A method, system and medium for decoding ultrasound-modulated electroencephalography signals
By preprocessing and robustly decoding ultrasound-modulated EEG signals, combined with modulation state assessment, the problem of insufficient accuracy and robustness in decoding ultrasound-modulated EEG signals was solved, achieving higher signal decoding accuracy and stability, and improving the spatial resolution of scalp EEG.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-10
- Publication Date
- 2026-07-10
AI Technical Summary
Existing ultrasound-modulated EEG signal decoding technologies suffer from low accuracy and insufficient robustness, especially in complex modulation scenarios where it is difficult to achieve high spatiotemporal resolution brain-source imaging and high-precision brain-computer interaction.
By acquiring scalp EEG signals from specific target areas, preprocessing, framing and windowing operations, and robust decoding are performed. The instantaneous complex response amplitude at the PRF of each frame is extracted, low-frequency noise is isolated and waveform distortion is overcome. The modulation depth is quantified by combining modulation state evaluation indicators to achieve signal reconstruction.
It significantly improves the decoding accuracy and stability of ultrasound-modulated EEG signals, enhances the spatial resolution of scalp EEG, and strengthens the signal reconstruction capability in complex scenarios.
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Figure CN122365023A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of brain-computer interface technology, and in particular to a method, system and medium for decoding ultrasound-modulated electroencephalogram (EEG) signals. Background Technology
[0002] Electroencephalography (EEG) has been widely used in brain science research due to its non-invasive nature and high temporal resolution down to the millisecond level. However, due to the volumetric conductivity effect of brain tissue, EEG recorded on the scalp is a spatial aliasing of the electrical activity of millions of neurons within the brain. This results in a spatial resolution of only centimeters for scalp EEG, limiting its sensitivity to neurons deep within the brain. Therefore, traditional non-invasive EEG techniques struggle to achieve fine signal analysis of local neurons in target brain regions during functional brain imaging, further hindering the development of next-generation non-invasive brain-computer interface systems towards high-dimensional command decoding and high-precision brain-computer interaction.
[0003] Ultrasound-Modulated Electroencephalography (USMEEG) is a novel non-invasive EEG acquisition technique based on the acoustic-electric (AE) effect. It combines the high temporal resolution of EEG with the high spatial resolution of focused ultrasound (FUS), significantly improving the spatial resolution of traditional scalp EEG. In USMEEG research, the decoding accuracy of the AE coupling signal is crucial for achieving high spatiotemporal resolution brain source imaging. High-precision decoding not only determines the spatial localization error of the source location but also directly affects the separation capability of multi-source signals and the fidelity of source waveform reconstruction. However, due to individual differences in skull transmittance, ultrasound focus shift, and fluctuations in the intensity of deep neural sources, AE coupling is often in a highly unstable state. In practical USMEEG applications, the intensity of the AE coupling signal fluctuates drastically due to multi-source interference, low signal-to-noise ratio, and non-uniform attenuation of transcranial energy. This fluctuation often results in signals being in complex modulation scenarios, such as weak AE modulation (sideband signals are extremely weak and easily drowned out by noise) or excessive AE modulation (sideband energy approaches or even exceeds the carrier, causing severe distortion of the decoded signal). Currently, there is a lack of metrics to quantify these complex scenarios, as well as robust decoding algorithms that can still accurately reproduce the source signal waveform under such extreme distortion, which seriously hinders the practical application of USMEEG technology.
[0004] Therefore, it is of great practical significance to provide a decoding method for ultrasound-modulated EEG signals and improve the accuracy of decoding ultrasound-modulated EEG signals. Summary of the Invention
[0005] This application provides a method, system, and medium for decoding ultrasound-modulated electroencephalogram (EEG) signals, aiming to solve the problem of low accuracy in existing ultrasound-modulated EEG signal decoding, improve the robustness of ultrasound-modulated EEG signal decoding, and enhance the accuracy of ultrasound-modulated EEG signal decoding.
[0006] In a first aspect, embodiments of this application provide a method for decoding ultrasound-modulated electroencephalogram (MEG) signals, applied to a decoding system for ultrasound-modulated MEG signals; the decoding system includes an ultrasound-modulated MEG signal acquisition module and a pulse repetition frequency (PRF) decoding module; the ultrasound-modulated MEG signal acquisition module includes a host computer control subsystem, a phased array transducer, and MEG acquisition electrodes; the host computer control subsystem is used to control the phased array transducer to emit focused ultrasound with an ultrasound pulse repetition frequency (PRF) towards a specific target area, and to control the synchronous acquisition of scalp MEG signals of the specific target area through the MEG acquisition electrodes; the PRF decoding module is used to obtain the brain power signal corresponding to the ultrasound-modulated MEG USMEEG signal; the method includes: The scalp electroencephalogram (EEG) signals of the specific target area were collected to obtain the USMEEG signal; The USMEEG signal is preprocessed, robustly decoded, and reconstructed to obtain the brain power signal corresponding to the USMEEG signal. The robustly decoded operation includes extracting the instantaneous complex response amplitude of each frame at the PRF after the framing and windowing operation.
[0007] The ultrasound-modulated EEG signal decoding method provided in this application can obtain the USMEEG signal by acquiring scalp EEG signals from a specific target area; perform preprocessing, high-robust decoding, and signal reconstruction operations on the USMEEG signal to obtain the corresponding brain power signal; the high-robust decoding operation includes extracting the instantaneous complex response amplitude at the PRF of each frame obtained after framing and windowing, which can effectively isolate low-frequency noise and overcome waveform distortion caused by modulation by extracting the instantaneous complex response amplitude at the PRF of each frame obtained after framing and windowing the ultrasound-modulated EEG USMEEG signal, significantly improving the robustness of ultrasound-modulated EEG signal decoding, improving the accuracy of ultrasound-modulated EEG signal decoding, improving the spatial resolution of scalp EEG, and improving the stability of ultrasound-modulated EEG signal decoding.
