A method and system for neuromodulation of affective disorders based on individual alpha frequencies

Abstract

This invention belongs to the field of neuromodulation and mental illness technology, and discloses a neuromodulation method for affective disorders based on individual alpha frequencies. This invention provides precise individualized treatment: through a 64-lead high-density EEG module (including temporal lobe leads) and an optimized electrode layout (target electrode + 6 loop electrodes), signal acquisition accuracy is improved by more than 35%, overcoming the limitations of traditional systems such as limited monitoring range and unstable signal quality. Combined with a real-time IAF detection algorithm, the matching accuracy between treatment parameters and individual EEG characteristics reaches 0.2Hz, an improvement of more than 50% compared to traditional fixed-frequency TMS systems. Intelligent closed-loop modulation: the closed-loop system built based on a PID control algorithm achieves a 50ms-level response speed, improving parameter adjustment timeliness by 5 times compared to traditional open-loop TMS systems. The treatment effectiveness rate is increased from 45% to 65-72%, especially for drug-resistant patients, with an effectiveness rate of 58%.

Description

Technical Field

[0001] This invention belongs to the field of neuromodulation and mental illness technology, and in particular relates to a method and system for neuromodulation of emotional disorders based on individual alpha frequencies. Background Technology

[0002] Currently, traditional drug therapy has limitations in the treatment of mood disorders such as depression and anxiety disorders. Some patients do not respond well to drug therapy, and drug therapy may be accompanied by side effects. In recent years, neuromodulation technology has played an increasingly important role in the treatment of mental illnesses. Among them, transcranial magnetic stimulation (TMS) has become an important treatment for mood disorders such as depression and anxiety disorders due to its non-invasiveness, safety, and good tolerability. TMS generates induced currents in brain tissue through time-varying magnetic fields, thereby achieving precise regulation of neural activity in specific brain regions. However, the current TMS technology lacks individualization in clinical application, which severely restricts the maximization of its therapeutic effect. Studies have shown that individual alpha frequency (8-13Hz), as an important indicator reflecting brain functional state, is closely related to emotion regulation function, but traditional TMS systems generally fail to fully consider this key physiological characteristic, resulting in significant individual differences in treatment effects.

[0003] Currently, TMS devices on the market are mainly divided into three categories: fixed-frequency systems use standardized parameters (such as 10Hz or 1Hz), but this fixed-frequency approach cannot adapt to individual differences in alpha frequency (IAF), resulting in insufficient response in approximately 30%-40% of patients; phase-synchronized TMS systems, while achieving EEG phase-triggered stimulation, still use the population average frequency (9-11Hz), failing to fully consider individual differences; and navigation TMS systems, although improving the accuracy of anatomical localization, lack a real-time EEG feedback mechanism and cannot dynamically adjust stimulation parameters based on the patient's real-time brain response. These systems generally suffer from problems such as mismatch between stimulation parameters and individual neural characteristics, and the inability to dynamically adjust, severely restricting the improvement of clinical efficacy. Therefore, developing a personalized closed-loop neuromodulation system with real-time monitoring and dynamic adjustment capabilities has become an urgent need in this field.

[0004] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0005] (1) Traditional fixed-frequency stimulation patterns cannot adapt to IAF differences, resulting in insufficient response in about 30%-40% of patients;

[0006] (2) Existing open-loop systems lack a real-time EEG feedback mechanism and cannot dynamically adjust treatment parameters according to changes in neural activity. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides a neuromodulation method for affective disorders based on individual alpha frequencies.

[0008] This invention is implemented as follows: A neural modulation method for affective disorders based on individual alpha frequencies includes:

[0009] Step 1: System initialization and calibration;

[0010] Complete multi-channel EEG electrode impedance detection and environmental noise calibration, establish signal acquisition benchmarks, and ensure that equipment operating parameters meet preset thresholds;

[0011] Step 2: EEG signal acquisition and individual alpha frequency calibration;

[0012] Step 3: Parameter setting and dynamic adjustment;

[0013] Step 4: Efficacy assessment;

[0014] A multidimensional efficacy evaluation report is generated by comprehensively analyzing alpha band power, neural synchronicity indicators, and clinical scale data before and after treatment.

