Information processing method, program, and information processing apparatus

Abstract

To elucidate brain activity for obtaining a feeling of happiness like that after sauna use and to stimulate the brain of a user so as to obtain the brain activity like that after the sauna.SOLUTION: The information processing method includes, by a processor included in the information processing apparatus, acquiring sound data, generating stimulation sound data for stimulating a brain of a user with a frequency in a frequency band of theta waves based on the sound data, and outputting the stimulation sound data to induce the theta waves in the brain of the user.SELECTED DRAWING: Figure 9

Description

[Technical Field]

[0001] The present invention relates to an information processing method, a program, and an information processing device. [Background technology]

[0002] In recent years, the effects of saunas have been attracting attention, and proper sauna use can activate various bodily functions. For example, it is said that proper sauna use can lead to the so-called "totono" state. The "totono" state refers to the intense feeling of euphoria that occurs after a hot sauna and then a cold bath, and is thought to be caused by a significant increase in beta-endorphins produced by the sauna.

[0003] Specific examples of sauna effects that induce a state of "tono" (totono), such as relief from fatigue, increased decision-making, concentration, and inspiration, and the ability to control emotions, are cited. Regarding these sauna effects, a Finnish study (Non-Patent Document 1) revealed a correlation between sauna use and a reduced risk of age-related diseases such as cardiovascular disease, neurodegenerative disease, metabolic dysfunction, and decreased immune function. The study also showed that men who used saunas 4 to 7 times a week had a 65% lower risk of developing Alzheimer's disease than men who used saunas only once a week. Other studies have also reported reduced incidence of depression and colds. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Laukkanen T, Khan H, Zaccardi F, Laukkanen JA. Association between sauna bathing and fatal cardiovascular and all-cause mortality events. JAMA Intern Med. 2015; 175(4):542-8. [Retrieved June 12, 2020], Internet<https: / / doi.org / 10.1001 / jamainternmed.2014.8187> Summary of the Invention [Problem to be solved by the invention]

[0005] However, while the experimental results in Non-Patent Document 1 and other experimental results mention many of the physical effects of saunas, there has been almost no research focusing on the perspectives of neuroscience or psychology. In other words, it has yet to be clarified what state of brain activity occurs in relation to the sensory state of "totono" (comfort) experienced after sauna use.

[0006] Therefore, one aspect of the disclosed technology aims to provide an information processing method, program, and information processing device that elucidates the brain activity that produces a sense of happiness similar to that after using a sauna, and that can stimulate the user's brain to produce brain activity similar to that after using a sauna. [Means for solving the problem]

[0007] In one aspect of the disclosed technology, an information processing method includes a processor included in an information processing device acquiring sound data, generating stimulation sound data based on the sound data that stimulates a user's brain with frequencies in the theta wave frequency band, and outputting the stimulation sound data to induce theta waves in the user's brain. [Effects of the Invention]

[0008] According to one aspect of the disclosed technology, it is possible to clarify the brain activity that produces a sense of happiness similar to that after using a sauna, and to stimulate the user's brain to produce brain activity similar to that after using a sauna. [Brief explanation of the drawings]

