Cognitive ability testing devices and methods

Abstract

The cognitive signal generation unit (10) of the cognitive ability testing device (30) includes a brain signal acquisition unit (11), a database (20), an MRCP correction data selection unit (132), and a calculation unit (133). The brain signal acquisition unit (11) acquires brain signals containing event-related potentials. The database (20) stores motor preparation potential correction data corresponding to the subject's actions. The MRCP correction data selection unit (132) selects motor preparation potential correction data based on prior information including the type of action and outputs the motor preparation potential correction data to the calculation unit (133). The calculation unit (133) uses the motor preparation potential correction data to correct the brain signals and generate cognitive signals.

Description

Technical Field

[0001] This invention relates to a cognitive ability testing device and a cognitive ability testing method for detecting cognitive abilities in response to external stimuli. Background Technology

[0002] Patent Document 1 describes a cognitive ability detection technique that utilizes brain signals. The technique described in Patent Document 1 detects event-related potentials (ERPs) from brain signals and uses ERPs to detect cognitive abilities.

[0003] Patent Document 2 describes a brain motor function analysis and diagnostic technique using electroencephalogram (EEG) data. The technique in Patent Document 2 detects motor readiness potentials based on EEG data and uses these potentials to diagnose brain motor function.

[0004] Patent document 3 describes a motion prediction technology using electroencephalograms (EEGs). The technology in patent document 3 uses motor preparation potentials to predict human actions.

[0005] Existing technical documents

[0006] Patent documents

[0007] Patent Document 1: Japanese Patent Application Publication No. 2002-272692

[0008] Patent Document 2: Japanese Patent Application Publication No. 2018-192909

[0009] Patent Document 3: International Publication No. 2020 / 138012 Summary of the Invention

[0010] The problem the invention aims to solve

[0011] However, in situations where a motor preparation potential is generated as shown in Patent Documents 2 and 3, the event-related potentials in the technology described in Patent Document 1 include event-related potentials such as P300 generated during cognition (hereinafter referred to as cognitive system potentials), and also include motor preparation potentials.

[0012] If such a motor preparation potential exists, the measurement accuracy of cognitive system potentials may sometimes decrease.

[0013] Therefore, the purpose of this invention is to provide a technique for improving the measurement accuracy of cognitive system potentials such as P300.

[0014] Solution for solving the problem

[0015] The cognitive ability testing 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 brain signals including event-related potentials (ERPs). The correction data storage unit stores motor preparation potential correction data corresponding to the subject's actions. The cognitive signal generation unit uses the motor preparation potential correction data to correct the brain signals and generate cognitive signals.

[0016] In this structure, the motor preparation potential contained in the event-related potentials (cognitive system potentials) is suppressed.

[0017] The effects of the invention

[0018] According to the present invention, the measurement accuracy of cognitive system potential can be improved. Attached Figure Description

[0019] Figure 1 This is a functional block diagram showing the structure of the cognitive signal generation unit according to the first embodiment.

[0020] Figure 2 This is a diagram showing the structure of the cognitive ability detection system according to the first embodiment.

[0021] Figure 3 (A) Figure 3 (B) Figure 3 Table (C) is an example of the correction data stored in the database.

[0022] Figure 4 (A) is a diagram showing an example of a brain signal waveform. Figure 4 (B) is to Figure 4 The image shown in (A) is an enlarged view of the region containing EOG and P300 in the waveform.

[0023] Figure 5 This is a diagram illustrating an example of motion preparation potential correction data.

[0024] Figure 6 This is a diagram showing an example of a cognitive signal waveform.

[0025] Figure 7 This is a diagram showing an example of a brain signal waveform.

[0026] Figure 8 This is a diagram illustrating an example of motion preparation potential correction data.

[0027] Figure 9 This is a diagram showing an example of a cognitive signal waveform.

[0028] Figure 10 This is a flowchart illustrating an example of a database generation method.

[0029] Figure 11(A) Figure 11 (B) Figure 11 (C) Figure 11 (D) is a diagram showing an example of an image generated when a database is created.

[0030] Figure 12 This is a flowchart illustrating an example of a method for generating cognitive signals.

[0031] Figure 13 (A) Figure 13 (B) Figure 13 (C) and Figure 13 (D) shows the waveforms for the case of multiple actions performed for one cognition.

[0032] Figure 14 (A) Figure 14 (B) Figure 14 (C) and Figure 14 (D) shows the waveforms for the case where multiple actions are performed independently for multiple cognitions in succession.

[0033] Figure 15 This is a functional block diagram showing the structure of the cognitive signal generation unit according to the second embodiment.

[0034] Figure 16 This is a diagram illustrating the structure of the cognitive ability detection system according to the second embodiment.

[0035] Figure 17 This is a diagram showing a portion of the structure of the cognitive ability detection system according to the third embodiment.

[0036] Figure 18 This is a diagram showing the structure of a cognitive ability detection system for games.

[0037] Figure 19 This is a diagram illustrating the structure of a cognitive ability detection system for multiplayer games. Detailed Implementation

[0038] (First Implementation)

[0039] The cognitive ability detection device according to the first embodiment of the present invention will be described with reference to the accompanying drawings. Figure 1 This is a functional block diagram showing the structure of the cognitive signal generation unit according to the first embodiment. Figure 2 This diagram illustrates the structure of the cognitive ability detection system according to the first embodiment. Furthermore, in this embodiment, an example of conducting a cognitive ability test for driving will be described. In other words, this embodiment shows an example of applying a cognitive ability test to a driving simulator.