[0015] Furthermore, the acquisition of the electroencephalogram (EEG) signals and the calibration of individual alpha frequencies are as follows:

[0016] By acquiring resting-state EEG signals using high-density EEG and combining noise suppression and spectral analysis techniques, the individual alpha peak frequency characteristics can be accurately extracted.

[0017] Furthermore, the parameter settings and dynamic control are as follows:

[0018] Based on IAF, initial TMS parameters are set, and the neural oscillation state is monitored through real-time EEG feedback. The stimulation frequency, intensity, and phase synchronization are dynamically optimized to achieve closed-loop regulation of "stimulation-feedback-adjustment".

[0019] Another objective of this invention is to provide a neuromodulation system for affective disorders based on individual alpha frequencies, comprising:

[0020] Multi-channel EEG signal acquisition and noise suppression module: includes 64 leads, signal amplification and filtering circuit, and power frequency noise suppression module, used for real-time monitoring of brain activity;

[0021] The power spectral density-based IAF peak frequency detection module includes a fast Fourier transform processor, an IAF dynamic tracking algorithm, and an adaptive algorithm for dynamically adjusting TMS parameters.

[0022] TMS stimulation module: used for transcranial magnetic stimulation based on individualized parameters.

[0023] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the neuromodulation method for affective disorders based on individual alpha frequencies.

[0024] Another object of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of the neuromodulation method for affective disorders based on individual alpha frequencies.

[0025] Another objective of this invention is to provide an information data processing terminal for implementing the aforementioned neuromodulation system for emotional disorders based on individual alpha frequencies.

[0026] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0027] Addressing the core technical challenges of existing transcranial magnetic stimulation (TMS) treatment systems, such as limited monitoring range, insufficient signal acquisition accuracy, fixed treatment parameters, and lagging control modes, this invention, based on measured data and technological achievements during its development, deeply analyzes the problem-solving path and innovative technical effects of the protected technical solution. It also elucidates the derived social value, specific advantages, and positive effects as follows:

[0028] First, the effectiveness of core technologies

[0029] Firstly, it achieves high-precision, individualized treatment. This invention features a 64-lead high-density EEG module with an additional dedicated temporal lobe lead. Combined with an optimized layout of target electrodes and six loop electrodes, it completely overcomes the shortcomings of traditional equipment, such as incomplete monitoring coverage, susceptibility to signal interference, and large quality fluctuations. Actual measurements show an improvement of over 35% in EEG signal acquisition accuracy and significantly optimized signal stability. Simultaneously, it incorporates a self-developed real-time IAF detection algorithm, overcoming the drawbacks of fixed-frequency treatment in traditional TMS systems. This enables precise matching of treatment parameters with individual EEG characteristics, achieving a matching accuracy of up to [percentage missing]. With a frequency of 0.2Hz, the parameter adaptability is improved by more than 50% compared with traditional equipment, truly achieving individualized intervention for each person and avoiding ineffective treatment and overstimulation.

[0030] Secondly, it achieves high-speed intelligent closed-loop control. A full-process intelligent closed-loop control system is built based on PID control algorithms, achieving a system response speed of 50ms. The real-time parameter adjustment efficiency far surpasses that of traditional open-loop TMS systems, improving overall control efficiency by 5 times. Relying on the closed-loop real-time feedback mechanism, the treatment process can be dynamically optimized based on the patient's real-time EEG status. The clinical treatment effectiveness rate has increased from 45% in traditional systems to 65%-72%. Even for the highly challenging group of patients with drug-resistant depression, the treatment effectiveness rate can still reach 58%, breaking through the bottleneck of poor efficacy of existing technologies for treatment-resistant cases and demonstrating significant clinical innovation.

[0031] Second, the derivative social effects

[0032] On the one hand, it significantly saves medical resources and reduces the economic burden on patients. Thanks to the precision treatment model, targeted intervention directly improves the efficiency of a single treatment, shortening the average treatment course for patients from 30 times to 20-25 times. This reduces repeated treatments and the consumption of medical resources, allowing each patient to reduce medical expenses by 25%-30%, alleviating the economic pressure on patients seeking medical treatment, and also optimizing the allocation of medical resources in medical institutions.

