Transcranial Magnetic Stimulation Pulse Signal Control Method, Device, Equipment and Medium

Abstract

Embodiments of the present invention disclose a transcranial magnetic stimulation pulse signal control method, device, equipment, and medium. The method includes: obtaining an electroencephalogram signal of a target brain region of a target object, and determining a real-time neural excitation feature of the target brain region based on the electroencephalogram signal; determining whether the real-time neural excitation feature matches a reference neural excitation feature corresponding to a target neural excitation state; when the real-time neural excitation feature matches the reference neural excitation feature, triggering the output of a preset transcranial magnetic stimulation pulse signal. The technical solution of the embodiments of the present invention controls the output of each transcranial magnetic stimulation pulse signal at the time point of the target neural excitation state, which can effectively enhance the regulation effect of transcranial magnetic stimulation on brain activities and achieve transcranial magnetic stimulation regulation with high temporal precision.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of transcranial magnetic stimulation, and in particular, to a method, device, equipment, and medium for controlling transcranial magnetic stimulation pulse signals. Background Art

[0002] Transcranial magnetic stimulation is a neuroregulation technology with high temporal and spatial resolution. Currently, the implementation of transcranial magnetic stimulation is that an experimenter pre-sets the output mode of transcranial magnetic stimulation and outputs pulse signals to a target brain region according to the setting to achieve the regulation of brain function.

[0003] However, the instantaneous state of cranial nerve activity when transcranial magnetic stimulation outputs pulse signals affects the effect of transcranial magnetic stimulation on the brain, resulting in different responses of the brain to each transcranial magnetic stimulation pulse signal, with significant intra-individual and inter-individual differences, reducing its neuroregulation effect on the brain. Summary of the Invention

[0004] Embodiments of the present invention provide a method, device, equipment, and medium for controlling transcranial magnetic stimulation pulse signals, which controls each transcranial magnetic stimulation pulse signal to be output at the moment of the target nerve excitation state, can effectively enhance the regulation effect of transcranial magnetic stimulation on brain activity, and achieve transcranial magnetic stimulation regulation with high temporal precision.

[0005] In a first aspect, embodiments of the present invention provide a method for controlling transcranial magnetic stimulation pulse signals, the method comprising:

[0006] Obtaining an electroencephalogram signal of a target brain region of a target object, and determining a real-time nerve excitation feature of the target brain region based on the electroencephalogram signal;

[0007] Determining whether the real-time nerve excitation feature matches a reference nerve excitation feature corresponding to a target nerve excitation state;

[0008] When the real-time nerve excitation feature matches the reference nerve excitation feature, triggering the output of a preset transcranial magnetic stimulation pulse signal.

[0009] In a second aspect, embodiments of the present invention further provide a device for controlling transcranial magnetic stimulation pulse signals, the device comprising:

[0010] An electroencephalogram signal feature extraction module, configured to obtain an electroencephalogram signal of a target brain region of a target object, and determine a real-time nerve excitation feature of the target brain region based on the electroencephalogram signal;

[0011] An electroencephalogram signal feature analysis module, configured to determine whether the real-time nerve excitation feature matches a reference nerve excitation feature corresponding to a target nerve excitation state;

[0012] A pulse signal control module, configured to trigger and output a preset transcranial magnetic stimulation pulse signal when the real-time neural excitation feature matches the reference neural excitation feature.

[0013] In a third aspect, an embodiment of the present invention further provides a computer device, which includes:

[0014] One or more processors;

[0015] A memory, configured to store one or more programs;

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the transcranial magnetic stimulation pulse signal control method provided in any embodiment of the present invention.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the transcranial magnetic stimulation pulse signal control method provided in any embodiment of the present invention.

[0018] The embodiments in the above-mentioned invention have the following advantages or beneficial effects:

[0019] In the embodiment of the present invention, by acquiring the electroencephalogram signal of the target brain region of the target object and determining the real-time neural excitation feature of the target brain region based on the electroencephalogram signal; determining whether the real-time neural excitation feature matches the reference neural excitation feature corresponding to the target neural excitation state; when the real-time neural excitation feature matches the reference neural excitation feature, triggering and outputting a preset transcranial magnetic stimulation pulse signal, the problem that the effect of transcranial magnetic stimulation is affected by the instantaneous change of brain nerve activity is solved, and each transcranial magnetic stimulation pulse signal is controlled to be output at the time point of the target neural excitation state, which can effectively enhance the regulation effect of transcranial magnetic stimulation on brain activity and achieve high-time-precision transcranial magnetic stimulation regulation. Description of the Drawings

[0020] Figure 1 is a flowchart of a transcranial magnetic stimulation pulse signal control method provided by an embodiment of the present invention;

[0021] Figure 2 is a flowchart of another transcranial magnetic stimulation pulse signal control method provided by an embodiment of the present invention;

[0022] Figure 3 is a schematic diagram of an electroencephalogram electrode for acquiring an electroencephalogram signal provided by an embodiment of the present invention;

[0023] Figure 4 is a schematic diagram of the hardware connection of a transcranial magnetic stimulation pulse signal control provided by an embodiment of the present invention;

[0024] Figure 5 It is a schematic structural diagram of a transcranial magnetic stimulation pulse signal control device provided by an embodiment of the present invention;

[0025] Figure 6 It is a schematic structural diagram of a computer device provided by an embodiment of the present invention. Detailed implementation manners

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. In addition, it should be noted that for the convenience of description, only parts related to the present invention rather than all structures are shown in the drawings.

