Systems and methods for simultaneously performing time-interference electrical stimulation and recording neuronal peak potentials

Abstract

A system for simultaneously performing temporal intervention (TI) electrical stimulation and recording neuronal peak potentials is disclosed. The system includes a signal acquisition device that acquires neurophysiological signals from the target region in parallel via multiple channels and converts them into digital neurophysiological signals. A signal processing device performs multiple filtering, baseline drift correction, and noise reduction on the neurophysiological signals before extracting neuronal peak potential activity features to detect and classify the neuronal peak potential signals. The system then outputs the neuronal peak potential signals and their classification and pattern recognition results. This system addresses the problem of mutual interference between electrical signals during real-time recording of neuronal peak potential signals during TI electrical stimulation, thereby providing dose-response assessment for TI electrical stimulation and helping users better study the effects of TI electrical stimulation on neurons in specific neural regions.

Description

Technical Field

[0001] This application belongs to the field of neurophysiological signal acquisition and processing technology, specifically relating to a system and method for simultaneously performing time-interference electrical stimulation and recording neuronal peak potentials. Background Technology

[0002] Temporal interference (TI) electrical stimulation, due to its non-invasiveness and selectivity, has significant advantages in deep brain stimulation and is becoming a research hotspot in the field of neurological disease treatment. TI electrical stimulation generates low-frequency envelope signals in the target area by applying two or more high-frequency alternating electric fields, utilizing their nonlinear superposition effect to induce neural excitation or inhibition in specific brain regions. It has advantages such as good spatial focusing and non-invasiveness, and its clinical translation potential is high, especially in the fields of neurological / psychiatric diseases, brain dysfunction, and neurorehabilitation.

[0003] Real-time electrophysiological recordings of neuronal spikes directly reflect the transient regulatory effect of temporal stimulation (TI) on neurons and are the gold standard for assessing the dose-response relationship of TI stimulation. However, both the TI stimulation signal generated by the time-interference electric field and the neuronal spike signal generated by nerve cells are electrical signals, and the intensity of the TI stimulation signal is much higher than the amplitude of the neuronal signal. There is a high degree of overlap between the two in both time and frequency dimensions, which seriously interferes with the real-time acquisition and analysis of neuronal spike signals and hinders the conduct of TI stimulation experiments.

[0004] Therefore, there is an urgent need for a system that can effectively distinguish, detect, and record neuronal peak potentials while performing time-interference electrical stimulation to solve the above problems. Summary of the Invention

[0005] To address the problems existing in the prior art, the purpose of this application is to provide a system for simultaneously implementing time-interference electrical stimulation (TI) and recording neuronal peak potentials. This system generates a stimulating current through a time-interference stimulation device and applies TI electrical stimulation to the target neural region. A high-temporal-resolution, interference-resistant signal acquisition device is used to acquire the neurophysiological signals of the target neural region. Then, a signal processing device is used to process and analyze the neurophysiological signals to obtain the neuronal peak potentials. Thus, this application achieves a solution that provides both TI stimulation and stimulation verification, thereby providing precise data support for the research and clinical translation of TI-based neural regulation mechanisms, which is conducive to the long-term development of this technology.

[0006] Specifically, this application relates to the following aspects:

[0007] According to one aspect of this application, a system for simultaneously performing time-interference electrical stimulation and recording neuronal peak potentials is provided, comprising: a time-interference stimulation device configured to generate multiple high-frequency sinusoidal signals, convert the multiple high-frequency sinusoidal signals into stimulation currents, and deliver the stimulation currents to a target region to form a time-interference electrical stimulation signal; a signal acquisition device configured to acquire neurophysiological signals from the target region in parallel via multiple channels, and perform analog-to-digital conversion on the neurophysiological signals to obtain digital neurophysiological signals; and a signal processing device configured to, after acquiring the neurophysiological signals by the signal acquisition device, perform preprocessing on the neurophysiological signals including filtering and baseline drift correction; extract neural electrical activity features from the digital neurophysiological signals after denoising, to detect and classify neuronal peak potential signals in the digital neurophysiological signals; and output the neuronal peak potential signals and their classification results; wherein the number of multiple high-frequency sinusoidal signals is 2, 4, or 8.

[0008] According to some embodiments of this application, the signal acquisition device includes: a multi-channel instrumentation amplifier configured to perform preliminary amplification and common-mode suppression of neurophysiological signals in parallel through multiple channels, with a maximum output amplitude of ±5000mV; and an analog-to-digital converter configured to convert the neurophysiological signals preprocessed by the signal processing device into neurophysiological digital signals in parallel through multiple channels, with a maximum sampling frequency of not less than 250kHz and an amplitude resolution of not less than 24 bits. According to some embodiments of this application, the signal acquisition device further includes a first notch filter, which includes: capacitors C1' and C2', resistors R1', R2', R3, R4, R5, R6, and R7, and operational amplifiers U1' and U2; the neurophysiological signal passes through C1' and R1' in parallel and is then connected to the positive input terminals of R3, R2', and U2 respectively, and the other end of R2' is connected to the output terminal of U1'; the output terminal of U1' passes through C2 and is connected to the inverting input terminals of R4 and U2 respectively; the inverting input terminal of U1' is connected to the inverting input terminal of U2 through C2' and R4, and the positive input terminal of U1' is connected to the midpoint of R6; the output terminal of U2 is connected to the input terminal of the neurophysiological signal through R5, R6, and R7.