[0040] (Structure of Cognitive Ability Testing System 1)

[0041] like Figure 2 As shown, 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 simulated pedal 392, and a simulated steering wheel 393.

[0042] The display 391 is positioned in front of the subject 80. The simulated pedals 392 and simulated steering wheel 393 are positioned in a manner that allows the subject 80 to operate them. Furthermore, in... Figure 2 The diagrams of the specific (physical) structure of the cognitive ability detection system 1 (driving simulator), excluding the display 391, simulated pedals 392, and simulated steering wheel 393, are omitted.

[0043] A brain signal sensor 111 is installed on the subject 80. More specifically, the brain signal sensor 111 is installed at a location including the top of the subject 80's head (the location of CZ in the scalp potential distribution map (international 10-20 method)).

[0044] The cognitive ability testing device 30 is connected to the brain signal sensor 111 and the display 391. The cognitive ability testing device 30 is implemented by a computing device such as a personal computer.

[0045] The cognitive ability testing device 30 includes a cognitive signal generation unit 10, a control unit 31, an image output unit 32, a judgment unit 33, and an operation input unit 300.

[0046] The operation input unit 300 accepts operation inputs from users such as the start and end of cognitive ability test, and the selection of the type of cognitive ability test, and outputs the operation inputs to the control unit 31.

[0047] The control unit 31 performs overall control of the cognitive ability testing device 30. Based on the operation input from the operation input unit 300, the control unit 31 controls the start and end of the cognitive ability testing. Additionally, the control unit 31 instructs the image output unit 32 to output the selected cognitive ability testing image.

[0048] Furthermore, the control unit 31 outputs prior information corresponding to the selected cognitive ability test to the cognitive signal generation unit 10. The prior information corresponding to the cognitive ability test defines the types of actions the subject 80 might take due to danger perception. For example, it defines the actions taken to operate the brake pedal or steering wheel due to the perception of a sudden person's presence. Additionally, the prior information may include, for example, the subject 80's identification information and the subject 80's type information.

[0049] The image output unit 32 outputs the image of the selected cognitive ability test to the display 391. The display 391 displays the image. Thus, the test subject 80 is able to see the image of the cognitive ability test.

[0050] When the subject 80 sees the image and operates the simulated pedal 392 and simulated steering wheel 393, brain signals (electroencephalograms) contain event-related potentials. The brain signal sensor 111 detects these brain signals and outputs them to the cognitive signal generation unit 10.

[0051] More specific structures and processing will be described later. The cognitive signal generation unit 10 generates cognitive signals based on brain signals detected by the brain signal sensor 111.

[0052] The judgment unit 33 analyzes the cognitive signals and determines the cognitive abilities of the subject 80, including the presence or absence of cognitive abilities and the level of cognitive abilities. Furthermore, the determination of cognitive abilities using cognitive signals can be achieved, for example, by using the appearance of P300. Various known methods can be used to determine cognitive abilities, which will not be described here.

[0053] (Structure of the cognitive signal generation unit 10)

[0054] like Figure 1 As shown, the cognitive signal generation unit 10 includes a brain signal acquisition unit 11, an information input unit 12, an EOG detection unit 131, an MRCP correction data selection unit 132, a calculation unit 133, and a database 20. The database 20 corresponds to the correction data storage unit of the present invention. Furthermore, MRCP stands for movement-related cortical potential, which in the present invention refers to movement-related potentials (motor preparation potentials).

[0055] The brain signal acquisition unit 11 acquires brain signals from the brain signal sensor 111 and outputs the brain signals to the arithmetic unit 133 and the EOG detection unit 131. The brain signal acquisition unit 11 may also include an amplification circuit and a filtering circuit. By including the amplification circuit, the brain signal acquisition unit 11 can amplify the brain signal to a predetermined signal level (amplitude). By including the filtering circuit, the brain signal acquisition unit 11 can suppress noise components in the brain signal other than event-related potentials.

[0056] The information input unit 12 is the input interface for prior information. The information input unit 12 receives prior information from the control unit 31 and outputs this prior information to the MRCP calibration data selection unit 132. Alternatively, the information input unit 12 may also have a user interface, allowing it to receive prior information via external operation input. Furthermore, prior information from the control unit 31 can also be directly input to the MRCP calibration data selection unit 132. That is, the information input unit 12 can be omitted.

[0057] The EOG detection unit 131 detects electrooculogram (EOG) signals from brain signals. The EOG detection unit 131 detects saccades and fixations from the EOG signals. The EOG detection unit 131 detects the timing of the change from saccade to fixation, sets this timing as a reference timing, and outputs it to the calculation unit 133.

[0058] Furthermore, the EOG detection unit 131 can also output the detection results of saccades and fixations to the calculation unit 133. In this case, the calculation unit 133 can detect the timing of the change from saccades to fixations and set that timing as the reference timing.

[0059] Database 20 stores correction data (motor preparation potential correction data) corresponding to each action or subject's motor preparation potential. The correction data is data that analogously represents the waveform of the motor preparation potential corresponding to the action or subject. This correction data is acquired through prior data sampling processing (details will be described later) and stored in database 20.

[0060] Figure 3 (A) Figure 3 (B) Figure 3 Table (C) is an example of a table showing the correction data stored in the database. Furthermore, in Figure 3 In each table, the motion preparation potential correction data is recorded as MRCP correction data.