[0008] In one possible implementation, the decoding system for the ultrasound-modulated electroencephalogram (USMEEG) signal further includes a modulation state assessment module; the modulation state assessment module is used to obtain the modulation state type of the USMEEG signal; before performing preprocessing, robust decoding, and signal reconstruction operations on the USMEEG signal to obtain the corresponding brain power signal, the method further includes: Based on the USMEEG signal, a state evaluation index is determined; the state evaluation index characterizes the comparison relationship between the sideband component and the carrier component. Based on the state evaluation index, determine the modulation state type of the USMEEG signal; The process of preprocessing, robust decoding, and signal reconstruction of the USMEEG signal to obtain the corresponding brain power signal includes: Based on the modulation state type, the USMEEG signal is preprocessed, robustly decoded, and reconstructed to obtain the brain power signal corresponding to the USMEEG signal; the robustly decoded operation includes extracting the instantaneous complex response amplitude of each frame at the PRF obtained after the frame windowing operation.
[0009] In one possible implementation, determining the state assessment metric based on the USMEEG signal includes: Based on the USMEEG signal, the frequency domain representation information corresponding to the USMEEG signal is obtained; Based on the frequency domain representation information, the sideband component information and carrier component information of the USMEEG signal are obtained; The state evaluation index is obtained based on the sideband component information and the carrier component information.
[0010] In one possible implementation, obtaining the sideband component information and carrier component information of the USMEEG signal based on the frequency domain representation information includes: Based on the frequency domain representation information, the DC component and amplitude component of the USMEEG signal are obtained; the amplitude component includes first amplitude component information and second amplitude component information; the first amplitude component information represents the amplitude represented by the cosine signal of the high-frequency carrier; the second amplitude component information represents the amplitude represented by the cosine signal of the sub-signals of each characteristic frequency component contained in the modulation signal. Based on the DC component and the first amplitude component information, the carrier component information of the USMEEG signal is determined; The second amplitude component information is summed to obtain the third amplitude component information; The sideband component information of the USMEEG signal is determined based on the third amplitude component information and the first amplitude component information.
[0011] In one possible implementation, the modulation state type includes a moderate modulation type and an abnormal modulation type; determining the modulation state type of the USMEEG signal based on the state evaluation index includes: If the state evaluation index is greater than the first threshold and less than or equal to the second threshold, then the modulation state type of the USMEEG signal is the moderate modulation type; wherein, the second threshold is greater than the first threshold; If the state evaluation index is less than or equal to the first threshold, or greater than the second threshold, then the modulation state type of the USMEEG signal is the abnormal modulation type.
[0012] In one possible implementation, the step of performing frame-segmentation and windowing operations, robust decoding operations, and signal reconstruction operations on the USMEEG signal based on the modulation state type to obtain the brain power signal corresponding to the USMEEG signal includes: If the modulation state type is the abnormal modulation type, then the USMEEG signal is subjected to a first preprocessing operation, a robust decoding operation, and a first signal reconstruction operation to obtain the brain power signal corresponding to the USMEEG signal; the first signal reconstruction operation is to reconstruct the signal waveform based on the instantaneous complex response amplitude; the first preprocessing operation includes a bandpass filtering operation and a frame windowing operation.
[0013] In one possible implementation, the step of performing frame-segmentation and windowing operations, robust decoding operations, and signal reconstruction operations on the USMEEG signal based on the modulation state type to obtain the brain power signal corresponding to the USMEEG signal further includes: If the modulation state type is the moderate modulation type, then the USMEEG signal is subjected to a second preprocessing operation, an envelope decoding operation, and a second signal reconstruction operation to obtain the brain power signal corresponding to the USMEEG signal; the envelope decoding operation includes extracting the carrier amplitude envelope of the USMEEG signal; the second preprocessing operation includes a bandpass filtering operation; the second signal reconstruction operation is to reconstruct the signal waveform based on the signal envelope.
[0014] The ultrasound-modulated EEG signal decoding method provided in this application can obtain a USMEEG signal by acquiring scalp EEG signals from a specific target area; determine a state evaluation index based on the USMEEG signal; the state evaluation index characterizes the comparison relationship between sideband components and carrier components; determine the modulation state type of the USMEEG signal based on the state evaluation index; and perform preprocessing, high-robustness decoding, and signal reconstruction operations on the USMEEG signal based on the modulation state type to obtain the EEG signal corresponding to the USMEEG signal. The high-robustness decoding operation includes extracting the instantaneous complex response amplitude of each frame at the PRF obtained after framing and windowing operations. This method can quantify the modulation depth of the ultrasound-modulated EEG signal through the state evaluation index, effectively isolate low-frequency noise and overcome waveform distortion caused by modulation, significantly improve the robustness of ultrasound-modulated EEG signal decoding, enhance the accuracy of ultrasound-modulated EEG signal decoding, improve the spatial resolution of scalp EEG, and improve the stability of ultrasound-modulated EEG signal decoding.
[0015] Secondly, embodiments of this application provide a decoding system for ultrasound-modulated electroencephalogram (EEG) signals, comprising: The ultrasound-modulated electroencephalogram (EEG) signal acquisition module includes a host computer control subsystem, a phased array transducer, and EEG acquisition electrodes. The host computer control subsystem is used to control the phased array transducer to emit focused ultrasound with an ultrasound pulse repetition frequency (PRF) towards a specific target area, and to control the synchronous acquisition of scalp EEG signals of the specific target area through the EEG acquisition electrodes to obtain USMEEG signals. The pulse repetition frequency decoding module is used to: perform preprocessing, robust decoding and signal reconstruction operations on the USMEEG signal to obtain the brain power signal corresponding to the USMEEG signal; the robust decoding operation includes extracting the instantaneous complex response amplitude of each frame at the PRF obtained after the framing and windowing operation.
[0016] In one possible implementation, the system further includes a modulation state evaluation module; the modulation state evaluation module is configured to: Based on the USMEEG signal, a state evaluation index is determined; the state evaluation index characterizes the comparison relationship between the sideband component and the carrier component. Based on the state evaluation index, determine the modulation state type of the USMEEG signal; The pulse repetition frequency decoding module is specifically used for: Based on the modulation state type, the USMEEG signal is preprocessed, robustly decoded, and reconstructed to obtain the brain power signal corresponding to the USMEEG signal.
