Immersion degree determination method and immersion degree determination apparatus

Abstract

To improve the determination accuracy of determining the degree of immersion of a subject on the basis of brain activity signals from the subject.SOLUTION: An immersion degree determination method includes; detecting brain wave signals of a central region Rc, a left front region Rlf, left rear regions Rlr1, Rlr2, right front regions Rrf1, Rrf2, and right rear regions Rrr1, Rrr2 of the subject's brain (S1); based on the detected brain wave signals, detecting connectivity between the central region Rc and the left front region Rlf, connectivity between the central region Rc and the left rear regions Rlr1, Rlr2, connectivity between the central region Rc and the right front regions Rrf1, Rrf2, and connectivity between the central region Rc and the right rear regions Rrr1, Rrr2 (S2); and determining the degree of immersion of the subject on the basis of calculated connectivity (S3-S10).SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to an immersion degree determination method and an immersion degree determination device. [Background technology]

[0002] Patent document 1 proposes a driver state determination device that calculates first to third index values ​​indicating the strength of alpha waves, beta waves, and theta waves from the driver's brain waves, and determines the driver's state of alertness and concentration based on these first to third index values. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2011-248535 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, because the frequency intensity of brain waves varies from subject to subject, there was a problem in that it was difficult to accurately determine the mental state of a subject based solely on the intensity of frequency bands such as alpha waves, beta waves, and theta waves. The present invention has been made in consideration of the above circumstances, and aims to improve the accuracy of determining a subject's level of immersion based on brain activity signals from the subject. [Means for solving the problem]

[0005] In order to achieve the above-mentioned object, one embodiment of the present invention provides a method for determining the degree of immersion, which detects EEG signals from the central region, left front region, left rear region, right front region, and right rear region of a subject's brain, and detects a first functional brain connectivity between the central region and the left front region, a second functional brain connectivity between the central region and the left rear region, a third functional brain connectivity between the central region and the right front region, and a fourth functional brain connectivity between the central region and the right rear region based on the detected EEG signals, and determines the subject's degree of immersion in a specific or unspecified target based on the calculated first to fourth functional brain connectivity. Effect of the Invention

[0006] According to the present invention, it is possible to improve the accuracy of determining the degree of immersion of a subject based on a brain activity signal from the subject. [Brief description of the drawings]

[0007] [Figure 1] FIG. 2 is a diagram illustrating an example of a hardware configuration of an immersion degree determination device according to an embodiment. [Diagram 2] FIG. 1 is a schematic diagram of each area of ​​the subject's brain. [Diagram 3] 2 is a block diagram illustrating an example of a functional configuration of a controller illustrated in FIG. 1. [Figure 4] 11 is a flowchart of an example of an immersion degree determination method according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In the description of the drawings, the same or similar parts are given the same or similar reference numerals, and duplicated explanations are omitted. Each drawing is a schematic diagram, and may differ from the actual one. The embodiments shown below are examples of devices and methods for embodying the technical idea of ​​the present invention, and the technical idea of ​​the present invention is not limited to the devices and methods exemplified in the following embodiments. The technical idea of ​​the present invention can be modified in various ways within the technical scope described in the claims.

[0009] (composition) 1 is a diagram showing an example of a hardware configuration of an immersion degree determination device according to an embodiment. The immersion degree determination device 1 is a device that detects brain activity in each region of a subject's brain using a sensor and determines whether the subject is immersed in a specific or unspecified target based on the detected brain activity. In the following explanation, an example will be described in which the immersion degree determination device 1 is installed in a vehicle and determines the driver's degree of immersion, but the present invention is not limited to such applications and can be widely applied to determining the degree of immersion of a subject in various applications.

[0010] The immersion degree determination device 1 includes a brain activity sensor 2, a visually recognized object sensor 3, a display device 4, a speaker 5, and a controller 6. Brain activity sensor 2 is a sensor that detects brain activity of a driver who is a subject. For example, brain activity sensor 2 may be an electroencephalogram sensor that detects brain waves of the driver. Furthermore, when the driver's brain activity is measured by functional Magnetic Resonance Imaging (fMRI), the brain activity sensor 2 may include a magnetic field application mechanism that applies a magnetic field to the driver's brain and a receiving coil that receives a response wave. When the driver's brain activity is measured by functional Near-InfraRed Spectroscopy (fNIRS), the brain activity sensor 2 may include a near-infrared light transmitting unit and a near-infrared light receiving unit.

