Biological information presentation device, biological information presentation method, and biological information presentation program

Abstract

A biological information presentation device (10) according to an embodiment comprises a biological information measurement unit (11), a biological information analysis unit (14), and a display unit (16). The biological information measurement unit (11) measures biological information of a user to whom stimuli under a plurality of conditions have been presented. The biological information analysis unit (14) calculates respective scores for the plurality of conditions on the basis of the feature amount of the biological information. The display unit (16) presents the scores to the user.

Description

Biometric information display device, biological information display method, and biological information display program

[0001] The present invention relates to a biological information display device, a biological information display method, and a biological information display program.

[0002] Biofeedback, a technology that provides real-time feedback of biological information obtained by measuring respiration and electroencephalograms, is known. It has also been reported that biofeedback can be used to induce relaxation and improve athletic performance (see, for example, Non-Patent Document 1 and Patent Document 1).

[0003] In earlier biofeedback systems, biological information is acquired from the user, and data is recorded during task execution. This data is analyzed in real time, and feature data is fed back to the user. The user can then voluntarily control this feedback to modulate physical activity. This enables voluntary control of the autonomic nervous system and induction of a relaxed state (see, for example, Patent Document 1).

[0004] Japanese Patent Publication No. 2016-67537

[0005] Brain Tech Guidebook / Evidence Book ver2.0, [online], [Accessed December 20, 2024], Internet (https: / / brains.link / braintech_guidebook)

[0006] However, conventional methods have the problem of not being able to sufficiently improve the user's ability to distinguish between different perceptual stimuli.

[0007] For example, conventional biofeedback provides feedback for a single task, making it difficult to separate responses to different stimuli or tasks (e.g., measured biological information). Distinguishing between different sensory stimuli is particularly important in fields such as anomaly detection and product quality control.

[0008] Therefore, the object of the present invention is to sufficiently improve the user's ability to distinguish between perceptual stimuli.

[0009] To solve the problem, the present invention provides a biological information presentation device that includes: a biological information measurement unit that measures the biological information of a user who has been presented with stimuli under multiple conditions; a biological information analysis unit that calculates a score for each of the multiple conditions based on the characteristic quantities of the biological information; and a presentation unit that presents the scores to the user.

[0010] According to the present invention, the user's ability to distinguish between perceptual stimuli can be significantly improved.

[0011] Figure 1 shows an example configuration of a biometric information presentation system. Figure 2 shows an example configuration of a biometric information presentation device. Figure 3 is a flowchart showing the processing flow in a calibration session of the biometric information presentation device. Figure 4 is a flowchart showing the processing flow in a feedback session of the biometric information presentation device. Figure 5 is a diagram illustrating the effects of the embodiment. Figure 6 shows an example configuration of a computer that executes a biometric information presentation program.

[0012] The embodiments for carrying out the present invention will be described below with reference to the drawings. The present invention is not limited to these embodiments.

[0013] [Configuration of the First Embodiment] The configuration of the biometric information presentation system of the first embodiment will be explained using Figure 1. Figure 1 is a diagram showing an example of the configuration of the biometric information presentation system.

[0014] As shown in Figure 1, the biometric information presentation system 1 includes a biometric information presentation device 10 and a display device 20. The biometric information presentation device 10 acquires the biometric information of user U1. The biometric information presentation device 10 also displays a feedback score on the display device 20 based on the acquired biometric information.

[0015] Figure 2 shows an example of the configuration of a biometric information display device. The biometric information display device 10 may be a computer having a memory unit and a control unit. The memory unit stores data, programs, etc., that are referenced when the control unit performs various processes. The memory unit is implemented by semiconductor memory elements such as RAM (Random Access Memory) and flash memory, or by storage devices such as hard disks and optical discs. The control unit is responsible for the overall control of the biometric information display device 10. The functions of the control unit are realized, for example, by the CPU (Central Processing Unit) executing programs stored in the memory unit.

