Cognitive ability detection device and cognitive ability detection method

Abstract

A cognitive signal generation part (10) of a cognitive ability detection apparatus (30) includes a brain signal acquisition part (11), a database (20), an MRCP correction data selection part (132), and a calculation part (133). The brain signal acquisition part (11) acquires a brain signal that includes an event related potential. The database (20) memorizes a motion readiness potential correction data in response to the kind of motions. The MRCP correction data selection part (132) selects the motion readiness potential correction data on the basis of prior information including the kind of motions to output the same to the calculation part (133). The calculation part (133) corrects the brain signal with the motion readiness potential correction data to generate a cognitive signal.

Description

[Technical field]

[0001] The present invention relates to a cognitive ability detection device and a cognitive ability detection method for detecting cognitive ability in response to an external stimulus. [Background technology]

[0002] Patent Document 1 describes a cognitive ability detection technology that utilizes brain signals. The technology described in Patent Document 1 detects event-related potentials from brain signals and detects cognitive ability using the event-related potentials.

[0003] Patent Document 2 describes a technique for analyzing and diagnosing brain motor function using electroencephalogram data. The technique of Patent Document 2 detects a readiness potential from electroencephalogram data and diagnoses brain motor function using the readiness potential.

[0004] Patent Document 3 describes a behavior prediction technology that uses electroencephalograms. The technology in Patent Document 3 predicts human behavior using the readiness potential. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2002-272692 A [Patent Document 2] JP 2018-192909 A [Patent Document 3] International Publication No. 2020 / 138012 Summary of the Invention [Problem to be solved by the invention]

[0006] However, in situations where motor readiness potentials occur as shown in Patent Documents 2 and 3, the event-related potentials in the technology described in Patent Document 1 include motor readiness potentials as well as event-related potentials such as P300 that occur during cognition (hereinafter referred to as cognitive system potentials).

[0007] The presence of such a motor readiness potential can sometimes reduce the measurement accuracy of cognitive system potentials.

[0008] Therefore, an object of the present invention is to provide a technique for improving the measurement accuracy of cognitive potentials such as P300. [Means for solving the problem]

[0009] The cognitive ability detection device of the present invention includes a brain signal acquisition unit, a correction data storage unit, and a cognitive signal generation unit. The brain signal acquisition unit acquires a brain signal including an event-related potential. The correction data storage unit stores readiness potential correction data corresponding to a type of movement. The cognitive signal generation unit corrects the brain signal with the readiness potential correction data to generate a cognitive signal.

[0010] In this configuration, the motor preparation potential included in the event-related potential (cognitive system potential) is suppressed. Effect of the Invention

[0011] According to the present invention, it is possible to improve the measurement accuracy of cognitive system potentials. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a functional block diagram showing a configuration of a recognition signal generation unit according to the first embodiment. [Diagram 2] FIG. 2 is a diagram showing the configuration of a cognitive ability detection system according to the first embodiment. [Diagram 3] 3(A), 3(B), and 3(C) are tables showing an example of correction data stored in the database. [Figure 4] FIG. 4(A) is a diagram showing an example of the waveform of a brain signal, and FIG. 4(B) is a diagram in which a region including EOG and P300 in the waveform shown in FIG. 4(A) is enlarged. [Diagram 5] FIG. 5 is a diagram showing an example of the corrected readiness potential data. [Figure 6] FIG. 6 is a diagram showing an example of a waveform of the recognition signal. [Figure 7] FIG. 7 is a diagram showing an example of the waveform of a brain signal. [Figure 8] FIG. 8 is a diagram showing an example of the readiness potential corrected data. [Figure 9] FIG. 9 is a diagram showing an example of a waveform of the recognition signal. [Figure 10] FIG. 10 is a flowchart showing an example of a method for generating a database. [Figure 11] 11(A), 11(B), 11(C) and 11(D) are diagrams showing an example of an image when a database is generated. [Figure 12] FIG. 12 is a flow chart illustrating an example of a method for generating an acknowledgement signal. [Figure 13] 13(A), 13(B), 13(C), and 13(D) show waveforms when multiple actions are performed for one recognition. [Figure 14] 14(A), 14(B), 14(C), and 14(D) show waveforms when a plurality of actions are individually performed in response to a plurality of successive cognitions. [Figure 15] FIG. 15 is a functional block diagram showing a configuration of a recognition signal generating unit according to the second embodiment. [Figure 16] FIG. 16 is a diagram showing the configuration of a cognitive ability detection system according to the second embodiment. [Figure 17] FIG. 17 is a diagram showing a part of the configuration of a cognitive ability detection system according to the third embodiment. [Figure 18] FIG. 18 is a diagram showing the configuration of a cognitive ability detection system for a game. [Figure 19] FIG. 19 is a diagram showing the configuration of a cognitive ability detection system for a multiplayer game. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] (First embodiment) A cognitive ability detection device according to a first embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a functional block diagram showing the configuration of a cognitive signal generation unit according to the first embodiment. FIG. 2 is a diagram showing the configuration of a cognitive ability detection system according to the first embodiment. Note that in this embodiment, a case where a cognitive ability test for driving is performed will be described as an example. In other words, this embodiment shows an example where a cognitive ability test is applied to a driving simulator.

