A method and device for constructing a brain-computer interface system for neurofeedback training

Abstract

The present invention discloses a method and apparatus for constructing a brain-computer interface system for neurofeedback training, relating to the field of neurofeedback training. The method comprises: designing a T-shaped channel layout for a near-infrared brain imaging device, including: employing a T-shaped arrangement of paired light sources and receivers on a plane, wherein adjacent light sources and receivers are combined to form data acquisition channels, and the multiple data acquisition channels thus formed can be mapped to the entire brain; configuring a BCI communication module, a bandpass filtering module, a baseline calculation and adaptation module, a feedback mode selection and calculation module, and an interactive display module within a brain-computer interface computer connected to the near-infrared brain imaging device. The present invention can determine the regulatory effects of resting-state brain states during various task feedback training sessions.

Description

Technical Field

[0001] The present invention relates to the field of neurofeedback training, and in particular to a method and device for constructing a brain-computer interface system for neurofeedback training. Background Art

[0002] Traditional neurofeedback training methods improve cognitive functions related to specific brain regions through closed-loop regulation of activation levels in that region, but traditional neurointervention methods have limited effectiveness in improving cognitive function. Brain functions, especially higher-level cognitive functions, are supported by a wider range of brain regions. These interconnected brain regions form a complex brain network, and cognitive functions emerge on the basis of this complex brain network. Constructing a closed-loop neurofeedback training method based on brain-computer interface technology can effectively regulate the overall topological properties of the brain network rather than simply intervening in individual brain regions. It more directly reflects specific cognitive functions and is of great significance for the rehabilitation treatment of mental illness and cognitive intervention for special populations. In addition, the resting brain reflects important inherent characteristics of the individual, which can effectively identify mental illness.

[0003] Existing technologies use electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) as imaging modalities to construct brain-computer interfaces (BCIs) for closed-loop neuromodulation. EEG records the brain's electrical activity through electrodes placed on the scalp. Its high temporal resolution enables real-time monitoring of brain activity, but its drawbacks include a low signal-to-noise ratio and low spatial resolution. fMRI uses magnetic fields and radiofrequency pulses to measure changes in the brain's blood oxygen levels to monitor neural activity. Its high spatial resolution allows for precise localization of active brain regions, resulting in good imaging quality. However, its drawbacks include sensitivity to head movements and high cost.

[0004] However, the above-mentioned technology does not take into account the resting-state regulation effect of the brain in the feedback training of the brain network, and does not fully consider the cognitive function of the brain, and thus cannot fully regulate the overall topological properties of the brain network affected by feedback training. Summary of the Invention

[0005] The embodiments of the present invention provide a method and device for constructing a brain-computer interface system for neurofeedback training, which can solve the problem in the prior art that training does not take into account the resting state of the brain and thus cannot fully adjust the overall topological properties of the brain network.

[0006] An embodiment of the present invention provides a method for constructing a brain-computer interface system for neurofeedback training, comprising the following steps: designing a T-shaped channel layout for a near-infrared brain imaging device; the T-shaped channel layout comprising: employing a pair of light sources and receivers in a T-shaped layout on a plane, wherein adjacent light sources and receivers are combined to form data acquisition channels, and the multiple combined data acquisition channels can be mapped to the entire brain; wherein the T-shaped channel layout is used to image the functions of brain regions whose brain activity is undergoing neurofeedback training; the near-infrared brain imaging device is used to convert the received imaging results into mental state data of the brain; and configuring a brain-computer interface computer connected to the near-infrared brain imaging device with a BCI communication module, a bandpass filtering module, a baseline calculation and adaptation module, a feedback mode selection and calculation module, and an interactive display module; wherein the BCI communication module is used to convert the mental state data into electroencephalogram (EEG) signals; the bandpass filtering module is used to extract signals in a desired frequency range from the EEG signals; the baseline calculation and adaptation module is used to determine the level of resting brain activity; the feedback mode selection and calculation module is used to feedback changes between the EEG signals and the baseline; and the interactive display module is used to display the feedback results and adjust brain activity based on the displayed results.

