fNIRS (Functional Optical Brain Imaging) signal quality assessment methods, devices, storage media, and electronic equipment.

Abstract

This application discloses a method, apparatus, storage medium, and electronic device for evaluating the signal quality of fNIRS (functional optical brain imaging). The method includes: acquiring incident light intensity data and emitted light intensity data emitted by a light source; determining the coefficient of variation based on the incident light intensity data, and determining the signal-to-noise ratio based on the incident light intensity data and the emitted 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 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. The evaluation method of this application 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 iterative hardware design.

Description

Technical Field

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

[0002] Currently, signal quality assessment methods can only measure the stability of optical signals and are relatively sensitive to detecting abnormal signals such as motion artifacts. However, they cannot determine whether light actually passes through the skull, thus failing to accurately reflect the true hemodynamic changes in the cortical region, especially the dynamic changes in oxygenation and deoxygenated hemoglobin. This limits the application of the coefficient of variation (CV) in assessing the validity of near-infrared signals. While heartbeat signal analysis can provide some physiological relevance, it easily ignores low-frequency noise interference (such as scalp blood flow and environmental noise). Furthermore, the selection of peak heart rate varies significantly between individuals and in different usage scenarios. For example, under exercise or high-load conditions, the heart rate range may deviate from the set reference value. This variability leads to unstable quality assessment results based on heartbeat signals, making them unsuitable for diverse application scenarios. Summary of the Invention

[0003] This application aims to at least partially address one of the technical problems in related technologies. To this end, the first objective of this application is to propose a method for assessing the signal quality of fNIRS (functional optical brain imaging) signals. This method involves acquiring incident and emitted light intensity data from a light source, determining the coefficient of variation based on the incident light intensity data, and determining the signal-to-noise ratio (SNR) based on the incident and emitted light intensity data. The emitted light intensity data is then processed to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range. Based on the coefficient of variation, SNR, number of peaks, and heartbeat signal characteristics, the quality assessment result of the fNIRS signal is determined. This method can comprehensively measure the quality of near-infrared signals and is adaptable to the physiological characteristics of different individuals, thus guiding real-time signal quality detection and iterative hardware design.

[0004] The second objective of this application is to provide a device for assessing the signal quality of fNIRS functional optical brain imaging.

[0005] The third objective of this application is to provide a computer-readable storage medium.

[0006] The fourth objective of this application is to propose an electronic device.

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

[0008] The fNIRS (functional optical brain imaging) signal quality assessment method according to embodiments of this application acquires incident and emitted light intensity data from a light source. Based on the incident light intensity data, a coefficient of variation is determined, and based on the incident and emitted light intensity data, a signal-to-noise ratio (SNR) is determined. The emitted light intensity data is processed to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range. Based on the coefficient of variation, SNR, number of peaks, and heartbeat signal characteristics, the quality assessment result of the fNIRS signal is determined. Therefore, this method can comprehensively measure the quality of near-infrared signals and can adapt to the physiological characteristics of different individuals, guiding real-time signal quality detection and iterative hardware design.

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

[0010] According to one embodiment of this application, the heartbeat signal features include the average value of the heartbeat interval, the average heart rate, and the standard deviation of the continuous heartbeat interval, as well as the cross-correlation coefficient and peak power of the red and infrared light corresponding to the heartbeat signal. Obtaining the heartbeat signal features includes: converting the emitted light intensity data into optical 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 interval based on a sliding window method.

[0011] According to one embodiment of this application, obtaining the number of peaks within a preset frequency range includes: performing a Fourier transform on the emitted light intensity data to obtain a frequency domain signal of the emitted light intensity data; sorting the amplitudes of the frequency domain signal within the preset frequency range in descending order; and taking the amplitudes of the first preset number in the sort as the number of peaks.

