FNIRS functional optical brain imaging signal quality evaluation method and device, storage medium and electronic equipment

Abstract

The invention discloses an fNIRS functional optical brain imaging signal quality evaluation method and device, a storage medium and electronic equipment. The method comprises the steps that incident light intensity data and emergent light intensity data emitted by a light source are acquired; determining a variable coefficient based on the incident light intensity data, and determining a signal-to-noise ratio based on the incident light intensity data and the emergent light intensity data; processing the incident light intensity data to obtain the number of peak values in a preset frequency range and heartbeat signal characteristics; and determining a quality evaluation result of the fNIRS signal based on the variable coefficient, the signal-to-noise ratio, the number of peak values and the heartbeat signal characteristics. According to the evaluation method, the quality of the near-infrared signal can be measured, and the evaluation method can adapt to physiological features of different individuals so as to guide real-time signal quality detection and hardware iteration design.

Description

Technical Field

[0001] The present application relates to the technical field of signal quality assessment, and in particular to a method for assessing the quality of fNIRS functional optical brain imaging signals, a device for assessing the quality of fNIRS functional optical brain imaging signals, a computer-readable storage medium, and an electronic device. Background Art

[0002] At present, when evaluating signal quality, only the stability of the optical signal can be measured, and it is more sensitive to detecting abnormal signals such as motion artifacts. However, it cannot determine whether the light actually passes through the skull, so it cannot accurately reflect the true hemodynamic changes in the cortical area, especially the dynamic changes of oxygenated and deoxygenated hemoglobin, which limits the application of the coefficient of variation in evaluating the true effectiveness of near-infrared signals. Although the analysis method of heartbeat signals can provide certain physiological relevance, it is easy to ignore low-frequency noise interference (such as scalp blood flow, environmental noise). In addition, there are significant differences in the selection of heartbeat frequency peaks between individuals and in different usage scenarios. For example, under exercise or high-load conditions, the heartbeat frequency range may deviate from the set reference value. This difference makes the quality assessment results based on heartbeat signals unstable and unable to adapt to diverse application scenarios. Summary of the invention

[0003] The present application aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the first purpose of the present application is to propose a method for evaluating the quality of fNIRS functional optical brain imaging signals, obtain incident light intensity data and outgoing light intensity data emitted by a light source, determine the coefficient of variation based on the incident light intensity data, and determine the signal-to-noise ratio based on the incident light intensity data and the outgoing light intensity data, process the incident light intensity data to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range, and determine the quality evaluation result of the fNIRS signal based on the coefficient of variation, signal-to-noise ratio, number of peaks and heartbeat signal characteristics, thereby being able to comprehensively measure the quality of near-infrared signals and being able to adapt to the physiological characteristics of different individuals to guide real-time signal quality detection and hardware iterative design.

[0004] The second objective of the present application is to propose a fNIRS functional optical brain imaging signal quality assessment device.

[0005] The third object of the present application is to provide a computer-readable storage medium.

[0006] The fourth objective of the present application is to provide an electronic device.

[0007] To achieve the above-mentioned objectives, the first aspect of the present application proposes a method for evaluating the quality of fNIRS functional optical brain imaging signals, the method comprising obtaining incident light intensity data and exiting light intensity data emitted by a light source; determining a coefficient of variation based on the incident light intensity data, and determining a signal-to-noise ratio based on the incident light intensity data and the exiting light intensity data; processing the incident light intensity data to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range; and determining a quality evaluation result of the fNIRS signal based on the coefficient of variation, the signal-to-noise ratio, the number of peaks and the heartbeat signal characteristics.

[0008] According to the fNIRS functional optical brain imaging signal quality assessment method of the embodiment of the present application, the incident light intensity data and the outgoing light intensity data emitted by the light source are obtained, the coefficient of variation is determined based on the incident light intensity data, and the signal-to-noise ratio is determined based on the incident light intensity data and the outgoing light intensity data, and the incident light intensity data is processed to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range, and the quality evaluation result of the fNIRS signal is determined based on the coefficient of variation, signal-to-noise ratio, number of peaks and heartbeat signal characteristics. Thus, the method can measure the quality of near-infrared signals, and can adapt to the physiological characteristics of different individuals to guide real-time signal quality detection and hardware iterative design.

[0009] In addition, the fNIRS functional optical brain imaging signal quality assessment method according to the above embodiment of the present application may also have the following additional technical features:

[0010] According to one embodiment of the present application, the heartbeat signal characteristics include the average value of the heartbeat intervals, the average heart rate and the standard deviation of consecutive heartbeat intervals, and the correlation coefficient and peak power of the red light and infrared light corresponding to the heartbeat signal. Obtaining the heartbeat signal characteristics includes: converting the incident light intensity data into light density data, filtering the color light of the infrared light wavelength and performing R point detection, and calculating the average value of the heartbeat intervals, the average heart rate and the standard deviation of the consecutive heartbeat intervals based on a sliding window method.

[0011] According to one embodiment of the present application, obtaining the number of peak values ​​within a preset frequency range includes: performing Fourier transform on the incident light intensity data to obtain a frequency domain signal of the incident light intensity data; sorting the amplitudes of the frequency domain signals within the preset frequency range in order from large to small; and taking the amplitudes of the first preset number in the sorting as the number of peak values.