[0007] Furthermore, the tasks of the neurofeedback training include: a resting state test task, a real-time feedback training task, and a delayed feedback training task; the resting state test task requires observing the symbol "+" in the center of the display device and keeping the brain in a resting state without thinking for three minutes to obtain the first mental state data of the brain; the real-time feedback training task requires observing the status bar in the center right of the display device, adjusting the brain's mental state to produce changes according to the prompt information displayed by the display device, and obtaining the second mental state data of the brain; the status bar is raised by the change of the second mental state data, maintained for thirty seconds, and then rested for fifteen seconds, and repeated twenty times; the delayed feedback training task requires observing the status bar in the center right of the display device and keeping the brain in a resting state without thinking for three minutes to obtain the first mental state data of the brain; the real-time feedback training task requires observing the status bar in the center right of the display device, adjusting the brain's mental state to produce changes according to the prompt information displayed by the display device, and obtaining the second mental state data of the brain; the status bar is raised according to the change of the second mental state data, maintained for thirty seconds, and then rested for fifteen seconds, and repeated twenty times; The training task requires continuous observation of the status bar and adjustment of the brain's mental state to produce changes, and obtain the brain's third mental state data; after keeping the status bar still for twenty-five seconds, adjust the status bar according to the third mental state data, and obtain the corresponding score according to the status bar after five seconds, rest for fifteen seconds, and repeat twenty times; construct a seven-day brain network adjustment task as neurofeedback training, and the brain network adjustment task performs a resting state test task before the start, a real-time feedback training task is performed on the first, third and fifth days respectively, and a delayed feedback training task is performed on the second, fourth and sixth days respectively. After the brain network adjustment task is completed, a resting state test task is performed.

[0008] Furthermore, the user needs to sit upright in front of the display device to observe the display device, with the eyes 60 to 70 centimeters away from the screen.

[0009] Furthermore, the T-shaped channel layout is used to image the functions of brain areas whose brain activity is under neurofeedback training. The specific steps include: imaging the functions of the prefrontal lobe area of ​​the brain through the T-shaped channel layout set in the forehead part of the whole-brain nylon cap; uploading the imaging results to the brain-computer interface computer.

[0010] Furthermore, the T-shaped channel layout specifically includes: the channel layout adopts a combination of twelve pairs of light sources and receivers in a T-shaped layout on the plane, each light source and each receiver constitutes a data acquisition channel, and a total of 35 data acquisition channels are combined, and the distance between adjacent light sources and receivers is three centimeters.

[0011] An embodiment of the present invention provides a device for constructing a brain-computer interface system for neurofeedback training, comprising:

[0012] A device setup module is configured to design a T-shaped channel layout for a near-infrared brain imaging device; the T-shaped channel layout comprises: a T-shaped layout on a plane using paired combinations of light sources and receivers, wherein adjacent light sources and receivers form data acquisition channels, and the multiple data acquisition channels formed are capable of being mapped to the entire brain; wherein the T-shaped channel layout is configured to image the functions of brain regions whose brain activity is undergoing neurofeedback training; the near-infrared brain imaging device is configured to convert the received imaging results into mental state data of the brain; a signal processing module is configured to configure a BCI communication module, a bandpass filtering module, a baseline calculation and adaptation module, a feedback mode selection and calculation module, and an interactive display module within a brain-computer interface computer connected to the near-infrared brain imaging device; wherein the BCI communication module is configured to convert mental state data into electroencephalogram (EEG) signals; the bandpass filtering module is configured to extract signals within a desired frequency range from the EEG signals; the baseline calculation and adaptation module is configured to determine the level of resting brain activity; the feedback mode selection and calculation module is configured to provide feedback on changes between the EEG signals and the baseline; and the interactive display module is configured to display the feedback results and adjust brain activity based on the displayed results.