[0014] (Configuration of cognitive ability detection system 1) As shown in FIG. 2, the cognitive ability detection system 1 includes a cognitive ability detection device 30 including a cognitive signal generation unit 10, a brain signal sensor 111, a display 391, a pseudo pedal 392, and a pseudo handlebar 393.

[0015] The display 391 is disposed in front of the subject 80. The pseudo pedals 392 and the pseudo steering wheel 393 are disposed in positions where they can be operated by the subject 80. Note that in Fig. 2, the specific (physical) configuration of the cognitive ability detection system 1 (driving simulator) other than the display 391, the pseudo pedals 392, and the pseudo steering wheel 393 is omitted from the illustration.

[0016] The brain signal sensor 111 is attached to the subject 80. More specifically, the brain signal sensor 111 is attached to a position including the top of the head of the subject 80 (the position of CZ on a scalp potential distribution map (International 10-20 system)).

[0017] Cognitive ability detection device 30 is connected to brain signal sensor 111 and display device 391. Cognitive ability detection device 30 is realized by an arithmetic processing device such as a personal computer.

[0018] The cognitive ability detection device 30 includes a cognitive signal generation unit 10 , a control unit 31 , a video output unit 32 , a determination unit 33 , and an operation input unit 300 .

[0019] The operation input unit 300 receives operation input from a user, etc., such as input of triggers such as the start and end of a cognitive ability detection test, and selection of the type of cognitive ability detection test, and outputs it to the control unit 31.

[0020] The control unit 31 performs overall control of the cognitive ability detection device 30. The control unit 31 performs control such as starting and ending the cognitive ability detection test in response to operation input from the operation input unit 300. The control unit 31 also instructs the video output unit 32 to output a video of the selected cognitive ability detection test.

[0021] Furthermore, the control unit 31 outputs prior information corresponding to the selected cognitive ability detection test to the cognitive signal generation unit 10. The prior information corresponding to the cognitive ability detection test is information that defines the type of action that the subject 80 will take in danger perception. For example, it is information that defines that the brake pedal or steering wheel will be operated in response to perception of a person jumping out. The prior information may include, for example, identification information of the subject 80, type information of the subject 80, etc.

[0022] The video output section 32 outputs the video of the selected cognitive ability detection test to the display device 391. The display device 391 displays this video, thereby enabling the subject 80 to view the video of the cognitive ability detection test.

[0023] When subject 80 watches this image and operates pseudo pedal 392 and pseudo handle 393, a brain signal (electroencephalogram) contains an event-related potential. Brain signal sensor 111 detects this brain signal and outputs it to cognitive signal generation section 10.

[0024] A more specific configuration and processing will be described later, but the recognition signal generation unit 10 generates a recognition signal from a brain signal detected by the brain signal sensor 111.

[0025] The determination unit 33 analyzes the cognitive signal and determines the cognitive ability of the subject 80, such as the presence or absence of cognitive ability and the level of cognitive ability of the subject 80. Note that the determination of cognitive ability using the cognitive signal uses, for example, the appearance of P300, and various known methods can be used, and the description thereof will be omitted here.

[0026] (Configuration of the cognitive signal generating unit 10) 1, the cognitive signal generating unit 10 includes a brain signal acquiring unit 11, an information input unit 12, an EOG detecting unit 131, an MRCP correction data selecting unit 132, a calculating unit 133, and a database 20. The database 20 corresponds to the correction data storage unit of the present invention. Note that MRCP stands for movement related cortical potential, which means a movement-related potential (movement preparation potential) in the present invention.

[0027] The brain signal acquiring section 11 acquires brain signals from the brain signal sensor 111 and outputs them to the calculation section 133 and the EOG detection section 131. The brain signal acquiring section 11 may include an amplifier circuit and a filter circuit. By including an amplifier circuit, the brain signal acquiring section 11 can amplify the brain signal to a predetermined signal level (amplitude). By including a filter circuit, the brain signal acquiring section 11 can suppress noise components other than the event-related potential included in the brain signal.

[0028] The information input unit 12 is an input interface for advance information. The information input unit 12 receives advance information from the control unit 31 described above, and outputs it to the MRCP correction data selection unit 132. The information input unit 12 also has a user interface, and may receive advance information by an external operation input. The advance information from the control unit 31 may be directly input to the MRCP correction data selection unit 132. That is, the information input unit 12 may be omitted.

[0029] The EOG detection unit 131 detects an electrooculogram (EOG) from a brain signal. The EOG detection unit 131 detects saccades and fixations from the electrooculogram. The EOG detection unit 131 detects the timing of a change from a saccade to a fixation, and outputs this change timing to the calculation unit 133 as a reference timing.

[0030] The EOG detection unit 131 may output the detection results of the saccade and fixation to the calculation unit 133. In this case, the calculation unit 133 may detect the timing of the change from the saccade to the fixation and set this timing as the reference timing.

