Brain workload index calculation system and brain workload index calculation method

Abstract

This brain workload index calculation system (100) comprises: a brain activity measurement device (1) which measures the brain activity of a subject (P); a data processing unit (21) which calculates a brain workload index on the basis of the degree of change in the measurement data, which is acquired from the brain activity measurement device (1), of the brain activity of the subject during the execution of a plurality of tasks of different levels of difficulty with respect to the work performance of the subject for each of the plurality of tasks of different levels of difficulty; and a display unit (24) which displays the brain workload index calculated by the data processing unit (21).

Description

[Technical field]

[0001] The present invention relates to a brain workload index calculation system and a brain workload index calculation method, and in particular to a brain workload index calculation system and a brain workload index calculation method that include a brain activity measuring device, a data processing unit, and a display unit. [Background technology]

[0002] 2. Description of the Related Art Conventionally, an optical measurement device including a brain activity measuring device, a data processing unit, and a display unit has been known. Such an optical measurement device is disclosed in, for example, JP 2015-229095 A.

[0003] The above-mentioned Japanese Patent Application Laid-Open No. 2015-229095 discloses an optical measurement device that performs optical measurement (brain function measurement) by near-infrared spectroscopy (NIRS). This optical measurement device includes an optical measurement unit (brain activity measurement device) and a control unit (data processing unit) including a display unit. The light transmitting probe and the light receiving probe of the optical measurement device are attached to a holder for fixing the probe attached to the head of the subject, and are arranged at a predetermined position on the surface of the head of the subject. The optical measurement device irradiates measurement light in the wavelength region of near-infrared light from the light transmitting probe, and detects the measurement light reflected inside the head of the subject by making it incident on the light receiving probe, thereby acquiring the intensity of the measurement light (amount of received light). Based on the acquired intensity of the measurement light, it is possible to acquire the change in the amount of hemoglobin (oxygenated hemoglobin, deoxygenated hemoglobin, and total hemoglobin) associated with brain activity. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2015-229095 A Summary of the Invention [Problem to be solved by the invention]

[0005] Although not disclosed in the above-mentioned JP 2015-229095 A, it is conceivable to utilize data showing the state of the brain during work, etc., in the skill evaluation of a worker, etc. However, in the skill evaluation of a worker, etc., there has been no index showing the brain's workload for objectively judging or evaluating the brain's state during work, etc. Therefore, there is a demand for the emergence of an index showing the brain's workload that can be used as objective data showing the brain's state.

[0006] The present invention has been made to solve the above-mentioned problems, and one object of the present invention is to provide a brain workload index calculation system and a brain workload index calculation method that are capable of presenting an index indicating brain workload that can be utilized as objective data showing the state of the brain when a worker performs work. [Means for solving the problem]

[0007] A brain workload index calculation system according to a first aspect of the present invention includes a brain activity measuring device that measures the brain activity of a subject, a data processing unit that calculates a brain workload index based on a degree of change in measurement data of the brain activity of the subject while performing a plurality of tasks of different difficulty levels with respect to the subject's task performance for each of the plurality of tasks of different difficulty levels obtained from the brain activity measuring device, and a display unit that displays the brain workload index calculated by the data processing unit. Here, the "task performance" is a score that is an evaluation of the results of the tasks performed by the subject during the brain activity measurement.

[0008] A brain workload index calculation method in a second aspect of the present invention includes the steps of acquiring measurement data of a subject's brain activity while performing a plurality of tasks of differing difficulty, acquiring the subject's performance for each of the plurality of tasks of differing difficulty, calculating a brain workload index based on the degree of change in the acquired measurement data of the subject's brain activity relative to the acquired task performance of the subject, and displaying the calculated brain workload index. Effect of the Invention

[0009] In the brain workload index calculation system according to the first aspect of the present invention, as described above, the data processing unit calculates a brain workload index based on the degree of change in measurement data of the subject's brain activity during the performance of a plurality of tasks of different difficulty levels, which is acquired from a brain activity measuring device, with respect to the subject's task performance for each of the tasks of different difficulty levels. By acquiring the degree of change in the measurement data of the subject's brain activity with respect to the task performance of the subject as the brain workload index, it is possible to evaluate that the acquired degree of change is larger as being closer to the brain activity change of a young person. Therefore, it is possible to present an index indicating the brain workload that can be used as objective data indicating the state of the brain when the worker performs the task.

[0010] The method for calculating a brain workload index according to the second aspect of the present invention includes a step of calculating a brain workload index based on the degree of change in measurement data of the subject's brain activity while performing a plurality of tasks of different difficulty levels, with respect to the subject's task performance for each of the tasks of different difficulty levels. As in the first aspect, by acquiring the degree of change in measurement data of the subject's brain activity with respect to the subject's task performance as the brain workload index, it is possible to evaluate that the acquired degree of change is closer to the brain activity change of a young person. Therefore, it is possible to present an index indicating the brain workload that can be used as objective data indicating the brain state when the worker performs the task. [Brief description of the drawings]

[0011] [Figure 1] 1 is a schematic diagram showing an overall configuration of a brain workload index calculation system according to an embodiment of the present invention; [Diagram 2] 1 is a block diagram of a brain workload index calculation system according to an embodiment of the present invention. [Diagram 3] FIG. 4 is a schematic diagram showing an example of a screen displayed by a display unit according to an embodiment of the present invention. [Figure 4] 13 is a graph showing a young person model and an elderly person model for evaluating differences in brain activity in response to differences in task performance. [Diagram 5] FIG. 1 is a schematic diagram showing a measurement design including a work execution period and a rest period according to an embodiment of the present invention. [Figure 6] 1 is a graph for explaining a plateau region. [Figure 7] 13 is a graph showing a first example of a change to a young person model after training in an evaluation of the suitability of the training. [Figure 8] 13 is a graph showing a second example of a change to a young person model after training in an evaluation of the suitability of the training. [Figure 9] 13 is a graph showing a third example of a change to a young person model after training in an evaluation of the suitability of the training. [Figure 10] 13 is a graph showing a first example of a change to an elderly model after training in an evaluation of the suitability of the training. [Figure 11] 13 is a graph showing a second example of a change to an elderly model after training in an evaluation of the suitability of the training. [Figure 12] 13 is a graph showing a third example of a change to an elderly model after training in an evaluation of the suitability of the training. [Figure 13] FIG. 11 is a flow chart for explaining a process of calculating a brain workload index by a control unit. [Figure 14] 1 is a graph showing a first example of models of product A and product B in product evaluation. [Figure 15] 11 is a graph showing a second example of models of products A and B in product evaluation. [Figure 16] 13 is a graph showing a third example of models of products A and B in product evaluation. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings.