[0033] On the other hand, it effectively improves public health and reduces the burden of social care. High-precision individualized treatment effectively improves the clinical cure rate of depression, shortens the average recovery period for patients, helps patients return to normal life and family more quickly, significantly reduces the long-term care burden on patients' families, and at the same time helps improve the current situation of clinical diagnosis and treatment of depression, promoting the transformation of mental illness diagnosis and treatment towards precision and efficiency, and has outstanding public health value. Attached Figure Description

[0034] Figure 1 This is a flowchart of a neuromodulation method for affective disorders based on individual alpha frequencies provided in an embodiment of the present invention.

[0035] Figure 2 This is a block diagram of the neuromodulation system for affective disorders based on individual alpha frequencies provided in an embodiment of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0037] like Figure 1 As shown, an embodiment of the present invention provides a method for neuromodulation of affective disorders based on individual alpha frequencies, comprising the following steps:

[0038] S101: System initialization and calibration;

[0039] Complete multi-channel EEG electrode impedance detection and environmental noise calibration, establish signal acquisition benchmarks, and ensure that equipment operating parameters meet preset thresholds;

[0040] S102: EEG signal acquisition and individual alpha frequency calibration;

[0041] S103: Parameter setting and dynamic control;

[0042] S104: Efficacy assessment;

[0043] Comprehensive analysis before and after treatment Multidimensional efficacy assessment reports are generated from band power, neural synchronicity indicators, and clinical scale data.

[0044] The EEG signal acquisition and individual alpha frequency calibration provided in this embodiment of the invention:

[0045] By acquiring resting-state EEG signals using high-density EEG and combining noise suppression and spectral analysis techniques, the individual alpha peak frequency characteristics can be accurately extracted.

[0046] The parameter settings and dynamic control provided in this embodiment of the invention are as follows:

[0047] Based on IAF, initial TMS parameters are set, and the neural oscillation state is monitored through real-time EEG feedback. The stimulation frequency, intensity, and phase synchronization are dynamically optimized to achieve closed-loop regulation of "stimulation-feedback-adjustment".

[0048] like Figure 2 As shown, an embodiment of the present invention provides a neural modulation system for affective disorders based on individual alpha frequencies, comprising:

[0049] Multi-channel EEG signal acquisition and noise suppression module: includes 64 leads, signal amplification and filtering circuit (0.5-100Hz bandwidth), and power frequency noise suppression module (50 / 60Hz adaptive cancellation), used for real-time monitoring of EEG activity;

[0050] The IAF peak frequency detection module based on power spectral density includes a Fast Fourier Transform (FFT) processor, an IAF dynamic tracking algorithm (sliding window + peak identification), and an adaptive algorithm (PID control or machine learning model) for dynamically adjusting TMS parameters.

[0051] TMS stimulation module: used for transcranial magnetic stimulation based on individualized parameters;

[0052] These three modules work together to form a closed-loop control mechanism of "signal acquisition - feature extraction - parameter optimization - precise stimulation - real-time feedback". Its core working principle is as follows: First, the patient's EEG signal is acquired through a 64-lead EEG electrode array. After amplification, filtering and analog-to-digital conversion by the signal acquisition card, it is transmitted to the control module. The control module uses FFT and sliding window algorithms to analyze the EEG signal in real time, accurately identify the individual alpha frequency, and dynamically optimize the TMS stimulation parameters (including frequency, intensity and pulse width) through a PID control algorithm. The optimized parameters drive the TMS coil through a power amplifier to generate a precise magnetic field to stimulate the target brain region. At the same time, the system continuously monitors the changes in the EEG response after stimulation, forming a closed-loop control loop of "signal acquisition - feature extraction - parameter optimization - precise stimulation - real-time feedback" to realize the real-time adjustment and optimization of treatment parameters.

[0053] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the neuromodulation method for affective disorders based on individual alpha frequencies.

[0054] Another object of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of the neuromodulation method for affective disorders based on individual alpha frequencies.

[0055] Another objective of this invention is to provide an information data processing terminal for implementing the neuromodulation system for emotional disorders based on individual alpha frequencies.

[0056] Specific implementation of the present invention:

[0057] Step 1: System initialization and calibration.

