Multifunctional wearable phototherapy glasses with electroencephalogram detection function

Abstract

The invention discloses multifunctional wearable phototherapy glasses with an electroencephalogram detection function, and relates to the technical field of phototherapy light sources. An electroencephalogram detection unit obtains an electroencephalogram signal of a current user, predicts a time sequence electroencephalogram signal of a next time period based on the electroencephalogram signal of the current user, obtains prediction data, and recommends phototherapy related data according to the prediction data; the electroencephalogram signal, the prediction data and the phototherapy related data of the current user are sent to an individual user side by a wireless transmission unit through an MCU, and the individual user side adjusts the light intensity and time of a phototherapy unit according to the recommended phototherapy related data; the serial port screen is connected with the MCU and is used for displaying the current user electroencephalogram signal, the prediction data and the phototherapy related data; and the phototherapy unit receives a control instruction sequentially passing through the individual user side, the MCU and the driving unit, and performs phototherapy according to the control instruction. The intelligent phototherapy glasses provided by the invention have the advantages of self-inspection and self-therapy, simple operation, safety, stability, uniform light field and adjustable mode.

Description

Technical Field

[0001] The present invention relates to the technical field of phototherapy light sources, and more particularly to a multifunctional wearable phototherapy glasses with electroencephalogram detection. Background Art

[0002] Currently, photobiomodulation technology (PBM) uses light irradiation of specific wavelengths to regulate cell functions, promote tissue repair, and treat diseases. Research shows that PBM can effectively relieve the depressive mood of patients with seasonal affective disorder (SAD), and also has a certain therapeutic effect on non-seasonal depression. For the phototherapy of depression, the light wavelength range commonly used is mainly blue light, with a wavelength range of about 460 - 495 nm. This method can regulate the human biological clock and help improve mood stability, etc. Blue light irradiation can inhibit the secretion of melatonin, thereby adjusting the human circadian rhythm and having a good therapeutic effect on SAD and sleep disorders. White light (full-spectrum light, containing blue light components) is also used to treat depression and sleep disorders because it is closer to natural light and can more comprehensively affect the human biological clock and mood regulation.

[0003] In addition, PBM has effects on preventing myopia and improving sleep disorders. Red light with a wavelength of 630 - 660 nm can be used to treat myopia. Red light is considered to be able to penetrate the skin and soft tissues and has a positive effect on the retina and choroid of the eyes. Near-infrared light with a wavelength range between 800 - 1000 nm can penetrate deeper into tissues and play a role in promoting cell metabolism and blood circulation. In addition, light in this wavelength band can also promote wound healing through mechanisms such as increasing cell energy production, reducing inflammation, promoting angiogenesis, and enhancing cell proliferation.

[0004] In the treatment of skin diseases, blue light with a wavelength range of 405 - 420 nm is usually used to kill acne-causing bacteria; red light with a wavelength range of about 630 - 660 nm is used to treat skin diseases, promote wound healing, reduce inflammation, improve skin texture, and reduce wrinkles; near-infrared light with a wavelength range of about 800 - 1100 nm penetrates deeper skin tissues and is used to treat chronic pain, promote blood circulation, and skin repair; yellow light with a wavelength range of about 570 - 590 nm is used to treat skin inflammations such as eczema and contact dermatitis.

[0005] Although the application prospect of phototherapy technology is broad, there are still many deficiencies in the current phototherapy devices on the market. For example, there may be problems with uneven light intensity distribution in phototherapy glasses, resulting in excessive light exposure in some areas and insufficient light in other areas; due to the large differences in eye structures and conditions among different users, phototherapy glasses may be difficult to meet the needs of all users; currently, phototherapy glasses can alleviate eye fatigue and have the function of treating depression to a certain extent, but there is no specific scientific principle for prevention and specific detection methods to evaluate the disease conditions of patients.

[0006] The utility model patent with the application number "202211014103.4" and the name "A wearable phototherapy glasses" discloses a wearable phototherapy glasses. This glasses uses LEDs as light sources, and a secondary optical lens is installed outside each group of LED light sources. The light beam emitted by the LED light source is focused by the secondary optical lens and transmitted onto a reflective film, and then white light is reflected to irradiate the eyes, improving the luminous efficiency of the light source. Although this patent provides an innovative design of wearable phototherapy glasses, there are problems such as poor light uniformity, insufficient efficiency of the reflective film, and excessive energy after light focusing, which may cause injury to the user's eyes during use.

