Integrated recognition and detection system based on electroencephalogram signals and brain function near-infrared signals

Abstract

The invention discloses an integrated recognition and detection system based on electroencephalogram signals and brain function near-infrared signals, and particularly relates to the technical field of medical diagnosis, which comprises a signal acquisition module, a main control module and PC (Personal Computer) upper computer software. Data processing and transmission are carried out through the main control module, display, analysis and real-time monitoring of the data are achieved in cooperation with PC upper computer software, detailed brain wave shape and near-infrared brain function imaging results can be provided, early diagnosis and intervention can be assisted, biomarkers in EEG and fNIRS data can be extracted through a classification recognition algorithm, and the accuracy of diagnosis and intervention is improved. The method helps to identify potential features, evaluates the severity of the illness state in combination with a clinical information database, and supports the formulation and monitoring of a personalized rehabilitation treatment scheme, thereby providing real-time and accurate diagnosis and treatment effect evaluation for a child patient.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical diagnosis, and in particular to an integrated recognition and detection system based on electroencephalogram signals and brain function near-infrared signals. BACKGROUND

[0002] Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are two important biological signals widely used in neuroscience, medical diagnosis, and brain science research. EEG reflects the neural activity of the brain by recording the electrical activity of the cerebral cortex, while fNIRS reflects the changes in blood oxygenation in the brain by monitoring changes in cerebral blood flow. These two signals can provide strong data support for the functional state of the brain, and therefore have important significance in the diagnosis of neurological diseases, functional brain imaging, and early intervention. With the advancement of brain science technology, multi-modal signal fusion methods based on EEG and fNIRS have gradually become a research hotspot, as they can provide more comprehensive and accurate assessments of brain state.

[0003] Existing EEG and fNIRS signal acquisition and analysis systems have many problems. For example, some systems cannot achieve synchronous acquisition of EEG and fNIRS signals during data acquisition, which makes the diagnosis results of single signals less accurate and unable to fully reflect the functional state of the brain. Motion artifact interference is a common problem in existing technologies, and traditional systems have difficulty in effectively distinguishing between motion artifact and real changes in brain signals, resulting in reduced data accuracy. Many systems lack effective motion artifact detection modules, which can affect the accuracy of EEG waveforms and cerebral blood flow signals, and thus affect the accuracy of diagnosis. Traditional brain signal analysis systems usually rely on single signal analysis methods and lack the fusion of multi-modal data, making it difficult to provide more accurate assessments of brain state. Although some systems have adopted computer-aided diagnosis (CAD) technology, they are mostly limited to simple EEG waveform recognition and lack comprehensive analysis and evaluation of multi-dimensional brain function information. Existing technologies lack sufficient support for personalized treatment plans and cannot effectively develop precise rehabilitation treatment plans based on patients' EEG and brain function signals. Therefore, we provide an integrated recognition and detection system based on EEG and fNIRS signals. SUMMARY

[0004] The purpose of the present application is to solve the problems existing in the prior art and to provide an integrated recognition and detection system based on EEG and fNIRS signals.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solutions: The integrated recognition and detection system based on EEG and fNIRS signals comprises a signal acquisition module, a main control module, and a PC host computer software. The signal acquisition module comprises a brain function near-infrared (fNIRS) data acquisition module, an electroencephalogram (EEG) data acquisition module and a motion state detection module. The main control module comprises the functions of power management, power supply management and data transmission. The PC host computer software comprises basic information management functions, EEG signal acquisition and display settings and fNIRS signal acquisition and display functions.

[0006] The EEG acquisition module acquires the electroencephalogram signal through the electrode sensor placed on the scalp, and then filters and digitizes the electroencephalogram signal through filtering, amplification and analog-to-digital conversion, and converts the electroencephalogram data through the main control module to obtain the original electroencephalogram signal time domain change.

[0007] The fNIRS acquisition module uses three AD7689 chips for signal acquisition, uses continuous wave (CW) detection, selects a double-wavelength near-infrared LED product SMT735D / 850D as the light source, and selects a Vishay VEMD5060X01 photodiode as the photodetector of the system.

