional regions to form an activation map, and mark significant points of blood oxygen concentration change on a preset blood oxygen response curve to form a blood oxygen distribution curve.

2. The template-based automatic mapping and display method for near-infrared channel brain regions according to claim 1, characterized in that, The interference removal process for each of the near-infrared signals includes: S1021. Perform wavelet multi-resolution decomposition on each of the near-infrared signals and use an adaptive threshold strategy based on signal local energy to remove high-frequency noise. S1022. After denoising is completed, adaptive sliding window regression is performed to eliminate low-frequency drift. S1023. The transient artifacts of each near-infrared signal are detected by the preset time derivative distribution repair method, and repaired by combining multi-channel consistency judgment to obtain the repaired near-infrared signal.

3. The template-based automatic mapping and display method for near-infrared channel brain regions according to claim 2, characterized in that, The threshold of the adaptive threshold strategy is dynamically adjusted based on the signal variance and decomposition level of the near-infrared signal.

4. The template-based automatic mapping and display method for near-infrared channel brain regions according to claim 3, characterized in that, The adaptive sliding window is adjusted in real time according to the stability of the near-infrared signal. When the signal variance increases, the window is shortened to improve the response speed; when the near-infrared signal is stable, the window is extended to reduce the risk of overfitting.

5. The template-based automatic mapping and display method for near-infrared channel brain regions according to claim 4, characterized in that, After step S1023, the method further includes: S1024. Perform a comprehensive signal quality score on each of the repaired near-infrared signals to obtain multiple signal scores; S1025. Determine whether the signal score meets the standard threshold. S1026. If it does not meet the requirements, return to step S1021 and reprocess the near-infrared signal to remove interference. S1027. If the condition is met, output the repaired near-infrared signal.

6. The template-based automatic mapping and display method for near-infrared channel brain regions according to claim 1, characterized in that, Step 103 further includes: S1031. Map the position of each acquisition channel onto a preset standard brain template, and use a preset iterative registration algorithm based on minimum distance and combine the probe spacing parameter to correct the deviation, so that each acquisition channel corresponds to each brain region. S1032. The near-infrared signals in each acquisition channel are modeled using a pre-set generalized linear model combined with a standard blood oxygen response function to calculate the regression coefficient β value, wherein the β value is used to characterize the activation degree of the brain region.

7. The template-based automatic mapping and display method for near-infrared channel brain regions according to claim 6, characterized in that, The step of drawing the boundaries of functional regions on a preset three-dimensional brain map based on each activation level includes: The significance of the β value was tested to obtain the test value; When the β value > 0.2 and the test value < 0.05, the corresponding brain region is automatically marked as an active region, and the boundaries of the functional region are drawn on a preset three-dimensional brain map.

8. A template-based near-infrared channel brain region automatic mapping and display system, characterized in that, include: The configuration module is used to select any of the templates, set the layout positions of multiple near-infrared light sources and multiple detectors, record the layout positions, and load a standard brain spatial model. The acquisition module is used to use each of the detectors as acquisition channels and acquire near-infrared signals accordingly, and to perform interference removal processing on each of the near-infrared signals in order to calculate the blood oxygen concentration change value of each acquisition channel. The calculation module is used to establish the correspondence between the signal intensity of the near-infrared signal and the activation level of the brain region, and to calculate the activation level of each brain region based on the signal intensity of each acquisition channel. The output module is used to draw the boundaries of functional regions on a preset three-dimensional brain map according to the activation levels, mark activation hotspots in the functional regions to form an activation map, and mark significant points of blood oxygen concentration change on a preset blood oxygen response curve to form a blood oxygen distribution curve.

9. A template-based near-infrared channel brain region automatic mapping and display device, characterized in that, The template-based near-infrared channel brain region automapping and display device includes: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a circuit; The at least one processor invokes the instructions in the memory to cause the template-based near-infrared channel brain region automapping and display device to perform the template-based near-infrared channel brain region automapping and display method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by the processor, it implements the template-based automatic mapping and display method for near-infrared channel brain regions as described in any one of claims 1-7.

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