[0013] The embodiments of the present invention provide a method and apparatus for constructing a brain-computer interface system for neurofeedback training. Compared with the prior art, the methods and apparatus have the following beneficial effects:

[0014] A T-shaped channel layout for a near-infrared brain imaging device is designed. The T-shaped channel layout includes: a T-shaped arrangement of paired light sources and receivers on a plane, with adjacent light sources and receivers forming data acquisition channels, and multiple data acquisition channels that can be mapped to the entire brain. The T-shaped channel layout is used to image the functions of brain regions undergoing neurofeedback training; the near-infrared brain imaging device is used to convert the received imaging results into mental state data. A brain-computer interface (BCI) computer connected to the near-infrared brain imaging device is configured with a BCI communication module, a bandpass filtering module, a baseline calculation and adaptation module, a feedback mode selection and calculation module, and an interactive display module. The BCI communication module is used to convert mental state data into electroencephalogram (EEG) signals; the bandpass filtering module is used to extract signals in the desired frequency range from the EEG signals; the baseline calculation and adaptation module is used to determine the level of resting brain activity; the feedback mode selection and calculation module is used to provide feedback on changes between the EEG signals and the baseline; and the interactive display module is used to display the feedback results and adjust brain activity based on the displayed results.

[0015] Among them, the T-shaped channel layout of the near-infrared brain imaging device is used to image the functions of brain areas whose brain activity is under neurofeedback training. In the brain-computer interface computer connected to the near-infrared brain imaging device, the baseline calculation and adaptive modules are used to determine the resting-state brain activity level. Therefore, resting-state brain activity is added as a reference in neurofeedback training, and a comprehensive study of the brain's mental state is conducted. The resting-state regulation effect of the brain network during neurofeedback training is taken into account, and the neurofeedback training of the brain network affects the overall topological properties of the brain network, thereby achieving full regulation of the overall topological properties of the brain network. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of the system architecture provided by an embodiment of the present invention;

[0017] Figure 2 Schematic diagram of channel layout and whole-brain channel positioning provided by an embodiment of the present invention;

[0018] Figure 3 A schematic diagram of the brain network feedback form provided by an embodiment of the present invention;

[0019] Figure 4 Schematic diagram of the brain network modulation task provided by an embodiment of the present invention (A is the resting-state test task, B is the real-time feedback training task, C is the delayed feedback training task, and D is the entire training process);

[0020] Figure 5 Schematic diagram of improving brain network regulation and inhibitory control capabilities provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] To make the above-mentioned objects, features, and advantages of the present invention more readily apparent, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following description sets forth numerous specific details to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art may make similar modifications without departing from the scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] See also Figures 1 to 5 , an embodiment of the present invention provides a method for constructing a brain-computer interface system for neurofeedback training, comprising the following steps:

[0023] Step 1: Design a T-shaped channel layout for the near-infrared brain imaging device. This T-shaped channel layout involves pairing light sources and receivers in a T-shaped arrangement on a plane. Adjacent light sources and receivers form data acquisition channels, and these multiple data acquisition channels can be mapped across the entire brain. The T-shaped channel layout is used to image the functions of brain regions undergoing neurofeedback training. The near-infrared brain imaging device converts the received imaging results into data on the brain's mental state.

[0024] Specifically, the channel layout uses a combination of twelve pairs of light sources and receivers in a T-shaped layout on the plane. Each light source and each receiver constitutes a data acquisition channel, and a total of 35 data acquisition channels are combined. The distance between adjacent light sources and receivers is three centimeters.

[0025] The neurofeedback training tasks include a resting-state test task, a real-time feedback training task, and a delayed feedback training task. The trainee observes the "+" symbol in the center of the display device and remains in a resting state without thinking for three minutes as the resting-state test task. The trainee observes the status bar in the center-right corner of the display device, which rises and falls according to changes in their mental state. The trainee adjusts their mental state according to the displayed prompts to raise the status bar for 30 seconds, then rests in a resting state for 15 seconds. This cycle repeats 20 times as the real-time feedback training task. Based on previous training experience, the trainee continuously adjusts their mental state to raise the status bar. However, the screen remains static for the first 25 seconds, and the trainee receives no feedback. After the last 5 seconds, the progress bar begins to change in real time, allowing the trainee to receive feedback. After a total of 30 seconds of training, the trainee receives a corresponding score. After completing the training, the trainee rests in a resting state for 15 seconds. This cycle repeats 20 times as the delayed feedback training task.

[0026] The resting-state test task, real-time feedback training task and delayed feedback training task were used as brain network regulation training tasks; brain network regulation training was carried out for seven days, with a resting-state test task before the start of training, a real-time feedback training task on the first, third and fifth days respectively, a delayed feedback training task on the second, fourth and sixth days respectively, and a resting-state test task after the training.