[0027] Figure 1 It is a flowchart of a transcranial magnetic stimulation pulse signal control method provided by an embodiment of the present invention. This embodiment is applicable to the situation of manipulating pulse signals in neuroregulation technology, especially applicable to the manipulation of transcranial magnetic stimulation pulse signals. This method can be executed by a transcranial magnetic stimulation pulse signal control device, and this device can be implemented in a software and / or hardware manner and integrated in a computer device with application development functions.

[0028] As Figure 1 shown, the transcranial magnetic stimulation pulse signal control method of this embodiment includes the following steps:

[0029] S110. Obtain the electroencephalogram signal of the target brain region of the target object, and determine the real-time neural excitation characteristics of the target brain region based on the electroencephalogram signal.

[0030] The target object is an object that needs to receive transcranial magnetic stimulation. For example, it can be a human or an experimental animal.

[0031] Among them, the target brain region is the brain region of the target object that is preset for transcranial magnetic stimulation. Different target brain regions can be selected according to needs. For example, it can be brain regions such as the sensorimotor cortex, prefrontal cortex, parietal cortex, and occipital cortex. It can be a single brain region or a collection of multiple brain regions.

[0032] The electroencephalogram signal is the overall reflection of the electrophysiological activities of the cranial nerve tissue on the surface of the cerebral cortex. The characteristics of the electroencephalogram signal frequency reflect different physiological information. The electroencephalogram signal contains different frequency components, and different frequency components have different meanings. Therefore, it is necessary to filter the electroencephalogram signal according to the research purpose to obtain electroencephalogram signals of different frequency components.

[0033] Obtaining the electroencephalogram signal of the target brain region and real-time extracting the neural activity characteristics of the electroencephalogram signal are the basic conditions for determining the real-time neural excitation characteristics.

[0034] The real-time neural excitation feature is the instantaneous feature of the neural oscillation rhythm of the electroencephalogram (EEG) signal, reflecting the instantaneous state of the current neural activity. Different neural excitation features correspond to the features of different neural oscillation rhythms of the EEG signal, and the neural excitation feature is related to the state of the cranial nerve activity. The neural excitation state of the target brain region is further determined according to the real-time neural excitation feature of the EEG signal.

[0035] Among them, the process of determining the real-time neural excitation feature of the target brain region based on the EEG signal may include steps such as EEG signal filtering processing and EEG signal feature extraction, and obtaining the instantaneous phase and instantaneous power of the EEG signal as the real-time neural excitation feature of the target brain region.

[0036] S120. Determine whether the real-time neural excitation feature matches the reference neural excitation feature corresponding to the target neural excitation state.

[0037] The target neural excitation state may be the target neural excitation state that the predetermined target brain region reaches.

[0038] The reference neural excitation feature may be the neural excitation feature corresponding to the target neural excitation state that the predetermined target brain region reaches.

[0039] The real-time neural excitation feature is related to the frequency components of the EEG signal. The frequency components of the EEG signal usually include gamma wave (30 - 100 Hz), beta wave (14 - 30 Hz), alpha wave (8 - 13 Hz), theta wave (4 - 7 Hz) and delta wave (below 4 Hz), and also include special mu wave, specifically referring to the activity in the alpha segment of the sensorimotor cortex, mainly related to movement, and can be used as an indicator to determine whether the sensorimotor cortex is activated. When the mu rhythm phase of the sensorimotor cortex is at the peak, the cranial nerve excitability is weak, and the regulatory effect of transcranial magnetic stimulation on cranial nerve activity at this moment is weak. When the mu rhythm phase of the sensorimotor cortex is at the trough, the cranial nerve excitability is high, and the regulatory effect of transcranial magnetic stimulation on brain nerve activity at this moment is strong; the relationship between the mu rhythm phase and the excitability of the sensorimotor cortex is affected by the mu rhythm power, that is: when the mu rhythm power is high, the above relationship between the mu rhythm phase and the excitability of the sensorimotor cortex holds, but when the mu rhythm power is low, the above relationship is not significantly shown. In summary, the instantaneous phase and instantaneous power are selected as the real-time neural excitation feature of the target brain region.