[0012] According to one embodiment of the present application, the quality evaluation result of the fNIRS signal determined based on the coefficient of variation, the signal-to-noise ratio, the number of peaks and the heartbeat signal characteristics includes: determining a first signal quality index based on the coefficient of variation, wherein the coefficient of variation is negatively correlated with the first signal quality index; determining a second signal quality index based on the number of peaks and a preset number of peaks, wherein the difference between the number of peaks and the preset number of peaks is negatively correlated with the second signal quality index; determining a third signal quality index based on the average value of the heartbeat intervals, the average heart rate and the first standard deviation of the continuous heartbeat intervals and the average value of the reference heartbeat intervals, the reference average heart rate and the second standard deviation of the continuous heartbeat intervals; and determining a fourth signal quality index based on the cross-correlation coefficient difference between the cross-correlation coefficient and the preset cross-correlation coefficient, and the peak power difference between the peak power and a preset peak power threshold, wherein the first standard The standard deviation and the second standard deviation determine a target difference by means of correlation and variance analysis, the target difference is negatively correlated with the third signal quality index, and the cross-correlation coefficient difference and the peak power difference are positively correlated with the fourth signal quality index; a fifth signal quality index is determined based on the signal-to-noise ratio, wherein the signal-to-noise ratio is positively correlated with the fifth signal quality index; a quality evaluation result of the fNIRS signal is determined based on a first product of the first signal quality index and a corresponding weight coefficient, a second product of the second signal quality index and a corresponding weight coefficient, a third product of the third signal quality index and a corresponding weight coefficient, a fourth product of the fourth signal quality index and a corresponding weight coefficient, and a fifth product of the fifth signal quality index and a corresponding weight coefficient, and the sum of the first product, the second product, the third product and the fourth product, wherein the evaluation result is positively correlated with the signal quality index.

[0013] According to one embodiment of the present application, the method further includes: when the quality evaluation result of the fNIRS signal is less than a preset result threshold, adjusting the signal-to-noise ratio.

[0014] According to an embodiment of the present application, adjusting the signal-to-noise ratio includes: increasing the intensity of the infrared light emitted by the light source.

[0015] According to one embodiment of the present application, the method further includes: acquiring head data of the person being measured; acquiring hair density based on the head data, and when the hair density is dense, increasing the intensity of infrared light emitted by the light source.

[0016] To achieve the above-mentioned purpose, the second aspect of the present application proposes an fNIRS functional optical brain imaging signal quality assessment device, the device comprising: a first acquisition module, used to acquire incident light intensity data and exiting light intensity data emitted by a light source; a first determination module, used to determine a coefficient of variation based on the incident light intensity data, and to determine a signal-to-noise ratio based on the incident light intensity data and the exiting light intensity data; a second acquisition module, used to process the incident light intensity data to acquire the number of peaks and heartbeat signal characteristics within a preset frequency range; and a second determination module, used to determine a quality evaluation result of the fNIRS signal based on the coefficient of variation, the signal-to-noise ratio, the number of peaks and the heartbeat signal characteristics.

[0017] According to the fNIRS functional optical brain imaging signal quality assessment device of the embodiment of the present application, the first acquisition module is used to obtain the incident light intensity data and the exit light intensity data emitted by the light source, the first determination module is used to determine the coefficient of variation based on the incident light intensity data, and the signal-to-noise ratio based on the incident light intensity data and the exit light intensity data, the second acquisition module is used to process the incident light intensity data to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range, and the second determination module is used to determine the quality evaluation result of the fNIRS signal based on the coefficient of variation, signal-to-noise ratio, number of peaks and heartbeat signal characteristics. Thus, the device can measure the quality of near-infrared signals and can adapt to the physiological characteristics of different individuals to guide real-time signal quality detection and hardware iterative design.

[0018] To achieve the above objectives, the third aspect of the present application provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the above fNIRS functional optical brain imaging signal quality assessment method.

[0019] According to the computer-readable storage medium of the embodiment of the present application, the above-mentioned fNIRS functional optical brain imaging signal quality assessment method is implemented during execution, which can comprehensively measure the quality of near-infrared signals and can adapt to the physiological characteristics of different individuals to guide real-time signal quality detection and hardware iterative design.

[0020] To achieve the above-mentioned purpose, an electronic device proposed in the fourth aspect of the present application includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned fNIRS functional optical brain imaging signal quality assessment method is implemented.

[0021] The electronic device according to the embodiment of the present application can measure the quality of near-infrared signals by executing the above-mentioned fNIRS functional optical brain imaging signal quality assessment method, and can adapt to the physiological characteristics of different individuals to guide real-time signal quality detection and hardware iterative design.

[0022] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Flow chart of a method for evaluating the quality of fNIRS functional optical brain imaging signals according to an embodiment of the present application;

[0024] Figure 2 A flowchart of a method for evaluating the quality of fNIRS functional optical brain imaging signals according to a specific example of the present application;

[0025] Figure 3 Schematic diagram of a block diagram of an fNIRS functional optical brain imaging signal quality assessment device according to an embodiment of the present application;

[0026] Figure 4 Schematic diagram of a block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0028] The following describes the fNIRS functional optical brain imaging signal quality assessment method, fNIRS functional optical brain imaging signal quality assessment device, computer-readable storage medium and electronic device proposed in the embodiments of the present application with reference to the accompanying drawings.

[0029] Figure 1 Flow chart of a method for evaluating the quality of fNIRS functional optical brain imaging signals according to an embodiment of the present application.

[0030] like Figure 1 As shown, the fNIRS functional optical brain imaging signal quality assessment method of the embodiment of the present application may include the following steps:

[0031] S1, obtaining incident light intensity data and outgoing light intensity data emitted by a light source.

[0032] S2, determining a coefficient of variation based on the incident light intensity data, and determining a signal-to-noise ratio based on the incident light intensity data and the exiting light intensity data.

[0033] S3, processing the incident light intensity data to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range.

[0034] S4, determine the quality evaluation results of the fNIRS signal based on the coefficient of variation, signal-to-noise ratio, number of peaks and heartbeat signal characteristics.

