Neuroregulation multi-modal antidepressive therapeutic apparatus

Abstract

The present application relates to the technical field of medical equipment, specifically a nerve regulation multi-modal anti-depression therapeutic instrument, which is used to solve the problem that the prior art usually only involves a single treatment method for depression treatment. The present application dynamically adjusts multi-modal data, including electroencephalogram signals, stimulation frequency or intensity of transcranial magnetic stimulation, stroboscopic frequency, light intensity, light color, music rhythm, music frequency band, volume, through real-time acquisition and analysis of electroencephalogram signals, to achieve personalized depression treatment. In addition, the present application greatly improves the accuracy of EEG signal acquisition through an innovative artifact elimination algorithm and electromagnetic shielding technology, and solves the problem that existing TMS and EEG combined devices are often subject to electromagnetic interference and artifact problems.

Description

Technical Field

[0001] This invention relates to the technical field of medical devices, and more particularly to a neuromodulation multimodal antidepressant therapy device. Background Technology

[0002] Most existing depression treatment devices can only apply a single technology, lacking effective integration of multimodal therapies. Furthermore, most existing devices do not incorporate real-time monitoring and feedback adjustment of electroencephalogram (EEG) signals, resulting in insufficient personalized treatment. Because traditional devices lack real-time monitoring of biological signals such as EEG, it is difficult to optimize treatment intensity and parameters based on neural feedback during treatment. Moreover, during simultaneous TMS and EEG acquisition, electromagnetic interference and motion artifacts significantly impact signal quality and are difficult to eliminate effectively. Summary of the Invention

[0003] The technical objective of this invention is to address the problems in current treatment of depression, such as insufficient integration of multimodal treatment methods, low degree of personalization, and difficulty in real-time monitoring and regulation. To achieve the above technical objective, the specific technical solution is as follows.

[0004] A neuromodulation multimodal antidepressant therapy device is disclosed. The device dynamically adjusts multimodal data based on electroencephalogram (EEG) signal analysis results to achieve personalized depression treatment. The multimodal data includes EEG signals, transcranial magnetic stimulation (TMS) frequency or intensity, strobe frequency, light intensity, light color, music rhythm, music frequency band, and volume. The EEG signals are acquired through an EEG signal acquisition circuit; the TMS frequency or intensity is adjusted through a TMS circuit; the strobe frequency, light intensity, or light color is adjusted through a strobe therapy control circuit; and the music rhythm and frequency band are adjusted via an audio DAC (audio-to-analysis) circuit to achieve the desired music playback and volume adjustment. The TMS circuit, strobe therapy control circuit, audio DAC circuit, and EEG signal acquisition circuit are integrated using a microcontroller.

[0005] In one embodiment of the above technical solution, the therapeutic device further includes a Bluetooth module and a host computer. The Bluetooth module exchanges data with the microcontroller via serial communication and exchanges data with the host computer in a transparent manner.

[0006] In one embodiment of the above technical solution, the EEG signal acquisition circuit uses an ADS1299 chip and a shielded cable to connect the electrodes and the front-end amplifier to reduce electromagnetic interference from transcranial magnetic stimulation pulses to the circuit. The shielding layer should be grounded to ensure effective shielding against external electromagnetic interference. The EEG signals acquired by the EEG signal acquisition circuit are then processed using an artifact removal algorithm to eliminate artifacts.

[0007] In one embodiment of the above technical solution, the antidepressant treatment device is a helmet-type device, which is worn on the head during use.

[0008] In one embodiment of the above technical solution, the multimodal data is timestamped and uploaded to the host computer in real time. The data of different modalities are stored in a queue through a data buffer and matched and merged according to the timestamp.

[0009] In one embodiment of the above technical solution, a machine learning model is deployed in the host computer, and the machine learning model adjusts the stimulation parameters in real time based on multimodal data.

[0010] In one embodiment of the above technical solution, the step of dynamically adjusting multimodal data based on the EEG signal analysis results includes: performing frequency analysis on the acquired EEG signals to extract amplitude information of alpha, beta, gamma, delta, and theta waves; increasing the frequency and intensity of transcranial stimulation, the frequency and intensity of phototherapy strobe, and playing fast-paced music when beta and gamma waves exceed a set threshold; and maintaining a low stimulation intensity when alpha and theta waves exceed a set threshold and beta and gamma waves are below a set threshold, with the stimulation intensity determined according to a set intensity threshold.

[0011] In one embodiment of the above technical solution, the dynamic adjustment of the multimodal data includes: the frequency of the transcranial magnetic stimulation (TMS) is adjusted to 15Hz as the β wave increases, to 18-20Hz as the γ wave increases, and to 1-5Hz as the δ and θ waves increase; the intensity of the TMS is adjusted to [60%, 70%] when the α wave is stable, and to [80%, 90%] as the β and γ waves increase; the stimulation pulse width varies within the range of 150-400 microseconds to achieve effective stimulation of different frequency bands; the stroboscopic frequency is adjusted to 12-20Hz as the β wave increases, to 30-40Hz as the γ wave increases, and to 5-8Hz as the α and θ waves increase; the light intensity is adjusted as the β wave increases... The light intensity is adjusted to 60%-80% with a yellow or orange light color, increasing to 90%-100% as the gamma wave intensifies, and to 30%-50% with a blue or green light color as the alpha and theta waves intensify. The music tempo is adjusted to 40-60 BPM when the alpha wave is stable, to 80-100 BPM when the beta wave intensifies, and to 100-120 BPM when the gamma wave intensifies. The music frequency band is adjusted to mid-frequency when the alpha wave intensifies and to high-frequency when the beta wave intensifies, with the mid-frequency range being 500-1000 Hz and the high-frequency range being 1000-3000 Hz. The volume is adjusted to 30%-50% when the alpha wave intensifies and to 70%-80% when the beta wave intensifies. Within the same time window, if the absolute value of the wave amplitude change does not exceed a set threshold, the wave is considered stable.