[0040] Match the real-time neural excitation feature of the target brain region with the neural excitation feature corresponding to the target neural excitation state that the target brain region reaches as preset, and determine whether to output the preset transcranial magnetic stimulation pulse signal according to the matching result, so as to ensure that the transcranial magnetic stimulation pulse signal is output when the target brain region reaches the target neural excitation state.

[0041] Specifically, determine whether the instantaneous phase in the real-time neural excitation feature is the trough phase in the reference neural excitation feature, and determine whether the instantaneous power in the real-time neural excitation feature is greater than the power threshold corresponding to the reference neural excitation feature.

[0042] When the instantaneous phase in the real-time neural excitation feature of the target frequency band in the target brain region is the trough phase, the neural excitability of the target brain region is high. When the instantaneous phase in the real-time neural excitation feature of the target frequency band in the target brain region is the peak phase, the neural excitability of the target brain region is weak.

[0043] When the neural excitability of the target frequency band in the target brain region of the target object is high, output a transcranial magnetic stimulation pulse signal for transcranial magnetic stimulation. It is also necessary to further determine whether the instantaneous power value in the real-time neural excitation feature is greater than the preset power threshold of the reference neural excitation feature.

[0044] Among them, the determination process of the power threshold includes: obtaining the resting electroencephalogram signal of the target brain region of the target object in the resting state; extracting the target frequency band resting electroencephalogram signal associated with the excitation rhythm of the target brain region from the resting electroencephalogram signal; taking the mean value of the squares of the signal amplitudes at each moment in the target frequency band resting electroencephalogram signal as the power threshold.

[0045] Specifically, before performing transcranial magnetic stimulation, obtain the resting electroencephalogram signal of the target brain region of the target object in the resting state, and extract the resting electroencephalogram signal of the target frequency band of the target brain region from the resting electroencephalogram signal. For example, it can be the mu rhythm electroencephalogram signal of the sensorimotor cortex for 3 minutes in the resting state with eyes open of a person. Take the mean value of the squares of the signal amplitudes at each moment in the target frequency band resting electroencephalogram signal as the power threshold.

[0046] Optionally, the determination process of the power threshold can also include: determining the frequency band peak of the electroencephalogram signal of the target frequency band based on a preset spectrum analysis model, and determining the high-pass cut-off frequency and low-pass cut-off frequency for filtering the electroencephalogram signal based on the frequency band peak.

[0047] Specifically, use the periodogram method or autoregressive model power spectral density estimation to obtain the spectrum of the electroencephalogram signal of the target brain region of the target object, and obtain the maximum value of the frequency band spectrum within the target frequency band range. The frequency value corresponding to this maximum value is the frequency band peak of the target frequency band of the target object. The high-pass cut-off frequency and low-pass frequency for filtering the electroencephalogram signal can be further determined through this frequency band peak. For example, the high-pass cut-off frequency and low-pass frequency for filtering the electroencephalogram signal are the frequency band peak of the target frequency band of the target object plus or minus 2 Hz.

[0048] S130. When the real-time neural excitation feature matches the reference neural excitation feature, trigger the output of a preset transcranial magnetic stimulation pulse signal.

[0049] When the real-time neural excitation feature of the electroencephalogram signal of the target brain region of the target object matches the reference neural excitation feature of the target brain region of the target object, it indicates that the target brain region reaches the target neural excitation state, and the target object can receive transcranial magnetic stimulation and control the output of transcranial magnetic stimulation pulse signals.

[0050] When the instantaneous phase in the real-time neural excitation feature of the target frequency band of the target brain region of the target object is the trough phase and the instantaneous power is greater than the power threshold corresponding to the reference neural excitation feature, trigger the output of a preset transcranial magnetic stimulation pulse signal.

[0051] The technical solution of this embodiment obtains the electroencephalogram signal of the target brain region of the target object, determines the real-time neural excitation feature of the target brain region based on the electroencephalogram signal, and determines whether the real-time neural excitation feature matches the reference neural excitation feature corresponding to the target neural excitation state. When the real-time neural excitation feature matches the reference neural excitation feature, trigger the output of a preset transcranial magnetic stimulation pulse signal to ensure that each transcranial magnetic stimulation pulse signal is output at the moment of the target neural excitation state, which can effectively enhance the regulation effect of transcranial magnetic stimulation on brain activity and achieve transcranial magnetic stimulation regulation with high temporal precision.

[0052] Figure 2 It is a flowchart of another data processing method for controlling transcranial magnetic stimulation pulse signals provided by an embodiment of the present invention. This embodiment and the data processing method for controlling transcranial magnetic stimulation pulse signals in the above embodiment belong to the same inventive concept, and further describes the solution for determining the real-time excitation feature of the target brain region based on the electroencephalogram signal. This method can be executed by a device for controlling transcranial magnetic stimulation pulse signals, and this device can be implemented in a software and / or hardware manner and integrated in a computer device with application development functions.

