JP2020028430A - Electroencephalogram analysis system, visibility evaluation system, electroencephalogram analysis method, and program - Google Patents

Abstract

To provide a system that improves the accuracy of addition average electroencephalogram data while decreasing the number of electroencephalogram datasets to be added and averaged.SOLUTION: An acquisition unit 1 acquires information on an electroencephalogram signal and a plurality of stimulus presentation timings related to the electroencephalogram signal. A preprocessing unit 2 extracts a plurality of electroencephalogram datasets corresponding to the plurality of stimulus presentation timings on a one-to-one basis from the electroencephalogram signal on the basis of the information on the electroencephalogram signal and the plurality of stimulus presentation timings acquired by the acquisition unit 1. A correlation calculation unit 3 calculates correlation data relating to the plurality of electroencephalogram datasets extracted by the preprocessing unit 2. A selection unit 4 selects a group of electroencephalogram datasets from the plurality of electroencephalogram datasets on the basis of the correlation data calculated by the correlation calculation unit 3. A data calculation unit 5 calculates addition average electroencephalogram data by adding and averaging the group of electroencephalogram datasets selected by the selection unit 4. An output unit 6 outputs electroencephalogram information including the addition average electroencephalogram data calculated by the data calculation unit 5.SELECTED DRAWING: Figure 1

Description

  The present disclosure generally relates to an electroencephalogram analysis system, a visibility evaluation system, an electroencephalogram analysis method and a program, and more specifically, an electroencephalogram analysis system that calculates electroencephalogram data (average electroencephalogram data) that is averaged from a plurality of electroencephalogram data, Also, the present invention relates to a visibility evaluation system, an electroencephalogram analysis method, and a program including the electroencephalogram analysis system.

  BACKGROUND ART Conventionally, as an example of an electroencephalogram analysis device, a driving attention amount determination device that determines a driver's state using electroencephalograms and performs safe driving assistance has been proposed (Patent Document 1).

  The driving attention amount determination device described in Patent Literature 1 includes an electroencephalogram measurement unit, an attention amount determination unit, and an output unit.

  The electroencephalogram measurement unit measures the electroencephalogram signal of the driver. The attention amount determination unit determines an attention amount for the driver's peripheral visual field region from an electroencephalogram signal measured from a point of occurrence of a visual stimulus generated in the driver's peripheral visual field region. The output unit alerts the driver by outputting a signal based on the determination result of the attention amount determination unit.

  Patent Literature 1 describes that in the study of event-related potential, analysis is performed after obtaining an average of brain wave data.

  In addition, the attention amount determination unit described in Patent Document 1 performs an averaging process of the accumulated required number of EEG data, and further calculates the event-related potential from 300 ms to 600 ms from the EEG data after the addition averaging. Is analyzed, and the amount of attention is determined based on the magnitude of the amplitude.

International Publication No. WO 2010/016244

  In the electroencephalogram analysis system, it may be desired to reduce the number of electroencephalogram data to be averaged.

  However, when performing the averaging process of the required number of EEG data, the EEG data when the driver did not notice the occurrence of the visual stimulus, and the driver's attention was directed to objects other than the visual stimulus. In some cases, brain wave data when the recognition of the stimulus is delayed is included. In such a case, if the number of electroencephalogram data to be averaged is reduced, the accuracy of the electroencephalogram data after the averaging process may be reduced.

  An object of the present disclosure is to provide an electroencephalogram analysis system, a visibility evaluation system, an electroencephalogram analysis method, and a program capable of improving the accuracy of the averaged electroencephalogram data while reducing the number of electroencephalogram data to be averaged. is there.

  An electroencephalogram analysis system according to an aspect of the present disclosure includes an acquisition unit, a preprocessing unit, a correlation calculation unit, a selection unit, a data calculation unit, and an output unit. The acquisition unit acquires information on an electroencephalogram signal and a plurality of stimulus presentation timings related to the electroencephalogram signal. The preprocessing unit extracts a plurality of electroencephalogram data corresponding to the plurality of stimulus presentation timings from the electroencephalogram signal based on the information on the electroencephalogram signals and the plurality of stimulus presentation timings acquired by the acquisition unit. . The correlation calculation unit calculates correlation data on the plurality of brain wave data extracted by the preprocessing unit. The selection unit selects a group of brain wave data from the plurality of brain wave data based on the correlation data calculated by the correlation calculation unit. The data calculation unit calculates the averaged electroencephalogram data by averaging the group of electroencephalogram data selected by the selection unit. The output unit outputs brain wave information including the averaged brain wave data calculated by the data calculation unit.

  A visibility evaluation system according to an aspect of the present disclosure includes the brain wave analysis system, a determination unit, and a result output unit. The determination unit determines the visibility of the subject based on the brain wave information output from the output unit of the brain wave analysis system. The result output unit outputs a result determined by the determination unit.

  An electroencephalogram analysis method according to an aspect of the present disclosure includes a first step, a second step, a third step, a fourth step, a fifth step, and a sixth step. The first step obtains information on an electroencephalogram signal and a plurality of stimulus presentation timings related to the electroencephalogram signal. The second step extracts a plurality of electroencephalogram data corresponding to the plurality of stimulus presentation timings from the electroencephalogram signal based on the information on the electroencephalogram signals and the plurality of stimulus presentation timings acquired in the first step. I do. The third step calculates correlation data relating to the plurality of brain wave data extracted in the second step. The fourth step selects a group of brain wave data from the plurality of brain wave data based on the correlation data calculated in the third step. In the fifth step, the averaged brain wave data is calculated by averaging the group of brain wave data selected in the fourth step. The sixth step outputs brain wave information including the averaged brain wave data calculated in the fifth step.

  A program according to an embodiment of the present disclosure is a program for causing a computer system to execute a first process, a second process, a third process, a fourth process, a fifth process, and a sixth process. It is. The first processing acquires information on an electroencephalogram signal and a plurality of stimulus presentation timings related to the electroencephalogram signal. The second processing extracts a plurality of electroencephalogram data corresponding to the plurality of stimulus presentation timings one-to-one from the electroencephalogram signal based on the information on the electroencephalogram signals and the plurality of stimulus presentation timings acquired in the first processing. I do. The third process calculates correlation data relating to the plurality of brain wave data extracted in the second process. The fourth process selects a group of brain wave data from the plurality of brain wave data based on the correlation data calculated in the third process. In the fifth process, the averaged brain wave data is calculated by averaging the group of brain wave data selected in the fourth process. The sixth process outputs brain wave information including the averaged brain wave data calculated in the fifth process.

  The electroencephalogram analysis system, the visibility evaluation system, the electroencephalogram analysis method, and the program according to the present disclosure can improve the accuracy of the averaged electroencephalogram data while reducing the number of electroencephalogram data to be averaged.

FIG. 1 is a configuration diagram of a visibility evaluation system including an electroencephalogram analysis system according to the first embodiment. FIG. 2 is a waveform diagram of a plurality of electroencephalogram data extracted from an electroencephalogram signal acquired by an acquisition unit in the electroencephalogram analysis system of the above. FIG. 3A is a waveform diagram of the averaged electroencephalogram data calculated by the simple averaging unit performing the averaging of a plurality of electroencephalogram data in the electroencephalogram analysis system of the above. FIG. 3B is a waveform diagram of brain wave data not selected by the selection unit in the brain wave analysis system of the above. FIG. 3C is a waveform diagram of the averaged electroencephalogram data calculated by averaging the group of electroencephalogram data selected by the selection unit in the electroencephalogram analysis system of the above, by the data calculation unit. FIG. 3D is a comparison diagram of the waveform diagram of the averaged electroencephalogram data shown in FIG. 3A and the waveform diagram of the averaged electroencephalogram data shown in FIG. 3C. FIG. 4A is a waveform diagram of the averaged electroencephalogram data calculated by the electroencephalogram analysis system of the above. FIG. 4B is an explanatory diagram of a latency extracted in the visibility evaluation system including the brain wave analysis system according to the first embodiment. FIG. 5 is a flowchart illustrating an operation of the visibility evaluation system including the brain wave analysis system according to the first embodiment. FIG. 6A is a diagram illustrating an electroencephalogram analysis system according to the first embodiment, in which all of a plurality of electroencephalogram data extracted from electroencephalogram signals corresponding to visual stimuli presented to a subject under the first condition, the second condition, the third condition, and the fourth condition are respectively obtained. FIG. 9 is a waveform diagram of the averaged electroencephalogram data assuming that the averaging is performed. FIG. 6B shows a group of a plurality of electroencephalogram data extracted from electroencephalogram signals corresponding to visual stimuli presented to the subject under the first condition, the second condition, the third condition, and the fourth condition in the electroencephalogram analysis system of the above. FIG. 7 is a waveform diagram of the averaged brain wave data when the brain wave data of FIG. FIG. 7A is a diagram illustrating the relationship between the latency and the number of brain wave data extracted in the visibility evaluation system including the brain wave analysis system according to the first embodiment. FIG. 7B is a diagram illustrating the relationship between the latency and the number of brain wave data extracted in the visibility evaluation system including the brain wave analysis system according to the comparative example. FIG. 8 is a configuration diagram of a visibility evaluation system including the electroencephalogram analysis system according to the second embodiment. FIG. 9 is a waveform diagram of the averaged electroencephalogram data calculated by averaging the group of electroencephalogram data selected by the selection unit in the electroencephalogram analysis system of the above, by the data calculation unit. FIG. 10 is a flowchart illustrating an operation of the visibility evaluation system including the brain wave analysis system according to the first embodiment. FIG. 11 is a configuration diagram of a visibility evaluation system including the electroencephalogram analysis system according to the third embodiment. FIG. 12A is a waveform diagram of a plurality of averaging data calculated by the averaging unit in the electroencephalogram analysis system of the Embodiment. FIG. 12B is a waveform diagram of the averaged electroencephalogram data calculated by averaging the group of electroencephalogram data selected by the selection unit in the electroencephalogram analysis system of the above, by the data calculation unit.