[0061] exist Figure 3 In case (A), motion preparation potential correction data is set according to the type of action. For example, for each action in action ACT(A), action ACT(B), action ACT(C), and action ACT(D), motion preparation potential correction data MRCPc(A), motion preparation potential correction data MRCPc(B), motion preparation potential correction data MRCPc(C), and motion preparation potential correction data MRCPc(D) are set respectively. As examples of each action in action ACT(A), action ACT(B), action ACT(C), and action ACT(D), in the case of a driving simulator, these are steering wheel operation, acceleration / braking operation, etc., in a specific environment.

[0062] exist Figure 3In case (B), the motion preparation potential correction data are set according to the subject. For example, motion preparation potential correction data MRCPC(1), motion preparation potential correction data MRCPC(2), motion preparation potential correction data MRCPC(3), and motion preparation potential correction data MRCPC(4) are set for each of the subjects SUB(1), SUB(2), SUB(3), and SUB(4).

[0063] exist Figure 3 In case (C), the motion preparation potential correction data is set according to the combination of the subject and the type of action. Although the details of each combination are omitted, for example, the motion preparation potential correction data MRCPc(A1) is set for the combination of action ACT(A) and subject SUB(1), and the motion preparation potential correction data MRCPc(D4) is set for the combination of action ACT(D) and subject SUB(4).

[0064] The MRCP correction data selection unit 132 uses prior information from the information input unit 12 to select and read the motion preparation potential correction data stored in the database 20. For example, if the action ACT(A) is specified in the prior information, the MRCP correction data selection unit 132 selects the motion preparation potential correction data MRCPc(A). Similarly, if the subject SUB(2) is specified in the prior information, the MRCP correction data selection unit 132 selects the motion preparation potential correction data MRCPc(2). For example, if both the action ACT(A) and the subject SUB(2) are specified in the prior information, the MRCP correction data selection unit 132 selects the motion preparation potential correction data MRCPc(A2).

[0065] Furthermore, the MRCP correction data selection unit 132 can also select motion preparation potential correction data based on importance. For example, when motion preparation potential correction data corresponding to multiple types of actions are stored, the importance of each action is assigned. When multiple types of actions exist in the prior information, the MRCP correction data selection unit 132, for example, selects the motion preparation potential correction data corresponding to the action with the highest importance.

[0066] The MRCP correction data selection unit 132 outputs the selected motion preparation potential correction data to the calculation unit 133.

[0067] The computation unit 133 uses the motor preparation potential correction data (selected correction data) selected by the MRCP correction data selection unit 132 to correct the brain signal, thereby generating a cognitive signal. More specifically, for example, the computation unit 133 generates a cognitive signal by differentiating the brain signal from the selected correction data. At this time, the computation unit 133 performs differential processing based on a reference timing set by the EOG detection unit 131 or the computation unit 133.

[0068] (Specific methods for generating cognitive signals)

[0069] Figure 4 (A) is a diagram showing an example of a brain signal waveform. Figure 4 (B) is to Figure 4 The image shown in (A) is an enlarged view of the region containing EOG and P300 in the waveform. Figure 5 This is a diagram illustrating an example of motion preparation potential correction data. Figure 6 This is a diagram showing an example of a cognitive signal waveform.

[0070] like Figure 4 (A) Figure 4 As shown in (B), the brain signals include electrooculogram (EOG) with saccades and fixation, cognitive event-related potentials (P300), and motor preparation potentials (MRCP).

[0071] Moreover, such as Figure 4 (A) Figure 4 As shown in (B), the electrooculogram (EOG), cognitive system event-related potential (P300), and motor preparation potential (MRCP) each have inherent waveforms (characteristic waveforms). For example, the EOG includes saccades and fixations. Saccades are generated by eye movements caused by cognition, resulting in a rapid voltage change (towards a negative potential) due to eye movement during cognition. During fixation, the voltage stabilizes as eye movement ceases and the cognitive object is fixed. The cognitive system event-related potential (P300) is a temporary voltage (a temporary voltage in the positive potential direction) generated when the subject 80 recognizes the object, approximately 300 ms after the reference moment of cognition. The motor preparation potential (MRCP) is the voltage generated when the subject 80 performs a movement due to cognition of the aforementioned cognitive object; after cognition, the voltage gradually increases (negative potential) and decreases (approaching 0V) as the movement is completed.

[0072] like Figure 5 As shown, the motion preparation potential correction data is set based on the motion preparation potential MRCP. Taking advantage of the aforementioned characteristics of the MRCP waveform, the motion preparation potential correction data, for example... Figure 5As shown, the maximum voltage value V1, time difference S1, time difference t11, and time difference t12 are used to set the time difference.

[0073] The maximum voltage value V1 is set using the maximum value (negative potential) of the motion preparation potential MRCP. The timing difference S1 is set using the time difference between the reference timing and the maximum voltage value V1 (maximum value time). As described above, the reference timing is set using the timing of the change from saccade to fixation.

[0074] The time difference t11 is set using the time difference between the maximum value time and the start time of the change in the motion preparation potential MRCP. For example, based on approximating the motion preparation potential MRCP and linearly approximating the voltage rise region, the start time of the change can be set using the time when the approximate straight line intersects with the 0V line. The setting of the start time of the change is not limited to this.

[0075] The time difference t12 is set using the time difference between the maximum value time and the end time of the change in the motion preparation potential MRCP. For example, based on approximating the motion preparation potential MRCP and linearly approximating the voltage drop region, the end time of the change can be set using the time when the approximate straight line intersects with the 0V line. The setting of the end time of the change is not limited to this.

