JP2020043951A - Multifunctional nerve feedback system and multifunctional nerve feedback method - Google Patents

Abstract

To provide a display in which a multifunctional feedback system including both a network and an activity is useful.SOLUTION: A multifunctional nerve feedback system includes: a sensor for measuring an activity of a region of interest consisting of at least two places of the brain of a subject; a control unit for analyzing a brain activity amount of the region of interest and a brain network of the region of interest from the activity of each of the region of interest measured by the sensor; and a display unit for displaying the brain activity amount of each of the region of interest and the brain network of the region of interest analyzed by the control unit. The control unit plots the magnitude of the brain activity amount of the region of interest on a graph with axes corresponding to the region of interest, and displays in the display unit the strength of the brain network of the region of interest by the size of a circle at plotted positions.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a multifunctional neural feedback system, a multifunctional neural feedback device, and a multifunctional neural feedback method.

  Nerve feedback is widely used in training to enhance brain function. Subjects typically control and regulate brain activity and the like in a unique manner to achieve specific goals related to biological responses (eg, brain activity).

  In connection with neural feedback, for example, US Pat. In connection with the neural feedback strategy, for example, Patent Document 2 discloses a technique for determining a stimulus load based on brain activity measurement.

US Patent Application Publication No. 2015/0294074 US Patent Application Publication No. 2006/0078183

  Two failure factors in training with neural feedback are inefficient neural feedback and improper strategies.

  In Patent Literature 1 described above, in order to control the connectivity of a specific region, the connectivity of a complicated and wide range of brain regions is simply displayed collectively, so that it may be difficult for the subject to make a strategy. Patent Literature 2 determines a stimulus load based on a single brain feature (one type of activity measurement value).

  It is an object of the present invention to provide a multi-function feedback system, device and multi-function feedback system method that includes both brain network (connectivity) and brain activity in a useful display.

  In order to solve the above problems, the multifunctional neural feedback system of the present invention includes, for example, a sensor for measuring the activity of a region of interest consisting of at least two places in the brain of a subject, and a sensor for measuring the activity of the sensor. A control unit that analyzes the brain activity of the region of interest and the brain network of the region of interest from each activity of the region of interest, and the brain activity of each region of interest and the brain network of the region of interest analyzed by the control unit. And a display device for displaying.

  In order to solve the above-mentioned problem, the multifunctional nerve feedback method of the present invention, for example, by selecting a region of interest consisting of at least two places in the brain of the subject, the selected region of interest The activity is measured, the brain activity of the region of interest and the brain network of the region of interest are analyzed from each activity of the region of interest measured by the sensor, and the analyzed interest is plotted on a graph of an axis corresponding to the region of interest. The magnitude of the brain activity in the region is plotted, and the strength of the brain network in the region of interest is represented by the size of a circle at the plotted position, and the target data is displayed on a graph.

  Further features related to the present invention will become apparent from the description of the present specification and the accompanying drawings. Further, aspects of the present invention are attained and attained by the elements and combinations of various elements described in the following detailed description and the appended claims.

  The description in this specification is merely exemplary, and is not intended to limit the scope or applications of the present invention in any way.

  According to the present invention, both the amount of activity in the relevant area of the brain and the network of the relevant area (connectivity) are displayed, and the subject is displayed, thereby providing efficient feedback to the subject so that the predetermined brain activity is obtained. It can be carried out.

It is a figure showing an example of a brain network and activity in a region of interest (ROI: regions-of-Interest) of a plurality of regions of the brain. FIG. 3 is a diagram illustrating an example of a hardware configuration of a multifunction feedback system. FIG. 7 is a diagram illustrating an example of a processing flow of a control process. FIG. 7 is a diagram illustrating an example of a processing flow of a feedback system when an abnormality of a subject is discovered. FIG. 4 is a diagram illustrating an example of a user interface of a control process for a new subject. FIG. 6 is a diagram illustrating an example of a registered user interface of a control process for a subject. It is a figure showing an example of a brain network function and a target value. It is the figure which showed an example of the analysis result of a brain network and brain activity. It is the figure which showed an example of the comparative display of the target with respect to the brain network and the amount of brain activity. It is a figure showing an example of a display of a screening result. It is a figure showing an example of a display of a session history of a subject. FIG. 6 is a diagram illustrating an example of a user interface for setting a neural feedback goal. It is a figure showing an example of a display screen of a measured value and a target. It is a figure showing an example which displayed a result of nerve feedback. It is a figure showing an example of task guidance nerve feedback.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. The following description and drawings are exemplifications for describing the present invention, and are omitted and simplified as appropriate for clarification of the description. The present invention can be implemented in other various forms. Unless otherwise specified, each component may be singular or plural.