[0009] [Figure 1A] FIG. 1 is a diagram showing the experimental procedure of the first experiment. [Figure 1B] This figure shows the auditory task results of the sauna group and control group before and after the sauna in the first experiment. [Figure 1C] This figure shows the reaction times of the sauna group and the control group before and after the sauna in the first experiment. [Figure 1D] FIG. 10 is a diagram showing the results of a questionnaire in the first experiment. [Figure 1E] FIG. 10 is a diagram showing the relationship between theta waves and alpha waves caused by sauna in the first experiment. [Figure 1F] FIG. 10 is a diagram showing the classification accuracy of subjects in the sauna group in the first experiment. [Figure 2A] FIG. 10 is a diagram showing the experimental procedure of the second experiment. [Figure 2B] FIG. 10 is a diagram showing the results of a questionnaire in the second experiment. [Figure 2C] FIG. 10 is a diagram showing an example of normal music used in the second experiment. [Figure 2D] FIG. 10 is a diagram showing an example of special music used in the second experiment. [Figure 2E] The mean event-related potentials (ERPs) of the subjects measured at the Cz electrode are shown. [Figure 2F] This figure shows quantitative analysis of MMN and P3 region at three sites (Cz, T3, T4) before (pre) and after (post) listening to music for each condition. [Figure 2G] This figure compares the amplitude spectral power of theta waves for each condition at each music listening stage (pre, post1, post2, post3). [Figure 3] 1 is a block diagram illustrating an example of an information processing device according to a first embodiment. [Figure 4] 5A and 5B are diagrams illustrating an example of generating a sine wave according to the first embodiment. [Figure 5] FIG. 2 is a diagram showing an example of processing process A according to the first embodiment. [Figure 6] FIG. 10 is a diagram showing an example of processing B according to the first embodiment. [Figure 7] FIG. 10 is a diagram showing an example of processing C according to the first embodiment. [Figure 8] FIG. 2 is a diagram showing an example of remix processing according to the first embodiment. [Figure 9] 5 is a flowchart showing an example of processing by the information processing device according to the first embodiment. [Figure 10] 4 is a flowchart showing an example of processing related to the first embodiment. [Figure 11] 10 is a diagram showing an example of an earphone set 20 according to a second embodiment. FIG. [Figure 12] FIG. 10 is a diagram showing an example of a schematic cross section of an earphone 200R according to a second embodiment. [Figure 13] 10 is a flowchart showing an example of processing by the information processing device 10 according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present invention will be described with reference to the drawings. However, the embodiments described below are merely examples, and are not intended to exclude various modifications or applications of techniques not explicitly described below. In other words, the present invention can be implemented with various modifications within the scope of its spirit. Furthermore, in the following description of the drawings, identical or similar parts are denoted by identical or similar reference numerals. The drawings are schematic and do not necessarily correspond to actual dimensions, ratios, etc. Parts in the drawings may have different dimensional relationships or ratios.

[0011] Before explaining the outline of the embodiments of the disclosed technology, we will explain the first experiment to elucidate brain activity and mood changes after a sauna, and the second experiment to investigate whether music can induce brain activity that leads to a state of "totono."

[0012] [First experiment] <Experiment details> (Participant) Twenty healthy subjects (14 men and 6 women, aged 21-41) participated in the experiment. All subjects were right-handed, and none had hearing or speech disabilities. The subjects were divided into two groups: a sauna group and a control group. The sauna group consisted of eight men and two women, while the control group consisted of six men and four women. All subjects in the sauna group had experience with sauna bathing.

[0013] (procedure) The experimental procedure for the sauna group consisted of three phases: pre-sauna, sauna, and post-sauna (see Figure 1A). Figure 1A illustrates the experimental procedure for the first experiment. In the example shown in Figure 1A, during the sauna, three sets of alternating hot and cold baths were performed: sauna (high temperature, 85-90°C), followed by a cold bath (low temperature, 16°C), and finally a break (20°C). One set consisted of sauna (10 minutes), cold bath (1-2 minutes), and a break (7 minutes). During the break, subjects were instructed to close their eyes and relax. During the pre-sauna and post-sauna phases, scalp EEG and heart rate were measured while the subjects performed an auditory oddball task. Similarly, to assess the subjects' stress levels, salivary α-amylase activity (SAA) was measured before and after the sauna session using an SAA biosensor (salivary amylase monitor, NIPRO Co., Japan) (Yamaguchi M, Kanemori T, Kanemaru M, Mizuno Y, Yoshida H. Test strip-type salivary amylase activity monitor and its evaluation. Sensors and materials. 2003;15(5):283-294.). Furthermore, the subjects' brain waves were analyzed using an ear-mounted electroencephalograph (EEG) (see Figures 11 and 12 below) before the sauna session and immediately after the cold water bath. Their responses to questionnaires about their physical and mental states were also recorded.