[0011] The viewed object sensor 3 is a sensor that can be used to detect an object that the driver is viewing. For example, the viewed object sensor 3 may be one or more cameras that capture images of the driver and his / her surroundings. The display device 4 is an output device that is provided at a position visible to the driver and presents visual information generated by the immersion degree determination device 1. The display device 4 may be, for example, a display screen of a navigation system, or a display device provided near a meter in front of the driver's seat or at another position. The speaker 5 is an output device that presents the auditory information generated by the immersion degree determination device 1. A buzzer may be provided as a device that outputs the auditory information. For example, if the immersion degree determination device 1 determines that the driver's level of immersion is high, it may determine that the driver is not concentrating on driving and may output an alarm display or a visual alarm message from the display device 4, or output an alarm sound or an auditory alarm message from the speaker 5 or a buzzer.

[0012] The controller 6 is an electronic circuit that determines the degree of immersion of the driver based on the detection signal output from the brain activity sensor 2. The controller 6 includes a processor 6a and peripheral components such as a storage device 6b. The processor 6a may be, for example, a CPU (Central Processing Unit) or an MPU (Micro-Processing Unit). The storage device 6b may include a semiconductor storage device, a magnetic storage device, an optical storage device, etc. The storage device 6b may include a register, a cache memory, a memory such as a ROM (Read Only Memory) and a RAM (Random Access Memory) used as a main memory device. The functions of the controller 6 described below are realized, for example, by the processor 6a executing a computer program stored in the storage device 6b. The controller 6 may be formed of dedicated hardware for executing each information processing described below. For example, the controller 6 may include a functional logic circuit set in a general-purpose semiconductor integrated circuit. The controller 6 may include a programmable logic device (PLD) such as a field programmable gate array (FPGA).

[0013] When determining the degree of immersion of the driver, the controller 6 detects brain activity signals from multiple partial regions of the driver's brain by analyzing the detection signal output from the brain activity sensor 2. For example, EEG (ElectroEncephaloGraphy) data, MRI data, and NIRS data from multiple partial regions are detected as brain activity signals. 2 is a schematic diagram of each region of the subject's brain. In this embodiment, the controller 6 detects brain activity signals from the central region Rc, the left front region Rlf, the left rear region Rlr, the right front region Rrf, and the right rear region Rrr of the brain.

[0014] The controller 6 then detects functional brain connectivity between the central brain region Rc and each of the other regions Rlf, Rlr, Rrf, and Rrr based on the brain activity signals of these regions. Functional brain connectivity is a pattern of statistical dependencies between different regions in the nervous system. In the following description, functional brain connectivity may be simply referred to as "connectivity."

[0015] In the example of Figure 2, the controller 6 detects a first connectivity C1 between the central region Rc and the left front region Rlf, a second connectivity C2 between the central region Rc and the left rear region Rlr, a third connectivity C3 between the central region Rc and the right front region Rrf, and a fourth connectivity C4 between the central region Rc and the right rear region Rrr.

[0016] The inventors of the present invention reproduced a state where the driver's immersion degree to a specific object is high and a state where the driver's immersion degree to a specific object is low through an experiment, and observed the difference between the first connectivity C1 to the fourth connectivity C4. In this experiment, a driver who was given a task of simulating driving monitoring was made to watch a video, and a state where the driver's immersion degree to the video viewing was high and a state where the driver's immersion degree to the video viewing was low were prepared. As a result, the inventors of the present invention confirmed that the first connectivity C1 to the fourth connectivity C4 become large when the driver's level of immersion is high.

[0017] Therefore, the immersion degree determination device 1 of the embodiment determines the degree of immersion of the driver with respect to a specific or unspecified target based on the first connectivity C1 to the fourth connectivity C4. In this way, the immersion degree determination device 1 of the embodiment determines the immersion degree of the subject based on the functional brain connectivity of specific brain regions, thereby reducing erroneous determinations compared to determinations based simply on the frequency intensity of electroencephalograms as in Patent Document 1. This improves the accuracy of determining the immersion degree of the subject.

[0018] Fig. 3 is a block diagram of an example of a functional configuration of the controller 6 in Fig. 1. The controller 6 includes a brain activity analysis unit 10, a visually recognized object determination unit 11, a subject state determination unit 12, and an alarm generation unit 13. The brain activity analysis unit 10 analyzes the detection signals output from the brain activity sensor 2 and detects brain activity signals (e.g., EEG data, MRI data, NIRS data, etc. as brain activity signals) from multiple partial regions of the driver's brain, namely, the central region Rc, the left front region Rlf, the left rear region Rlr, the right front region Rrf, and the right rear region Rrr.