  The position, size, shape, range, and the like of each component illustrated in the drawings may not necessarily represent the actual position, size, shape, range, or the like, in order to facilitate understanding of the present invention. Therefore, the present invention is not necessarily limited to the position, size, shape, range, and the like disclosed in the drawings.

  In the following description, various types of information may be described using expressions such as “table”, “list”, and “queue”. However, various types of information may be expressed by a data structure other than these. The “XX table”, “XX list”, etc. may be referred to as “XX information” to indicate that the data structure does not depend on the data structure. In describing the identification information, expressions such as “identification information”, “identifier”, “name”, “ID”, “number”, and “Region” are used, but these can be replaced with each other.

  When there are a plurality of components having the same or similar functions, the same reference numerals may be given different subscripts for explanation. However, when it is not necessary to distinguish between these components, the description may be omitted with suffixes omitted.

  In the following description, processing performed by executing a program may be described. However, the program is executed by a processor (for example, a CPU or a GPU), so that a predetermined processing can be appropriately performed by a storage resource ( Since the processing is performed using a storage device and / or an interface device (for example, a communication port) or the like, the processor may be a processor. Similarly, the subject of the processing that executes the program may be a controller having a processor, an apparatus, a system, a computer, or a node. The subject of the processing performed by executing the program may be a control unit, and may include a dedicated circuit (for example, an FPGA or an ASIC) for performing a specific processing.

  The program may be installed on a device such as a computer from a program source. The program source may be, for example, a program distribution server or a computer-readable storage medium. When the program source is a program distribution server, the program distribution server includes a processor and storage resources for storing the program to be distributed, and the processor of the program distribution server may distribute the program to be distributed to another computer. Further, in the following description, two or more programs may be realized as one program, or one program may be realized as two or more programs.

  This system aims to simultaneously display multiple features of the brain in order for the subject to train his brain and adjust and control the brain to be in a predetermined active state. . Conventional neural feedback is a method of summarizing and displaying a wide range of brain activities, or displaying one type of brain activity as a feature. And the information itself may be insufficient.

  In neural feedback, information for accurately grasping brain activity is not organized, and if the amount of information is insufficient, the subject may incorrectly set the feedback target and perform inefficient training. is there. An essential feedback system is required to minimize the risk of training failure. For example, executive function is one of the cognitive processes that is of wide training interest. In order to enhance executive function, brain networks (eg, connectivity) and activity in regions-of-interest (ROI) must be improved.

  FIG. 1 is a graph showing the magnitude of brain activity in each region, with region 1 (eg, lower frontal cortex) on the horizontal axis and region 2 (eg, frontal cortex) on the vertical axis as the region of interest (ROI). . The size of the circle plotted in FIG. 1 indicates the strength of the brain network in region 1 and region 2. For example, data plotted with a white circle indicates that the strength of the brain network is high because the size of the circle is larger than data plotted in gray.

  Therefore, one of the goals of the training is to improve ROI activities and connectivity. Connectivity is defined, for example, as two signals (eg, correlation) indicating activity in both regions. The activity amount is defined, for example, as the signal strength of the measurement area. However, the purpose of the training does not always increase the brain network and the amount of brain activity, but the brain network and the amount of brain activity in the ROI are set according to the purpose of the training. For example, in order to (1) maintain a calm and concentrated state, the brain network strength is high and the ROI brain activity is low, or (2) the calm state To be in a relaxed state without concentrating on the crab, the strength of the brain network is low and the brain activity of ROI is low, or (3) in order to be in a concentrated state, The strength of the brain network is high, and the brain activity of the ROI is also high. Therefore, a target of the brain activity amount of the brain network and ROI (for example, a target value of the brain network and the brain activity amount) is set according to the purpose of the training.

  By displaying the display of FIG. 1 to the subject, in addition to the activity of each area, the connectivity (connectivity) of each area can be fed back simultaneously. Therefore, reaching the target brain activity state can be efficiently supported.