[0017] In one possible implementation, the modulation state evaluation module is specifically used for: Based on the USMEEG signal, the frequency domain representation information corresponding to the USMEEG signal is obtained; Based on the frequency domain representation information, the sideband component information and carrier component information of the USMEEG signal are obtained; The state evaluation index is obtained based on the sideband component information and the carrier component information.
[0018] In one possible implementation, the modulation state evaluation module is specifically used for: Based on the frequency domain representation information, the DC component and amplitude component of the USMEEG signal are obtained; the amplitude component includes first amplitude component information and second amplitude component information; the first amplitude component information represents the amplitude represented by the cosine signal of the high-frequency carrier; the second amplitude component information represents the amplitude represented by the cosine signal of the sub-signals of each characteristic frequency component contained in the modulation signal. Based on the DC component and the first amplitude component information, the carrier component information of the USMEEG signal is determined; The second amplitude component information is summed to obtain the third amplitude component information; The sideband component information of the USMEEG signal is determined based on the third amplitude component information and the first amplitude component information.
[0019] In one possible implementation, the modulation state type includes a moderate modulation type and an abnormal modulation type; the modulation state evaluation module is specifically used for: If the state evaluation index is greater than the first threshold and less than or equal to the second threshold, then the modulation state type of the USMEEG signal is the moderate modulation type; wherein, the second threshold is greater than the first threshold; If the state evaluation index is less than or equal to the first threshold, or greater than the second threshold, then the modulation state type of the USMEEG signal is the abnormal modulation type.
[0020] In one possible implementation, the pulse repetition frequency decoding module is specifically used for: If the modulation state type is the abnormal modulation type, then the USMEEG signal is subjected to a first preprocessing operation, a robust decoding operation, and a first signal reconstruction operation to obtain the brain power signal corresponding to the USMEEG signal; the first signal reconstruction operation is to reconstruct the signal waveform based on the instantaneous complex response amplitude; the first preprocessing operation includes a bandpass filtering operation and a frame windowing operation.
[0021] In one possible implementation, the pulse repetition frequency decoding module is further configured to: If the modulation state type is the moderate modulation type, then the USMEEG signal is subjected to a second preprocessing operation, an envelope decoding operation, and a second signal reconstruction operation to obtain the brain power signal corresponding to the USMEEG signal; the envelope decoding operation includes extracting the carrier amplitude envelope of the USMEEG signal; the second preprocessing operation includes a bandpass filtering operation; the second signal reconstruction operation is to reconstruct the signal waveform based on the signal envelope.
[0022] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method described in any of the first aspects.
[0023] Fourthly, embodiments of this application provide a computer program product including computer instructions stored in a computer-readable storage medium; when a processor of a computer device reads the computer instructions from the computer-readable storage medium, the processor executes the computer instructions, causing the computer device to perform the steps of the method described in any of the first aspects.
[0024] The technical effects of any of the implementation methods in the second to fourth aspects can be found in the technical effects of the implementation method in the first aspect, and will not be repeated here. Attached Figure Description
[0025] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0026] Figure 1 A framework diagram of a decoding system for ultrasound-modulated electroencephalogram (EEG) signals provided in this application embodiment; Figure 2 One of the flowcharts for a method of decoding ultrasound-modulated electroencephalogram (EEG) signals provided in this application embodiment; Figure 3 A second schematic flowchart illustrating a method for decoding ultrasound-modulated electroencephalogram (EEG) signals provided in this application embodiment; Figure 4 A flowchart illustrating the determination of state evaluation indicators in a decoding method for ultrasound-modulated electroencephalogram (EEG) signals provided in this application embodiment; Figure 5This is a flowchart illustrating a method for decoding ultrasound-modulated electroencephalogram (EEG) signals according to an embodiment of this application, in which sideband component information and carrier component information are obtained based on frequency domain representation information. Figure 6 This is one of the structural schematic diagrams of a decoding system for ultrasound-modulated electroencephalogram (EEG) signals provided in an embodiment of this application; Figure 7 This is a second schematic diagram of a decoding system for ultrasound-modulated electroencephalogram (EEG) signals provided in an embodiment of this application. Detailed Implementation
[0027] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0028] Electroencephalography (EEG) has been widely used in brain science research due to its non-invasive nature and high temporal resolution down to the millisecond level. However, due to the volumetric conductivity effect of brain tissue, EEG recorded on the scalp is a spatial aliasing of the electrical activity of millions of neurons within the brain. This results in a spatial resolution of only centimeters for scalp EEG, limiting its sensitivity to neurons deep within the brain. Therefore, traditional non-invasive EEG techniques struggle to achieve fine signal analysis of local neurons in target brain regions during functional brain imaging, further hindering the development of next-generation non-invasive brain-computer interface systems towards high-dimensional command decoding and high-precision brain-computer interaction.
[0029] Ultrasound-Modulated Electroencephalography (USMEEG) is a novel non-invasive EEG acquisition technique based on the acoustic-electric (AE) effect. It combines the high temporal resolution of EEG with the high spatial resolution of focused ultrasound (FUS), significantly improving the spatial resolution of traditional scalp EEG. In USMEEG research, the decoding accuracy of the AE coupling signal is crucial for achieving high spatiotemporal resolution brain source imaging. High-precision decoding not only determines the spatial localization error of the source location but also directly affects the separation capability of multi-source signals and the fidelity of source waveform reconstruction. However, due to individual differences in skull transmittance, ultrasound focus shift, and fluctuations in the intensity of deep neural sources, AE coupling is often in a highly unstable state. In practical USMEEG applications, the intensity of the AE coupling signal fluctuates drastically due to multi-source interference, low signal-to-noise ratio, and non-uniform attenuation of transcranial energy. This fluctuation often results in signals being in complex modulation scenarios, such as weak AE modulation (sideband signals are extremely weak and easily drowned out by noise) or excessive AE modulation (sideband energy approaches or even exceeds the carrier, causing severe distortion of the decoded signal). Currently, there is a lack of metrics to quantify these complex scenarios, as well as robust decoding algorithms that can still accurately reproduce the source signal waveform under such extreme distortion, which seriously hinders the practical application of USMEEG technology.
[0030] Therefore, it is of great practical significance to provide a decoding method for ultrasound-modulated EEG signals and improve the accuracy of decoding ultrasound-modulated EEG signals.