[0007] Therefore, there is an urgent need for an intelligent phototherapy glasses with functions of self - inspection, self - treatment, simple operation, safety and stability, uniform light field, and adjustable modes. Summary of the Invention

[0008] In view of this, the present invention provides a multifunctional wearable phototherapy glasses with electroencephalogram detection to solve the problems existing in the background technology.

[0009] To achieve the above - mentioned purpose, the present invention adopts the following technical solutions:

[0010] A multifunctional wearable phototherapy glasses with electroencephalogram detection, comprising:

[0011] PC housing, on which a serial port screen, an MCU, a switch unit, a light therapy unit, a wireless transmission unit, an electroencephalogram (EEG) detection unit, and a driving unit are provided. The switch unit controls the turning on and off of the light therapy glasses. The driving unit is respectively connected to the EEG detection unit and the light therapy unit. The driving unit sends a collection instruction to the EEG detection unit. The EEG detection unit acquires the current user's EEG signal, predicts the sequential EEG signal for the next time period based on the current user's EEG signal, obtains prediction data, and recommends light therapy-related data according to the prediction data. The current user's EEG signal, prediction data, and light therapy-related data are sent to the personal user terminal through the wireless transmission unit by the MCU. The personal user terminal adjusts the brightness intensity and time of the light therapy unit according to the recommended light therapy-related data. The serial port screen is connected to the MCU and is used to display the current user's EEG signal, prediction data, and light therapy-related data. The light therapy unit receives the control instructions that sequentially pass through the personal user terminal, the MCU, and the driving unit, and performs light therapy according to the control instructions.

[0012] Preferably, the housing includes a front frame, a first temple, a second temple, a light source fixing shell, an electrode fixing shell, and a connecting frame. The first temple and the second temple are respectively located on both sides of the front glasses frame. The MCU and an antenna are arranged inside the first temple, and the antenna is connected to the wireless transmission unit. The switch unit is arranged at the upper right end of the front glasses frame. The light source fixing shell is located directly below the front glasses frame and is connected to the front glasses frame in a foldable manner and can be folded to the rear of the front glasses frame after use. The electrode fixing shell is located directly above the front glasses frame, and the two are connected through a connecting frame. The electrode fixing shell sends the collected data to the EEG detection unit. The serial port screen is arranged at the lens part of the front glasses frame.

[0013] Preferably, it further includes a wireless charging module, which is arranged inside the second temple and is connected to the energy storage unit to provide energy for the light therapy glasses.

[0014] Preferably, the EEG detection unit includes an EEG signal amplification circuit, a filtering circuit, an analog-to-digital conversion circuit, and a signal processing module that are connected in sequence. The signal processing module predicts the sequential EEG signal for the next time period for the current user's EEG signal after analog-to-digital conversion, obtains prediction data, and recommends light therapy-related data according to the prediction data.

[0015] Preferably, the signal processing module includes:

[0016] Prediction module: perform phase space reconstruction on the current user's EEG signals to obtain a phase space; input the phase space into a Convolutional Neural Network (CNN) to obtain a time series with high-dimensional feature information; input the time series into a Deep Learning Long Short-Term Memory (LSTM) model for feature time series prediction to obtain prediction data; determine whether the accuracy of the prediction data exceeds a threshold. If the accuracy of the prediction result exceeds the threshold, output the prediction data.

[0017] Data recommendation module: segment the prediction data and calculate the global fluctuation similarity between the segmented prediction data and historical data, and obtain the relevant prediction data curves for each prediction data curve based on the global fluctuation similarity.

[0018] In each prediction data curve and its corresponding relevant prediction data curve, obtain the abnormality degree of each prediction data in each prediction data curve according to the relative outlier degree of the prediction data at the same moment in all the prediction data in the corresponding prediction data curve.

[0019] Calculate the light therapy-related data according to the abnormality degree of each prediction data.