[0035] Specifically, first obtain the incident light intensity data and the outgoing light intensity data emitted by the light source. The incident light intensity data refers to the light intensity emitted from the light source, that is, the light intensity measured before the object under test (such as the human head). This data is crucial for evaluating the initial output of the light source and the propagation characteristics of the light. The outgoing light intensity data refers to the light intensity after absorption and scattering by the object under test, that is, the light intensity measured after the object under test. The outgoing light intensity data provides information about the interaction between light and tissue, which is very important for evaluating the penetration and absorption characteristics of the signal.

[0036] After obtaining the incident light intensity data and the outgoing light intensity data, the coefficient of variation can be determined based on the incident light intensity data, and the signal-to-noise ratio can be determined based on the incident light intensity data and the outgoing light intensity data. That is, the coefficient of variation is an indicator to measure the stability of the signal, and the calculation formula is the ratio of the standard deviation to the mean. Based on the incident light intensity data, the coefficient of variation of the signal can be calculated to evaluate the stability and consistency of the signal. The signal-to-noise ratio is the ratio of the signal intensity to the background noise intensity, which is used to measure the quality of the signal. Based on the incident light intensity and the outgoing light intensity data, the signal-to-noise ratio can be determined to evaluate the ratio of useful information to noise in the signal.

[0037] The incident light intensity data can be processed to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range. For example, by performing frequency domain analysis on the incident light intensity data, the number of peaks within a specific frequency range (such as the breathing and heartbeat frequency range) can be identified. These peaks may be related to physiological activities, such as heartbeat and breathing, and their existence and number can provide additional information about signal quality. By analyzing these peaks, physiological related signals and noise components in the signal can be identified. A good signal should have an appropriate number of peaks, neither too many nor too few, which indicates that the signal contains valid physiological information without being overwhelmed by noise. By analyzing the heartbeat related signals in the incident light intensity data, the characteristics of the heartbeat signal, such as heartbeat frequency, amplitude, etc., can be extracted. These characteristics help to evaluate the relevant information of physiological activities in the signal and can be used to further analyze and optimize the signal quality.

[0038] Thus, after obtaining the coefficient of variation, signal-to-noise ratio, number of peaks and heartbeat signal characteristics, the quality evaluation result of the fNIRS signal can be determined according to the coefficient of variation, signal-to-noise ratio, number of peaks and heartbeat signal characteristics. That is, the coefficient of variation, signal-to-noise ratio, number of peaks and heartbeat signal characteristics are combined to evaluate the quality of the fNIRS signal. For example, the quality of the fNIRS signal is evaluated according to each indicator, such as each indicator can correspond to an evaluation result, such as a quality score. The better the evaluation result, the greater the quality score. Then the score can be calculated by weighted summation to determine the final quality evaluation result. That is, each indicator provides different aspects of signal quality. Combining these indicators can more comprehensively evaluate the accuracy, stability and physiological relevance of the signal. The final quality evaluation result is obtained based on the comprehensive analysis of the above indicators, which can be used to determine whether the signal is suitable for further analysis and application. If the signal quality evaluation result is lower than the preset threshold, adjustment measures may need to be taken, such as adjusting the light source intensity or improving the signal processing method to improve the signal quality, or the detector sensitivity may be improved or the optical path design may be optimized to improve the signal quality.

[0039] In this way, the quality of near-infrared signals can be measured comprehensively and can adapt to the physiological characteristics of different individuals to guide real-time signal quality detection and hardware iterative design.

[0040] According to one embodiment of the present application, the characteristics of the heartbeat signal include the average value of the heartbeat interval, the average heart rate and the standard deviation of consecutive heartbeat intervals, and the correlation coefficient and peak power of the red light and infrared light corresponding to the heartbeat signal. The characteristics of the heartbeat signal are obtained, including: converting the incident light intensity data into light density data, filtering the color light of the infrared light wavelength and performing R point detection, and calculating the average value of the heartbeat interval, the average heart rate and the standard deviation of consecutive heartbeat intervals based on a sliding window method.

[0041] Specifically, incident light intensity data refers to the intensity of light emitted from a light source, while optical density data refers to the intensity of light absorbed and scattered after passing through tissues (such as the scalp and brain). By converting incident light intensity data into optical density data, the interaction between light and tissue can be more accurately reflected, which is a key step in evaluating the quality of fNIRS signals. That is, in fNIRS (functional near-infrared spectroscopy), infrared wavelength light is used to monitor changes in cerebral hemodynamics, and filtering of infrared wavelength color light is to remove irrelevant noise and interference and retain signal components related to the heartbeat. This usually involves a bandpass filter to limit the signal to a specific frequency range, such as 0.5-4.5Hz, which is the main frequency range of the heartbeat signal. R-point detection refers to the detection of the peak point of the heartbeat in an electrocardiogram or similar biological signal. In fNIRS, the R point corresponds to the maximum point of the light intensity change caused by the heartbeat. By detecting these points, heartbeat events can be accurately identified, providing a basis for subsequent heartbeat signal feature extraction.

[0042] Thus, the mean value of the heartbeat interval, the average heart rate, and the standard deviation of the continuous heartbeat interval can be calculated according to the sliding window method. Among them, the heartbeat interval (Inter-Beat Interval, IBI) refers to the time interval between two consecutive heartbeats. By calculating the average value of a series of heartbeat intervals, the average heartbeat interval can be obtained, which is an important indicator for evaluating heart rate variability. The average heart rate refers to the average number of heartbeats per unit time, usually expressed in heartbeats per minute. The average heart rate can be calculated by dividing 60000 (the number of milliseconds in a minute) by the average heartbeat interval (in milliseconds). The standard deviation of the continuous heartbeat interval is an indicator to measure the variability of the heartbeat interval, which is obtained by calculating the standard deviation of the difference between continuous heartbeat intervals. SDNN reflects the overall heart rate variability. Thus, the characteristics of the heartbeat signal can be comprehensively obtained, which are crucial for evaluating the quality of fNIRS signals. These characteristics not only reflect the regularity and variability of the heartbeat, but also reveal the health status of the cardiovascular system and the activity state of the autonomic nervous system.