(Embodiment 1)
Hereinafter, an electroencephalogram analysis system according to the first embodiment and a visibility evaluation system including the same will be described with reference to FIGS.

(1) Outline An electroencephalogram analysis system 10 (see FIG. 1) according to the first embodiment is a system used for analyzing, for example, an electroencephalogram (electroencephalogram signal) of a subject who drives an automobile (moving body). Here, the electroencephalogram analysis system 10 is used, for example, to analyze the electroencephalogram of the subject when the subject is shown a simulated visual environment that simulates the visual environment while driving a car. The “visual environment” is an environment recognized by the subject through the subject's vision. The electroencephalogram analysis system 10 can analyze a subject's electroencephalogram obtained without having the subject actually drive a car.

  The visibility evaluation system 100 (see FIG. 1) is a system that evaluates, for example, the visibility of a subject who appears in a visual environment of a subject who drives an automobile (moving body) in a visual environment. The visibility evaluation system 100 uses an event-related potential (ERP) of a subject's electroencephalogram obtained when a visual target as a sensory stimulus (visual stimulus) to the subject is presented in the subject's visual environment. To evaluate the visibility. The “event-related potential” is a part of an electroencephalogram, and refers to a temporal change in the potential of the brain that is temporally related to an external event. In the visibility evaluation system 100, as an “event-related potential”, a positive component (referred to as “P300”) that appears in the driver's brain wave in the vicinity of about 300 milliseconds starting from the timing at which an external visual stimulus occurs. ). “Positive component” refers to a potential greater than 0 μV. The latency of P300 is, for example, in the range of 250 ms to 600 ms.

(2) Configuration (2.1) Overall Configuration of EEG Analysis System and Visibility Evaluation System The EEG analysis system 10 includes an acquisition unit 1, a pre-processing unit 2, a correlation calculation unit 3, as shown in FIG. It includes a selection unit 4, a data calculation unit 5, and an output unit 6. The acquisition unit 1 acquires information on an electroencephalogram signal and a plurality of stimulus presentation timings related to the electroencephalogram signal. The preprocessing unit 2 extracts a plurality of electroencephalogram data corresponding to the plurality of stimulus presentation timings one-to-one from the electroencephalogram signal based on the electroencephalogram signal acquired by the acquisition unit 1 and information on the plurality of stimulus presentation timings. The correlation calculation unit 3 calculates correlation data relating to the plurality of brain wave data extracted by the pre-processing unit 2. The selection unit 4 selects a group of brain wave data from a plurality of brain wave data based on the correlation data calculated by the correlation calculation unit 3. The data calculation unit 5 calculates the averaged electroencephalogram data by averaging the group of electroencephalogram data selected by the selection unit 4. The output unit 6 outputs brain wave information including the averaged brain wave data calculated by the data calculation unit 5.

  The acquisition unit 1 acquires an electroencephalogram signal from the electroencephalogram measurement unit 11. Further, the acquisition unit 1 acquires information on a plurality of stimulus presentation timings related to the brain wave signal from the visual stimulus control unit 15. The electroencephalogram measurement unit 11 and the visual stimulus control unit 15 are not components of any of the electroencephalogram analysis system 10 and the visibility evaluation system 100, but for convenience of explanation, before describing each component of the electroencephalogram analysis system 10 in more detail. The brain wave measurement unit 11 and the visual stimulus control unit 15 will be described. The electroencephalogram measurement unit 11 may be a component of the electroencephalogram analysis system 10. At least one of the electroencephalogram measurement unit 11 and the visual stimulus control unit 15 may be a component of the visibility evaluation system 100.

  The electroencephalogram measurement unit 11 measures the electroencephalogram of the subject. The electroencephalogram measurement unit 11 is, for example, an electroencephalograph. The electroencephalogram measurement unit 11 detects an electroencephalogram signal by, for example, measuring potential changes at a plurality of electrodes mounted on the subject's head.

  The plurality of electrodes mounted on the subject's head include a lead electrode, a reference electrode, and a ground electrode. The plurality of electrodes are arranged, for example, on the basis of the international 10-20 method, for example, by arranging a lead electrode at Pz (median parietal), a reference electrode at A1 (right earlobe), and a ground electrode at the forehead. The lead-out electrode may be arranged at Cz (cranial parietal) or Oz (occipital) around Pz instead of Pz (median parietal). The electroencephalograph may be a head-mounted electroencephalograph.

  The acquisition unit 1 acquires, for example, information on a plurality of stimulus presentation timings related to the electroencephalogram signal from the visual stimulus control unit 15. The visual stimulus control unit 15 controls a stimulus presentation timing for presenting a visual target (visual stimulus) in the simulated visual environment of the subject. The visual stimulus is, for example, a disc-shaped presentation member rotatably supported by a support member that is driven to rotate by a motor. In the presentation member, the first main surface is a presentation surface, the second main surface is a non-presentation surface, the color of the first main surface is white, and the color of the second surface is black. In the presenting member, the first main surface is located on the subject side when the visual stimulus is presented, and the second principal surface is located on the subject side when the visual stimulus is not presented. The visual stimulus control unit 15 can be realized, for example, by causing a computer to execute an appropriate program.

  The simulated visual environment is an image generated by photographing a visual environment (front of the vehicle) that is visible while the vehicle is driving while the driver sitting on the driver's seat of the vehicle faces forward. The visual environment is an environment that can be seen by the driver when the vehicle is running on a road with the headlights (headlights) of the vehicle turned on at night. Therefore, the simulated viewing environment is an image that reproduces a situation in which the headlights of the vehicle illuminate the front of the vehicle. The image here is a moving image. The simulated visual environment is displayed as an image on a display unit in front of the subject.

  The video is not limited to the captured image itself, and may be, for example, an image obtained by performing image processing on the captured image, or a CG (Computer Graphics) image created based on the captured image. For example, since an image captured by a camera becomes dark at night, an image based on a captured image may be an image obtained by performing brightness correction on an image captured by a camera. Further, the image based on the captured image may be an image in which an obstacle is extracted from an image captured by a camera and a CG (Computer Graphics) image indicating the obstacle is superimposed on the captured image.

  A gaze point that the subject gazes at is superimposed on the image displayed by the display unit. The fixation point is displayed near the center in the left-right direction of the simulated visual environment displayed on the display unit. The position of the gazing point may be changed according to the shape of the road in the video displayed as the simulated viewing environment. For example, when the road curves to the left, the gazing point may be displayed so as to be deviated leftward from the center of the simulated viewing environment in the left-right direction.