[0012] According to one embodiment of this application, 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 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 heartbeat interval, the average heart rate, and the first standard deviation of the continuous heartbeat interval and the average heartbeat interval, the reference average heart rate, and the second standard deviation of the reference continuous heartbeat interval; and determining a fourth signal quality index based on the difference between the cross-correlation coefficient and the preset cross-correlation coefficient, and the difference between the peak power and a preset peak power threshold, wherein the first standard deviation is negatively correlated with the second signal quality index; and determining a third signal quality index based on the difference between the cross-correlation coefficient and the preset cross-correlation coefficient, and the difference between the peak power and a preset peak power threshold, wherein the first standard deviation is negatively correlated with the second signal quality index; and determining a fourth signal quality index based on the difference between the cross-correlation coefficient and the preset cross-correlation coefficient, and the difference between the peak power and a preset peak power threshold, wherein the first standard deviation is negatively correlated with the second signal quality index; and determining a third signal quality index based on the difference between the average heartbeat interval, the average heart rate, and the first standard deviation of the continuous heartbeat interval, and the first standard deviation of the continuous heartbeat interval, and the second standard deviation of the reference heartbeat interval, and the second standard deviation of the reference average heart rate and the reference continuous heartbeat interval; and determining a fourth signal quality index based on the difference between the cross-correlation coefficient and the preset cross-correlation coefficient, and the difference between the peak power and a preset peak power threshold, wherein the first standard deviation is negatively correlated with the second signal quality index; and determining a fourth signal quality index based on The target difference between the standard deviation and the second standard deviation is determined through correlation and variance analysis. The target difference is negatively correlated with the third signal quality index. The difference in cross-correlation coefficients and the difference in peak power are positively correlated with the fourth signal quality index. A fifth signal quality index is determined based on the signal-to-noise ratio (SNR), wherein the SNR 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 its corresponding weight coefficient, the second product of the second signal quality index and its corresponding weight coefficient, the third product of the third signal quality index and its corresponding weight coefficient, the fourth product of the fourth signal quality index and its corresponding weight coefficient, and the fifth product of the fifth signal quality index and its corresponding weight coefficient, as well as the sum of the first product, the second product, the third product, and the fourth product. The evaluation result is positively correlated with the signal quality index.

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

[0014] According to one embodiment of this 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 this application, the method further includes: acquiring head data of the subject; acquiring hair density based on the head data; and increasing the intensity of infrared light emitted by the light source when the hair density is dense.

[0016] To achieve the above objectives, a second aspect of this application provides an fNIRS (functional optical brain imaging) signal quality assessment device. The device includes: a first acquisition module for acquiring incident light intensity data and emitted light intensity data emitted by a light source; a first determination module for determining a coefficient of variation based on the incident light intensity data and a signal-to-noise ratio based on the incident light intensity data and the emitted light intensity data; a second acquisition module for processing the emitted light intensity data to acquire the number of peaks and heartbeat signal characteristics within a preset frequency range; and a second determination module for determining the quality assessment 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 this application, a first acquisition module is used to acquire incident light intensity data and emitted light intensity data emitted by the light source; a first determination module is used to determine the coefficient of variation based on the incident light intensity data, and to determine the signal-to-noise ratio based on the incident light intensity data and the emitted light intensity data; a second acquisition module is used to process the emitted light intensity data to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range; and a second determination module is used to determine the quality assessment result of the fNIRS signal based on the coefficient of variation, signal-to-noise ratio, number of peaks, and heartbeat signal characteristics. Therefore, this device 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.

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

[0019] The computer-readable storage medium according to the embodiments of this application implements the above-described fNIRS functional optical brain imaging signal quality assessment method during execution. It can comprehensively measure the quality of near-infrared signals and adapt to the physiological characteristics of different individuals to guide real-time signal quality detection and hardware iterative design.

[0020] To achieve the above objectives, an electronic device is proposed in the fourth aspect of this application, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-described fNIRS functional optical brain imaging signal quality assessment method.

[0021] The electronic device according to the embodiments of this application, by performing the above-described fNIRS functional optical brain imaging signal quality assessment method, 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.

[0022] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0023] Figure 1 This is a flowchart of a method for assessing the signal quality of fNIRS functional optical brain imaging according to an embodiment of this application;

[0024] Figure 2 A flowchart illustrating a specific example of an fNIRS functional optical brain imaging signal quality assessment method according to this application;

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

[0026] Figure 4 This is a block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

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

[0028] The following description, with reference to the accompanying drawings, 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 embodiments of this application.

[0029] Figure 1 This is a flowchart of a method for assessing the signal quality of fNIRS functional optical brain imaging according to an embodiment of this application.