[0043] According to an embodiment of the present application, obtaining the number of peaks within a preset frequency range includes: performing Fourier transform on incident light intensity data to obtain a frequency domain signal of the incident light intensity data; sorting the amplitudes of the frequency domain signals within the preset frequency range in descending order; and using the amplitudes of the first preset number in the sorting as the number of peaks. The preset number and the preset range may be determined according to actual conditions.

[0044] Specifically, after obtaining the number of peaks within the preset frequency range, the incident light intensity data can be Fourier transformed to obtain the frequency domain signal of the incident light intensity data. Among them, the Fourier transform can efficiently calculate the discrete Fourier transform and decompose the signal into the superposition of various frequency components. After the Fourier transform, a series of complex numbers are obtained, each of which represents the signal component at a specific frequency, the modulus (absolute value) of these complex numbers represents the amplitude of the signal at the corresponding frequency, and the phase angle represents the phase of the signal. Determine the preset frequency range, such as the frequency range of heartbeat and breathing determined in advance according to the physiological characteristics of the fNIRS signal. Within this range, extract the corresponding frequency domain signal amplitude and sort it in order from large to small. In the sorted amplitude list, select the amplitude of the preset number as the peak value. These peaks represent the strongest frequency components in the signal, which may be signals of physiological activities such as heartbeat and breathing. In addition, the preset number can be determined according to the experimental design or analysis purpose. For example, if the strongest heartbeat signal is of interest, several peaks with the highest amplitude can be selected, and the number of these peaks is the number of peaks we are looking for. For example, the observable frequency range is 0-fs / 2, and 5 significant peaks are extracted within this frequency range to check the frequency band where the peaks are located. If the frequency band is within the range of breathing, heartbeat, etc., it is considered that there is no other large frequency band interference.

[0045] In this way, the main peaks within the preset frequency range can be extracted from the fNIRS signal. These peaks reflect the most important physiological activity information in the signal and are crucial for evaluating signal quality and further analysis.

[0046] According to one embodiment of the present application, the quality evaluation result of the fNIRS signal is determined based on the coefficient of variation, the signal-to-noise ratio, the number of peaks and the characteristics of the heartbeat signal, including: determining a first signal quality index based on the coefficient of variation, wherein the coefficient of variation is negatively correlated with the first signal quality index; determining a second signal quality index based on the number of peaks and a preset number of peaks, wherein the difference between the number of peaks and the preset number of peaks is negatively correlated with the second signal quality index; determining a third signal quality index based on the average value of heartbeat intervals, the average heart rate and the first standard deviation of continuous heartbeat intervals and the average value of reference heartbeat intervals, the reference average heart rate and the second standard deviation of continuous heartbeat intervals; and determining a fourth signal quality index based on the difference between the cross-correlation coefficient and the preset cross-correlation coefficient, and the peak power difference between the peak power and the preset peak power threshold, wherein the first The standard deviation and the second standard deviation determine the target difference by means of correlation and variance analysis, the target difference is negatively correlated with the third signal quality index, and the cross-correlation coefficient difference and the peak power difference are positively correlated with the fourth signal quality index; the fifth signal quality index is determined based on the signal-to-noise ratio, wherein the signal-to-noise ratio is positively correlated with the fifth signal quality index; the quality evaluation result of the fNIRS signal is determined based on the first product of the first signal quality index and the corresponding weight coefficient, the second product of the second signal quality index and the corresponding weight coefficient, the third product of the third signal quality index and the corresponding weight coefficient, the fourth product of the fourth signal quality index and the corresponding weight coefficient, the fifth product of the fifth signal quality index and the corresponding weight coefficient, and the sum of the first product, the second product, the third product and the fourth product, wherein the evaluation result is positively correlated with the signal quality index.

[0047] Specifically, when determining the quality evaluation result of the fNIRS signal according to the coefficient of variation, signal-to-noise ratio, number of peaks and heartbeat signal characteristics, the first signal quality index can be determined first according to the coefficient of variation, wherein the coefficient of variation is negatively correlated with the first signal quality index, that is, the larger the coefficient of variation, the smaller the first signal quality index, and the smaller the coefficient of variation, the larger the first signal quality index. That is, the coefficient of variation is an indicator of signal stability, which is defined as the ratio of the standard deviation to the mean. The higher the value of the coefficient of variation, the greater the variability of the signal and the worse the stability. Therefore, the coefficient of variation is negatively correlated with the first signal quality index, that is, the lower the coefficient of variation, the higher the first signal quality index.

[0048] Then, the second signal quality index can be determined based on the number of peaks and the preset number of peaks, wherein the difference between the number of peaks and the preset number of peaks is negatively correlated with the second signal quality index. That is, the greater the difference between the number of peaks and the preset number of peaks, the smaller the second signal quality index, and the smaller the difference between the number of peaks and the preset number of peaks, the larger the second signal quality index. That is, by performing Fourier transform on the incident light intensity data, the number of peaks within the preset frequency range is extracted, and the smaller the difference between the number of peaks and the preset number of peaks, the more effective physiological information is contained in the signal, and therefore the higher the second signal quality index.