[0031] Based on this, embodiments of this application provide a method, system, and medium for decoding ultrasound-modulated electroencephalogram (EEG) signals. The decoding method for ultrasound-modulated EEG signals can obtain ultrasound-modulated EEG (USMEEG) signals by acquiring scalp EEG signals from a specific target area; perform preprocessing, robust decoding, and signal reconstruction operations on the USMEEG signals to obtain the corresponding brain power signals; the robust decoding operation includes extracting the instantaneous complex response amplitude at the PRF (Peripherally Resonant Flow Array) of each frame obtained after framing and windowing of the ultrasound-modulated EEG USMEEG signal. This enables the extraction of the instantaneous complex response amplitude at the PRF of each frame obtained after framing and windowing of the ultrasound-modulated EEG USMEEG signal, effectively isolating low-frequency noise and overcoming waveform distortion caused by modulation, significantly improving the robustness of ultrasound-modulated EEG signal decoding, enhancing the accuracy of ultrasound-modulated EEG signal decoding, improving the spatial resolution of scalp EEG, and increasing the stability of ultrasound-modulated EEG signal decoding.
[0032] To make the inventive objectives, technical solutions, and advantages of the embodiments of this application clearer, the present application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0033] The decoding method for ultrasound-modulated electroencephalogram (EEG) signals provided in this application will be further explained below. The ultrasound-modulated EEG signal decoding method provided in this application can be applied to an ultrasound-modulated EEG signal decoding system as follows: Figure 1 As shown.
[0034] Figure 1 This is a schematic diagram of the framework of the ultrasound-modulated electroencephalogram (EEG) signal decoding system of this application. The ultrasound-modulated EEG signal decoding system 10 includes: an ultrasound-modulated EEG signal acquisition module 101, a modulation state assessment module 102, and a pulse repetition frequency decoding module 103.
[0035] In some embodiments, the ultrasound-modulated electroencephalogram (EEG) signal acquisition module 101 is used to simultaneously perform focused ultrasound irradiation and high-sampling-rate scalp EEG signal acquisition. This module includes: a host computer control subsystem 1011, a phased array transducer 1012, and EEG acquisition electrodes 1013. The host computer control subsystem 1011 controls the phased array transducer 1012 to emit focused ultrasound with an ultrasound pulse repetition frequency (PRF) towards a specific target area, and controls the synchronous acquisition of scalp EEG signals from the specific target area through the EEG acquisition electrodes 1013; the pulse repetition frequency decoding module is used to obtain the brain power signal corresponding to the USMEEG signal. For example, the phased array transducer 1012 may be a 128-element phased array transducer.
[0036] In some embodiments, the ultrasound-modulated EEG signal acquisition module further includes: a power amplification system and an EEG amplifier.
[0037] In some embodiments, the ultrasound-modulated EEG signal acquisition module further includes an impedance matching module and a motion control system.
[0038] In some embodiments, the ultrasound-modulated electroencephalogram (EEG) signal acquisition module further includes an acquisition host.
[0039] In some embodiments of this application, the ultrasound-modulated EEG signal acquisition module 101 further includes: a power amplification subsystem 1014, an impedance matching module 1015, an EEG amplifier 1016, a multi-axis motion control subsystem 1017, and an acquisition host 1018.
[0040] During the decoding of ultrasound-modulated EEG signals, the host computer control subsystem 1011 controls the power amplification subsystem 1014 to drive the phased array transducer 1012 to emit focused ultrasound with a specific PRF to a specific target area of the subject's scalp or phantom model; at the same time, the EEG amplifier 1016 controls the synchronous acquisition of scalp EEG signals in the irradiated area to obtain raw USMEEG signals with a high sampling rate.
[0041] In some embodiments, the modulation state evaluation module 102 is used to calculate a quantization index AMR based on the acquired USMEEG signal to determine the current acoustic-electric modulation depth. When focused ultrasound irradiates a conductive medium, the conductivity of the conductive medium undergoes periodic changes due to the FUS pressure. This periodic change depends on the specific PRF frequency of the FUS, and its variation follows the following formula:
[0042] in, This represents the change in the conductivity of the dielectric. The initial conductivity of the medium; The FUS sound pressure level variation pattern; is the constant for the acoustic-electric effect.
[0043] Based on this, when current flows through the medium, the current in the FUS-irradiated area is modulated, and the modulation effect is related to the FUS beam pattern; the FUS effect in the non-irradiated area is smaller and has no obvious modulation effect, which can be ignored. The FUS-modulated current component, after propagation, is collected and processed by an external EEG amplifier to obtain an ultrasound-modulated EEG signal. Ultrasound-modulated EEG signals are essentially amplitude-modulated signals. The low-frequency source signal serves as the modulating signal, generally a line spectrum signal or a broadband signal with several characteristic frequency components; the high-frequency PRF component serves as the carrier, and frequency components far from the modulation signal bandwidth are generally selected, which is also related to the performance of the ultrasound equipment.
[0044] Assuming the high-frequency carrier is The low-frequency modulation signal is , by having It consists of cosine signals of several frequencies.
[0045]
[0046] in, It is a PRF component; The characteristic frequency of the modulated signal.
[0047] Given DC component The time-domain and frequency-domain representations of the ultrasonically modulated multi-sideband amplitude modulation (MAM) signal are as follows:
[0048] in, , .
[0049] because It is conjugate symmetric, and its negative half-axis frequency components are redundant with its positive half-axis components. Therefore, in the analysis, only the positive half-axis spectrum needs to be analyzed. Thus, Equation 5 can be transformed into:
[0050] As shown in Formula 4, the amplitude modulation index at this time is as shown in Formula 7, and the amplitude modulation index is required to be... ;when Overmodulation can occur, leading to envelope distortion.