[0008] The signal acquisition module mainly comprises an electrode sensor, a pre-processing circuit, an analog front-end circuit, a communication interface and a power management circuit in hardware, and the acquisition of the electroencephalogram data is realized by writing a control program through the main control STM32 processor in software.

[0009] The fNIRS acquisition module moves when the head or body moves during operation, causes the change of the optical signal, or introduces the interference of the electroencephalogram signal.

[0010] The PC host computer software can realize data acquisition, display, analysis and control functions through communication with the embedded system or other hardware modules, and the main use is to obtain real-time or timing acquisition data from the hardware device, and display the data to the user through a graphical interface for monitoring and analysis.

[0011] The PC host computer software has basic window function, user information management, real-time data display, device control, parameter setting, alarm prompt function, the software supports dynamic addition of new users, and provides add, delete, modify and query operation on the entered user information, facilitates user to maintain and manage patient data, in the electroencephalogram (EEG) and near-infrared brain function imaging (fNIRS) monitoring system, the software can display the waveform of brain wave and the light wave of different wavelengths collected by near-infrared brain function imaging in real time, these waveforms can be further analyzed and recognized to help the intervention and treatment of children, the software can also control the working state of the device, start or stop the data acquisition function, set the opening and closing of EEG acquisition channel, configure the S-D (arrangement of light source and detector) of fNIRS, the software can continuously monitor the connection between the device and the user, and timely remind the user to pay attention to abnormal data.

[0012] The EEG (EEG) acquisition module outputs digitized EEG data by collecting scalp electrode signals and provides a time reference for the brain function near-infrared (fNIRS) acquisition module; the brain function near-infrared (fNIRS) acquisition module detects light source and photodetector signals and outputs light intensity change data, and provides a comparison reference of optical signal change and motion state for the motion state detection module; the motion state detection module collects head motion data and generates motion compensation information, and provides motion artifact correction reference data for the main control module.

[0013] The power management unit provides stable working power for the power supply management unit through voltage stabilization and power distribution; the power supply management unit provides stable power supply support for the data transmission unit by controlling the power supply state of each sub-module; the data transmission unit sends the digital signals of the EEG and fNIRS acquisition modules to the PC host computer software through SPI or serial communication.

[0014] The information management function module is further configured to maintain user and device information, and provide the EEG signal acquisition and display setting module with acquisition parameters corresponding to the user; the EEG signal acquisition and display setting module is further configured to provide the fNIRS signal acquisition and display module with a synchronous time reference by receiving and displaying electroencephalogram waveform data in real time; and the fNIRS signal acquisition and display module is further configured to provide the integrated monitoring interface with fusion display data by displaying and processing multi-wavelength optical signals.

[0015] The present application has the following advantages: The present application has the following advantages: The present application has the following advantages: The present application has the following advantages:

[0016] The present application has the following advantages: Figure 1 The present application has the following advantages: The present application has the following advantages: Figure 2 The present application has the following advantages: The present application has the following advantages: Figure 3 The present application has the following advantages: The present application has the following advantages: Figure 4 The present application has the following advantages: The present application has the following advantages: Figure 5 The present application has the following advantages: DETAILED DESCRIPTION

[0017] In order to make the technical problems, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the described examples are only some of the embodiments of the present application, rather than all the embodiments. The specific examples described herein are only used to explain the present application, and are not used to limit the present application. Based on the examples in the present application, all other examples obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0018] It needs to be further explained that the drawings and embodiments of the present application mainly describe the concept of the present application, and on the basis of the concept, the specific forms and settings of some connection relationships, position relationships, power mechanisms, power supply systems, hydraulic systems and control systems may not be completely described, but the skilled in the art can realize the above-mentioned specific forms and settings in a well-known manner on the premise of understanding the concept of the present application.

[0019] When an element is referred to as being "fixed" or "set" on another element, it can be directly on the other element or indirectly on the other element. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or indirectly connected to the other element.