  The electroencephalogram analysis system 10 further includes the simple averaging unit 7. The simple averaging unit 7 calculates averaging electroencephalogram data (simple averaging electroencephalogram data) by performing averaging of a plurality of electroencephalogram data extracted by the preprocessing unit 2. The correlation calculation unit 3 calculates a correlation coefficient between each of the plurality of brain wave data extracted by the preprocessing unit 2 and the averaged brain wave data calculated by the simple averaging unit 7 as correlation data. The selecting unit 4 selects, from the plurality of brain wave data, brain wave data corresponding to a correlation coefficient that is equal to or greater than a predetermined value among the plurality of correlation coefficients calculated by the correlation calculating unit 3.

  The electroencephalogram analysis system 10 further includes an analysis unit 8. The analysis unit 8 extracts a feature amount related to the event-related potential from the averaged electroencephalogram data calculated by the data calculation unit 5. The electroencephalogram information output from the output unit 6 includes information on the feature amount extracted by the analysis unit 8.

  The feature quantity relating to the event-related potential is, for example, an event-related potential and the latency of the event-related potential. Here, the “latency” is the time from when the visual stimulus occurs to when the peak of P300 appears. In other words, the “latency” is a point in time from when the visual stimulus is presented (the point of occurrence) to when the potential reaches a peak in the above-described range corresponding to P300 of the electroencephalogram.

  The visibility evaluation system 100 includes an electroencephalogram analysis system 10, a determination unit 12, and a result output unit 13. The electroencephalogram measurement unit 11 measures the electroencephalogram of the subject and outputs an electroencephalogram signal. The determination unit 12 determines the visibility of the subject based on the brain wave information output from the output unit 6 of the brain wave analysis system 10. The result output unit 13 outputs the result determined by the determination unit 12.

(2.2) Details of EEG analysis system and visibility evaluation system Hereinafter, the EEG analysis system and the visibility evaluation system will be described in more detail.

  As described above, the electroencephalogram analysis system 10 includes the acquisition unit 1, the preprocessing unit 2, the correlation calculation unit 3, the selection unit 4, the data calculation unit 5, and the output unit 6. Further, the electroencephalogram analysis system 10 further includes the simple averaging unit 7 and further includes the analysis unit 8.

  The acquisition unit 1 acquires an electroencephalogram signal from the electroencephalogram measurement unit 11. Further, the acquisition unit 1 acquires information on a plurality of visual stimulus presentation timings from the visual stimulus control unit 15. The acquisition unit 1 may acquire at least one of an electroencephalogram signal and information on a plurality of visual stimulus presentation timings from an external storage device.

  The preprocessing unit 2 divides the electroencephalogram signal acquired from the electroencephalogram measurement unit 11 into analysis sections for each visual stimulus presentation timing based on the information on the plurality of visual stimulus presentation timings acquired from the visual stimulus control unit 15, and Extract data. Each of the plurality of analysis sections is, for example, a section from 100 ms before the corresponding visual stimulus presentation timing to 600 ms after the visual stimulus presentation timing. The preprocessing unit 2 can align the starting points T0 of a plurality of (for example, 20) electroencephalogram data corresponding to a plurality of visual stimulus presentation timings on one time axis (see FIG. 2). The acquisition unit 1 includes a storage unit that stores a plurality of brain wave data.

  The preprocessing unit 2 determines an addition start point T0 of a plurality of brain wave data corresponding to a plurality of visual stimulus presentation timings one-to-one (see FIGS. 3A and 3C). More specifically, the preprocessing unit 2 extracts, from the electroencephalogram signals acquired by the acquisition unit 1, electroencephalogram data in an analysis section from -100 ms to 600 milliseconds starting from each visual stimulus presentation timing. Note that the time width for extracting the electroencephalogram data (time width of the analysis section) is defined as a range that always includes the P300 component of the event-related potential. If the P300 component is included, the brain wave data may be extracted with a time width different from this time width.

  The pre-processing unit 2 includes, for example, a bandpass filter of 0.05 to 30 Hz in order to reduce noise included in brain wave data. In addition, it is preferable that the pre-processing unit 2 removes the influence of the potential change on the brain activity irrespective of the visual target, which constantly and frequently occurs in the brain wave signal of the subject. For example, the preprocessing unit 2 removes electroencephalogram data including a potential equal to or higher than a predetermined potential (for example, 80 μV) in order to remove in advance the electroencephalogram data including the potential change due to the subject's blink during the trial. Here, the trial includes presenting a visual stimulus to a subject whose brain waves are being measured.

The correlation calculation unit 3 calculates correlation data relating to the plurality of brain wave data extracted by the pre-processing unit 2. By the way, as described above, the electroencephalogram analysis system 10 includes the simple averaging unit 7, and the correlation calculation unit 3 uses the plurality of brain wave data (see FIG. 2) extracted by the preprocessing unit 2 and the simple averaging unit. The correlation function with the averaged electroencephalogram data (see FIG. 3A) calculated in step 7 is calculated as correlation data. Correlation function, for example, each of the plurality of brain wave data x, the average of each of the plurality of brain wave data x _a, the total arithmetic mean waveform data y, the mean of all the arithmetic mean waveform data and y _a, the correlation coefficient Is calculated by the following formula (1), where r is r. Here, r is a correlation coefficient between x and y. Further, each electroencephalogram data is time-series data, x _i is the value of the i-th data of x, and y _i is the value of the i-th data of y.

  Table 1 below shows the correlation coefficients calculated by the equation (1). In Table 1, the phases calculated from the electroencephalogram signal obtained when 20 trials were performed on one subject and the 20 electroencephalogram data and the averaged electroencephalogram data corresponding to each of the first to 20th trials. The relation number is described.

  The selection unit 4 selects a group of brain wave data from a plurality of brain wave data based on the correlation data (correlation coefficient) calculated by the correlation calculation unit 3. The selecting unit 4 selects a group of electroencephalogram data corresponding to a correlation coefficient that is equal to or greater than a predetermined value among the plurality of correlation coefficients calculated by the correlation calculation unit 3 from the plurality of electroencephalogram data. Here, the predetermined value is, for example, a value of [average value of a plurality of correlation coefficients] − [standard deviation of a plurality of correlation coefficients]. Thus, the selecting unit 4 excludes brain wave data (see FIG. 3B) having a small correlation coefficient from the plurality of brain wave data. In Table 1, the trial and the correlation coefficient corresponding to the brain wave data having a small correlation coefficient are shaded. That is, in Table 1, the electroencephalogram data at the time of the first trial, the electroencephalogram data at the time of the third trial, the electroencephalogram data at the time of the tenth trial, and the averaging electroencephalogram calculated by the simple averaging unit 7 The correlation coefficient with the data is smaller than a predetermined value.

  The data calculation unit 5 calculates the averaged electroencephalogram data by averaging the group of electroencephalogram data selected by the selection unit 4. FIG. 3C is a waveform of the averaged electroencephalogram data calculated by the data calculation unit 5. In FIG. 3D, the averaged electroencephalogram data (selective averaged waveform) calculated by the data calculation unit 5 is indicated by a solid line, and the averaged electroencephalogram data (all trials averaged waveform) calculated by the simple averaged unit 7 is shown. Shown by broken lines. From FIG. 3D, it can be seen that the waveform of P300 is sharper in the selective averaging waveform.

  The output unit 6 outputs brain wave information including the averaged brain wave data calculated by the data calculation unit 5. In the electroencephalogram analysis system 10 according to the first embodiment, the output unit 6 outputs the electroencephalogram information including the averaged electroencephalogram data calculated by the data calculation unit 5 and the feature amount related to the event-related potential extracted by the analysis unit 8. For example, it outputs to the determination unit 12 of the visibility evaluation system 100. As the feature quantity relating to the event-related potential, there is, for example, the latency and the amplitude of P300.

  FIG. 4A shows the averaged electroencephalogram data (event-related potential) calculated by the data calculation unit 5 for each of the different conditions 1, 2, 3, and 4. FIG. 4B is an explanatory diagram of the latency of the event-related potential in FIG. 4A. More specifically, FIG. 4B shows the time point at which the plus potential reaches the maximum value around 300 msec (for example, 250 msec to 600 msec) from the visual stimulus presentation timing in relation to the latency of P300.

  Conditions 1, 2, 3, and 4 have different experimental conditions. Experimental conditions are trial conditions. Condition 4, condition 3, condition 2, and condition 1 are conditions in which the subject is likely to notice the visual stimulus in this order. That is, condition 4 is a condition in which the subject is most likely to notice the visual stimulus, and condition 1 is a condition in which the subject is least noticeable to the visual stimulus. The experimental conditions include, for example, the optical characteristics (light distribution characteristics, luminance, color, etc.) of the headlight of the automobile, the magnitude, color, moving speed, position, and the like of the visual stimulus.