[0016] As shown in Figure 2, the biological information presentation device 10 includes a biological information measurement unit 11, a stimulus presentation unit 12, a biological information storage unit 13, a biological information analysis unit 14, a biological information storage unit 15, and a display unit 16.

[0017] The biological information measurement unit 11 is a device that measures biological information. The biological information measurement unit 11 has the function of measuring the biological information of user U1 in real time and transmitting it to a subsequent stage.

[0018] The bio-information measurement unit 11 measures heart rate variability and scalp electroencephalogram (EEG), etc. For example, the bio-information measurement unit 11 may be an electrocardiograph, electroencephalograph, eye movement measuring device (eye tracker), respiratory measuring device, etc. Alternatively, the bio-information measurement unit 11 may be other devices that measure bio-information or movements that can indirectly estimate the autonomic nervous system activity and brain activity of user U1.

[0019] The stimulus presentation unit 12 presents stimuli to the user U1 indicating the start and end times of the perceptual stimulus task. The stimulus presentation unit 12 also presents stimuli that are the target of discrimination training.

[0020] For example, the stimulus presentation unit 12 presents stimuli such as images, sounds, smells, and electrical stimuli at regular intervals. For example, the stimulus presentation unit 12 may also have a function to play back recorded images and sounds.

[0021] Furthermore, the perceptual stimulus task includes presenting stimuli to user U1 and allowing user U1 to control the feature quantities of biological information corresponding to the presented stimuli. The feature quantities and the operations for controlling them will be explained in detail later.

[0022] The biological information storage unit 13 is connected to the biological information measurement unit 11 and the stimulus presentation unit 12. The biological information storage unit 13 receives biological signal data indicating biological information transmitted from the biological information measurement unit 11 at regular intervals and temporarily stores the received biological signal data as biological signal data during the execution of the perceptual stimulus task.

[0023] The bio-information analysis unit 14 is connected to the bio-information holding unit 13, the bio-information storage unit 15, and the display unit 16. The bio-information analysis unit 14 calculates feature quantities from the bio-signal data received from the bio-information holding unit 13 during the execution of a perceptual stimulus task, and from the bio-signal data recorded in the bio-information storage unit 15.

[0024] The bioinformation analysis unit 14 preprocesses the biosignal data to reduce the influence of noise originating from non-biological signals. Subsequently, the bioinformation analysis unit 14 performs analysis to calculate features of interest (hereinafter simply referred to as features), such as amplitude information and phase information. For example, since electroencephalograms (EEGs) are obtained as waveforms, the bioinformation analysis unit 14 can calculate features from EEGs based on amplitude, phase, etc.

[0025] The biological information analysis unit 14 performs different processes during calibration (calibration session) and feedback (feedback session).

[0026] During the calibration session, the bio-information analysis unit 14 transmits the stimulus conditions (hereinafter referred to as "conditions") and feature quantities to the bio-information storage unit 15 to create a set of feature quantities for each condition.

[0027] The conditions represent, for example, the type, content, and intensity of the stimulus. For example, suppose the perceptual stimulus targeted for discrimination training is an auditory stimulus. In this case, the first condition may be "outputting a sound with a frequency of 440 Hz (the note A)." The second condition may be "outputting a sound with a frequency of 493.8 Hz (the note B)."

[0028] In the feedback session, the bio-information analysis unit 14 converts the current bio-signal data (for example, the bio-signal data from which the last feature was calculated) into a feedback score based on the statistics of the feature set stored in the bio-information storage unit 15. The bio-information analysis unit 14 then transmits the feedback score to the display unit 16.

[0029] The biological information storage unit 15 acquires feature quantities of biological signal data from the biological information analysis unit 14 and stores the acquired biological signal data.