[0049] Then, the third signal quality index can be determined according to the average value of the heartbeat interval, the average heart rate and the first standard deviation of the continuous heartbeat interval and the average value of the reference heartbeat interval, the reference average heart rate and the second standard deviation of the reference continuous heartbeat interval. Among them, the average value of the reference heartbeat interval, the reference average heart rate and the reference continuous heartbeat interval can be combined with other heart rate equipment (such as PPG (Photoplethysmogram, photoplethysmogram equipment)) to collect PPG signals, and calculate the R point in the time process, thereby obtaining the average value of the reference heartbeat interval, the reference average heart rate and the second standard deviation of the reference continuous heartbeat interval. The smaller the deviation of these indicators from the reference value, the better the physiological relevance of the signal. The target difference can be determined by correlation and variance analysis, and this target difference is negatively correlated with the third signal quality index, that is, the larger the target difference, the smaller the third signal quality index, and the signal quality is considered to be poor, and the smaller the target difference, the larger the third signal quality index, and the signal quality is considered to be good.

[0050] And the fourth signal quality index can be determined according to the cross-correlation coefficient difference between the cross-correlation coefficient and the preset cross-correlation coefficient, and the peak power difference between the peak power and the preset peak power threshold. The cross-correlation coefficient and the peak power are indicators for measuring the correlation between the red light and the infrared light signal. The cross-correlation coefficient difference and the peak power difference are positively correlated with the fourth signal quality index, that is, the smaller these differences are, the higher the fourth signal quality index is, indicating that the quality of the signal is better, and the larger the difference is, the smaller the fourth signal quality index is, indicating that the quality of the signal is worse.

[0051] In addition, the fifth signal quality index can also be determined according to the signal-to-noise ratio, wherein the signal-to-noise ratio is positively correlated with the fifth signal quality index, that is, the larger the signal-to-noise ratio, the more useful information components and the fewer noise components in the received signal, and the better the signal quality. That is, the larger the fifth signal quality index, that is, the smaller the signal-to-noise ratio, the more noise components in the signal and the poorer the signal quality. That is, the smaller the fifth signal quality index, the signal-to-noise ratio can be used to measure the rationality of the light intensity setting.

[0052] Finally, the above five signal quality indexes are multiplied by the corresponding weight coefficients, and then summed to obtain the quality evaluation result of the fNIRS signal. That is, the quality evaluation result of the fNIRS signal is determined according to the first product of the first signal quality index and the corresponding weight coefficient, the second product of the second signal quality index and the corresponding weight coefficient, the third product of the third signal quality index and the corresponding weight coefficient, the fourth product of the fourth signal quality index and the corresponding weight coefficient, and the fifth product of the fifth signal quality index and the corresponding weight coefficient, as well as the sum of the first product, the second product, the third product and the fourth product. And this evaluation result is positively correlated with the signal quality index, that is, the higher the index, the better the signal quality evaluation result, indicating that the accuracy and reliability of the signal are higher. Thus, the quality of the fNIRS signal can be comprehensively evaluated, and an important reference can be provided for subsequent data analysis and application.

[0053] According to one embodiment of the present application, the fNIRS functional optical brain imaging signal quality assessment method further includes: when the quality assessment result of the fNIRS signal is less than a preset result threshold, adjusting the signal-to-noise ratio. The preset result threshold can be determined according to actual conditions.

[0054] Specifically, the evaluation results can be determined based on a series of indicators, such as coefficient of variation, signal-to-noise ratio, correlation coefficient, peak power, etc. For example, the better the evaluation result, the larger the corresponding signal quality index, and the quality of the evaluation result can be determined based on the size of the signal quality index. The preset result threshold refers to a standard value set before the signal quality evaluation is performed, which is used to determine whether the signal quality reaches an acceptable level. This threshold is set based on experimental data, previous research, or the performance standards of the equipment.

[0055] When the quality evaluation result of the fNIRS signal is lower than the preset threshold, for example, the signal quality index of the current fNIRS signal is low, which means that the signal may be interfered by too much noise, or the signal itself is not stable and accurate enough. In this case, the signal-to-noise ratio needs to be adjusted to improve the signal quality. For example, by adjusting the light intensity of the light source, the intensity of the signal can be changed, thereby affecting the signal-to-noise ratio, that is, increasing the light intensity of the light source can increase the signal intensity; conversely, reducing the light intensity of the light source can reduce noise, but may reduce the detectability of the signal. In addition, different filtering techniques can be applied, such as bandpass filters, to remove noise outside a specific frequency band and retain important physiological signals. Through the above adjustments, the signal acquisition quality can be optimized, making the fNIRS signal more accurate and stable, thereby improving the reliability of the monitoring results.

[0056] Therefore, in order to ensure the accuracy and reliability of fNIRS signals in practical applications, especially in neuroscience research and clinical diagnosis, high-quality signals are essential for obtaining accurate information on cerebral hemodynamic changes. By dynamically adjusting the signal-to-noise ratio, external noise can be effectively suppressed and the stability and accuracy of the signal can be improved.

[0057] Further, according to an embodiment of the present application, adjusting the signal-to-noise ratio includes: increasing the intensity of infrared light emitted by the light source.

[0058] Specifically, the signal-to-noise ratio is one of the key indicators for measuring signal quality. The signal-to-noise ratio directly affects the detectability and accuracy of the signal. The light intensity of the light source is an important factor affecting the signal-to-noise ratio, because the light intensity is directly related to the strength of the signal and the level of background noise. When the quality evaluation result of the fNIRS signal is lower than the preset threshold, it means that the signal may be interfered by too much noise, or the signal itself is not stable and accurate enough. In this case, the signal-to-noise ratio can be improved by increasing the intensity of the infrared light emitted by the light source.

[0059] That is, when adjusting the signal-to-noise ratio, the adjustable light source intensity can increase the strength of the signal, thereby suppressing the influence of noise to a certain extent and improving the detectability of the signal. This adjustment helps to improve the quality of the signal so that it reaches or exceeds the preset threshold. In addition, after adjusting the light source intensity, the signal quality evaluation needs to be re-performed to ensure that the adjusted signal quality reaches the preset threshold, to verify the effect of the light intensity adjustment, and to ensure that the signal quality is actually improved. In this way, the signal-to-noise ratio of the fNIRS signal can be effectively adjusted and optimized, thereby improving the accuracy and stability of the signal.