[0020] Preferably, the specific steps for obtaining the phase space in the prediction module include:

[0021] Use the false nearest neighbor method to calculate the embedding dimension m of each lead signal c , use the mutual information method to calculate the delay time τ of each lead signal c , and use the embedding dimension m c and the delay time τ c of each lead signal to perform phase space reconstruction on the current user's EEG signals to obtain a phase space.

[0022] For the time series data of the c-th lead its reconstructed space state is: Merge the reconstruction matrices of all leads into a multi-dimensional phase space X n =[X 1,n ; X 2,n ;...; X C,n ; where C is the total number of leads, X c,n is the phase space point of the c-th lead, m c is the embedding dimension of the c-th lead, and τ c is the delay time of the c-th lead.

[0023] Preferably, the data recommendation module specifically includes:

[0024] Extract all extreme points from the prediction data obtained in the prediction module, and divide the curve into N with the extreme points as the segmentation points pSub - segments Align the historical data of different electrodes with the predicted data after segmentation according to time;

[0025] The calculation of fluctuation similarity includes:

[0026]

[0027] Among them, θ pi,qi is the fluctuation similarity between the matching sub - segments (S pi , S qi ) of the electroencephalogram curves p and q; is the global frequency band power mean of curve p; S pi,qi is the number of initial sub - segments in the corresponding matching sub - segment of the q - th electroencephalogram curve in the i - th pair of matching sub - segments of the p - th and q - th electroencephalogram curves; norm is the normalization function, l pi is the sub - segment slope of the matching sub - segment corresponding to the p - th electroencephalogram curve in the i - th pair of matching sub - segments; l qi is the sub - segment slope of the matching sub - segment corresponding to the q - th electroencephalogram curve in the i - th pair of matching sub - segments; M is the total number of matching sub - segments; Θ p,q is the mean similarity. If Θ p,q ≥γ, then p is determined to be the relevant predicted data curve of q;

[0028] Calculate the local outlier factor LOF p (t j ) of the electroencephalogram data point E pi in the sub - segment S p (t j ) for each electroencephalogram curve p;

[0029]

[0030] Among them, ρ p (t j ) is the degree of abnormality of the electroencephalogram data at the t j th moment in the p - th electroencephalogram curve; LOF p (t j ) is the LOF value of the electroencephalogram at the t j th moment in the p - th electroencephalogram curve; LOF k (t j ) is the LOF value of the electroencephalogram data at the t j th moment in the k - th relevant electroencephalogram curve of the p - th electroencephalogram curve, K is the total number of all relevant electroencephalogram curves of the p - th electroencephalogram curve, ω p (t j ) is the outlier weight of the electroencephalogram data at the t j th moment in the p - th electroencephalogram curve; Calculate the light therapy - related data according to the degree of abnormality of each predicted data.

[0031] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a multifunctional wearable light therapy glasses with electroencephalogram (EEG) detection, integrating a multifunctional module: multiple functional modules such as a control unit, a switch unit, a driving unit, a light therapy unit, a wireless transmission module, an EEG detection unit, and a wireless charging module are integrated inside the light therapy glasses, realizing the organic combination of light therapy and EEG detection; a microprocessor is used as the control unit, which has functions such as instruction interpretation and execution, arithmetic and logical operations, coordinated control of hardware components, memory management, and communication with external devices, improving the intelligent level of the light therapy glasses; users can input program operations through a serial port screen to manually adjust the light therapy intensity to meet the needs of different users; a constant current module and a filtering circuit are adopted to ensure a constant light power and improve the light therapy effect; it can monitor the EEG state of users in real time and provide accurate physiological data. Through a differential amplification circuit and a filtering circuit, environmental noise and power line interference are effectively suppressed, ensuring the accurate acquisition of EEG signals. An analog-to-digital conversion circuit converts analog signals into digital signals for subsequent data processing and analysis. The EEG signals are sent to the user's mobile device through a wireless transmission module, facilitating the user to view and analyze data at any time. Users can understand their own EEG state in real time, which helps to improve their health and provides a scientific basis for the diagnosis and treatment of psychological diseases such as depression. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0033] Figure 1 It is the system structure diagram provided by the present invention;

[0034] Figure 2 It is the physical structure diagram provided by the present invention;

[0035] Figure 3 It is the schematic diagram of the internal circuit functions provided by the present invention.