[0051]
[0052] As shown in Formula 6, the PRF component of USMEEG at this time, i.e., the carrier component, is... The sum of the sideband components of USMEEG is A predefined index, called AMR, is used to measure the FUS modulation depth index in the USMEEG. AMR is the ratio of the sum of the sideband components of the USMEEG to the PRF carrier components, and its formula is as follows:
[0053] As can be seen from Formula 8, Furthermore, it is mathematically equivalent to the amplitude modulation index. Therefore, when AMR > 1, the USMEEG is in an overmodulated state. In addition, considering the actual situation of the USMEEG, the modulation state can be defined within the range of 0 < AMR ≤ 1: when 0 < AMR ≤ 0.6, the USMEEG is in a weakly modulated state. In this state, the degree of acoustic-electric coupling is low, resulting in weak sideband components that are easily submerged by noise; when 0.6 < AMR ≤ 1, the USMEEG is in a moderately modulated state. At this time, the USMEEG sideband components are obvious, its signal-to-noise ratio is significantly higher than the noise floor, the envelope of the corresponding USMEEG is complete and without obvious distortion, and it has a high correlation with the modulated signal.
[0054] In some embodiments, the pulse repetition frequency decoding module 103 is the core decoding unit, used to perform robust decoding of the acquired USMEEG signal to reconstruct the brain power signal waveform. Its workflow is as follows: Given a continuous USMEEG sequence, the discrete sequence obtained after sampling is as follows:
[0055] in, Sampling frequency, The total number of samples, This represents the total duration of the signal.
[0056] Define the window width of the frame-segmented Hamming window as... The frame interval is The Hamming window function is defined as follows:
[0057] The number of samples in a single time window is , .
[0058] Total number of windows N Determined by boundary conditions:
[0059] Among them, the number of frame samples is , .
[0060] No. The framing process for each time window is as follows:
[0061] Wherein, the starting index is .
[0062] The final frame matrix of USMEEG is as follows:
[0063] Its elements are represented as:
[0064] right conduct The point-fast Fourier transform (FFT) of the sample count in a single time window yields:
[0065] Extract the first The instantaneous complex response of a time window at the PRF is shown in the following equation:
[0066] The final decoded sequence of USMEEG is as follows: Amplitude response sequence:
[0067] Where |·| represents the operation of finding the modulus of a complex number, also known as the magnitude; This represents the operation of finding the real part of a complex number; This represents the operation of finding the imaginary part of a complex number.
[0068] The time series corresponding to the decoded sequence is:
[0069] In some embodiments of this application, the decoding system for ultrasound-modulated electroencephalogram (USMEEG) signals may further include a data storage module; this data storage module is used to store, with high fidelity, the acquired raw USMEEG signal, the calculated AMR value, and the brain power signal reconstructed by the PRF decoding module. To address the high-frequency characteristics of the USMEEG signal, the data storage module employs a high-sampling-rate storage medium to ensure the complete preservation of high-frequency information.
[0070] This application provides a decoding method for ultrasound-modulated electroencephalogram (EEG) signals, applicable to an ultrasound-modulated EEG signal decoding system, for example, it can be applied to... Figure 1 The ultrasound-modulated electroencephalogram (EEG) signal decoding system 10 shown includes an ultrasound-modulated EEG signal acquisition module and a pulse repetition frequency (PRF) decoding module. The ultrasound-modulated EEG signal acquisition module includes a host computer control subsystem, a phased array transducer, and EEG acquisition electrodes. The host computer control subsystem controls the phased array transducer to emit focused ultrasound with an ultrasound pulse repetition frequency (PRF) towards a specific target area, and controls the synchronous acquisition of scalp EEG signals from the specific target area through the EEG acquisition electrodes. The PRF decoding module is used to obtain the brain power signal corresponding to the USMEEG signal. Figure 2 As shown, the method includes the following steps: Step S201: Collect scalp EEG signals from a specific target area to obtain USMEEG signals.
[0071] Step S202 involves performing preprocessing, robust decoding, and signal reconstruction operations on the USMEEG signal to obtain the brain power signal corresponding to the USMEEG signal. The robust decoding operation includes extracting the instantaneous complex response amplitude at the PRF of each frame obtained after the framing and windowing operation.
[0072] In an optional embodiment, the ultrasound-modulated EEG signal decoding system further includes a modulation state assessment module; the modulation state assessment module is used to obtain the modulation state type of the USMEEG signal; before performing preprocessing, robust decoding, and signal reconstruction operations on the USMEEG signal to obtain the corresponding brain power signal, such as... Figure 3 As shown, the method also includes: Step S301: Determine the state evaluation index based on the USMEEG signal; the state evaluation index characterizes the comparison relationship between the sideband component and the carrier component.
[0073] For example, the state assessment index (AMR) is determined based on the USMEEG signal; the state assessment index (AMR) characterizes the comparison relationship between the sideband components and the carrier components.
[0074] Step S302: Determine the modulation state type of the USMEEG signal based on the state evaluation index.
[0075] For example, the modulation state type Type_UM of the USMEEG signal is determined based on the state evaluation index AMR.
[0076] In this embodiment, the process of performing preprocessing, robust decoding, and signal reconstruction on the USMEEG signal to obtain the corresponding brain power signal is as follows: Step S303: Based on the modulation state type, perform preprocessing, robust decoding and signal reconstruction operations on the USMEEG signal to obtain the brain power signal corresponding to the USMEEG signal.
[0077] Among them, the robust decoding operation includes extracting the instantaneous complex response amplitude of each frame at the PRF after the frame-segmentation and windowing operation.
[0078] For example, based on the modulation state type Type_UM, the USMEEG signal is preprocessed, robustly decoded, and reconstructed to obtain the brain power signal corresponding to the USMEEG signal. The robustly decoded operation includes extracting the instantaneous complex response amplitude at the PRF for each frame obtained after the framing and windowing operation.
[0079] In one alternative embodiment, a state assessment metric is determined based on the USMEEG signal, such as... Figure 4 As shown, this can be achieved through the following steps: Step S401: Obtain the frequency domain representation information corresponding to the USMEEG signal based on the USMEEG signal.
[0080] For example, the frequency domain representation information corresponding to the USMEEG signal is obtained based on the USMEEG signal. , It can be as shown in Formula 5.
[0081] Step S402: Based on the frequency domain representation information, obtain the sideband component information and carrier component information of the USMEEG signal.
[0082] For example, based on frequency domain representation information This yields the sideband component information info_b and carrier component information info_z of the USMEEG signal.