[0014] In the control group, subjects did not enter the sauna and instead rested. During the bathing phase, subjects in the control group entered a 37°C hot water bath. This was designed to ensure that subjects in both groups had the same amount of skin exposure time.

[0015] (Auditory oddball task) In the auditory oddball task, subjects were presented with a frequent standard stimulus, a 1000 Hz tone (80%), and an infrequent "target" stimulus, a 2000 Hz tone (20%), with a stimulus onset asynchrony (SOA) of 1.5 seconds. Auditory stimuli were presented to subjects via headphones at a sound pressure level of 60 dB for 50 ms (rise / fall time 5 ms). The sequential order of stimuli was pseudorandom, with the restriction that at least two standard stimuli were presented between infrequent "target" stimuli. During the auditory oddball task, subjects were instructed to press a response button in response to the target stimuli. Reaction times (RTs, in milliseconds) were measured, and only correct responses were averaged. A total of 200 stimuli were presented in the task.

[0016] (Electroencephalogram recording) Resting EEG after the cold water bath during the sauna phase must be recorded immediately, but this is difficult to achieve with conventional EEG recording equipment due to the limited time available for setup. Therefore, two types of EEG recording equipment were used in this experiment. During the oddball task performed in the pre-sauna and post-sauna phases, EEG was recorded using a wireless biosignal amplifier system (Polymate Mini AP108, Miyuki Giken Co., Ltd., Tokyo, Japan) and gel electrodes (METS INC., Chiba, Japan). Hereafter, this EEG data is referred to as scalp EEG data. Recordings were made at Cz, Fz, and Pz (at a 500 Hz sampling rate) according to the International 10-20 system. Furthermore, ground and reference electrodes were attached to the left and right humers, respectively.

[0017] Electroocular activity (EOG) was assessed by measuring blinks and vertical eye movements using one electrode attached to the upper outer edge of the left eye. Electrocardiograms (ECGs) were obtained using one electrode attached to the upper left chest to measure heart rate. Data recorded during the oddball task were filtered using a 0.1 Hz low-cut filter and a 30 Hz high-cut filter.

[0018] The baseline epoch for either the standard or target stimulus began 100 ms before stimulus presentation and ended 600 ms after stimulus onset. Epochs in which the EEG or EOG signal exceeded ±50 μV due to artifacts from vertical eye movements or muscle contractions were automatically excluded. Standard and target ERPs were calculated by averaging the epochs for each stimulus. The difference waveform was calculated by subtracting the target ERP from the standard ERP. The negative peak in the difference waveform between 100 and 250 ms after stimulus presentation was calculated as the area of ​​the mismatch negativity (MMN), and the positive peak in the difference waveform between 240 and 400 ms was calculated as the area of ​​the P300.

[0019] Furthermore, because of the advantage of extremely short wearing time, an intra-auricular electrode system (VIE ZONE®, VIE STYLE Inc., reference electrode: back of neck) was used to record EEG from the left and right Eustachian tubes. Hereinafter, this EEG data will be referred to as intra-auricular EEG data. The sampling rate of the EEG data was 600 Hz. The intra-auricular EEG data was filtered with a 2 Hz low-cutoff filter and a 30 Hz high-cutoff filter.

[0020] The data were divided into equally spaced segments (8 seconds each). Segments in which the EEG signal exceeded ±100 μV due to artifacts from vertical eye movements or muscle contractions were automatically excluded, followed by Fourier transformation. To overcome the limitations of individual differences and incomparability in conventional EEG frequency bands, we used a frequency band division method based on individual alpha frequency (IAF). The peak power frequency between 8 and 13 Hz obtained from the data from the pre-sauna phase was defined as the IAF. The IAF was used as the boundary point between the lower and upper limits of the alpha band to determine the frequency band for each subject. Each subject's data was averaged for each epoch in the left and right ear channels, and the total absolute spectral power was calculated for five frequency bands.