[0019] The brain activity analysis unit 10 may detect brain activity signals from a plurality of left rear regions Rlr1 and Rlr2 as the left rear region Rlr. Brain activity signals may be detected from each of the right front regions Rrf1 and Rrf2. Brain activity signals may be detected from each of the right rear regions Rrr1 and Rrr2 as the right rear region Rrr.

[0020] For example, the central region Rc may be the Posterior Cingulate Cortex (PCC). The left anterior region Rlf may be the left frontal gyrus (LFG), the left posterior region Rlr1 may be the left angular gyrus (LAG), and the left posterior region Rlr2 may be the left posterior inferior parietal sulcus (LplPS).

[0021] The right anterior region Rrf1 may be the right dorsolateral prefrontal cortex (RdlPFC). The right anterior region Rrf2 may be the right frontal eye field (RFEF). The right posterior region Rrr1 may be the right angular gyrus (RAG). The right posterior region Rrr2 may be the right posterior inferior parietal sulcus (RplPS).

[0022] The brain activity analysis unit 10 detects, at a certain time, a first connectivity C1 between the central region Rc and the left front region Rlf, a second connectivity C2 between the central region Rc and the left rear region Rlr, a third connectivity C3 between the central region Rc and the right front region Rrf, and a fourth connectivity C4 between the central region Rc and the right rear region Rrr. For example, the brain activity analysis section 10 calculates a correlation coefficient between a brain activity signal from the central region Rc and a brain activity signal from the left front region Rlf at a certain time.

[0023] For example, the brain activity analysis section 10 may calculate, as the correlation coefficient between the brain activity signals, a normal correlation coefficient between the time-series data of the brain activity signals. Furthermore, for example, the brain activity analysis unit 10 may calculate the coherence between time series data of the brain activity signals as the correlation coefficient between the brain activity signals. For example, if the autospectrum of the time series data x, y of the brain activity signals of the target region for calculating the correlation coefficient is expressed as Pxx(f) and Pyy(f), and the cross spectrum is expressed as Pxy(f), the coherence Cxy(f) of the brain activity signals of these regions can be calculated by the following formula. Cxy(f) = |Pxy(f)| 2 / (|Pxx(f)|×|Pyy(f)|) Furthermore, for example, the brain activity analysis section 10 may calculate an autoregressive coefficient of Partial Directed Coherence (PDC) as a correlation coefficient between brain activity signals.

[0024] The brain activity analysis section 10 may detect the first connectivity C1 based on the correlation coefficient between the brain activity signals of these regions. For example, it may be determined that the first connectivity C1 exists when the correlation coefficient is equal to or greater than a threshold, and that the first connectivity C1 exists when the correlation coefficient is less than the threshold. Similarly, the brain activity analysis unit 10 may calculate a correlation coefficient between the brain activity signal from the central region Rc and the brain activity signal from the left rear region Rlr, and detect the second connectivity C2 based on the correlation coefficient between the brain activity signals of these regions. Also, the brain activity analysis unit 10 may calculate a correlation coefficient between the brain activity signal from the central region Rc and the brain activity signal from the right front region Rrf, and detect the third connectivity C3 based on the correlation coefficient between the brain activity signals of these regions. Also, the brain activity analysis unit 10 may calculate a correlation coefficient between the brain activity signal from the central region Rc and the brain activity signal from the right rear region Rrr, and detect the fourth connectivity C4 based on the correlation coefficient between the brain activity signals of these regions.

[0025] Also, for example, the first connectivity C1 may be calculated as an index value that is larger when the correlation coefficient between the brain activity signal from the central region Rc and the brain activity signal from the left front region Rlf is large compared to when it is small. For example, the first connectivity C1 may be calculated as an index value that is larger as the correlation coefficient is larger. The same applies to the second connectivity C2 to the fourth connectivity C4.

[0026] Here, when detecting brain activity signals from multiple left rear regions Rlr1 and Rlr2 as the left rear region Rlr, correlation coefficients between the brain activity signal from the central region Rc and each of the brain activity signals from the left rear regions Rlr1 and Rlr2 may be calculated, and the second connectivity C2 between the central region Rc and the left rear region Rlr may be detected based on the average, median, maximum or minimum value of these correlation coefficients. Alternatively, second connectivity C2a and C2b between the left posterior regions Rlr1 and Rlr2, respectively, may be detected based on these correlation coefficients.