  FIG. 2 is a diagram illustrating an example of a hardware configuration diagram of the multifunction feedback system. The multifunction feedback device (200) has a control unit (201) and a storage device (203). The control unit (201) includes a control process (202) and a feedback system (204). The control process (202) also functions as an initial interface between the operator and the subject.

  The multifunction feedback device (200) can be realized by using the configuration of a general personal computer PC. It has a storage device (203) such as a PC central processing unit (CPU, not shown), memory (DRAM, etc., not shown), HDD (hard disk drive) or SSD solid state drive). The central processing unit, memory, storage device, and the like of the PC are connected by communication means such as a bus. The functions of the control process (202) and the feedback system (204) shown in FIG. 2 are realized by the central processing unit (the control unit (201)) reading the software stored in the storage device (203) into the memory and executing the software. Is achieved.

  The functions of the control process (202) and the feedback system (204) can be realized not only by software but also by dedicated hardware such as ASIC and FPGA. The storage device (203) stores the subject data (203a), the target data (203b), and the task data (203c) in addition to various software. The target data is given as a target value or a target range indicating the amount of activity in the relevant region of the brain or the strength of the brain network. The memory temporarily stores various data in addition to the resident software for the central processing unit to realize various functions.

The steps performed in the control process (202) are described in FIG.
First, the control process (202) determines whether the subject is a new subject or has been previously registered based on information entered by the operator via the user interface (see FIGS. 5 and 6). Is determined (301).

  If the subject is new, the process proceeds to step (302), and the control process (202) executes the subject data (203a), the target data (203b), and the task data (203) via the user interface of the peripheral device (205a). Enter the information (303) in 203c). The information set and registered in the control process (202) is stored in the storage device (203) according to the information type (eg, subject / goal / task).

  On the other hand, if the subject has been registered, the process proceeds to step (304), and the control process (202) determines whether or not the history mode is valid based on an instruction from the subject or the operator. The history mode is an operation mode for reading and displaying subject history information such as a measured value of a brain activity state as past subject data, target data as a target value, and a task as a training menu. .

  When the history mode is determined to be valid in step (304), the process proceeds to step (305), the subject history information is read, and the information read in step (311) is displayed on the display device (206) as a result. . When the history mode is invalidated in step (304), the subject receives the initial measurement in the same manner as the subject without neural feedback.

  On the other hand, if the subject is a new subject, the registration of the subject's data is completed in step (302), the process proceeds to step (306), and the initial measurement (306) by the control process is performed. The initial measurement (306) includes a connection between the multi-function feedback device (200) and the biological sensor (205b), and the sensor data (307) is input to the multi-function feedback device (200). Biological sensors measure activity in a region of interest comprising at least two portions of the subject's brain. Here, the biological sensor (205b) refers to functional near-infrared / fNIRS, functional magnetic resonance imaging / fMRI, magnetoencephalogram / EEG, and the like.

  Next, after acquiring the sensor data, the control process (202) analyzes the brain network (for example, connectivity) and the activity amount, which are the relevant regions (step 308).

  Brain networks (eg, connectivity) use signal correlation, covariance, coherence, causality, and other methods of expressing temporal signal relationships. The amount of activity may be the amplitude of the time signal integration, the peak amplitude, the peak time, the waiting time, or another method of expressing the characteristics of each single signal. In this embodiment, the correlation coefficient and the integration of the time signal are used as the brain network and the activity, respectively.

  Since the brain measurements may be performed in a limited or entire area, the brain network and activity are compared to a target value (203b) associated with the measured area (step 309). This comparison is performed by the control process (202) retrieving the target value stored as the target data from the storage device (203) and comparing it with the measured value.

  Next, the control process (202) associates the results of the screening including the initial measurement (306), the analysis of connectivity and activity (308), and the comparison with the target (309) with the subject data (203a). It is stored in a storage device (step 310). The reading of the screening results and the subject history is visualized (step 311) by the control process via the display device (206) to actually indicate the subject's condition and / or progress to target training. You. The display device (206) is shown in the user interface and feedback result display area (206a), and the task stimulus display area (206b) (step 311).

  The control process (202) determines an abnormality of the current subject state (low brain network, low activity, etc.) (step 312). For example, a state where the brain activity of the brain network or the measurement point does not reach the target value, or the time until reaching the target value is too long is determined to be abnormal. If there is no abnormality, the training can be completed without the need for training. If there is any abnormality, feedback training by the feedback system (204) is strongly recommended. The feedback training will be described in detail with reference to FIG.