[0027] Step 2: Configure the BCI communication module, bandpass filtering module, baseline calculation and adaptation module, feedback mode selection and calculation module, and interactive display module within the brain-computer interface computer connected to the near-infrared brain imaging device. The BCI communication module converts mental state data into EEG signals. The bandpass filtering module extracts signals within the desired frequency range from the EEG signals. The baseline calculation and adaptation module determines resting-state brain activity levels. The feedback mode selection and calculation module provides feedback on changes between the EEG signals and the baseline. The interactive display module displays the feedback results and adjusts brain activity accordingly.

[0028] The fNIRS near-infrared brain imaging device is connected to the BCI computer via a network switch, which transmits brain imaging data to the BCI computer in real time. The BCI computer is also connected to the fNIRS near-infrared brain imaging device via a serial cable, which transmits data marker information from the computer to the fNIRS device. The BCI computer is also connected to peripherals such as a monitor, keyboard, and mouse, and receives user input.

[0029] The human brain accounts for 5% of the body's weight but consumes 20% of its energy. Furthermore, energy consumption increases by less than 5% during active tasks, demonstrating that the brain is not only a highly energy-intensive organ but also constantly active, even when deliberately avoiding activity. The resting state of the brain is the state in which an individual's brain remains inactive or without deliberate thought. Research has shown that the resting state reflects important, inherent characteristics of the individual, which can effectively identify psychiatric disorders such as Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), and Antisocial Personality Disorder (ASPD). Even some important individual differences in performance can be reflected in the resting state brain. Unlike in the task state, the resting state brain lacks significant activation, or, if there is activation, these random activations are annihilated after long-term averaging, thus exhibiting a steady-state characteristic. Independent component analysis (ICA) is a key method for extracting resting-state network features. By analyzing the correlation between time series between brain regions, a collection of brain regions with synchronous fluctuations in the resting state can be extracted. This is how the default mode network (DMN) was isolated. This is a brain network state that occurs when individuals are not performing specific tasks or engaging in self-referential tasks. Research has shown that resting-state brain networks contain rich information, potentially reflecting inherent characteristics of brain structural connectivity. Effective intervention in resting-state brain networks is an important target for treating mental illness.

[0030] Current neurofeedback techniques generally employ task-based regulation. While these task-based regulation methods are believed to have a corresponding impact on resting-state brain networks, the direction and effectiveness of regulation remain controversial. This is primarily because the feedback itself, as a constantly changing stimulus, can distract trainees, leaving them too busy processing the stimulus information to establish a deeper connection between brain activity and active will. This present invention, however, employs an intermittent feedback method, which is more consistent with learning principles, allowing individuals ample time to verify their hypotheses and, through continuous experimentation, improve their ability to regulate brain networks.

[0031] Compared to traditional EEG and fMRI neurofeedback methods, this invented method is more suitable for widespread application. It can be used for rehabilitation treatment of people with mental illnesses, such as attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and antisocial personality disorder (ASPD). It can also be used to improve the symptoms of obsessive-compulsive disorder, addiction, obesity, mood disorders, and other related conditions. This method can also be used for cognitive enhancement training in special health groups, such as those undergoing high-intensity work, pilots, and military personnel.

[0032] The present invention also has the following beneficial effects:

[0033] (1) Using the overall topological properties of the brain network as feedback cues to achieve comprehensive regulation of the whole brain and effectively improve cognitive performance such as inhibitory control.

[0034] (2) The neurofeedback training method in the form of brain networks does not depend on the activation state of brain regions, and can regulate the resting brain network, thereby improving behavioral efficacy and maintaining effects.

[0035] (3) Use a variety of feedback presentation forms, combined with intermittent feedback strategies, to motivate trainees.

[0036] (4) The existing technology uses feedback from a single brain region (bilateral dorsolateral prefrontal cortex DLPFC) to improve cognitive ability, but fails to fully consider the brain network properties of cognitive ability, resulting in limited training effects. The present invention uses a brain network-based regulation training method to regulate the overall topological properties of the brain, which is more in line with the brain's learning laws and has better cognitive improvement and disease symptom intervention effects;

[0037] (5) The existing technology has not fully examined the functional changes of the brain, and the regulatory effect on the resting-state brain is unknown. The present invention adopts a training method in the form of delayed feedback, fully considering the changing laws of resting-state regulation, and has better training transfer and maintenance effects.