[0027] Similarly, when detecting brain activity signals from multiple right front regions Rrf1 and Rrf2 as the right front region Rrf, correlation coefficients between the brain activity signal from the central region Rc and each of the brain activity signals from the right front regions Rrf1 and Rrf2 may be calculated, and the third connectivity C3 between the central region Rc and the right front region Rrf may be detected based on the average, median, maximum or minimum value of these correlation coefficients. Alternatively, third connectivity C3a and C3b between the right anterior regions Rrf1 and Rrf2, respectively, may be detected based on these correlation coefficients.

[0028] Similarly, when detecting brain activity signals from multiple right rear regions Rrr1 and Rrr2 as the right rear region Rrr, correlation coefficients between the brain activity signal from the central region Rc and each of the brain activity signals from the right rear regions Rrr1 and Rrr2 may be calculated, and the fourth connectivity C4 between the central region Rc and the right rear region Rrr may be detected based on the average, median, maximum or minimum value of these correlation coefficients. Alternatively, fourth connectivity C4a and C4b between the right posterior regions Rrr1 and Rrr2, respectively, may be detected based on these correlation coefficients.

[0029] The visually recognized object determination unit 11 determines an object visually recognized by the driver based on a detection signal output from the visually recognized object sensor 3. For example, the visually recognized object is determined based on an image output from a camera, which is the visually recognized object sensor 3. For example, the visual target determination unit 11 determines whether the driver is viewing a display screen of a navigation system, a display device provided near a meter in front of the driver's seat, or another display device provided in another position. Also, for example, the visual target determination unit 11 determines whether the driver is gazing ahead.

[0030] The subject state determination unit 12 determines the mental state of the driver based on the first connectivity C1 to the fourth connectivity C4 detected by the brain activity analysis unit 10. For example, the subject state determination unit 12 determines the degree of immersion of the subject in a specific or unspecified target based on the first connectivity C1 to the fourth connectivity C4. For example, the subject state determination unit 12 determines the degree of immersion of the subject in the visual target determined by the visual target determination unit 11.

[0031] For example, the subject state determination section 12 may determine that the driver's degree of immersion is high (i.e., the driver is immersed) when all of the first connectivity C1 to the fourth connectivity C4 are detected (i.e., when all of the first connectivity C1 to the fourth connectivity C4 are present). For example, the subject state determination unit 12 may determine that the subject has a high degree of immersion in the visual objects determined by the visual object determination unit 11 (such as the display screen of a navigation system or a display device provided near the meter or in another location) (i.e., it may determine that the subject is immersed in these visual objects).

[0032] Also, for example, if the visual object determination unit 11 determines that the driver is not looking ahead and the driver's level of immersion is high, the subject state determination unit 12 may determine that the driver's level of immersion in some kind of "distraction" is high (i.e., determine that the driver is immersed in a "distraction"). Also, for example, even if the visual object determination unit 11 determines that the driver is looking ahead and the driver is highly immersed, the subject state determination unit 12 may determine that the driver is highly immersed in "mental wondering" (i.e., determine that the driver is immersed in "mental wondering").

[0033] In addition, when second connectivity C2a and C2b are detected for each of multiple left rear regions Rlr1 and Rlr2 as the left rear region Rlr, it is possible to determine that second connectivity C2 between the central region Rc and the left rear region Rlr exists if either the second connectivity C2a or C2b is detected, and to determine that second connectivity C2 does not exist if neither the second connectivity C2a nor C2b is detected. Alternatively, if both second connectivity C2a and C2b are detected, it may be determined that second connectivity C2 exists, and if at least one of second connectivity C2a and C2b is not detected, it may be determined that second connectivity C2 does not exist.

[0034] Similarly, when third connectivity C3a and C3b are detected for each of multiple right front regions Rrf1 and Rrf2 as the right front region Rrf, it may be determined that third connectivity C3 between the central region Rc and the right front region Rrf exists if either the third connectivity C3a or C3b is detected, and that third connectivity C3 does not exist if neither the third connectivity C3a nor C3b is detected. Alternatively, the third connectivity C3 may be determined to be present when both the third connectivity C3a and C3b are detected, and the third connectivity C3 may be determined to be absent when at least one of the third connectivity C3a and C3b is not detected.