  From Table 2, it can be understood that the latency is shortest in the case of condition 4 among the conditions 1 to 4, and the latency is increased in the order of condition 4, condition 3, condition 2, and condition 1.

  The determination unit 12 of the visibility evaluation system 100 determines the visibility of the driver based on the latency of the event-related potential included in the brain wave information. The determination unit 12 determines that the visibility is higher as the latency is shorter, and determines that the visibility is lower as the latency is longer. The determination unit 12 outputs the determination result to the result output unit 13.

  The result output unit 13 of the visibility evaluation system 100 may be, for example, a device that can output at least one of an image and a sound. The image is output using a display device such as a liquid crystal display device or an OLED (Organic Light Emitting Diode) display. The sound is output using a speaker. The result output unit 13 presents the determination result of the determination unit 12 by at least one of an image and a sound.

  The electroencephalogram analysis system 10 according to the present disclosure includes a computer system. The computer system mainly has a processor and a memory as hardware. When the processor executes the program recorded in the memory of the computer system, the functions as the electroencephalogram analysis system 10 according to the present disclosure (the acquisition unit 1, the preprocessing unit 2, the correlation calculation unit 3, the selection unit 4, the data calculation unit 5) , The output unit 6, the simple averaging unit 7, and the analysis unit 8). The program may be pre-recorded in the memory of the computer system, may be provided through an electric communication line, or may be recorded in a non-transitory recording medium such as a memory card, an optical disk, or a hard disk drive readable by the computer system. May be provided. A processor of a computer system is composed of one or more electronic circuits including a semiconductor integrated circuit (IC) or a large-scale integrated circuit (LSI). An integrated circuit such as an IC or an LSI referred to here differs depending on the degree of integration, and includes an integrated circuit called a system LSI, a VLSI (Very Large Scale Integration), or a ULSI (Ultra Large Scale Integration). Furthermore, an FPGA (Field-Programmable Gate Array), which is programmed after the manufacture of the LSI, or a logic device capable of reconfiguring the connection relation inside the LSI or reconfiguring the circuit section inside the LSI, is also adopted as a processor. Can be. The plurality of electronic circuits may be integrated on one chip, or may be provided separately on a plurality of chips. The plurality of chips may be integrated in one device, or may be provided separately in a plurality of devices. The computer system includes a microcontroller having one or more processors and one or more memories. Therefore, the microcontroller is also composed of one or more electronic circuits including a semiconductor integrated circuit or a large-scale integrated circuit.

  In addition, the fact that a plurality of functions in the electroencephalogram analysis system 10 are integrated in one housing is not an essential configuration of the electroencephalogram analysis system 10, and the components of the electroencephalogram analysis system 10 are distributed over a plurality of housings. May be provided. Furthermore, at least a part of the function of the electroencephalogram analysis system 10 may be realized by a cloud (cloud computing) or the like.

  The visibility evaluation system 100 according to the present disclosure includes a computer system of the electroencephalogram analysis system 10. Further, the visibility evaluation system 100 includes a computer system separately from the electroencephalogram analysis system 10. The computer system mainly has a processor and a memory as hardware. When the processor executes the program recorded in the memory of the computer system, the function of the visibility evaluation system 100 (the function of the determination unit 12) in the present disclosure is realized. The program may be pre-recorded in the memory of the computer system, may be provided through an electric communication line, or may be recorded in a non-transitory recording medium such as a memory card, an optical disk, or a hard disk drive readable by the computer system. May be provided. The determination unit 12 of the visibility evaluation system 100 may be realized by a computer system of the electroencephalogram analysis system 10 executing a program.

(2.2) Operation of EEG Analysis System The operation of the EEG analysis system 10 will be described based on the flowchart of FIG.

  First, the acquisition unit 1 acquires data (information on an electroencephalogram signal and a plurality of stimulus presentation timings) (step S1).

  Next, the pre-processing unit 2 performs pre-processing of the data acquired by the acquiring unit 1 (step S2). In step S2, the preprocessing unit 2 extracts a plurality of electroencephalogram data corresponding to the plurality of stimulus presentation timings one-to-one from the electroencephalogram signal based on the electroencephalogram signal acquired by the acquisition unit 1 and the information on the plurality of stimulus presentation timings. (Step S2). Note that the pre-processing includes a filtering process for removing noise of the brain wave signal and a process of extracting brain wave data for each of a plurality of analysis sections from the brain wave signal. Each of the plurality of stimulus presentation timings corresponds to one trial. Therefore, if there are a plurality of stimulus presentation timings, it means that the same number of trials as the number of stimulus presentation timings have been performed.

  Next, the simple averaging unit 7 performs a first averaging process (step S3). Here, the first averaging process is a process of calculating averaging electroencephalogram data (simple averaging electroencephalogram data) by averaging a plurality of electroencephalogram data extracted by the preprocessing unit 2. Here, the plurality of EEG data are EEG data of all trials. The simple averaging electroencephalogram data is averaging electroencephalogram data obtained by averaging a plurality of electroencephalogram data.

  Next, the correlation calculation unit 3 calculates correlation data regarding the plurality of brain wave data extracted by the pre-processing unit 2, and the selection unit 4 calculates the correlation data from the plurality of brain wave data based on the correlation data calculated in the third step. A group of brain wave data is selected (step S4). In step S4, first, the correlation calculation unit 3 calculates correlation data regarding a plurality of brain wave data corresponding to all trials extracted by the preprocessing unit 2. After that, the selecting unit 4 selects a group of brain wave data from the plurality of brain wave data based on the correlation data calculated by the correlation calculating unit 3. Here, the selecting unit 4 selects a group of electroencephalogram data corresponding to a group of trials to be subjected to a second averaging process to be described later among all the electroencephalogram data corresponding to all trials.

  Next, the data calculation unit 5 performs a second averaging process (step S5). Here, the second averaging process is a process of calculating averaging brain wave data by averaging the group of brain wave data selected by the selection unit 4.

  Next, the analysis unit 8 extracts a feature amount of an event-related potential (ERP) (step S6). In step S <b> 6, the analysis unit 8 extracts a feature amount related to the event-related potential from the averaged electroencephalogram data calculated by the data calculation unit 5. The analysis unit 8 extracts the latency and the amplitude of P300 from the averaged electroencephalogram data as the feature amount of the event-related potential.

  Next, the output unit 6 outputs data (electroencephalogram information including the averaged electroencephalogram data calculated by the data calculation unit 5 and the information on the feature amount of the event-related potential extracted by the analysis unit 8) ( Step S7).

(3) Effects The electroencephalogram analysis system 10 according to the first embodiment includes an acquisition unit 1, a preprocessing unit 2, a correlation calculation unit 3, a selection unit 4, a data calculation unit 5, and an output unit 6. . The acquiring unit 1 acquires information on an electroencephalogram signal and a plurality of stimulus presentation timings related to the electroencephalogram signal. The preprocessing unit 2 extracts a plurality of electroencephalogram data corresponding to the plurality of stimulus presentation timings one-to-one from the electroencephalogram signal based on the electroencephalogram signal acquired by the acquisition unit 1 and information on the plurality of stimulus presentation timings. The correlation calculation unit 3 calculates correlation data relating to the plurality of brain wave data extracted by the pre-processing unit 2. The selection unit 4 selects a group of brain wave data from a plurality of brain wave data based on the correlation data calculated by the correlation calculation unit 3. The data calculation unit 5 calculates the averaged electroencephalogram data by averaging the group of electroencephalogram data selected by the selection unit 4. The output unit 6 outputs brain wave information including the averaged brain wave data calculated by the data calculation unit 5.

  In the electroencephalogram analysis system 10 according to the first embodiment, it is possible to improve the accuracy of the averaged electroencephalogram data while reducing the number of electroencephalogram data to be averaged.

  In each of the above conditions 1 to 4, the number of trials is set to 15, and the averaged electroencephalogram data obtained by averaging a plurality of (15) brain wave data corresponding to the total number of trials is shown in FIG. FIG. 6B shows the averaged electroencephalogram data obtained by averaging the selected group (13) of electroencephalogram data. As described above, conditions 1, 2, 3, and 4 are different from each other in experimental conditions. The condition 4, the condition 3, the condition 2, and the condition 1 are conditions in which the driver is more likely to notice the visual stimulus in this order: latency in the case of condition 4 <latency in the case of condition 3, and latency in the case of condition 2. Ideally, the latency in the case of condition <condition 1 is satisfied. Comparing FIG. 6A and FIG. 6B, it can be seen that the waveform of P300 is sharper in FIG. 6B.