[0036] In the figure, 1 is the front glasses frame; 2 is the first temple; 3 is the second temple; 4 is the light therapy unit; 5 is the switch unit; 6 is the EEG detection unit; 7 is the electrode; 8 is the light source module; 9 is the MCU; 10 is the wireless transmission unit; 11 is the heat dissipation hole; 12 is the charging coil; 13 is the adjustment unit; 14 is the serial port screen. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] An embodiment of the present invention discloses a multifunctional wearable light therapy glasses with electroencephalogram (EEG) detection. As shown in Figure 1 , 2 , 3, it includes:

[0039] A PC housing, on which a serial port screen 14, an MCU 9, a switch unit 5, a light therapy unit 4, a wireless transmission unit 10, an EEG detection unit 6, and a driving unit are provided. The switch unit 5 controls the opening and closing of the light therapy glasses. The driving unit is respectively connected to the EEG detection unit 6 and the light therapy unit 4. The driving unit sends a collection instruction to the EEG detection unit 6. The EEG detection unit 6 acquires the current user's EEG signal, predicts the sequential EEG signal in the next time period based on the current user's EEG signal, obtains prediction data, and recommends light therapy-related data according to the prediction data. The current user's EEG signal, prediction data, and light therapy-related data are sent to the personal user terminal through the MCU 9 by the wireless transmission unit 10. The personal user terminal adjusts the brightness intensity and time of the light therapy unit 4 according to the recommended light therapy-related data. The serial port screen 14 is connected to the MCU 9 and is used to display the current user's EEG signal, prediction data, and light therapy-related data. The light therapy unit 4 receives the control instructions sequentially through the personal user terminal, the MCU 9, and the driving unit, and performs light therapy according to the control instructions.

[0040] In a specific embodiment, the shell adopts a PC plastic shell, which has excellent thermal conductivity and heat resistance. The driving module transmits the output control instructions to the phototherapy unit 4 in a steady flow manner to ensure the stability of the light source during use. Among them, the phototherapy unit 4 improves the uniformity of the light source by adding a light-homogenizing sheet; the PCB of the phototherapy unit 4 is made of an aluminum substrate to improve the heat dissipation function; the phototherapy unit 4 is also provided with a connected temperature sensing device to detect that the temperature exceeds a certain threshold and alarms, and transmits the interrupt signal to the MCU9; the light source includes a plurality of parallel light source modules 8. A nose pad is provided on the side of the nose pad that contacts the nose. The nose pad is detachably connected to the shell. The nose pad is made of silicone material, which is non-slip and safe, and prevents fatigue of the nose caused by long-term wearing, and the nose pad cover can be removed and cleaned to keep it clean and hygienic. The nose pad is made of silicone material, which is non-slip and safe, and can be removed and cleaned to improve wearing comfort and hygiene. MCU9 has the functions of interpreting and executing instructions stored in memory, performing arithmetic and logical operations, coordinating and controlling the operations of other hardware components, managing data and instructions in memory, including cache and virtual memory, and handling communications with external devices.

[0041] In one specific embodiment, a filter film is added in front of the light source module 8 to prevent the ultraviolet light and short-wave blue light from the light source from penetrating and damaging the eyes during prolonged use. This further improves the safety of the phototherapy glasses and reduces eye fatigue. The MCU 9 inside the glasses has a timer program that shuts down the power supply and stops operation if the light source is not turned off beyond the scheduled usage time, thus preventing eye discomfort caused by prolonged use. The addition of a temperature sensor, a timing device, and a filter film effectively prevents eye damage caused by high temperatures, prolonged use, and harmful light.

[0042] In a specific embodiment, the light source of the light therapy unit 4 is an OLED or an LED.

[0043] In a specific embodiment, the wireless charging module includes a charging coil 12, a voltage stabilization protection circuit, an LC resonant rectifier circuit, a filtering circuit, an adjustment circuit and an ASIC chip with a power management unit, thereby realizing efficient and safe wireless charging; an adjustment unit 13 is provided at the upper left end of the front eyeglass frame 1, which can be used to directly adjust the light therapy mode.