[0053] As Figure 2 shown, the method for controlling transcranial magnetic stimulation pulse signals in this embodiment includes the following steps:

[0054] S210. Obtain the electroencephalogram signal of the target brain region of the target object, perform filtering processing on the electroencephalogram signal, and obtain the target frequency band electroencephalogram signal associated with the excitation rhythm of the target brain region.

[0055] Specifically, determining the real-time neural excitation feature of the target brain region based on the electroencephalogram signal may include the following steps:

[0056] First, perform Laplacian spatial filtering on the electroencephalogram signal to obtain a spatially filtered signal.

[0057] Performing Laplacian spatial filtering on the electroencephalogram signal of the target brain region can reduce noise, improve the signal-to-noise ratio of the obtained electroencephalogram signal, and obtain a spatially filtered signal. For example, as Figure 3Place the target brain region electrode E0 at the preset spatial position point in the target brain region, place the ground electrode at the tip of the nose, place 2 reference electrodes at the bilateral mastoids, and place 4 spatially filtered electroencephalogram (EEG) electrodes E1, E2, E3, and E4 at the front and back on the same sagittal line as the target brain region electrode E0 and at the left and right on the same coronal plane and at the same distance from the target brain region electrode E0. Place four spatially filtered EEG electrodes E1, E2, E3, and E4 on the sagittal line and the coronal line at a distance of 2 cm from the target brain region electrode E0 near the target brain region electrode E0 to form a Laplacian spatial filter for the EEG signal of the target brain region electrode E0. The EEG signal of the target brain region electrode E0 takes 500 ms as a real-time processing time period. After the 500-ms EEG signal passes through the Laplacian spatial filter, a high signal-to-noise ratio spatially filtered signal can be obtained.

[0058] Then, input the spatially filtered signal into a zero-phase finite impulse response band-pass filter for frequency-domain filtering to obtain the EEG signal in the target frequency band.

[0059] Input the spatially filtered signal into a zero-phase finite impulse response band-pass filter for frequency-domain filtering to obtain the frequency-domain EEG signal in the target frequency band. For example, input the spatially filtered signal into a zero-phase finite impulse response frequency-domain filter with an order of Fs / 2, where Fs is the sampling rate of the EEG signal, to obtain the μ-rhythm time-domain waveform.

[0060] S220. Perform distortion signal processing and forward prediction processing on the EEG signal in the target frequency band to obtain the forward prediction EEG signal.

[0061] During the process of frequency-domain filtering, the filtering process causes distortion of the obtained frequency-domain EEG signal in the target frequency band. Therefore, it is necessary to cut off the distorted signal, and then perform forward prediction processing on the EEG signal segment to obtain the forward prediction EEG signal.

[0062] In this process, first filter out the distorted signal segments corresponding to the starting signal and the ending signal of the EEG signal in the target frequency band to obtain an EEG signal segment without filtering distortion.

[0063] To obtain an EEG signal segment without filtering distortion, it is necessary to filter out the distorted signals at the starting signal and the ending signal of the frequency-domain EEG signal in the target frequency band. The remaining EEG signal in the target frequency band after filtering is the EEG signal segment without filtering distortion, which is used for forward prediction of the signal. For example, remove the first 64 ms and the last 64 ms of the 500-ms EEG signal segment of the target brain region to obtain a remaining 372-ms EEG signal segment without filtering distortion for forward prediction of the signal.

[0064] Then, use a preset signal prediction method to perform forward prediction on the EEG signal segment without filtering distortion to obtain the forward prediction EEG signal.

[0065] An autoregressive forward prediction model can be established using the filtered distortion-free signal segment to perform forward prediction on the filtered distortion-free EEG signal segment; alternatively, a sine wave fitting method can be used to perform forward prediction on the filtered distortion-free EEG signal segment to obtain the forward predicted EEG signal. For example, based on the filtered distortion-free EEG signal segment of the remaining 372 ms, an autoregressive model based on Yule-Walker of order P is established, where the number P can be determined by the Bayesian information criterion method to obtain the forward predicted signal.

[0066] S230. Determine the instantaneous phase and instantaneous power of the forward predicted EEG signal as the real-time neural excitation feature of the target brain region.

[0067] Among them, determining the instantaneous phase and instantaneous power of the forward predicted EEG signal as the real-time neural excitation feature of the target brain region includes:

[0068] Specifically, the Hilbert-Huang transform can be performed on the forward predicted EEG signal to obtain the instantaneous phase at the target moment, and the square of the amplitude of the EEG signal at the target moment is calculated as the instantaneous power.