[0020] The orientation words "inner, outer" refer to the inner and outer of the contour of each component itself, and the orientation or position relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" is based on the orientation or position relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0021] For the convenience of description, spatial relative terms such as "above", "upper", "upper surface", "upper", etc. can be used herein to describe the spatial positional relationship of one device or feature with other devices or features as shown in the drawings. It should be understood that the spatial relative terms are intended to include different orientations in use or operation in addition to the orientation of the device described in the drawings. For example, if the device in the drawings is inverted, the device described as "above" or "above" other devices or structures will be positioned "below" or "below" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below" orientations. The device can also be positioned in other different ways, and the spatial relative description used herein is interpreted accordingly.

[0022] The terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, and the meaning of "several" is one or more, unless otherwise explicitly specified.

[0023] The integrated identification and detection system based on electroencephalogram (EEG) signals and near-infrared signals of brain function provided by this invention will now be described.

[0024] like Figures 1-5 As shown, the present invention provides a technical solution: an integrated identification and detection system based on electroencephalogram (EEG) signals and near-infrared signals of brain function, comprising: a signal acquisition module, a main control module, and PC host computer software; The signal acquisition module includes a functional near-infrared spectroscopy (fNIRS) data acquisition module, an electroencephalogram (EEG) data acquisition module, and a motion state detection module; The main control module includes functions for power management, power supply management, and data transmission. The PC host computer software includes basic information management functions, EEG signal acquisition and display settings, and fNIRS signal acquisition and display functions; The EEG acquisition module acquires EEG signals through electrode sensors placed on the scalp. The EEG signals are then filtered, amplified, and converted from analog to digital to obtain the original EEG signal time-domain variation. Based on the performance indicators of small size, high bandwidth, low noise, high precision, and low power consumption, the EEG acquisition module is designed based on the analog front-end acquisition chip ADS1299. Data transmission and command transmission between the main control module and the EEG acquisition module are realized through SPI communication. The brain functional near-infrared (fNIRS) acquisition module uses three AD7689 chips for signal acquisition and continuous wave (CW) detection. The dual-wavelength near-infrared LED product SMT735D / 850D is selected as the light source, and Vishay's VEMD5060X01 photodiode is selected as the photodetector of the system. The signal acquisition module mainly includes electrode sensors, preprocessing circuits, analog front-end circuits, communication interfaces, and power management circuits in its hardware. In terms of software, it mainly uses a STM32 main control processor to write control programs to acquire EEG data. By setting the sampling rate, sampling mode, sampling time, and gain factor, it continuously acquires scalp EEG data, converts the acquired data into digital signals, returns feature values ​​after acquisition, and sends the data to the processor via the SPI serial port. The processor then converts the digital signals into the original analog signals and stores them. The movement of the head or body during the operation of the brain functional near-infrared (fNIRS) acquisition module causes the probe to move, resulting in changes in the optical signal or introducing interference with the electroencephalogram (EEG) signal. The motion sensor is fixed to the subject's head to collect head motion data. The selected ICM-20948 nine-axis sensor is capable of measuring. The PC host software can realize data acquisition, display, analysis and control functions through communication with embedded systems or other hardware modules. Its main purpose is to obtain real-time or timed data collected from hardware devices and display these data to users through a graphical interface for monitoring and analysis. The PC host software has basic window functions, user information management, real-time data display, device control, parameter setting, alarm prompt functions, and supports dynamic addition of new users. It also provides add, delete, modify and query operations on the entered user information, making it easy for users to maintain and manage patient data. In the electroencephalogram (EEG) and near-infrared brain function imaging (fNIRS) monitoring system, the software can display the waveform of brain waves and the light waves of different wavelengths collected by the near-infrared brain function imaging in real time. These waveforms can be further analyzed and recognized to help children's intervention and treatment. The software can also control the working state of the device, start or stop data collection, set the opening and closing of EEG collection channels, configure the S-D (arrangement of light source and detector) of fNIRS, and continuously monitor the connection between the device and the user to remind the user to pay attention to abnormal data in a timely manner. The EEG acquisition module collects scalp electrode signals and outputs digitized EEG data, providing a time reference for the fNIRS acquisition module. The fNIRS acquisition module detects light source and photodetector signals and outputs light intensity change data, providing a comparison reference for the motion state detection module. The motion state detection module collects head motion data and generates motion compensation information, providing motion artifact correction reference data for the main control module. The power management unit provides stable working power for the power supply management unit through voltage stabilization and distribution. The power supply management unit provides stable power support for the data transmission unit by controlling the power supply state of each sub-module. The data transmission unit sends digital signals from the EEG and fNIRS acquisition modules to the PC host software through SPI or serial communication. The information management function module maintains user and device information to provide acquisition parameters for the EEG signal acquisition and display setting module. The EEG signal acquisition and display setting module receives and displays EEG waveform data in real time to provide a synchronous time reference for the fNIRS signal acquisition and display module. The fNIRS signal acquisition and display module displays multi-wavelength light signals and processes data to provide fusion display data for the overall monitoring interface.