[0076] Furthermore, as mentioned above, these settings are implemented using pre-sampled data. Pre-sampling can be conducted by the subjects themselves or using brain signals obtained during past cognitive ability tests of the subjects. Additionally, these settings can also use statistical values ​​(e.g., mean, median, etc.) of motor readiness potentials (MRCPs) detected from multiple subjects. When using statistical values ​​of MRCPs detected from multiple subjects, attributes such as the gender and age of the subjects can also be considered in the settings.

[0077] In this way, multiple values ​​characterizing the motion preparation potential (MRCP) are used to set the motion preparation potential correction data. As a result, the storage capacity of the motion preparation potential correction data can be minimized without suppressing the characteristic MRCP.

[0078] In addition, motion preparation potential correction data can also use waveform data of pre-sampled motion preparation potential MRCP (all sampled voltage values).

[0079] The computation unit 133 uses a reference timing set by the EOG detection unit 131 or the computation unit 133 as a reference to perform differential calculation on the brain signal and the motor readiness potential correction data set in this way. At this time, the computation unit 133 uses linear interpolation or the like to restore the brain signal based on the aforementioned motor readiness potential correction data. Figure 5The solid line shows the waveform of the motor preparation potential correction data. Then, the arithmetic unit 133 performs a difference operation between the brain signal (the waveform of the brain signal) and the waveform of the restored motor preparation potential correction data.

[0080] Here, the motor readiness potential correction data set as described above is similar to or largely consistent with the motor readiness potentials (MRCP) contained in the brain signals obtained from subject 80. Therefore, as Figure 6 As shown, the cognitive signal obtained by differentiating the brain signal from the motor readiness potential correction data becomes a signal in which the brain signal suppresses the motor readiness potential MRCP. In other words, the cognitive signal becomes a waveform that more clearly represents the electrooculogram (EOG) and the event-related potential (P300) of the cognitive system.

[0081] Therefore, the cognitive signal becomes a signal that enables easier and more reliable detection of cognitive abilities. As a result, the measurement accuracy of cognitive system potentials such as P300 is improved. Furthermore, the determination unit 33 can determine cognitive abilities with higher accuracy by using this cognitive signal.

[0082] Furthermore, the above description shows a method where the arithmetic unit 133 directly differentially distinguishes the brain signal from the motor preparation potential correction data. However, it is also possible that the arithmetic unit 133 corrects the voltage value of the motor preparation potential correction data using the maximum voltage value of the acquired brain signal and the maximum voltage value of the motor preparation potential correction data, and then differentially distinguishes the brain signal from the motor preparation potential correction data. For example, the arithmetic unit 133 calculates the ratio of the maximum voltage value of the acquired brain signal to the maximum voltage value of the motor preparation potential correction data. After correcting the voltage value of the motor preparation potential correction data using this ratio, the arithmetic unit 133 differentially distinguishes the brain signal from the motor preparation potential correction data. This allows for more effective suppression of motor preparation potentials contained in the brain signal.

[0083] In the above Figure 4 (A) Figure 4 (B) Figure 5 , Figure 6 The diagram shows a case where the voltage change region of the motor preparation potential MRCP does not overlap with the event-related potential P300 of the cognitive system, but... Figure 7 , Figure 8 , Figure 9 As shown, even when the voltage change region of the motion preparation potential MRCP overlaps with the cognitive system event-related potential P300, by performing the above processing, the cognitive signal will become a waveform that more clearly represents the cognitive system event-related potential P300. Figure 7 This is a diagram showing an example of a brain signal waveform. Figure 8 This is a diagram illustrating an example of motion preparation potential correction data. Figure 9This is a diagram showing an example of a cognitive signal waveform.

[0084] like Figure 8 As shown, for actions and subjects with rapid voltage changes in the motion preparation potential (MRCP), motion preparation potential correction data (time difference S2, time difference t21, time difference t22) corresponding to the speed are set. Moreover, since the action or subject is set as prior information, the MRCP correction data selection unit 132 can use this prior information to select appropriate motion preparation potential correction data.

[0085] Therefore, even though the waveforms of motor preparatory potential correction data vary depending on the action and the subject, the cognitive signals are similar. Figure 9 As shown, this becomes a more explicit representation of the waveform of the event-related potential P300 in the cognitive system. For example, even if the event-related potential P300 in the cognitive system is as shown... Figure 7 As shown, it is buried in the motion preparation potential MRCP, and also as Figure 9 As shown, the motor preparation potential MRCP is suppressed, making it easy to detect the cognitive system event-related potential P300.

[0086] (Database generation methods)

[0087] The motion preparation potential correction data stored in the aforementioned database 20 is generated as shown below, for example.

[0088] Figure 10 This is a flowchart illustrating an example of a database generation method. Figure 11 (A) Figure 11 (B) Figure 11 (C) Figure 11 (D) is a diagram showing an example of an image generated when a database is created.

[0089] First, the cognitive ability assessor selects an event as the object of cognitive ability assessment (S21). In other words, the cognitive ability testing device accepts the selection of the event.

[0090] The cognitive ability testing device presents triggering information corresponding to the selected event, used to generate a database, to the subject or other entity generating motor preparatory potential correction data (S22). For example, using... Figure 11 (A) Figure 11 (B) Figure 11 (C) Figure 11 The image shown in (D) is used to present the triggering information used to generate the database. Furthermore, the triggering information is not limited to images; it can also be sounds, stimuli, etc.