[0012] In one embodiment of the above technical solution, the electroencephalogram (EEG) signal is visualized on a host computer.

[0013] The beneficial technical effects of this invention are as follows: By organically combining transcranial magnetic stimulation, stroboscopic therapy, and music therapy, and integrating real-time EEG monitoring, treatment parameters are dynamically adjusted to provide a personalized and intelligent multimodal treatment plan for depression. Through innovative artifact elimination algorithms and electromagnetic shielding technology, the accuracy of EEG signal acquisition is greatly improved, solving the problems of electromagnetic interference and artifacts often encountered in existing TMS and EEG combined devices. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 , one A schematic diagram illustrating the use of the helmet-mounted neuromodulation multimodal antidepressant therapy device in one embodiment.

[0016] Figure 2 , one A general block diagram of the circuit in one implementation method.

[0017] Figure 3 , one A flowchart of the audio DAC parsing circuit in one implementation method.

[0018] Figure 4 , one A flowchart of the integrated transcranial magnetic stimulation circuit in one embodiment.

[0019] Figure 5 , one A flowchart of the stroboscopic therapy control circuit in one embodiment.

[0020] Figure 6 , one A flowchart of the EEG acquisition circuit in one implementation method.

[0021] In the diagram, 1 is the frontal EEG acquisition dry electrode, 2 is the LED array, 3 is the high-fidelity headphones, and 4 is the TMS magnetic coil; E is the EEG signal, L is the light signal, M is HiFi music, P is the pulse magnetic field signal, D is data, 1-1 is the EEG acquisition circuit, 2-1 is the stroboscopic therapy control circuit, 3-1 is the audio DAC parsing circuit, 4-1 is the integrated transcranial magnetic stimulation circuit, 5 is the microcontroller, 6 is the Bluetooth module, 7 is the SD card storage circuit, 8 is the host computer software, 9 is the power management module, 10 is the crystal oscillator, 11 is the master tape music, 12 is the DAC chip, 13 is the first operational amplifier circuit, 14 is the first filter circuit, 15 is the pulse current control, 16 is the first temperature control and protection circuit, 17 is the second temperature control and protection circuit, 18 is the stroboscopic control circuit, 19 is the second operational amplifier circuit, 20 is the second filter circuit, and 21 is the analog-to-digital converter (ADC). Detailed Implementation

[0022] The following, with reference to the accompanying drawings, clearly and completely describes how the technical solution of this case organically combines transcranial magnetic stimulation (TMS), stroboscopic therapy, and music therapy, and dynamically adjusts treatment parameters in conjunction with real-time EEG monitoring to provide a personalized and intelligent multimodal treatment plan for depression. Obviously, the described embodiments are only a part of the embodiments of this case, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this case without inventive effort are within the scope of protection of this application.

[0023] (I) Composition of the treatment device

[0024] This case presents a helmet-style neuromodulation multimodal antidepressant therapy device. The head-integrated input / output terminals include: frontal EEG acquisition dry electrodes (1), an LED array (2), high-fidelity headphones (HiFi headphones) (3), and a magnetic coil (4). The magnetic coil is a transcranial magnetic stimulation output device used to generate a high-intensity magnetic field. Placed on the scalp, the generated magnetic pulses can penetrate the skull and stimulate brain neurons. This magnetic coil is typically fixed to the scalp corresponding to the left dorsolateral prefrontal cortex (DLPFC). The DLPFC is a key area in the brain responsible for mood regulation and cognitive function. Studies have shown that the DLPFC of depressed patients often exhibits reduced activity; therefore, activating this area through TMS can improve mood and alleviate depressive symptoms. Sometimes, low-frequency TMS stimulation of the right DLPFC is used to inhibit overactive neural activity. This method allows for adjustments to the stimulation protocol based on the patient's specific situation. The LED array is usually placed in front of the face to ensure that light can enter the brain through the eyes and stimulate the area regulating circadian rhythms. The device typically includes a phototherapy lamp or head-mounted device that directs light of a specific wavelength directly onto the patient's face, particularly the area around the eyes. The light should avoid direct contact with the retina to prevent discomfort, and the angle and distance are adjusted according to the device design and treatment plan. A diagram illustrating the head-mounted device is shown below. Figure 1 As shown.

[0025] See Figure 2 The therapeutic device uses a microcontroller (5) to achieve data communication and control between the integrated transcranial magnetic stimulation circuit (4-1), stroboscopic therapy control circuit (2-1), audio DAC parsing circuit (3-1), EEG acquisition circuit (1-1), Bluetooth module (6), and SD card storage circuit (7). The power management module (9) supplies power to each circuit. Data transmission with the host computer software is completed via Bluetooth data pass-through. Under the microcontroller's control, precise control of each circuit can be achieved, including setting the mode and intensity of the output pulse magnetic field (P) of the transcranial magnetic stimulation circuit; switching the specific HiFi music (M) content and playback duration for music therapy; the stroboscopic mode and intensity of the LED light signal (L); the start and stop times of the acquired EEG (E) data; data (D) storage in the SD card storage circuit; and communication with the host computer software (8) via Bluetooth.