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

[0031] S1, acquire the incident light intensity data and the emitted light intensity data emitted by the light source.

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

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

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

[0035] Specifically, the first step is to acquire incident and emitted light intensity data from the light source. Incident light intensity data refers to the light intensity emitted from the light source, measured before the object being tested (e.g., the human head). This data is crucial for assessing the initial output of the light source and the propagation characteristics of the light. Emitted light intensity data refers to the light intensity after absorption and scattering by the object being tested, measured after the object. Emitted light intensity data provides information about the interaction between light and tissue, which is important for assessing the penetration and absorption characteristics of the signal.

[0036] After acquiring incident and emitted light intensity data, the coefficient of variation (COP) and signal-to-noise ratio (SNR) can be determined based on the incident and emitted light intensity data. The COP is a measure of signal stability, calculated as the ratio of standard deviation to mean. Based on the incident light intensity data, the COP can be calculated to assess the signal's stability and consistency. The SNR, the ratio of signal strength to background noise strength, is used to measure signal quality. By determining the SNR based on the incident and emitted light intensity data, the proportion of useful information to noise in the signal can be evaluated.

[0037] The emitted 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 emitted light intensity data, the number of peaks within a specific frequency range (such as the respiratory and heartbeat frequency range) can be identified. These peaks may be related to physiological activities, such as heartbeat and respiration, and their presence and quantity can provide additional information about signal quality. By analyzing these peaks, physiologically relevant 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, indicating that the signal contains effective physiological information without being overwhelmed by noise. By analyzing heartbeat-related signals in the emitted light intensity data, features of the heartbeat signal, such as heart rate and amplitude, can be extracted. These features help assess relevant information about physiological activities in the signal and can be used for further analysis and optimization of signal quality.

[0038] Therefore, after obtaining the coefficient of variation, signal-to-noise ratio, number of peaks, and heartbeat signal characteristics, the quality assessment result of the fNIRS signal can be determined based on these indicators. This involves comprehensively evaluating the quality of the fNIRS signal by integrating these indicators. For example, the quality of the fNIRS signal can be evaluated separately for each indicator, with each indicator corresponding to an assessment result, such as a quality score. A better assessment result results in a higher quality score. The scores can then be weighted and summed to calculate a comprehensive score, determining the final quality assessment result. In other words, each indicator provides different aspects of signal quality, and combining these indicators allows for a more comprehensive assessment of the signal's accuracy, stability, and physiological relevance. The final quality assessment result is derived from the comprehensive analysis of the above indicators and can be used to determine whether the signal is suitable for further analysis and application. If the signal quality assessment result is below a preset threshold, adjustment measures may be necessary, such as adjusting the light source intensity or improving the signal processing method to improve signal quality, or improving detector sensitivity or optimizing the optical path design to enhance signal quality.

[0039] Therefore, it is possible to comprehensively measure the quality of near-infrared signals and 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 this application, the heartbeat signal features include the average value of the heartbeat interval, the average heart rate, and the standard deviation of the continuous heartbeat interval, as well as the cross-correlation coefficient and peak power of the red and infrared light corresponding to the heartbeat signal. Obtaining the heartbeat signal features includes: converting the emitted light intensity data into optical 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 interval 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). Converting emitted light intensity data into optical density data allows for a more accurate reflection of the interaction between light and tissue, a crucial step in assessing fNIRS signal quality. In fNIRS (functional near-infrared spectroscopy), infrared wavelengths of light are used to monitor changes in cerebral hemodynamics. Filtering the infrared wavelengths removes irrelevant noise and interference, preserving the signal components relevant to the heartbeat. This typically involves bandpass filters to limit the signal to a specific frequency range, such as 0.5–4.5 Hz, which is the dominant frequency range for heartbeat signals. R-point detection refers to detecting the peak points of the heartbeat in an electrocardiogram or similar biosignal. In fNIRS, R-points correspond to the points of maximum light intensity change caused by the heartbeat. Detecting these points allows for accurate identification of heartbeat events, providing a foundation for subsequent heartbeat signal feature extraction.