[0058] (1) After the patient wears the electrode cap, the system automatically detects the electrode contact impedance (target value <5kΩ), abnormal electrodes are highlighted in red, and position correction suggestions are generated (based on 3D head model, registration error <1 mm).

[0059] (2) Environmental parameter calibration: noise control (target value <30 dB(A)), temperature and humidity regulation (20-25) (Humidity 40-60%).

[0060] Step 2: EEG signal acquisition and IAF calibration.

[0061] (1) Signal acquisition: The subject remained in a resting state with eyes closed, and the system was set to (1000Hz) Continuous recording at a sampling rate of 10Hz for 5 minutes 0.5 minutes of EEG signals.

[0062] (2) Preprocessing: First, an adaptive filter is used to eliminate power frequency interference (50 / 60Hz) to ensure that the peak-to-peak value of residual noise is below 3. Next, signal components were separated by independent component analysis, retaining neurogenic components with a variance contribution rate of over 95%, effectively removing motion artifacts caused by eye movements, electromyography, etc.

[0063] (3) Power spectral analysis: The system uses the improved Welch periodogram method (window length 2s, overlap rate 75%) to estimate the spectrum of the preprocessed EEG signal, calculates the power spectral density in the 8-13Hz band, and determines the IAF main peak (e.g., 10.2Hz) through a peak detection algorithm. (0.2Hz), confidence interval 95%.

[0064] Step 3: Parameter setting and dynamic control.

[0065] (1) Initial parameter settings: The initial treatment parameters were set based on individualized test results, and the stimulation frequency was IAF plus (0.5 Hz). 0.1 Hz), ensuring precise matching with the patient's neural oscillation characteristics; the stimulation intensity is set to 110% of the motor threshold (MT). 2%); pulse width is fixed at (200) ), corresponding to the magnetic field penetration depth (10-15mm) (1mm), balancing stimulation intensity and safety.

[0066] (2) Real-time monitoring and dynamic adjustment: During the treatment process, the system monitors the patient's temperature and humidity at 2 seconds per second. Continuous monitoring of key indicators with a cycle of 0.1 seconds: Band power change rate exceeds Frequency adjustment is triggered at 20% (step size 0.1 Hz, range 8-13 Hz); the Phase Locking Value (PLV) is used to assess neural synchronicity; when the PLV is below 0.8... Increment the stimulus intensity by 5% when the value is 0.05. 1% (upper limit 120% MT); Coil temperature is monitored in real time via a PT100 sensor (accuracy). 0.1℃), exceeding the limit (40℃) When the temperature reaches 1℃, the frequency is automatically forced to drop to 8 Hz and an audible and visual alarm is triggered. This closed-loop control mechanism achieves millisecond-level response through sliding window analysis (window length 2s) and fuzzy logic decision-making, ensuring dynamic optimization of treatment parameters.

[0067] Step 4: Efficacy assessment.

[0068] After treatment, the system automatically analyzes key data from the treatment process, including electroencephalogram (EEG) signals. Changes in band power, adjustment frequency and amplitude of stimulation parameters, etc. By comparing IAF values ​​before and after treatment, Band power spectral density and neural synchronicity indices (such as PLV) are used to assess treatment efficacy. Simultaneously, clinical assessment scales are combined to comprehensively evaluate the patient's symptom improvement, generating a detailed efficacy evaluation report.

[0069] Example 1: Precise calibration of individual alpha frequencies (IAF) based on resting-state electroencephalography (rsEEG)

[0070] This embodiment uses a high-resolution EEG acquisition system to acquire spontaneous EEG activity of subjects in a resting, eyes-closed state. The acquired continuous EEG signals are analyzed using the power spectral density (PSD) estimation method. Within the 8-13Hz alpha band, the frequency point corresponding to the peak power is identified and defined as the individual alpha frequency (IAF). Technical details: The peak frequency method is used for further refinement calibration to ensure spatial coupling between the stimulation frequency fstimulation and the subject's intrinsic oscillatory rhythm fIAF. Results: Experimental verification shows that IAF-based specific modulation has a higher resonance-inducing effect than conventional modulation fixed at 10Hz.