[0031] The database 20 stores correction data (ready potential correction data) corresponding to the readiness potential for each movement or each subject. The correction data is data that simulates the waveform of the readiness potential corresponding to the movement or the subject. The correction data is acquired by a prior data sampling process (details of which will be described later) and stored in the database 20.

[0032] 3A, 3B, and 3C are tables showing an example of the correction data stored in the database. In each of the figures in FIG. 3, the readiness potential correction data is described as MRCP correction data.

[0033] In the case of Fig. 3(A), the readiness potential correction data is set for each type of action. For example, for each of the actions ACT(A), ACT(B), ACT(C), and ACT(D), the readiness potential correction data MRCPc(A), MRCPc(B), MRCPc(C), and MRCPc(D) are set, respectively. In the case of a driving simulator, examples of each of the actions ACT(A), ACT(B), ACT(C), and ACT(D) are steering operation, accelerator operation, and brake operation under a specific environment.

[0034] 3B, the readiness potential correction data is set for each subject. For example, the readiness potential correction data MRCPc(1), the readiness potential correction data MRCPc(2), the readiness potential correction data MRCPc(3), and the readiness potential correction data MRCPc(4) are set for each of the subjects SUB(1), SUB(2), SUB(3), and SUB(4).

[0035] In the case of Fig. 3(C), the readiness potential correction data is set for each combination of the subject and the type of movement. Although details of each combination are omitted, for example, the readiness potential correction data MRCPc(A1) is set for the combination of movement ACT(A) and subject SUB(1), and the readiness potential correction data MRCPc(D4) is set for the combination of movement ACT(D) and subject SUB(4).

[0036] The MRCP correction data selection unit 132 selects and reads out the readiness potential correction data stored in the database 20 using the prior information from the information input unit 12. For example, if the prior information specifies action ACT(A), the MRCP correction data selection unit 132 selects the readiness potential correction data MRCPc(A). If the prior information specifies subject SUB(2), the MRCP correction data selection unit 132 selects the readiness potential correction data MRCPc(2). If the prior information specifies action ACT(A) and subject SUB(2), the MRCP correction data selection unit 132 selects the readiness potential correction data MRCPc(A2).

[0037] The MRCP correction data selection unit 132 may select the readiness potential correction data by referring to the importance. For example, when the readiness potential correction data corresponding to a plurality of types of motions is stored, the importance is associated with each motion. When the prior information includes a plurality of types of motions, the MRCP correction data selection unit 132 selects, for example, the readiness potential correction data corresponding to the motion with the highest importance.

[0038] The MRCP correction data selection unit 132 outputs the selected readiness potential correction data to the calculation unit 133 .

[0039] The calculation unit 133 generates a cognitive signal by correcting the brain signal using the readiness potential correction data (selected correction data) selected by the MRCP correction data selection unit 132. More specifically, for example, the calculation unit 133 generates a cognitive signal by subtracting the selected correction data from the brain signal. At this time, the calculation unit 133 performs the subtraction process based on the reference timing set by the EOG detection unit 131 or the calculation unit 133.

[0040] (Specific methods for generating cognitive signals) Fig. 4(A) is a diagram showing an example of the waveform of a brain signal, and Fig. 4(B) is a diagram showing an enlarged area including EOG and P300 in the waveform shown in Fig. 4(A). Fig. 5 is a diagram showing an example of corrected data of the readiness potential. Fig. 6 is a diagram showing an example of the waveform of a cognitive signal.

[0041] As shown in Figures 4(A) and 4(B), the brain signals include electrooculograms (EOG) including saccades and fixations, cognitive event-related potentials (P300), and motor readiness potentials (MRCP).

[0042] As shown in FIG. 4(A) and FIG. 4(B), the electrooculogram EOG, the cognitive event-related potential P300, and the MRCP each have a unique waveform (characteristic waveform). For example, the electrooculogram EOG is generated by the movement of the eyeball due to cognition, and includes a saccade in which the voltage changes suddenly (changes in the negative potential direction) due to the movement of the eyeball due to cognition, and a fixation in which the voltage stabilizes by the stopping of the movement of the eyeball and gazing at the recognized object. The cognitive event-related potential P300 is a temporary voltage (temporary voltage in the positive potential direction) generated when the subject 80 recognizes an object, and occurs about 300 msec. after the reference timing of recognition. The MRCP is a voltage generated when the subject 80 performs a movement due to the recognition of the above-mentioned recognition object, and after recognition, the voltage value gradually increases (negative potential), and decreases (approaching 0 V) ​​as the movement is completed.

[0043] As shown in Fig. 5, the readiness potential correction data is set based on the readiness potential MRCP. Taking advantage of the fact that the waveform of the readiness potential MRCP has the above-mentioned characteristics, the readiness potential correction data is set using, for example, the maximum voltage value V1, and the readiness potential correction data is set using the time difference S1, the time difference t11, and the time difference t12, as shown in Fig. 5.

[0044] The maximum voltage value V1 is set by the maximum value (negative potential) of the motor preparation potential MRCP. The time difference S1 is set by the time difference between the reference timing and the time of the maximum voltage value V1 (maximum time). The reference timing is set by the timing of the change in saccade and fixation, as described above.