[0038] An embodiment of the present invention provides a device for constructing a brain-computer interface system for neurofeedback training, comprising:

[0039] The device setting module is used to design the T-shaped channel layout of the near-infrared brain imaging device; the T-shaped channel layout includes: using a pair of light sources and receivers to form a T-shaped layout on a plane, adjacent light sources and receivers are combined to form a data acquisition channel, and the combined multiple data acquisition channels can be mapped to the whole brain; wherein, the T-shaped channel layout is used to image the functions of brain areas whose brain activity is under neurofeedback training; the near-infrared brain imaging device is used to convert the received imaging results into mental state data of the brain.

[0040] A signal processing module is used to configure a BCI communication module, a bandpass filtering module, a baseline calculation and adaptation module, a feedback mode selection and calculation module, and an interactive display module in a brain-computer interface computer connected to a near-infrared brain imaging device; wherein the BCI communication module is used to convert mental state data into EEG signals; the bandpass filtering module is used to extract signals in a required frequency range from EEG signals; the baseline calculation and adaptation module is used to determine the level of resting brain activity; the feedback mode selection and calculation module is used to feedback changes between EEG signals and the baseline; and the interactive display module is used to display the feedback results and adjust brain activity according to the displayed results.

[0041] A specific embodiment is as follows:

[0042] 1. System construction

[0043] The system hardware includes near-infrared brain imaging equipment fNIRS, brain-computer interface computer and display equipment, as well as necessary data link equipment such as network switches, serial communication cables, etc. The system architecture is as follows Figure 1 shown.

[0044] (1) Connect the fNIRS device to the BCI computer through a network switch, and correctly set the LAN IP address and subnet mask of the fNIRS device and computer. Use the ping command to debug and test the network connectivity for real-time transmission of brain imaging data from the fNIRS device to the BCI computer.

[0045] (2) Connect the computer and the fNIRS device via a serial cable, and set the corresponding serial port number on the computer for the computer to send data tag information to the fNIRS device.

[0046] (3) Connect the brain-computer interface computer to the monitor, keyboard, mouse and other peripherals. The subject sits in front of the screen with their eyes 60-70 cm away from the screen.

[0047] (4) The channel layout design is illustrated by taking the targeted rehabilitation treatment of ADHD patients as an example. The core cognitive defect of ADHD patients is inhibitory control, and the corresponding key functional brain area is located in the frontal lobe. The present invention selects this brain area as the intervention and regulation brain area. Other disease types or cognitive ability interventions can flexibly select the brain area of ​​interest according to the specific situation. The present invention adopts a 12-pair light source-receiver combination scheme, with a distance of 3 cm between the light source and the receiver. A T-shaped layout is adopted on the plane, and a total of 35 data acquisition channels are combined. These 35 data channels are mapped to the forehead of the whole-brain nylon cap, and the motion tracking and positioning system FSTRAK is used to measure the actual spatial coordinates of the channel position and convert it into the MNI standard brain coordinates of the standard spatial coordinate system for neuroimaging data, and the Brodmann brain area division of the cerebral cortex is performed. The registration operation shows that the data channel can basically cover most of the brain areas of the frontal lobe. The channel layout is consistent with the whole-brain channel positioning as shown in the figure. Figure 2 shown.

[0048] (5) The system software has functions such as brain-computer interface communication and network topology calculation and display, and specifically includes five modules: BCI communication module, bandpass filtering module, baseline calculation and adaptation module, feedback mode selection and calculation module, and interactive display module. The feedback mode selection and calculation module includes three feedback forms, namely the traditional single brain region feedback form, the functional connection feedback form between brain regions, and the brain network feedback form adopted by the present invention. The software design process is as follows: Figure 3 shown.

[0049] 2. Brain Network Neurofeedback Training

[0050] The brain network regulation task includes three independent tasks: resting-state test task, real-time feedback training task, and delayed feedback training task.