[0030] Furthermore, during the feedback session, the biometric information storage unit 15 fits the feature sets that match the conditions and the feature sets that do not match to arbitrary distributions. The biometric information storage unit 15 then transmits statistics representing each distribution to the biometric information analysis unit 14, enabling the calculation of the feedback score. The biometric information storage unit 15 may update the feature sets as appropriate based on newly obtained features during the feedback session. The biometric information storage unit 15 shall also maintain a number of feature sets corresponding to the number of conditions in the task (for example, two or more).

[0031] The display unit 16 is connected to the biological information analysis unit 14. The display unit 16 presents the feedback score received from the biological information analysis unit 14 to the user U1.

[0032] For example, the display unit 16 presents the feedback score as the position of the cursor 21 on the display of the display device 20.

[0033] For example, if we consider the display as an xy-plane, let x be the coordinate of the x-axis (horizontal axis) and y be the coordinate of the y-axis (vertical axis). The display unit 16 represents x and y using feature quantities obtained from different conditions. For example, the display unit 16 substitutes the first feature quantity obtained from the first condition into x, and the second feature quantity obtained from the second condition into y. Alternatively, the display unit 16 may substitute normalized values ​​of the feature quantities into x and y.

[0034] The display unit 16 displays the cursor 21 at the position (x, y) on the display of the display device 20. Further, the display unit 16 receives an operation for moving the cursor 21 by the user U1. Note that the display unit 16 receiving an operation means that the display unit 16 continues to acquire biometric signal data via the biometric information measurement unit 11 and the biometric information holding unit 13. The user U1 performs an operation by spontaneously changing his or her own biometric signal. The display unit 16 moves the cursor 21 in response to the change in the biometric signal data. Thereby, the user U1 spontaneously controls two feature amounts by spontaneously controlling the position of the cursor 21 in a specific direction.

[0035] Note that the display unit 16 may substitute the method of presenting the feedback score not only for the position of the cursor 21 but also for the color (chroma / brightness) of the object to be displayed, the type of sound, the size of the cursor 21, etc. For example, the display unit 16 displays an object having each of a plurality of feature amounts as a parameter. The parameter determines the position, shape, color, etc. of the object.

[0036] The display unit 16 is an example of the presentation unit. The display unit 16 may present the feedback score by other methods such as voice in addition to the screen.

[0037] The biometric information presentation device 10 executes processing in two sessions: a calibration session and a feedback session.

[0038] [Calibration Session] In the calibration session, the biometric information presentation device 10 acquires biometric signal data from the user U1 during a perceptual stimulation task, and creates a set of feature amounts for each condition during multiple executions of the perceptual stimulation task.

[0039] For example, when the number of trials for each perceptual stimulation task in the calibration session is about 20 to 100 times, the biometric information presentation device 10 may be able to create a set of feature amounts for which a stable distribution can be calculated.

[0040] Note that since the number of trials required to obtain a stable distribution varies depending on the feature amount used, the number of trials in the calibration session does not necessarily have to be within the range of 20 to 100. Also, if a target feature amount distribution is defined in advance, the biological information presentation device 10 may omit the calibration session.

[0041] [Processing of the First Embodiment] Using FIG. 3, the processing in the calibration session will be described. FIG. 3 is a flowchart showing the flow of processing in the calibration session of the biological information presentation device.

[0042] As shown in FIG. 3, the stimulus presentation unit 12 performs a plurality of trials (for example, a predetermined number of times) of the target perceptual stimulus task (step S101). Each time the perceptual stimulus task is performed, the processing from step S102 to step S104 is executed (step S105).

[0043] The biological information measurement unit 11 measures the biological information at the time of the first stimulus presentation (step S102). Also, the biological information measurement unit 11 measures the biological information at the time of the second stimulus presentation (step S103). The first stimulus and the second stimulus may be perceptual stimuli with different conditions from each other.

[0044] Subsequently, the biological information analysis unit 14 calculates the feature amount of a single trial from the biological information (step S104).

