A device for quantitatively evaluating transcranial magnetic stimulation individualized threshold

Abstract

The present application relates to the technical field of transcranial magnetic stimulation regulation, and particularly relates to a device for quantitatively evaluating a personalized threshold of transcranial magnetic stimulation. The device comprises the following steps: installing electroencephalogram and electromyogram electrodes, setting TMS pre-stimulation parameters; starting stimulation, synchronously recording electroencephalogram and electromyogram data, calculating a TMS stimulation moment, taking the stimulation moment calculated each time as a reference, saving electroencephalogram data and electromyogram data from t / 2 before the stimulation moment to t / 2 after the stimulation moment; filtering the electroencephalogram data and electromyogram data with a length of t, then calculating power spectral density values of each lead under different frequency bands for data segments S1 from 0 to t / 2 to Delta and data segment S2 with a length of t, and Delta is a data length control parameter; comprehensively calculating power spectral density values of multi-channel electroencephalogram leads and electromyogram leads under different frequency bands under N times of stimulation, and calculating a TMS stimulation threshold. The technical scheme can more accurately determine a magnetic stimulation intensity threshold.

Description

A device for quantitatively assessing personalized thresholds for transcranial magnetic stimulation Technical Field

[0001] This invention relates to the field of transcranial magnetic stimulation modulation technology, specifically to a device for quantitatively assessing personalized thresholds for transcranial magnetic stimulation. Background Technology

[0002] Transcranial magnetic stimulation (TMS) works by using pulsed magnetic fields to penetrate the skin and skull in a non-invasive and painless manner, precisely targeting the cerebral cortex. By flexibly adjusting the frequency and intensity of stimulation, TMS can effectively regulate the excitation or inhibition state of local neurons in the brain.

[0003] In clinical applications, TMS technology has brought new technological solutions to multiple disciplines, including mental health and neurorehabilitation. In the field of mental health, TMS can be widely used for various diseases such as depression, anxiety, obsessive-compulsive disorder, auditory hallucinations, and sleep disorders, providing new rehabilitation pathways for subjects. In the field of neurorehabilitation, TMS also plays an important therapeutic role in various conditions such as motor dysfunction after stroke, speech disorders, dysphagia, dystonia, spinal cord injury, epilepsy, Parkinson's disease, and chronic pain, effectively improving the symptoms and quality of life of subjects.

[0004] However, accurately determining the stimulation intensity threshold is a crucial prerequisite for ensuring the desired therapeutic effect of transcranial magnetic stimulation (TMS). Due to significant individual differences, each person responds differently to different stimulation intensities. Using a uniform stimulation threshold would inevitably lead to some subjects failing to achieve the expected stimulation effect due to insufficient stimulation intensity, while others might suffer serious brain damage due to excessive stimulation intensity, thus affecting both efficacy and safety.

[0005] Currently, threshold determination relies primarily on physician experience. In practice, physicians typically attach electromyography (EMG) electrodes to the back of the subject's hand, stimulate the motor cortex of the brain, and manually assess the EMG amplitude during magnetic stimulation. This traditional method has significant limitations. Firstly, its criterion is too singular, relying solely on EMG amplitude, failing to comprehensively and accurately reflect the true cortical response under different stimulation intensities. Secondly, the lack of scientific and objective quantitative standards makes it difficult to guarantee the scientific validity and rationality of threshold determination, leading to suboptimal treatment outcomes and limiting the further promotion and application of TMS technology in clinical practice. Summary of the Invention

[0006] The purpose of this invention is to provide a device for quantitatively assessing personalized thresholds for transcranial magnetic stimulation, which can more accurately determine the magnetic stimulation intensity threshold.

[0007] To achieve the above objectives, in a first aspect, the present invention provides a device for quantitatively assessing personalized thresholds for transcranial magnetic stimulation, comprising:

[0008] Install EEG and EMG electrodes and set TMS pre-stimulation parameters;

[0009] Start stimulation, simultaneously record EEG and EMG data, calculate TMS stimulation time, and save EEG and EMG data from t / 2 before to t / 2 after each stimulation time as a baseline.

[0010] The EEG and EMG data of length t are filtered separately. Then, the power spectral density values ​​of each lead in different frequency bands are calculated for the data segment S1 from 0 to t / 2 to Δ and the entire data segment S2 of length t. Δ is the data length control parameter.