[0042] Therefore, the mean of the inter-beat interval, the mean heart rate, and the standard deviation of consecutive heartbeat intervals can be calculated using a sliding window method. The inter-beat interval (IBI) refers to the time interval between two consecutive heartbeats. By calculating the average of a series of IBIs, the mean heartbeat interval can be obtained, which is an important indicator for assessing heart rate variability. The mean heart rate refers to the average number of heartbeats per unit time, usually expressed as heartbeats per minute. The mean heart rate can be calculated by dividing 60,000 (the number of milliseconds in one minute) by the mean heartbeat interval (in milliseconds). The standard deviation of consecutive heartbeat intervals is an indicator of heartbeat interval variability, obtained by calculating the standard deviation of the differences between consecutive heartbeat intervals. SDNN reflects the overall heart rate variability. Thus, comprehensive characteristics of the heartbeat signal can be obtained, which are crucial for assessing the quality of the fNIRS signal. 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 one embodiment of this application, obtaining the number of peaks within a preset frequency range includes: performing a Fourier transform on the emitted light intensity data to obtain a frequency domain signal of the emitted 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 of peaks in the sorted range as the number of peaks. The preset number and preset range can be determined according to actual conditions.

[0044] Specifically, after obtaining the number of peaks within a preset frequency range, a Fourier transform can be performed on the emitted light intensity data to obtain the frequency domain signal of the emitted light intensity data. The Fourier transform can efficiently calculate the Discrete Fourier Transform, decomposing the signal into a superposition of various frequency components. After the Fourier transform, a series of complex numbers are obtained, each representing a signal component at a specific frequency. The magnitude (absolute value) of these complex numbers represents the amplitude of the signal at the corresponding frequency, while the phase angle represents the phase of the signal. A preset frequency range is determined, such as one pre-determined based on the physiological characteristics of the fNIRS signal, like the frequency range of heartbeat and respiration. Within this range, the corresponding frequency domain signal amplitudes are extracted and sorted in descending order. From the sorted amplitude list, the first preset number of amplitudes are selected as peaks. These peaks represent the strongest frequency components in the signal, which may be signals from physiological activities such as heartbeat and respiration. Furthermore, the preset number can be determined based on the experimental design or analytical purpose. For example, if the focus is on the strongest heartbeat signal, the highest peak values ​​can be selected, and the number of these peaks is the desired number of peaks. For instance, if the observable frequency range is 0-fs / 2, five significant peaks can be extracted within this range. The frequency band in which these peaks are located can be examined. If the frequency band is within the range of breathing, heartbeat, etc., then it can be assumed that there are no other significant frequency interferences.

[0045] Therefore, it is possible to extract the main peaks within a preset frequency range 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 this application, determining the quality evaluation result of an fNIRS signal based on the coefficient of variation, signal-to-noise ratio, number of peaks, and 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 heartbeat interval, average heart rate, and a first standard deviation of a continuous heartbeat interval and a second standard deviation of a reference heartbeat interval, a reference average heart rate, and a reference continuous heartbeat interval; and determining a fourth signal quality index based on the difference between the cross-correlation coefficient and a preset cross-correlation coefficient, and the difference between the peak power and a preset peak power threshold, wherein the first... The target difference between the standard deviation and the second standard deviation is determined through correlation and variance analysis. The target difference is negatively correlated with the third signal quality index, while the difference in cross-correlation coefficients and the difference in peak power are positively correlated with the fourth signal quality index. The fifth signal quality index is determined based on the signal-to-noise ratio (SNR), which 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 its corresponding weight coefficient, the second product of the second signal quality index and its corresponding weight coefficient, the third product of the third signal quality index and its corresponding weight coefficient, the fourth product of the fourth signal quality index and its corresponding weight coefficient, and the fifth product of the fifth signal quality index and its corresponding weight coefficient, as well as the sum of the first, second, third, and fourth products. The evaluation result is positively correlated with the signal quality index.

[0047] Specifically, when determining the quality assessment results of fNIRS signals based on coefficient of variation, signal-to-noise ratio, number of peaks, and heartbeat signal characteristics, the first signal quality index can be determined first based on the coefficient of variation. The coefficient of variation and the first signal quality index are negatively correlated; that is, the larger the coefficient of variation, the smaller the first signal quality index, and vice versa. In other words, the coefficient of variation is an indicator of signal stability, defined as the ratio of the standard deviation to the mean. A higher coefficient of variation indicates greater signal variability and poorer stability. Therefore, the coefficient of variation and the first signal quality index are negatively correlated; the lower the coefficient of variation, the higher the first signal quality index.