  In this way, for registered subjects, the screening data performed by new subjects can be omitted by utilizing previous data / history, and brain networks and brain activities in specific areas of the brain can be omitted. The measurement can be started. Even for a registered subject, the screening measurement can be performed to determine the abnormality in the current state, as in the case of a new user.

FIG. 4 is a processing flow of the feedback system (204) when an abnormality of the subject is found.
The feedback system (204) operates in several steps. The feedback system (204) selects relevant regions (eg, lower frontal cortex and frontal cortex) based on the detected abnormality (step 401).

  For the current session, it is determined whether the measurement is the first session on the same day (step 402).

  First, in the case of the first session, the subject tries to reach his / her target brain state (brain network and brain activity in the related area) according to his / her own strategy. The feedback system (204) obtains the sensor data (405) from the biological sensor measurement (205b) and starts a feedback measurement (step 404). Thereafter, the feedback system (204) analyzes the connectivity and brain activity of the relevant area according to the biological signal (step 406).

  The feedback system (204) displays the analysis result in real time (Step 407). The feedback system (204) compares the analysis result with the target value (Step 408). That is, the connectivity and the activity amount of the related area are analyzed in real time, and at the same time as the analysis, the analysis result is sent to the display device (206) and displayed on the feedback result display area (206a) in real time.

  The feedback system (204) displays the analysis result together with the target value on the display device (206) in real time, so that the subject can compare the target value and the measured value in real time. After completing the training session, the feedback system (204) stores the results in the storage device (203) (step 409) and determines whether the goal has been achieved (step 410). When a failure occurs, the feedback system (204) selects new task data to perform training by a new task. Once the goal is achieved, complete the training session.

  If the target is not reached by the user's own strategy, the feedback system (202) of the multifunctional feedback device (200) recommends an appropriate task (step 403) from the task data (203c) of the storage device (203). For example, (1) in order to maintain a calm state and a focused state, the strength of the attention network of the brain is high, and the brain activity in the dorsolateral prefrontal cortex is low, or (2) the person is calm In order to maintain a relaxed state without concentrating on something, the intensity of the attention network is low and the brain activity in the dorsolateral prefrontal cortex is low, or (3) the person is concentrated To do so, the task is selected according to the goal, such as when the strength of the attention network is high and the possible activity in the dorsolateral prefrontal cortex is high.

  For example, the feedback system (204) displays "candle flame" on the task stimulation unit (206b) when the goal is the calm state of (1) and the state of concentration. For example, the feedback system (204) displays the `` healing image '' on the task stimulation unit (206b) when the goal is the calm state of (2) and the state is relaxed without concentrating on something. I do. For example, the feedback system (204) displays a "computation problem" on the task stimulation unit (206b) when the target is (3) concentrated.

  In the storage device (203), the above-described target and task are stored in association with each other, and the feedback system (204) responds when a target is input from an input device (not shown). The task data (203c) is read from the storage device (203), and the task is displayed on the display device (206). The subject can efficiently reach the target by being given the task displayed on the task stimulation unit (206b) of the display device (206). However, the above-described target and task are merely examples, and a suitable task for reaching the target is updated as needed by the operator and stored in the task data (203c) of the storage device (203). Suitable tasks may be stored individually for each subject.

  The real-time analysis of connectivity and activity in relevant areas of the brain is displayed on the display together with the target values, so that the subject can evaluate the adequacy of the strategy for reaching each target value to the target value. it can. In addition, it is possible to provide the subject with an appropriate task of causing the measured value to reach the target value for the connectivity and the activity amount of the relevant region of the brain, and efficiently achieve the target.

  FIG. 5 is a diagram showing a user interface of the control process (202) displayed on the display device (206). The user interface is used by new (500) or registered (600) subjects. Therefore, it is first necessary to specify whether the subject is new or registered (501). If the subject is new, (501a) is checked. Thereafter, personal information (502) such as a registration date (503), a subject ID (504), a birthday (505), and a gender (506) is registered. The control process (202) can automatically generate the subject ID as the recommended ID, and the operator can change it.

  Executing Reset (507) deletes all information. By executing [Save and Start] (508), the input information is saved in the storage device (203), and the screening measurement is started.

  FIG. 6 is a view showing a user interface of the registered subject, which is a user interface of the control process (202) displayed on the display device (206), similarly to FIG.