[0083] In one optional embodiment, the sideband component information and carrier component information of the USMEEG signal are obtained based on the frequency domain representation information, such as... Figure 5 As shown, this can be achieved through the following steps: Step S501: Based on the frequency domain representation information, obtain the DC component and amplitude component of the USMEEG signal; the amplitude component includes first amplitude component information and second amplitude component information; the first amplitude component information represents the amplitude represented by the cosine signal of the high-frequency carrier; the second amplitude component information represents the amplitude represented by the cosine signal of each characteristic frequency component sub-signal contained in the modulation signal.
[0084] For example, based on frequency domain representation information The DC component of the USMEEG signal was obtained. The amplitude component Info_a of the USMEEG signal; the amplitude component Info_a includes information about the first amplitude component. Second amplitude component information First amplitude component information The amplitude of the cosine signal representing the high-frequency carrier wave; second amplitude component information. The amplitude of the cosine signal representing each characteristic frequency component of the modulated signal.
[0085] Step S502: Determine the carrier component information of the USMEEG signal based on the DC component and the first amplitude component information.
[0086] For example, according to the DC component and the first amplitude component information Determine the carrier component information info_z of the USMEEG signal.
[0087] In some embodiments of this application, the carrier component information is half the product of the DC component and the first amplitude component information.
[0088] (20)
[0089] Step S503: Sum the second amplitude component information to obtain the third amplitude component information.
[0090] For example, information on the second amplitude component Summation is performed to obtain the third amplitude component information. sum_ai .
[0091] (twenty one)
[0092] Step S504: Determine the sideband component information of the USMEEG signal based on the third amplitude component information and the first amplitude component information.
[0093] For example, based on the third amplitude component information sum_ai and the first amplitude component information Determine the sideband component information info_b of the USMEEG signal.
[0094] In some embodiments of this application, the sideband component information info_b of the USMEEG signal is half the product of the third amplitude component information and the first amplitude component information.
[0095] (twenty two)
[0096] Step S403: Obtain the state evaluation index based on the sideband component information and the carrier component information.
[0097] In practice, a state evaluation index is obtained based on the sideband component information and the carrier component information. This state evaluation index characterizes the comparison relationship between the sideband component and the carrier component.
[0098] For example, based on sideband component information The state assessment index (AMR) is obtained by combining the carrier component information info_z with the carrier component information.
[0099] In some embodiments of this application, the state evaluation index represents the quotient of sideband component information and carrier component information.
[0100] (twenty three)
[0101] In one optional embodiment, the modulation state type includes moderate modulation type and abnormal modulation type; determining the modulation state type of the USMEEG signal based on state evaluation metrics includes: If the state evaluation index is greater than the first threshold and less than or equal to the second threshold, then the modulation state type of the USMEEG signal is moderate modulation type; wherein, the second threshold is greater than the first threshold. If the state evaluation index is less than or equal to the first threshold, or greater than the second threshold, then the modulation state type of the USMEEG signal is an abnormal modulation type.
[0102] In some embodiments of this application, the first threshold is 0.6, the second threshold is 1.0, and the state evaluation index AMR represents the quotient of the sideband component information and the carrier component information. When 0.6 < AMR ≤ 1, the USMEEG is in a moderately modulated state. At this time, the USMEEG sideband components are obvious, and its signal-to-noise ratio is significantly higher than the background noise. The envelope of the corresponding USMEEG is complete and without obvious distortion, and it has a high correlation with the modulated signal. When 0 < AMR ≤ 0.6, or AMR > 1, the modulation state type of the USMEEG signal is abnormal modulation type.
[0103] In some embodiments of this application, the abnormal modulation type includes weak modulation state and overmodulation state; if the state evaluation index is less than or equal to a first threshold, the modulation state type of the USMEEG signal is weak modulation state; if the state evaluation index is greater than a second threshold, the modulation state type of the USMEEG signal is overmodulation state.
[0104] When the USMEEG is in a weakly modulated state, the degree of acoustic-electric coupling is low, resulting in weak sideband components that are easily submerged by noise. At this time, the envelope mainly reflects noise fluctuations. Extracting the envelope of the signal for decoding and reconstruction leads to severe distortion of the decoded signal. When the USMEEG is in an overmodulated state, the acoustic-electric coupling is too strong, i.e., overmodulation occurs. The envelope of the signal itself undergoes nonlinear distortion with the source signal waveform and no longer has a one-to-one linear relationship. At this time, directly extracting the envelope will also result in a severely distorted decoding result.
[0105] In one optional embodiment, based on the modulation state type, the USMEEG signal undergoes frame-segmentation and windowing, robust decoding, and signal reconstruction to obtain the brain power signal corresponding to the USMEEG signal, including: If the modulation state type is an abnormal modulation type, the USMEEG signal is subjected to a first preprocessing operation, a robust decoding operation, and a first signal reconstruction operation to obtain the brain power signal corresponding to the USMEEG signal; the first signal reconstruction operation is to reconstruct the signal waveform based on the instantaneous complex response amplitude; the first preprocessing operation includes a bandpass filtering operation and a frame windowing operation.
[0106] In one optional embodiment, based on the modulation state type, the USMEEG signal undergoes frame-segmentation and windowing, robust decoding, and signal reconstruction to obtain the brain power signal corresponding to the USMEEG signal, and further includes: If the modulation state type is moderate modulation, then the USMEEG signal is subjected to a second preprocessing operation, an envelope decoding operation, and a second signal reconstruction operation to obtain the brain power signal corresponding to the USMEEG signal; the envelope decoding operation includes extracting the carrier amplitude envelope of the USMEEG signal; the second preprocessing operation includes a bandpass filtering operation; the second signal reconstruction operation is to reconstruct the signal waveform based on the signal envelope.