[0051] Task 1: Resting state test task, used to check and verify the intervention effect. The trainees are required to sit in front of the screen, keep their eyes open, and look at the "+" symbol in the center of the screen. They should relax their body and mind as much as possible and do not need to think about anything. This task lasts for 3 minutes. Figure 4 As shown in A.

[0052] Task 2: Real-time feedback training task, used for real-time feedback regulation of brain networks. A status bar in the form of a thermometer appears in the center right of the screen. This status bar is a real-time measurement of the state of the brain network and does not represent the actual temperature. Trainees need to adjust their mental state according to the prompts on the screen so that the status bar is as high as possible, and gradually establish a connection between their mental state and the topological characteristics of the brain network, and master the regulation rules. Each adjustment training trial lasts 30 seconds, followed by a 15-second break. A total of 20 trials are conducted, taking 15 minutes. Figure 4 As shown in B.

[0053] Task three: Delayed feedback training task, used for regulation training of resting-state brain networks. This form of intermittent feedback can effectively transfer the regulatory ability learned in real-time feedback training to the resting state. The brain-computer interface computer continuously obtains and analyzes the brain mental state data collected by the near-infrared brain imaging device. Within 25 seconds after the start of training, the analysis results are stored in the computer but are not fed back to the trainees. The display device screen always remains static and does not change. Within the last 5 seconds of training, the analysis results are fed back to the trainees in real time through changes in the status bar as a delayed feedback training task. After completion, rest for 15 seconds and proceed to the next trial. A total of 20 trials were conducted, taking 15 minutes. Figure 4 As shown in C.

[0054] The entire training process is completed within 7 days. Before the start of the first day of training and after the end of the seventh day of training, a resting state test task is performed; on the 1st, 3rd, and 5th days, a real-time feedback training task is performed every day; on the 2nd, 4th, and 6th days, a delayed feedback training task is performed every day. Figure 4 As shown in D.

[0055] A comparative analysis of the training methods was conducted. By setting up a pseudo-feedback group and a traditional continuous feedback group for comparison, the individuals' brain network regulation level was significantly improved after intermittent neurofeedback training, but there was no significant change in the pseudo-feedback group and the continuous feedback group. The cognitive performance before and after training (using the Stroop task used to study attention, cognitive control and interference processing) was tested. Except for the pseudo-feedback group, the cognitive performance of the intermittent feedback group and the continuous feedback group was significantly improved, but the cognitive performance of the intermittent feedback group improved more. The results are as follows Figure 5 This shows that neurofeedback training can significantly improve cognitive performance, and intermittent feedback can not only effectively improve cognitive performance, but also deeply change the resting-state brain network.

[0056] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A method for constructing a brain-computer interface system for neurofeedback training, characterized in that: The following steps are involved: Design a T-shaped channel layout for near-infrared brain imaging equipment; The T-shaped channel layout includes: using a combination of light sources and receivers in pairs to form a T-shaped layout on a plane, adjacent light sources and receivers are combined to form data acquisition channels, and the multiple data acquisition channels formed can be mapped to the entire brain; the channel layout uses a combination of twelve pairs of light sources and receivers to form a T-shaped layout on a plane, each light source and each receiver forming a data acquisition channel, for a total of 35 data acquisition channels, and the distance between adjacent light sources and receivers is three centimeters; The T-shaped channel layout is used to image the functions of brain regions undergoing neurofeedback training; the near-infrared brain imaging device is used to convert the received imaging results into mental state data of the brain, specifically including: imaging the functions of the prefrontal lobe region of the brain through the T-shaped channel layout set on the forehead part of the whole-brain nylon cap; uploading the imaging results to the brain-computer interface computer; A BCI communication module, a bandpass filtering module, a baseline calculation and adaptation module, a feedback mode selection and calculation module, and an interactive display module are configured in a brain-computer interface computer connected to the near-infrared brain imaging device; The BCI communication module is used to convert mental state data into EEG signals; the bandpass filtering module is used to extract signals in the required frequency range from the EEG signals; the baseline calculation and adaptation module is used to determine the resting brain activity level; the feedback mode selection and calculation module is used to feedback the changes between the EEG signals and the baseline; and the interactive display module is used to display the feedback results and adjust brain activity according to the displayed results. The neurofeedback training includes: a resting state test task, a real-time feedback training task, and a delayed feedback training task; The resting state test task requires observing the symbol "+" in the center of the display device and keeping the brain in a resting state without thinking for three minutes to obtain the first mental state data of the brain; The real-time feedback training task requires observing the status bar on the right side of the center of the display device, adjusting the brain's mental state to change according to the prompt information displayed on the display device, and obtaining the brain's second mental state data; the status bar is raised according to the change of the second mental state data, maintained for 30 seconds, and then rested for 15 seconds, and repeated 20 times; The delayed feedback training task requires continuously observing the status bar and adjusting the brain's mental state to produce changes, thereby obtaining the brain's third mental state data; after keeping the status bar still for 25 seconds, adjusting the status bar according to the third mental state data, and obtaining the corresponding score based on the status bar after 5 seconds, resting for 15 seconds, and repeating the cycle 20 times; A seven-day brain network regulation task was constructed as neurofeedback training. The brain network regulation task included a resting-state test task before the start, a real-time feedback training task on the first, third, and fifth days, and a delayed feedback training task on the second, fourth, and sixth days. A resting-state test task was performed after the brain network regulation task was completed.