[0091] exist Figure 11 (A) Figure 11 (B) Figure 11(C) Figure 11 In (D), a car 901 and a reaction start line 910 are shown in image 90. Image 90 is moved downwards (refer to the thick arrow in the image) so that the car 901 moves upwards towards the image 90 without changing its position. At this time, the positional relationship between the car 901 and the reaction start line 910 remains unchanged.

[0092] At some point, such as Figure 11 As shown in (B), the object to be avoided, 902, appears from the top of image 90. This is explained as follows: the person generating the motion preparation potential correction data begins an avoidance maneuver after the object to be avoided, 902, reaches the reaction start line 910. Therefore, in this state, the person generating the motion preparation potential correction data tracks the object to be avoided, 902, with their eyes. This generates an electrooculogram (EOG).

[0093] Next, as Figure 11 As shown in (C), when the object to be avoided 902 reaches the reaction start line 910, the person generating the motion preparation potential correction data operates the aforementioned simulated steering wheel, as follows: Figure 11 The avoidance action is performed as shown in (D). This generates the awareness of the avoidance action and the preparatory potential for the action.

[0094] The cognitive ability detection device measures and acquires brain signals during this series of actions (S23).

[0095] The cognitive ability detection device extracts the waveform of the motor preparation potential from the brain signal (S24). As described above, the timing of the avoidance start is roughly obtained from the image. Therefore, by using the timing of the avoidance object 902 set in the image reaching the reaction start line 910 as a reference, the cognitive ability detection device can extract the motor preparation potential more accurately.

[0096] The cognitive ability detection device generates the above-mentioned motor preparation potential correction data based on the extracted waveform of the motor preparation potential, and registers the motor preparation potential correction data in the database 20 (S25).

[0097] In this way, by using the above method, a database 20 of motion preparation potential correction data can be generated.

[0098] (Cognitive Ability Testing Methods (Cognitive Signal Generation Methods))

[0099] Figure 12 This is a flowchart illustrating an example of a method for generating cognitive signals. The cognitive signal generation unit 10 generates the cognitive signal by performing... Figure 12The processes shown are used to generate cognitive signals. Furthermore, details of each process are described above, except where further explanation is required.

[0100] The cognitive signal generation unit 10 acquires brain signals (S11). The cognitive signal generation unit 10 detects the electrooculogram (EOG) (S12). The cognitive signal generation unit 10 uses the EOG to determine the reference timing (S13).

[0101] The cognitive signal generation unit 10 reads out the pre-generated motor preparation potential correction data as described above based on prior information (S14). The cognitive signal generation unit 10 uses the read (selected) motor preparation potential correction data to correct brain signals and generate cognitive signals (S15).

[0102] Furthermore, for example, this process is programmed and stored on a storage medium, an external server, or the like. This process can be read and executed by a processing device such as a personal computer used to implement the cognitive signal generation unit 10.

[0103] (Situations where multiple actions occur)

[0104] The above description illustrates a method for generating cognitive signals when only one action occurs. However, there are cases where multiple actions overlap or occur consecutively.

[0105] Figure 13 (A) Figure 13 (B) Figure 13 (C) and Figure 13 (D) shows the waveforms for a scenario where multiple actions are performed in response to a single cognition. This scenario is, for example, equivalent to the following situation: a pedestrian suddenly appears from a crosswalk and simultaneously applies the brakes and turns the steering wheel.

[0106] Figure 13 (A) shows the waveform of the brain signal. Figure 13 (B) Figure 13 (C) shows the waveforms of motion preparation potential correction data for different types of movements. Figure 13 (D) shows the waveform of the cognitive signal.

[0107] like Figure 13 (B) Motion preparation potential correction data MRCPc(A), Figure 13 As shown in the motion preparation potential correction data MRCPCc(B) (C), the motion preparation potential correction data is set according to the action. Therefore, even if... Figure 13 As shown in (A), brain signals containing multiple motor preparation potentials can also be suppressed. Therefore, as Figure 13As shown in (D), the cognitive signal becomes a signal that can easily detect the event-related potential P300 of the cognitive system.

[0108] Figure 14 (A) Figure 14 (B) Figure 14 (C) and Figure 14 (D) shows the waveforms for a situation where multiple actions are performed independently for multiple consecutive cognitions. This situation is equivalent to, for example, the following scenario: after braking to slow down near a crosswalk, turning the steering wheel due to a pedestrian suddenly appearing.

[0109] Figure 14 (A) shows the waveform of the brain signal. Figure 14 (B) Figure 14 (C) shows the waveforms of motion preparation potential correction data for different types of movements. Figure 14 (D) shows the waveform of the cognitive signal.

[0110] like Figure 14 (B) Motion preparation potential correction data MRCPc(A), Figure 14 As shown in the motion preparation potential correction data MRCPCc(B) (C), the motion preparation potential correction data is set according to the action. Therefore, even if... Figure 14 As shown in (A), brain signals containing multiple motor preparation potentials can also be suppressed. Therefore, as Figure 14 As shown in (D), the cognitive signal becomes a signal that can independently and easily detect cognitive system event-related potentials P300A and P300B.

[0111] (Second Implementation)

[0112] The cognitive ability detection device according to the second embodiment of the present invention will be described with reference to the accompanying drawings. Figure 15 This is a functional block diagram showing the structure of the cognitive signal generation unit according to the second embodiment. Figure 16 This is a diagram illustrating the structure of the cognitive ability detection system according to the second embodiment.