[0045] After a plurality of trials of the perceptual stimulus task are completed, the biological information analysis unit 14 calculates the statistic for each stimulus condition from the plurality of trial feature amounts (step S106). The biological information analysis unit 14 calculates, for example, the average of the feature amounts corresponding to perceptual stimuli with common conditions (for example, the average of the feature amounts corresponding to the first stimulus and the average of the feature amounts corresponding to the second stimulus). Then, the biological information analysis unit 14 stores the statistic in the biological information storage unit 15 (step S107).

[0046] [Feedback Session] In the feedback session, the bio-information display device 10 measures the user U1's biosignals when the perceptual stimulus task is performed, displays the feature quantities in real time, and accepts user input on the feature quantities. This allows the bio-information display device 10 to encourage user U1 to spontaneously modulate their biosignals.

[0047] In the feedback session, the biometric information display device 10 calculates features for each trial, and may update the feature distribution using the obtained features. However, the biometric information display device 10 does not have to update the feature distribution.

[0048] Figure 4 illustrates the processing in a feedback session. Figure 4 is a flowchart showing the flow of processing in a feedback session of a biometric information presentation device.

[0049] As shown in Figure 4, the stimulus presentation unit 12 performs the target stimulus once (step S201). The number of repetitions from step S201 to step S205 may be two or more times instead of just once.

[0050] Here, either a first stimulus or a second stimulus is presented. The biological information measurement unit 11 measures biological information at the time of presentation of the first or second stimulus (step S202).

[0051] Next, the biological information analysis unit 14 converts the biological information into features in real time based on statistical values ​​(step S203).

[0052] The display unit 16 then presents the feature quantities to the user in real time (step S204).

[0053] The biological information analysis unit 14 modifies the statistical quantities based on the acquired data (step S206). The acquired data is, for example, the details of an operation to change the feedback score. The biological information analysis unit 14 stores the statistical quantities in the biological information storage unit 15 (step S207).

[0054] The biometric information display device 10 determines whether or not to continue the feedback session (step S208). If the biometric information display device 10 decides to continue the feedback session (step S208: Yes), it returns to step S201 and repeats the process. If the biometric information display device 10 decides not to continue the feedback session (step S208: No), it terminates the process. For example, the biometric information display device 10 determines not to continue the feedback session when the feedback session has been repeated a certain number of times, when the duration of the feedback session exceeds a specified time, or when user U1 performs an operation to end the feedback session.

[0055] [Examples] An example of modulating the ability to distinguish olfactory stimuli based on the first embodiment will be described. An example of improving the user U1's ability to distinguish perceptual stimuli will be called neurofeedback.

[0056] First, the bio-information presentation device 10 performs a calibration session to acquire a baseline feature distribution. During the calibration session, the stimulus presentation unit 12 presents two types of olfactory stimuli, the first stimulus and the second stimulus, 30 times each. For example, in this case, the multiple trials in S101 of Figure 3 consist of 30 trials. The bio-information measurement unit 11 measures the brainwaves (an example of biosignal data) of user U1 when the stimulus is presented.

[0057] The biological information analysis unit 14 performs preprocessing on the measured electroencephalogram (EEG). The preprocessing includes bandpass filtering to extract frequencies of interest, notch filtering to reduce power supply noise, and spatial filtering to extract local signals.

[0058] After preprocessing, the biological information analysis unit 14 obtains amplitude information of the frequency band of interest by performing a discrete Fourier transform on the electroencephalogram (EEG) segments cut into 500ms windows at 50ms intervals based on the timing of olfactory stimulus presentation.

[0059] The bio-information analysis unit 14 then stores the amplitude information for each stimulus condition (the first stimulus and the second stimulus) in the bio-information storage unit 15 as a set of features that will serve as the baseline for the feedback session.