[0048] The second signal quality index can then be determined based on the number of peaks and a preset number of peaks. 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 larger the difference between the number of peaks and the preset number of peaks, the smaller the second signal quality index; conversely, the smaller the difference, the larger the second signal quality index. In other words, by performing a Fourier transform on the emitted light intensity data and extracting the number of peaks within a preset frequency range, a smaller difference between the number of peaks and the preset number of peaks indicates that the signal contains more effective physiological information, thus resulting in a higher second signal quality index.

[0049] Next, the third signal quality index can be determined based on the average heart rate, mean heart rate, and first standard deviation of the continuous heart rate interval, compared with the second standard deviation of the reference heart rate interval, reference mean heart rate, and reference continuous heart rate interval. The reference heart rate interval, reference mean heart rate, and reference continuous heart rate interval can be obtained by acquiring PPG signals using other heart rate monitoring devices (such as PPG (Photoplethysmogram)) and calculating the R-point over time, thereby obtaining the average reference heart rate interval, reference mean heart rate, and second standard deviation of the reference continuous heart rate interval. The smaller the deviation of these indicators from the reference values, the better the physiological correlation of the signal. The target difference can then be determined through correlation and variance analysis. 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, indicating poor signal quality; conversely, the smaller the target difference, the larger the third signal quality index, indicating good signal quality.

[0050] The fourth signal quality index can be determined based on the difference between the cross-correlation coefficient and a preset cross-correlation coefficient, and the difference between the peak power and a preset peak power threshold. The cross-correlation coefficient and peak power are indicators of the correlation between red and infrared light signals. The difference between the cross-correlation coefficient and the difference between the peak power are positively correlated with the fourth signal quality index; that is, the smaller these differences, the higher the fourth signal quality index, indicating better signal quality; conversely, the larger the differences, the lower the fourth signal quality index, indicating worse signal quality.

[0051] In addition, the fifth signal quality index can be determined based on the signal-to-noise ratio (SNR). The SNR and the fifth signal quality index are positively correlated. That is, the higher the SNR, the more useful information components and the less noise components the received signal contains, and the better the signal quality. Conversely, the higher the fifth signal quality index, the lower the SNR, indicating that there are more noise components in the signal and the poorer the signal quality. The lower the fifth signal quality index, the better. Therefore, the SNR can be used to measure the rationality of the light intensity setting.

[0052] Finally, the five signal quality indices are multiplied by their corresponding weighting coefficients and then summed to obtain the quality evaluation result of the fNIRS signal. Specifically, the quality evaluation result of the fNIRS signal is determined by the first product of the first signal quality index and its corresponding weighting coefficient, the second product of the second signal quality index and its corresponding weighting coefficient, the third product of the third signal quality index and its corresponding weighting coefficient, the fourth product of the fourth signal quality index and its corresponding weighting coefficient, and the fifth product of the fifth signal quality index and its corresponding weighting coefficient, as well as the sum of the first, second, third, and fourth products. Furthermore, 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 higher accuracy and reliability of the signal. Therefore, a comprehensive assessment of the fNIRS signal quality is achieved, providing important reference for subsequent data analysis and applications.

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

[0054] Specifically, evaluation results can be based on a series of indicators, such as coefficient of variation, signal-to-noise ratio, cross-correlation coefficient, and peak power. For example, a better evaluation result corresponds to a larger signal quality index, and the quality of the evaluation result can be determined based on the magnitude of the signal quality index. A preset result threshold is a standard value set before conducting a signal quality evaluation to determine whether the signal quality has reached an acceptable level. This threshold is set based on experimental data, previous research, or equipment performance standards.

[0055] When the quality assessment result of the fNIRS signal is lower than a preset threshold, for example, if the current fNIRS signal quality index is low, it means that the signal may be subject to excessive noise interference, or the signal itself may not be stable or accurate enough. In this case, the signal-to-noise ratio (SNR) needs to be adjusted to improve signal quality. For example, adjusting the light intensity of the light source can change the signal strength, thereby affecting the SNR; that is, increasing the light intensity can increase the signal strength, while decreasing the light intensity can reduce noise, but may reduce the detectability of the signal. Additionally, different filtering techniques, such as bandpass filters, can be applied to remove noise outside specific frequency bands while retaining important physiological signals. Through these 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 crucial for obtaining accurate information on changes in cerebral hemodynamics. 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] Furthermore, according to one embodiment of this application, adjusting the signal-to-noise ratio includes increasing the intensity of infrared light emitted by the light source.