[0013] (Configuration of the brain workload index calculation system) The overall configuration of a brain workload index calculation system 100 according to one embodiment of the present invention will be described with reference to FIGS. 1 to 9.

[0014] As shown in FIG. 1, a brain workload index calculation system 100 according to this embodiment includes a brain activity measuring device 1 and a data processing device 2.

[0015] The brain activity measuring device 1 is a device (optical measuring device) that optically measures the brain activity of the subject P using near-infrared spectroscopy (NIRS) and generates time-series measurement result data. Specifically, the brain activity measuring device 1 is a NIRS device. The brain activity measuring device 1 irradiates measurement light in the wavelength region of near-infrared light from a light transmitting probe (not shown) arranged on the head surface of the subject P. Then, the measurement light reflected inside the head is made to enter a light receiving probe (not shown) arranged on the head surface and detected, thereby obtaining the intensity (amount of received light) of the measurement light. A plurality of light transmitting probes and a plurality of light receiving probes are provided, and are attached to a holder 11 for fixing each probe at a predetermined position on the head surface. The brain activity measuring device 1 measures the change amount of oxygenated hemoglobin, deoxygenated hemoglobin, and total hemoglobin based on the intensity (amount of received light) of measurement light of a plurality of wavelengths (for example, three wavelengths of 780 nm, 805 nm, and 830 nm) and the light absorption characteristics of hemoglobin. In this way, the brain activity measuring device 1 measures changes in the blood state in the brain that accompany brain activity as changes in the amount of hemoglobin.

[0016] Brain activity measurement is performed by alternating between task performance periods in which tasks of different difficulty levels are performed and rest periods in which no tasks are performed (see FIG. 5). Note that brain activity measurement may or may not include rest periods. Areas of the brain related to memory, cognition, motivation, judgment, etc. are set as the measurement site when obtaining measurement data of subject P's brain activity. For example, the prefrontal cortex of the brain is set as the measurement site. More specifically, the dorsolateral prefrontal cortex of the brain is set as the measurement site.

[0017] Moreover, the brain activity measuring device 1 is communicably connected to a data processing device 2, and transmits the obtained measurement data to the data processing device 2.

[0018] Next, the configuration of the data processing device 2 will be described.

[0019] As shown in FIG. 2, the data processing device 2 is composed of a PC (personal computer) equipped with a control unit 21 including a CPU and memory, a storage unit 22 such as a hard disk drive or flash memory, an input unit 23 such as a keyboard, and a display unit 24. The control unit 21 (CPU) executes a program stored in the storage unit 22, so that the PC functions as the data processing device 2 of the brain workload index calculation system. The control unit 21 acquires measurement data by communicating with the brain activity measurement device 1. The control unit 21 also acquires file data for data processing into which the task performance is input by the input unit 23. The task performance is a score obtained by evaluating the results of the tasks performed on the subject P during the brain activity measurement. In detail, the task performance is a score obtained by evaluating the results of multiple tasks with different levels of difficulty performed on the subject P during the brain activity measurement. Multiple tasks set to different levels of difficulty are performed on the subject P, and multiple tasks are also performed for each level of difficulty. Therefore, the task performance is, in more detail, an average value of the task performance, which is the result of multiple tasks for each level of difficulty. The control unit 21 is an example of the "data processing unit" of the present invention.

[0020] In this embodiment, the control unit 21 calculates a brain workload index based on the degree of change in measurement data of the brain activity of the subject P while performing a plurality of tasks of different difficulty levels, which is acquired from the brain activity measuring device 1, with respect to the task performance of the subject P for each of a plurality of tasks of different difficulty levels. The control unit 21 is configured to calculate, as the brain workload index, a change in brain activity in the measurement data with respect to a change in task performance order in the task performance for each of a plurality of tasks of different difficulty levels. The change in brain activity is, for example, the change in oxygenated hemoglobin, deoxygenated hemoglobin, and total hemoglobin. The details of the brain workload index will be described later.

[0021] The input unit 23 accepts user input of numerical values ​​related to task performance. The task performance is input by the user into file data for data processing according to the behavioral data type and the subject P. The file data is data in Excel format, CSV format, text format, or the like.

[0022] The display unit 24 displays various information including the brain workload index output from the control unit 21. Fig. 3 is a schematic diagram showing an example of a screen displayed by the display unit 24. Examples of information displayed on the display unit 24 include subject information 30, measurement information 31, current brain workload index 32, current behavioral data 33, workload index history 34, behavioral data history 35, and the like.

[0023] In this embodiment, the brain workload index is calculated multiple times for the same subject P. The current brain workload index 32 is the amount of change in brain activity in the measurement data relative to the amount of change in task performance for each of a plurality of tasks of different difficulty levels, which is obtained by performing all of a plurality of tasks of different difficulty levels and through all the performed tasks. The workload index history 34 is a chronological arrangement of previously calculated brain workload indexes.

[0024] The current behavioral data 33 is an average value of task performance, which is the result of multiple tasks of different difficulty levels, and the task performance is displayed according to the number of difficulty levels. The behavioral data history 35 is a chronological order of previously acquired task performance according to the number of difficulty levels.

[0025] Further, the information displayed on the display unit 24 may include graphs showing approximate straight lines (see Figs. 7 to 12 and Figs. 14 to 16) and the presence or absence of a plateau region T (see Fig. 6) as a stagnation region (described later). As long as the information displayed on the display unit 24 is at least the current brain workload index 32, any of the other information may not be displayed on the display unit 24.