[0025] In this embodiment, the EEG and cerebral blood flow signals of the subject are collected in real time by the integrated EEG and fNIRS signal acquisition module, the motion state detection module is combined to effectively distinguish the motion artifacts, the data accuracy is ensured, the data processing and transmission are performed by the main control module, the data display, analysis and real-time monitoring are realized by the PC upper computer software, the detailed brain waveforms and near-infrared brain function imaging results can be provided to assist early diagnosis and intervention, the system can extract biomarkers in EEG and fNIRS data through classification recognition algorithm, help to identify potential features, and evaluate the severity of the disease combined with clinical information database, so as to provide real-time and accurate diagnosis and treatment effect evaluation.

[0026] Among them: the system classifies EEG data through EEGPT network, classifies fNIRS data through fNIRS-T network, and classifies EEG and fNIRS fusion data through FGANet network, and classifies the classification results of the three networks, finds the most relevant biomarkers in EEG signals and fNIRS signals through brain network analysis, brain activation analysis and correlation analysis.

[0027] Among them: the main control module uses STM32H743IIT6 chip as the core control unit, integrates ArmCortex-M7 kernel, and has a working frequency of 480MHz, is equipped with large-capacity storage space and rich peripheral interface; the power management unit supports 24V medical adapter and 16.8V lithium battery dual power supply, all power inputs are processed through isolation power module to ensure electrical isolation and system safety, meet the medical safety requirements; the communication system includes SPI protocol interface (communicates with fNIRS and EEG module), RS485 protocol interface (communicates with motion detection module), USB protocol interface (equipped with isolation module and communicates with upper computer) and ESP32-S3 wireless module (interacts with upper computer through WIFI and TCP / IP protocol); the storage system includes Flash chip for storing system configuration information and calibration parameters, and SDRAM chip for temporarily storing real-time data collected by each module.

[0028] The electroencephalogram (EEG) acquisition module adopts 2 pieces of ADS1299 chip cascade to realize 16-channel signal acquisition, which can be expanded to 32 channels; each piece of ADS1299 integrates 8 low-noise programmable gain amplifiers (PGA) and 8 24-bit high-resolution analog-to-digital converters, the amplification factor is 1-24 times programmable adjustment; the sampling rate supports 250 SPS to 16 KSPS adjustable, the signal bandwidth is DC to 27 KHz; the input impedance is greater than 1 GΩ, the common-mode rejection ratio reaches 110 dB, the equivalent input noise is less than 1 μVpp (DC-70 Hz frequency band), the low input bias current is 300 pA; the pre-processing circuit includes ESD static protection circuit and RC anti-aliasing filter circuit, the cutoff frequency is 339 Hz, which can effectively attenuate interference signals outside the sampling frequency band; the electrode adopts Ag-AgCl cup-shaped wet electrode, which is used with medical electroencephalogram conductive paste, and the electrode placement follows the international 10-20 electrode system standard, and long-time stable recording is supported.