[0060] In the feedback session, the stimulus presentation unit 12 presents one of two types of stimuli (the first stimulus or the second stimulus). In the example in Figure 4, the stimulus is presented only once (one trial), but the stimulus may be presented two or more times (multiple trials).

[0061] The bio-information analysis unit 14 performs the same processing as in the calibration session on the electroencephalogram (EEG) segments cut into 500ms windows at 50ms intervals, based on the trigger output of the stimulus presentation (for example, the time when the stimulus presentation started) for each trial. In other words, the bio-information analysis unit 14 obtains amplitude information of the frequency band of interest by performing a discrete Fourier transform on the EEG.

[0062] Let Pt be the amplitude information of the frequency band of interest acquired at time t. The bio-information analysis unit 14 uses the bio-signal data acquired in the calibration session to calculate the feedback scores corresponding to the first stimulus and the second stimulus as shown in equations (1) and (2), respectively. Furthermore, generalizing the feedback score for the k-th stimulus (the k-th stimulus) results in equation (3).

[0063]

[0064]

[0065]

[0066] The k stimuli are multiple stimuli with different conditions. As shown in equations (1) to (3), the bio-information analysis unit 14 calculates a feedback score for each of the multiple conditions.

[0067] Here, the bio-information analysis unit 14 fits the set of features obtained in the calibration session when the k-th stimulus (the k-th stimulus) is presented (see equation (4)) to a normal distribution. （ｋ） σ is the mean of the normal distribution.（ｋ） This is the standard deviation of the normal distribution in question.

[0068]

[0069] Note that the subscript P in equation (4) is a number that identifies the feature. In this case, there are n features. For example, the feature is the amplitude of the frequency component of interest for each trial.

[0070] The display unit 16 presents a feedback score as a cursor 21 that moves on the two-dimensional plane (xy plane) of the display.

[0071] User U1 is trained to increase the feedback score (amplitude synchronization) for the same stimulus and decrease the feedback score for different stimuli by manipulating the cursor 21 to any position.

[0072] Furthermore, the bio-information analysis unit 14 may update the feature set during the feedback session. For example, the bio-information analysis unit 14 modifies the feature set for each stimulus after each trial to use the bio-signal data from the most recent 60 trials. This allows user U1 to learn different electroencephalogram responses to different stimulus presentations. As a result, user U1's olfactory discrimination ability can be improved.

[0073] Figure 5 illustrates the effects of the embodiment. The normal distribution 51A in Figure 5 is the distribution of feature quantities in the electroencephalogram (EEG) of user U1 before learning, when the first stimulus is presented. The normal distribution 51B is the distribution of a single feature quantity in the EEG of user U1 after learning, when the second stimulus is presented.

[0074] Learning, in this context, refers to conducting feedback sessions. It is believed that learning modulates the brainwaves of user U1 when the first or second stimulus is presented. In other words, learning makes the differences in user U1's brainwaves in response to multiple different stimuli more pronounced. To put it another way, learning increases the degree of dissimilarity, or difference, between the characteristics of user U1's brainwaves in response to multiple different stimuli.

[0075] The normal distribution 52A in FIG. 5 is the distribution of the feature amounts of the brain waves of the user U1 after learning when the first stimulus is presented. The normal distribution 52B is the distribution of the feature amounts of the brain waves of the user U1 after learning when the second stimulus is presented.

[0076] The distance between the normal distribution 52A and the normal distribution 52B is larger than the distance between the normal distribution 51A and the normal distribution 51B. Note that the distance between two distributions represents the dissimilarity or the degree of difference between the two distributions.

[0077] Note that X Ａｉ is an example of the feature amount of the brain waves of the user U1 when the first stimulus is presented. When the first stimulus is presented, with the same probability as the feature amount X Ａｉ before learning, the feature amount X Ａｉ after learning occurs. When the feature amount X Ａｉ before learning occurs, it cannot be said that the user U1 can accurately distinguish between the first stimulus and the second stimulus. In contrast, when the feature amount X Ａｉ after learning occurs, it can be said that the user U1 can accurately distinguish between the first stimulus and the second stimulus. That is, the discrimination ability of the user U1 is improved by the feedback session.