  FIG. 7A shows the latency obtained from the averaged electroencephalogram data obtained by averaging a plurality of electroencephalogram data corresponding to the total number of trials for each of the cases where the number of trials is changed in each of the conditions 1 to 4. FIG. 7B shows the latency obtained from the averaged electroencephalogram data obtained by adding and averaging the group of electroencephalogram data selected in.

  From FIG. 7A, when the latency is obtained from the averaged electroencephalogram data obtained by averaging a plurality of electroencephalogram data corresponding to the total number of trials, when the number of trials is 14 or less, the ranking of the latency among the conditions 1 to 4 However, it turns out that it changes compared with the case where the number of trials is 15 or more. On the other hand, from FIG. 7B, when the latency is obtained from the averaged electroencephalogram data obtained by averaging the group of electroencephalogram data selected by the selection unit 4, if the number of trials becomes 12 or less, the conditions between conditions 1 to 4 It can be seen that the ranking of the latency in fluctuates compared to the case where the number of trials is 13 or more. From the above, it is understood that the electroencephalogram analysis system 10 according to the first embodiment can improve the accuracy of the averaged electroencephalogram data while reducing the number of electroencephalogram data to be averaged.

  The visibility evaluation system 100 according to the first embodiment includes an electroencephalogram analysis system 10, a determination unit 12, and a result output unit 13. The determination unit 12 determines the visibility of the subject based on the brain wave information output from the output unit 6 of the brain wave analysis system 10. The result output unit 13 outputs the result determined by the determination unit 12. Here, the subject is a person who has generated an electroencephalogram signal acquired by the electroencephalogram analysis system 10.

  In the visibility evaluation system 100 according to the first embodiment, it is possible to improve the accuracy of the averaged electroencephalogram data while reducing the number of electroencephalogram data to be averaged.

  Further, the electroencephalogram analysis system 10 according to the first embodiment further includes the simple averaging unit 7. The simple averaging unit 7 calculates averaging electroencephalogram data by performing averaging of a plurality of electroencephalogram data extracted by the preprocessing unit 2. The correlation calculation unit 3 calculates a correlation coefficient between each of the plurality of brain wave data extracted by the preprocessing unit 2 and the averaged brain wave data calculated by the simple averaging unit 7 as correlation data. The selecting unit 4 selects, from the plurality of brain wave data, brain wave data corresponding to a correlation coefficient that is equal to or greater than a predetermined value among the plurality of correlation coefficients calculated by the correlation calculating unit 3. Thus, in the electroencephalogram analysis system 10 according to the first embodiment, since the electroencephalogram data whose correlation coefficient with the averaging electroencephalogram data calculated by the simple averaging unit 7 is less than the predetermined value is not selected by the selection unit 4, the averaging is performed. It is possible to improve the accuracy of the averaged EEG data while reducing the number of EEG data to be executed.

  Further, the electroencephalogram analysis system 10 according to the first embodiment further includes an analysis unit 8. The analysis unit 8 extracts a feature amount related to the event-related potential from the averaged electroencephalogram data calculated by the data calculation unit 5. The electroencephalogram information output from the output unit 6 includes information on the feature amount extracted by the analysis unit 8. Thus, the electroencephalogram analysis system 10 according to the first embodiment can output a feature amount related to an event-related potential.

  Further, the electroencephalogram analysis method according to the first embodiment includes a first step, a second step, a third step, a fourth step, a fifth step, and a sixth step. The first step is to acquire information on an electroencephalogram signal and a plurality of stimulus presentation timings related to the electroencephalogram signal. The second step extracts a plurality of electroencephalogram data corresponding to the plurality of stimulus presentation timings one-to-one from the electroencephalogram signal based on the information on the electroencephalogram signals and the plurality of stimulus presentation timings acquired in the first step. The third step calculates correlation data relating to the plurality of brain wave data extracted in the second step. The fourth step selects a group of brain wave data from the plurality of brain wave data based on the correlation data calculated in the third step. In the fifth step, the averaged brain wave data is calculated by averaging the group of brain wave data selected in the fourth step. The sixth step outputs brain wave information including the averaged brain wave data calculated in the fifth step.

  In the electroencephalogram analysis method according to the first embodiment, it is possible to improve the accuracy of the averaged electroencephalogram data while reducing the number of electroencephalogram data to be averaged.

  The program according to the first embodiment is a program for causing a computer system to execute a first process, a second process, a third process, a fourth process, a fifth process, and a sixth process. . The first process acquires information on an electroencephalogram signal and a plurality of stimulus presentation timings related to the electroencephalogram signal. The second process extracts a plurality of electroencephalogram data corresponding to the plurality of stimulus presentation timings one-to-one from the electroencephalogram signal based on the electroencephalogram signal and the information on the plurality of stimulus presentation timings acquired in the first process. The third process calculates correlation data relating to the plurality of brain wave data extracted in the second process. The fourth process selects a group of brain wave data from the plurality of brain wave data based on the correlation data calculated in the third process. The fifth process calculates the averaged electroencephalogram data by averaging the group of electroencephalogram data selected in the fourth process. The sixth process outputs brain wave information including the averaged brain wave data calculated in the fifth process.

  In the program according to the first embodiment, it is possible to improve the accuracy of the averaged electroencephalogram data while reducing the number of electroencephalogram data to be averaged.

(Embodiment 2)
Hereinafter, an electroencephalogram analysis system 10a according to the second embodiment and a visibility evaluation system 100a including the same will be described with reference to FIG.

  The electroencephalogram analysis system 10a according to the second embodiment does not include the simple averaging unit 7 of the electroencephalogram analysis system 10 according to the first embodiment, and calculates the correlation of the electroencephalogram analysis system 10 according to the first embodiment. Instead of the unit 3 and the selection unit 4, a correlation calculation unit 3a and a selection unit 4a are provided. Regarding the electroencephalogram analysis system 10a according to the second embodiment, the same components as those of the electroencephalogram analysis system 10 according to the first embodiment are denoted by the same reference numerals, and description thereof will be omitted as appropriate.

  In the electroencephalogram analysis system 10a according to the second embodiment, similarly to the electroencephalogram analysis system 10 according to the first embodiment, the preprocessing unit 2 extracts a plurality of electroencephalogram data from the electroencephalogram signal acquired by the acquisition unit 1. The plurality of electroencephalogram data correspond one-to-one to all trials (multiple trials). The preprocessing unit 2 can align the starting points T0 of a plurality of (for example, 20) electroencephalogram data corresponding to a plurality of visual stimulus presentation timings on one time axis (see FIG. 2).

  The correlation calculation unit 3a calculates the correlation coefficient of each of the plurality of electroencephalogram data extracted by the preprocessing unit 2 with each of the remaining electroencephalogram data on a brute force basis, and then calculates a correlation coefficient average for each of the plurality of electroencephalogram data. The value is calculated as correlation data. The selecting unit 4a selects, from the plurality of brain wave data, brain wave data corresponding to a correlation coefficient average value that is equal to or more than a predetermined value among the plurality of correlation coefficient average values calculated by the correlation calculation unit 3.

  Table 3 below shows examples of correlation coefficients for each combination of round robin calculated by the above-described correlation calculation unit 3a.

  Table 4 below shows an example of the average correlation coefficient for each of a plurality of brain wave data calculated by the above-described correlation calculation unit 3a.

  The selecting unit 4a selects, from the plurality of brain wave data, brain wave data corresponding to a correlation coefficient average value that is equal to or more than a predetermined value among the plurality of correlation coefficient average values calculated by the correlation calculation unit 3. Here, the predetermined value is, for example, a value of [average of a plurality of correlation coefficient averages] − [standard deviation of a plurality of correlation coefficient averages]. Thus, the selecting unit 4a excludes the electroencephalogram data having a small average correlation coefficient from the plurality of electroencephalogram data. In Table 4, the brain wave data having a small average correlation coefficient and the average correlation coefficient are shaded. That is, in Table 4, the average value of the correlation coefficients of the brain wave data at the first trial and the brain wave data at the tenth trial is small.

  The data calculation unit 5 calculates the averaged electroencephalogram data (see FIG. 9) by averaging the group of electroencephalogram data selected by the selection unit 4a.