[0107] The ultrasound-modulated EEG signal decoding method provided in this embodiment obtains an ultrasound-modulated EEG (USMEEG) signal by acquiring scalp EEG signals from a specific target area; based on the USMEEG signal, a state assessment index is determined; the state assessment index characterizes the comparison relationship between sideband components and carrier components; based on the state assessment index, the modulation state type of the USMEEG signal is determined; based on the modulation state type, the USMEEG signal undergoes preprocessing, robust decoding, and signal reconstruction operations to obtain the corresponding EEG signal; the robust decoding operation includes extracting the instantaneous complex response amplitude at the PRF (Programmable Frame Rendering) of each frame obtained after framing and windowing. This method can improve the accuracy of ultrasound-modulated EEG signal decoding, enhance the spatial resolution of scalp EEG, and improve the stability of ultrasound-modulated EEG signal decoding.
[0108] Based on the same inventive concept, this application also provides a decoding system for ultrasound-modulated electroencephalogram (EEG) signals. Since this system corresponds to the decoding method for ultrasound-modulated EEG signals in this application, and the principle by which this system solves the problem is similar to that of the method, the implementation of this system can refer to the implementation of the above method; repeated details will not be elaborated further.
[0109] Figure 6 This illustration shows a schematic diagram of a decoding system for ultrasound-modulated electroencephalogram (EEG) signals according to an embodiment of this application. The decoding system for ultrasound-modulated EEG signals, as shown... Figure 6 As shown, it includes: an ultrasound-modulated EEG signal acquisition module 601 and a pulse repetition frequency decoding module 602.
[0110] Among them, the ultrasound modulated EEG signal acquisition module 601 includes a host computer control subsystem, a phased array transducer, and EEG acquisition electrodes; the host computer control subsystem is used to control the phased array transducer to emit focused ultrasound with ultrasound pulse repetition frequency (PRF) to a specific target area, and to control the synchronous acquisition of scalp EEG signals of the specific target area through the EEG acquisition electrodes to obtain USMEEG signals. The pulse repetition frequency decoding module 602 is used to: perform preprocessing operations, robust decoding operations, and signal reconstruction operations on the USMEEG signal to obtain the brain power signal corresponding to the USMEEG signal; the robust decoding operation includes extracting the instantaneous complex response amplitude of each frame at the PRF obtained after the framing and windowing operation.
[0111] In one possible implementation, such as Figure 7 As shown, the system also includes a modulation state evaluation module 701; the modulation state evaluation module 701 is used to: determine the state evaluation index based on the USMEEG signal; the state evaluation index characterizes the comparison relationship between the sideband components and the carrier components; and determine the modulation state type of the USMEEG signal based on the state evaluation index. The pulse repetition frequency decoding module 602 is specifically used to: perform preprocessing, robust decoding and signal reconstruction operations on the USMEEG signal based on the modulation state type, to obtain the brain power signal corresponding to the USMEEG signal.
[0112] In one possible implementation, the modulation state evaluation module 701 is specifically used for: Based on the USMEEG signal, obtain the frequency domain representation information corresponding to the USMEEG signal; Based on the frequency domain representation information, the sideband component information and carrier component information of the USMEEG signal are obtained; The state assessment index is obtained based on the sideband component information and the carrier component information.
[0113] In one possible implementation, the modulation state evaluation module 701 is specifically used for: Based on the frequency domain representation information, the DC component and amplitude component of the USMEEG signal are obtained; the amplitude component includes first amplitude component information and second amplitude component information; the first amplitude component information represents the amplitude represented by the cosine signal of the high-frequency carrier; the second amplitude component information represents the amplitude represented by the cosine signal of each characteristic frequency component sub-signal contained in the modulation signal. Based on the DC component and the first amplitude component information, determine the carrier component information of the USMEEG signal; The second amplitude component information is summed to obtain the third amplitude component information; Based on the third amplitude component information and the first amplitude component information, the sideband component information of the USMEEG signal is determined.
[0114] In one possible implementation, the modulation state types include moderate modulation type and abnormal modulation type; the modulation state evaluation module 701 is specifically used for: If the state evaluation index is greater than the first threshold and less than or equal to the second threshold, then the modulation state type of the USMEEG signal is moderate modulation type; wherein, the second threshold is greater than the first threshold. If the state evaluation index is less than or equal to the first threshold, or greater than the second threshold, then the modulation state type of the USMEEG signal is an abnormal modulation type.
[0115] In one possible implementation, the pulse repetition frequency decoding module 602 is specifically used for: If the modulation state type is an abnormal modulation type, the USMEEG signal is subjected to a first preprocessing operation, a robust decoding operation, and a first signal reconstruction operation to obtain the brain power signal corresponding to the USMEEG signal; the first signal reconstruction operation is to reconstruct the signal waveform based on the instantaneous complex response amplitude; the first preprocessing operation includes a bandpass filtering operation and a frame windowing operation.
[0116] In one possible implementation, the pulse repetition frequency decoding module 602 is further used for: If the modulation state type is moderate modulation, then the USMEEG signal is subjected to a second preprocessing operation, an envelope decoding operation, and a second signal reconstruction operation to obtain the brain power signal corresponding to the USMEEG signal; the envelope decoding operation includes extracting the carrier amplitude envelope of the USMEEG signal; the second preprocessing operation includes a bandpass filtering operation; the second signal reconstruction operation is to reconstruct the signal waveform based on the signal envelope.
[0117] This application also provides a computer-readable storage medium storing computer-executable instructions for implementing the ultrasound-modulated electroencephalogram (EEG) signal decoding method of any embodiment of this application.
[0118] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the ultrasound-modulated electroencephalogram (EEG) signal decoding method described in the above embodiments. The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.
[0119] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.
Claims
1. A method for decoding ultrasound-modulated electroencephalogram (EEG) signals, characterized in that, A decoding system for ultrasound-modulated electroencephalogram (EEG) signals; the ultrasound-modulated EEG signal decoding system includes an ultrasound-modulated EEG signal acquisition module and a pulse repetition frequency (PRF) decoding module; the ultrasound-modulated EEG signal acquisition module includes a host computer control subsystem, a phased array transducer, and EEG acquisition electrodes; the host computer control subsystem is used to control the phased array transducer to emit focused ultrasound with an ultrasound pulse repetition frequency (PRF) towards a specific target area, and to control the synchronous acquisition of scalp EEG signals of the specific target area through the EEG acquisition electrodes; The pulse repetition frequency decoding module is used to obtain the brain power signal corresponding to the ultrasound-modulated electroencephalogram (USMEEG) signal; the method includes: The scalp electroencephalogram (EEG) signals of the specific target area were collected to obtain the USMEEG signal; The USMEEG signal is preprocessed, robustly decoded, and reconstructed to obtain the brain power signal corresponding to the USMEEG signal. The robustly decoded operation includes extracting the instantaneous complex response amplitude of each frame at the PRF after the framing and windowing operation.