  In the case of a registered subject, (501b) is checked. By the control process (202), the user interface displays the data selection list (601), so that keywords (602) such as registration date, subject ID, gender, birthday, and age can be easily searched. All or keyword related data searched by the keyword are displayed as a list. A user who is a registered subject can select his or her own record as history information from the displayed list. For the registered subject, when the history mode (604) is activated (605), no screening measurement is required. The results of the previous session are displayed when you click [Display all] (607). If the history mode is disabled (606), a [Save and Start] button (508) can be executed to perform the screening measurement.

  FIG. 7 shows a relationship among a brain function (701), a brain region (702), a target value of brain activity (704), and a target value of connectivity (705). The network function can be selected from the network with the lowest screening or the network associated with diseases such as depression, stroke, ADHD. For example, a patient after a stroke has impaired motor networks. In the post-screening, the listed individual functions (701) corresponding to the regions (702, 703) are evaluated based on whether the current conditions (eg, activation (704) and connectivity (705)) are within the target. Is done.

  FIG. 8 shows three examples of screening measurements. Graphs 801a-c show two signals from the lower frontal cortex (bold line) and medial frontal cortex (fine line) that make up the executive function network.

  The goal of executive function is a discharge with both high activation and connectivity in both the inferior and medial frontal cortex. In this case, there are three conditions: (a) high connectivity, low activity, (b) low connectivity, low activity, and (c) high connectivity, high activity. Connectivity is defined as how similar (eg, correlation) the two signals are. The amount of activity is defined by the signal strength in the whole time unit (802) or the task-related section (803), that is, by comparing two signals in each region with a baseline (for example, amplitude level 0).

  The states (a) to (c) represent, for example, the following states. (a) When the connectivity of the attention network is high and the amount of activity of the relevant part is low, it indicates a state of calmness and concentration. (b) The state of low connectivity and low activity indicates a state of being calm and relaxing without concentrating on something. (c) The state of high connectivity and high activity indicates a state of deep concentration.

  In task-related screening measures, activity can be expressed using beta estimated using a linear regression model (804). The model (805) follows a brain activity prediction function during the task-related interval.

  FIG. 9 shows the measured distribution (901) of multiple regions, for example, the lower frontal cortex and the medial frontal cortex. 8, circles without boundaries (902a-c) represent exemplary conditions. The x-axis and the y-axis correspond to the region 1 (for example, the lower frontal cortex) and the region 2 (for example, the medial frontal cortex), and represent the characteristic of each region as the characteristic 1 (for example, the amount of activity) of each region. Further, the circular area is represented by the following equation.

  Here, D is the area of the circle (the square of the point adjusted to the plot scale), and Rho is the strength of the feature (for example, correlation coefficient) of the brain network.

  As described above, the activity of each region and the connectivity of each region can be effectively visualized with respect to the state of the brain in each region that is the related region.

  FIG. 10 shows a window of screening results (1000). The results are displayed in a brain diagram (1001) consisting of the ROI (lower frontal cortex and frontal cortex respectively 1002-1003) and its network (1004).

  Multifunctional graph (1005) includes target (1006), target model (1007), target area (1008) obtained from successful training data and 95% confidence intervals (1008a and 1008b), running as a screening measurement One or more of the state (1009) and the difference between the current and target conditions (1010) are displayed. If the current state is out of the prediction range, it is determined to be abnormal and displayed (1011). The control process (202) quantifies the difference by a standard score (1012) according to the following equation (Equation 2).

  Here, d is the Euclidean distance between the current condition and the target condition (normalized in the same range of the row and a correlation coefficient).

  If nothing is wrong, the control process (202) is that the brain diagram (1001) and the multi-function graph (1005) are the least active functions of some tested brain network activity functions (Eg, visual movement, default mode brain network, etc.). If there is no abnormality, click the [Close] (1013) button to complete the screening. It is then trained (1014) via the feedback system by initializing some neural feedback parameters (814).

  As shown in FIG. 10, a detailed result can be visually confirmed from the related area, function, comparison, inference, and standard score. A subject who has no knowledge of brain activity can easily understand his / her condition, dysfunction, and training target through the screen of the display device, and can encourage efforts to achieve the target.

  FIG. 11 is a diagram illustrating an example in which the [Display All] (607) button is clicked to display the session history of the registered subject. The interface by the control process (202) presents a subject ID (Subject ID) (1101), a score graph (1102), and an efficiency graph (1105) as information of the previous result.