[0026] The aforementioned Bluetooth module exchanges data with the microcontroller via serial communication and with the host computer software in a transparent manner.

[0027] The aforementioned SD card storage circuit is used to store real-time data from EEG acquisition, TMS treatment parameters, patient treatment records, and audio files from music therapy. The treatment records include EEG data, TMS parameters, phototherapy parameters, and music therapy parameters recorded during each treatment session.

[0028] The following section will provide a detailed introduction to each circuit.

[0029] (1.1) Audio DAC Resolution Circuit

[0030] The core function of a DAC (Digital Audio Decoding) circuit is to convert digital audio signals into analog signals. In music therapy, outputting high-fidelity, pleasant music is crucial to the entire therapeutic process. Therefore, a well-designed audio DAC circuit is essential, and selecting a high-quality DAC chip is the primary task in audio circuit design.

[0031] In one implementation, the Texas Instruments PCM1794A chip is selected: a 24-bit DAC that supports a 192kHz sampling rate, suitable for high-precision audio. The input interface uses an I2S interface: widely used in audio transmission, supporting high-precision and high-sampling-rate audio transmission, suitable for audio processing.

[0032] The flowchart of the audio DAC parsing circuit is as follows: Figure 3 As shown, the dashed box represents the audio DAC parsing circuit (3-1). After the master music (11) is stored via the SD card storage circuit (7), the DAC chip (12) converts the digital audio signal into an analog signal. A first operational amplifier circuit (13) is used as the output buffer circuit to amplify and filter the signal, ensuring that the output audio signal is clean and distortion-free. Next, a low-pass filter is used at the analog output terminal by the first filter circuit (14), using a typical third-order filter to remove high-frequency noise.

[0033] In one implementation, the operational amplifier in the first operational amplifier circuit is selected as the high-performance op-amp-OPA1612, which has low noise and low distortion and is suitable for high-fidelity audio systems, while the amplifier in the high-fidelity headphones (3) uses the TPA6120A2 chip to ensure high power output and no distortion.

[0034] Meanwhile, clock jitter can affect the accuracy of audio signals, so a high-precision clock source is crucial. The CCHD-957 low-jitter crystal oscillator (10) is used, and the microcontroller (5) controls it to provide a stable clock signal for the DAC and reduce distortion.

[0035] To avoid power supply noise affecting audio quality, the power supply provided by the power management module (9) is processed by the DAC and op-amp using a low-noise LDO regulator of TPS7A47, so as to provide a stable and low-noise power supply to the audio DAC resolution circuit (3-1). At the same time, capacitors are used to decouple the power supply and filter out high-frequency noise.

[0036] (1.2) Integrated transcranial magnetic stimulation circuit

[0037] The basic principle of transcranial magnetic stimulation (TMS) is to generate a pulsed magnetic field through rapidly changing electrical currents, which in turn induces currents in the cerebral cortex and affects neuronal activity. The control of the strength and frequency of this magnetic field directly determines the stimulation effect.

[0038] The core task of integrated transcranial magnetic stimulation (TMS) circuits is to non-invasively stimulate specific brain regions, such as the dorsolateral prefrontal cortex, using electromagnetic pulses to modulate neural activity. Specifically, an electric current passes through a magnetic coil, generating a momentary strong magnetic field. This strong magnetic field is typically 1-2.5 Tesla. This magnetic field penetrates the scalp and skull, inducing a current within the cerebral cortex. This induced current stimulates neurons, achieving a therapeutic effect.

[0039] The integrated transcranial magnetic stimulation circuit consists of the following main parts: ① High-voltage power supply: provides a large current that switches rapidly to ensure that the coil can generate sufficient magnetic field strength. ② Pulse current control circuit (15): controls the current intensity and pulse width through the magnetic coil (4), thereby adjusting the intensity of the magnetic field and the intensity of stimulation. ③ Selection and design of magnetic coil (4): the shape and size of the magnetic coil directly affect the distribution of the magnetic field and the depth of stimulation. ④ First temperature control and protection circuit (16): used to protect the main circuit and the human body, and to prevent damage caused by excessive current or voltage by fusing when the current or voltage is too high. The details are as follows.

[0040] The TMS circuit requires a strong magnetic field generated by an inductor, necessitating a high-voltage source capable of providing instantaneous high current. A common design for high-voltage power supplies is a capacitor discharge circuit, using high-quality, high-voltage capacitors charged by a high-voltage DC power supply. When the control circuit sends a signal, the capacitor discharges rapidly, generating a large current that flows through the magnetic coil, thus producing a high-intensity pulsed magnetic field. The high-voltage range is [250V, 400V].

[0041] Next is the design of the pulse current control circuit. Transcranial magnetic stimulation requires rapidly rising and falling current pulses, which are achieved through a pulse generator circuit: Semiconductor switches (such as IGBTs or MOSFETs) are used to precisely control the opening and closing of capacitor discharge, determining the pulse frequency and duration. The pulse control unit, controlled by a microcontroller (such as an STM32), controls the pulse frequency and duty cycle, allowing adjustment of the stimulation intensity according to the treatment plan. The pulse frequency is typically between 1Hz and 10Hz, used for different treatment scenarios.

[0042] Output magnetic coil design: The coil type usually adopts the "8" shaped coil (butterfly coil), which can concentrate the magnetic field and increase the local stimulation intensity. The number of turns and inductance value of the coil are designed according to the required magnetic field strength. Too many turns will result in too large an inductance value, which will affect the current rise time. At the same time, the coil is wrapped with high temperature resistant material to ensure that overheating will not occur during long-term use.