[0045] The time difference t11 is set by the time difference between the maximum value time and the time when the MRCP starts to change. The start time of the change can be set by, for example, approximating the MRCP and linearly approximating the voltage rise region, and then by the time when the MRCP crosses the 0V line. The setting of the start time of the change is not limited to this.

[0046] The time difference t12 is set by the time difference between the maximum value time and the time when the MRCP ends changing. The end time of the change can be set by, for example, approximating the MRCP and linearly approximating the voltage drop region, and then by the time when the MRCP crosses the 0V line. The setting of the end time of the change is not limited to this.

[0047] As described above, these settings are realized by pre-sampling. Pre-sampling may be performed on the subject 80 in advance, or brain signals acquired during a past cognitive ability test of the subject 80 may be used. Also, statistical values ​​(e.g., average value, median value, etc.) of the MRCP readiness potentials detected from multiple people may be used. When using statistical values ​​of the MRCP readiness potentials detected from multiple people, the settings may be made taking into consideration attributes of the subject 80, such as the sex and age.

[0048] In this way, the readiness potential correction data is set by a plurality of values ​​that characterize the readiness potential MRCP, which makes it possible to reduce the storage capacity of the readiness potential correction data without suppressing the characteristics of the readiness potential MRCP.

[0049] As the corrected data for the readiness potential, it is also possible to use waveform data (all sampled voltage values) of the readiness potential MRCP sampled in advance.

[0050] The calculation unit 133 subtracts the thus-set corrected readiness potential data from the brain signal, based on the reference timing set by the EOG detection unit 131 or the calculation unit 133. At this time, the calculation unit 133 restores the waveform of the corrected readiness potential data as shown by the solid line in Fig. 5 from the above-mentioned corrected readiness potential data by using linear interpolation or the like. Then, the calculation unit 133 subtracts the waveform of the restored corrected readiness potential data from the brain signal (waveform of the brain signal).

[0051] Here, the readiness potential correction data set as described above is similar or substantially identical to the readiness potential MRCP included in the brain signal acquired from the subject 80. Therefore, as shown in Fig. 6, the cognitive signal obtained by subtracting the correction data from the brain signal is a signal in which the readiness potential MRCP is suppressed from the brain signal. In other words, the cognitive signal is a waveform in which the electrooculogram EOG and the cognitive event-related potential P300 appear more clearly.

[0052] This allows the cognitive signal to be a signal that can detect cognitive ability more easily and reliably. As a result, the measurement accuracy of cognitive potentials such as P300 is improved. The judgment unit 33 can use this cognitive signal to more accurately judge cognitive ability.

[0053] In the above description, the calculation unit 133 has shown an aspect in which the calculation unit 133 directly subtracts the readiness potential correction data from the brain signal. However, the calculation unit 133 may correct the voltage value of the readiness potential correction data by the maximum voltage value of the acquired brain signal and the maximum voltage value of the readiness potential correction data, and then subtract it from the brain signal. For example, the calculation unit 133 calculates the ratio between the maximum voltage value of the acquired brain signal and the maximum voltage value of the readiness potential correction data. The calculation unit 133 corrects the voltage value of the readiness potential correction data by this ratio, and subtracts it from the brain signal. This makes it possible to more effectively suppress the readiness potential contained in the brain signal.

[0054] In the above-mentioned Fig. 4(A), Fig. 4(B), Fig. 5 and Fig. 6, the case where the voltage change area of ​​the MRCP of the readiness potential does not overlap with the cognitive event-related potential P300 is shown. However, as shown in Fig. 7, Fig. 8 and Fig. 9, even if the voltage change area of ​​the MRCP of the readiness potential overlaps with the cognitive event-related potential P300, the above-mentioned processing is performed to make the cognitive signal have a waveform in which the cognitive event-related potential P300 appears more clearly. Fig. 7 is a diagram showing an example of the waveform of the brain signal. Fig. 8 is a diagram showing an example of the readiness potential correction data. Fig. 9 is a diagram showing an example of the waveform of the cognitive signal.

[0055] 8, for actions or subjects with rapid changes in the voltage of the readiness potential MRCP, readiness potential correction data (time difference S2, time difference t21, time difference t22) corresponding to this speed is set. Since the action or subject is set as prior information, the MRCP correction data selection unit 132 can select suitable readiness potential correction data based on this prior information.

[0056] Therefore, even if the waveform of the corrected readiness potential data differs depending on the action or subject, the cognitive signal has a waveform in which the cognitive event-related potential P300 appears more clearly, as shown in Fig. 9. For example, even if the cognitive event-related potential P300 is buried in the readiness potential MRCP as shown in Fig. 7, the readiness potential MRCP is suppressed, as shown in Fig. 9, and the cognitive event-related potential P300 becomes easily detectable.

[0057] (How to generate a database) The readiness potential correction data stored in the above-mentioned database 20 is generated, for example, as follows.

[0058] Fig. 10 is a flow chart showing an example of a method for generating a database. Fig. 11(A), Fig. 11(B), Fig. 11(C), and Fig. 11(D) are diagrams showing an example of an image when a database is generated.