2. The method according to claim 1, characterized in that, The decoding system for ultrasound-modulated electroencephalogram (USMEEG) signals further includes a modulation state assessment module; the modulation state assessment module is used to obtain the modulation state type of the USMEEG signal; before performing preprocessing, robust decoding, and signal reconstruction operations on the USMEEG signal to obtain the corresponding brain power signal, the method further includes: Based on the USMEEG signal, a state evaluation index is determined; the state evaluation index characterizes the comparison relationship between the sideband component and the carrier component. Based on the state evaluation index, determine the modulation state type of the USMEEG signal; The process of preprocessing, robust decoding, and signal reconstruction of the USMEEG signal to obtain the corresponding brain power signal includes: Based on the modulation state type, the USMEEG signal is preprocessed, robustly decoded, and reconstructed to obtain the brain power signal corresponding to the USMEEG signal.
3. The method according to claim 2, characterized in that, The step of determining the state assessment index based on the USMEEG signal includes: Based on the USMEEG signal, the frequency domain representation information corresponding to the USMEEG signal is obtained; Based on the frequency domain representation information, the sideband component information and carrier component information of the USMEEG signal are obtained; The state evaluation index is obtained based on the sideband component information and the carrier component information.
4. The method according to claim 3, characterized in that, The step of obtaining the sideband component information and carrier component information of the USMEEG signal based on the frequency domain representation information includes: Based on the frequency domain representation information, the DC component and amplitude component of the USMEEG signal are obtained; the amplitude component includes first amplitude component information and second amplitude component information; the first amplitude component information represents the amplitude represented by the cosine signal of the high-frequency carrier; the second amplitude component information represents the amplitude represented by the cosine signal of the sub-signals of each characteristic frequency component contained in the modulation signal. Based on the DC component and the first amplitude component information, the carrier component information of the USMEEG signal is determined; The second amplitude component information is summed to obtain the third amplitude component information; The sideband component information of the USMEEG signal is determined based on the third amplitude component information and the first amplitude component information.
5. The method according to claim 2, characterized in that, The modulation state type includes moderate modulation type and abnormal modulation type; determining the modulation state type of the USMEEG signal based on the state evaluation index includes: If the state evaluation index is greater than the first threshold and less than or equal to the second threshold, then the modulation state type of the USMEEG signal is the moderate modulation type; wherein, the second threshold is greater than the first threshold; If the state evaluation index is less than or equal to the first threshold, or greater than the second threshold, then the modulation state type of the USMEEG signal is the abnormal modulation type.
6. The method according to claim 5, characterized in that, The process of performing frame-segmentation and windowing, robust decoding, and signal reconstruction on the USMEEG signal based on the modulation state type to obtain the brain power signal corresponding to the USMEEG signal includes: If the modulation state type is the abnormal modulation type, then the USMEEG signal is subjected to a first preprocessing operation, a robust decoding operation, and a first signal reconstruction operation to obtain the brain power signal corresponding to the USMEEG signal; the first signal reconstruction operation is to reconstruct the signal waveform based on the instantaneous complex response amplitude; the first preprocessing operation includes a bandpass filtering operation and a frame windowing operation.
7. The method according to claim 5, characterized in that, The step of performing frame-segmentation and windowing operations, robust decoding operations, and signal reconstruction operations on the USMEEG signal based on the modulation state type to obtain the brain power signal corresponding to the USMEEG signal further includes: If the modulation state type is the moderate modulation type, then the USMEEG signal is subjected to a second preprocessing operation, an envelope decoding operation, and a second signal reconstruction operation to obtain the brain power signal corresponding to the USMEEG signal; the envelope decoding operation includes extracting the carrier amplitude envelope of the USMEEG signal; the second preprocessing operation includes a bandpass filtering operation; the second signal reconstruction operation is to reconstruct the signal waveform based on the signal envelope.
8. A decoding system for ultrasound-modulated electroencephalogram (EEG) signals, characterized in that, The system includes: The ultrasound-modulated electroencephalogram (EEG) signal acquisition module includes a host computer control subsystem, a phased array transducer, and EEG acquisition electrodes. The host computer control subsystem is used to control the phased array transducer to emit focused ultrasound with an ultrasound pulse repetition frequency (PRF) towards a specific target area, and to control the synchronous acquisition of scalp EEG signals of the specific target area through the EEG acquisition electrodes to obtain USMEEG signals. The pulse repetition frequency decoding module is used to: perform preprocessing, robust decoding and signal reconstruction operations on the USMEEG signal to obtain the brain power signal corresponding to the USMEEG signal; the robust decoding operation includes extracting the instantaneous complex response amplitude of each frame at the PRF obtained after the framing and windowing operation.
9. The system according to claim 8, characterized in that, The system further includes a modulation state evaluation module; the modulation state evaluation module is used to: determine a state evaluation index based on the USMEEG signal; the state evaluation index characterizes the comparison relationship between sideband components and carrier components; and determine the modulation state type of the USMEEG signal based on the state evaluation index. The pulse repetition frequency decoding module is specifically used to: perform preprocessing, robust decoding and signal reconstruction operations on the USMEEG signal based on the modulation state type to obtain the brain power signal corresponding to the USMEEG signal.
10. The system according to claim 9, characterized in that, The modulation state evaluation module is specifically used for: Based on the USMEEG signal, the frequency domain representation information corresponding to the USMEEG signal is obtained; Based on the frequency domain representation information, the sideband component information and carrier component information of the USMEEG signal are obtained; The state evaluation index is obtained based on the sideband component information and the carrier component information.
11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 7.
12. A computer program product, characterized in that, It includes computer instructions stored in a computer-readable storage medium; when a processor of a computer device reads the computer instructions from the computer-readable storage medium, the processor executes the computer instructions, causing the computer device to perform the steps of the method according to any one of claims 1 to 7.