[0043] Furthermore, since high current can cause overheating of coils and other components, a temperature monitoring circuit must be designed. In case of excessively high temperatures, the current should be adjusted via PWM or the power should be cut off directly, and fuses should be added to protect the equipment and the user. The current limit must be strictly limited to ensure no harm to the user; in particular, the pulse rise time and current peak value need to be controlled.

[0044] (1.3) Strobophic therapy control circuit

[0045] The core task of the stroboscopic therapy control circuit is to generate light waves of specific wavelengths and frequencies using LEDs for the treatment of neurological disorders such as depression. Stroboscopic therapy aims to modulate brain activity through light stimulation, particularly neural circuits related to mood and sleep regulation. A flowchart of the stroboscopic therapy control circuit is shown below. Figure 5 As shown.

[0046] Firstly, regarding the selection of LEDs, blue and green light are commonly used wavelengths for the treatment of depression because these wavelengths can regulate circadian rhythms and improve mood. The blue light range is 450-495nm, and the green light range is 495-570nm, with each range including its two boundary values.

[0047] Frequency Selection: Different strobe frequencies have different effects on the brain. Common therapeutic frequency ranges are 10Hz-40Hz: Low frequency: suitable for relaxation and mood regulation. High frequency: can activate cognitive functions and improve brain activity levels. The low frequency range is 10-20Hz, and the high frequency range is 30-40Hz, with each range including its two boundary values.

[0048] The LED's switching frequency and brightness are controlled by pulse width modulation using a microcontroller and a strobe control circuit (18). Various control modes can be designed or customized according to the user's needs.

[0049] Security design:

[0050] 1. Eye Protection: During phototherapy, direct light exposure to the retina must be avoided to prevent eye damage. This can be achieved by limiting the angle of illumination or designing light-shielding devices to ensure the light is concentrated on the treatment area.

[0051] 2. Overcurrent and overvoltage protection: Overcurrent and overvoltage protection functions are designed for the LED driver circuit to prevent damage to the LED or control circuit from unexpected voltage fluctuations or excessive current.

[0052] 3. Temperature monitoring: The light source may overheat after working for a long time. By designing a second temperature control and protection circuit (17), the power will be automatically reduced or the operation will be stopped when the LED temperature is too high.

[0053] (1.4) EEG acquisition circuit

[0054] The function of the EEG acquisition circuit is to collect the patient's brain signals in real time to assess neural activity and treatment effectiveness. EEG signals are mainly composed of waveforms of different frequencies, including alpha waves, beta waves, gamma waves, and delta waves. These waveforms represent different brain activity states and can reflect psychological states such as attention, relaxation, and anxiety.

[0055] Specifically, EEG is acquired using highly conductive dry electrodes. When designing the EEG acquisition circuit, it is essential to consider how to reduce electromagnetic interference during the TMS process and ensure the accuracy of the acquired signal. The electrical signal is at the microvolt level and is very weak, so it is amplified by a second operational amplifier circuit (19). In addition, to reduce EMG interference and high-frequency noise, a second filter circuit (20) should be designed at the front end. A low-pass filter is used in the second filter circuit to remove high-frequency interference, and a high-pass filter can also be added to remove baseline drift. The amplified analog signal is converted into a digital signal using an analog-to-digital converter (ADC) (21). For example, the division point of the low-pass filter is set below 100Hz, and the cutoff frequency of the high-pass filter is set at 0.1Hz.

[0056] To minimize the impact of TMS on EEG acquisition, electromagnetic interference must be considered. Shielded cables should be used to connect the electrodes and the front-end amplifier to reduce electromagnetic interference from the TMS magnetic pulses. The shielding layer should be grounded to ensure effective shielding against external electromagnetic interference. Artifacts should be eliminated on the host computer using an artifact removal algorithm. The EEG acquisition circuit flowchart is shown below. Figure 6 As shown.

[0057] In one implementation, a high-gain, low-noise differential amplifier is used in the second operational amplifier circuit to amplify the signal. Specifically, the differential amplifier is the AD8237, which features low input noise and a high common-mode rejection ratio (CMRR), effectively suppressing power supply noise and interference signals.

[0058] In one implementation, the analog-to-digital converter uses the ADS1299 chip, which has a 24-bit high resolution to ensure sufficient dynamic range and a sampling rate between 250-1000Hz, enabling it to capture key frequency band information in the EEG, such as alpha, beta, gamma, and delta waves.

[0059] Alpha waves, with a frequency range of 8-13 Hz, are typically associated with relaxation and a state of rest with eyes closed. Alpha waves increase when a person is relaxed and meditating with their eyes closed. Moderate alpha wave activity can reflect a calm and focused state of mind.

[0060] Beta waves, with a frequency range of 13-30 Hz, are typically associated with alertness, thinking, concentration, and stress. Beta wave activity increases when a person is focused on a task, particularly in the frontal and parietal lobes.

[0061] Gamma waves, with a frequency range of 30-100Hz, are associated with higher cognitive functions, learning, and memory. Gamma waves are active when the brain is in a state of high concentration and information processing.

[0062] Delta waves (δ waves): With a frequency range of 0.5-4 Hz, they are typically associated with deep sleep. Increased delta wave activity usually reflects the brain's repair and recovery processes. Theta waves (θ waves): With a frequency range of 4-8 Hz, they are associated with light sleep, relaxation, and meditation. Increased theta wave activity usually indicates that the brain is relaxed but still alert.