[0069] Using the forward predicted EEG signal in the target frequency band, perform the Hilbert-Huang transform to obtain the instantaneous phase of the target brain region at the target moment, and then take the square of the amplitude of the EEG signal at the target moment as the instantaneous power value. The real-time neural excitation feature at the target moment can include this instantaneous power value. For example, perform the Hilbert-Huang transform to obtain the instantaneous phase value at the 500 ms time point, and obtain the instantaneous power value by calculating the square of the signal amplitude at the 500 ms time point.

[0070] S240. Determine whether the real-time neural excitation feature matches the reference neural excitation feature corresponding to the target neural excitation state.

[0071] S250. When the real-time neural excitation feature matches the reference neural excitation feature, trigger the output of a preset transcranial magnetic stimulation pulse signal.

[0072] Specifically, determine whether the instantaneous phase is at the trough. If it is at the trough, then determine whether the instantaneous power value is greater than the power threshold. If it is greater than the power threshold, trigger the output of a preset transcranial magnetic stimulation pulse signal. If the trigger condition is not met, read the next 500 ms of data and make the judgment again.

[0073] In the technical solution of this embodiment, by filtering the electroencephalogram (EEG) signal, an EEG signal in a target frequency band associated with the excitation rhythm of the target brain region is obtained; then, the distorted signal processing and forward prediction processing are performed on the EEG signal in the target frequency band to obtain a forward prediction EEG signal; the instantaneous phase and instantaneous power of the forward prediction EEG signal are determined as the real-time neural excitation characteristics of the target brain region; it is determined whether the real-time neural excitation characteristics match the reference neural excitation characteristics corresponding to the target neural excitation state; finally, when the real-time neural excitation characteristics match the reference neural excitation characteristics, a preset transcranial magnetic stimulation (TMS) pulse signal is triggered for output. The technical solution of the embodiment of the present invention ensures that the TMS pulse signal is output at the moment when the triggering condition, that is, the target neural excitation state, is satisfied, realizing TMS regulation with high time accuracy.

[0074] In a specific TMS example, a schematic diagram of the hardware connection for controlling a TMS pulse signal is as Figure 4 shown, including a TMS instrument 410, a coil 420, a target brain region 430, EEG acquisition electrodes 440, an EEG amplification module 450, a data acquisition module 460, an EEG signal storage module 470, an EEG signal processing module 1 (480), and an EEG signal processing module 2 (490).

[0075] The specific implementation steps are as follows:

[0076] 1. The TMS instrument 410 outputs a TMS pulse signal and is connected to the coil 420.

[0077] Among them, the TMS instrument 410 can be the Magstim Rapid2 TMS instrument produced in the UK, or the MAG&More etc. TMS instrument with external input / output ports produced in Germany, or any other TMS instrument with an external input / output trigger port. The coil 420 is placed over the target brain region 430 of the target subject. The coil can use an eight-shaped coil, a double-cone coil, a circular coil, etc. that match the TMS instrument.

[0078] 2. The EEG acquisition electrodes 440 acquire the high-time-resolution EEG signal of the target brain region 430, and the EEG acquisition electrodes 440 are connected to the EEG amplification module 450.

[0079] 3. The EEG amplification module 450 amplifies the weak EEG signal. The EEG amplification module 450 can be the NeurOne commercial EEG amplifier produced in Finland, or a domestic EEG amplifier or a fabricated EEG amplification device with good EEG signal amplification function, as long as it has an output port after EEG signal amplification, and this output port can be connected to the data acquisition module 460 to input the amplified analog EEG signal into the data acquisition module 460.

[0080] 4. The data acquisition module 460 converts the acquired analog EEG signals into digital EEG signals through analog-to-digital conversion. The data acquisition module 460 is also connected to the transcranial magnetic stimulator (410), the EEG signal storage module 470, and the EEG signal processing module 1 (480).

[0081] 5. The EEG storage module 470 stores the digital EEG signals. The EEG storage module 470 is connected to the EEG signal processing module 2 (490).

[0082] 6. The EEG signal processing module 2 (490) determines the target frequency band value of the target brain region 430. The EEG signal processing module 2 (490) is connected to the EEG signal processing module 1 (480).