[0060] According to one embodiment of the present application, the fNIRS functional optical brain imaging signal quality assessment method also includes: obtaining head data of the person being measured; obtaining hair density based on the head data, and when the hair density is dense, increasing the intensity of infrared light emitted by the light source.

[0061] Specifically, before implementing the fNIRS signal quality assessment method, it is first necessary to obtain the head data of the person being measured. This data may include information such as the shape and size of the head and the density of the hair. This information is crucial for the subsequent adjustment of the light source intensity because they directly affect the ability of near-infrared light to penetrate the scalp and skull. Hair density is an important factor affecting the quality of fNIRS signals. Areas with denser hair may absorb or scatter more near-infrared light, thereby reducing the intensity and quality of the signal. Therefore, evaluating hair density is crucial for determining the appropriate light source intensity. When the assessment results show that the person being measured has a high hair density, in order to ensure that the near-infrared light can effectively penetrate the scalp and skull and reach the cerebral cortex, it is necessary to increase the intensity of the infrared light emitted by the light source. Doing so can increase the intensity of the signal, thereby improving the signal-to-noise ratio and improving the signal quality. That is, increasing the intensity of the light source can help overcome the additional scattering and absorption caused by the high hair density, ensuring that enough light can reach the detector, thereby obtaining more accurate information on changes in cerebral blood oxygen concentration.

[0062] In addition, after adjusting the light intensity, the quality of the fNIRS signal needs to be re-evaluated to ensure that the adjusted signal quality reaches the preset threshold. This step is necessary to verify the effect of the light intensity adjustment and ensure that the signal quality is actually improved. Therefore, the fNIRS signal quality assessment method can adapt to the physiological characteristics of different individuals, especially in the case of high hair density, by adjusting the light intensity to optimize the signal acquisition quality, thereby improving the reliability of the monitoring results and the performance of the equipment.

[0063] Combine the following Figure 2 To describe the evaluation method of this application.

[0064] As a specific example, the fNIRS functional optical brain imaging signal quality assessment method of the present application may include the following steps:

[0065] S101, obtaining incident light intensity data and outgoing light intensity data emitted by a light source.

[0066] S102, determining a coefficient of variation based on the incident light intensity data, and determining a signal-to-noise ratio based on the incident light intensity data and the exiting light intensity data.

[0067] S103, converting the incident light intensity data into light density data, filtering the infrared light wavelength, and performing R point detection, and calculating the average value of the heartbeat interval, the average heart rate, and the standard deviation of the continuous heartbeat intervals based on a sliding window method.

[0068] S104, perform Fourier transform on the incident light intensity data to obtain a frequency domain signal of the incident light intensity data, sort the amplitudes of the frequency domain signal within a preset frequency range in descending order, and use the amplitudes of the first preset number in the sorting as the number of peaks.

[0069] S105, determining a first signal quality index based on the coefficient of variation, wherein the coefficient of variation is negatively correlated with the first signal quality index, and determining a second signal quality index based on the number of peaks and a preset number of peaks, wherein a difference between the number of peaks and the preset number of peaks is negatively correlated with the second signal quality index.

[0070] S106, determining a third signal quality index based on the average value of heartbeat intervals, the average heart rate, and the first standard deviation of continuous heartbeat intervals and the average value of reference heartbeat intervals, the reference average heart rate, and the second standard deviation of reference continuous heartbeat intervals, and determining a fourth signal quality index based on the cross-correlation coefficient difference between the cross-correlation coefficient and the preset cross-correlation coefficient, and the peak power difference between the peak power and the preset peak power threshold, wherein the first standard deviation and the second standard deviation determine a target difference by means of correlation and variance analysis, the target difference is negatively correlated with the third signal quality index, and the cross-correlation coefficient difference and the peak power difference are positively correlated with the fourth signal quality index.

[0071] S107: Determine a fifth signal quality index based on the signal-to-noise ratio, wherein the signal-to-noise ratio and the fifth signal quality index are positively correlated.

[0072] S108, determining a first product of a first signal quality index and a corresponding weight coefficient, a second product of a second signal quality index and a corresponding weight coefficient, a third product of a third signal quality index and a corresponding weight coefficient, a fourth product of a fourth signal quality index and a corresponding weight coefficient, and a fifth product of a fifth signal quality index and a corresponding weight coefficient.

[0073] S109, determining a quality evaluation result of the fNIRS signal according to the sum of the first product, the second product, the third product and the fourth product, wherein the evaluation result is positively correlated with the signal quality index

[0074] S110, determining whether the quality evaluation result of the fNIRS signal is less than a preset result threshold and whether the hair density of the person being tested is dense. If yes, execute step S111; if no, execute step S101.

[0075] S111, increasing the intensity of the infrared light emitted by the light source.

[0076] In summary, according to the fNIRS functional optical brain imaging signal quality assessment method of the embodiment of the present application, the incident light intensity data and the outgoing light intensity data emitted by the light source are obtained, the coefficient of variation is determined based on the incident light intensity data, and the signal-to-noise ratio is determined based on the incident light intensity data and the outgoing light intensity data, and the incident light intensity data is processed to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range, and the quality evaluation result of the fNIRS signal is determined based on the coefficient of variation, signal-to-noise ratio, number of peaks and heartbeat signal characteristics. Thus, the method can measure the quality of near-infrared signals, and can adapt to the physiological characteristics of different individuals to guide real-time signal quality detection and hardware iterative design.