[0026] As the current brain workload index 32, the value (numerical value) of the change in brain activity in the measurement data relative to the change in task performance for each of a plurality of tasks with different levels of difficulty is displayed on the display unit 24. As the current brain workload index 32, a mark or color based on the above value that is set in advance may be displayed on the display unit 24. Examples of the mark include "◯", "△", "×", etc., depending on the value. As the color, for example, by comparing the value (numerical value) of the change in brain activity relative to the change in task performance before and after training described below, if the value of the change in brain activity relative to the change in task performance after training is large, the value may be displayed in a dark color, and if the value of the change in brain activity relative to the change in task performance after training is small, the value may be displayed in a light color.

[0027] The subject information 30 includes the name, sex, age, and so on of the subject P, which are displayed on the display unit 24. The measurement date and time of brain activity measured by the brain activity measuring device 1, the measured part, and so on are displayed on the display unit 24 as the measurement information 31. The task performance of the subject P for each of a plurality of tasks of different difficulty levels is displayed on the display unit 24 as the current behavioral data 33. The brain workload index calculated in the past for the same subject P is displayed on the display unit 24 as the workload index history 34. The task performance of the same subject P for each of a plurality of tasks of different difficulty levels obtained in the past is displayed on the display unit 24 as the behavioral data history 35. The workload index history 34 and the behavioral data history 35 may be displayed as line graphs instead of bar graphs.

[0028] (Brain Workload Index) We explain brain workload indicators.

[0029] The brain workload index is based on a model that evaluates the difference in brain activity relative to the difference in task performance. Figure 4 is a graph with the feature amount in brain activity (amount of brain activity) on the vertical axis (explained variable) and task performance on the horizontal axis (explanatory variable). A large feature amount in brain activity indicates a large amount of brain activity, and a small feature amount in brain activity indicates a small amount of brain activity.

[0030] FIG. 4 shows a young person model (solid line) which is an example of a model for young people, and an elderly person model (dashed line) which is an example of a model for elderly people, as models for evaluating differences in brain activity relative to differences in task performance. When comparing the young person model with the elderly person model, the young person model has a larger difference in brain activity relative to differences in task performance. In other words, the young person model has a larger slope, which is the change in brain activity relative to the change in task performance, than the elderly person model. Also, the young person model has a stronger brain response (larger brain activity) at lower task performance (high difficulty) than the elderly person model. As an index of brain workload, the young person model is more desirable than the elderly person model.

[0031] Here, the graphs representing the young person model and the elderly person model have task performance set as the horizontal axis. For example, even if tasks are set to the same level of difficulty, some subjects P may find the tasks difficult, while other subjects P may find the tasks easy. Even if a subject becomes accustomed to a task after performing the same level of difficulty multiple times, the task is treated as having the same level of difficulty, and the influence of accustomization is not taken into account. In order to suppress the influence of the different perceptions of difficulty and accustomization by such subjects P, and to obtain a more objective index that is not dependent on "difficulty," a model with task performance as the horizontal axis is used.

[0032] (Summary of training, tasks, task difficulty and task performance) The training may be, for example, driving training using a driving simulator 3 (see FIG. 1) at a driving school or the like. The training may also be, for example, flight training for pilots using a flight simulator (not shown). The training is not limited to the driving training and flight training described above.

[0033] The measurement data of the brain activity of the subject P measured by the brain activity measuring device 1 is measured while the subject P is performing a plurality of tasks of different difficulty levels. If the training is driving training using a drive simulator 3 (see FIG. 1), the task is performing simulated driving using the same drive simulator 3 as the training. If the training is flight training using a flight simulator (not shown), the task is performing simulated flight using the same flight simulator as the training. The task may be the same as the training, or may be different from the training.

[0034] When the brain activity is measured by the brain activity measuring device 1, a plurality of tasks with different levels of difficulty are performed on the subject P. As shown in FIG. 5, the tasks are performed in the order of low to high difficulty. At least two tasks with different levels of difficulty are performed as tasks with different levels of difficulty. The number of tasks with different levels of difficulty is preferably large, but is not particularly limited as long as it is two or more. In this embodiment, five tasks with different levels of difficulty are performed. Moreover, tasks at each level of difficulty are performed multiple times for each level of difficulty. In this embodiment, tasks at each level of difficulty are performed five times for each level of difficulty. It is preferable that the difference in difficulty between the tasks with different levels of difficulty is large. Moreover, a rest period is set between the tasks with different levels of difficulty. It is also possible that the rest period is set, or not set.

[0035] The task performance at each level of difficulty is the average of the task performance in the tasks performed multiple times for each level of difficulty. Also, the brain activity in the tasks at each level of difficulty is, for example, the average or integral values ​​of oxygenated hemoglobin, deoxygenated hemoglobin, and total hemoglobin in the tasks performed multiple times for each level of difficulty.

[0036] For example, when the task is to carry out a simulated driving using the driving simulator 3 (see FIG. 1), the difficulty level is set according to the difficulty level of the driving course. Also, for example, the difficulty level is set according to the number of events that occur during the simulated driving. Note that the setting of different difficulty levels in the task is not limited to the above example.

[0037] The task performance is a score obtained by evaluating the results of a number of tasks with different levels of difficulty. For example, if the task is to perform a simulated driving using a driving simulator 3 (see FIG. 1), the task performance is a score obtained by calculating various behavioral data such as the time spent driving at a certain speed and the time spent driving from one section to another, based on a predetermined standard.

[0038] (Method of calculating brain workload index) A method for calculating the brain workload index will now be described.

[0039] The brain workload index is calculated, for example, based on a regression model of brain activity and task performance in a graph (see Figs. 7 to 12) with the feature amount (amount of brain activity) in brain activity on the vertical axis (explained variable) and task performance on the horizontal axis (explanatory variable). An example of the brain workload index is the amount of change in brain activity in the measured data relative to the amount of change in task performance order in the task performance for each of a plurality of tasks with different levels of difficulty. The horizontal axis in the graph is set so that the task performance decreases as the axis moves away from the origin. The vertical axis in the graph is set so that the amount of change in brain activity increases as the axis moves away from the origin.