[0009] According to some embodiments of this application, the signal acquisition device further includes a bandpass filter, which includes: capacitors C1”, C2”, C3, C4; resistors R1”, R2”, R3’, R4’; and operational amplifiers U3 and U4; C3 and C4 are connected in series, one end of which is connected to a neurophysiological signal, and the other end is connected to the non-inverting input terminal of U3; one end of R4 is connected to a reference voltage V. refR3' is connected to the non-inverting input of U3 at one end; R3' is connected between C3 and C4 at one end and to the output and inverting input of U3 at the other end; R1" and R2" are connected in series, with one end connected to the output of U3 and the other end connected to the inverting input of U4 at the other end; C2" is grounded and connected to the inverting input of U4 at the other end; C1" is connected between R1" and R2" at one end and to the output and non-inverting input of U4 at the other end.

[0010] According to some embodiments of this application, the signal processing device includes a hardware processing unit comprising: a second notch filter for suppressing multiple high-frequency sinusoidal signals; and a baseline drift correction circuit for filtering out background drift in neurophysiological signals.

[0011] According to some embodiments of this application, the signal processing device further includes a software processing unit configured to execute embedded program code instructions to perform the following functions: removing noise and artifacts from the neurophysiological digital signal; extracting neural electrical activity features from the neurophysiological digital signal; identifying neuronal peak potential signals from the neurophysiological digital signal; and determining the classification result of the neuronal peak potential signal based on the neuronal peak potential signal and the neural electrical activity features.

[0012] According to some embodiments of this application, the characteristics of neural electrical activity include: the peak value of the neuronal peak potential, the duration of the neuronal peak potential, and the amplitude of the neuronal peak potential.

[0013] According to some embodiments of this application, the time-interference stimulation device includes: an MCU control unit configured to set and adjust time interference parameters for each of a plurality of high-frequency sinusoidal signals, the time interference parameters including frequency, amplitude, and phase; a signal generator configured to generate a plurality of high-frequency sinusoidal signals based on the interference parameters set by the MCU control unit; a high-pass filter circuit configured to suppress DC components and interference in the plurality of high-frequency sinusoidal signals; and a constant current source circuit configured to convert the plurality of high-frequency sinusoidal signals into a plurality of stimulation currents.

[0014] According to some embodiments of this application, the system for simultaneously performing time-interference electrical stimulation and neuronal peak potential recording further includes: an analysis device configured to group neuronal activity patterns to distinguish neuronal populations and their response patterns based on neuronal peak potential signals output by a signal processing device, classification results of neuronal peak potential signals, and / or neural electrical activity characteristics.

[0015] According to another aspect of this application, a method for recording neuronal peak potentials during time-interference electrical stimulation is provided, comprising: setting parameters of two high-frequency sinusoidal current signals required for time-interference stimulation according to the needs of the target neural modulation frequency band, the parameters including frequency, phase, and amplitude; and determining the frequency difference between the two high-frequency sinusoidal current signals to match the target neural modulation rhythm; the MCU control unit receiving the set parameters and controlling a signal generator to output two high-frequency sinusoidal current signals to the target head to form an interference envelope waveform Δf in the target neural region; acquiring the raw signal of neural electrical activity in the target neural region using a signal acquisition device; performing preliminary amplification and common-mode interference suppression on the raw signal using a multi-channel instrumentation amplifier; suppressing power frequency interference and screening neural electrical activity frequency bands using a notch filter and a bandpass filter, respectively; eliminating background drift using a baseline drift correction circuit, and finally obtaining a neural electrophysiological signal; converting the neural electrophysiological signal into a digital signal using an analog-to-digital converter; identifying the neuronal peak potential signal in the digital signal and determining the classification result of the neuronal peak potential signal using a peak detection algorithm; and outputting the neuronal peak potential signal and its classification result.

[0016] The system and method for simultaneously performing time-interference electrical stimulation (TI) and recording neuronal peak potentials provided in this application can simultaneously perform TI electrical stimulation and acquire neurophysiological signals, avoiding mutual interference between the two. By acquiring, recording, and processing neuronal peak potentials, researchers and medical personnel can monitor the intervention effect of TI electrical stimulation on the target neural region in real time, thereby providing a feasible solution for optimizing TI electrical stimulation parameters and improving the effect of target stimulation. Attached Figure Description

[0017] Figure 1 The figure shows a schematic diagram of the system structure according to the implementation scheme of this application.

[0018] Figure 2 The figure shows a schematic diagram of the structure of a time-interference stimulation device according to an embodiment of this application.

[0019] Figure 3 The diagram illustrates a functional block diagram of a signal acquisition device and a hardware processing unit used in conjunction with an embodiment of this application.

[0020] Figure 4 The figure shows a functional block diagram of the software processing unit according to the embodiment of this application.

[0021] Figure 5A The diagram illustrates a circuit of a first notch filter according to an embodiment of this application.

[0022] Figure 5B The diagram illustrates a circuit diagram of a bandpass filter according to an embodiment of this application.

[0023] Figure 6 The diagram illustrates a flowchart of a method according to an embodiment of this application.

[0024] Figure 7 The diagram illustrates the analysis of the system classification results according to the implementation scheme of this application. Detailed Implementation

[0025] The present application is further illustrated below with reference to embodiments. It should be understood that the embodiments are only used to further illustrate and explain the present application and are not intended to limit the present application.

[0026] Unless otherwise defined, technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art. While similar or identical methods and materials may be applied in experimental or practical applications, materials and methods are described herein. In case of conflict, the definitions included herein shall prevail. Furthermore, materials, methods, and examples are for illustrative purposes only and are not intended to be limiting. The present application is further described below with reference to specific embodiments, but is not intended to limit the scope of the application.