[0035] Similarly, when detecting fourth connectivity C4a and C4b for each of multiple right rear regions Rrr1 and Rrr2 as the right rear region Rrr, it may be determined that fourth connectivity C4 between the central region Rc and the right rear region Rrr exists if either the fourth connectivity C4a or C4b is detected, and that fourth connectivity C4 does not exist if neither the fourth connectivity C4a nor C4b is detected. Alternatively, the fourth connectivity C4 may be determined to be present when both the fourth connectivity C4a and C4b are detected, and the fourth connectivity C4 may be determined to be absent when at least one of the fourth connectivity C4a and C4b is not detected.

[0036] Furthermore, when the second connectivity C2 to the fourth connectivity C4 are detected but the first connectivity C1 is not detected (i.e., when the second connectivity C2 to the fourth connectivity C4 are present but the first connectivity C1 is not present), the subject state determination unit 12 may determine that the driver's immersion level is "very high." In other words, the subject state determination unit 12 may determine that the driver's immersion level is higher than when all of the first connectivity C1 to the fourth connectivity C4 are detected.

[0037] Furthermore, the subject state determination unit 12 may determine that the driver's immersion level is low when it detects the first connectivity C1, the second connectivity C2, and the fourth connectivity C4, but does not detect the third connectivity C3 (i.e., when the first connectivity C1, the second connectivity C2, and the fourth connectivity C4 are present, but the third connectivity C3 is absent). For example, it may determine that the driver is not immersed. In other words, it may determine that the driver's immersion level is lower than when all of the first connectivity C1 to the fourth connectivity C4 are detected. In this case, the subject state determination unit 12 determines that the driver is concentrating on driving.

[0038] In addition, as in the above example, when the first connectivity C1 to the fourth connectivity are calculated as index values ​​that are larger when the correlation coefficient between the brain activity signal from the central region Rc and the brain activity signals from the left front region Rlf, the left rear region Rlr, the right front region Rrf, and the right rear region Rrr is small, the strength of the immersion degree may be determined according to the magnitude of the first connectivity C1 to the fourth connectivity C4. That is, the strength of the immersion degree may be determined according to the magnitude of the correlation coefficient.

[0039] In addition, when the second connectivity C2a and C2b are calculated for each of the multiple left rear regions Rlr1 and Rlr2 as the left rear region Rlr, the average value, median value, maximum value, or minimum value of the second connectivity C2a and C2b may be calculated as the second connectivity C2 of the left rear region Rlr. Similarly, when the third connectivity C3a and C3b are calculated for each of the multiple right front regions Rrf1 and Rrf2 as the right front region Rrf, the average value, median value, maximum value, or minimum value of the third connectivity C3a and C3b may be calculated as the third connectivity C3 of the right front region Rrf. Similarly, when the fourth connectivity C4a and C4b are calculated for each of the multiple right rear regions Rrr1 and Rrr2 as the right rear region Rrr, the average value, median value, maximum value, or minimum value of the fourth connectivity C4a and C4b may be calculated as the fourth connectivity C4 of the right rear region Rrr.

[0040] For example, the degree of immersion may be determined to be higher when the first connectivity C1 to the fourth connectivity C4 are small or large. For example, the degree of immersion may be determined to be higher as the first connectivity C1 to the fourth connectivity C4 are large. Specifically, the degree of immersion may be determined to be higher when the average value, median value, maximum value, or minimum value of the first connectivity C1 to the fourth connectivity C4 is small or large. For example, the degree of immersion may be determined to be higher as the average value, median value, maximum value, or minimum value of the first connectivity C1 to the fourth connectivity C4 is large.

[0041] Specifically, for example, when the average value, median value, maximum value, or minimum value of the first connectivity C1 to the fourth connectivity C4 is equal to or greater than a predetermined first threshold, the degree of immersion of the driver may be determined to be high (i.e., the driver is immersed), and when the average value, median value, maximum value, or minimum value is less than the first threshold, the degree of immersion of the driver may be determined to be low (i.e., the driver is not immersed). Furthermore, when the average value, median value, maximum value, or minimum value of the first connectivity C1 to the fourth connectivity C4 is equal to or greater than a second threshold that is greater than the first threshold, the degree of immersion of the driver may be determined to be "very high."