  The operation of the electroencephalogram analysis system 10a according to the second embodiment will be described with reference to the flowchart in FIG. The description of the same operation as that of the electroencephalogram analysis system 10 according to the first embodiment will be appropriately omitted.

  First, the acquisition unit 1 acquires data (information on an electroencephalogram signal and a plurality of stimulus presentation timings) (step S11). Step S11 is the same as step S1 in the follow chart of FIG. 5 for explaining the operation of the electroencephalogram analysis system 10 according to the first embodiment.

  Next, the preprocessing unit 2 performs preprocessing of the data acquired by the acquisition unit 1 (step S12). In step S12, the preprocessing unit 2 extracts a plurality of electroencephalogram data corresponding to the plurality of stimulus presentation timings one-to-one from the electroencephalogram signal based on the electroencephalogram signal acquired by the acquisition unit 1 and information on the plurality of stimulus presentation timings. . Step S12 is the same as step S2 in the flowchart of FIG.

  Next, the correlation calculating unit 3a calculates the above-described average values of the plurality of correlation coefficients, and the selecting unit 4a selects a group of EEG data from the plurality of EEG data based on the plurality of average correlation coefficient values ( Step S13).

  Next, the data calculation unit 5 performs an averaging process (step S14). Here, the averaging process is a process of calculating the averaging brain wave data by averaging the group of brain wave data selected by the selection unit 4a.

  Next, the analysis unit 8 extracts a feature amount of an event-related potential (ERP) (step S15). Here, in step S <b> 15, the analysis unit 8 extracts a feature amount related to an event-related potential from the averaged electroencephalogram data calculated by the data calculation unit 5. The analysis unit 8 extracts the latency and the amplitude of P300 from the averaged electroencephalogram data as the feature amount of the event-related potential.

  Next, the output unit 6 outputs data (electroencephalogram information including the averaged electroencephalogram data calculated by the data calculation unit 5 and the information on the feature amount of the event-related potential extracted by the analysis unit 8) ( Step S16).

  The electroencephalogram analysis system 10a according to the second embodiment includes an acquisition unit 1, a preprocessing unit 2, a correlation calculation unit 3a, a selection unit 4a, a data calculation unit 5, and an output unit 6. The acquiring unit 1 acquires information on an electroencephalogram signal and a plurality of stimulus presentation timings related to the electroencephalogram signal. The preprocessing unit 2 extracts a plurality of electroencephalogram data corresponding to the plurality of stimulus presentation timings one-to-one from the electroencephalogram signal based on the electroencephalogram signal acquired by the acquisition unit 1 and information on the plurality of stimulus presentation timings. The correlation calculation unit 3a calculates correlation data relating to the plurality of brain wave data extracted by the preprocessing unit 2. The selection unit 4a selects a group of brain wave data from a plurality of brain wave data based on the correlation data calculated by the correlation calculation unit 3a. The data calculation unit 5 calculates the averaged electroencephalogram data by averaging the group of electroencephalogram data selected by the selection unit 4a. The output unit 6 outputs brain wave information including the averaged brain wave data calculated by the data calculation unit 5.

  In the electroencephalogram analysis system 10a according to the second embodiment, it is possible to improve the accuracy of the averaged electroencephalogram data while reducing the number of electroencephalogram data to be averaged.

  Further, in the electroencephalogram analysis system 10a according to the second embodiment, the correlation calculation unit 3a calculates a correlation coefficient in each brute force combination of the plurality of electroencephalogram data extracted by the preprocessing unit 2, and then calculates a plurality of electroencephalogram data. The average value of the correlation coefficient for each is calculated as correlation data. The selecting unit 4a selects, from the plurality of brain wave data, brain wave data corresponding to a correlation coefficient average value that is equal to or greater than a predetermined value among the plurality of correlation coefficient average values calculated by the correlation calculation unit 3a. Thereby, in the electroencephalogram analysis system 10a according to the second embodiment, the electroencephalogram data corresponding to the correlation coefficient average value smaller than the predetermined value among the plurality of correlation coefficient average values calculated by the correlation calculation unit 3a is selected by the selection unit 4a. Is not selected, it is possible to improve the accuracy of the averaged EEG data while reducing the number of EEG data to be averaged.

  Further, the visibility evaluation system 100a according to the second embodiment includes an electroencephalogram analysis system 10a, a determination unit 12, and a result output unit 13. The determination unit 12 determines the visibility of the subject based on the brain wave information output from the output unit 6 of the brain wave analysis system 10a. The result output unit 13 outputs the result determined by the determination unit 12.

  In the visibility evaluation system 100a according to the second embodiment, it is possible to improve the accuracy of the averaged electroencephalogram data while reducing the number of electroencephalogram data to be averaged.

(Embodiment 3)
Hereinafter, an electroencephalogram analysis system 10b according to the third embodiment and a visibility evaluation system 100b including the same will be described with reference to FIG.

  The electroencephalogram analysis system 10b according to the third embodiment includes an averaging unit 9, instead of the simple averaging unit 7, the correlation calculation unit 3, and the selection unit 4 of the electroencephalogram analysis system 10 according to the first embodiment. The apparatus includes a correlation calculator 3b and a selector 4b. Regarding the electroencephalogram analysis system 10b according to the third embodiment, the same components as those of the electroencephalogram analysis system 10 according to the first embodiment are denoted by the same reference numerals, and description thereof will be omitted as appropriate.

  In the electroencephalogram analysis system 10b according to the third embodiment, similarly to the electroencephalogram analysis system 10 according to the first embodiment, the preprocessing unit 2 extracts a plurality of electroencephalogram data from the electroencephalogram signal acquired by the acquisition unit 1. The plurality of electroencephalogram data correspond one-to-one to all trials (multiple trials). The preprocessing unit 2 can align the starting points T0 of a plurality of (for example, 20) electroencephalogram data corresponding to a plurality of visual stimulus presentation timings on one time axis (see FIG. 2).

  The averaging unit 9 averages the plurality of averaging data (see FIG. 12A) by averaging the remaining brain wave data for each of the cases where one of the plurality of brain wave data extracted by the preprocessing unit 2 is removed. calculate.

  The correlation calculator 3b calculates the correlation coefficient for each of all combinations of the plurality of averaged data calculated by the averaging unit 9, and then calculates the average correlation coefficient for each averaged data as the correlation data. .

  The selection unit 4b selects brain wave data corresponding to the correlation coefficient average value that is equal to or greater than a predetermined value among the plurality of correlation coefficient average values calculated by the correlation calculation unit 3b. Here, the predetermined value is, for example, a value of [average of a plurality of correlation coefficient averages] − [standard deviation of a plurality of correlation coefficient averages]. Thus, the selecting unit 4b excludes the electroencephalogram data having a small average correlation coefficient from the plurality of electroencephalogram data.

  Table 5 below shows an example of the average value of the correlation function calculated by the above-described correlation calculation unit 3b. In Table 5 below, a trial that corresponds one-to-one to one brain wave data that has been removed from a plurality of brain wave data is set as a removal trial. In Table 5, the electroencephalogram data whose correlation coefficient average value is smaller than a predetermined value and the correlation coefficient average value are shaded. That is, in Table 5, the correlation coefficient average value of the brain wave data at the 13th trial, the brain wave data at the 15th trial, and the brain wave data at the 16th trial is smaller than the predetermined value.

  The data calculation unit 5 calculates the averaged electroencephalogram data (see FIG. 12B) by averaging the group of electroencephalogram data selected by the selection unit 4b.

  The electroencephalogram analysis system 10b according to the third embodiment includes an acquisition unit 1, a preprocessing unit 2, a correlation calculation unit 3b, a selection unit 4b, a data calculation unit 5, and an output unit 6. The acquiring unit 1 acquires information on an electroencephalogram signal and a plurality of stimulus presentation timings related to the electroencephalogram signal. The preprocessing unit 2 extracts a plurality of electroencephalogram data corresponding to the plurality of stimulus presentation timings one-to-one from the electroencephalogram signal based on the electroencephalogram signal acquired by the acquisition unit 1 and information on the plurality of stimulus presentation timings. The correlation calculation unit 3b calculates correlation data relating to the plurality of brain wave data extracted by the preprocessing unit 2. The selection unit 4b selects a group of brain wave data from a plurality of brain wave data based on the correlation data calculated by the correlation calculation unit 3b. The data calculation unit 5 calculates the averaged electroencephalogram data by averaging the group of electroencephalogram data selected by the selection unit 4b. The output unit 6 outputs brain wave information including the averaged brain wave data calculated by the data calculation unit 5.