[0044] In a specific embodiment, the housing includes a front frame, a first mirror arm 2, a second mirror arm 3, a light source fixing case, an electrode 7 fixing case, and a connecting frame. The first mirror arm 2 and the second mirror arm 3 are respectively located on both sides of the front spectacle frame 1. The first mirror arm 2 is internally provided with an MCU9 and an antenna, and the antenna is connected to a wireless transmission unit 10; a switch unit 5 is provided at the upper right end of the front spectacle frame 1; the light source fixing case is located directly below the front spectacle frame 1 and is connected to the front spectacle frame 1 in a foldable manner, and can be folded to the rear of the front spectacle frame 1 after use; the electrode 7 fixing case is located directly above the front spectacle frame 1, and the two are connected by a connecting frame. The electrode 7 fixing case sends the collected data to the electroencephalogram detection unit 6; a serial port screen 14 is provided at the lens part of the front spectacle frame 1. A plurality of adhesive patch electrodes 7 are placed in the electrode 7 fixing case for recording the electrical activity of the brain, and are fixed on the forehead of the user. The electrode 7 design at the forehead position can not only effectively collect electroencephalogram signals, but also has the advantages of comfort, portability, and multi-functional integration.

[0045] In a specific embodiment, the circuit of the electroencephalogram detection unit 6 is connected to the MCU9. The user inputs an operation signal to the switch unit 5 to open or close the instruction, and the driving module drives the operation of the electroencephalogram detection module; a plurality of adhesive patch electrodes 7 are placed in the electrode 7 fixing case for recording the electrical activity of the brain, and are fixed on the forehead of the user. The electrode 7 design at the forehead position can not only effectively collect electroencephalogram signals, but also has the advantages of comfort, portability, and multi-functional integration. An electrode cap with a fixed position is added outside the electrode 7. The electrode cap is made of a soft conductive material, such as silver-plated glass silicone, which can better contact the user's skin and improve the user's experience.

[0046] In a specific embodiment, a wireless charging module is further included. The wireless charging module is arranged inside the second mirror arm 3 and is connected to the energy storage unit to provide energy for the light therapy glasses.

[0047] In a specific embodiment, heat dissipation holes 11 are also provided on the second mirror arm 3 for heat dissipation.

[0048] In a specific embodiment, the electroencephalogram detection unit 6 includes an electroencephalogram signal amplification circuit, a filtering circuit, an analog-to-digital conversion circuit, and a signal processing module connected in sequence. The signal processing module predicts the electroencephalogram signal in the next time period for the current user's electroencephalogram signal after analog-to-digital conversion, obtains prediction data, and recommends light therapy-related data according to the prediction data. The amplification circuit adopts a differential amplification circuit, which can effectively suppress common-mode interference, such as power line noise and other environmental interference. The filtering circuit is used to remove noise and unnecessary frequency components in the signal, such as 50 / 60Hz power line interference. The analog-to-digital conversion circuit converts the analog electroencephalogram signal into a digital signal for computer processing.

[0049] In a specific embodiment, the electroencephalogram (EEG) detection unit 6 further includes a storage module. The above-mentioned signals are transmitted into the storage module and then transmitted to the user's mobile device through the antenna by the wireless transmission module 10. The user device software analyzes the EEG data and evaluates the intervention effect. At the same time, the signals are transmitted to the display module (serial port screen 14) for display via Bluetooth synchronization.

[0050] In a specific embodiment, the wireless transmission module 10 is Bluetooth.

[0051] In a specific embodiment, it further includes a user graphic module, which includes an optional combination of a touch screen or a liquid crystal screen and keys. The user graphic module can perform data and instruction interaction with the control unit through communication methods such as serial port and IIC.

[0052] In a specific embodiment, the signal processing module includes:

[0053] A prediction module that performs phase space reconstruction on the current user's EEG signal to obtain a phase space. The phase space is input into a convolutional neural network (CNN) to obtain a time series with high-dimensional feature information. The time series is input into a deep learning long short-term memory (LSTM) model for feature time series prediction to obtain prediction data. It is determined whether the accuracy of the prediction data exceeds a threshold. If the accuracy of the prediction result exceeds the threshold, the prediction data is output.