  The score in the score graph represents the proximity of the final training condition compared to the goal, and the efficiency parameters indicate the training process, such as the time required to achieve the goal and the track. The two lines represent different functions for each score (1103-1104) and efficiency (1106-1107) graph. For example, bold and thin lines are for executive and motor functions, respectively. The current session goal can be selected from previously tested functions (eg, executive function and motor function) (1108) by executing the [Neurofeedback Setting] (1109) button. [Close] (1110) button closes the session history window.

  As shown in FIG. 11, a graph showing the training progress status of the session can be presented. You can also select specific features from the saved history to train in the current session.

  FIG. 12 shows a screen of neural feedback settings (1200) that terminates the neural feedback process controlled by the feedback system (204).

  There are two options: training time (1201a) and 2) stability (1201b). When a time is selected, neural feedback ends at a set time (eg, 3 minutes). In the case of stability, if the subject is able to control features (brain activity and connectivity of the region of interest) in the target for a set time (eg, 1 minute), neural feedback is terminated. Clicking the [Reset] (1202) button resets the selection, and pressing the [Start Neurofeedback] (1203) button starts neural feedback. As described above, the method of terminating the neural feedback can be selected according to the training purpose of the subject.

  FIG. 13 is a diagram showing an example of real-time feedback display when the training target (1301) is set by being controlled by the feedback system (204). Real-time connectivity-activity is active beyond the time that is indicative of the training process. Thus, feature analysis is performed in real time, either in motion or in a discrete time window (eg, 5 seconds). The training process is indicated by the moving circles 1302a-1302d. The process track (for example, between lines) is also shown for the latest measurement point. The feedback processing is continued until the processing ends according to the end setting (1201).

  While the achievement of Feature 1 (ie, the amount of activity) is seen within the target area, the achievement of Feature 2 (ie, connectivity) is indicated by a change in color, change in shape, or a blinking circle.

  As shown in FIG. 13, the subject can easily grasp his / her situation in real time, and can examine the self-strategic effect of both functions.

  FIG. 14 is a diagram showing an example in which the result of neural feedback by the feedback system (204) is displayed. The results are shown in three types: 1) Multi-function graph (1401), 2) Previous neural feedback training (1404), and 3) Inference box (1409) of neural feedback training result.

  The multifunction graph (1401) shows the state before (1402) and after (1403) the neural feedback training. According to this example, the neural feedback training shows feature 1 (eg, activity) in both areas related to connectivity. In addition, the size of the circle indicates the strength of connectivity of brain activity in both regions. After the training condition is within the target prediction limits (eg, 95%), the score (1405) and efficiency (1406) parameters are displayed before and after the training condition (1404). The score is calculated using Equation 2, and the efficiency parameter is calculated using Equation 3.

  dtotal (1408) is the sum of the Euclidean distance starting from the feedback response point until the end of the feedback session, low target is the connectivity training target value, low total is the average correlation coefficient during the training interval, and t is the target achieved It is the time needed to be done.

  Performing neural feedback training improves the performance score for the goal. The result is shown in region (1409). By executing the [Close] (1410) button, the neural feedback system can be closed. To improve the current goal function using task-guided neurofeedback, the [Task-guided Neurofeedback] (1411) button can be processed. To train other functions with your own strategy, you can execute the [Training of other functions] (1412) button.

  As described above, it is possible to efficiently evaluate the training effect using the multi-function graph, the comparison tally, and the abstract inference. The window may also use self or self-guided strategies or task leader feedback to initiate the next training session for the same or different functional goals.

  FIG. 15 shows an example of task-guided neural feedback (1500). Tasks can also be as a single rule, such as "Remember friend's birthday", "Count after 6 steps from 1000", or include several rules such as Working Memory Task (1501) It may be a complicated paradigm.

  The working memory task has several steps: encoding, acquisition, and testing. Each task is repeated until the feedback ends according to the end setting (1201). If the improvement of function is questioned, the task can be changed in the middle of neural feedback.

  For example, one minute without positive training progress. The tasks are displayed simultaneously with the multi-function feedback graph (1502), allowing the subject to intermittently check their performance during the task. The feedback result is displayed in the same manner as in the window of FIG. Instruct the subject to find the best strategy for regulating brain activity.