[0021] Theta band: [IAF-4.0Hz to IAF-6.0Hz], Lower 1 alpha band: [IAF-2.0Hz to IAF-4.0Hz], Lower 2 alpha bands: [IAF~IAF-2.0Hz], Upper alpha band: [IAF to IAF+2.0Hz], Beta band: 15-30Hz

[0022] <Experimental Results> In an auditory oddball task performed before and after the sauna, P300 amplitude and reaction time to target stimuli (sound discrimination) significantly decreased after the sauna, while MMN amplitude significantly increased (Figures 1B and 1C). Figure 1B shows the auditory task results for the sauna and control groups before and after the sauna in Experiment 1. The example shown in Figure 1B shows the average event-related potential (ERP) waveforms at three sites (Fz, Cz, Pz) and demonstrates the difference between the sauna and control groups in the auditory task before (pre: dotted line) and after (post3: dashed-dotted line).

[0023] As shown in Figure 1B, the P300 is closely related to attention, so a decrease in the P300 indicates that the task required less attention. On the other hand, an increase in the absolute value of the MMN is generally said to represent an increase in sound discrimination. Therefore, the result showing an increase in the MMN after sauna suggests that the subjects became more sensitive to auditory stimuli during sauna bathing.

[0024] This may have led to a decrease in P300 amplitude, as participants were able to focus less attention on identifying the sounds and engage in the task. This result is consistent with the question "Images from memory or imagination appear very vividly" in the altered states of consciousness questionnaire (see below) (Figure 1D), suggesting that saunas may clear people's minds and make them more lucid.

[0025] To assess whether cognitive performance changed after sauna bathing, the mean reaction times (RTs) for the oddball task were calculated for all subjects in the sauna and control groups before and after sauna bathing and compared (Figure 1C). Figure 1C shows the reaction times of the sauna and control groups before and after sauna bathing in the first experiment. As shown in Figure 1C, a two-way repeated measures analysis of variance (ANOVA) revealed a marginally significant main effect of stage (F(1,18) = 3.24, p < 0.1) and a marginally significant interaction between group and stage (F(1,18) = 3.04, p < 0.1). A simple main effect test for stage revealed a significant decrease in RTs in the sauna group (F(1,18) = 6.29, p < 0.05), but not in the control group (F(1,18) = 0.00, ns).

[0026] To assess whether sauna bathing altered the subjects' subjective mental and physical states, we calculated questionnaire scores before sauna bathing and at each rest stage during sauna bathing and compared the scores for all subjects in the sauna and control groups. Figure 1D shows the questionnaire results from the first experiment. Figure 1D shows an example of the results of a two-way repeated measures analysis of all questions on the altered states of consciousness scale.

[0027] The results, shown in Figure 1D, indicate that sauna bathing altered subjective feelings regarding physical and mental states. The Altered State of Consciousness Scale assessed whether sauna bathing altered subjective feelings regarding physical and mental states. Significant changes were observed in the Altered State of Consciousness Scale for questions regarding physical sensations, such as "I feel a sense of great enjoyment and pleasure in my body," "My muscles feel relaxed," and "I feel a sense of floating." These descriptions indicate a relaxed physical state. Responses to relaxation-related questions, such as "I feel very relaxed," also indicate a sense of relaxation after sauna bathing. Most importantly, responses to the question about "feeling in a state of 'totono' (feeling in a state of 'totono')" showed a significant interaction and a main effect of set. This indicates that the sauna group achieved a state of 'totono' (feeling in a state of 'totono') after sauna bathing, but the control group did not.

[0028] Based on the above results, the "totono" state can be considered to manifest as physical and mental feelings such as relaxation, joy, and mental clarity, accompanied by feelings of happiness and positive emotions. Furthermore, because the "totono" state manifested during rest, measuring brain waves at this time revealed that sauna use gradually increased theta and alpha waves (Figure 1E). Figure 1E shows the relationship between theta and alpha waves induced by sauna use in the first experiment.

[0029] As shown in Figure 1E, the increase in theta waves is thought to reflect not only increased cognitive and emotional processing, but also increased awareness and attention. Furthermore, as shown in Figure 1D, this is consistent with the result of a question related to emotional processing: "I feel like I am isolated from the things and people around me."