[0083] Among them, the EEG signal processing module 1 (480) can be a lower computer based on Simulink or LabVIEW or ARM, etc., and is used to determine the real-time neural excitation characteristics. Further, it is used to judge whether it matches the reference neural excitation characteristics according to the real-time neural excitation characteristics. If it matches, a TTL level signal is generated and transmitted to the data acquisition module. Specifically, the EEG signals acquired by the electrodes E0 of the target brain region are subjected to Laplacian space filtering to obtain the space-filtered signals; the space-filtered signals are input into a zero-phase finite impulse response bandpass filter for frequency domain filtering to obtain the target frequency band neural oscillation signals, that is, the frequency domain filtered signals. The high-pass cut-off frequency and low-pass frequency of the frequency band filter can be determined by the neural oscillation frequency band. Filtering causes distortion at the start and end of the frequency domain filtered signals. Therefore, the distorted signals in a period of time after the start and before the end are cut off, and the remaining signals are the signal segments without filtering distortion. Then, the signal segments without filtering distortion are used for forward prediction of the signal, and the methods applied can be any one of the following: (1) establishing an autoregressive forward prediction model using the signal segments without filtering distortion to perform forward prediction on the signal; (2) performing forward prediction on the signal using the method of sine wave fitting. Hilbert-Huang transform is performed on the forward prediction signal to obtain the instantaneous phase value of the signal, and the instantaneous power value is obtained by taking the square of the amplitude. Then, it is judged whether the triggering condition is satisfied. If the triggering condition is satisfied, a TTL level is output from the output port of the data acquisition module 460 to trigger the output of the control pulse signal of the transcranial magnetic stimulator 410. If the condition is not satisfied, the next cycle detection is performed.

[0084] The acquisition of the target band neural oscillation signal includes: (1) Before outputting the transcranial magnetic stimulation pulse signal, first acquire the electroencephalogram (EEG) signal of the target brain region 430 of the target object in the eyes-open resting state for 3 minutes; (2) Store it in the EEG signal storage module 470, and the EEG signal is transmitted to the EEG signal processing module 2 (490) via the EEG signal storage module 470; (3) In the signal processing module 2 (490), use the periodogram method or the autoregressive model power spectral density estimation to obtain the frequency spectrum of the EEG signal of the target brain region (430). Since the positions of the spectral peaks of different target objects are different, the maximum value of the frequency band spectrum is obtained within the target frequency band range, and the frequency value corresponding to the maximum value of the frequency band spectrum is used as the frequency band peak of the target object. The high-pass cut-off frequency and the low-pass frequency for filtering the EEG signal are the frequency band peak of the target frequency band of the target object plus or minus 2 Hz. Calculate the squared amplitude of the EEG signal of the target brain region at each time point as the instantaneous power, and obtain the average value of the instantaneous power of all time points as the power threshold.

[0085] 7. The output end of the data acquisition module (460) is connected to the external interface of the transcranial magnetic stimulator (410). After receiving the TTL level output by the EEG signal processing module 1 (480), it is transmitted to the transcranial magnetic stimulator.

[0086] 8. After the transcranial magnetic stimulator 410 receives the TTL level output by the data acquisition module 460, it outputs a preset transcranial magnetic stimulation pulse signal.

[0087] Figure 5 It is a schematic structural diagram of a transcranial magnetic stimulation pulse signal control device provided by an embodiment of the present invention. This embodiment is applicable to the pulse signal output control of a transcranial magnetic stimulator. The device can be implemented in a software and / or hardware manner and integrated into a computer terminal device with application development functions.

[0088] As Figure 5 shown, the transcranial magnetic stimulation pulse signal control device includes: an EEG signal feature extraction module 510, an EEG signal feature analysis module 520, and a pulse signal control module 530.

[0089] Among them, the EEG signal feature extraction module 510 is used to acquire the EEG signal of the target brain region of the target object and determine the real-time neural excitation feature of the target brain region based on the EEG signal; the EEG signal feature analysis module 520 is used to determine whether the real-time neural excitation feature matches the reference neural excitation feature corresponding to the target neural excitation state; the pulse signal control module 530 is used to trigger the output of a preset transcranial magnetic stimulation pulse signal when the real-time neural excitation feature matches the reference neural excitation feature.

[0090] The technical solution of this embodiment realizes operations such as extracting electroencephalogram (EEG) signal features, analyzing EEG signal features, and controlling pulse signals through the mutual cooperation among various modules. The embodiment of the present invention solves the problem that the effect of transcranial magnetic stimulation (TMS) is affected by the instantaneous changes in brain nerve activities, controls the output of each TMS pulse signal at the moment of the nerve excitation state, can effectively enhance the regulation effect of TMS on brain activities, and realizes TMS regulation with high time precision.

[0091] Optionally, the EEG signal feature extraction module 510 is specifically configured to: perform filtering processing on the EEG signal to obtain a target band EEG signal associated with the excitation rhythm of the target brain region; perform distortion signal processing and forward prediction processing on the target band EEG signal to obtain a forward prediction EEG signal; determine the instantaneous phase and instantaneous power of the forward prediction EEG signal as the real-time nerve excitation feature of the target brain region.

[0092] Optionally, the EEG signal feature extraction module 510 may also be configured to: perform Laplacian space filtering on the EEG signal to obtain a space filtering signal; input the space filtering signal into a zero-phase finite impulse response band-pass filter for frequency domain filtering to obtain a target band EEG signal.