[0029] The brain function near-infrared (fNIRS) acquisition module adopts a continuous wave (CW) detection method, and a light source system is configured with 16 double-wavelength LED light sources (Marubeni SMT735D / 850D), and the emission wavelengths are 735 nm and 850 nm, respectively, and the two wavelengths are located on both sides of the 800 nm intersection point of HbR and HbO2 absorption spectrum, wherein the 735 nm wavelength is sensitive to deoxyhemoglobin (HbR), and the 850 nm wavelength is sensitive to oxyhemoglobin (HbO2); the single-wavelength luminous power is 24 mW@50 mA forward current, the wavelength spread error is only ±20 nm, and the spectral purity is high; a detection system is configured with 24 silicon photodiodes (Vishay VEMD5060X01), the spectral response range is 350-1070 nm, the peak sensitivity wavelength is about 800 nm, the detection sensitivity is 1.74 pW, and the half-sensitivity angle is ±65°; a 16-bit analog-to-digital converter is used for signal processing, and the system supports 24*16 channel configuration; in the optical transmission system, the detector cable core is a glass optical fiber, and the outer layer is protected by a PVC sleeve, the optical fiber only transmits optical signals and does not conduct electricity, which can avoid electromagnetic interference; based on the modified Beer-Lambert law (MBLL), the absorption difference of HbR and HbO2 at 735 nm and 850 nm is used to calculate the relative blood oxygen concentration change.

[0030] The motion state detection module adopts an ICM-20948 nine-axis sensor, is fixed on the head of a subject, and measures linear acceleration, angular velocity and geomagnetic field information in real time; during signal acquisition, the motion of the head or the body may cause the probe to move, causing changes in the optical signal or interference of the electroencephalogram signal; the motion data and the EEG and fNIRS data are collected synchronously and transmitted to the main control module through an RS485 protocol; in subsequent signal processing, the motion data can be used to effectively distinguish between real brain activity signals and artifacts introduced by motion, improve the accuracy of signal analysis, and provide data support for the identification and correction of motion artifacts.

[0031] The PC host software is used as a computer application program for interacting with the hardware device, communicates with the device host through a USB or WIFI mode, and realizes functions such as data acquisition, display, analysis and control; the software main interface can realize real-time viewing of the device connection state and patient information, synchronous display of the EEG signal waveform and the fNIRS signal waveform, and support for amplifying any signal waveform for detailed viewing; the user information management module supports dynamic addition of new users, provides add, delete, modify and query operations on the entered user information, inputs patient basic information including name, gender, birth date, contact phone number, etc., and supports query using ID, age range, name, birth date range and gender as single or combined index items; the EEG acquisition control module supports a maximum of 16 channel configurations, users can select test channels according to needs, and after configuring the channels, data acquisition or pre-acquisition can be started, the waveform display amplitude and moving speed can be adjusted, the relative time or absolute time can be selected for horizontal axis display, and whether to display a specific channel can be selected; the fNIRS acquisition control module supports a maximum of 24 near-infrared acquisition channels, the current channel acquisition sequence and the light source-detector (S-D) arrangement can be configured by importing an SD design file, and after the configuration is completed, data acquisition can be started; the device control function includes starting or stopping data acquisition, setting the opening and closing of the EEG acquisition channel, and configuring the light source and detector arrangement of the fNIRS; the data management function supports real-time data display, automatic saving of acquisition data, data import and export, and subsequent analysis; the system can continuously monitor the connection between the device and the user, and timely remind the user to pay attention to the device state and abnormal data.

[0032] In the starting stage: enter patient basic information, confirm normal operation of the device, configure acquisition parameters (including EEG channel selection, S-D arrangement setting of the fNIRS, sampling rate setting, etc.), and prepare the patient (install the brain cap, apply conductive paste, and check the electrode impedance); In the data acquisition stage: perform synchronous acquisition of the electroencephalogram and near-infrared data, perform real-time preprocessing of the original data, monitor the data waveform and signal quality in real time (including checking the electrode contact, monitoring the motion artifact, and evaluating the signal-to-noise ratio), and the system automatically saves the acquisition data; End stage: stop data collection, save complete data files, generate collection reports (including collection duration, data quality assessment, abnormal event records), archive data to the system database for subsequent analysis.

[0033] Modular structure design of the device The device adopts a modular design, facilitating assembly and maintenance, with clear internal device layout and clear functional partitioning. The device is mainly divided into three circuit boards: a near-infrared function board, a main control board, and an electroencephalogram board. The near-infrared function board is located in the middle of the device, with 24 photodiode matrices and 16 LED control matrices arranged, fixed by buckles and sockets, and the wire harness is led out through the opening at the top of the device to connect to the brain cap. The main control board is located below, arranged in an L shape, with interfaces for various functional modules and connected to the functional modules through the wire harness. The electroencephalogram board is located at the top left side of the device, with 18 electrode interfaces (16 electroencephalogram leads + 1 reference lead + 1 bias lead) distributed, connected to the brain cap using dry electrode connecting lines. The key module is located on the upper shell of the device, including one power button and three LED status indicator lights, used to control the power state of the device and indicate the working state of the device. The device shell adopts a rectangular design with smooth corners, and the plastic material has good mechanical strength. An external antenna interface is provided on the left side of the device to enhance the wireless communication signal. Each functional module can be replaced independently, with high integration, easy functional expansion, and device upgrade and maintenance.