[0030] On the other hand, increased alpha waves may be associated with relaxation, which is consistent with what is observed after high-intensity exercise, where increased alpha energy is accompanied by a heightened subjective experience of well-being and positive emotions, consistent with the questionnaire items "feeling very relaxed" and "feeling incredibly good."

[0031] <AIデコードアルゴリズム> Here, the inventors conducted an experiment to determine whether intra-auricular EEG data could be used to classify two states: "in-tune" and "non-in-tune." Here, intra-auricular EEG data before the sauna is defined as "non-in-tune," and intra-auricular EEG data after the sauna is defined as "in-tune." In other words, the data in the "in-tune" category includes data collected during the three breaks in the post-sauna phase (post1, post2, post3). The data in the "non-in-tune" category includes only data collected during the pre-sauna phase (pre).

[0032] The intraauricular EEG data were divided into equal intervals (4 seconds) and Fourier transformed. Power in the 4-40 Hz band was calculated at 0.5 Hz intervals. Noise quantification was performed for each segment based on predefined criteria obtained from noise-free intraauricular EEG data. Segments exceeding 2 SD were excluded. Power was then standardized across participants. For future applications, three signals (left ear, right ear, and the difference between the left and right ear signals) were used to classify participants as "in tune" or "not in tune."

[0033] Because the number of data points in the "Totonou" category was greater than that in the "Non-Totonou" category, data points in the "Totonou" category were randomly selected so that the number of data points was the same as that in the "Non-Totonou" category. The number of feature dimensions was reduced by applying principal component analysis (PCA) to all components 1 through 150. The training data included 90% of the data points in each category, and 10% of the data points in each category was used as test data. Linear discriminant analysis (LDA) using least squares and a selection operator (LASSO) was used to distinguish between the two categories. Ten-fold cross-validation of the training data was performed to select the optimal parameter λ. The optimal value was considered to be the λ value that resulted in the lowest test error in cross-validation, and the accuracy was finally evaluated using the test data.

[0034] Figure 1F shows the classification accuracy of the sauna group in Experiment 1. As shown in Figure 1F, the sauna group's mean classification accuracy was 88.34% (SD = 6.71), which was significantly different from chance (50%) according to a one-sample t-test (t(9) = 18.08, p < 0.01). This suggests that the mean performance for decoding the "tono" state using the in-ear EGG was approximately 90%, demonstrating that the in-ear EGG can be used to classify brain states as "tono" or "non-tono."

[0035] [Second experiment] Next, the inventors focused on the fact that music not only promotes relaxation but also induces significant changes in brain activity. They attempted to recreate the same relaxing effect as the "totono" state felt after a sauna bath by using an innovative approach: listening to music incorporating stimulating sounds proven by neuroscience. For example, previous research has shown that low-frequency auditory stimulation, such as theta waves, can help suppress anxiety and enhance happiness. However, such auditory stimulation can be too monotonous and can cause discomfort and distraction. Therefore, the inventors hypothesized that by integrating regular music with auditory stimulation, they could stimulate specific brain activity patterns and create a comfortable experience similar to the "totono" state.

[0036] <Experiment details> (subject) Eight healthy subjects (four males, aged 21-41) participated in the experiment. All participants were right-handed, and none had hearing or speech disabilities. The experiment consisted of three conditions: modified music (normal music with specific pure tones added, hereafter referred to as "music+"), normal music (hereafter referred to as "normal music"), and silence. Each subject received the three types of stimuli on different days. To ensure fairness and avoid bias, the order of participation in each condition was balanced among the participants.

[0037] (procedure) The second experiment consisted of three phases: a pre-test, music listening (silenced state), and a post-test (Figure 2A). Figure 2A illustrates the experimental procedure for the second experiment. In the example shown in Figure 2A, scalp electroencephalogram (EEG) and heart rate (HR) were measured during the auditory oddball task in both the pre-test and post-test. Furthermore, salivary alpha-amylase activity (SAA) was measured using a biosensor (Salivary Amylase Monitor, NIPRO Corporation, Japan) before and after music listening to assess stress levels. Furthermore, after the pre-test phase, EEG data were recorded for 5 minutes in silence as a baseline. After EEG data recording, participants were administered a questionnaire regarding their physical and mental state.