[0042] When the subject state determination unit 12 determines that the driver's level of immersion is high (i.e., the driver is immersed), for example, when all of the first connectivity C1 to the fourth connectivity C4 are detected, the alarm generation unit 13 generates a first alarm signal and outputs it from the display device 4, the speaker 5, or a buzzer. For example, the first alarm signal may be an alarm display or a visual alarm message displayed on the display device 4, or an alarm sound or an auditory alarm message output from the speaker 5 or a buzzer.

[0043] In addition, if the subject state determination unit 12 determines that the driver's level of immersion is "very high", for example, if it detects the second connectivity C2 to the fourth connectivity C4 but does not detect the first connectivity C1, the alarm generation unit 13 generates a second alarm signal and outputs it from the display device 4, the speaker 5, or a buzzer. For example, the second alarm signal may be an alarm display or a visual alarm message displayed on the display device 4, or an alarm sound or an auditory alarm message output from the speaker 5 or a buzzer.

[0044] For example, the warning generation unit 13 may generate a second warning signal that is stronger than the first warning signal to inform the driver of a high urgency. Also, by generating a second warning signal that is stronger than the first warning signal, the driver can be made aware of the warning signal even if the driver is immersed in the driving with a very high degree of immersion. For example, the first alarm signal may generate only an alarm display or a visual alarm message to be displayed on the display device 4, and the second alarm signal may generate both an alarm display or a visual alarm message to be displayed on the display device 4, and an alarm sound or an auditory alarm message to be output from the speaker 5 or a buzzer.

[0045] Further, for example, the warning display or visual warning message displayed on the display device 4 as the second warning signal may be a more noticeable display or message than the warning display or visual warning message displayed as the first warning signal, or may be a display or message that attracts more attention. For example, the volume of the alarm sound or auditory alarm message output from the speaker 5 or buzzer as the second alarm signal may be made louder than the alarm sound or auditory alarm message output as the first alarm signal, or the alarm sound or alarm message may be made to be more attention-grabbing.

[0046] Also, when the subject state determination unit 12 determines that the driver's immersion level is low (for example, the driver is not immersed), for example, when the first connectivity C1, the second connectivity C2, and the fourth connectivity C4 are detected but the third connectivity C3 is not detected, the warning generation unit 13 does not generate a warning signal, i.e., stops presenting the first warning signal and the second warning signal.

[0047] (operation) FIG. 4 is a flowchart of an example of a method for determining a degree of immersion according to an embodiment. In step S1, brain activity sensor 2 outputs a detection signal that detects the brain activity of the driver. In step S2, the brain activity analysis unit 10 analyzes the detection signal output from the brain activity sensor 2 to detect, at a certain time, a first connectivity C1 between the central region Rc and the left front region Rlf, a second connectivity C2 between the central region Rc and the left rear region Rlr, a third connectivity C3 between the central region Rc and the right front region Rrf, and a fourth connectivity C4 between the central region Rc and the right rear region Rrr.

[0048] In step S3, the subject condition determination unit 12 determines whether or not all of the first connectivity C1 to the fourth connectivity C4 have been detected. If all of the first connectivity C1 to the fourth connectivity C4 have been detected (step S3: Y), the process proceeds to step S4. If any of the first connectivity C1 to the fourth connectivity C4 has not been detected (step S3: N), the process proceeds to step S6. In step S4, the subject state determining section 12 determines that the driver's immersion level is high (that is, the driver is immersed). In step S5, the alarm generating unit 13 generates a first alarm signal and outputs it from the display device 4, the speaker 5, and the buzzer. After that, the process proceeds to step S11.

[0049] On the other hand, in step S6, the subject condition determination unit 12 determines whether the second connectivity C2 to the fourth connectivity C4 are detected and the first connectivity C1 is not detected. If the second connectivity C2 to the fourth connectivity C4 are detected and the first connectivity C1 is not detected (step S6: Y), the process proceeds to step S7. If any of the second connectivity C2 to the fourth connectivity C4 is not detected or the first connectivity C1 is detected (step S6: N), the process proceeds to step S9. In step S7, the subject's state determining section 12 determines that the driver's immersion level is "highly intense." In step S8, a second alarm signal is generated and output from the display device 4, the speaker 5, and the buzzer. After that, the process proceeds to step S11.