[0063] (II) Data Processing of Therapeutic Instruments

[0064] (2.1) Synchronous Analysis and Processing of Multimodal Data

[0065] Multimodal data includes EEG signals, TMS parameters, phototherapy frequency, and music therapy rhythm. To ensure the accuracy of treatment, these data need to be analyzed and processed simultaneously.

[0066] In synchronous analysis, a synchronization mechanism is required for the data, including timestamp synchronization, data buffering, and synchronous transmission. Timestamp synchronization means that all acquired EEG signals, TMS trigger signals, phototherapy strobe signals, and music playback data should be timestamped by a unified clock source (implemented through a crystal oscillator in hardware circuitry), ensuring that all data is analyzed synchronously on the same timeline. A high-precision crystal oscillator can be used as the master clock source for the entire system. Data buffering and synchronous transmission refer to the real-time transmission of data from various data sources to the host computer software. Data of different modalities is stored in a queue through a data buffer and matched and merged according to timestamps.

[0067] Processing multimodal data refers to fusing and analyzing the multimodal data, and the steps include:

[0068] (2.1.1) Fast Fourier Transform: Perform frequency domain analysis on EEG data to extract the power spectrum intensity of α, β, γ, θ, and δ waves.

[0069] (2.1.2) Multi-source data fusion algorithm: By using weighted fusion algorithms, such as Kalman filtering and Bayesian fusion, the patient's attention, relaxation level, cognitive state and other information are obtained by comprehensively analyzing the signals of each frequency band of EEG, TMS parameters, phototherapy frequency and music rhythm.

[0070] (2.1.3) Joint analysis of frequency and time domains: This includes analyzing the frequency domain components and time domain signals of EEG to capture transient EEG changes and the immediate effects of TMS pulses on EEG.

[0071] In one implementation, radar charts are used in electroencephalogram (EEG) signal analysis to display the amplitudes of different brain waves. For example, each corner represents a brain wave component, such as alpha, beta, and gamma waves. The amplitude (power) of each waveform represents its proportion in the overall EEG activity. Radar charts allow for a visual observation of the strength of different brain frequency bands at a given moment, facilitating the assessment of a patient's emotional and attentional states. The radar chart is a commonly used visualization tool for displaying multidimensional data.

[0072] In one implementation, attention was specifically analyzed within the EEG signal analysis. Attention analysis is primarily based on the activity levels of beta and gamma waves. The following is the approach to attention analysis based on different EEG waves: Beta wave activity: Beta waves are typically associated with wakefulness, focus, and cognitive tasks. During focused tasks such as reading, calculation, or manipulation, beta waves in the frontal and parietal lobes are significantly enhanced. Therefore, attentional states can be analyzed by detecting the intensity of beta wave activity. Gamma wave activity: Gamma waves are associated with higher cognitive functions, information processing, and learning. The frequency and intensity of gamma waves increase during periods of high concentration. Therefore, increased gamma wave activity usually reflects a high level of attention. Alpha wave inhibition: Alpha waves are inhibited when the brain switches from a relaxed state to a focused state. Therefore, if alpha wave activity is low while beta and gamma waves are strong, it indicates a state of high concentration.

[0073] In one implementation, an attention index is calculated when analyzing attention. An attention index can be calculated based on the power of different waveforms. For example, a baseline value is set, and a quantified attention level is obtained according to the relative strengths of alpha, beta, and gamma waves, serving as a reference for real-time analysis.

[0074] In one implementation, the above-mentioned analysis of EEG signal components, attention analysis, and calculation of the attention index can be performed by host computer software. Specifically, the host computer software completes the above analysis and calculation by receiving the collected EEG data and control and feedback data from the treatment device, reading audio files from the SD card, performing data preprocessing and TMS artifact elimination, and storing and displaying the time-domain and frequency-domain graphs of the EEG signals. The analysis results are then stored for subsequent comparison and long-term tracking. Furthermore, the host computer software provides a human-computer interaction platform, allowing it to monitor and control the treatment process, adjust treatment parameters in real time, and provide personalized intelligent feedback adjustments.

[0075] The control and feedback data of the aforementioned treatment equipment includes feedback data from transcranial magnetic stimulation (TMS) devices, control data from stroboscopic phototherapy devices, and control data from music therapy. Specifically: the feedback data from the TMS devices includes parameters such as pulse frequency, intensity, and pulse width. The host computer receives this data and, in conjunction with EEG signal analysis, adjusts the TMS parameters in real time. The control data from the stroboscopic phototherapy devices includes parameters such as the stroboscopic frequency and light intensity. This data can also be received and controlled via the host computer software to adjust the intensity and rhythm of the phototherapy. The control data for music therapy includes receiving and reading the music playback status and dynamically adjusting the rhythm and volume of the music based on changes in EEG signals. The playback status includes the currently playing music rhythm, frequency, and volume.

[0076] (2.2) Synchronous Feedback and Adjustment Mechanism for Multimodal Data

[0077] Real-time feedback algorithm: By integrating EEG signals, TMS output, phototherapy parameters, and music rhythm, a real-time feedback closed-loop system is formed to continuously adjust stimulation parameters. Machine learning models are used to predict the patient's condition and optimize the adjustment of stimulation parameters. These machine learning models include, for example, LSTM, BiLSTM, and RAN.

[0078] Adaptive parameter adjustment: Based on real-time EEG data from different patients, the system automatically learns the patient's response to various treatment parameters and forms a personalized treatment model. In each cycle, the system updates and adjusts the strategy based on EEG data and patient feedback. For example, a cycle is set to 10 seconds.