[0059] First, the cognitive ability assessor selects an event for which cognitive ability is to be assessed (S21). In other words, the cognitive ability detection device accepts the selection of the event.

[0060] The cognitive ability detection device presents trigger information for database generation corresponding to the selected event to a person for whom corrected readiness potential data is to be generated, such as a test subject (S22). The trigger information for database generation is presented by images such as those shown in Fig. 11(A), Fig. 11(B), Fig. 11(C), and Fig. 11(D), for example. Note that the trigger information is not limited to images, and may be sounds, stimuli, etc.

[0061] 11(A), 11(B), 11(C), and 11(D), an automobile 901 and a reaction start line 910 are displayed in an image 90. The automobile 901 moves toward the top of the image 90 without changing its position, and the image 90 changes to move downward (see the thick arrow in the figure). At this time, the positional relationship between the automobile 901 and the reaction start line 910 does not change.

[0062] At a certain timing, as shown in Fig. 11(B), an avoidance target 902 appears from the upper end of the image 90. The person for whom the readiness potential correction data is to be generated has been instructed to start an avoidance action after the avoidance target 902 reaches the reaction start line 910. Therefore, in this state, the person for whom the readiness potential correction data is to be generated follows the avoidance target 902 with his / her eyes. This generates an electrooculogram EOG.

[0063] Next, as shown in Fig. 11(C), when the object to be avoided 902 reaches the reaction start line 910, the subject for whom the corrected data for the motor readiness potential is generated operates the above-mentioned pseudo handle to perform the avoidance action as shown in Fig. 11(D). This causes the subject to recognize the avoidance action and generates a motor readiness potential for performing the avoidance action.

[0064] The cognitive ability detection device measures and acquires brain signals during this series of movements (S23).

[0065] The cognitive ability detection device extracts a waveform of the readiness potential from the brain signal (S24). As described above, the timing of starting the avoidance is roughly obtained from the video. Therefore, by using the timing when the avoidance target 902 set in the video reaches the reaction start line 910 as a reference, the cognitive ability detection device can extract the readiness potential more accurately.

[0066] The cognitive ability detection device generates the above-mentioned corrected readiness potential data from the extracted waveform of the readiness potential, and registers it in the database 20 (S25).

[0067] Thus, using the above described method, a database 20 of corrected readiness potential data can be generated.

[0068] (Method for detecting cognitive ability (method for generating cognitive signal)) Fig. 12 is a flowchart showing an example of a method for generating a recognition signal. The recognition signal generating unit 10 generates a recognition signal by performing the processes shown in Fig. 12. Note that the details of each process are described above, and descriptions will be omitted except for points where further additional explanation is required.

[0069] The recognition signal generating unit 10 acquires a brain signal (S11). The recognition signal generating unit 10 detects an electrooculogram EOG (S12). The recognition signal generating unit 10 determines a reference timing using the electrooculogram EOG (S13).

[0070] The cognitive signal generating unit 10 reads out the pre-generated readiness potential correction data in accordance with the prior information (S14). The cognitive signal generating unit 10 corrects the brain signal using the read (selected) readout readiness potential data to generate a cognitive signal (S15).

[0071] For example, this processing can be programmed and stored in a storage medium or an external server, and can be realized by a processing device such as a personal computer that realizes the recognition signal generation unit 10 reading and executing this program.

[0072] (When multiple actions occur) The above description has been given of how to generate a cognitive signal when one action is performed. However, there are cases where multiple actions are performed overlapping or consecutively.

[0073] Figures 13(A), 13(B), 13(C), and 13(D) show waveforms when multiple actions are performed in response to one recognition, such as when a pedestrian jumps out from a crosswalk, applies the brakes, and turns the steering wheel.

[0074] FIG. 13(A) shows the waveform of a brain signal, FIG. 13(B) and FIG. 13(C) show the waveforms of corrected readiness potential data for different types of movements, and FIG. 13(D) shows the waveform of a cognitive signal.

[0075] As shown in the corrected data MRCPc(A) of Fig. 13(B) and the corrected data MRCPc(B) of Fig. 13(C), the corrected data MRCPc(A) of the readiness potential is set for each movement. Therefore, as shown in Fig. 13(A), even if the brain signal contains multiple readiness potentials, each of the readiness potentials can be suppressed. As a result, as shown in Fig. 13(D), the cognitive signal becomes a signal in which the cognitive event-related potential P300 can be easily detected.

[0076] Figures 14(A), 14(B), 14(C), and 14(D) show waveforms of a case where a driver performs a plurality of actions individually in response to a plurality of consecutive recognitions. For example, this case corresponds to a case where the driver applies the brakes to slow down when approaching a pedestrian crossing, and then turns the steering wheel when a pedestrian jumps out.

[0077] FIG. 14(A) shows the waveform of a brain signal, FIG. 14(B) and FIG. 14(C) show the waveforms of corrected readiness potential data for different types of movements, and FIG. 14(D) shows the waveform of a cognitive signal.