[0011] The TMS stimulation threshold was calculated by combining the power spectral density values ​​of multi-channel EEG and EMG leads at different frequency bands under N stimulations, including the following:

[0012] Calculate the joint power spectral density on data segments S1 and S2 respectively. and The calculation formula is as follows:

[0013]

[0014]

[0015] Where k is the number of leads collected by the EEG; ~ The following figures represent the power spectral density of lead i in data segment S1 across the six frequency bands. ~ The power spectral density of lead i in data segment S2 is represented in six frequency bands in sequence. ~ The following figures sequentially represent the power spectral density of electromyography leads in the six frequency bands of data segment S1. ~ The power spectral density of electromyography leads in data segment S2 is represented sequentially across the six frequency bands.

[0016] The corresponding values ​​for N TMS stimulations are calculated. and ,in, The combined EEG and EMG power spectral density corresponding to data segment S1; The combined EEG and EMG power spectral density corresponding to data segment S2;

[0017] The stimulus response index K value is calculated for each of N consecutive TMS stimuli using the following formula:

[0018]

[0019] Obtain the array corresponding to N TMS stimuli { }, select array { The TMS stimulation intensity corresponding to the maximum value in} is used as the personalized stimulation threshold.

[0020] Beneficial effects of the basic approach: Traditional TMS threshold assessment often relies on single EMG or simplified EEG signals, which are easily affected by individual muscle state and signal interference. This approach simultaneously collects EEG and EMG data. The former reflects the electrical activity of cortical neurons, while the latter captures the peripheral muscle electrical responses evoked by stimuli. Combining the two provides a complete coverage of neural conduction pathways from the central nervous system to the periphery, reducing bias caused by single data sources. For example, analyzing EEG data to determine cortical excitability, combined with EMG motor evoked potential data, can more accurately correspond the stimulus intensity to the neuromuscular linkage response.

[0021] The solution first filters the data, effectively removing artifacts such as environmental electromagnetic noise and involuntary muscle contractions, ensuring the quality of the original data. Simultaneously, by calculating the power spectral density values ​​of different data segments and then integrating multi-channel data from N stimuli, it avoids random errors from single stimuli, ensuring that the final threshold calculation result stably reflects the individual's true physiological response. This solves the problem of threshold deviation caused by abnormal data in single stimuli in traditional assessments.

[0022] The intensity of transcranial magnetic stimulation (TMS) is closely related to its safety. Excessive intensity may induce adverse reactions such as epilepsy and severe muscle spasms, while insufficient intensity may fail to achieve the desired therapeutic effect. The personalized and precise thresholds derived in this approach provide doctors with a clear reference range for safe stimulation.

[0023] Its dynamic assessment of cortical excitability and electromyographic responses can also provide real-time feedback on the subject's physiological tolerance during stimulation. If the assessment reveals abnormally strong electromyographic responses or epileptiform waveforms in the electroencephalogram (EEG), the doctor can promptly reduce the stimulation intensity.

[0024] MS threshold is directly related to cortical excitability, and abnormal cortical excitability is a characteristic of many neuropsychiatric disorders such as depression, epilepsy, and multiple sclerosis. The multi-band power spectral density values ​​and EEG / EMG linkage data recorded in this protocol can serve as auxiliary indicators for assessing the subject's brain function status, in addition to calculating the threshold.

[0025] This technical solution does not directly intervene in the disease process. Instead, it uses scientific calculations to determine a safe and effective technical parameter. The core of this solution is not to directly implement treatment, but to provide a technical measurement and evaluation method to determine a personalized threshold for transcranial magnetic stimulation (TMS). This threshold is an objective parameter setting before treatment, not the treatment itself.

[0026] As a feasible and preferred option, EEG and EMG electrodes are installed, and TMS pre-stimulation parameters are set, including the following:

[0027] A k-channel EEG lead was installed in the subject's head using a 10-20 lead system, and an EMG lead was installed in the abductor pollicis brevis muscle on the back of the subject's hand. The stimulation parameters included at least: the number of stimulations N, the stimulation frequency F, and N intensity levels. The stimulus intensity sequence A, EEG and EMG sampling rates f, and data truncation length t constitute the data, where 1 / F <t<2 / F。

[0028] As a feasible and preferred approach, the timing of TMS stimulation is calculated, including the following:

[0029] Peak detection is performed on continuous EEG data segments corresponding to two adjacent stimuli. The time corresponding to the maximum absolute value is determined as the stimulation time of that stimulus. EEG and EMG data of t / 2 time before and after this time are extracted to form an analysis window of length t.