[0093] Optionally, the EEG signal feature extraction module 510 may also be configured to: filter out the distortion signal segments corresponding to the start signal and the end signal of the target band EEG signal to obtain a non-filtered distortion EEG signal segment; perform forward prediction on the non-filtered distortion EEG signal segment by using a preset signal prediction method to obtain a forward prediction EEG signal.

[0094] Optionally, the EEG signal feature extraction module 510 is further configured to: perform Hilbert-Huang transform on the forward prediction EEG signal to obtain the instantaneous phase at the target moment, and calculate the square of the amplitude of the EEG signal at the target moment as the instantaneous power.

[0095] Optionally, the TMS pulse signal control device further includes:

[0096] An EEG signal feature analysis module 520, configured to determine whether the instantaneous phase in the real-time nerve excitation feature is the trough phase in the reference nerve excitation feature, and determine whether the instantaneous power in the real-time nerve excitation feature is greater than the power threshold corresponding to the reference nerve excitation feature.

[0097] Optionally, the EEG signal feature analysis module 520 is further configured to: obtain the resting EEG signal of the target brain region of the target object in the resting state. Extract the target band resting EEG signal associated with the excitation rhythm of the target brain region from the resting EEG signal. Take the mean value of the squares of the signal amplitudes at each moment in the target band resting EEG signal as the power threshold.

[0098] Optionally, the EEG signal feature analysis module 520 can also be used to: determine a target frequency band for the EEG signal based on a preset spectrum analysis model to obtain the frequency band peak value of the EEG signal, and determine the high-pass cut-off frequency and low-pass cut-off frequency for filtering the EEG signal based on the frequency band peak value.

[0099] The transcranial magnetic stimulation pulse signal control device provided by the embodiments of the present invention can execute the transcranial magnetic stimulation pulse signal control method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0100] Figure 6 It is a schematic structural diagram of a computer device provided by an embodiment of the present invention. Figure 6 The block diagram of an exemplary computer device 12 suitable for implementing the embodiments of the present invention is shown. Figure 6 The displayed computer device 12 is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention. The computer device 12 can be any terminal device with computing capabilities, such as intelligent controllers, servers, mobile phones and other terminal devices.

[0101] Such as Figure 6 As shown, the computer device 12 is presented in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).

[0102] The bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the multiple bus structures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0103] The computer device 12 typically includes a variety of computer system-readable media. These media can be any available media accessible by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0104] The system memory 28 may include computer system-readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 can be used to read and write non-removable, non-volatile magnetic media ( Figure 6not shown, typically referred to as a "hard disk drive"). Although Figure 6 not shown in Figure 6 , a disk drive for reading and writing to a removable non-volatile disk (such as a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM, or other optical medium) may be provided. In these cases, each drive may be connected to the bus 18 through one or more data medium interfaces. The system memory 28 may include at least one program product having a set (such as at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0105] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. The program modules 42 generally perform the functions and / or methods in the embodiments described in the present invention.

[0106] The computer device 12 may also communicate with one or more external devices 14 (such as a keyboard, a pointing device, a display 24, etc.), and may also communicate with one or more devices that enable a user to interact with the computer device 12, and / or communicate with any device that enables the computer device 12 to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication may be through the input / output (I / O) interface 22. Also, the computer device 12 may communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 20. As shown, the network adapter 20 communicates with other modules of the computer device 12 through the bus 18. It should be understood that although Figure 6 not shown in Figure 6 , other hardware and / or software modules may be used in conjunction with the computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0107] The processing unit 16 executes various functional applications and transcranial magnetic stimulation pulse signal control by running programs stored in the system memory 28. For example, it implements the transcranial magnetic stimulation pulse signal control method provided by the embodiments of the present invention. The method includes:

[0108] Obtaining an electroencephalogram signal of a target brain region of a target object, and determining real-time neural excitation characteristics of the target brain region based on the electroencephalogram signal;

[0109] Determine whether the real-time nerve excitation feature matches the reference nerve excitation feature corresponding to the target nerve excitation state;

[0110] When the real-time nerve excitation feature matches the reference nerve excitation feature, trigger the output of a preset transcranial magnetic stimulation pulse signal.

[0111] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the transcranial magnetic stimulation pulse signal control method provided in any embodiment of the present invention. The method includes:

[0112] Obtain the electroencephalogram signal of the target brain area of the target object, and determine the real-time nerve excitation feature of the target brain area based on the electroencephalogram signal;

[0113] Determine whether the real-time nerve excitation feature matches the reference nerve excitation feature corresponding to the target nerve excitation state;

[0114] When the real-time nerve excitation feature matches the reference nerve excitation feature, trigger the output of a preset transcranial magnetic stimulation pulse signal.

[0115] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, apparatus, or device.

[0116] The computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and the computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0117] The program code contained on a computer-readable medium can be transmitted by any suitable medium, including but not limited to: wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the above.