[0038] The music listening phase was divided into three parts, each consisting of 5 minutes of music listening or silence followed by a questionnaire. The questionnaire used in the second experiment was based on a visual analogue scale (VAS) and was administered on a PC using MATLAB®.

[0039] Figure 2B shows the results of the questionnaire in Experiment 2. As shown in Figure 2B, items 1–10 (Q1–Q10) were extracted from the “Altered States of Consciousness Rating Scale” (Studerus, E., Gamma, A., and Vollenweider, FX (2010). Psychometric evaluation of the altered states of consciousness rating scale (OAV). PLoS One 5:e12412. doi: 10.1371 / journal.pone.0012412). For these items, responses of “No, same as usual” were scored as 0 points, and responses of “Yes, much more than usual” were scored as 100 points.

[0040] Items 11 to 13 (Q11-13) were extracted from the Short-Form Self-Report Scale for Assessing Relaxation Effects (S-MARE) (Sakakibara, M., Teramoto, Y., and Tani, I. (2014). Development of a short-form self-report measure to assess relaxation effects. Jpn. J. Psychol 85, 284-293. doi: 10.4992 / jjpsy.85.13210). For these items, responses of "No, I don't think so" were scored as 0, and responses of "Yes, I definitely think so" were scored as 100. Significant differences were observed in subjects' responses to these items before and after sauna bathing.

[0041] (Electroencephalogram recording) In the auditory oddball task, a series of 200 stimuli consisting of a 1000 Hz standard tone (80% of the time) and a rare 2000 Hz "target" tone (20% of the time) were presented 1.5 seconds after the onset of each stimulus. These auditory stimuli lasted 50 ms (5 ms rise / fall time) and were delivered through headphones at a sound pressure level of 60 dB. Stimuli were presented pseudorandomly, ensuring that at least two standard stimuli preceded each "target" stimulus.

[0042] Subjects were instructed to press a response button with their preferred hand upon detecting the target stimulus. Reaction times (RTs) were measured in milliseconds, and the average of correct responses was calculated. EEG was recorded using a wireless biosignal amplifier system (Polymate Mini AP108, Miyuki Giken Co., Ltd., Tokyo, Japan) and gel electrodes (METS INC., Chiba, Japan). According to the International 10-20 system, recordings were made from the Cz, T3, and T4 electrodes at a sampling rate of 500 Hz. Ground and reference electrodes were placed on the left and right lateral cortical areas, respectively. Electroocular activity (EOG) was assessed by measuring eyeblinks and vertical eye movements with an electrode placed on the upper outer edge of the left eye. An electrocardiogram (ECG) was obtained by measuring heart rate with an electrode placed on the upper left chest. Recorded data were filtered with a 0.1 Hz low-cut filter and a 40 Hz high-cut filter.

[0043] During the oddball task, epochs of standard or target stimuli began 100 ms before stimulus presentation (baseline) and continued for 600 ms after stimulus onset. Epochs in which the EEG or EOG signal exceeded ±50 μV were automatically excluded due to artifacts from vertical eye movements or muscle contractions. Event-related potentials (ERPs) for standard and target stimuli were then calculated by averaging the epochs for each stimulus. A difference waveform was calculated by subtracting the target ERP from the standard ERP. The MMN (100–250 ms) was calculated as the negative peak of the difference waveform, and the P300 (240–400 ms) was calculated as the positive peak of the difference waveform using a time window. The area under the curve (μV × ms) of the difference waveform peak was calculated, and the MMN and P300 areas were calculated.

[0044] During baseline and music / silence conditions, subjects were instructed to sit with their eyes closed for 5 min for brain activity measurements. Brain activity data were divided into equally spaced segments (10 s) and subjected to fast Fourier transform (FFT). Data for each subject were averaged for each epoch at Cz, T3, and T4 electrodes, respectively, and gross absolute spectral power was calculated for theta (4-8 Hz) and alpha (8-12 Hz) frequency bands.