[0071] Example 2: Closed-loop neuromodulation of IAF based on real-time EEG feedback

[0072] This embodiment introduces a closed-loop feedback mechanism. During neuromodulation, the system simultaneously acquires EEG signals and performs online short-time Fourier transform (STFT) using sliding window technology. Dynamic adjustment logic: Real-time monitoring of the relative power spectrum intensity and main peak frequency shift in the alpha band. If IAF drift due to subject fatigue or modulation is detected, the system automatically triggers parameter compensation, fine-tuning the stimulation frequency Δf in real time. Results: This embodiment effectively solves the "phase lockout" problem during stimulation and significantly improves the synchronization level of neuronal clusters.

[0073] Example 3: Adaptive modulation based on phase-amplitude coupling (PAC) and neural synchronization

[0074] This embodiment further quantifies neural synchronicity indicators (such as phase-locked value (PLV) or coherence). The system monitors not only power changes but also the signal phase consistency within the target brain region. Intensity control algorithm: A synchronicity threshold Tsync is set. When PLV < Tlow, the system gradually increases the stimulation current intensity (e.g., in 0.1mA steps); when PLV > Thigh, the intensity is maintained or moderately reduced to prevent over-excitation or side effects. Effect: A dynamic nonlinear adaptation between stimulation intensity and brain functional state is achieved, ensuring the safety and effectiveness of the modulation.

[0075] Example 4: Multi-stage segmented regulation and neuroplasticity guidance strategy

[0076] This embodiment divides the entire regulation process into three stages: induction period, maintenance period, and consolidation period.

[0077] Phase Logic: After each phase, a 3-5 minute washout or observation period is performed to recalculate the current IAF. Based on the neurophysiological response characteristics of the previous phase (such as the alpha rebound effect), the initial stimulation parameters for the next phase are dynamically updated. Effect: This quasi-continuous update mechanism can cope with the adaptive tolerance (Habituation) developed by long-term stimulation of the nervous system, enhancing the long-term effectiveness of regulation in improving mood disorders.

[0078] Example 5: Modular System Collaborative Neural Modulation Implementation Architecture

[0079] This embodiment illustrates the integration of hardware and algorithm logic. The system consists of a signal sensing module, a feature extraction engine, a decision logic unit, and a physical stimulus driver. Interaction mechanism: The feature extraction engine uses a fast algorithm to calculate the IAF and entropy weight index; the decision logic unit calculates the target voltage / current vector according to a preset control law. Results: The modular design ensures extremely low latency from signal acquisition to feedback. Experiments demonstrate that this system architecture exhibits good robustness and real-time performance when processing high-throughput EEG data.

[0080] Example 6: Long-term regulation optimization based on multimodal efficacy assessment feedback

[0081] This embodiment combines electrophysiological indicators (alpha gain) with clinical affective scales (such as HAM-D, PHQ-9) for closed-loop assessment. Optimization of the closed loop: The differential signal before and after regulation and the improvement rate of scale scores are used as input variables, and a machine learning model (such as support vector machine) is used to fit the subject's response curve to specific parameters. Results: This embodiment provides a precise parameter library tailored to each patient with affective disorders, realizing the translational application from a single experiment to long-term clinical treatment.

[0082] Example 7: Individual Alpha Frequency Calibration and Neural Modulation Based on Resting-State EEG

[0083] In this embodiment, the subject is in a quiet and awake state, preferably in an environment with stable lighting and minimal noise interference, to complete the resting EEG data acquisition with eyes closed. A multi-channel EEG acquisition method is used to obtain continuous and stable raw signal data, with an acquisition duration preferably of 2–10 minutes to ensure the stability of the spectrum estimation. During the acquisition process, the electrode impedance is monitored to ensure it meets a preset range (e.g., not higher than 10). (or the device's recommended threshold), and record the acquisition status for subsequent artifact removal.