[0027] Exemplary System

[0028] Figure 1 The figure shows a schematic block diagram of a system for simultaneously performing time-interference electrical stimulation and recording neuronal peak potentials according to an embodiment of this application.

[0029] like Figure 1 As shown, the system for synchronously implementing time-interference electrical stimulation and recording neuronal peak potentials according to an embodiment of this application includes the following modules.

[0030] A time-interference stimulation device is used to output two or more high-frequency sinusoidal current signals to a target head. Taking two high-frequency sinusoidal current signals as an example, these two signals have a certain frequency difference Δf. After being output to the scalp surface of the target head (e.g., an animal's head), a time-interference electrical stimulation signal can be formed in the target neural region based on the characteristics of time-interference electrical stimulation. This signal has a low frequency Δf and can modulate the neural electrical activity in the deep brain regions of the target brain. The time-interference stimulation device consists of several parts, such as... Figure 2 As shown, it first includes an MCU control unit, which is used to receive parameter inputs from the user. These parameter inputs come from the input signals provided by the user, including the frequency, amplitude, and phase of multiple high-frequency sinusoidal current signals applied to the target, and the frequency, amplitude, and phase of the time interference electrical stimulation signal generated by the interference. The MCU control unit that receives the input signals sends control commands to the signal generator to make it generate a specific high-frequency sinusoidal current signal.

[0031] The MCU control unit has an independent user interface to receive user input signals, which can take the form of keypad input, rotary encoder input, knob input, numeric keypad input, etc. The MCU control unit also has a display screen that can display the parameter values ​​of the aforementioned multiple high-frequency sinusoidal current signals in real time, allowing users to view and adjust them at any time. It can be understood that the parameters of the time-interference electrical stimulation signal are adjusted by the two or more high-frequency sinusoidal current signals that generate it. For example, changing the frequency or phase of at least one of the two high-frequency sinusoidal current signals will change the frequency Δf and phase of the time-interference electrical stimulation signal. Therefore, the MCU control unit can also calculate and display the frequency, amplitude, and phase values ​​of the time-interference electrical stimulation signal itself in real time based on the relationship between the high-frequency stimulation current and the interference stimulation signal.

[0032] Continue to refer to Figure 2 The signal generator, under the instruction of the MCU control unit, generates multiple high-frequency sinusoidal current signals. Before output, these signals pass through a high-pass filter circuit to remove DC components and low-frequency interference. The signals then pass through a constant current source circuit, where they are processed into stable, constant high-frequency stimulation currents with high stability and low noise characteristics. Each channel ultimately outputs a high-frequency stimulation current to a time-interference electrode electrically connected to the constant current source circuit, which is then applied to a specific target neural region in the experimental animal (e.g., a rat). The time-interference electrode can be a differential pair electrode, a multi-channel array electrode, etc., all of which can be used to generate an interference electric field based on multiple high-frequency stimulation currents, thereby producing a low-frequency envelope as a time-interference electrical stimulation signal in the target neural region.

[0033] An example of the structure of a high-pass filter circuit is as follows: Figure 2 As shown, each of the multiple high-frequency sinusoidal current signals is used as the input V. IN After connecting the series-connected filter capacitors C1 and C2, and the reference voltage V provided through resistor R1 ref The voltage is supplied to the positive input of operational amplifier U1 (model TLV9062IDR); the output of U1 is connected to the inverting input, and it is also connected to the midpoint of C1 and C2 through resistor R2 to form a voltage follower and amplification. Specifically, the capacitance values ​​of C1 and C2 are each 9.8nF to allow the high-frequency component of the high-frequency sinusoidal current signal to pass through and effectively attenuate the low-frequency component. Finally, an output voltage V with a frequency range of 200Hz–20kHz is obtained using a time-interference stimulation device. OUTThe maximum value is ±120V, the maximum output current is ±12mA, and the inter-channel frequency difference Δf is 0.01Hz-500Hz. The low-frequency envelope formed by the time interference electric field generated by these signals has a frequency of 0.01Hz-500Hz, which can match the time window of neuronal membrane potential depolarization to regulate its electrical activity.

[0034] That is, the system for simultaneously performing time-interference electrical stimulation and recording neuronal peak potentials according to the implementation scheme of this application includes: a time-interference stimulation device configured to generate multiple high-frequency sinusoidal signals, convert the multiple high-frequency sinusoidal signals into stimulation currents, and deliver the stimulation currents to the target region to form a time-interference electrical stimulation signal.

[0035] Furthermore, the time-interference stimulation device includes: an MCU control unit configured to set and adjust the time interference parameters of each of the plurality of high-frequency sinusoidal signals, the time interference parameters including frequency, amplitude, and phase; a signal generator configured to generate the plurality of high-frequency sinusoidal signals based on the interference parameters set by the MCU control unit; a high-pass filter circuit configured to suppress DC components and interference in the plurality of high-frequency sinusoidal signals; and a constant current source circuit configured to convert the plurality of high-frequency sinusoidal signals into a plurality of stimulation currents; the number of the plurality of high-frequency sinusoidal signals is 2, 4, or 8.

[0036] The signal acquisition device is used to acquire multiple neuronal electrical signals during the stimulation current provided by the time-interference stimulation device. Specifically, the multiple electrical signals of the target neural region are acquired by a multi-channel instrumentation amplifier. The multi-channel instrumentation amplifier has electrodes that provide multiple acquisition channels. Each electrode provides multiple acquisition channels for neurophysiological signals and is located at different positions in the target neural region. This redundancy ensures that at least some channels can acquire the required signals, thus avoiding the problem of not being able to acquire neurophysiological signals using a single-channel instrumentation amplifier. In addition, the multi-channel instrumentation amplifier used also needs to have low noise characteristics to avoid introducing additional noise into the neuronal electrical signals.