[0054] A data recommendation module that segments the prediction data and calculates the global fluctuation similarity between the segmented prediction data and the historical data. Relevant prediction data curves for each prediction data curve are obtained based on the global fluctuation similarity. The historical data is the EEG data of patients with seasonal or non-seasonal affective disorder (SAD).

[0055] In each prediction data curve and the corresponding relevant prediction data curve, the outlier degree of each prediction data in each prediction data curve is obtained according to the relative outlier degree of the prediction data at the same time in all the prediction data of the corresponding prediction data curve.

[0056] Based on the outlier degree of each prediction data, the light therapy-related data is calculated.

[0057] In a specific embodiment, obtaining the phase space in the prediction module specifically includes:

[0058] Using the false nearest neighbor method to calculate the embedding dimension m of each lead signal c , using the mutual information method to calculate the delay time τ of each lead signal c , using the embedding dimension m of each lead signal c and the delay time τ c , performing phase space reconstruction on the current user's EEG signal to obtain a phase space;

[0059] For the time series data of the c-th lead Its reconstructed space state is Merge the reconstruction matrices of all leads into a multi-dimensional phase space X n =[X 1,n ; X 2,n ;...; X C,n ; where C is the total number of leads X c,n is the phase space point of the c-th lead, m c is the embedding dimension of the c-th lead, τ c is the delay time of the c-th lead. The lead signal is the time series electrical signal collected by each electrode. Each electrode corresponds to an independent "lead", which is used to capture the electrical activities of different regions of the brain and form multi-channel EEG data

[0060] In a specific embodiment, the data recommendation module specifically includes

[0061] Extract all extreme points from the prediction data obtained in the prediction module, and divide the curve into N p segments with the extreme points as the segmentation points Align the historical data of different electrodes with the segmented prediction data in time

[0062] The calculation of fluctuation similarity includes

[0063]

[0064] where θ pi,qi is the fluctuation similarity between the matching segments (S pi , S qi ) of the EEG curves p and q, is the global frequency band power mean of curve p; S pi,qi is the initial segment number in the corresponding matching segment of the q-th EEG curve in the i-th pair of matching segments of the p-th EEG curve and the q-th EEG curve; norm is the normalization function, l pi is the segment slope of the corresponding matching segment of the p-th EEG curve in the i-th pair of matching segments; l qi is the segment slope of the corresponding matching segment of the q-th EEG curve in the i-th pair of matching segments; M is the total number of matching segments; Θ p,q is the similarity mean. If Θ p,q ≥γ, then p is determined to be the relevant prediction data curve of q, and γ is the preset threshold

[0065] Calculate the local outlier factor LOF of each EEG data point E p (t j ) in the EEG curve p within the segment S pi ​p (t j ), filter pseudo anomalies through a double verification mechanism (Outlier Factor LOF + correlation curve comparison) to avoid misjudgment;

[0066]

[0067] Among them, ρ p (t j ) is the anomaly degree of the EEG data at the t j -th moment in the p-th EEG curve; LOF p (t j ) is the LOF value of the EEG at the t j -th moment in the p-th EEG curve; LOF k (t j ) is the LOF value of the EEG data at the t j -th moment in the k-th related EEG curve of the p-th EEG curve, K is the total number of all related EEG curves of the p-th EEG curve, ω p (t j ) is the outlier weight of the EEG data at the t j -th moment in the p-th EEG curve; Calculate the phototherapy-related data according to the anomaly degree of each predicted data.

[0068] ω p (t j ) = norm(LOF p (t j ) - LOF p,K (t j )) × σ p,K (t j );

[0069] Among them, LOF p,K (t j ) is the average LOF of curve p and its K related EEG curves at time t j ; σ p,K (t j ) is the variance of the corresponding LOF value.

[0070] Calculate the phototherapy-related data according to the anomaly degree of each predicted data, specifically including:

[0071] Wavelength selection:

[0072] Among them, λ0 is the base wavelength, and Δλ is the wavelength adjustment range;

[0073] Intensity adjustment I p = I min + (I max - I min ) · exp(-ρp (t j ));

[0074] wherein, I min 、I max are the lower limit and upper limit of the safety intensity range respectively;

[0075] Duration determination:

[0076] wherein, T0 is the base duration.