[0079] Synergistic Adjustment: TMS, phototherapy, and music therapy are adjusted synchronously based on EEG analysis results. For example, when a patient is detected to be in a highly focused state, their beta and gamma waves are significantly enhanced, the TMS stimulation frequency increases, the phototherapy strobe frequency increases synchronously, and the music rhythm speeds up, achieving a synergistic effect of multimodal therapy.

[0080] (2.3) Data Recording and Optimization

[0081] Data recording: Record the EEG data, TMS parameters, phototherapy and music therapy parameters during each treatment process for later analysis and model optimization, to form a more accurate adjustment plan.

[0082] Model training: Using personal historical data, a neural network model is trained, and the multimodal parameter adjustment scheme is gradually optimized to ensure more precise future treatments.

[0083] (III) Personalized use of the therapeutic device

[0084] The neuromodulation multimodal antidepressant therapy device dynamically adjusts its treatment based on EEG signal analysis results. This is primarily achieved by real-time monitoring of the patient's brain activity and timely adjusting parameters such as the intensity, frequency, and rhythm of TMS, stroboscopic therapy, and music therapy to achieve personalized and precise treatment outcomes. The correlation between EEG signal analysis results and treatment parameters is shown below.

[0085] (3.1) Alpha waves at 8-13 Hz indicate a relaxed emotional state. Higher alpha wave intensity indicates a relaxed state, and treatment can focus on maintaining or reinforcing this state. Adjustment measures include: TMS: Reducing the intensity or frequency of TMS stimulation to avoid overstimulation and maintain the patient's relaxed state. Phototherapy: Reducing the frequency and intensity of phototherapy to keep the patient relaxed. Music therapy: Playing slow, soothing music to maintain a relaxing atmosphere.

[0086] (3.2) Beta waves at 13-30Hz indicate a state of focused attention. Increased beta wave intensity indicates the patient is concentrating or is awake. Adjustment measures: TMS: Increase the frequency of TMS stimulation to enhance stimulation of the prefrontal cortex and promote attention and cognitive abilities. Phototherapy: Moderately increase the frequency and intensity of phototherapy to stimulate the visual cortex and enhance the patient's alertness. Music therapy: Select fast-paced music to enhance brain alertness and attention.

[0087] (3.3) Gamma waves in the 30-100Hz range indicate higher cognitive activity in the brain. Increased gamma wave activity indicates that the patient's brain is in a state of higher cognition and information processing. Adjustment measures: TMS: Maintain a high stimulation frequency to support higher cognitive activity and ensure that stimulation is not excessive. Light therapy: Moderately increase the flicker frequency to synchronize gamma wave activity and enhance cognitive efficacy. Music therapy: Select music with complex rhythms and melodies to further activate cognitive functions.

[0088] (3.4) When delta waves are between 0.5-4 Hz and theta waves are between 4-8 Hz, it indicates a state of rest and meditation. Increased delta and theta waves suggest the patient may be in a state of deep relaxation, rest, or sleep. Adjustments: TMS: Reduce or pause TMS stimulation to avoid interfering with the patient's deep rest. Phototherapy: Reduce light intensity and adjust the frequency to a lower level to avoid stimulating the patient. Music therapy: Play meditation music to help the patient enter a deeper state of relaxation.

[0089] In one embodiment, treatment parameters are dynamically adjusted in conjunction with real-time EEG monitoring. The dynamic adjustment process is as follows.

[0090] First, EEG signal data is acquired in real time using an EEG acquisition system and transmitted to a host computer for processing. The Fast Fourier Transform (FFT) algorithm is then used to perform frequency analysis on the EEG signals, extracting the amplitude information of the frequency components of various brain waves, including alpha, beta, gamma, delta, and theta waves.

[0091] Next, threshold ranges are set based on the amplitude of different brain waves to determine the patient's current neurological state. When beta and gamma waves exceed the set thresholds, it indicates that the patient is in a state of focused attention, and the TMS frequency, light therapy strobe frequency, and music rhythm are increased. When alpha waves are above the set thresholds and beta and gamma waves are below the thresholds, it indicates that the patient is relaxed or has slight attention, and a low stimulation intensity needs to be maintained. The stimulation intensity is determined according to the set intensity thresholds. The parameters of TMS, light therapy, and music therapy are automatically adjusted through the control algorithm in the host computer software.

[0092] A more specific dynamic adjustment method is shown below.

[0093] (3.4.1) TMS parameter adjustment

[0094] (3.4.1.1) Frequency range, from 1Hz to 20Hz, dynamically adjusted according to the activity of gamma and beta waves in the EEG:

[0095] Significant enhancement of beta waves indicates focused attention: The TMS frequency was gradually adjusted from the initial 10Hz to 15Hz to further activate the cognitive function of the prefrontal cortex.

[0096] Enhanced gamma waves indicate higher cognitive activity: further adjusting the TMS frequency to 18-20Hz enhances higher cognitive processing capabilities.

[0097] Increased delta and theta waves indicate a relaxed state: Adjust the TMS frequency to 1-5 Hz to reduce stimulation to the patient and maintain a relaxed state.

[0098] (3.4.1.2) Stimulation intensity: Initially set to 70% to 100% of maximum output, with intensity adjustment based on real-time feedback from the EEG signal.

[0099] When the alpha wave is stable: Gradually adjust the TMS intensity to [60%, 70%) to avoid overstimulation. If the absolute value of the wave amplitude change does not exceed the set threshold within the same time window, the alpha wave is considered stable.