[0084] During signal processing, the raw EEG undergoes preprocessing and noise suppression, including but not limited to: bandpass filtering (covering the alpha-related frequency band), notch filtering (suppressing power line interference), independent component analysis or regression analysis to remove eye movement / EMG artifacts, and segmented removal of abnormally high-amplitude segments. Subsequently, spectral analysis is performed, with spectral estimation methods such as Welch power spectral density estimation or multiple cone window spectral estimation. The power spectral density is calculated within the alpha band (e.g., 8–13 Hz, or 7–14 Hz depending on population characteristics), and the frequency with the highest power is used as the subject's individual alpha frequency (IAF). To improve robustness, the IAF can also be obtained using a "maximum power frequency + neighborhood weighted average" approach, and the IAF confidence interval or stability index (such as the window standard deviation) is output.

[0085] Based on the IAF, a target frequency parameter for neural stimulation is set, and neural modulation is performed on the subject according to this parameter. The stimulation method can be transcranial alternating current stimulation, transcranial magnetic stimulation, transcranial noise stimulation, or other forms of neural stimulation with controllable frequency; the stimulation frequency is preferably set to IAF or IAF. f( f is a small offset used to achieve up / down tuning. Stimulation intensity, pulse width / duty cycle, and stimulation duration are set according to safety thresholds and treatment requirements.

[0086] Through the above implementation process, the frequency of neural stimulation is matched with the subject's own neural oscillation rhythm, reducing individual differences and the risk of mismatch caused by uniform frequency stimulation. Repeated implementations show that the determined IAF remains stable in the short term, and neural modulation based on this frequency has good repeatability and consistency, providing a reliable basis for subsequent closed-loop and dynamic modulation.

[0087] Example 8: Closed-loop modulation of individual alpha frequency based on EEG feedback

[0088] In this embodiment, after IAF calibration is completed, continuous neural stimulation is applied to the subject, and EEG signals are acquired in real time during the stimulation process. To reduce the impact of stimulation artifacts on the analysis, the system can employ artifact suppression strategies, including but not limited to: stimulation synchronization marking, template subtraction, reference channel regression, or selecting an analysis window without saturation segments within the stimulation cycle. The system performs online analysis on the acquired EEG signals, extracting Alpha band power, peak frequency position (whether it deviates from IAF), and its changing trend in real time, and comparing the current state with the baseline state before stimulation.

[0089] Closed-loop parameter tuning is triggered when any of the following conditions are detected: 1) Alpha band power decreases or increases abnormally relative to the baseline by more than a threshold; 2) Alpha peak frequency drifts relative to IAF (e.g., deviates from the preset Hz threshold); 3) Indicators reflecting neural synchronicity or phase stability shift.

[0090] Upon triggering, the system automatically adjusts the stimulation frequency and intensity: frequency adjustment preferably uses a small-step search centered on the IAF or proportional adjustment (e.g., IAF). (Progressive updates of 0.1–0.5 Hz) are preferred. Intensity adjustments are preferably made in steps or proportionally within a safe range, and an upper limit is set on the rate of change to avoid instantaneous overstimulation.

[0091] This embodiment achieves dynamic updates of stimulation parameters through real-time EEG feedback, enabling adaptive regulation of neural stimulation based on the subject's current neural oscillation state. Experimental results show that this closed-loop regulation method can effectively suppress stimulus mismatch, maintain the stability of neural oscillations, and improve the precision and individualization of the regulation process.

[0092] Example 9: Adaptive Adjustment of Stimulus Intensity Based on Neural Synchronicity Indicators

[0093] In this embodiment, during neural modulation, in addition to the alpha band power, neural synchronicity indicators reflecting the degree of coordinated brain region activity are further calculated. These synchronicity indicators may include phase lock value (PLV), coherence, weighted phase lag index (wPLI), or other measures reflecting connection strength and phase consistency. The system continuously monitors the time-series changes of the synchronicity indicators and compares them with an individualized baseline or target interval.

[0094] When the synchronicity level is below a preset threshold or below the lower bound of the target interval, the stimulus intensity is automatically increased; when the synchronicity level is above the preset threshold or above the upper bound of the target interval, the stimulus intensity is correspondingly reduced to avoid overstimulation and discomfort. To ensure the stability of regulation, the intensity adaptive strategy can introduce hysteresis interval and minimum duration constraints: parameter tuning is only performed when the synchronicity index continuously deviates from the threshold for a preset duration; at the same time, a maximum step size and a maximum change frequency are set for intensity changes.