[0113] like Figure 15 , Figure 16 As shown, 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 the cognitive signal generation unit 10A in the cognitive ability detection device 30A includes an action detection unit 14, and utilizes the timing of the action detected by the action detection unit 14. The other structures of the cognitive ability detection system 1A are the same as those of the cognitive ability detection device 30, and descriptions of identical parts are omitted.

[0114] The cognitive ability detection system 1A includes a camera 394. The camera 394 acquires images, such as those of the subject 80, including body movements, facial expressions, and eye movements, and outputs these images to the cognitive signal generation unit 10A. Additionally, motion detection sensors, such as accelerometers and angular velocity sensors, are installed on the simulated pedal 392 and the simulated steering wheel 393. These motion detection sensors detect the movements of the simulated pedal 392 (operation by the subject 80) and the simulated steering wheel 393 (operation by the subject 80), and output detection signals to the cognitive signal generation unit 10A. Furthermore, a unit may be included to mechanically detect the movements of the simulated pedal 392 and the simulated steering wheel 393, and output detection signals based on the results of the mechanical detection.

[0115] The motion detection unit 14 of the cognitive signal generation unit 10A analyzes the eye movements and actions of the subject 80 based on the acquired images, and detects the types of eye movements and actions. Furthermore, the motion detection unit 14 detects the types of actions (operations) of the subject 80 based on the detection signals. The motion detection unit 14 outputs the detected types of actions, etc., to the MRCP correction data selection unit 132.

[0116] The MRCP correction data selection unit 132 selects motion preparation potential correction data based on the type of motion detected by the motion detection unit 14. Therefore, the MRCP correction data selection unit 132 can select appropriate motion preparation potential correction data even without prior information.

[0117] Alternatively, the MRCP correction data selection unit 132 can also select motor preparation potential correction data based on the detection results of the motion detection unit 14 and prior information. For example, if the detection results of the motion detection unit 14 are consistent with the prior information, the MRCP correction data selection unit 132 selects motor preparation potential correction data based on the type of the consistent motion. If the detection results of the motion detection unit 14 are inconsistent with the prior information, the MRCP correction data selection unit 132 selects motor preparation potential correction data based on the preference of one of them. In addition, if the detection results of the motion detection unit 14 are inconsistent with the prior information, the MRCP correction data selection unit 132 displays a warning indicating the inconsistency. Thus, for example, a cognitive ability assessor can input the appropriate type of motion into the cognitive ability detection device 30A.

[0118] Furthermore, the detection results of the motion detection unit 14 can also be used for the generation of cognitive signals in the calculation unit 133. For example, if the detection results of the motion detection unit 14 include eye movements, the calculation unit 133 can use the detection results of the motion detection unit 14 to set the reference timing even if the reference timing cannot be detected by the EOG detection unit 131.

[0119] Furthermore, if the detection result of the action detection unit 14 includes an action (operation), the calculation unit 133 uses the timing of the action (operation) to estimate the generation period of the motor preparation potential. The calculation unit 133 performs correction based on the motor preparation potential correction data for this estimated period in the brain signal. As a result, the calculation unit 133 is able to generate a cognitive signal that is easy to detect for cognitive system event-related potentials P300.

[0120] (Third Implementation)

[0121] The cognitive ability detection device according to the third embodiment of the present invention will be described with reference to the accompanying drawings. Figure 17 This is a diagram showing a portion of the structure of the cognitive ability detection system according to the third embodiment.

[0122] like Figure 17 As shown, 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 structure for detecting EOG. The other structures of the cognitive ability detection system according to the third embodiment are the same as those of the cognitive ability detection system 1 according to the first embodiment; descriptions of identical parts are omitted.

[0123] A brain signal sensor 112 is installed on the subject 80. More specifically, the brain signal sensor 112 is installed at the location FP1 (international 10-20 method) including the subject 80. The brain signal sensor 112 outputs the detected brain signals to the brain signal acquisition unit 11B of the cognitive signal generation unit 10B.

[0124] The brain signal acquisition unit 11B outputs the brain signal (CZ brain signal) detected by the brain signal sensor 111 to the arithmetic unit 133. The brain signal acquisition unit 11B outputs the brain signal (FP1 brain signal) detected by the brain signal sensor 112 to the EOG detection unit 131.

[0125] The EOG detection unit 131 detects electrooculogram (EOG) from the brain signals (FP1 brain signals) detected by the brain signal sensor 112.

[0126] By configuring the structure in this way, brain signals that serve as the detection source for EOG are detected from near the eyes of the subject 80. Therefore, the EOG detection unit 131 can detect EOG with higher accuracy.

[0127] Furthermore, in the above description, P300 was used as an example of a cognitive system event-related potential. Cognitive system event-related potentials can also be P100, N400, etc. By using the above structure and processing, the cognitive signal generation unit can generate cognitive signals that can detect these cognitive system event-related potentials.

[0128] Furthermore, the above explanation uses a cognitive ability test for driving as an example. However, the above structure and processing can be applied to any event where an action is performed after visual confirmation.

[0129] For example, by using game consoles to measure the cognitive abilities of test subjects, it can be applied to cognitive tests of e-sports players, athletes, and students in schools.