2. A method for constructing a brain-computer interface system for neurofeedback training according to claim 1, characterized in that: The user needs to sit upright in front of the display device to observe the display device, with the eyes 60 to 70 centimeters away from the screen.

3. A device for constructing a brain-computer interface system for neurofeedback training, characterized in that: include: The device setup module is used to design the T-shaped channel layout of the near-infrared brain imaging device; The T-shaped channel layout includes: using a combination of light sources and receivers in pairs to form a T-shaped layout on a plane, adjacent light sources and receivers are combined to form data acquisition channels, and the multiple data acquisition channels formed can be mapped to the entire brain; the channel layout uses a combination of twelve pairs of light sources and receivers to form a T-shaped layout on a plane, each light source and each receiver forming a data acquisition channel, for a total of 35 data acquisition channels, and the distance between adjacent light sources and receivers is three centimeters; wherein the T-shaped channel layout is used to image the functions of brain areas whose brain activity is under neurofeedback training; the near-infrared brain imaging device is used to convert the received imaging results into mental state data of the brain, specifically including: imaging the functions of the prefrontal lobe area of ​​the brain through the T-shaped channel layout set on the forehead part of the whole-brain nylon cap; uploading the imaging results to the brain-computer interface computer; a signal processing module configured to configure a BCI communication module, a bandpass filtering module, a baseline calculation and adaptation module, a feedback mode selection and calculation module, and an interactive display module within a brain-computer interface computer connected to the near-infrared brain imaging device; wherein the BCI communication module is configured to convert mental state data into EEG signals; the bandpass filtering module is configured to extract signals within a desired frequency range from the EEG signals; the baseline calculation and adaptation module is configured to determine the level of resting brain activity; the feedback mode selection and calculation module is configured to provide feedback on changes between the EEG signals and the baseline; and the interactive display module is configured to display the feedback results and adjust brain activity based on the displayed results; The neurofeedback training includes: resting state test tasks, real-time feedback training tasks and delayed feedback training tasks; The resting state test task requires observing the symbol "+" in the center of the display device and keeping the brain in a resting state without thinking for three minutes to obtain the first mental state data of the brain; The real-time feedback training task requires observing the status bar on the right side of the center of the display device, adjusting the brain's mental state to change according to the prompt information displayed on the display device, and obtaining the brain's second mental state data; the status bar is raised according to the change of the second mental state data, maintained for 30 seconds, and then rested for 15 seconds, and repeated 20 times; The delayed feedback training task requires continuously observing the status bar and adjusting the brain's mental state to produce changes, thereby obtaining the brain's third mental state data; after keeping the status bar still for 25 seconds, adjusting the status bar according to the third mental state data, and obtaining the corresponding score based on the status bar after 5 seconds, resting for 15 seconds, and repeating the cycle 20 times; A seven-day brain network regulation task was constructed as neurofeedback training. The brain network regulation task included a resting-state test task before the start, a real-time feedback training task on the first, third, and fifth days, and a delayed feedback training task on the second, fourth, and sixth days. A resting-state test task was performed after the brain network regulation task was completed.

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