[0078] Thus, by learning, the modulation of the brain waves is promoted, and the discrimination ability of the user U1 with respect to the first stimulus and the second stimulus is improved. Further, by causing the user U1 to perform an operation for controlling the feature amount, the modulation of the brain waves of the user is further promoted.

[0079] For example, consider the case of improving the discrimination ability to identify whether or not a certain fragrance is included according to the above-described embodiment. The first stimulus is "generating a gas that does not contain ammonia". The second stimulus is "generating a gas that contains ammonia". Also, assume that one of the feature amounts is regarded as representing the degree of discomfort (hereinafter, discomfort degree). Also, assume that a person feels discomfort when smelling ammonia.

[0080] In the calibration session, the parameter of the normal distribution of the discomfort degree of the first stimulus is (μ （１） , σ （１）Let's assume that ) = (0.3, 1). Also, in the calibration session, the parameter of the normal distribution of the discomfort level of the second stimulus is (μ （２） , σ （２） Let's assume that ) = (0.6, 1). In this case, as shown in Figure 5 before training, the two normal distributions largely overlap, and the difference between the distributions is unclear. This means that user U1 has a low ability to distinguish between the presence or absence of ammonia.

[0081] In a feedback session, suppose the stimulus presentation unit 12 presents the first stimulus. At the same time, suppose the bio-information analysis unit 14 obtains a displeasure score of 0.4 based on the electroencephalogram (EEG) at time t obtained from user U1. In this case, according to equations (1) and (2), the feedback scores x and y become 0.1 and -0.2, respectively.

[0082] Here, for the first stimulus, the correct response is for the feedback score to approach 0. For the second stimulus, the correct response is for the absolute value of the feedback score to move away from 0.

[0083] User U1 confirms that the presented feedback scores of 0.1 and -0.2 are far from the respective target feedback scores of 0 and -0.6 (feedback scores when the response matches the first stimulus but does not match the second stimulus). This prompts User U1 to think, "I must move the cursor closer to the target," and modulates their physical activity (e.g., the way they generate brain waves) in real time. It is expected that this thought will become stronger by manipulating the feedback scores.

[0084] User U1 performs actions to bring the presented feedback score of 0.1 closer to the correct answer, that is, voluntarily controls their physical activity.

[0085] Furthermore, as explained in Figure 5, when user U1's physical activity is altered, the distribution of features based on biometric information changes.

[0086] [Effects of the First Embodiment] As described above, the bio-information measurement unit 11 measures the bio-information of a user who has been presented with stimuli under multiple conditions. The bio-information analysis unit 14 calculates a score for each of the multiple conditions based on the characteristic quantities of the bio-information. The display unit 16 presents the scores to the user. The display unit 16 may also accept an operation from the user to change the score.

[0087] As a result, the biometric information presentation device 10 can compare the biometric information of the user who has been presented with a stimulus with past biosignal data in real time and provide feedback, thereby modulating the user's biosignals and improving their ability to distinguish perceptual stimuli. Thus, the first embodiment can be used for training in sensory evaluation, such as in product quality control.

[0088] Furthermore, the first embodiment can be described as a method that applies contrastive learning, a learning method in the field of machine learning, to biofeedback.

[0089] [System Configuration, etc.] Furthermore, the components of each part shown in the diagram are functional concepts and do not necessarily need to be physically configured as shown. In other words, the specific forms of distribution and integration of each device are not limited to those shown in the diagram, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions. In addition, all or any part of the processing functions performed by each device can be realized by a CPU and the program executed on that CPU, or by hardware using wired logic.

[0090] Furthermore, among the processes described in the embodiments described above, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by known methods. In addition, the processing procedures, control procedures, specific names, and information including various data and parameters shown in the above document and drawings can be arbitrarily changed unless otherwise specified.