[0130] Figure 18 This is a diagram illustrating the structure of a cognitive ability detection system for games. Below, regarding... Figure 18 The cognitive ability detection system 1C shown is only different from the cognitive ability detection system 1A according to the second embodiment.

[0131] like Figure 18 As shown, the cognitive ability detection system 1C includes a cognitive ability detection device 30C, a display 391, and an operating device 394.

[0132] The cognitive ability detection device 30C includes an application execution unit 39. The application execution unit 39 executes a game application.

[0133] The application execution unit 39 outputs the game's image to the image output unit 32. The image output unit 32 outputs the game's image to the display 391. Thus, the game's image is displayed on the display 391.

[0134] The application execution unit 39 outputs event information (such as specific operations input from game images) that can be used to detect cognitive abilities in the game to the control unit 31.

[0135] Based on the event information, the control unit 31 outputs prior information corresponding to the cognitive ability test to the cognitive signal generation unit 10.

[0136] The operating device 394, such as a keyboard or mouse, accepts the input from the subject 80, who is a game player. The operating device 394 outputs the input to the cognitive signal generation unit 10 and the application execution unit 39.

[0137] The application execution unit 39 performs processing within the game application based on the operation input.

[0138] The cognitive signal generation unit 10A uses the operation input content from the operation device 394 to detect the type of action (operation) of the subject 80.

[0139] Using this structure, the cognitive ability detection system 1C can detect a gamer's cognitive ability regarding the game. Furthermore, for example, the cognitive ability detection system 1C can detect game operation characteristics that the gamer may not have noticed themselves based on the cognitive ability detection results, and can provide feedback to the gamer. Feedback methods include, for example, visual data of cognitive ability, and visual data of weaknesses (problems) based on the cognitive ability detection results. Thus, gamers can recognize their weaknesses, thereby increasing their progress in the game.

[0140] In addition, Figure 18 The example shown depicts a game console implemented using a PC, but the structure of this invention can also be applied to console-type game consoles. In this case, the operating device 394 is not limited to a keyboard, but can also be a controller specifically designed for game consoles.

[0141] In addition, Figure 18 The example shown illustrates the use of a general game application, but a test game application for assessing cognitive abilities can also be used. In this case, the control unit 31 can also be used to execute the test game application.

[0142] In addition, Figure 18 The diagram illustrates a single-player game, but the structure of this invention can also be applied to other scenarios, such as... Figure 19 The example shown is a multiplayer game.

[0143] Figure 19 This is a diagram illustrating the structure of a cognitive ability detection system for multiplayer games.

[0144] like Figure 19 As shown, the cognitive ability detection system 1D corresponding to the multiplayer game environment has multiple (in Figure 19 The system consists of four components: a cognitive ability detection system 1C, a comprehensive judgment unit 50, and a data communication network 500.

[0145] Multiple cognitive ability detection systems 1C are connected to a data communication network 500, enabling them to send and receive data via the data communication network 500. A comprehensive judgment unit 50 is also connected to the data communication network 500, acquiring cognitive ability detection results and various data and information used for cognitive ability detection from the multiple cognitive ability detection systems 1C.

[0146] The comprehensive judgment unit 50 uses the cognitive ability detection results of multiple cognitive ability detection systems 1C to determine features related to cognitive ability in a multiplayer game. For example, based on the comparison results of the cognitive abilities of multiple players detected by multiple cognitive ability detection systems 1C, the comprehensive judgment unit 50 determines and visualizes the weaknesses of the group in the multiplayer game.

[0147] At this time, the comprehensive judgment unit 50 can also use the operation input content for judgment. For example, in a cooperative game, the role of each player in the group is determined based on the operation input content. The comprehensive judgment unit 50 stores the judgment criteria corresponding to the cognitive abilities of each role and uses the judgment criteria to determine the cognitive abilities. As a result, it is possible to more accurately determine the weaknesses of each player in fulfilling their respective roles, and to provide these weaknesses in a visual manner.

[0148] Furthermore, the comprehensive judgment unit 50 pre-stores characteristics of the cognitive abilities of each character suitable for cooperative games. Moreover, the comprehensive judgment unit 50 can determine suitable characters based on the acquired cognitive abilities of each player and provide this information visually. Therefore, groups playing cooperative games can enable each player to play with a more suitable character. Consequently, for example, they can tackle more challenging tasks, thereby increasing their enthusiasm for cooperative games.

[0149] Furthermore, in the case of a battle game, the actions (attack, defense, etc.) of each player are determined based on the input input. The comprehensive judgment unit 50 stores a judgment criterion for cognitive ability corresponding to each action and uses this criterion to determine cognitive ability. Therefore, when each player battles against an opponent, it can more accurately determine aspects where the player is inferior to the opponent, i.e., weaknesses in the battle game, and provide this information in a visual manner. In this case, the comprehensive judgment unit 50 can not only use the timing of input detection to detect cognitive ability but also the reaction speed of the action. Moreover, the comprehensive judgment unit 50 can use the reaction speed of such actions to determine weaknesses and provide this information in a visual manner.

[0150] Explanation of reference numerals in the attached figures

[0151] 1, 1A, 1C, 1D: Cognitive Ability Detection System; 10, 10A, 10B: Cognitive Signal Generation Unit; 11, 11B: Brain Signal Acquisition Unit; 12: Information Input Unit; 14: Motion Detection Unit; 20: Database; 30, 30A, 30C: Cognitive Ability Detection Device; 31: Control Unit; 32: Image Output Unit; 33: Judgment Unit; 39: Application Program Execution Unit; 80: Subject; 90: Image; 111: Brain Signal Sensor; 112: Brain Signal Sensor; 131: EOG Detection Unit; 132: MRCP Correction Data Selection Unit; 133: Calculation Unit; 300: Operation Input Unit; 391: Display; 392: Simulated Pedal; 393: Simulated Steering Wheel; 394: Camera; 901: Car; 902: Avoidance Object; 910: Reaction Start Line.