[0050] In step S9, the subject condition determination unit 12 determines whether the first connectivity C1, the second connectivity C2, and the fourth connectivity C4 are detected, and the third connectivity C3 is not detected. If the first connectivity C1, the second connectivity C2, and the fourth connectivity C4 are detected, and the third connectivity C3 is not detected (step S9: Y), the process proceeds to step S10. If any of the first connectivity C1, the second connectivity C2, and the fourth connectivity C4 is not detected, or the third connectivity C3 is detected (step S9: N), the process proceeds to step S11. In step S10, the subject state determination section 12 determines that the driver's immersion level is low (for example, the driver is not immersed). After that, the process proceeds to step S11. In step S11, the controller 6 determines whether or not the ignition key of the vehicle has been turned off. If the ignition key has not been turned off (step S11: N), the process returns to step S1. If the ignition key has been turned off (step S11: Y), the process ends.

[0051] (Effects of the embodiment) (1) The brain activity sensor 2 and the controller 6 detect brain activity signals from the central region, the left front region, the left rear region, the right front region, and the right rear region of the subject's brain. Based on the detected brain activity signals, the controller 6 detects a first functional brain connectivity between the central region and the left front region, a second functional brain connectivity between the central region and the left rear region, a third functional brain connectivity between the central region and the right front region, and a fourth functional brain connectivity between the central region and the right rear region, and determines the subject's degree of immersion in a specific or unspecified target based on the calculated first to fourth functional brain connectivity. In this way, by determining the degree of immersion of a subject based on the functional brain connectivity of specific brain regions, it is possible to reduce erroneous determinations and improve the accuracy of determining the degree of immersion.

[0052] (2) When all of the first to fourth functional brain connectivity are detected, the controller 6 may determine that the subject is immersed in a specific or unspecified object. This makes it possible to accurately determine whether the subject is immersed in something. (3) The controller 6 may determine that the degree of immersion is higher when the second functional brain connectivity to the fourth functional brain connectivity are detected and the first functional brain connectivity is not detected, compared to when all of the first functional brain connectivity to the fourth functional brain connectivity are detected. This makes it possible to judge the subject's level of immersion in stages.

[0053] (4) The controller 6 may determine that the degree of immersion is lower when the first functional brain connectivity, the second functional brain connectivity, and the fourth functional brain connectivity are detected but the third functional brain connectivity is not detected, compared to when all of the first functional brain connectivity to the fourth functional brain connectivity are detected. This makes it possible to determine whether the subject's level of immersion is low, for example, whether the subject is not immersed.

[0054] (5) The controller 6 may present an alert to the subject depending on the determined degree of immersion. For example, the controller 6 may present an alert to the subject when it is determined that the subject is immersed in a specific or unspecified object. This makes it possible to attract the subject's attention if the subject is engrossed in something. (6) The controller 6 may present a first alert to the subject if it determines that the subject is immersed in a specific or unspecified object, and may present a second alert, which is stronger than the first alert, to the subject if it determines that the level of immersion is greater. This allows the subject to be made aware of the alarm even when the subject is more immersed. (7) The controller 6 may stop presenting the alert to the subject when it is determined that the level of immersion is lower. This makes it possible to stop unnecessary alerts from being displayed if the subject is not immersed in something.

[0055] (8) Brain activity signals may be detected from the posterior cingulate cortex as the central region, from the left frontal gyrus as the left anterior region, from the left angular gyrus and left posterior inferior intraparietal sulcus as the left posterior region, from the right dorsolateral prefrontal cortex and right frontal eye field as the right anterior region, and from the right angular gyrus and right posterior inferior intraparietal sulcus as the right posterior region. Based on the functional brain connectivity between these regions, it is possible to improve the accuracy of judging the subject's level of immersion.

[0056] (9) The controller 6 may determine the strength of the degree of immersion based on the magnitude of the correlation coefficient between brain activity signals. This makes it possible to determine the level of immersion of the subject. (10) The controller 6 calculates a plurality of correlation coefficients which are the correlation coefficients between each of the brain activity signals of multiple sites in any one of the left rear region, the right front region, and the right rear region and the brain activity signal of the central region, and calculates an average value of the plurality of correlation coefficients as the correlation coefficient between the brain activity signal of any one of the regions and the brain activity signal of the central region. This makes it possible to individually detect the functional brain connectivity between each of the multiple sites and the central region in each of the left rear region, right front region, and right rear region, thereby determining the subject's degree of immersion. [Explanation of symbols]