[0045] (musical stimulation) One of the inventors created original music using Ableton Live software. This music included bass, synthesizer, and piano sounds, along with the software's internal sound instruments. The bass was "Ambient Encounters Bass," the synthesizers "Simple One Pad," "The Greatest Pad," and "Megaphatness Pad," and the piano was "Modular Pianos: 11 Tape Pianos." To create a low-frequency hum, the notes C, C#, and E were played on the bass. The chord Cadd9, consisting of the notes C, E, G, and D, was played on the synthesizer. The arpeggio melody, consisting of the notes C, D, E, and D, was played on the piano. This original music served as the basis for the special music.

[0046] Here, the difference between normal music and special music will be explained using waveforms. Figure 2C is a diagram showing an example of normal music used in the second experiment. The example shown in Figure 2C shows the waveform of the sound of normal music, the power after FFT transformation, and the envelope after FFT transformation.

[0047] FIG. 2D shows an example of the special music used in the second experiment. The example shown in FIG. 2D shows the waveform of the special music, the power after FFT transformation, and the envelope after FFT transformation. The Fourier transform of original music created by one of the inventors showed a peak around 82 Hz (middle of FIG. 2D). Therefore, θ1 of the original music is around 82 Hz.

[0048] To shift the power peak of the original music's envelope to the theta band (around 7 Hz), a 75 Hz pure tone (θ2) was added to the original music. The FFT results and frequency response of the modified original music (special music) envelope are shown in Figure 2D. This special music was used to stimulate the theta wave frequency band in the user's brain.

[0049] Figures 2E-G show examples of various experimental results from the second experiment. Figure 2E shows the average event-related potential (ERP) of the subjects measured at the Cz electrode. Figure 2F shows quantitative analysis of the MMN and P3 region at three sites (Cz, T3, and T4) before (pre) and after (post) music listening for each condition. Figure 2G compares the amplitude spectral power of theta waves at each music listening stage (pre, post1, post2, and post3) for each condition.

[0050] Based on the above experimental results, an auditory oddball task was performed in the session after listening to music using regular music (music) shown in Figure 2C and special music (music+) shown in Figure 2D. Listening to the special music significantly increased the MMN (Figures 2E and 2G). Furthermore, theta wave activity significantly increased while listening to the special music (Figure 2G). A questionnaire survey showed that the mean score for feeling relaxed significantly increased only when listening to the special music (Figure 2B).

[0051] Therefore, when comparing the results of the second experiment with those of the first experiment, the significant increase in MMN was the same as that obtained in the sauna, indicating a significant improvement in sensitivity to auditory stimuli. The significant increase in theta waves was also the same as that in the first experiment. Theta waves are closely related to relaxation, and when combined with the questionnaire results, this suggests that when people are able to separate themselves from the outside world, their brains enter a theta state, which in turn promotes more creative thinking.

[0052] In the questionnaire survey for the second experiment, only the items that showed a significant interaction in the first experiment were used, and the results showed that only the items "My heart is beating faster than usual" and "My back is stiff" showed no change, while the other items showed changes similar to those seen in the sauna (Figure 2B). These two items may be due to the alternating hot and cold effects in the sauna, which may be difficult to reproduce by listening to music.

[0053] As described above, in Experiments 1 and 2, despite differences in the trends of changes in alpha wave activity, there was a consistent significant increase in theta wave and MMN amplitude. This suggests that listening to music incorporating monaural beats may have succeeded in creating a state similar to the "Totono" state. Music that does not require a special environment, such as a sauna, can more easily achieve a relaxation effect.

[0054] Based on the results of the first and second experiments described above, the disclosed technology elucidates the brain activity that produces a sense of happiness similar to that experienced after using a sauna, and by stimulating the user's brain with the frequency band corresponding to theta waves, which are characteristic of brain activity similar to that experienced after using a sauna, it becomes possible to provide the user with a relaxation effect similar to that experienced after using a sauna.