[0078] As shown in the corrected data MRCPc(A) of Fig. 14(B) and the corrected data MRCPc(B) of Fig. 14(C), the corrected data MRCPc(A) of the readiness potential is set for each movement. Therefore, even if the brain signal contains multiple readiness potentials as shown in Fig. 14(A), each of the readiness potentials can be suppressed. As a result, as shown in Fig. 14(D), the cognitive signal becomes a signal in which the cognitive event-related potentials P300A and P300B can be detected individually and easily.

[0079] Second embodiment A cognitive ability detection device according to a second embodiment of the present invention will be described with reference to the drawings. Fig. 15 is a functional block diagram showing the configuration of a cognitive signal generation unit according to the second embodiment. Fig. 16 is a diagram showing the configuration of a cognitive ability detection system according to the second embodiment.

[0080] 15 and 16, the cognitive ability detection system 1A according to the second embodiment differs from the cognitive ability detection system 1 according to the first embodiment in that a cognitive signal generation unit 10A in a cognitive ability detection device 30A is provided with a movement detection unit 14 and in that it utilizes the timing of the movement detected by the movement detection unit 14. Other configurations of the cognitive ability detection system 1A are similar to those of the cognitive ability detection device 30, and therefore descriptions of similar parts will be omitted.

[0081] The cognitive ability detection system 1A includes a camera 394. The camera 394 captures an image including, for example, the body behavior, facial expression, eye movement, etc. of the subject 80, and outputs the captured image to the cognitive signal generation unit 10A. In addition, the pseudo pedal 392 and the pseudo handle 393 are fitted with motion detection sensors such as an acceleration sensor and an angular velocity sensor. These motion detection sensors detect the motion of the pseudo pedal 392 (operation of the subject 80) and the motion of the pseudo handle 393 (operation of the subject 80), and output a detection signal to the cognitive signal generation unit 10A. Note that a means for mechanically detecting the motion of the pseudo pedal 392 and the motion of the pseudo handle 393 may be provided, and a detection signal may be output based on the mechanically detected result.

[0082] The motion detection unit 14 of the recognition signal generation unit 10A analyzes the eye movement and motion of the subject 80 from the acquired video and detects the type of eye movement and motion. The motion detection unit 14 also detects the type of motion (operation) of the subject 80 from the detection signal. The motion detection unit 14 outputs the detected type of motion, etc. to the MRCP correction data selection unit 132.

[0083] The MRCP correction data selection unit 132 selects the readiness potential correction data based on the type of movement detected by the movement detection unit 14. This allows the MRCP correction data selection unit 132 to select appropriate readiness potential correction data even without prior information.

[0084] Alternatively, the MRCP correction data selection unit 132 may select the readiness potential correction data based on the detection result of the motion detection unit 14 and the prior information. For example, if the detection result of the motion detection unit 14 matches the prior information, the MRCP correction data selection unit 132 selects the readiness potential correction data based on the type of the matched motion. If the detection result of the motion detection unit 14 does not match the prior information, the MRCP correction data selection unit 132 selects the readiness potential correction data using one of them as a preferential criterion. Alternatively, if the detection result of the motion detection unit 14 does not match the prior information, the MRCP correction data selection unit 132 displays a warning indicating that they do not match. Thereby, for example, the assessor of cognitive ability may input the type of appropriate motion to the cognitive ability detection device 30A.

[0085] The detection result of the motion detection unit 14 can also be used to generate a recognition signal in the calculation unit 133. For example, if the detection result of the motion detection unit 14 includes eye movement, the calculation unit 133 can set the reference timing using the detection result of the motion detection unit 14 even if the EOG detection unit 131 cannot detect the reference timing.

[0086] Furthermore, if the detection result of the motion detection unit 14 includes a motion (operation), the calculation unit 133 estimates the occurrence period of the readiness potential using the timing of the motion (operation). The calculation unit 133 performs correction using the readiness potential correction data for this estimated period in the brain signal. This allows the calculation unit 133 to generate a cognitive signal that makes it easy to detect the cognitive event-related potential P300.

[0087] (Third embodiment) A cognitive ability detection device according to a third embodiment of the present invention will be described with reference to Fig. 17. Fig. 17 is a diagram showing a part of the configuration of a cognitive ability detection system according to the third embodiment.

[0088] 17, the cognitive ability detection system according to the third embodiment differs from the cognitive ability detection system 1 according to the first embodiment in the configuration for detecting EOG. Other configurations of the cognitive ability detection system according to the third embodiment are similar to those of the cognitive ability detection system 1 according to the first embodiment, and a description of similar parts will be omitted.

[0089] Brain signal sensor 112 is attached to subject 80. More specifically, brain signal sensor 112 is attached to a position including the position of FP1 (international 10-20 system) of subject 80. Brain signal sensor 112 outputs the detected brain signal to brain signal acquisition section 11B of cognitive signal generation section 10B.

[0090] Brain signal acquirer 11B outputs the brain signal (CZ brain signal) detected by brain signal sensor 111 to calculation section 133. Brain signal acquirer 11B outputs the brain signal (FP1 brain signal) detected by brain signal sensor 112 to EOG detection section 131.