[0037] More specifically, a multi-channel instrumentation amplifier is an amplifier capable of simultaneously processing multiple input signals. It can be a single chip integrating multiple relatively independent signal amplification channels, or a combination of multiple single-channel signal amplifiers. In this application, multi-channel instrumentation amplifiers with low input voltage and current noise and low distortion, such as the AD8421 and AD8422, can be selected to perform preliminary amplification and common-mode interference suppression on weak neuronal electrical signals. The maximum output amplitude of the multi-channel instrumentation amplifier reaches ±5000mV, effectively amplifying neuronal electrical signals that were originally only at the 0.3μV level. It can also suppress common-mode signals, such as high-frequency stimulation currents coupled to the electrodes in the signal amplification channels or low-frequency noise at the electrode-tissue interface; these interferences will be further filtered out subsequently. The neurophysiological signals acquired from each channel will be processed and recorded separately for independent analysis or signal optimization.

[0038] Then, the signal amplified by the multi-channel instrumentation amplifier passes through a first notch filter and a bandpass filter to filter out noise in the power frequency band and the effective frequency band of non-neuronal electrical signals, respectively. Figure 5A As shown, the circuit of the first notch filter consists of two operational amplifiers U1' and U2, resistors R1', R2', R3, R4, R5, R6, and R7, capacitors C1' and C2', and a resistor network for feedback and bias. R6 is an adjustable resistor. The input neurophysiological signal is V. IN The filtered output neurophysiological signal is represented as V. OUT '. C1' is connected in series with V IN Between node A and node B, node A is simultaneously connected to the non-inverting input of U2, one end of R1', and one end of R2'. The other end of R1' is connected to node B, and the other end of R2' is connected to the inverting input of U1' through capacitor C2'. Node B is connected to one end of R3, and the other end of R3 is grounded. R1 and R3 form a pre-stage voltage divider network. OUT Connect one end of R5 and one end of R4 simultaneously; connect the other end of R5 to one end of adjustable R6, and connect the other end of R4 to the output terminal of U1' and one end of C2'. Connect the other end of R6 to one end of R7, and ground the other end of R7. R5, R6, and R7 form a voltage divider network to extract V. OUT The voltage divider signal is connected to the non-inverting input of U1'. The inverting input of U1' is connected to node A, the non-inverting input is connected to the adjustable resistor R6, and the output is connected to one end of C2' and one end of R4. The non-inverting input of U2 is connected to node A, the inverting input is connected to the other end of C2', and the output is the required V. OUT In this way, the RC filter circuit composed of C1', R1' and C2', R4 effectively isolates the output of the power frequency signal, and ensures the normal output of the neurophysiological signal through the feedback network.

[0039] Then, refer to Figure 5B The bandpass filter circuit consists of two operational amplifiers U3 and U4, several resistors R1”, R2”, R3', R4', and capacitors C1”, C2”, C3, C4, and mainly includes a high-pass filter section and a low-pass filter section. The input signal V... IN (i.e., the V of the aforementioned notch filter) OUT The high-frequency signal passes through the filter front end via capacitors C4 and C3, while the DC and low-frequency signals are isolated by these two capacitors. The signal then reaches the non-inverting input of the first operational amplifier U3. R4' converts the reference voltage V... ref R3" is also provided to the positive input terminal of U3. R3" is connected between the output terminal and the inverting input terminal of the op-amp. The high-pass filtered signal is amplified and output from U3, then divided by R1" and R2" before being sent to the inverting input terminal of the second op-amp U4. U4 also includes a filter C2" grounded at its input terminal for stable filtering in the mid-frequency band. Thus, U4 constitutes an active low-pass filter. Its feedback path is connected to C1", and the cutoff frequency is set through R2' and C2" to ultimately amplify the output signal V. OUT "The next component provided to the signal acquisition equipment."

[0040] In this way, interference signals in the power frequency band, high-frequency interference signals above 6kHz, and low-frequency interference signals below 300Hz are effectively filtered out. Combined with the common-mode suppression of the original signal by the previous multi-channel instrumentation amplifier, the neuronal electrical signals are effectively acquired, and their quality is improved through preprocessing. The signal acquisition equipment also includes an analog-to-digital converter (ADC) to convert the aforementioned amplified, filtered, and other preprocessed neuronal electrical signals into analyzable digital signals. This ADC also supports multi-channel synchronous sampling, such as using a TI ADS1274 that supports 4-channel or 8-channel synchronous sampling, or a multi-channel isolated ADC, to ensure high-precision, high-time-resolution recording of neuronal peak potential signals. The maximum sampling frequency of this ADC is configured to be no less than 250kHz, and the amplitude resolution no less than 24 bits to ensure accurate recording of neuronal electrical signals for analysis.

[0041] That is, the system for synchronously performing time-interference electrical stimulation and recording neuronal peak potentials according to the implementation scheme of this application includes: a signal acquisition device configured to acquire neurophysiological signals of the target region in parallel through multiple channels, and to perform analog-to-digital conversion on the neurophysiological signals to obtain neurophysiological digital signals.