[0095] This implementation method achieves dynamic adaptation between stimulus intensity and neural network state. Multiple implementations demonstrate that this method can maintain the modulatory effect while reducing the risk of stimulus discomfort, making the neural modulation process safer and more stable, and thus possessing clinical applicability.

[0096] Example 10: Application of Emotional State Improvement Based on Multi-Stage Regulation

[0097] In this embodiment, the neural modulation process is divided into multiple stages. Each stage reconfirms the IAF characteristics based on real-time or stage-initial EEG signals and updates the stimulation parameters accordingly. Each stage can have different modulation objectives; for example, the early stage focuses on improving alpha stability, the middle stage on optimizing network synchronization, and the later stage on consolidating the effect and reducing intensity. After each stage, the system records changes in alpha power, peak frequency stability, and synchronization indicators, which are used as the basis for parameter updates before the next stage begins.

[0098] To avoid the attenuation of regulation caused by long-term fixed parameters, this embodiment adopts a phased dynamic update mechanism: when IAF drift or power response decline is observed, frequency recalibration or fine-tuning of the stimulation frequency is preferred; when synchronicity is too strong or too weak, stimulation intensity and duration are preferred. During the treatment course, phased assessments of subjective scales / behavioral tasks can be superimposed as auxiliary constraints for parameter optimization in the next phase.

[0099] The results showed that multi-stage regulation can better adapt to changes in the subject's neurological state over time, maintain a stable regulatory effect, and improve the consistency and controllability of the overall treatment course.

[0100] Example 11: Implementation of Neural Modulation Based on System Collaboration

[0101] In this embodiment, the system includes an EEG signal acquisition unit, an individual alpha frequency extraction unit, a parameter decision unit, and a neural stimulation unit. The EEG signal acquisition unit acquires the subject's resting-state and stimulated-state EEG signals and provides time synchronization markers; the individual alpha frequency extraction unit performs preprocessing, spectrum analysis, and IAF extraction, and outputs features such as power, peak frequency, stability, and synchronicity; the parameter decision unit generates or updates stimulation parameters (including at least target frequency and stimulation intensity, and may also include waveform, duty cycle, stimulation duration, phase division rules, etc.) based on the above features; the neural stimulation unit implements neural modulation based on the updated parameters.

[0102] Each unit operates collaboratively according to a predetermined logical sequence, forming a continuous control process: resting-state data acquisition → IAF calibration → stimulus execution → online feature extraction → closed-loop parameter tuning → recording and evaluation. The system may also include a safety control module for setting upper and lower limits for stimulus parameters, issuing abnormal alarms, and automatically downgrading or stopping stimulation when signal saturation, strong artifacts, or subject discomfort are detected.

[0103] This implementation demonstrates that each functional unit achieves closed-loop collaboration through parameter and signal interaction, making the neural modulation process structure clear, the realization path well-defined, and enabling the stable implementation of the IAF-based neural modulation method for affective disorders.

[0104] Example 12: Optimization Implementation Method Based on Therapeutic Effect Assessment Feedback

[0105] In this embodiment, after completing the neuromodulation, the EEG signals before and after the modulation are compared and analyzed to evaluate objective neurophysiological indicators such as changes in Alpha band power, peak frequency stability, and synchronicity. Simultaneously, efficacy evaluation data is generated by combining standardized clinical scales or emotional state assessment tools (such as depression, anxiety, and emotional arousal / pleasure scales), and can be further combined with behavioral indicators (reaction time, performance on emotion recognition tasks, etc.) to form a comprehensive evaluation.

[0106] The evaluation results are used to optimize and adjust subsequent regulatory parameters, serving as a reference for the next round of neural modulation.

[0107] When objective indicators improve but subjective efficacy is insufficient, the preferred strategy is to extend the treatment course or adjust the treatment phase.

[0108] When subjective improvement is significant but synchronicity is too strong or unduly increased, it is preferable to reduce the intensity or increase the hysteresis constraint.

[0109] When IAF drift is significant, it is preferable to shorten the recalibration interval or increase the closed-loop update frequency.

[0110] By introducing a efficacy evaluation feedback mechanism, neuromodulation is expanded from a single implementation to an iterative optimization process. Implementation results show that this approach helps to gradually improve the precision and individualization of modulation, enabling neuromodulation protocols to possess long-term stability and scalability.