[0058] Specifically, signal-to-noise ratio (SNR) is one of the key indicators for measuring signal quality. A high SNR directly affects the detectability and accuracy of a signal. Light intensity is a significant factor influencing SNR, as it directly relates to signal strength and background noise levels. When the quality evaluation result of the fNIRS signal is below a preset threshold, it means the signal may be subject to excessive noise interference, or the signal itself may be unstable and inaccurate. In this case, the SNR can be improved by increasing the intensity of the infrared light emitted by the light source.

[0059] In other words, when adjusting the signal-to-noise ratio (SNR), increasing the light intensity of the light source can increase the signal strength, thereby suppressing the influence of noise to a certain extent and improving the signal's detectability. This adjustment helps improve signal quality, bringing it to or exceeding a preset threshold. Furthermore, after adjusting the light intensity, the signal quality needs to be re-evaluated to ensure that the adjusted signal quality reaches the preset threshold, verifying the effect of the light intensity adjustment and ensuring that the signal quality is actually improved. Therefore, the SNR of the fNIRS signal can be effectively adjusted and optimized, thereby improving the signal's accuracy and stability.

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

[0061] Specifically, before implementing the fNIRS signal quality assessment method, it is necessary to first acquire head data of the subject. This data may include information such as head shape, size, and hair density. This information is crucial for subsequent adjustment of the light source intensity, as it directly affects the ability of near-infrared light to penetrate the scalp and skull. Hair density is a significant factor affecting fNIRS signal quality; areas with denser hair may absorb or scatter more near-infrared light, thus reducing signal strength and quality. Therefore, assessing hair density is essential for determining the appropriate light source intensity. When the assessment results show that the subject has high hair density, the intensity of the infrared light emitted by the light source needs to be increased to ensure that near-infrared light can effectively penetrate the scalp and skull and reach the cerebral cortex. Doing so can improve signal strength, thereby improving the signal-to-noise ratio and signal quality. In other words, increasing the light source intensity can help overcome the additional scattering and absorption caused by high hair density, ensuring sufficient light energy reaches the detector, thereby obtaining more accurate information on changes in cerebral blood oxygen concentration.

[0062] Furthermore, after adjusting the light source intensity, the quality of the fNIRS signal needs to be reassessed 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 cases of high hair density, by adjusting the light source intensity to optimize signal acquisition quality, thereby improving the reliability of monitoring results and the performance of the equipment.

[0063] The following is combined 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 this application may include the following steps:

[0065] S101, acquire incident light intensity data and outgoing light intensity data emitted by the light source.

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

[0067] S103 converts the emitted light intensity data into light density data, filters the infrared wavelength light, performs R-point detection, and calculates the average heart rate, average heart rate, and standard deviation of continuous heartbeat intervals based on a sliding window method.

[0068] S104, perform Fourier transform on the emitted light intensity data to obtain the frequency domain signal of the emitted light intensity data, sort the amplitude of the frequency domain signal within the preset frequency range in descending order, and take the first preset number of amplitudes in the sort as the number of peak values.

[0069] S105, 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, and 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.

[0070] S106, a third signal quality index is determined based on the average value of the heartbeat interval, the average heart rate, and the first standard deviation of the continuous heartbeat interval, and the second standard deviation of the average value of the reference heartbeat interval, the reference average heart rate, and the reference continuous heartbeat interval. A fourth signal quality index is determined based on the difference between the cross-correlation coefficient and the preset cross-correlation coefficient, and the 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 difference between the cross-correlation coefficient and the difference between the peak power are positively correlated with the fourth signal quality index.

[0071] S107, determine the 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, determine 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.

[0073] S109, determine the quality evaluation result of the fNIRS signal based on 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, determine whether the quality evaluation result of the fNIRS signal is less than the preset result threshold and whether the hair density of the person being tested is dense. If yes, proceed to step S111; if no, proceed to step S101.