[0042] Furthermore, the signal acquisition device includes: a multi-channel instrumentation amplifier configured to perform preliminary amplification and common-mode suppression of the neurophysiological signal in parallel across multiple channels, with a maximum output amplitude of ±5000mV; and an analog-to-digital converter configured to convert the pre-processed neurophysiological signal from the signal processing device into a multi-channel parallel digital neurophysiological signal, with a maximum sampling frequency of not less than 250kHz and a replication resolution of not less than 24 bits. The signal acquisition device further includes a first notch filter, which comprises: capacitors C1' and C2', resistors R1', R2', R3, R4, R5, R6, and R7, and operational amplifiers U1' and U2; the neurophysiological signal is connected to the positive input terminals of R3, R2', and U2 respectively after passing through C1' and R1' in parallel, and the other end of R2' is connected to the output terminal of U1'; the output terminal of U1' is connected to the inverting input terminals of R4 and U2 respectively through C2; the inverting input terminal of U1' is connected to the inverting input terminal of U2 through C2' and R4, and the positive input terminal of U1' is connected to the midpoint of R6; the output terminal of U2 is connected to the input terminal of the neurophysiological signal through R5, R6, and R7.

[0043] Furthermore, the signal acquisition device further includes a first bandpass filter, which includes: capacitors C1, C2, C3, and C4; resistors R1, R2, R3, and R4; and operational amplifiers U3 and U4. C3 and C4 are connected in series, with one end connected to the neurophysiological signal and the other end connected to the non-inverting input of U3. One end of R4 is connected to the reference voltage VREF and the other end is connected to the non-inverting input of U3. One end of R3 is connected between C3 and C4 and the other end is connected to the output and inverting input of U3. R1 and R2 are connected in series, with one end connected to the output of U3 and the other end connected to the inverting input of U4. C2 is grounded and connected to the inverting input of U4. One end of C1 is connected between R1 and R2 and the other end is connected to the output and non-inverting input of U4.

[0044] The signal processing device includes a hardware processing unit and a software processing unit, which process the obtained neuronal electrical signals through hardware circuits and software algorithms, respectively. The hardware processing unit also includes a notch filter circuit and a bandpass filter circuit. The former is no longer used to suppress power frequency noise, but rather to suppress the TI electrical stimulation carrier frequency in real time, such as the high-frequency components f1 and f2 at the thousands of Hz level output from the electrodes by two high-frequency sinusoidal signals. This means that after the stimulation current signal has been initially filtered by the signal acquisition device, its corresponding digital signal is filtered again. The latter is used to retain the effective frequency band of the neuronal peak potential signal. In this application, this effective frequency band is set to 300Hz-6kHz to ensure that the peak potential is within this band. The hardware processing unit also includes a baseline drift correction circuit to filter out low-frequency background drift during signal transmission, improving signal stability and accuracy. Figure 3 The diagram illustrates the function of signal acquisition equipment in conjunction with hardware processing units to process and convert neurophysiological signals.

[0045] refer to Figure 4 The digital signal, after being processed again by the aforementioned circuit, enters the software processing unit to perform neural signal recognition and classification. First, a denoising algorithm removes noise and artifacts from the signal, and an extraction algorithm extracts key features of neural electrical activity, such as the peak value, duration, and amplitude of neuronal peak potentials. This step provides the necessary neural electrical activity features for subsequent classification. Then, a peak detection algorithm is applied to identify neuronal peak potential signals, which represent neuronal activation events. Finally, a machine learning algorithm is used to classify the detected neuronal peak potential signals, i.e., to identify the firing waveform of each peak potential.

[0046] The denoising algorithms include existing algorithms for removing electromyographic noise and eye movement noise to denoise other electrical signals. Extraction algorithms include local extremum methods or interpolation methods to extract peak potential peak values, half-peak width methods or peak-to-peak duration methods to calculate peak potential duration and phase between peaks. Machine learning classification algorithms include supervised learning-based peak potential classification (traditional machine learning classifiers such as SVM and logistic regression or deep neural networks such as CNN and Transformer) and / or unsupervised learning-based peak potential clustering (k-nearest neighbors, hidden Markov models), etc.

[0047] In one example, peak detection includes two steps: setting a peak threshold and classifying the neurophysiological digital signal based on the peak threshold. Specifically, peak values ​​below the peak threshold are considered the desired peak potential value, and their phases are considered the peak potential positions. The system described in this application sets the peak threshold to -40 μV, and uses peak values ​​below -40 μV as peak potential values. The peak potential classification results obtained by the classification algorithm can be monophasic / biphasic peak potentials, regular / irregular discharge peak potentials, and / or excitatory / inhibitory peak potentials, etc. Obtaining these results through the software processing unit can help researchers initially determine the nature of the peak potentials to preliminarily judge the effect of TI stimulation, and can also be used by researchers as input information for further research.

[0048] That is, the system for synchronously implementing time-interference electrical stimulation and recording neuronal peak potentials according to the embodiments of this application includes: a signal processing device configured to, after the signal acquisition device acquires the neurophysiological signal, perform preprocessing on the neurophysiological signal including filtering and baseline drift correction; extract the neural electrical activity features from the neurophysiological digital signal after denoising, so as to detect and classify the neuronal peak potential signals in the neurophysiological digital signal; and output the neuronal peak potential signals and their classification results.

[0049] Furthermore, the signal processing device includes a hardware processing unit comprising: a second notch filter for suppressing the plurality of high-frequency sinusoidal signals; a bandpass filter for filtering the effective frequency band of the neuronal peak potential signal, wherein the effective frequency band is 300Hz-6kHz; and a baseline drift correction circuit for filtering out background drift in the neurophysiological signal. The signal processing device also includes a software processing unit configured to execute embedded program code instructions to perform the following functions: removing noise and artifacts from the neurophysiological digital signal; extracting the neural electrical activity features from the neurophysiological digital signal; identifying the neuronal peak potential signal from the neurophysiological digital signal; and determining the classification result of the neuronal peak potential signal based on the neuronal peak potential signal and the neural electrical activity features.