[0040] The control unit 21 is configured to acquire measurement data of the brain activity of the subject P from the brain activity measuring device 1. The control unit 21 acquires the amount of change in the brain activity as the measurement data of the brain activity. In detail, the control unit 21 acquires the amount of change in oxygenated hemoglobin, deoxygenated hemoglobin, and total hemoglobin as the measurement data of the brain activity. The control unit 21 calculates a feature amount in a certain section of the measurement data for each of a plurality of tasks with different levels of difficulty. The feature amount is an average value or an integral value of the amount of change in oxygenated hemoglobin, deoxygenated hemoglobin, and total hemoglobin as the measurement data of the brain activity in the certain section of the measurement data.

[0041] The control unit 21 is also configured to acquire task performance, which is input via the input unit 23 and is obtained by converting the evaluation of the results of a plurality of tasks having different levels of difficulty into scores.

[0042] The control unit 21 is configured to calculate an approximation line for plots on a graph showing the brain activity amount for each of a plurality of tasks with different levels of difficulty. The approximation line showing the relationship between the task performance X and the brain activity amount (feature amount) Y is expressed by the following formula (1). Y = aX + b (1)

[0043] The control unit 21 obtains the slope a of the calculated approximate line as a brain workload index. The control unit 21 also obtains the intercept b of the calculated approximate line as a secondary brain workload index.

[0044] The control unit 21 also calculates a coefficient of determination R 2 It is configured to calculate the coefficient of determination R 2 is calculated by squaring the correlation coefficient R expressed by the following formula (2). The control unit 21 calculates the coefficient of determination R 2 is taken as a secondary brain workload index. TIFF0007619467000001.tif21148

[0045] The user can evaluate that the larger the slope a of the approximation line as the primary evaluation item, the closer it is to the brain activity change of a young person and the more desirable the change. In addition, when evaluation of the primary evaluation item is difficult, the user can evaluate that the larger the intercept b of the approximation line as the secondary evaluation item, the closer it is to the brain activity change of a young person and the more desirable the change. Furthermore, the user can evaluate that the coefficient of determination R 2 The reliability of the primary endpoint can be evaluated by the coefficient of determination R 2 The closer to 1, the higher the efficacy of the primary endpoint.

[0046] In addition, the control unit 21 calculates the coefficient of determination R 2 is less than a preset threshold, the presence or absence of a plateau region T (see FIG. 6) as a stagnant region in which the amount of brain activity for each task performance is within a predetermined range is determined.

[0047] Specifically, assume that five plots of brain activity versus task performance are provided as shown in Fig. 6. In this case, when the gradient of an approximation line calculated using the plot of brain activity versus the lowest task performance (the rightmost plot in the graph) and the plot of brain activity versus the second lowest task performance (the second plot from the right in the graph) is within a preset range, the control unit 21 determines that the region between the two plots (the portion indicated by the dashed line) is a plateau region T as a stagnant region.

[0048] (Evaluation of training suitability using brain workload index) With reference to Figures 7 to 12, the evaluation of the suitability of training using a brain workload index according to one embodiment of the present invention will be described. In this embodiment, the brain workload index calculated by a data processing device is used to evaluate the suitability of training. For the sake of simplicity in the following explanation, the number of plots of brain activity versus task performance shown in Figures 7 to 12 is two.

[0049] As described above, a younger model is more desirable than an older model for the brain workload index. Therefore, if the brain workload index calculated after training has changed to a younger model compared to the brain workload index calculated before training (if the slope, which is the amount of change in brain activity relative to the amount of change in task performance, is larger), it can be evaluated that the training was suitable for the subject P. Also, if the brain workload index calculated after training has changed to an older model compared to the brain workload index calculated before training (if the slope, which is the amount of change in brain activity relative to the amount of change in task performance, is smaller), it can be evaluated that the training was not suitable for the subject P.

[0050] Before and after training, the subject P is given a number of tasks with different levels of difficulty. The data processing device 2 acquires measurement data of brain activity before and after training and task performance for each task from the brain activity measuring device 1. As shown in Figs. 7 to 12, the control unit 21 calculates a first approximation line (before training) 41 for a plot of brain activity versus task performance for each task with different levels of difficulty before training. The control unit 21 also calculates a second approximation line (after training) 42 for a plot of brain activity versus task performance for each task with different levels of difficulty after training. In Figs. 7 to 12, the plots of brain activity versus task performance are indicated by black circles.

[0051] Then, the slope of the calculated first approximate line 41 is compared with the slope of the second approximate line 42. At this time, as shown in Figs. 7 to 9, if the slope of the second approximate line 42 is greater than the slope of the first approximate line 41, it can be said that the brain workload index after training has changed to a young person model compared to the brain workload index before training. In this case, the user can evaluate that the training was suitable for the subject P. In Figs. 7 to 12, the actual measurement value is the value of the feature amount of brain activity in the value of the task performance acquired after training. Also, the predicted value is the value of the feature amount of brain activity in the value of the task performance acquired after training (shown by a white circle) on the calculated first approximate line.

[0052] In addition, as a result of comparing the first approximate line 41 and the second approximate line 42 in FIG. 7, after training, the task performance and the brain activity are generally improved. In addition, as a result of comparing the first approximate line 41 and the second approximate line 42 in FIG. 8, after training, the task performance is improved and the brain activity is decreased on the side where the task performance is high, and the task performance and the brain activity are improved on the side where the task performance is low. In addition, as a result of comparing the first approximate line 41 and the second approximate line 42 in FIG. 9, after training, the task performance is improved and the brain activity is decreased on the whole. That is, the change in the brain activity relative to the change in the task performance (the slope of the approximate line) is meaningful and important. In all of FIG. 7 to FIG. 9, the change in the brain activity relative to the change in the task performance (the slope of the approximate line) is large after training. In addition, in the case shown in FIG. 9, it is possible that the person became accustomed to the task by repeating the training.

[0053] 10 to 12, if the slope of the second approximation line 42 is smaller than the slope of the first approximation line 41, it can be said that the brain workload index after training has changed to an elderly model compared to the brain workload index before training. In this case, the user can evaluate that the training was not suitable for subject P.