[0030] As a feasible and preferred approach, EEG and EMG data of length t are filtered separately, including the following:

[0031] The electroencephalogram (EEG) data and electromyogram (EMG) data were subjected to 0-250 Hz bandpass filtering and 50 Hz power frequency interference was removed.

[0032] As a feasible and preferred approach, the power spectral density values ​​of each lead in different frequency bands are calculated, including the following:

[0033] For each stimulus, the EEG and EMG data segments were subjected to Fast Fourier Transform, and their power spectral density in six frequency bands (0.5-4Hz, 4-8Hz, 8-13Hz, 13-30Hz, 30-80Hz, and 80-250Hz) was calculated.

[0034] As a feasible and preferred approach, the original data from each channel are subjected to frequency domain feature extraction using Fast Fourier Transform, as shown in the following formula:

[0035]

[0036] in, For length is Time series, For frequency domain indexing, For time-domain indexing, The imaginary unit;

[0037] The power spectral density values ​​of each frequency band are extracted from the calculated frequency domain features, as shown in the following formula:

[0038]

[0039] in, , For each frequency band, the corresponding frequency domain index. .

[0040] As a feasible preferred solution, it also includes a stimulation threshold display module for dynamically displaying the calculated stimulation threshold. The display interface may include threshold values, stimulation parameter settings, EEG and EMG data waveforms, and power spectral density graphs. Attached Figure Description

[0041] Figure 1 is a schematic diagram of the architecture of a device for quantitatively assessing personalized thresholds for transcranial magnetic stimulation according to the present invention.

[0042] Figure 2 is a schematic diagram of the working principle of the device for quantitatively assessing the personalized threshold of transcranial magnetic stimulation according to the present invention. Detailed Implementation

[0043] To make the technical solution and advantages of this application clearer, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings. It is understood that the specific embodiments described herein are only some embodiments of the present invention, and are only used to explain this application, not to limit it. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be considered isolated; they can be combined with each other to achieve better technical effects. The same reference numerals appearing in the accompanying drawings of the following embodiments represent the same features or components, and can be applied to different embodiments.

[0044] Furthermore, unless otherwise defined, the technical or scientific terms used in this invention description shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains.

[0045] The present invention will now be described in further detail with reference to the accompanying drawings.

[0046] Referring to Figure 1, this disclosure proposes a device for quantitatively assessing personalized thresholds for transcranial magnetic stimulation, including an electroencephalogram (EEG) acquisition module, an electromyogram (EMG) acquisition module, a data preprocessing module, a stimulation time calculation module, a stimulation threshold calculation module, and a stimulation threshold display module.

[0047] The EEG acquisition module is used to install EEG acquisition leads on the subject's head according to the 10-20 international standard lead system, set the EEG data sampling rate and data truncation length, and acquire EEG data.

[0048] The electromyography (EMG) acquisition module is used to install EMG acquisition leads on the back of the subject's hand, set the EMG data sampling rate and data truncation length, and acquire EMG data.

[0049] The data preprocessing module is used to save the currently acquired data segments in the form of a matrix, and to perform filtering and truncation processing on the acquired EEG and EMG data.

[0050] The stimulation timing calculation module calculates the TMS stimulation timing by jointly calculating the peak values ​​of two adjacent EEG data blocks.

[0051] The stimulation threshold calculation module, for each TMS stimulation during the pre-stimulation process, calculates the power spectral density of EEG and EMG data in different frequency bands for the data T1 consisting of 0~t / 2~Δ before stimulation and the data of length t consisting of t / 2 before and after stimulation. and The formula is as follows:

[0052]

[0053]

[0054] Where k is the number of leads collected by the EEG; ~ The following represent the power spectral density of lead i in data segment S1 in six frequency bands (including 0.5~4Hz, 4~8Hz, 8~13Hz, 13~30Hz, 30Hz~80Hz, and 80~250Hz). ~ The power spectral density of lead i in data segment S2 is represented in six frequency bands in sequence. ~ The following figures sequentially represent the power spectral density of electromyography leads in the six frequency bands of data segment S1. ~ The power spectral density of electromyography leads in data segment S2 is represented sequentially across the six frequency bands.