[0091] The EOG detection unit 131 detects an electrooculogram (EOG) from the brain signal detected by the brain signal sensor 112 (the brain signal of FP1).

[0092] With this configuration, the brain signal that is the detection source of the EOG is detected from near the eyes of the subject 80. Therefore, the EOG detection section 131 can detect the EOG with higher accuracy.

[0093] In the above description, the cognitive event-related potential is described by taking P300 as an example. The cognitive event-related potential may be P100, N400, etc., and the cognitive signal generating unit can generate a cognitive signal capable of detecting these cognitive event-related potentials by using the above-described configuration and processing.

[0094] Although the above description has been given of an example of a cognitive ability test for driving, the above-described configuration and processing can be applied to any event in which a visual recognition and action are performed.

[0095] For example, by measuring the cognitive ability of subjects using a game console or the like, the present invention can be applied to cognitive tests for e-sports players and athletes, and for cognitive tests for students in schools.

[0096] Fig. 18 is a diagram showing the configuration of a cognitive ability detection system for a game. In the following, only the points of a cognitive ability detection system 1C shown in Fig. 18 that are different from the cognitive ability detection system 1A according to the second embodiment will be described.

[0097] As shown in FIG. 18, a cognitive ability detection system 1C includes a cognitive ability detection apparatus 30C, a display 391, and an operation device 394.

[0098] The cognitive ability detection device 30C includes an application execution unit 39. The application execution unit 39 executes a game application.

[0099] The application execution unit 39 outputs the game video to the video output unit 32. The video output unit 32 outputs the game video to the display device 391. As a result, the game video is displayed on the display device 391.

[0100] The application execution unit 39 outputs to the control unit 31 event information (such as a specific operation to be input in accordance with a game image) that can be used to detect cognitive ability in a game.

[0101] The control unit 31 outputs, based on the event information, prior information corresponding to the cognitive ability detection test to the cognitive signal generation unit 10.

[0102] The operation device 394 is, for example, a keyboard, a mouse, or the like, and receives operation input from the subject 80 who is a game player. The operation device 394 outputs the operation input content to the recognition signal generation unit 10 and the application execution unit 39.

[0103] The application execution unit 39 executes processing within the game application in accordance with the operational input contents.

[0104] The recognition signal generator 10A detects the type of motion (operation) of the subject 80 by using the operation input contents from the operation device 394.

[0105] With such a configuration, the cognitive ability detection system 1C can detect the cognitive ability of a game player with respect to a game. For example, the cognitive ability detection system 1C can detect features of game operation that the game player himself is not aware of from the detection result of cognitive ability, and can provide feedback to the game player. Methods of feedback include, for example, visualized data of cognitive ability and visualized data of weak points (challenges) based on the detection result of cognitive ability. This allows the game player to recognize his own weak points and improve his speed in the game.

[0106] 18 shows a case where the game machine is realized by a PC, the configuration of the present invention can also be applied to a console type game machine. In this case, the operation device 394 is not limited to a keyboard, and may be a controller dedicated to the game machine.

[0107] 18 shows a case where a general game application is used, but a test game application for detecting cognitive ability may be used. In this case, the control unit 31 may execute the test game application.

[0108] Also, while FIG. 18 shows the case of solo play, the configuration of the present invention can also be applied to the case of multiplay, as shown in FIG.

[0109] FIG. 19 is a diagram showing the configuration of a cognitive ability detection system for a multiplayer game.

[0110] As shown in FIG. 19, a cognitive ability detection system 1D compatible with a multiplay environment includes a plurality of (four in FIG. 19) cognitive ability detection systems 1C, a comprehensive assessment section 50, and a data communication network 500.

[0111] The multiple cognitive ability detection systems 1C are connected to a data communication network 500 and are capable of transmitting and receiving data via the data communication network 500. The comprehensive judgment unit 50 is connected to the data communication network 500 and acquires cognitive ability detection results and various data and information for cognitive ability detection from the multiple cognitive ability detection systems 1C.

[0112] The comprehensive judgment unit 50 judges the characteristics related to the cognitive abilities of the multiplayer using the detection results of the cognitive abilities of the multiple cognitive ability detection systems 1C. For example, based on the comparison results of the cognitive abilities of the multiple players detected by the multiple cognitive ability detection systems 1C, the comprehensive judgment unit 50 judges and visualizes the weaknesses of the group as a multiplayer.

[0113] At this time, the overall judgment unit 50 may use the operation input contents for the judgment. For example, in the case of cooperative play, the role of each player in the group is judged from the operation input contents. The overall judgment unit 50 stores judgment criteria for cognitive ability according to each role, and judges cognitive ability using these judgment criteria. This makes it possible to more accurately judge and visualize and provide weaknesses, etc., of each player when playing their respective roles.

[0114] Furthermore, the overall judgment unit 50 stores the characteristics of the cognitive abilities suitable for each role in cooperative play. Then, the overall judgment unit 50 may determine the suitable role based on the acquired cognitive abilities of each player, and provide it in a visualized form. This allows each player in a group performing cooperative play to play the game in a role that suits them best. Therefore, for example, it becomes possible to challenge quests with higher difficulty levels, which increases motivation for cooperative play.