  In the electroencephalogram analysis system 10b according to the third embodiment, it is possible to improve the accuracy of the averaged electroencephalogram data while reducing the number of electroencephalogram data to be averaged.

  Further, the electroencephalogram analysis system 10b according to the third embodiment further includes the averaging unit 9. The averaging unit 9 calculates a plurality of averaging data by averaging the remaining brain wave data each time one of the plurality of brain wave data extracted by the preprocessing unit 2 is removed. The correlation calculating unit 3b calculates the correlation coefficient for each of all combinations of the plurality of averaged data calculated by the averaging unit 9, and then calculates the correlation coefficient average value for each of the averaged data as the correlation data. . The selecting unit 4b selects brain wave data corresponding to a correlation coefficient average value that is equal to or greater than a predetermined value among the plurality of correlation coefficient average values calculated by the correlation calculation unit 3b. Thereby, in the electroencephalogram analysis system 10b according to the third embodiment, the electroencephalogram data corresponding to the correlation coefficient average value smaller than the predetermined value among the plurality of correlation coefficient average values calculated by the correlation calculation unit 3b is selected by the selection unit 4b. Therefore, it is possible to improve the accuracy of the averaged EEG data while reducing the number of EEG data to be averaged.

  The visibility evaluation system 100b according to the third embodiment includes an electroencephalogram analysis system 10b, a determination unit 12, and a result output unit 13. The determination unit 12 determines the visibility of the subject based on the electroencephalogram information output from the output unit 6 of the electroencephalogram analysis system 10b. The result output unit 13 outputs the result determined by the determination unit 12.

  In the visibility evaluation system 100b according to the third embodiment, it is possible to improve the accuracy of the averaged electroencephalogram data while reducing the number of electroencephalogram data to be averaged.

  Embodiments 1 to 3 are only one of various embodiments of the present disclosure. Various changes can be made to the first to third embodiments according to the design and the like as long as the object of the present disclosure can be achieved.

  For example, the correlation data according to the first embodiment is not limited to the correlation coefficient obtained by Expression (1), and may be, for example, a correlation coefficient obtained by Expression (2) below.

  In the equation (2), X (t) is brain wave data, and Y (t + τ) is total addition average waveform data.

  Further, the electroencephalogram analysis systems 10, 10a, and 10b include the analysis unit 8, but are not limited thereto, and may have a configuration without the analysis unit 8. The electroencephalogram analysis systems 10, 10a, and 10b may be configured to output the averaged waveform data calculated by the data calculation unit 5 from the output unit 6 when the analysis unit 8 is not provided. In this case, the visibility evaluation systems 100, 100a, and 100b include an analysis unit that extracts a feature amount of an event-related potential from the averaged waveform data output from the output unit 6 of the electroencephalogram analysis systems 10, 10a, and 10b. Is also good.

  Further, the determination unit 12 of the visibility evaluation system 100 may determine the visibility based on not only the latency of P300 but also, for example, the latency and the amplitude of P300.

  The moving object assumed in the visibility evaluation system 100 is not limited to an automobile. For example, the moving object may be a moving object other than an automobile such as a motorcycle, a train, an aircraft, a construction machine, and a ship.

  In the electroencephalogram analysis system 10, the output unit 6 may be, for example, a device capable of outputting at least one of an image and a sound. The image is output using a display device such as a liquid crystal display device or an OLED (Organic Light Emitting Diode) display. The sound is output using a speaker. The output unit 6 presents the brain wave information by at least one of an image and a sound.

(5) Conclusion The following aspects are disclosed from the embodiments and the like described above.

  The electroencephalogram analysis system (10; 10a; 10b) according to the first aspect includes an acquisition unit (1), a preprocessing unit (2), a correlation calculation unit (3; 3a; 3b), and a selection unit (4; 4a; 4b), a data calculation unit (5), and an output unit (6). The acquisition unit (1) acquires information on an electroencephalogram signal and a plurality of stimulus presentation timings related to the electroencephalogram signal. The preprocessing unit (2) extracts a plurality of electroencephalogram data corresponding to the plurality of stimulus presentation timings one-to-one from the electroencephalogram signal based on the electroencephalogram signal acquired by the acquisition unit (1) and information on the plurality of stimulus presentation timings. . The correlation calculator (3; 3a; 3b) calculates correlation data relating to the plurality of brain wave data extracted by the preprocessor (2). The selection unit (4; 4a; 4b) selects a group of brain wave data from a plurality of brain wave data based on the correlation data calculated by the correlation calculation unit (3; 3a; 3b). The data calculation unit (5) calculates the averaged electroencephalogram data by averaging the group of electroencephalogram data selected by the selection unit (4; 4a; 4b). The output unit (6) outputs brain wave information including the averaged brain wave data calculated by the data calculation unit (5).

  In the electroencephalogram analysis system (10; 10a; 10b) according to the first aspect, it is possible to improve the accuracy of the averaged electroencephalogram data while reducing the number of electroencephalogram data to be averaged.

  The electroencephalogram analysis system (10) according to the second aspect is the same as the first aspect, further including a simple averaging unit (7). The simple averaging unit (7) calculates averaging brain wave data by performing averaging of the plurality of brain wave data extracted by the preprocessing unit (2). The correlation calculation unit (3) calculates, as correlation data, a correlation coefficient between each of the plurality of brain wave data extracted by the preprocessing unit (2) and the averaged brain wave data calculated by the simple averaging unit (7). . The selection unit (4) selects, from the plurality of brain wave data, brain wave data corresponding to a correlation coefficient that is equal to or greater than a predetermined value among the plurality of correlation coefficients calculated by the correlation calculation unit (3).

  In the electroencephalogram analysis system (10) according to the second aspect, the electroencephalogram data whose correlation coefficient with the averaging electroencephalogram data calculated by the simple averaging unit (7) is less than a predetermined value is not selected by the selection unit (4). Therefore, it is possible to improve the accuracy of the averaged EEG data while reducing the number of EEG data to be averaged.

  In the electroencephalogram analysis system (10a) according to the third aspect, in the first aspect, the correlation calculation unit (3a) includes a phase relationship in each of the brute force combinations of the plurality of electroencephalogram data extracted by the preprocessing unit (2). After calculating the number, the average value of the correlation coefficient for each of the plurality of brain wave data is calculated as the correlation data. The selection unit (4a) selects, from the plurality of brain wave data, brain wave data corresponding to a correlation coefficient average value that is equal to or greater than a predetermined value among the plurality of correlation coefficient average values calculated by the correlation calculation unit (3a). .

  In the electroencephalogram analysis system (10a) according to the third aspect, the electroencephalogram data corresponding to the correlation coefficient average value smaller than the predetermined value among the plurality of correlation coefficient average values calculated by the correlation calculation unit (3a) is selected. Since it is not selected in the section (4a), it is possible to improve the accuracy of the averaged electroencephalogram data while reducing the number of electroencephalogram data to be averaged.

  An electroencephalogram analysis system (10b) according to a fourth aspect is the first aspect, further comprising an averaging unit (9). The averaging unit (9) calculates a plurality of averaging data by averaging the remaining brain wave data in each case where one of the plurality of brain wave data extracted by the preprocessing unit (2) is removed. I do. The correlation calculating section (3b) calculates a correlation coefficient for each of all combinations of the plurality of averaged data calculated by the averaging section (9), and then calculates a correlation coefficient average value for each averaged data. Calculate as data. The selection unit (4b) selects brain wave data corresponding to a correlation coefficient average value that is equal to or greater than a predetermined value among the plurality of correlation coefficient average values calculated by the correlation calculation unit (3b).

  In the electroencephalogram analysis system (10b) according to the fourth aspect, the electroencephalogram data corresponding to the correlation coefficient average value smaller than the predetermined value among the plurality of correlation coefficient average values calculated by the correlation calculation unit (3b) is selected. Since the selection is not made by the section (4b), it is possible to improve the accuracy of the averaged brain wave data while reducing the number of the brain wave data to be averaged.

  An electroencephalogram analysis system (10; 10a; 10b) according to a fifth aspect is the electroencephalogram analysis system according to any one of the first to fourth aspects, further comprising an analysis unit (8). The analysis unit (8) extracts a feature amount related to the event-related potential from the averaged electroencephalogram data calculated by the data calculation unit (5). The electroencephalogram information output from the output unit (6) includes information on the feature amount extracted by the analysis unit (8).