[0054] In addition, as a result of comparing the first approximate line 41 and the second approximate line 42 in FIG. 10, after training, the task performance and the brain activity are generally increased. Also, as a result of comparing the first approximate line 41 and the second approximate line 42 in FIG. 11, after training, the task performance and the brain activity are increased on the side of high task performance, and the task performance is increased and the brain activity is decreased on the side of low task performance. Also, as a result of comparing the first approximate line 41 and the second approximate line 42 in FIG. 12, after training, the task performance is generally increased and the brain activity is decreased. In all of FIG. 10 to FIG. 12, the change in brain activity relative to the change in task performance (the slope of the approximate line) is smaller after training.

[0055] By calculating the brain workload index before and after training for the same subject P, the suitability of the training can be objectively indicated. If the brain workload index calculated after training is larger than the brain workload index calculated before training, it can be evaluated that the training is suitable for the subject P. If the brain workload index calculated after training is smaller than the brain workload index calculated before training, it can be evaluated that the training is not suitable for the subject P.

[0056] (Calculation process of brain workload index) The calculation process of the brain workload index according to this embodiment will be described with reference to Fig. 13. The calculation process of the brain workload index described below is executed by the control unit 21 consisting of a CPU as hardware. The control unit 21 is configured to obtain measurement data of brain activity and task performance before and after a predetermined training, and to calculate an approximation line before and after the predetermined training.

[0057] In step S1, the control unit 21 acquires measurement data of the brain activity of the subject P while the subject P is performing a plurality of tasks with different levels of difficulty from the brain activity measuring device 1. After that, the process proceeds to step S2.

[0058] In step S2, the control unit 21 calculates a feature amount for a certain section of the acquired measurement data of brain activity. After that, the process proceeds to step S3.

[0059] In step S3, the control unit 21 acquires task results for each of a plurality of tasks with different levels of difficulty inputted via the input unit 23. After that, the process proceeds to step S4.

[0060] In step S4, the control unit 21 calculates an approximation line for the plot of the calculated brain activity feature amount against the acquired task performance. After that, the process proceeds to step S5.

[0061] In step S5, the control unit 21 determines at least the gradient of the calculated approximate line as a brain workload index. The control unit 21 also determines the coefficient of determination R 2 Then, the process proceeds to step S6.

[0062] In step S6, the control unit 21 calculates the coefficient of determination R 2 It is judged whether the calculated coefficient of determination R is less than a preset threshold value. 2 If it is less than the threshold value (Yes in step S6), the process proceeds to step S7, and the calculated coefficient of determination R 2 If is equal to or greater than the threshold value (No in step S6), the process proceeds to step S8.

[0063] In step S7, the control unit 21 determines whether or not there is a plateau region T in the feature amount of brain activity. After that, the process proceeds to step S8.

[0064] In step S8, the control unit 21 causes the display unit 24 to display the brain workload index. Then, the process ends.

[0065] (Effects of the Brain Workload Index Calculation System According to the Present Embodiment) The brain workload index calculation system 100 according to this embodiment can provide the following effects.

[0066] In this embodiment, as described above, the brain workload index calculation system 100 includes the brain activity measurement device 1, the control unit 21 that calculates the brain workload index based on the degree of change in the measurement data of the brain activity of the subject P during the execution of the plurality of tasks with different levels of difficulty, which is acquired from the brain activity measurement device 1 with respect to the task performance of the subject P for each of the plurality of tasks with different levels of difficulty, and the display unit 24 that displays the calculated brain workload index. Here, the "task performance" is a score that is an evaluation of the result of the task performed on the subject P during the brain activity measurement. By acquiring the degree of change in the measurement data of the brain activity of the subject P with respect to the task performance of the subject P as the brain workload index, it can be evaluated that the larger the acquired degree of change, the closer it is to the brain activity change of a young person. Therefore, it is possible to present an index indicating the brain workload that can be used as objective data indicating the brain state when the worker performs the task.

[0067] In this embodiment, as described above, the control unit 21 is configured to calculate, as a brain workload index, the amount of change in brain activity in the measurement data relative to the amount of change in task performance order in the task performance for each of a plurality of tasks with different levels of difficulty. By using the amount of change in task performance order instead of the amount of change in difficulty order for the task performance acquired for each of a plurality of tasks with different levels of difficulty, the amount of change in brain activity relative to the amount of change in task performance can be quantified and acquired, so that an index indicating the brain workload that can be used as objective and clear data indicating the state of the brain can be presented.

[0068] In this embodiment, as described above, the control unit 21 is configured to acquire task performance, which is a score obtained by evaluating the results of a plurality of tasks with different levels of difficulty. This allows the task performance to be quantified and acquired. Since the amount of change in task performance varies depending on the value of the task performance, the amount of change in brain activity relative to the amount of change in task performance, which is an index of the brain's workload, can be calculated appropriately and precisely.

[0069] In addition, in this embodiment, as described above, the control unit 21 is configured to calculate an approximation line for the plots on the graph as the amount of brain activity based on the amount of change in brain activity with respect to the task performance for each of a plurality of tasks with different levels of difficulty, and to use the slope of the calculated approximation line as the brain workload index. This makes it possible to easily quantify and obtain the amount of change in brain activity with respect to the amount of change in task performance, and therefore to present an index indicating the brain workload that can be used as more objective data indicating the brain state when the worker performs the task.

[0070] In the present embodiment, as described above, the display unit 24 is configured to display a graph showing an approximate straight line together with the brain workload index. This allows the user or subject P to visually and intuitively recognize the brain workload index.

[0071] In this embodiment, as described above, the display unit 24 is configured to display a graph in which the vertical axis represents the amount of brain activity and the horizontal axis represents task performance, and the task performance decreases as the distance from the origin increases. Here, the lower the task performance (the higher the task difficulty), the greater the tendency for brain activity to increase, so the approximation line in the graph configured as described above tends to rise to the right. This allows the graph to display an approximation line rising to the right, allowing the user or subject P to recognize the brain's workload index more visually and intuitively.

[0072] In this embodiment, as described above, the display unit 24 is configured to display a graph showing a first approximate straight line 41 of a plot of brain activity for task performance for each of a plurality of tasks with different difficulty levels before the predetermined training, and a second approximate straight line 42 of a plot of brain activity for task performance for each of a plurality of tasks with different difficulty levels after the predetermined training. This allows the user or subject P to visually and intuitively recognize the brain workload index before and after the predetermined training, and therefore allows the user or subject P to easily recognize the evaluation of the suitability of the training.