[0050] Furthermore, the characteristics of the neural electrical activity include: the peak value of the neuronal peak potential, the duration of the neuronal peak potential, and the amplitude of the neuronal peak potential.

[0051] Specifically, this application does not limit the components used for acquiring and recording neuronal peak potentials in a system for synchronous implementation of time-interference electrical stimulation (TI) and neuronal peak potential recording according to the embodiments of this application to include all the aforementioned components. For example, neurophysiological signals can be acquired by a multi-channel instrumentation amplifier, processed by a first notch filter and a bandpass filter, and then converted from analog to digital for direct input to the software processing unit of a signal processing device. However, further extracting the peak potential signal and suppressing high-frequency components by using a second notch filter, and removing drift before signal processing such as by using a baseline drift correction circuit, can significantly improve the quality of the peak potential signal, which is very important for the evaluation of TI stimulation. Therefore, this application preferably uses all the aforementioned components in sequence to acquire the peak potential signal, although this may introduce a slight delay, it effectively eliminates interference from TI stimulation.

[0052] According to the implementation scheme of this application, the system for synchronous implementation of time-interference electrical stimulation and neuronal peak potential recording is electrically connected to an analysis device after the signal processing device. The analysis device integrates software programs to acquire neural electrical activity characteristics extracted by the software processing unit, and combines the time series of neurophysiological digital signals to group the neuronal activity patterns of the target neural region at different times and output visualization results to distinguish the response patterns of different neuronal groups. This helps researchers establish the dose-response relationship of TI stimulation, optimize stimulation strategies, and provides data support, thereby facilitating the clinical application effect verification of TI deep brain stimulation.

[0053] That is, the system for synchronously implementing time-interference electrical stimulation and recording of neuronal peak potentials according to the embodiments of this application further includes: an analysis device configured to group neuronal activity patterns to distinguish neuronal populations and their response patterns based on the neuronal peak potential signals output by the signal processing device, the classification results of the neuronal peak potential signals, and / or the characteristics of the neural electrical activity.

[0054] It is understood that the software programs of the software processing unit and the analysis device can be embedded program products. The analysis device can also be an electronic device other than an embedded device, such as a computer, whose software program is a computer program product. These embedded program products and / or computer program products include embedded program instructions or computer program instructions, which are stored on a readable storage medium of the embedded device or computer device. When run by a processor, these instructions enable the processor to execute the denoising algorithm, extraction algorithm, peak detection algorithm, machine learning classification algorithm, and neuron activity pattern grouping steps of the software processing unit according to the embodiments of this application.

[0055] The embedded program product and / or computer program product described herein can be written with program code in any combination of one or more programming languages ​​to perform the operations described in the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java, C++, and C#, as well as conventional procedural programming languages ​​such as C, Python, or similar languages. The program code can be executed entirely on the software processing unit and the analysis device's software program, or as a standalone software package. The analysis device's software program can also be executed partially on the analysis device and partially on a remote computing device, or entirely on a remote computing device or server, communicating only with the analysis device to transmit information such as visualization results of neuronal activity pattern groups.

[0056] In one implementation scheme for studying changes in neuronal peak potentials in response to TI stimulation using the system described in this application, such as Figure 7 As shown, the neuronal response signals, including those at baseline and after TI electrical stimulation, were acquired, identified, and compared. This comparison included the comparison of peak potential signals after further processing on a computer device. The four columns from left to right in the figure are: neuronal peak potential signal, classified neuronal peak potential signal, temporal distribution of neuronal peak potential signal, and statistics of phase-locked values ​​of neuronal peak potentials. For the neuronal peak potential signal, the baseline shows the raw waveform of the neural signal without stimulation, with black spikes representing neural discharge events; TI electrical stimulation represents the neuronal peak potential signal waveform under TI electrical stimulation (highlighted in blue). It can be seen that neuronal peak potential signals are still generated under stimulation, but they may change and require further comparison.

[0057] Furthermore, after classifying the neuronal peak potential signals, the waveforms of all detected peak potentials were aligned and superimposed. The resulting thin gray line represents the single-shot waveform, and the thick black line represents the average waveform. Compared to the baseline, the shape of the single-shot neuronal peak potential signal under TI electrical stimulation remained generally similar, indicating that the neuronal peak potential signals identified by the system were not severely distorted by the stimulation signal, consistent with the expected results. Regarding the temporal distribution of the neuronal peak potential signals, each black dot represents a neuronal peak potential signal detected at a specific time point during a specific stimulation, with multiple black dots forming a similar dot plot. The blue waveform below represents the envelope of the TI modulation wave, showing the modulation frequency of the stimulation signal. It can be preliminarily concluded that during TI electrical stimulation, the appearance of the neuronal peak potential signal is significantly more consistent with the specific phase of the TI modulation wave than with the baseline signal, that is, there is a phase-locking phenomenon in the neuronal peak potential signal. Further extraction of the phase-locking value statistics of the peak potential and comparison of the phase-locking value distribution under the baseline and TI electrical stimulation conditions revealed that the phase-locking value under the TI condition was significantly higher than that under the baseline, indicating that TI electrical stimulation may make the neural discharge more synchronized or modulated. This difference is statistically significant (marked by a horizontal line, p<0.001).