[0091] [Program] The biometric information display device 10 described above can be implemented by installing a program (biometric information display program) as packaged software or online software on a desired computer. For example, by having the computer run the above program, the computer can be made to function as the biometric information display device 10. The term "computer" here includes mobile communication terminals such as smartphones, mobile phones and PHS (Personal Handyphone System), as well as terminals such as PDA (Personal Digital Assistant).

[0092] Figure 6 shows an example configuration of a computer that executes a biometric information presentation program. Computer 1000 has, for example, memory 1010 and CPU 1020. Computer 1000 also has a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070. These components are connected by a bus 1080.

[0093] Memory 1010 includes ROM (Read Only Memory) 1011 and RAM (Random Access Memory) 1012. ROM 1011 stores, for example, a boot program such as BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to the hard disk drive 1090. The disk drive interface 1040 is connected to the disk drive 1100. For example, a removable storage medium such as a magnetic disk or optical disk is inserted into the disk drive 1100. The serial port interface 1050 is connected to, for example, a mouse 1110 and a keyboard 1120. The video adapter 1060 is connected to, for example, a display 1130.

[0094] The hard disk drive 1090 stores, for example, the OS 1091, application program 1092, program module 1093, and program data 1094. That is, the program that defines each process executed by the biometric information presentation device 10 is implemented as a program module 1093 in which code executable by a computer is written. The program module 1093 is stored, for example, in the hard disk drive 1090. For example, a program module 1093 for executing processes similar to the functional configuration of the biometric information presentation device 10 is stored in the hard disk drive 1090. Note that the hard disk drive 1090 may be replaced by an SSD (Solid State Drive).

[0095] Furthermore, the data used in the processing of the above-described embodiment is stored as program data 1094 in, for example, memory 1010 or hard disk drive 1090. The CPU 1020 then reads the program module 1093 and program data 1094 stored in memory 1010 or hard disk drive 1090 into RAM 1012 as needed and executes them.

[0096] Furthermore, the program module 1093 and program data 1094 are not limited to being stored in the hard disk drive 1090; for example, they may be stored in a removable storage medium and read by the CPU 1020 via a disk drive 1100 or the like. Alternatively, the program module 1093 and program data 1094 may be stored in another computer connected via a network (LAN (Local Area Network), WAN (Wide Area Network), etc.). The program module 1093 and program data 1094 may then be read by the CPU 1020 from the other computer via a network interface 1070.

[0097] U1 User 1 Biometric Information Presentation System 10 Biometric Information Presentation Device 11 Biometric Information Measurement Unit 12 Stimulus Presentation Unit 13 Biometric Information Storage Unit 14 Biometric Information Analysis Unit 15 Biometric Information Storage Unit 16 Display Unit 20 Display Device 21 Cursor

Claims

1. A biological information presentation device comprising: a biological information measurement unit that measures the biological information of a user presented with stimuli under multiple conditions; a biological information analysis unit that calculates a score for each of the multiple conditions based on the characteristic quantities of the biological information; and a presentation unit that presents the scores to the user.

2. The biometric information display device according to claim 1, characterized in that the display unit receives an operation from the user to change the score.

3. A method for presenting biometric information performed by a computer, comprising: a biometric information measurement step of measuring the biometric information of a user who has been presented with stimuli of multiple conditions; a biometric information analysis step of calculating a score for each of the multiple conditions based on the characteristic quantities of the biometric information; and a presentation step of presenting the scores to the user.

4. A biometric information presentation program characterized by causing a computer to perform the following steps: a biometric information measurement step of measuring the biometric information of a user who has been presented with stimuli under multiple conditions; a biometric information analysis step of calculating a score for each of the multiple conditions based on the characteristic quantities of the biometric information; and a presentation step of presenting the scores to the user.

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