[0055] The calculation yields the corresponding results of N TMS stimuli. and ,in, The combined power spectral density of EEG and EMG corresponding to each stimulus 0~t / 2~Δ data segment; The combined power spectral density of EEG and EMG for each stimulus with a total data length of t.

[0056] The joint power spectral density and K value corresponding to N stimulation times are calculated using the following formula:

[0057]

[0058] Obtain the array corresponding to N TMS stimuli { },choose{ The TMS stimulation intensity corresponding to the maximum value in} is used as the stimulation threshold.

[0059] The stimulation threshold display module dynamically displays the calculated stimulation threshold.

[0060] Referring to Figure 2, the working principle of a device for quantitatively assessing personalized thresholds for transcranial magnetic stimulation includes the following steps.

[0061] Step S100, install EEG and EMG acquisition leads, including:

[0062] Step S101: EEG lead installation. EEG acquisition leads are installed on the subject's scalp. The number of leads can be selected according to actual needs; common options include 8 channels, 16 channels, 32 channels, 64 channels, and 128 channels. The number of EEG acquisition leads is denoted as k. The lead positions must accurately correspond to different areas of the brain to ensure comprehensive EEG signal acquisition.

[0063] Step S102: Install electromyography (EMG) leads. Install EMG acquisition leads on the back of the subject's hand. Typically, the ground wire is placed on the inside of the wrist, the recording electrode is placed on the belly of the abductor pollicis brevis muscle, and the reference electrode is placed on the tendon (bony prominence) to accurately record the electrical activity of the hand muscles under TMS stimulation.

[0064] Step S200: Set stimulation parameters on the TMS control terminal, including:

[0065] The number of stimulations (N) is set according to the experimental design or clinical needs. In one embodiment, it can be set to 20 or 30 times to ensure the sufficiency and reliability of the data.

[0066] Stimulation frequency (F): Set the frequency of TMS stimulation, with the unit of Hertz (Hz). The common stimulation frequency range is from 0.1 Hz to 20 Hz, and the specific selection depends on the treatment purpose and the subject's condition.

[0067] Stimulation intensity (A): The stimulation intensity sequence is an arithmetic increasing sequence. Set the initial stimulation intensity and the increment step. In one embodiment, the initial intensity can be set to 20% of the maximum output intensity, and it increases by 5% each time until the maximum output intensity is reached or the subject shows an obvious muscle response; the pre - stimulation intensity A can be represented as an array ,

[0068] Data sampling rate (f): Set the sampling rate of EEG and EMG data. Usually, 1000 Hz or higher is selected to ensure that high - frequency signal components can be captured.

[0069] Data truncation length (t): Set the data truncation length according to the stimulation frequency to ensure that the data recording time after each stimulation is long enough to capture the complete EEG and EMG responses. Usually, the data truncation length should satisfy 1 / F < t < 2 / F. For example, when the stimulation frequency is 1 Hz, the data truncation length can be set between 1 s and 2 s.

[0070] Step S300, collect and pre - process data, including:

[0071] Step S301, data collection: Use the TMS stimulation coil to stimulate the head motor control area of the subject according to the preset stimulation parameters. At the same time, start the EEG analyzer and the EMG collector to synchronously collect the EEG and EMG data during the stimulation, and ensure that the distance between the magnetic stimulation coil and the scalp is less than 1 cm to reduce energy loss.

[0072] Step S302, data pre - processing, including:

[0073] EEG data pre - processing: Filter the collected EEG data with a length of t. Usually, a Butterworth zero - phase band - pass filter is used to retain the data information from 0 to 250 Hz, and at the same time remove the 50 Hz power - frequency interference. The filtered data is used for subsequent analysis.

[0074] EMG data pre - processing: Perform band - pass filtering on the collected EMG data with a length of t. The cut - off frequencies are usually set between 20 Hz and 250 Hz to remove low - frequency noise and high - frequency interference. The 50 Hz power - frequency interference also needs to be removed.

[0075] Step S400, calculate the stimulation moment, including:

[0076] Step S401, Peak Detection: Based on the currently acquired EEG data segment t and the next acquired EEG data segment t, the maximum and minimum values ​​of two adjacent EEG data segments are taken respectively. By comparing the absolute values ​​of the maximum and minimum values ​​of the two data segments, the time point corresponding to the maximum value is taken as the TMS stimulation time to improve the accuracy of stimulation time calculation.