[0057] Reference Signs List 1...Immersion level determination device, 2...Brain activity sensor, 3...Visual target sensor, 4...Display device, 5...Speaker, 6...Controller, 6a...Processor, 6b...Storage device, 10...Brain activity analysis unit, 11...Visual target determination unit, 12...Subject state determination unit, 13...Alarm generation unit, C1...First connectivity, C2, C2a, C2b...Second connectivity, C3, C3a, C3b...Third connectivity, C4, C4a, C4b...Fourth connectivity, Rc...Central region, Rlf...Left front region, Rlr, Rlr1, Rlr2...Left rear region, Rrf, Rrf1, Rrf2...Right front region, Rrr, Rrr1, Rrr2...Right rear region

Claims

1. Detecting brain activity signals from a central region, a left anterior region, a left posterior region, a right anterior region, and a right posterior region of the subject's brain; Detecting a first functional brain connectivity between the central region and the left front region, a second functional brain connectivity between the central region and the left rear region, a third functional brain connectivity between the central region and the right front region, and a fourth functional brain connectivity between the central region and the right rear region based on the detected brain activity signal; Based on the calculated first functional brain connectivity to the fourth functional brain connectivity, a degree of immersion of the subject in a specific or unspecified target is determined. A method for determining a degree of immersion.

2. The immersion degree determination method according to claim 1, characterized in that when all of the first functional brain connectivity to the fourth functional brain connectivity are detected, it is determined that the subject is immersed in a specific or unspecified object.

3. The immersion degree determination method according to claim 1 or 2, characterized in that the degree of immersion is determined to be higher when the second functional brain connectivity to the fourth functional brain connectivity are detected and the first functional brain connectivity is not detected, compared to when all of the first functional brain connectivity to the fourth functional brain connectivity are detected.

4. The immersion degree determination method according to any one of claims 1 to 3, characterized in that the degree of immersion is determined to be lower when the first functional brain connectivity, the second functional brain connectivity, and the fourth functional brain connectivity are detected and the third functional brain connectivity is not detected, compared to when all of the first functional brain connectivity to the fourth functional brain connectivity are detected.

5. 5. The immersion degree determination method according to claim 1, further comprising the step of presenting a warning to the subject in accordance with the determined immersion degree.

6. 3. The immersion degree determination method according to claim 2, further comprising the step of: presenting a warning to the subject when it is determined that the subject is immersed in a specific or unspecified object.

7. The method for determining the degree of immersion described in claim 3, characterized in that a first alarm is presented to the subject when it is determined that the subject is immersed in a specific or unspecified object, and a second alarm that is stronger than the first alarm is presented to the subject when it is determined that the degree of immersion is higher.

8. The method for determining a degree of immersion according to claim 4 , further comprising the step of ceasing presentation of a warning to the subject when the degree of immersion is determined to be lower.

9. The immersion degree determination method according to any one of claims 1 to 8, characterized in that brain activity signals are detected from the posterior cingulate cortex as the central region, brain activity signals are detected from the left frontal gyrus as the left anterior region, brain activity signals are detected from the left angular gyrus and left posterior inferior intraparietal sulcus as the left posterior region, brain activity signals are detected from the right dorsolateral prefrontal cortex and right frontal eye field as the right anterior region, and brain activity signals are detected from the right angular gyrus and right posterior inferior intraparietal sulcus as the right posterior region.

10. The immersion degree determination method according to any one of claims 1 to 9, characterized in that the strength of the immersion degree is determined based on the magnitude of the correlation coefficient between the brain activity signals.

11. Calculating a plurality of correlation coefficients between each of the brain activity signals of a plurality of sites in any one of the left rear region, the right front region, and the right rear region and the brain activity signal of the central region; calculating an average value of the plurality of correlation coefficients as a correlation coefficient between the brain activity signal of any one of the regions and the brain activity signal of the central region; The immersion degree determination method according to claim 10 .

12. Sensors for detecting brain activity signals from a central region, a left front region, a left rear region, a right front region, and a right rear region of the subject's brain; an electronic circuit that detects a first functional brain connectivity between the central region and the left front region, a second functional brain connectivity between the central region and the left rear region, a third functional brain connectivity between the central region and the right front region, and a fourth functional brain connectivity between the central region and the right rear region based on the detected brain activity signal, and determines a degree of immersion of the subject in a specific or unspecified target based on the calculated first to fourth functional brain connectivity; An immersion degree determination device comprising:

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