[0073] In the present embodiment, as described above, the control unit 21 is configured to determine the presence or absence of a plateau region T as a stagnant region where the brain activity amount for each task performance is within a predetermined range. This allows the user to easily recognize the presence or absence of a plateau region T that is an indicator of the stagnation of the increase in the brain activity amount.

[0074] In this embodiment, as described above, the brain activity measuring device 1 is configured to measure brain activity in the prefrontal cortex in the head of the subject P. This makes it possible to present an index indicating the workload of areas of the brain related to memory, cognition, motivation, judgment, and the like.

[0075] Furthermore, in this embodiment, as described above, the brain activity measuring device 1 is configured to optically measure the brain activity of the subject P by near-infrared spectroscopy. This does not require large-scale equipment compared to magnetic resonance imaging (MRI) or the like, and therefore makes it possible to easily measure changes in the brain activity of the subject P.

[0076] (Effects of the brain workload index calculation method according to the present embodiment) In addition, the brain workload index calculation method according to the present embodiment includes a step of calculating a brain workload index based on the degree of change in the measurement data of the brain activity of the subject P while performing a plurality of tasks with different levels of difficulty, with respect to the task performance of the subject P for each of the tasks with different levels of difficulty. As with the brain workload index calculation system, by acquiring the degree of change in the measurement data of the brain activity of the subject P with respect to the task performance of the subject P as the brain workload index, it is possible to evaluate that the acquired degree of change is closer to the brain activity change of a young person. Therefore, it is possible to present an index indicating the brain workload that can be used as objective data indicating the brain state when the worker performs the task.

[0077] In the method for calculating a brain workload index according to the present embodiment, as described above, the step of acquiring measurement data involves having the subject P perform a plurality of tasks with different levels of difficulty in the order of difficulty from the lowest to the highest, and acquiring measurement data of the brain activity of the subject P while performing the plurality of tasks. By gradually changing the difficulty of the tasks from the lowest to the highest, it is possible to prevent the subject P from giving up on the tasks midway.

[0078] In the method for calculating a brain workload index according to the present embodiment, as described above, the step of acquiring measurement data includes a step of acquiring measurement data of the brain activity of the subject P before the predetermined training and a step of acquiring measurement data of the brain activity of the subject P after the predetermined training, and the step of acquiring task performance includes a step of acquiring the task performance of the subject P for each of the multiple tasks before the predetermined training and a step of acquiring the task performance of the subject P for each of the multiple tasks after the predetermined training. This makes it possible to easily calculate the brain workload index before and after the predetermined training.

[0079] In the method for calculating a brain workload index according to the present embodiment, the step of calculating a brain workload index includes the steps of: calculating an approximation line for plots on a graph as brain activity amounts based on measurement data for task performance for each of a plurality of tasks before a predetermined training, and setting the slope of the calculated approximation line as the brain workload index before the predetermined training; and calculating an approximation line for plots on a graph as brain activity amounts for task performance for each of a plurality of tasks after a predetermined training, and setting the slope of the calculated approximation line as the brain workload index after the predetermined training. This allows the appropriateness of training to be evaluated based on the slope of the calculated approximation line.

[0080] [Variations] The embodiments disclosed herein should be considered to be illustrative and not restrictive in all respects. The scope of the present invention is indicated by the claims, not by the description of the embodiments above, and further includes all modifications (variations) within the meaning and scope of the claims.

[0081] For example, in the above embodiment, the brain workload index is used to evaluate the suitability of training, but the present invention is not limited to this. The brain workload index may be used, for example, for product evaluation during product development. In the above embodiment, the brain workload index is calculated before and after training, but in the modified example, the brain workload index is calculated when a different product is used.

[0082] As described above, the younger model is more desirable than the elderly model for the brain workload index. Assume that an approximate straight line is obtained for a plot of brain activity versus task performance for multiple tasks with different levels of difficulty when using the products for product A and product B. When comparing the approximate straight line for product A with the approximate straight line for product B, the product that has a higher brain activity at +∞ when estimating the difficulty of +∞ and a lower brain activity at -∞ when estimating the difficulty of -∞ can be evaluated as a product that shows brain activity closer to the younger model.

[0083] For example, when comparing the approximated line for Product A with the approximated line for Product B, it is assumed that the slope of the approximated line for Product A, which is the amount of change in brain activity relative to the amount of change in task performance, is greater than that of Product B. In this case, Product A can be evaluated as a product that shows brain activity closer to the young person model. Product A can also be evaluated as a product that can respond to the brain activity of subject P according to the level of difficulty better than Product B.

[0084] Whether it is desirable for the brain workload index of a product to be closer to the young person model depends on the purpose and use of the product. In the above example where the brain workload index of Product A is closer to the young person model than the brain workload index of Product B, for example, in a product intended to enable efficient work, Product A is considered to be easier for users to apply (use) than Product B. In contrast, in a product intended to maintain a state of mental and physical relaxation, Product B is considered to be easier for users to apply (use) than Product A.

[0085] (Product evaluation using brain workload index) Product evaluation using the brain workload index will be described with reference to Figs. 14 to 16. As in the above embodiment, when using product A, a plurality of tasks with different levels of difficulty are performed on subject P. The data processing device 2 acquires measurement data of brain activity and task performance for each task. The control unit 21 calculates a first approximation line 51 for a plot of brain activity versus task performance for each of a plurality of tasks with different levels of difficulty for product A. Also, when using product B, a plurality of tasks with different levels of difficulty are performed on subject P. The data processing device 2 acquires measurement data of brain activity and task performance for each task. The control unit 21 calculates a second approximation line 52 for a plot of brain activity versus task performance for each of a plurality of tasks with different levels of difficulty for product B. Then, the slope of the calculated first approximation line 51 is compared with the slope of the second approximation line 52. In this case, as shown in Figures 14 to 16, if the slope of the first approximation line 51 for Product A is greater than the slope of the second approximation line 52 for Product B, it can be evaluated that the brain workload index of Product A is more similar to the young person model than the brain workload index of Product B. In Figures 14 to 16, the plots of brain activity versus task performance are indicated by black circles, and the plots of brain activity versus task performance when the difficulty levels of +∞ and -∞ are estimated are indicated by white circles.