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

[0076] In summary, the fNIRS (functional optical brain imaging) signal quality assessment method according to the embodiments of this application acquires incident light intensity data and emitted light intensity data emitted by the light source, determines the coefficient of variation based on the incident light intensity data, and determines the signal-to-noise ratio based on the incident light intensity data and emitted light intensity data. The emitted light intensity data is processed to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range. The quality assessment result of the fNIRS signal is determined based on the coefficient of variation, signal-to-noise ratio, number of peaks, and heartbeat signal characteristics. Therefore, this method can comprehensively measure the quality of near-infrared signals and can adapt to the physiological characteristics of different individuals, guiding real-time signal quality detection and hardware iterative design.

[0077] Corresponding to the above embodiments, this application also proposes an 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 this application embodiment includes: a first acquisition module 110, a first determination module 120, a second acquisition module 130, and a second determination module 140.

[0079] The first acquisition module 110 is used to acquire incident light intensity data and emitted 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 to determine the signal-to-noise ratio based on the incident light intensity data and the emitted light intensity data. The second acquisition module 130 is used to process the emitted 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, signal-to-noise ratio, number of peaks, and heartbeat signal characteristics.

[0080] According to one embodiment of this application, the heartbeat signal features include the average value of the heartbeat interval, the average heart rate, and the standard deviation of the continuous heartbeat interval, as well as the cross-correlation coefficient and peak power of the red and infrared light corresponding to the heartbeat signal. The second acquisition module 130 acquires the heartbeat signal features, specifically for: converting the emitted light intensity data into optical 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 interval based on a sliding window method.

[0081] According to one embodiment of this application, the second acquisition module 130 acquires the number of peaks within a preset frequency range, specifically for: performing a Fourier transform on the emitted light intensity data to obtain the frequency domain signal of the emitted 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 sort as the number of peaks.

[0082] According to one embodiment of this application, the second determining module 140 determines 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. Specifically, it is 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 heartbeat interval, average heart rate, and the first standard deviation of the continuous heartbeat interval and the average heartbeat interval, the reference average heart rate, and the second standard deviation of the reference continuous heartbeat interval; and determine a fourth signal quality index based on the difference between the cross-correlation coefficient and a preset cross-correlation coefficient, and the difference between the peak power and a preset peak power threshold. Specifically, the target difference between the first and second standard deviations is determined through correlation and variance analysis. The target difference is negatively correlated with the third signal quality index, while the difference in cross-correlation coefficients and peak power is positively correlated with the fourth signal quality index. The fifth signal quality index is determined based on the signal-to-noise ratio (SNR), which 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 its corresponding weight coefficient, the second product of the second signal quality index and its corresponding weight coefficient, the third product of the third signal quality index and its corresponding weight coefficient, the fourth product of the fourth signal quality index and its corresponding weight coefficient, and the fifth product of the fifth signal quality index and its corresponding weight coefficient, as well as the sum of the first, second, third, and fourth products. The evaluation result is positively correlated with the signal quality index.

[0083] According to one embodiment of this application, the second determining 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 one embodiment of this application, the second determining module 140 adjusts the signal-to-noise ratio, specifically for: increasing the intensity of infrared light emitted by the light source.

[0085] According to one embodiment of this application, the second determining module 140 is further configured to: acquire head data of the person being tested; acquire hair density based on the head data; and increase the intensity of infrared light emitted by the light source when the hair density is dense.

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

[0087] According to the fNIRS functional optical brain imaging signal quality assessment device of this application, a first acquisition module is used to acquire incident light intensity data and emitted light intensity data emitted by the light source; a first determination module is used to determine the coefficient of variation based on the incident light intensity data, and to determine the signal-to-noise ratio based on the incident light intensity data and the emitted light intensity data; a second acquisition module is used to process the emitted light intensity data to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range; and a second determination module is used to determine the quality assessment result of the fNIRS signal based on the coefficient of variation, signal-to-noise ratio, number of peaks, and heartbeat signal characteristics. Therefore, this device 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.

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

[0089] The computer-readable storage medium of this application embodiment stores a program that, when executed by a processor, implements the above-described fNIRS functional optical brain imaging signal quality assessment method.