[0058] Thus, the system for simultaneously implementing time-interference electrical stimulation (TI) and neuronal peak potential recording according to the above-described implementation schemes can solve the mutual interference problem in existing TI stimulation schemes when simultaneously acquiring TI electrical stimulation signals and neuronal peak potential signals, especially the interference of the former on the identification of the latter. By constructing signal acquisition and analysis equipment with high input amplitude range, high sampling rate, and low noise characteristics, the interference of TI electrical stimulation artifacts on weak neuronal peak potential signals is effectively suppressed, achieving high-precision identification and recording of microvolt-level neuronal peak potential signals even against a background of hundreds of millivolt electrical stimulation artifacts. Therefore,

[0059] Therefore, in summary, the system provided in this application offers researchers and medical professionals a high-precision, stable, and easy-to-use technical solution for evaluating the effects of TI electrical stimulation on brain function rehabilitation and for studying the neuromodulation mechanisms during the stimulation process.

[0060] Exemplary methods

[0061] Figure 6 The illustration shows a flowchart of a method for recording neuronal peak potentials during time-interference electrical stimulation according to an embodiment of this application.

[0062] like Figure 6 As shown, the method for recording neuronal peak potentials during time-interference electrical stimulation according to an embodiment of this application includes the following steps.

[0063] Step S110: Based on the requirements of the target neural modulation frequency band, set the parameters of the two high-frequency sinusoidal current signals required for time interference stimulation, the parameters including frequency, phase and amplitude; and determine the frequency difference between the two high-frequency sinusoidal current signals to match the target neural modulation rhythm.

[0064] In step S120, the MCU control unit receives the set parameters and controls the signal generator to output two high-frequency sinusoidal current signals to the target head to form an interference envelope waveform Δf in the target nerve region.

[0065] Step S130: The raw signal of neural electrical activity is acquired using a multi-channel instrumentation amplifier and initially amplified and suppressed for common-mode interference; power frequency interference is suppressed and neural electrical activity frequency bands are screened using notch filters and bandpass filters, respectively; and background drift is eliminated using a baseline drift correction circuit to finally obtain the neural electrophysiological signal.

[0066] Step S140: The neurophysiological signal is converted into a digital signal using an analog-to-digital converter; a peak detection algorithm is used to identify the neuronal peak potential signal in the digital signal and determine the classification result of the neuronal peak potential signal; and the neuronal peak potential signal and its classification result are output.

[0067] Specifically, in step S130, the notch filter includes a first notch filter for power frequency filtering and a second notch filter for peak potential filtering. It can be considered that the second notch filter provides bandpass filtering for the peak potential frequency band signal. The original signal is filtered multiple times by passing through the first notch filter, the bandpass filter, the second notch filter and the baseline drift correction circuit, and then enters the analog-to-digital converter.

[0068] As can be seen from the above, the method for recording neuronal peak potentials during time-interference electrical stimulation according to the embodiments of this application is a method using a system for simultaneously implementing time-interference electrical stimulation and recording neuronal peak potentials according to the embodiments of this application. This method can acquire and record neuronal peak potentials in target neural regions while experimental animals or subjects receive non-invasive TI stimulation. For example, this method uses an MCU control unit, signal generator, multi-channel instrumentation amplifier, first notch filter, bandpass filter, second notch filter, baseline drift correction circuit, analog-to-digital converter, etc., as described in the "Exemplary System" to achieve functions such as stimulation signal delivery, and acquisition, filtering, identification, and classification of neurophysiological signals. However, those skilled in the art should understand that the method may not be limited to using a system for simultaneously implementing time-interference electrical stimulation and recording neuronal peak potentials according to the embodiments of this application to implement the above-described S110-S140 operation steps, so as to effectively record neuronal peak potentials during time-interference electrical stimulation and perform high-precision extraction and recording of microvolt-level neuronal peak potential signals in the context of multiple, for example, two, TI electrical stimulation artifacts.

[0069] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0070] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0071] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0072] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0073] The above description has been given for illustrative and descriptive purposes. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. Systems for simultaneously performing time-interference electrical stimulation and recording neuronal peak potentials, including: A time-interference stimulation device is configured to generate multiple high-frequency sinusoidal signals, convert the multiple high-frequency sinusoidal signals into stimulation currents, and deliver the stimulation currents to the target region to form a time-interference electrical stimulation signal. The signal acquisition device is configured to acquire the neurophysiological signals of the target region in parallel through multiple channels, and to perform analog-to-digital conversion on the neurophysiological signals to obtain digital neurophysiological signals; A signal processing device is configured to perform preprocessing on the neurophysiological signal, including filtering and baseline drift correction, after the signal acquisition device acquires the neurophysiological signal. After denoising the digital neurophysiological signal, the neural electrical activity features are extracted to detect and classify the neuronal peak potential signals in the digital neurophysiological signal; the neuronal peak potential signals and their classification results are output. The number of the plurality of high-frequency sinusoidal signals is 2, 4, or 8. The signal acquisition device further includes a bandpass filter, which includes: capacitors C1”, C2”, C3, and C4; resistors R1”, R2”, R3’, and R4’; and operational amplifiers U3 and U4. C3 and C4 are connected in series, with one end connected to the neurophysiological signal and the other end connected to the non-inverting input terminal of U3. One end of R4 is connected to a reference voltage V. ref R3' is connected to the non-inverting input of U3 at one end; R3' is connected between C3 and C4 at one end and to the output and inverting input of U3 at the other end; R1" and R2" are connected in series, with one end connected to the output of U3 and the other end connected to the inverting input of U4 at the other end; C2" is grounded and connected to the inverting input of U4 at the other end; C1" is connected between R1" and R2" at one end and to the output and non-inverting input of U4 at the other end.