  According to this embodiment, in addition to the activity of each region of the brain, the connectivity (connectivity) of each region can be fed back simultaneously. Therefore, reaching the target brain activity state can be efficiently supported.

  In addition, since the connectivity and activity amount of the relevant region of the brain are analyzed in real time and displayed on the display device together with the target value, the subject evaluates the validity of the strategy for causing each measured value to reach the target value. be able to.

  In addition, it is possible to provide the subject with an appropriate task of causing the measured value to reach the target value for the connectivity and the activity amount of the relevant region of the brain, and efficiently achieve the target.

  Further, regarding the state of the brain in each of the related regions, the activity of each region and the connectivity of each region can be effectively visualized.

  Further, a subject who has no knowledge of brain activity can easily understand his / her condition, dysfunction, and training target through the screen of the display device, and can encourage efforts to achieve the target.

  201: multifunctional feedback device, 201: control unit, 202: control process, 203: storage device, 204: feedback system, 205: input device, 206: output device.

Claims (13)

A sensor for measuring the activity of a region of interest comprising at least two places in the brain of the subject,
From the activity of each of the region of interest measured by the sensor, a control unit that analyzes the brain activity of the region of interest and the brain network of the region of interest,
A multifunctional nerve feedback system, comprising: a display device that displays a brain activity amount of each of the regions of interest and a brain network of the regions of interest analyzed by the control unit.   The control unit, on the display device, on a graph having an axis corresponding to the region of interest, plots the amount of brain activity of the region of interest, and the strength of the brain network of the region of interest at the plotted position The multifunctional nerve feedback system according to claim 1, wherein the system is represented by a circle. The control unit, the brain activity amount in the region of interest and brain network target data,
A control process for judging abnormality of the subject by comparing the brain activity of the region of interest analyzed based on the measurement of the sensor with the analysis result of the brain network of the region of interest. Item 3. The multifunctional nerve feedback system according to Item 2.   The said control process makes the said display apparatus display the brain activity of the said area of interest and the brain network of the said area of interest analyzed based on the said target data and the measurement of the said sensor, The said display device. A multifunctional neural feedback system as described.   The control unit has a feedback system that reads a task corresponding to the target data when the control process finds an abnormality in the subject, and displays the read task on the display device, 4. The multifunctional neural feedback system according to claim 3, wherein: In a sensor for measuring the activity of the region of interest consisting of at least two places in the brain of the subject, in a multi-functional nerve feedback device connected to the display device,
The multifunctional nerve feedback device has a control unit that analyzes a brain activity amount of the region of interest and a brain network of the region of interest from each activity of the region of interest measured by the sensor. Multifunctional nerve feedback device.   The control unit, on the display device, on a graph having an axis corresponding to the region of interest, plots the amount of brain activity of the region of interest, and the strength of the brain network of the region of interest at the plotted position 7. The multifunctional nerve feedback device according to claim 6, wherein the multifunctional nerve feedback device is represented by a circle. The control unit, the brain activity in the region of interest and brain network target data,
By comparing a brain activity amount of the region of interest analyzed based on the measurement of the sensor with an analysis result of the brain network of the region of interest, a control process for determining an abnormality of the subject is provided. The multifunctional nerve feedback device according to claim 7.   The said control process makes the said display apparatus display the brain activity of the said region of interest and the brain network of the said region of interest analyzed based on the said target data and the measurement of the said sensor, The said display device. A multifunctional neural feedback device as described.   The control unit has a feedback system that reads a task corresponding to the target data when the control process finds an abnormality in the subject, and displays the read task on the display device, The multifunctional nerve feedback device according to claim 8, wherein Selecting a region of interest consisting of at least two places in the subject's brain,
Measuring the activity of the selected region of interest,
Analyzing the brain activity of the region of interest and the brain network of the region of interest from each activity of the region of interest measured by a sensor,
The analyzed brain activity of the region of interest is plotted on a graph having an axis corresponding to the region of interest, and the strength of the brain network of the region of interest is represented by the size of a circle at the plotted position. Displaying a brain activity in the region of interest and target data of a brain network on the graph.   By comparing the target data and the brain activity of the region of interest analyzed based on the measurement of the sensor with the analysis result of the brain network of the region of interest, the abnormality of the subject is determined. The multifunctional nerve feedback method according to claim 11, wherein   The multifunctional nerve feedback method according to claim 11, wherein when an abnormality of the subject is found, a task corresponding to the target data is read, and the read task is displayed.

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