[0111] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0112] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A neural modulation method for affective disorders based on individual alpha frequencies, characterized in that, Includes the following steps: Signal preprocessing: acquire raw EEG signals of the subject in the resting state, and perform artifact removal processing on the raw EEG signals to obtain pure EEG signals; Individual frequency calibration involves time-frequency analysis of pure EEG signals to identify characteristic parameters within the 8Hz to 13Hz Alpha frequency band and determine the unique corresponding individual Alpha frequency. Stimulation protocol generation sets the individual's alpha frequency as the target stimulation frequency and combines it with preset stimulation waveforms and pulse widths to generate a personalized neuromodulation protocol. Closed-loop regulation is executed by initiating neural stimulation according to the neural regulation protocol, simultaneously collecting the subject's online EEG signals in real time, extracting Alpha band features from the online EEG signals, and dynamically correcting stimulation parameters based on the deviation between the online features and the baseline state to complete continuous closed-loop neural regulation.

2. The method according to claim 1, characterized in that, In the individual frequency calibration step, the power spectral density estimation method is used to calculate the energy distribution in the Alpha band, and the frequency point corresponding to the energy peak is determined as the individual Alpha frequency.

3. The method according to claim 1, characterized in that, In the individual frequency calibration step, the centroid frequency method is used to calculate the weighted average frequency within the Alpha band, and the calculation result is determined as the individual Alpha frequency.

4. The method according to claim 1, characterized in that, In the closed-loop regulation execution step, the neural synchronicity index of the target brain region is extracted in real time. The neural synchronicity index is obtained by calculating the phase-locked value of the signal in the target brain region. When the neural synchronicity index is lower than the preset threshold, the stimulation intensity is increased by a fixed step size until the neural synchronicity index returns to the target range.

5. The method according to claim 1, characterized in that, The neuromodulation protocol is divided into three progressive phases: induction, maintenance, and consolidation. After each phase, an observation window is set during which EEG signals are recollected to calibrate the individual alpha frequency parameters for the next phase.

6. The method according to claim 1, characterized in that, After the closed-loop regulation execution step, an efficacy feedback step is added. The difference data of EEG characteristics before and after regulation are extracted, and combined with the improvement rate of the clinical emotional assessment scale, a parameter optimization model is constructed. The improvement rate is used as a feedback variable to adjust the stimulation duty cycle and phase shift parameters of subsequent treatment courses.

7. A neural modulation system for affective disorders based on individual alpha frequencies, characterized in that, include: The EEG signal acquisition and processing module is used to acquire multi-channel EEG signals from subjects in both resting and real-time online states, amplify, filter, and eliminate artifacts from the acquired signals, and output standardized EEG signals. The individual Alpha frequency analysis module is connected to the EEG signal acquisition and processing module. It is used to receive standardized EEG signals, perform time-frequency analysis and energy calculation on the standardized EEG signals, and lock the individual Alpha frequency in the Alpha frequency band from 8Hz to 13Hz. The central control decision-making module is connected to the EEG signal acquisition and processing module and the individual Alpha frequency analysis module, respectively. It is used to receive individual Alpha frequency data, generate corresponding neuromodulation plans, and issue dynamic control instructions based on real-time online EEG signal feedback. The neural stimulation execution module, connected to the central control decision module, is used to receive dynamic regulation instructions, output corresponding physical stimulation signals, and implement precise neural regulation on the target brain regions of the subject.

8. The system according to claim 7, characterized in that, The EEG signal acquisition and processing module includes a 64-lead electrode assembly, a signal amplification circuit, and a power frequency noise suppression unit. The electrode assembly adopts a layout of target electrodes combined with 6 loop electrodes.

9. The system according to claim 7, characterized in that, The individual alpha frequency analysis module has a built-in fast Fourier transform processor and dynamic tracking algorithm to continuously track the frequency fluctuations of the subject's EEG signals and update the individual alpha frequency data in real time.

10. The system according to claim 7, characterized in that, The central control decision module has built-in PID control logic to achieve a 50ms response speed and to instantly adjust the stimulation frequency and intensity based on real-time EEG feedback data.

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