[0090] According to the computer-readable storage medium of the present application embodiment, by executing the above-described fNIRS functional optical brain imaging signal quality assessment method, the quality of near-infrared signals can be comprehensively measured, and it 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 embodiments, this application also proposes an electronic device.

[0092] like Figure 4 As shown, the electronic device 200 of this application embodiment 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, it implements the above-described fNIRS functional optical brain imaging signal quality assessment method.

[0093] The electronic device according to the embodiments of this application, by performing the above-described fNIRS functional optical brain imaging signal quality assessment method, 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.

[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, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0095] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0096] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0097] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0098] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0099] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for assessing the signal quality of fNIRS functional optical brain imaging, characterized in that, The method includes: Acquire incident light intensity data and outgoing light intensity data emitted by the light source; 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 emitted light intensity data, wherein the coefficient of variation is the ratio of the standard deviation to the mean of the incident light intensity. The emitted light intensity data is processed to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range, wherein the preset frequency range is a specific frequency range of breathing and heartbeat. 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 method for assessing the signal quality of fNIRS functional optical brain imaging according to claim 1, characterized in that, The heartbeat signal features include the average heartbeat interval, average heart rate, and standard deviation of consecutive heartbeat intervals, as well as the cross-correlation coefficient and peak power of the red and infrared light corresponding to the heartbeat signal. The heartbeat signal features are obtained by acquiring the cross-correlation coefficient and peak power, which are indicators measuring the correlation between red and infrared light signals, including: The emitted light intensity data is converted into optical density data, and the infrared wavelength color light is filtered and then R-point detection is performed. The average value of the heartbeat interval, the average heart rate, and the standard deviation of the continuous heartbeat interval are calculated based on a sliding window method.

3. The method for assessing the signal quality of fNIRS functional optical brain imaging according to claim 1, characterized in that, Obtain the number of peak values ​​within a preset frequency range, including: Perform a Fourier transform on the emitted light intensity data to obtain the frequency domain signal of the emitted light intensity data; The amplitudes of the frequency domain signals within the preset frequency range are sorted in descending order; The amplitude of the first preset number in the sorting is taken as the number of peak values.

4. The method for assessing the signal quality of fNIRS functional optical brain imaging according to claim 2, characterized in that, The determination of the quality assessment 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: A first signal quality index is determined based on the coefficient of variation, wherein the coefficient of variation is negatively correlated with the first signal quality index; A second signal quality index is determined 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. A third signal quality index is determined based on the average heart rate interval, the average heart rate, and the first standard deviation of the continuous heart rate interval compared with the average heart rate interval, the reference average heart rate, and the reference continuous heart rate interval. A fourth signal quality index is determined based on the difference between the cross-correlation coefficient and a preset cross-correlation coefficient, and the difference between the peak power and a preset peak power threshold. The first standard deviation and the second standard deviation are used to determine a target difference through correlation and variance analysis. The target difference is negatively correlated with the third signal quality index, and the difference between the cross-correlation coefficient 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 and the fifth signal quality index are positively correlated. 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. The evaluation result is positively correlated with the signal quality index.

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

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

7. The method for assessing the signal quality of fNIRS functional optical brain imaging according to claim 5 or 6, characterized in that, The method further includes: Obtain the head data of the subject being tested; Based on the head data, hair density is obtained, and when the hair density is dense, the intensity of infrared light emitted by the light source is increased.

8. A device for assessing the signal quality of fNIRS functional optical brain imaging, characterized in that, The device includes: The first acquisition module is used to acquire incident light intensity data and outgoing light intensity data emitted by the light source; The first determining module is used to determine the coefficient of variation based on the incident light intensity data, and to determine the signal-to-noise ratio based on the incident light intensity data and the emitted light intensity data, wherein the coefficient of variation is the ratio of the standard deviation to the mean of the incident light intensity. The second acquisition module is used to process the emitted light intensity data to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range, wherein the preset frequency range is a specific frequency range of breathing and heartbeat. The second determining 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, It stores a program that, when executed by a processor, implements the fNIRS functional optical brain imaging signal quality assessment method according to any one of claims 1-7.

10. An electronic device, characterized in that, include: The memory, the processor, and the program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the fNIRS functional optical brain imaging signal quality assessment method according to any one of claims 1-7.

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