[0077] Corresponding to the above embodiments, the present application also proposes a fNIRS functional optical brain imaging signal quality assessment device.

[0078] like Figure 3 As shown, the fNIRS functional optical brain imaging signal quality assessment device 100 of the embodiment of the present application includes: a first acquisition module 110, a first determination module 120, a second acquisition module 130 and a second determination module 140.

[0079] Among them, the first acquisition module 110 is used to obtain the incident light intensity data and the exit light intensity data emitted by the light source. The first determination module 120 is used to determine the coefficient of variation based on the incident light intensity data, and determine the signal-to-noise ratio based on the incident light intensity data and the exit light intensity data. The second acquisition module 130 is used to process the incident light intensity data to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range. The second determination module 140 is used to determine the quality evaluation result of the fNIRS signal based on the coefficient of variation, the signal-to-noise ratio, the number of peaks and the heartbeat signal characteristics.

[0080] According to one embodiment of the present application, the heartbeat signal characteristics include the average value of the heartbeat intervals, the average heart rate and the standard deviation of consecutive heartbeat intervals, and the correlation coefficient and peak power of the red light and infrared light corresponding to the heartbeat signal. The second acquisition module 130 acquires the heartbeat signal characteristics, and is specifically used for: converting the incident light intensity data into light density data, filtering the color light of the infrared light wavelength and performing R point detection, and calculating the average value of the heartbeat intervals, the average heart rate and the standard deviation of consecutive heartbeat intervals based on a sliding window method.

[0081] According to one embodiment of the present application, the second acquisition module 130 acquires the number of peak values ​​within a preset frequency range, and is specifically used for: performing Fourier transform on the incident light intensity data to obtain a frequency domain signal of the incident light intensity data; sorting the amplitudes of the frequency domain signals within the preset frequency range in descending order; and taking the amplitudes of the first preset number in the sorting as the number of peak values.

[0082] According to one embodiment of the present application, the second determination module 140 determines the quality evaluation result of the fNIRS signal based on the coefficient of variation, the signal-to-noise ratio, the number of peaks and the heartbeat signal characteristics, and is specifically used to: determine a first signal quality index based on the coefficient of variation, wherein the coefficient of variation is negatively correlated with the first signal quality index; determine a second signal quality index based on the number of peaks and a preset number of peaks, wherein the difference between the number of peaks and the preset number of peaks is negatively correlated with the second signal quality index; determine a third signal quality index based on the average value of heartbeat intervals, the first standard deviation of the average heart rate and continuous heartbeat intervals and the average value of the reference heartbeat intervals, the second standard deviation of the reference average heart rate and continuous heartbeat intervals, and determine a fourth signal quality index based on the difference between the cross-correlation coefficient and the preset cross-correlation coefficient, and the peak power difference between the peak power and the preset peak power threshold. The first standard deviation and the second standard deviation are used to determine the target difference through correlation and variance analysis, the target difference is negatively correlated with the third signal quality index, and the cross-correlation coefficient difference and the peak power difference are positively correlated with the fourth signal quality index; the fifth signal quality index is determined based on the signal-to-noise ratio, wherein the signal-to-noise ratio is positively correlated with the fifth signal quality index; the quality evaluation result of the fNIRS signal is determined based on the first product of the first signal quality index and the corresponding weight coefficient, the second product of the second signal quality index and the corresponding weight coefficient, the third product of the third signal quality index and the corresponding weight coefficient, the fourth product of the fourth signal quality index and the corresponding weight coefficient, and the fifth product of the fifth signal quality index and the corresponding weight coefficient, as well as the sum of the first product, the second product, the third product and the fourth product, wherein the evaluation result is positively correlated with the signal quality index.

[0083] According to an embodiment of the present application, the second determination module 140 is further configured to: adjust the signal-to-noise ratio when the quality evaluation result of the fNIRS signal is less than a preset result threshold.

[0084] According to an embodiment of the present application, the second determination module 140 adjusts the signal-to-noise ratio, specifically to increase the intensity of the infrared light emitted by the light source.

[0085] According to an embodiment of the present application, the second determination module 140 is further used to: obtain head data of the measured person; obtain hair density based on the head data, and when the hair density is dense, increase the intensity of infrared light emitted by the light source.

[0086] It should be noted that for details not disclosed in the fNIRS functional optical brain imaging signal quality assessment device in the embodiment of the present application, please refer to the details disclosed in the fNIRS functional optical brain imaging signal quality assessment method in the embodiment of the present application, and the details will not be repeated here.

[0087] According to the fNIRS functional optical brain imaging signal quality assessment device of the embodiment of the present application, the first acquisition module is used to obtain the incident light intensity data and the exit light intensity data emitted by the light source, the first determination module is used to determine the coefficient of variation based on the incident light intensity data, and the signal-to-noise ratio based on the incident light intensity data and the exit light intensity data, the second acquisition module is used to process the incident light intensity data to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range, and the second determination module is used to determine the quality evaluation result of the fNIRS signal based on the coefficient of variation, signal-to-noise ratio, number of peaks and heartbeat signal characteristics. Thus, the device can measure the quality of near-infrared signals and can adapt to the physiological characteristics of different individuals to guide real-time signal quality detection and hardware iterative design.

[0088] Corresponding to the above embodiments, the present application also proposes a computer-readable storage medium.

[0089] The computer-readable storage medium of the embodiment of the present application stores a program thereon, and when the program is executed by a processor, the above-mentioned fNIRS functional optical brain imaging signal quality assessment method is implemented.

[0090] According to the computer-readable storage medium of the embodiment of the present application, by executing the above-mentioned fNIRS functional optical brain imaging signal quality assessment method, the quality of near-infrared signals can be measured comprehensively, and can adapt to the physiological characteristics of different individuals to guide real-time signal quality detection and hardware iterative design.