[0086] The number of products compared in the product evaluation may be two, or may be three or more. Furthermore, the tasks with different levels of difficulty may be tasks that use the product itself, or may not use the product itself, as long as they are tasks that are performed while using the product.

[0087] The use of the brain workload index is not limited to the evaluation of the suitability of training, product evaluation in product development, etc. Another use of the brain workload index is, for example, calculating the brain workload index between different subjects P and evaluating which subject P's brain workload index is more similar to the young person model.

[0088] In the above embodiment, the control unit 21 calculates the coefficient of determination R 2is less than a preset threshold value, the presence or absence of a plateau region T is determined, but the present invention is not limited to this. When it is determined that a plateau region T exists, the control unit 21 may be configured to calculate an approximation line excluding the plots included in the plateau region T (plots of brain activity amount versus task performance). The slope of the approximation line calculated including the plots included in the plateau region T is smaller than the slope of the approximation line calculated excluding the plots included in the plateau region T, and therefore may not be appropriate as a brain workload index. By calculating the approximation line excluding the plots included in the plateau region T, an appropriate brain workload index may be calculated.

[0089] In the above embodiment, the control unit 21 calculates the coefficient of determination R 2 is less than a preset threshold value, the present invention is not limited to this. For example, when a plurality of tasks with a predetermined number or more different levels of difficulty are performed on the subject P, and the calculated coefficient of determination R 2 is less than a preset threshold value, the presence or absence of the plateau region T may be determined. An example of the above-mentioned predetermined number is a case where the subject P is given a plurality of tasks with different levels of difficulty, which are five or more. The control unit 21 also determines whether the calculated coefficient of determination R 2 The presence or absence of the plateau region T may be determined even if the difference is equal to or greater than a preset threshold value.

[0090] The user may select at least two of the plots of brain activity for task performance for each task of different difficulty levels shown in the graph, and the control unit 21 may be configured to calculate an approximation line based on the plots selected by the user. This allows the user to appropriately obtain a brain workload index in the area that the user wishes to obtain.

[0091] In the above embodiment, an example was shown in which an approximation line was calculated for the plot of brain activity versus task performance for each of a plurality of tasks with different levels of difficulty, but the present invention is not limited to this. For example, an approximation curve may be calculated for the plot of brain activity versus task performance for each of a plurality of tasks with different levels of difficulty.

[0092] In the above embodiment, an example is shown in which a NIRS device is used to measure changes in brain activity of the subject P, but the present invention is not limited to this. Changes in brain activity of the subject P may be measured using, for example, an MRI device (magnetic resonance imaging device) or an electroencephalograph.

[0093] In the above embodiment, an example is shown in which the brain activity of the prefrontal cortex in the head of the subject P is measured, but the present invention is not limited to this. The measurement site of the subject P can be set appropriately.

[0094] In the above embodiment, the task performance is input by the user, but the present invention is not limited to this. For example, the input unit 23 may be provided in a processing device provided separately from the data processing device 2. In this case, the processing device including the input unit 23 is communicably connected to the data processing device 2 and transmits the input task performance to the data processing device 2. The data processing device 2 may be configured to acquire, as task performance, input data input by the subject P using a keyboard of a PC, a touch panel of a tablet terminal, a smartphone, or the like. The data input by the user regarding numerical values ​​related to task performance may be accumulated in a database (not shown), and the data processing device 2 may be configured to acquire the input data from the database as task performance.

[0095] [Aspects] (First aspect) It will be understood by those skilled in the art that the above exemplary embodiment is a specific example of the first aspect below.

[0096] (Item 1) A brain activity measuring device for measuring the brain activity of a subject; a data processing unit that calculates a brain workload index based on a degree of change in measurement data of the brain activity of the subject while performing a plurality of tasks of different difficulty levels, the measurement data being acquired from the brain activity measuring device and corresponding to the subject's task performance for each of the plurality of tasks of different difficulty levels; A brain workload index calculation system comprising: a display unit that displays the brain workload index calculated by the data processing unit.

[0097] (Item 2) Item 2. The brain workload index calculation system according to item 1, wherein the data processing unit is configured to calculate, as the brain workload index, an amount of change in brain activity in the measurement data relative to an amount of change in the task performance order in the task performance for each of the tasks having different levels of difficulty.

[0098] (Item 3) 3. The brain workload index calculation system according to item 2, wherein the data processing unit is configured to acquire the task performance by scoring the evaluation of the results of a plurality of tasks having different levels of difficulty.

[0099] (Item 4) Item 4. The brain workload index calculation system according to item 2 or 3, wherein the data processing unit is configured to calculate an approximation line for a plot on a graph as a brain activity amount based on an amount of change in the brain activity with respect to the task performance for each of the plurality of tasks having different levels of difficulty, and to use a slope of the calculated approximation line as the brain workload index.

[0100] (Item 5) 5. The brain workload index calculation system according to item 4, wherein the display unit is configured to display the graph in which the approximation line is represented together with the brain workload index.

[0101] (Item 6) Item 6. The brain workload index calculation system according to item 5, wherein the display unit is configured to display the graph in which the vertical axis represents brain activity and the horizontal axis represents task performance, with the task performance decreasing as the distance from the origin increases.

[0102] (Item 7) 7. The brain workload index calculation system according to item 5 or 6, wherein the display unit is configured to display the graph in which, as the approximation lines, a first approximation line for a plot on the graph as the amount of brain activity for the task performance for each of the tasks having different difficulty levels before a predetermined training, and a second approximation line for a plot on the graph as the amount of brain activity for the task performance for each of the tasks having different difficulty levels after the predetermined training are represented.

[0103] (Item 8) The brain workload index calculation system according to any one of items 4 to 7, wherein the data processing unit is configured to determine the presence or absence of a plateau region as a stagnation region in which the brain activity amount for each task performance is within a predetermined range.

[0104] (Item 9) 9. The brain workload index calculation system according to any one of items 1 to 8, wherein the brain activity measuring device is configured to measure brain activity in a prefrontal cortex in the head of the subject.