[0077] The degree of abnormality is directly related to the three core parameters of phototherapy (wavelength, intensity, duration). Its calculation result is the mathematical basis for parameter adjustment. The calculation of the degree of abnormality is the key link connecting electroencephalogram signal analysis and phototherapy execution. Its essence is a quantitative bridge that converts physiological indicators into treatment instructions, which not only ensures the adaptability of the plan to individual differences, but also avoids the risk of over-treatment through a safety mechanism, realizing the closed-loop logic of "detection - analysis - treatment"; the higher the degree of abnormality, the more the wavelength shifts towards the long wavelength direction. Since long wavelength light has stronger penetration into deep brain regions, it is suitable for severe abnormal states; the degree of abnormality is exponentially positively correlated with the light intensity. When the degree of abnormality ρ = 0, the lowest safety intensity is maintained, and when ρ = 1, it rises to 5000 lux to avoid strong light damage to the retina; the higher the average value of the degree of abnormality, the longer the phototherapy duration, and the effect is improved by strengthening the treatment.

[0078] In summary, the present invention provides a device with functions of phototherapy, wireless charging, and electroencephalogram detection and its specific implementation method. Through the above technical solutions, the present invention has the following remarkable advantages: The present invention integrates three functions of phototherapy, wireless charging, and electroencephalogram detection, realizing multi-functional integration and improving the practicability of the device and the user experience; through wireless charging technology, the limitation of traditional wired charging is avoided, making the user more convenient and free during use; the design of the phototherapy module can adjust the light intensity and spectrum according to user needs, providing personalized phototherapy services for users; the electroencephalogram detection module can real-time monitor the electroencephalogram state of the user, providing accurate physiological data for the user, which helps to improve the user's health condition; the present invention has a compact structure and is easy to operate, and is suitable for use in various occasions such as families, medical treatment, and scientific research.

[0079] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0080] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multifunctional wearable light therapy glasses with electroencephalogram detection, characterized in that, Including: A PC shell, on which a serial port screen, an MCU, a switch unit, a light therapy unit, a wireless transmission unit, an electroencephalogram (EEG) detection unit, and a driving unit are provided. The switch unit controls the turning on and off of the light therapy glasses. The driving unit is respectively connected to the EEG detection unit and the light therapy unit. The driving unit sends a collection instruction to the EEG detection unit. The EEG detection unit acquires the current user's EEG signal, predicts the sequential EEG signal for the next time period based on the current user's EEG signal, obtains prediction data, and recommends light therapy-related data according to the prediction data. The current user's EEG signal, prediction data, and light therapy-related data are sent to the personal user terminal through the wireless transmission unit by the MCU. The personal user terminal adjusts the brightness intensity and time of the light therapy unit according to the recommended light therapy-related data. The serial port screen is connected to the MCU and is used to display the current user's EEG signal, prediction data, and light therapy-related data. The light therapy unit receives the control instructions sequentially passed through the personal user terminal, the MCU, and the driving unit and performs light therapy according to the control instructions.

2. The multifunctional wearable light therapy glasses with EEG detection according to claim 1, characterized in that, The shell includes a front frame, a first mirror arm, a second mirror arm, a light source fixing shell, an electrode fixing shell, and a connecting frame. The first mirror arm and the second mirror arm are respectively located on both sides of the front glasses frame. The MCU and an antenna are provided inside the first mirror arm, and the antenna is connected to the wireless transmission unit. The switch unit is provided at the upper right end of the front glasses frame. The light source fixing shell is located directly below the front glasses frame and is connected to the front glasses frame in a foldable manner and can be folded to the rear of the front glasses frame after use. The electrode fixing shell is located directly above the front glasses frame, and the two are connected through the connecting frame. The electrode fixing shell sends the collected data to the EEG detection unit. The serial port screen is provided at the lens part of the front glasses frame.

3. The multifunctional wearable light therapy glasses with EEG detection according to claim 2, characterized in that, It further includes a wireless charging module, which is provided inside the second mirror arm and is connected to the energy storage unit to provide energy for the light therapy glasses.