  In the electroencephalogram analysis system (10; 10a; 10b) according to the fifth aspect, it is possible to output a feature amount related to an event-related potential.

  A visibility evaluation system (100; 100a; 100b) according to a sixth aspect includes an electroencephalogram analysis system (10; 10a; 10b) according to any one of the first to fifth aspects, a determination unit (12), and a result. An output unit (13). The determination unit (12) determines the visibility of the subject based on the brain wave information output from the output unit (6) of the brain wave analysis system (10; 10a; 10b). The result output unit (13) outputs the result determined by the determination unit (12).

  In the visibility evaluation system (100; 100a; 100b) according to the sixth aspect, it is possible to improve the accuracy of the averaged electroencephalogram data while reducing the number of electroencephalogram data to be averaged.

  In the visibility evaluation system (100; 100a; 100b) according to the seventh aspect, in the sixth aspect, the determining unit (12) determines the visibility of the subject from the latency of the event-related potential based on the brain wave information. .

  In the visibility evaluation system (100; 100a; 100b) according to the seventh aspect, the visibility can be determined based on the length of the latency.

  An electroencephalogram analysis method according to an eighth aspect includes a first step, a second step, a third step, a fourth step, a fifth step, and a sixth step. The first step is to acquire information on an electroencephalogram signal and a plurality of stimulus presentation timings related to the electroencephalogram signal. The second step extracts a plurality of electroencephalogram data corresponding to the plurality of stimulus presentation timings one-to-one from the electroencephalogram signal based on the information on the electroencephalogram signals and the plurality of stimulus presentation timings acquired in the first step. The third step calculates correlation data relating to the plurality of brain wave data extracted in the second step. The fourth step selects a group of brain wave data from the plurality of brain wave data based on the correlation data calculated in the third step. In the fifth step, the averaged brain wave data is calculated by averaging the group of brain wave data selected in the fourth step. The sixth step outputs brain wave information including the averaged brain wave data calculated in the fifth step.

  In the electroencephalogram analysis method according to the eighth aspect, it is possible to improve the accuracy of the averaged electroencephalogram data while reducing the number of electroencephalogram data to be averaged.

  A program according to a ninth aspect is a program for causing a computer system to execute a first process, a second process, a third process, a fourth process, a fifth process, and a sixth process. is there. The first process acquires information on an electroencephalogram signal and a plurality of stimulus presentation timings related to the electroencephalogram signal. The second process extracts a plurality of electroencephalogram data corresponding to the plurality of stimulus presentation timings one-to-one from the electroencephalogram signal based on the electroencephalogram signal and the information on the plurality of stimulus presentation timings acquired in the first process. The third process calculates correlation data relating to the plurality of brain wave data extracted in the second process. The fourth process selects a group of brain wave data from the plurality of brain wave data based on the correlation data calculated in the third process. The fifth process calculates the averaged electroencephalogram data by averaging the group of electroencephalogram data selected in the fourth process. The sixth process outputs brain wave information including the averaged brain wave data calculated in the fifth process.

  In the program according to the ninth aspect, it is possible to improve the accuracy of the averaged electroencephalogram data while reducing the number of electroencephalogram data to be averaged.

Reference Signs List 1 acquisition unit 2 preprocessing unit 3, 3a, 3b correlation calculation unit 4, 4a, 4b selection unit 5 data calculation unit 6 output unit 7 simple averaging unit 8 analysis unit 9 addition averaging unit 10, 10a, 10b EEG analysis system 11 EEG measurement unit 12 Judgment unit 13 Result output unit 15 Visual stimulus control unit 100, 100a, 100b Visibility evaluation system

Claims (9)

An acquisition unit that acquires information on a brain wave signal and a plurality of stimulus presentation timings related to the brain wave signal,
A preprocessing unit that extracts a plurality of brain wave data corresponding to the plurality of stimulus presentation timings one-to-one from the brain wave signal based on the information of the brain wave signal and the plurality of stimulus presentation timings acquired by the acquisition unit,
A correlation calculation unit that calculates correlation data for the plurality of brain wave data extracted in the preprocessing unit,
A selection unit that selects a group of brain wave data from the plurality of brain wave data based on the correlation data calculated by the correlation calculation unit,
A data calculation unit that calculates an averaged brain wave data by averaging the group of brain wave data selected by the selection unit,
An output unit that outputs brain wave information including the averaged brain wave data calculated by the data calculation unit,
EEG analysis system. The apparatus further includes a simple averaging unit that calculates averaging brain wave data by performing averaging of the plurality of brain wave data extracted in the preprocessing unit,
The correlation calculation unit calculates, as the correlation data, a correlation coefficient between each of the plurality of brain wave data extracted by the preprocessing unit and the averaging brain wave data calculated by the simple averaging unit,
The selection unit selects, from the plurality of brain wave data, brain wave data corresponding to a correlation coefficient that is equal to or greater than a predetermined value among the plurality of correlation coefficients calculated by the correlation calculation unit,
The electroencephalogram analysis system according to claim 1. The correlation calculation unit calculates a correlation coefficient for each of all combinations of two brain wave data among the plurality of brain wave data extracted by the preprocessing unit, and then calculates a correlation coefficient for each of the plurality of brain wave data. Calculate the average value as correlation data,
The selection unit selects, from the plurality of brain wave data, brain wave data corresponding to a correlation coefficient average value that is equal to or greater than a predetermined value among the plurality of correlation coefficient average values calculated by the correlation calculation unit,
The electroencephalogram analysis system according to claim 1. An averaging unit that calculates a plurality of averaging data by averaging the remaining brain wave data for each case where one of each of the plurality of brain wave data extracted by the preprocessing unit is removed,
The correlation calculating unit calculates a correlation coefficient for each of all combinations of the plurality of averaging data calculated by the averaging unit, and then calculates a correlation coefficient average value for each of the averaging data to the correlation data. Calculated as
The selection unit selects brain wave data corresponding to a correlation coefficient average value that is equal to or greater than a predetermined value among the plurality of correlation coefficient average values calculated by the correlation calculation unit,
The electroencephalogram analysis system according to claim 1. An analysis unit that extracts a feature amount related to an event-related potential from the averaging electroencephalogram data calculated by the data calculation unit,
The brain wave information output from the output unit includes information of the feature amount extracted by the analysis unit,
The electroencephalogram analysis system according to claim 1. An electroencephalogram analysis system according to any one of claims 1 to 5,
A determination unit that determines the visibility of a subject based on the brain wave information output from the output unit of the brain wave analysis system,
And a result output unit that outputs a result determined by the determination unit,
Visibility evaluation system. The determination unit determines the visibility of the subject from the latency of the event-related potential based on the brain wave information,
A visibility evaluation system according to claim 6. A first step of acquiring information on an electroencephalogram signal and a plurality of stimulus presentation timings related to the electroencephalogram signal,
A second step of extracting a plurality of electroencephalogram data corresponding to the plurality of stimulus presentation timings from the electroencephalogram signal based on the information on the electroencephalogram signals and the plurality of stimulus presentation timings acquired in the first step,
A third step of calculating correlation data relating to the plurality of brain wave data extracted in the second step;
A fourth step of selecting a group of brain wave data from the plurality of brain wave data based on the correlation data calculated in the third step;
A fifth step of calculating an averaged electroencephalogram data by averaging the group of electroencephalogram data selected in the fourth step;
And outputting a brain wave information including the averaged brain wave data calculated in the fifth step.
EEG analysis method. For computer systems,
A first process for acquiring information on an electroencephalogram signal and a plurality of stimulus presentation timings related to the electroencephalogram signal,
A second process of extracting a plurality of electroencephalogram data corresponding to the plurality of stimulus presentation timings from the electroencephalogram signal based on the information on the electroencephalogram signals and the plurality of stimulus presentation timings acquired in the first process,
A third process of calculating correlation data relating to the plurality of brain wave data extracted in the second process;
A fourth process of selecting a group of brain wave data from the plurality of brain wave data based on the correlation data calculated in the third process;
A fifth process of calculating an averaged electroencephalogram data by averaging the group of electroencephalogram data selected in the fourth process;
And a sixth process of outputting brain wave information including the averaged brain wave data calculated in the fifth process.
Program for.