[0034] Working principle: As shown in Figures 1-5 , the present application combines electroencephalogram (EEG) and brain function near-infrared signal (fNIRS) for recognition and detection, uses an integrated acquisition device to realize simultaneous and synchronous acquisition of EEG and fNIRS data, avoiding the time synchronization error of traditional devices. The EEG signal is acquired by electrode sensors on the scalp, amplified, and then converted to digital, and then processed and transmitted by the main control module. The fNIRS signal is acquired by near-infrared light sources and photodetectors to collect brain blood flow change data. The two signal acquisition modules are controlled by the same main control board to ensure real-time and accuracy of data acquisition, and the data is transmitted to the host computer software for analysis and display through SPI or RS485 protocol.

[0035] During the acquisition process, the motion state detection module monitors the head motion through the nine-axis sensor, which can distinguish between motion artifacts and brain signal changes to ensure signal quality. The host computer software visualizes the acquisition data of EEG and fNIRS and monitors the data waveform in real time. The software not only displays real-time data, but also controls device startup, stop, and parameter setting, while supporting dynamic management of user information. After data acquisition, the data is classified and recognized by the system, combined with EEG, fNIRS signals, and clinical scale information, to output a diagnosis report for diagnosis and disease assessment.

[0036] The system has perfect disease management and treatment effect evaluation function, by integrating the physiological signals and clinical information of the patient into the database, the system can long-term track the brain development state and treatment effect of the patient, quantify the disease development and rehabilitation process, this systematic treatment monitoring can timely adjust the intervention strategy, provide personalized and real-time rehabilitation scheme, help the early intervention and rehabilitation effect evaluation of the patient, and provide scientific treatment basis and support for medical staff and parents.

[0037] The above merely provides the preferred embodiments of the present application, and is not intended to limit the present application, and any modification, equivalent replacement and improvement within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0038] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprising", "including", and / or "having", when used herein, specify the presence of stated features, steps, operations, devices, components and / or combinations thereof.

[0039] The relative arrangement of the components and steps, numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present application unless specifically stated otherwise. It should be understood that the sizes of the various portions shown in the drawings are not drawn to scale for the purpose of convenience in description. The techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail, but should be considered as part of the authorized description when appropriate. In all examples shown and discussed herein, any specific values should be interpreted as merely exemplary, and not as limiting. Therefore, other examples of the exemplary embodiments can have different values. It should be noted that similar reference numbers and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

Claims

1. An integrated identification and detection system based on electroencephalogram (EEG) signals and near-infrared signals of brain function, characterized in that, include: Signal acquisition module, main control module, and PC host computer software; The signal acquisition module includes a functional near-infrared spectroscopy (fNIRS) data acquisition module, an electroencephalogram (EEG) data acquisition module, and a motion state detection module; The main control module includes functions for power management, power supply management, and data transmission. The PC host computer software includes basic information management functions, EEG signal acquisition and display settings, and fNIRS signal acquisition and display functions.

2. The integrated identification and detection system based on electroencephalogram (EEG) signals and near-infrared brain function signals according to claim 1, characterized in that, The EEG acquisition module acquires EEG signals through electrode sensors placed on the scalp. The EEG signals are then filtered, amplified, and converted from analog to digital to obtain the original EEG signal time-domain variation via the main control module. Based on the performance indicators of small size, high bandwidth, low noise, high precision, and low power consumption, the EEG acquisition module is designed based on the analog front-end acquisition chip ADS1299. Data transmission and command transmission between the main control module and the EEG acquisition module are realized through SPI communication.