4. A multifunctional wearable light therapy glasses with electroencephalogram detection according to claim 1, characterized in that, The EEG detection unit includes an EEG signal amplification circuit, a filtering circuit, an analog-to-digital conversion circuit, and a signal processing module connected in sequence. The signal processing module predicts the sequential EEG signal for the next time period for the current user's EEG signal after analog-to-digital conversion, obtains prediction data, and recommends light therapy-related data according to the prediction data.

5. The multifunctional wearable light therapy glasses with electroencephalogram detection according to claim 4, characterized in that, The signal processing module includes: A prediction module that performs phase space reconstruction on the current user's EEG signal to obtain a phase space; inputs the phase space into a convolutional neural network (CNN) to obtain a time series with high-dimensional feature information; inputs the time series into a deep learning long short-term memory (LSTM) model to perform feature time series prediction and obtain prediction data; determines whether the accuracy of the prediction data exceeds a threshold. If the accuracy of the prediction result exceeds the threshold, the prediction data is output. A data recommendation module that segments the prediction data and calculates the global fluctuation similarity between the segmented prediction data and historical data, and obtains the relevant prediction data curves for each prediction data curve according to the global fluctuation similarity. In each of the predicted data curves and the corresponding relevant predicted data curves, according to the relative outlier degree of the predicted data at the same time among all the predicted data in the corresponding predicted data curve, obtain the abnormality degree of each predicted data in each predicted data curve; Calculate the phototherapy-related data according to the abnormality degree of each predicted data.

6. The multifunctional wearable light therapy glasses with EEG detection according to claim 5, characterized in that, The phase space obtained in the prediction module specifically includes: Using the false nearest neighbor method, calculate the embedding dimension m of each lead signal c , using the mutual information method, calculate the delay time τ of each lead signal c , using the embedding dimension m of each lead signal c and the delay time τ c , perform phase space reconstruction of the current user's EEG signal to obtain a phase space; The time series data for the c-th lead Its reconstructed space state is: Merge the reconstruction matrices of all leads into the multi-dimensional phase space X n =[X 1,n ; X 2,n ;...; X C,n ; where C is the total number of leads, X c,n is the phase space point of the c-th lead, m c is the embedding dimension of the c-th lead, and τ c is the delay time of the c-th lead.

7. A multifunctional wearable light therapy glasses with electroencephalogram detection according to claim 5, characterized in that, The data recommendation module specifically includes: Extract all extreme points from the prediction data obtained by the prediction module, and use the extreme points as segmentation points to divide the curve into N p segments Align the historical data of different electrodes with the segmented prediction data according to time; The calculation of fluctuation similarity includes: Among them, θ pi,qi is the fluctuation similarity between the matching segments (S pi , S qi ) of the EEG curves p and q, is the global frequency band power mean of the curve p; S pi,qi is the number of initial segments in the corresponding matching segment of the q-th EEG curve in the i-th pair of matching segments of the p-th EEG curve and the q-th EEG curve; norm is the normalization function, l pi is the segment slope of the matching segment corresponding to the p-th EEG curve in the i-th pair of matching segments; l qi is the segment slope of the matching segment corresponding to the q-th EEG curve in the i-th pair of matching segments; M is the total number of matching segments; Θ p,q is the similarity mean. If Θ p,q ≥γ, then p is determined to be the relevant prediction data curve of q; Calculate the EEG data points E in each EEG curve p through the LOF algorithm p (t j ), and the local outlier factor LOF pi within the segment S p (t j ); where ρ p (t j ) is the degree of abnormality of the EEG data at the t j -th moment in the p-th EEG curve; LOF p (t j ) is the LOF value of the EEG at the t j -th moment in the p-th EEG curve; LOF k (t j ) is the LOF value of the EEG data at the t j -th moment in the k-th related EEG curve of the p-th EEG curve, K is the total number of all related EEG curves of the p-th EEG curve, ω p (t j ) is the outlier weight of the EEG data at the t j -th moment in the p-th EEG curve; According to the degree of abnormality of each prediction data, the phototherapy-related data is calculated.

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