2. The system for simultaneously performing time-interference electrical stimulation and recording neuronal peak potentials according to claim 1, wherein, The signal acquisition device includes: A multi-channel instrumentation amplifier is configured to perform preliminary amplification and common-mode suppression of the neurophysiological signal in parallel through multiple channels, with a maximum output amplitude of ±5000mV; An analog-to-digital converter is configured to convert the preprocessed neurophysiological signal from the signal processing device into a multi-channel parallel neurophysiological digital signal, wherein the maximum sampling frequency is not less than 250 kHz and the amplitude resolution is not less than 24 bits.

3. The system for simultaneously performing time-interference electrical stimulation and recording neuronal peak potentials according to claim 1, wherein, The signal acquisition device further includes a first notch filter, which comprises: Capacitors C1' and C2', resistors R1', R2', R3, R4, R5, R6, and R7, and operational amplifiers U1' and U2 are used. The neurophysiological signal is connected to the positive input terminals of R3, R2', and U2 respectively after passing through C1' and R1' in parallel. The other end of R2' is connected to the output terminal of U1'. The output terminal of U1' is connected to the inverting input terminals of R4 and U2 respectively through C2'. The inverting input terminal of U1' is connected to the inverting input terminal of U2 through C2' and R4. The positive input terminal of U1' is connected to the midpoint of R6. The output terminal of U2 is connected to the input terminal of the neurophysiological signal through R5, R6, and R7.

4. The system for simultaneously performing time-interference electrical stimulation and recording neuronal peak potentials according to claim 1, wherein, The signal processing device includes a hardware processing unit, which comprises: A second notch filter is used to suppress the plurality of high-frequency sinusoidal signals; A baseline drift correction circuit is used to filter out background drift in the neurophysiological signal.

5. The system for simultaneously performing time-interference electrical stimulation and recording neuronal peak potentials according to claim 1, wherein, The signal processing device further includes a software processing unit configured to execute embedded program code instructions to perform the following functions: Remove noise and artifacts from the aforementioned neurophysiological digital signals; Extract the neural electrical activity features from the neural electrophysiological digital signals; Identify the neuronal peak potential signal in the said neurophysiological digital signal; Based on the neuronal peak potential signal and the characteristics of neural electrical activity, the classification result of the neuronal peak potential signal is determined.

6. The system for simultaneously performing time-interference electrical stimulation and recording neuronal peak potentials according to claim 5, wherein, The characteristics of the neural electrical activity include: the peak value of the neuronal peak potential, the duration of the neuronal peak potential, and the amplitude of the neuronal peak potential.

7. The system for simultaneously performing time-interference electrical stimulation and recording neuronal peak potentials according to claim 1, wherein, The time-interference stimulation device includes: An MCU control unit is configured to set and adjust the time interference parameters of each of the plurality of high-frequency sinusoidal signals, the time interference parameters including frequency, amplitude and phase; A signal generator is configured to generate the plurality of high-frequency sinusoidal signals based on the interference parameters set by the MCU control unit; A high-pass filter circuit is configured to suppress the DC component and interference in the plurality of high-frequency sinusoidal signals; A constant current source circuit is configured to convert the plurality of high-frequency sinusoidal signals into a plurality of stimulation currents.

8. The system for simultaneously performing time-interference electrical stimulation and recording neuronal peak potentials according to claim 1, further comprising: The analysis device is configured to group neuronal activity patterns to distinguish neuronal populations and their response patterns based on the neuronal peak potential signals output by the signal processing device, the classification results of the neuronal peak potential signals, and / or the neural electrical activity characteristics.

9. Methods for recording neuronal peak potentials during time-interference electrical stimulation, including: Based on the requirements of the target neural modulation frequency band, the parameters of the two high-frequency sinusoidal current signals required for time interference stimulation are set, including frequency, phase and amplitude; and the frequency difference between the two high-frequency sinusoidal current signals is determined to match the target neural modulation rhythm. The MCU control unit receives the set parameters and controls the signal generator to output two high-frequency sinusoidal current signals to the target head to form an interference envelope waveform Δf in the target nerve region; Raw signals of neural electrical activity in the target neural region are acquired using a signal acquisition device; the raw signals are initially amplified and common-mode interference suppressed using a multi-channel instrumentation amplifier; power frequency interference is suppressed and neural electrical activity frequency bands are selected using a notch filter and a bandpass filter, respectively. The bandpass filter includes capacitors C1”, C2”, C3, and C4, resistors R1”, R2”, R3’, and R4’, and operational amplifiers U3 and U4. C3 and C4 are connected in series, with one end connected to the neural electrophysiological signal and the other end connected to the non-inverting input of U3. One end of R4 is connected to a reference voltage V. ref One end of R3' is connected to the non-inverting input of U3, and one end of R3' is connected between C3 and C4, while the other end is connected to the output and inverting input of U3. R1" and R2" are connected in series, with one end connected to the output of U3 and the other end connected to the inverting input of U4. C2" is grounded and connected to the inverting input of U4. One end of C1" is connected between R1" and R2", while the other end is connected to the output and non-inverting input of U4. A baseline drift correction circuit is used to eliminate background drift, and finally, the neurophysiological signal is obtained. The neurophysiological signals are converted into digital signals using an analog-to-digital converter; a peak detection algorithm is used to identify neuronal peak potential signals in the digital signals and determine the classification results of the neuronal peak potential signals; And output the neuron peak potential signal and its classification result.

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