[0105] (Item 10) 10. The brain workload index calculation system according to any one of items 1 to 9, wherein the brain activity measuring device is configured to optically measure the subject's brain activity by near-infrared spectroscopy.

[0106] (Item 11) A step of acquiring measurement data of brain activity of a subject while the subject is performing a plurality of tasks having different levels of difficulty; acquiring task performances of the subject for each of a plurality of tasks having different levels of difficulty; Calculating a brain workload index based on a degree of change in the acquired measurement data of the subject's brain activity relative to the acquired task performance of the subject; and displaying the calculated brain workload index.

[0107] (Item 12) Item 12. The method for calculating a brain workload index according to item 11, wherein the step of acquiring the measurement data comprises performing a plurality of tasks having different levels of difficulty on the subject in order from the task with the lowest level of difficulty to the task with the highest level of difficulty, and acquiring measurement data of brain activity of the subject while performing the plurality of tasks.

[0108] (Item 13) The step of acquiring the measurement data includes a step of acquiring measurement data of the subject's brain activity before a predetermined training, and a step of acquiring measurement data of the subject's brain activity after the predetermined training, Item 13. The method for calculating a brain workload index according to item 11 or 12, wherein the step of acquiring the task performance includes a step of acquiring the task performance of the subject for each of the plurality of tasks before the predetermined training, and a step of acquiring the task performance of the subject for each of the plurality of tasks after the predetermined training.

[0109] (Item 14) Item 14. The method for calculating a brain workload index according to item 13, wherein the step of calculating the brain workload index includes the steps of: calculating an approximation line for a plot on a graph as the amount of brain activity based on the measurement data for the task performance for each of the plurality of tasks before the specified training, and setting a slope of the calculated approximation line as the brain workload index before the specified training; and calculating an approximation line for a plot on a graph as the amount of brain activity for the task performance for each of the plurality of tasks after the specified training, and setting a slope of the calculated approximation line as the brain workload index after the specified training. [Explanation of symbols]

[0110] 1. Brain activity measurement device 2 Data Processing Device 21 Control section 24 Display section 41 First approximation line (before training) 42 Second approximation line (after training) 100 Brain Workload Index Calculation System P Subject T plateau region

Claims

1. A brain activity measuring device for measuring the brain activity of a subject; a data processing unit that calculates a brain workload index based on a degree of change in measurement data of the brain activity of the subject while performing a plurality of tasks of different difficulty levels, the measurement data being acquired from the brain activity measuring device and corresponding to the subject's task performance for each of the plurality of tasks of different difficulty levels; A brain workload index calculation system comprising: a display unit that displays the brain workload index calculated by the data processing unit.

2. 2. The brain workload index calculation system according to claim 1, wherein the data processing unit is configured to calculate, as the brain workload index, a change in brain activity in the measurement data relative to a change in the task performance order in the task performance for each of the tasks having different levels of difficulty.

3. The brain workload index calculation system according to claim 2 , wherein the data processing unit is configured to acquire the task performance by converting evaluations of results of a plurality of tasks having different degrees of difficulty into scores.

4. 3. The brain workload index calculation system according to claim 2, wherein the data processing unit is configured to calculate an approximation line for a plot on a graph as brain activity based on an amount of change in the brain activity relative to the task performance for each of the tasks having different levels of difficulty, and to use a slope of the calculated approximation line as the brain workload index.

5. The brain workload index calculation system according to claim 4 , wherein the display unit is configured to display the graph in which the approximation line is represented together with the brain workload index.

6. 6. The brain workload index calculation system according to claim 5, wherein the display unit is configured to display the graph in which the vertical axis represents brain activity and the horizontal axis represents task performance, with the task performance decreasing the farther away from the origin.

7. The brain workload index calculation system of claim 5, wherein the display unit is configured to display the graph in which the approximate straight lines are a first approximate straight line for a plot dotted on the graph as the amount of brain activity for the task performance for each of the tasks having different levels of difficulty before the specified training, and a second approximate straight line for a plot dotted on the graph as the amount of brain activity for the task performance for each of the tasks having different levels of difficulty after the specified training.

8. The brain workload index calculation system according to claim 4 , wherein the data processing unit is configured to determine the presence or absence of a plateau region as a stagnation region in which the amount of brain activity for each task performance is within a predetermined range.

9. The brain workload index calculation system according to claim 1 , wherein the brain activity measuring device is configured to measure brain activity in a prefrontal cortex in the subject's head.

10. The brain workload index calculation system according to claim 1 , wherein the brain activity measuring device is configured to optically measure the subject's brain activity by near-infrared spectroscopy.

11. A step of acquiring measurement data of brain activity of a subject while the subject is performing a plurality of tasks having different levels of difficulty; acquiring task performances of the subject for each of a plurality of tasks having different levels of difficulty; Calculating a brain workload index based on a degree of change in the acquired measurement data of the subject's brain activity relative to the acquired task performance of the subject; and displaying the calculated brain workload index.

12. 12. The brain workload index calculation method according to claim 11, wherein the step of acquiring the measurement data comprises having the subject perform a plurality of tasks of differing difficulty in order from the least difficult task to the most difficult task, and acquiring measurement data of the subject's brain activity while performing the plurality of tasks.

13. The step of acquiring the measurement data includes a step of acquiring measurement data of the subject's brain activity before a predetermined training, and a step of acquiring measurement data of the subject's brain activity after the predetermined training, 12. The brain workload index calculation method of claim 11, wherein the step of acquiring the task performance includes a step of acquiring the task performance of the subject for each of the plurality of tasks before the specified training, and a step of acquiring the task performance of the subject for each of the plurality of tasks after the specified training.

14. 14. The method for calculating a brain workload index according to claim 13, wherein the step of calculating the brain workload index includes the steps of: calculating an approximation line for a plot that is dotted on a graph as the amount of brain activity based on the measurement data for the task performance for each of the plurality of tasks before the specified training, and setting a slope of the calculated approximation line as the brain workload index before the specified training; and calculating an approximation line for a plot that is dotted on a graph as the amount of brain activity for the task performance for each of the plurality of tasks after the specified training, and setting a slope of the calculated approximation line as the brain workload index after the specified training.

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