[0091] Corresponding to the above embodiment, the present application also proposes an electronic device.

[0092] like Figure 4 As shown, the electronic device 200 of the embodiment of the present application may include: a memory 210, a processor 220, and a program stored in the memory 210 and executable on the processor 220. When the processor 220 executes the program, the above-mentioned fNIRS functional optical brain imaging signal quality assessment method is implemented.

[0093] The electronic device according to the embodiment of the present application can measure the quality of near-infrared signals by executing the above-mentioned fNIRS functional optical brain imaging signal quality assessment method, and can adapt to the physiological characteristics of different individuals to guide real-time signal quality detection and hardware iterative design.

[0094] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0095] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0096] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0097] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0098] In this application, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0099] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for evaluating the quality of fNIRS functional optical brain imaging signals, characterized in that: The method comprises: Obtain incident light intensity data and outgoing light intensity data emitted by the light source; determining a coefficient of variation based on the incident light intensity data, and determining a signal-to-noise ratio based on the incident light intensity data and the exiting light intensity data; Processing the incident light intensity data to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range; The quality evaluation result of the fNIRS signal is determined based on the coefficient of variation, the signal-to-noise ratio, the number of peaks and the heartbeat signal characteristics.

2. The fNIRS functional optical brain imaging signal quality assessment method according to claim 1, characterized in that: The heartbeat signal characteristics include the average value of heartbeat intervals, the average heart rate and the standard deviation of consecutive heartbeat intervals, as well as the correlation coefficient and peak power of the red light and infrared light corresponding to the heartbeat signal. Acquiring the heartbeat signal characteristics includes: The incident light intensity data is converted into light density data, and the color light of the infrared light wavelength is filtered for R point detection, and the average value of the heartbeat interval, the average heart rate and the standard deviation of the continuous heartbeat intervals are calculated based on a sliding window method.

3. The fNIRS functional optical brain imaging signal quality assessment method according to claim 1, characterized in that: Get the number of peaks within a preset frequency range, including: Performing Fourier transform on the incident light intensity data to obtain a frequency domain signal of the incident light intensity data; Sorting the amplitudes of the frequency domain signals within the preset frequency range in descending order; The amplitude of the first preset number in the sorting is used as the peak number.

4. The fNIRS functional optical brain imaging signal quality assessment method according to claim 2, characterized in that: The method of determining the quality evaluation result of the fNIRS signal based on the coefficient of variation, the signal-to-noise ratio, the number of peaks and the heartbeat signal characteristics includes: Determining a first signal quality index based on the coefficient of variation, wherein the coefficient of variation is negatively correlated with the first signal quality index; Determining a second signal quality index based on the number of peaks and a preset number of peaks, wherein a difference between the number of peaks and the preset number of peaks is negatively correlated with the second signal quality index; A third signal quality index is determined based on the average value of the heartbeat intervals, the first standard deviation of the average heart rate and the continuous heartbeat intervals, and the average value of the reference heartbeat intervals, the second standard deviation of the reference average heart rate and the reference continuous heartbeat intervals, and a fourth signal quality index is determined based on the cross-correlation coefficient difference between the cross-correlation coefficient and the preset cross-correlation coefficient, and the peak power difference between the peak power and a preset peak power threshold, wherein the first standard deviation and the second standard deviation determine a target difference by means of correlation and variance analysis, the target difference is negatively correlated with the third signal quality index, and the cross-correlation coefficient difference and the peak power difference are positively correlated with the fourth signal quality index; Determining a fifth signal quality index based on the signal-to-noise ratio, wherein the signal-to-noise ratio is positively correlated with the fifth signal quality index; The quality evaluation result of the fNIRS signal is determined based on a first product of the first signal quality index and the corresponding weight coefficient, a second product of the second signal quality index and the corresponding weight coefficient, a third product of the third signal quality index and the corresponding weight coefficient, a fourth product of the fourth signal quality index and the corresponding weight coefficient, and a fifth product of the fifth signal quality index and the corresponding weight coefficient, and the sum of the first product, the second product, the third product and the fourth product, wherein the evaluation result is positively correlated with the signal quality index.

5. The fNIRS functional optical brain imaging signal quality assessment method according to claim 1, characterized in that: The method further comprises: When the quality evaluation result of the fNIRS signal is less than a preset result threshold, the signal-to-noise ratio is adjusted.

6. The fNIRS functional optical brain imaging signal quality assessment method according to claim 5, characterized in that: The signal-to-noise ratio is adjusted, comprising: Increase the intensity of the infrared light emitted by the light source.

7. The fNIRS functional optical brain imaging signal quality assessment method according to claim 5 or 6, characterized in that: The method further comprises: Obtain the head data of the person being tested; The hair density is acquired based on the head data, and when the hair density is thick, the intensity of the infrared light emitted by the light source is increased.

8. An fNIRS functional optical brain imaging signal quality assessment device, characterized in that: The device comprises: A first acquisition module is used to acquire incident light intensity data and outgoing light intensity data emitted by the light source; A first determination module, configured to determine a coefficient of variation based on the incident light intensity data, and to determine a signal-to-noise ratio based on the incident light intensity data and the exit light intensity data; A second acquisition module is used to process the incident light intensity data to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range; The second determination module is used to determine the quality evaluation result of the fNIRS signal based on the coefficient of variation, the signal-to-noise ratio, the number of peaks and the heartbeat signal characteristics.

9. A computer-readable storage medium, characterized in that: A program is stored thereon, and when the program is executed by a processor, the fNIRS functional optical brain imaging signal quality assessment method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: A memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for evaluating the quality of fNIRS functional optical brain imaging signals according to any one of claims 1 to 7 is implemented.

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