[0100] Enhancement of beta and gamma waves: Gradually adjust to 80%-90% to enhance the stimulation effect.

[0101] (3.4.1.3) Stimulation pulse width: initially set to 200 microseconds, dynamically adjusted according to the instantaneous changes in the EEG signal, varying within the range of 150-400 microseconds to achieve effective stimulation of different frequency bands.

[0102] One method for calculating the amplitude fluctuation of the aforementioned alpha wave is as follows: by calculating the power spectral density (PSD) within the frequency band, if the absolute value of the amplitude change is within a set threshold range, the alpha wave signal can be considered stationary. For example, in a relaxed state, if the power spectrum of the alpha wave remains at a high stable value without significant decreases or fluctuations. For instance, if the threshold is set to 10%, then amplitude fluctuations not exceeding ±10% are considered stationary.

[0103] (3.4.2) Adjustment of phototherapy parameters

[0104] (3.4.2.1) The flicker frequency is initially set to 10Hz, and the dynamic adjustment range is 5-40Hz:

[0105] Relaxation state: alpha and theta waves are enhanced, and the phototherapy strobe frequency is adjusted to 5-8 Hz to enhance the patient's sense of relaxation.

[0106] To improve concentration, beta waves are significantly enhanced. Adjust the light therapy strobe frequency to 12-20Hz and synchronize the EEG to promote concentration.

[0107] Advanced cognitive activity, enhanced gamma waves, and increasing the frequency of light therapy flashes to 30-40Hz activate the brain's advanced information processing functions.

[0108] (3.4.2.2) Illumination intensity: Set within the range of 10%-100% of the light intensity, and adjust in real time according to the intensity of the α, β, and γ waves of the EEG:

[0109] Relaxed state: Maintain light intensity at 30%-50% to avoid overstimulation.

[0110] Enhanced attention: Light intensity is increased to 60%-80%, activating the visual cortex.

[0111] Advanced cognitive activity: Light intensity can be increased to 90%-100%, enhancing the stimulation effect.

[0112] (3.4.2.3) Illumination Color: Based on the analysis of EEG bands and emotional state, the LED color is dynamically adjusted.

[0113] When relaxing: Choose gentle warm colors, such as yellow or orange light.

[0114] When attention is heightened: Choose blue, green, or white light to enhance alertness.

[0115] (3.4.3) Adjustment of Music Therapy Parameters

[0116] (3.4.3.1) Music rhythm: The initial rhythm is set to 60 BPM, which can be adjusted within the range of 40-120 BPM.

[0117] Relaxation state: alpha and theta waves are enhanced, and the music tempo is lowered to 40-60 BPM, providing a soothing environment.

[0118] Focus: Enhanced beta waves; adjust the music tempo to 80-100 BPM to improve attention and concentration.

[0119] Advanced cognitive activity: Enhanced gamma waves, which can increase the music tempo to 100-120 BPM, stimulate brain activity.

[0120] (3.4.3.2) Music Frequency Band: Analyze the EEG spectrum and select a music frequency band that is synchronized with brain waves:

[0121] Alpha wave enhancement: Select mid-frequency music to enhance the relaxation effect. For example, the mid-frequency range is [500Hz, 1000Hz].

[0122] Beta wave enhancement: Select high-frequency (1000-3000Hz) music to promote attention and cognitive abilities. For example, the high-frequency range is [1000Hz, 3000Hz].

[0123] (3.4.3.3) Volume Adjustment: Adjust the volume in real time based on the EEG signal, ranging from 30% to 80%. If a reference volume is used, it must be comfortable for the user at the reference volume, and the volume should not be too high. The reference volume is set to, for example, 50-60dB.

[0124] When concentrating: turn the volume up to 70%-80% to enhance the musical stimulation.

[0125] Relaxed state: Reduce the volume to 30%-50% and avoid interference.

[0126] The host computer software updates the EEG data based on each time window (e.g., 5 seconds) and automatically adjusts the TMS, phototherapy, and music parameters to optimize the treatment effect. Different treatment phase modes (e.g., relaxation mode, focus mode) can be set, enabling intelligent switching of treatment plans (initially, these plans can be customized for each patient under the guidance of clinicians; patient-specific data may also be collected for model training and precision treatment).

[0127] In one implementation, initially, TMS: the starting frequency is set at 10Hz, and the intensity is at a moderate level. Phototherapy: the starting frequency is set at 12Hz, and the light intensity is 50%. Music therapy: playing light music with a moderate tempo (60-80 BPM). When β waves significantly increase (patient attention is focused), TMS: increases to 15Hz, and the intensity increases by 10%. Phototherapy: the frequency increases to 18Hz, and the light intensity increases to 70%. Music therapy: playing music with a faster tempo (80-100 BPM) to enhance patient focus. When alpha waves significantly increase (patient relaxation), TMS: decreases to 8Hz, and the intensity decreases by 10%. Phototherapy: the frequency decreases to 10Hz, and the light intensity decreases to 40%. Music therapy: switching to more soothing music, reducing tempo changes. When gamma waves increase (higher cognitive activity), TMS: maintains high-frequency stimulation (20Hz) to promote cognitive activity. Phototherapy: the frequency is synchronized with gamma waves, reaching 30Hz. Music therapy: playing music with complex melodies to support cognitive function.