[0077] Step S402: Time alignment. Based on the calculated stimulation time, extract and save multi-channel EEG and EMG data with a data length of t (from t / 2 before the stimulation time to t / 2 after the stimulation time) for subsequent analysis of EEG and EMG changes before and after stimulation.

[0078] Step S500, calculate the power spectral density, including:

[0079] Step S501: Calculate the power spectral density of the EEG data by performing a Fast Fourier Transform (FFT) on an EEG data segment of length t.

[0080]

[0081] in, For length is Time series, For frequency domain indexing, For time-domain indexing, It is the imaginary unit.

[0082] Based on this, the power spectral density values ​​of each lead in different frequency bands were calculated:

[0083]

[0084] in, , For each frequency band, the corresponding frequency domain index. .

[0085] According to international standards, frequency bands can be divided into six bands: delta wave (0.5~4Hz), theta wave (4~8Hz), alpha wave (8~13Hz), beta wave (13~30Hz), gamma wave (30Hz~80Hz), and high gamma wave (80~250Hz).

[0086] The power spectral density values ​​of data segment S1 (0~t / 2~Δ, where Δ is a data length control parameter to control the analysis data between two adjacent stimulus times, Δ≤20ms) and data segment S2 (the entire data length t) were calculated in each channel and 6 frequency bands for subsequent comprehensive evaluation.

[0087] Step S502: Calculate the power spectral density of the electromyography (EMG) data. Perform an FFT transform on an EMG data segment of length t to calculate the power spectral density values ​​of the EMG leads at different frequency bands. The frequency band division is the same as for the EEG data.

[0088] The power spectral density values ​​of data fragment S1 and data fragment S2 in the six frequency bands were calculated respectively for subsequent comprehensive evaluation.

[0089] Step S600: Comprehensively evaluate and determine the threshold, including:

[0090] Step S601: Power spectral density calculation. Calculate the combined EEG and EMG power spectral density on data segments S1 and S2 for N stimuli. and :

[0091]

[0092]

[0093] Where k is the number of leads collected by the EEG; ~ The following represent the power spectral density of lead i in data segment S1 in six frequency bands (including 0.5~4Hz, 4~8Hz, 8~13Hz, 13~30Hz, 30~80Hz, and 80~250Hz). ~ The power spectral density of lead i in data segment S2 is represented in six frequency bands in sequence. ~ The following figures sequentially represent the power spectral density of electromyography leads in the six frequency bands of data segment S1. ~ The power spectral density of electromyography leads in data segment S2 is represented sequentially across the six frequency bands.

[0094] The calculation yields the corresponding results of N TMS stimuli. and That is, the first TMS stimulus corresponds to The second TMS correspondence The Nth TMS stimulus corresponds to The same applies to M2.

[0095] in, The combined power spectral density of EEG and EMG corresponding to data segment S1; This represents the combined power spectral density of EEG and EMG corresponding to data segment S2.

[0096] Step S602: Threshold determination. Calculate the stimulus response index K value for each of N consecutive TMS stimuli using the following formula:

[0097]

[0098] Obtain the array corresponding to N TMS stimuli { }, select array { The TMS stimulation intensity corresponding to the maximum value in} is used as the personalized stimulation threshold (when the stimulation intensity reaches the individual threshold, the difference between EEG and EMG before and after TMS stimulation is the most significant, that is, the stimulation response is the strongest).

[0099] Step S700: Dynamically display the threshold results, including:

[0100] Step S701: The calculated threshold is dynamically displayed on the TMS personalized threshold assessment device. The display interface may include information such as the threshold value, stimulation parameter settings, EEG and EMG data waveforms, and power spectral density graphs, so that doctors can intuitively understand the subject's response and the threshold determination process.

[0101] Step S702, feedback adjustment: Based on the displayed threshold results, the doctor can adjust the TMS stimulation parameters (such as stimulation intensity, frequency, etc.) in a timely manner. At the same time, the device can record the response after each adjustment.