[0115] In addition, in a battle play, the operation (attack, defense, etc.) of each player is judged from the operation input contents. The overall judgment unit 50 stores judgment criteria for cognitive ability corresponding to each operation, and judges cognitive ability using these judgment criteria. As a result, when each player plays against an opponent player, the points in which the player is inferior to the opponent player, that is, weak points during battle play, can be more accurately judged and visualized and provided. At this time, the overall judgment unit 50 can detect not only cognitive ability but also reaction speed of the operation using the detection timing of the operation input contents. The overall judgment unit 50 can also judge weak points using such reaction speed of the operation, and visualize and provide them. [Explanation of symbols]

[0116] 1, 1A, 1C, 1D: Cognitive Ability Detection System 10, 10A, 10B: Cognitive signal generation section 11, 11B: Brain signal acquisition unit 12: Information input section 14: Motion detection unit 20: Database 30, 30A, 30C: Cognitive ability detection device 31: Control unit 32: Video output section 33: Judgment section 39: Application execution unit 80: Subject 90:Video 111: Brain signal sensor 112: Brain signal sensor 131: EOG detector 132: MRCP correction data selection unit 133: Arithmetic section 300: Operation input section 391:Display unit 392: Pseudo pedal 393: Pseudo Handle 394: Camera 901: Automobiles 902: Objects to avoid 910: Reaction start line

Claims

1. a brain signal acquisition unit for acquiring a brain signal of the subject, the brain signal including an event-related potential generated when the subject recognizes an object; a correction data storage unit configured to store in advance readiness potential correction data simulating a waveform of a readiness potential generated in the brain signal of the subject by a movement made when the subject recognizes an object; a calculation unit that generates a cognitive signal by subtracting the corrected data of the readiness potential from the brain signal; Equipped with The waveform of the motor preparation potential has a gradually increasing voltage value as the recognition object is recognized, and a gradually decreasing voltage value upon completion of the movement. Cognitive ability detection device.

2. The readiness potential correction data is Voltage maximum, The time difference between the reference timing and the time of the maximum voltage value, The time difference between the time of the maximum voltage and the time of the start of the voltage change, The time difference between the time of the maximum voltage and the time of the end of the voltage change, Using, memorize, The calculation unit is A voltage waveform is reconstructed from the voltage maximum value and each time difference, and a difference process is performed using the voltage waveform to generate the recognition signal. The cognitive ability detection device according to claim 1 .

3. The calculation unit is When the motion is of a plurality of types, the difference processing is performed using the corrected readiness potential data for each of the plurality of motions. The cognitive ability detection device according to claim 1 or 2.

4. an electrooculogram detection unit that detects an electrooculogram from the brain signal; The calculation unit is performing a process of the difference based on the electrooculogram to generate the cognitive signal; The cognitive ability detection device according to any one of claims 1 to 3.

5. The calculation unit is The difference processing is executed based on a timing of change from a saccade to a fixation of the electrooculogram. The cognitive ability detection device according to claim 4.

6. a readiness potential correction data selection unit that selects the readiness potential correction data using advance information including a type of the predetermined movement; The calculation unit is performing the difference processing using the selected corrected readiness potential data to generate the cognitive signal; The cognitive ability detection device according to any one of claims 1 to 3.

7. the correction data storage unit stores the readiness potential correction data by setting a degree of importance to the readiness potential correction data; The readiness potential correction data selection unit is selecting the readiness potential correction data by referring to the importance; The cognitive ability detection device according to claim 6.

8. a motion detection unit that detects a motion of the subject that generates the brain signal, The readiness potential correction data selection unit is selecting the readiness potential correction data using the movement detected by the movement detection unit; The cognitive ability detection device according to claim 6 or 7.

9. The motion detection unit is an acceleration sensor or an angular velocity sensor. The cognitive ability detection device according to claim 8.

10. The calculation unit is and executing the process of the difference by referring to the timing of the motion detected by the motion detection unit. The cognitive ability detection device according to claim 8 or 9.

11. A determination unit that determines cognitive ability using the cognitive signal. The cognitive ability detection device according to any one of claims 1 to 3.

12. A video output unit is provided for outputting a video for assessing cognitive ability. The cognitive ability detection device according to claim 11.

13. A brain signal acquisition process for acquiring a brain signal of the subject, the brain signal including an event-related potential generated when the subject recognizes an object; a cognitive signal generation process for generating a cognitive signal by subtracting pre-stored readiness potential correction data, which simulates a waveform of a readiness potential generated in the brain signal of the subject by a movement made by the subject when the subject recognizes an object, from the brain signal; having The waveform of the motor preparation potential has a gradually increasing voltage value as the recognition object is recognized, and a gradually decreasing voltage value upon completion of the movement. Cognitive ability detection methods.

14. The cognitive ability detection device according to claim 1 , wherein the action is an action caused by the subject's danger perception.

15. The cognitive ability detection method according to claim 13 , wherein the action is an action caused by the subject's risk perception.

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