[0128] In the aforementioned music therapy, the volume needs to be adjusted in real time based on the EEG signals. For example, when concentrating, the volume should be increased to 70%-80% to enhance musical stimulation. When relaxed, the volume should be reduced to 30%-50% to avoid interference.

[0129] In the music therapy mentioned above, attention should be paid to the beats per minute (BPM) of the selected music. A higher BPM indicates a faster tempo, while a lower BPM indicates a slower tempo. Adjusting the BPM in music therapy can influence a patient's mood and attention span. For example, a lower BPM (e.g., 40-60 BPM) can help patients relax, while a higher BPM (e.g., 80-120 BPM) can enhance attention and improve cognitive activity.

[0130] The aforementioned adjustments to the stimulation intensity, frequency, and rhythm parameters of TMS, stroboscopic therapy, and music therapy are primarily achieved by the user setting treatment goals, such as relaxation, focus, and meditation, through a host computer software. The software then automatically adjusts the parameters of the TMS, light therapy, and music. Changes in brainwave frequency and intensity are monitored through the host computer interface, allowing for real-time adjustments to the treatment plan to ensure optimal therapeutic effects.

[0131] Although embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, and not restrictive. Those skilled in the art can make many other forms based on the guidance of this specification and without departing from the scope of protection of the claims of the present invention, and all of these are within the scope of protection of the present invention.

Claims

1. A neuromodulation multimodal antidepressant therapy device, characterized in that: The treatment device dynamically adjusts multimodal data based on the results of EEG signal analysis to achieve personalized depression treatment; The multimodal data includes electroencephalogram (EEG) signals, transcranial magnetic stimulation (TMS) frequency or intensity, strobe frequency, light intensity, light color, music rhythm, music frequency band, and volume. The electroencephalogram (EEG) signal is acquired by an EEG signal acquisition circuit; the stimulation frequency or intensity of the transcranial magnetic stimulation (TMS) is adjusted by a TMS circuit; the stroboscopic frequency, light intensity, or light color is adjusted by a stroboscopic therapy control circuit; and the music rhythm and music frequency band are played and the volume is adjusted by an audio DAC analysis circuit. The transcranial magnetic stimulation circuit, stroboscopic therapy control circuit, audio DAC parsing circuit, and electroencephalogram (EEG) signal acquisition circuit are integrated using a microcontroller. The dynamic adjustment of multimodal data based on the analysis results of EEG signals includes: performing frequency analysis on the acquired EEG signals to extract amplitude information of alpha, beta, gamma, delta, and theta waves; increasing the frequency and intensity of transcranial stimulation and phototherapy strobe frequency and intensity, and playing fast-paced music when beta and gamma waves exceed a set threshold; maintaining a low stimulation intensity when alpha and theta waves exceed a set threshold and beta and gamma waves are below a set threshold, with the stimulation intensity determined according to a set intensity threshold. The frequency of the transcranial magnetic stimulation (TMS) is adjusted to 15 Hz as the beta wave increases, to 18-20 Hz as the gamma wave increases, and to 1-5 Hz as the delta and theta waves increase; the intensity of the TMS is adjusted to [60%, 70%] when the alpha wave is stable, and to [80%, 90%] as the beta and gamma waves increase; the pulse width varies within the range of 150-400 microseconds to achieve effective stimulation of different frequency bands. The strobe frequency is adjusted to 12-20Hz as the β wave increases, to 30-40Hz as the γ wave increases, and to 5-8Hz as the α and θ waves increase; the light intensity is adjusted to 60%-80% and the light color is yellow or orange as the β wave increases, to 90%-100% as the γ wave increases, and to 30%-50% and the light color is blue or green as the α and θ waves increase. The music rhythm is adjusted to 40-60 BPM when the alpha wave is stable, to 80-100 BPM when the beta wave is enhanced, and to 100-120 BPM when the gamma wave is enhanced; the music frequency band is adjusted to mid-frequency when the alpha wave is enhanced and to high-frequency when the beta wave is enhanced, with the mid-frequency being 500-1000 Hz and the high-frequency being 1000-3000 Hz; the volume is adjusted to 30%-50% when the alpha wave is enhanced and to 70%-80% when the beta wave is enhanced. Within the same time window, if the absolute value of the wave amplitude change does not exceed a set threshold, the wave is judged to be stable.

2. The antidepressant treatment device according to claim 1, characterized in that, The therapeutic device also includes a Bluetooth module and a host computer. The Bluetooth module exchanges data with the microcontroller via serial communication and exchanges data with the host computer in a transparent manner.

3. The antidepressant treatment device according to claim 1, characterized in that, The EEG signal acquisition circuit uses the ADS1299 chip and uses shielded cables to connect the electrodes and the front-end amplifier to reduce electromagnetic interference from transcranial magnetic stimulation pulses to the circuit. The shielding layer should be grounded to ensure effective shielding against external electromagnetic interference. The EEG signals acquired by the EEG signal acquisition circuit are then processed using an artifact removal algorithm to eliminate artifacts.

4. The antidepressant treatment device according to claim 1, characterized in that, The antidepressant treatment device is a helmet-style device that is worn on the head during use.

5. The antidepressant treatment device according to claim 1, characterized in that, The multimodal data is timestamped and uploaded to the host computer in real time. The data of different modalities is stored in a queue through a data buffer and matched and merged according to the timestamp.

6. The antidepressant treatment device according to claim 2, characterized in that, The host computer is equipped with a machine learning model, which adjusts the stimulation parameters in real time based on multimodal data.

7. The antidepressant treatment device according to claim 2, characterized in that, The EEG signals are visualized on the host computer.

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