[0118] The computer program code for performing the operations of the present invention can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0119] Those of ordinary skill in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. Optionally, they can be implemented with program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be separately made into individual integrated circuit modules, or multiple modules or steps of them can be made into a single integrated circuit module to implement. Thus, the present invention is not limited to any specific combination of hardware and software.

[0120] Note that the above is only the preferred embodiment of the present invention and the applied technical principles. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A transcranial magnetic stimulation pulse signal control method, characterized in that Comprising: Obtaining electroencephalogram (EEG) signals of a target brain region of a target object, and determining real-time neural excitatory characteristics of the target brain region based on the EEG signals; Determining whether the real-time neural excitatory characteristics match reference neural excitatory characteristics corresponding to a target neural excitatory state; When the real-time neural excitatory characteristics match the reference neural excitatory characteristics, triggering an output of a preset transcranial magnetic stimulation pulse signal; Wherein, the determining the real-time neural excitatory characteristics of the target brain region based on the EEG signals includes: Performing filtering processing on the EEG signals to obtain target band EEG signals associated with the excitation rhythm of the target brain region; Performing distortion signal processing and forward prediction processing on the target band EEG signals to obtain forward prediction EEG signals; Determining the instantaneous phase and instantaneous power of the forward prediction EEG signals as the real-time neural excitatory characteristics of the target brain region; The determining whether the real-time neural excitatory characteristics match reference neural excitatory characteristics corresponding to a target neural excitatory state includes: Determining whether the instantaneous phase in the real-time neural excitatory characteristics is the trough phase in the reference neural excitatory characteristics, and determining whether the instantaneous power in the real-time neural excitatory characteristics is greater than a power threshold corresponding to the reference neural excitatory characteristics.

2. The method according to claim 1, wherein The performing distortion signal processing and forward prediction processing on the target band EEG signals to obtain forward prediction EEG signals includes: Filtering out distortion signal segments corresponding to the start signal and the end signal of the target band EEG signals to obtain a non-filtered distortion EEG signal segment; Performing forward prediction on the non-filtered distortion EEG signal segment by using a preset signal prediction method to obtain the forward prediction EEG signals.

3. The method according to claim 1, characterized in that, The determining the instantaneous phase and instantaneous power of the forward prediction EEG signals includes: Performing Hilbert-Huang transform on the forward prediction EEG signals to obtain the instantaneous phase at a target moment, and calculating the square of the amplitude of the EEG signals at the target moment as the instantaneous power.

4. The method according to claim 1, characterized in that The performing filtering processing on the EEG signals to obtain target band EEG signals associated with the excitation rhythm of the target brain region includes: Performing Laplace space filtering on the EEG signals to obtain space-filtered signals; Inputting the space-filtered signals into a zero-phase finite impulse response band-pass filter for frequency domain filtering to obtain the target band EEG signals.

5. The method according to claim 1, characterized in that, The determining process of the power threshold includes: Obtaining resting EEG signals of the target brain region of the target object in a resting state; Extracting target band resting EEG signals associated with the excitation rhythm of the target brain region from the resting EEG signals; Taking the mean of the squares of the signal amplitudes at each moment in the target band resting EEG signals as the power threshold.

6. The method according to claim 5, wherein The method further includes: Determining the frequency band peak of the target band resting EEG signals based on a preset spectrum analysis model, and determining the high-pass cut-off frequency and the low-pass cut-off frequency for filtering the EEG signals based on the frequency band peak.

7. A transcranial magnetic stimulation pulse signal control device, characterized in that, The device includes: An electroencephalogram (EEG) signal feature extraction module, configured to obtain EEG signals of a target brain region of a target object, and determine real-time neural excitation features of the target brain region based on the EEG signals; An EEG signal feature analysis module, configured to determine whether the real-time neural excitation features match reference neural excitation features corresponding to a target neural excitation state; A pulse signal control module, configured to trigger and output a preset transcranial magnetic stimulation pulse signal when the real-time neural excitation features match the reference neural excitation features; Wherein, the EEG signal feature extraction module is specifically configured to: Perform filtering processing on the EEG signals to obtain target band EEG signals associated with the excitation rhythm of the target brain region; perform distortion signal processing and forward prediction processing on the target band EEG signals to obtain forward prediction EEG signals; determine the instantaneous phase and instantaneous power of the forward prediction EEG signals as the real-time neural excitation features of the target brain region; The EEG signal feature analysis module is specifically configured to: Determine whether the instantaneous phase in the real-time neural excitation features is the trough phase in the reference neural excitation features, and determine whether the instantaneous power in the real-time neural excitation features is greater than a power threshold corresponding to the reference neural excitation features.

8. A computer device, characterized in that, The computer device includes: One or more processors; A memory, configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the transcranial magnetic stimulation pulse signal control method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the transcranial magnetic stimulation pulse signal control method according to any one of claims 1-6.

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