3. The integrated identification and detection system based on electroencephalogram (EEG) signals and near-infrared brain function signals according to claim 1, characterized in that, The brain functional near-infrared (fNIRS) acquisition module uses three AD7689 chips for signal acquisition and continuous wave (CW) detection. The dual-wavelength near-infrared LED product SMT735D / 850D is selected as the light source, and Vishay's VEMD5060X01 photodiode is selected as the system's photodetector.

4. The integrated identification and detection system based on electroencephalogram (EEG) signals and near-infrared brain function signals according to claim 1, characterized in that, The signal acquisition module mainly includes electrode sensors, preprocessing circuits, analog front-end circuits, communication interfaces, and power management circuits in its hardware. In terms of software, it mainly uses a STM32 main controller to write control programs to acquire EEG data. By setting the sampling rate, sampling mode, sampling time, and gain factor, it continuously acquires scalp EEG data, converts the acquired data into digital signals, returns feature values ​​after acquisition, and sends the data to the processor via the SPI serial port. The processor then converts the digital signals back into original analog signals and stores them.

5. The integrated identification and detection system based on electroencephalogram (EEG) signals and near-infrared brain function signals according to claim 1, characterized in that, The brain function near-infrared (fNIRS) acquisition module moves the probe due to head or body movement during operation, causing changes in the optical signal or introducing EEG signal interference. The motion sensor is fixed to the subject's head to collect head movement data. The selected ICM-20948 nine-axis sensor is capable of measuring this.

6. The integrated identification and detection system based on electroencephalogram (EEG) signals and near-infrared brain function signals according to claim 1, characterized in that, The PC host computer software, through communication with the embedded system or other hardware modules, can realize data acquisition, display, analysis and control functions. Its main purpose is to obtain real-time or timed data from hardware devices and display this data to users for monitoring and analysis through a graphical interface.

7. The integrated identification and detection system based on electroencephalogram (EEG) signals and near-infrared brain function signals according to claim 1, characterized in that, The PC-based host computer software features basic window functions, user information management, real-time data display, device control, parameter settings, and alarm prompts. The software supports dynamically adding new users and provides CRUD operations for already entered user information, facilitating user maintenance and management of patient data. In the electroencephalogram (EEG) and near-infrared brain functional imaging (fNIRS) monitoring system, the software can display real-time EEG waveforms and light waves of different wavelengths acquired by fNIRS. These waveforms can be further analyzed and identified to aid in intervention and treatment for children. The software can also control the device's operating status, enabling or stopping data acquisition, setting the EEG acquisition channel to open and close, and configuring the fNIRS SD (light source and detector arrangement). The software continuously monitors the connection between the device and the user, promptly alerting the user to abnormal data.

8. The integrated identification and detection system based on electroencephalogram (EEG) signals and near-infrared brain function signals according to claim 1, characterized in that, The EEG acquisition module collects scalp electrode signals and outputs digitized EEG data, providing a time reference for synchronous sampling to the functional near-infrared spectroscopy (fNIRS) acquisition module. The fNIRS acquisition module detects light source and photodetector signals and outputs light intensity change data, providing a comparison reference between optical signal changes and motion state for the motion state detection module. The motion state detection module collects head motion data and generates motion compensation information, providing motion artifact correction reference data for the main control module.

9. The integrated identification and detection system based on electroencephalogram (EEG) signals and near-infrared brain function signals according to claim 1, characterized in that, The power management unit provides a stable operating power supply to the power management unit by regulating and distributing electrical energy; the power management unit provides stable power support to the data transmission unit by controlling the power supply status of each sub-module; the data transmission unit sends the digital signals of the EEG and fNIRS acquisition modules to the PC host computer software through SPI or serial communication.

10. The integrated identification and detection system based on electroencephalogram (EEG) signals and near-infrared brain function signals according to claim 1, characterized in that, The information management module maintains user and device information to provide the corresponding user acquisition parameters to the EEG signal acquisition and display setting module; the EEG signal acquisition and display setting module receives and displays EEG waveform data in real time to provide a synchronized time reference for the fNIRS signal acquisition and display module; the fNIRS signal acquisition and display module provides fused display data for the overall monitoring interface through multi-wavelength optical signal display and data processing.

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