[0102] The above content is merely an embodiment of the present invention. Commonly known structures and characteristics of the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all prior art in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can improve and implement this solution based on the guidance provided in this application and their own capabilities. Some typical well-known structures or systems should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A device for quantitatively assessing personalized thresholds for transcranial magnetic stimulation, characterized in that, include: The EEG acquisition module and EMG acquisition module are used to install EEG and EMG electrodes and set TMS pre-stimulation parameters. After stimulation begins, EEG and EMG data are recorded synchronously, the TMS stimulation time is calculated, and the EEG and EMG data from t / 2 before to t / 2 after the stimulation time are saved based on the calculated stimulation time. The data preprocessing module filters EEG and EMG data of length t, and then calculates the power spectral density values ​​of each lead in different frequency bands for data segments S1 (0~t / 2~Δ) and the entire data segment S2 (length t), where Δ is a data length control parameter. The stimulation timing calculation module calculates the TMS stimulation timing by jointly calculating the peak values ​​of two adjacent EEG data blocks. The stimulation threshold calculation module calculates the TMS stimulation threshold by combining the power spectral density values ​​of multi-channel EEG and EMG leads in different frequency bands after N stimulations, including the following: calculating the joint power spectral density on data segments S1 and S2 respectively. and The calculation formula is as follows: Where k is the number of leads collected by the EEG; ~ The following figures represent the power spectral density of lead i in data segment S1 across the six frequency bands. ~ The power spectral density of lead i in data segment S2 is represented in six frequency bands in sequence. ~ The following figures sequentially represent the power spectral density of electromyography leads in the six frequency bands of data segment S1. ~ The power spectral density of the electromyography leads in data segment S2 is represented sequentially across six frequency bands; the corresponding values ​​for N TMS stimulations are calculated through N TMS stimulations. and ,in, The combined EEG and EMG power spectral density corresponding to data segment S1; The combined EEG and EMG power spectral density corresponds to data segment S2; the stimulus response index K value is calculated for each of the N consecutive TMS stimulations, using the following formula: Obtain the array corresponding to N TMS stimuli { }, select array { The TMS stimulation intensity corresponding to the maximum value in} is used as the personalized stimulation threshold.

2. The device for quantitatively assessing personalized thresholds for transcranial magnetic stimulation according to claim 1, characterized in that, Install EEG and EMG electrodes and set TMS pre-stimulation parameters, including the following: install k-channel EEG leads in a 10-20 lead system on the subject's head and install EMG leads in the abductor pollicis brevis muscle on the back of the subject's hand. The stimulation parameters include at least: stimulation count N, stimulation frequency F, and N intensity levels. The stimulus intensity sequence A, EEG and EMG sampling rates f, and data truncation length t constitute the data, where 1 / F <t<2 / F。 3. The device for quantitatively assessing personalized thresholds for transcranial magnetic stimulation according to claim 1, characterized in that, The calculation of TMS stimulation time includes the following: peak detection is performed on the continuous EEG data segments corresponding to two adjacent stimuli, the time corresponding to the maximum absolute value is determined as the stimulation time of the stimulus, and EEG and EMG data of t / 2 time before and after the time are extracted to form an analysis window of length t.

4. The device for quantitatively assessing personalized thresholds for transcranial magnetic stimulation according to claim 1, characterized in that, The EEG and EMG data of length t are filtered separately, including the following: the EEG data and the EMG data are bandpass filtered from 0 to 250 Hz and the 50 Hz power frequency interference is removed.

5. The device for quantitatively assessing personalized thresholds for transcranial magnetic stimulation according to claim 1, characterized in that, Calculate the power spectral density values ​​of each lead in different frequency bands, including the following: For each stimulus, the EEG and EMG data segments were subjected to Fast Fourier Transform, and their power spectral density in six frequency bands (0.5-4Hz, 4-8Hz, 8-13Hz, 13-30Hz, 30-80Hz, and 80-250Hz) was calculated.

6. The device for quantitatively assessing personalized thresholds for transcranial magnetic stimulation according to claim 1, characterized in that, The original data from each channel are used to extract frequency domain features using a Fast Fourier Transform, as shown in the following formula: in, For length is Time series, For frequency domain indexing, For time-domain indexing, The value is an imaginary unit; the power spectral density value of each frequency band is extracted from the calculated frequency domain features, as shown in the following formula: in, 、 For each frequency band, the corresponding frequency domain index. 。 7. The device for quantitatively assessing personalized thresholds for transcranial magnetic stimulation according to claim 1, characterized in that, It also includes a stimulation threshold display module, which dynamically displays the calculated stimulation threshold. The display interface may include threshold values, stimulation parameter settings, EEG and EMG data waveforms, and power spectral density graphs.

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