Claims

1. A cognitive ability testing device, comprising: The brain signal acquisition unit acquires brain signals containing event-related potentials, which are potentials generated when the subject cognizes an object; The calibration data storage unit stores motion preparation potential calibration data that analogically represents motion preparation potentials. The motion preparation potentials are potentials generated by actions caused when the subject perceives an object, and are different from potentials based on eye movements when the subject perceives an object. as well as The computation unit uses the motor preparation potential correction data to correct the brain signals in order to generate cognitive signals. The motion preparation potential correction data is set using the maximum voltage, the time difference between the reference timing and the maximum voltage, the time difference between the maximum voltage and the start time of the voltage change, and the time difference between the maximum voltage and the end time of the voltage change.

2. The cognitive ability detection device according to claim 1, wherein, When there are multiple types of actions, the arithmetic unit uses the motion preparation potential correction data of each of the multiple actions to perform the correction process.

3. The cognitive ability detection device according to claim 1 or 2, wherein, It includes an electrooculogram (EOG) detection unit, which detects the EOG from the brain signals. The arithmetic unit performs the correction process based on the electrooculogram.

4. The cognitive ability detection device according to claim 3, wherein, The arithmetic unit performs the correction process based on the timing of the change from saccades to fixation in the electrooculogram.

5. The cognitive ability detection device according to claim 1 or 2, wherein, The device includes a motion preparation potential correction data selection unit, which uses pre-set prior information including the type of the motion to select the motion preparation potential correction data. The arithmetic unit uses the selected motion preparation potential correction data to perform the correction process.

6. The cognitive ability detection device according to claim 5, wherein, The correction data storage unit stores the motion preparation potential correction data in a manner that assigns importance. The motion preparation potential correction data selection unit selects the motion preparation potential correction data with reference to the importance level.

7. The cognitive ability detection device according to claim 5, wherein, It includes a motion detection unit that detects the actions of the subject who emits the brain signals. The motion preparation potential correction data selection unit selects the motion preparation potential correction data using the motion detected by the motion detection unit.

8. The cognitive ability detection device according to claim 7, wherein, The calculation unit performs the correction process by referring to the timing of the action detected by the action detection unit.

9. The cognitive ability detection device according to claim 1 or 2, wherein, It has a determination unit that uses the cognitive signal to determine cognitive ability.

10. The cognitive ability detection device according to claim 1 or 2, wherein, It has an image output unit that outputs images for determining cognitive abilities.

11. The cognitive ability detection device according to claim 7 or 8, wherein, The motion detection unit is an acceleration sensor or an angular velocity sensor.

12. The cognitive ability detection device according to claim 1, wherein, The voltage value of the waveform of the motion preparation potential gradually increases as the object of cognition is perceived, and gradually decreases as the action is completed.

13. A cognitive ability testing device, comprising: The brain signal acquisition unit acquires brain signals containing event-related potentials, which are potentials generated when the subject cognizes an object; The correction data storage unit stores, in accordance with the type of action, correction data of motion preparation potentials that are analog representations of motion preparation potentials. These motion preparation potentials are potentials generated by actions caused when recognizing the object, and are different from potentials based on eye movements when the subject recognizes the object. as well as The computation unit uses the motor preparation potential correction data to correct the brain signals in order to generate cognitive signals. Specifically, the correction data storage unit stores the maximum voltage value, the time difference between the reference timing and the maximum voltage value, the time difference between the maximum voltage value and the start time of the voltage change, and the time difference between the maximum voltage value and the end time of the voltage change as the motion preparation potential correction data. The arithmetic unit reconstructs the voltage waveform used in the correction based on the maximum voltage value and each time difference, and performs the correction process.

14. The cognitive ability detection device according to claim 13, wherein, The voltage value of the waveform of the motion preparation potential gradually increases as the object of cognition is perceived, and gradually decreases as the action is completed.

15. A method for detecting cognitive ability, comprising: Brain signal acquisition and processing: acquiring brain signals containing event-related potentials, wherein the event-related potentials are potentials generated when the subject cognizes an object; as well as Cognitive signal generation processing uses motor readiness potential correction data, which analogically represents motor readiness potentials, to correct the brain signals in order to generate cognitive signals. These motor readiness potentials are potentials generated by actions induced when the subject cognizes an object, and are different from potentials based on eye movements during the subject's cognition of the object. Specifically, the motion preparation potential correction data is set using the maximum voltage value, the time difference between the reference timing and the maximum voltage value, the time difference between the maximum voltage value and the start time of the voltage change, and the time difference between the maximum voltage value and the end time of the voltage change.

16. The cognitive ability detection method according to claim 15, wherein, The voltage value of the waveform of the motion preparation potential gradually increases as the object of cognition is perceived, and gradually decreases as the action is completed.

17. A computer-readable storage medium storing a computer program that, when executed by a processor, performs the cognitive ability detection method according to claim 15 or 16.

18. A computer program product comprising a computer program that, when executed by a processor, performs the cognitive ability detection method according to claim 15 or 16.

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