[0055] <Summary of the Disclosed Technology> In the disclosed technology, in order to enhance a predetermined frequency band corresponding to theta waves in electroencephalograms, stimulation sound data is generated that stimulates the user's brain with frequencies in a predetermined frequency band. Also, in the disclosed technology, by generating stimulation sound data (e.g., stimulation instrument sound data) that causes as little discomfort as possible to the user, it is possible to achieve the effect of stimulating theta waves while reducing the discomfort felt by the user.

[0056] Furthermore, the stimulation sound data includes instrument sound data (referred to as "stimulating instrument sound data") that stimulates the user's brain with frequencies in a predetermined frequency band corresponding to theta waves, and it is possible to generate music using this stimulation instrument sound data. Furthermore, by having the user listen to the generated music, it is possible to provide the user with stimulation in the predetermined frequency band corresponding to theta waves. This reduces the discomfort felt by the user while stimulating the user with the predetermined frequency band corresponding to theta waves, thereby achieving the effects of the predetermined frequency band.

[0057] [First embodiment] <Configuration example of information processing device 10> 3 is a block diagram showing an example of an information processing device 10 according to the first embodiment. The information processing device 10 is, for example, a personal computer, and may be composed of one or more devices. The information processing device 10 processes sound data and generates, for example, instrument sound data that stimulates a user in a predetermined frequency band. The information processing device 10 does not necessarily have to be a personal computer, and may also be a server, a smartphone, a tablet terminal, or the like that has information processing capabilities.

[0058] The information processing device 10 includes one or more processors (CPUs: Central Processing Units) 110, one or more network communication interfaces 120, a storage device 130, a user interface 150, and one or more communication buses 170 for interconnecting these components.

[0059] The user interface 150 includes a display device (not shown) and an input device (not shown), such as a keyboard and / or a mouse or some other pointing device.

[0060] The storage device 130 may be, for example, a high-speed random access memory such as a DRAM, an SRAM, a DDR RAM, or other random access solid-state storage device, or may be a non-volatile memory such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The storage device 130 may be, for example, a computer-readable non-transitory recording medium that records a program that causes a processor to execute the processes described below.

[0061] Another example of storage device 130 may be one or more storage devices located remotely from processor 110. In one embodiment, storage device 130 stores the following programs, modules, and data structures, or a subset thereof:

[0062] One or more processors 110 read and execute a program from the storage device 130 as necessary. For example, one or more processors 110 execute a program stored in the storage device 130 to configure a control unit 111 that executes the processing of the disclosed technology. The control unit 111 may also configure an acquisition unit 112, a generation unit 113, an output unit 114, and a determination unit 115. The determination unit 115 will be described in a second embodiment.

[0063] The acquisition unit 112 acquires sound data. For example, the acquisition unit 112 may acquire predetermined sound data stored in the storage device 130, may acquire predetermined sound data from an external device via the network communication interface 120, or may acquire predetermined sound data generated by a user operating the user interface 150.

[0064] The generation unit 113 generates stimulation sound data that stimulates the user's brain with frequencies in the frequency band of theta waves, based on the sound data acquired by the acquisition unit 112. For example, the generation unit 113 may use the user interface 150 (see FIG. 4, etc., described later) to generate stimulation instrument sound data, which is an example of stimulation sound data, based on sound data that stimulates theta waves, described later. The generation unit 113 may also include first to third processing units 113a to 113c, and process the sound data acquired by the acquisition unit 112 into stimulation sound data. For example, the first to third processing units 113a to 113c perform predetermined processing on the sound data to generate stimulation sound data that includes, as main components, predetermined frequencies in a predetermined frequency band.

[0065] The predetermined frequency band of theta waves includes, for example, the frequency band of θ (theta) waves (4 to 8 Hz) related to electroencephalograms generated by brain activity. Furthermore, the generation unit 113 may generate stimulation sound data that includes a specific predetermined frequency as a main component within the frequency band of theta waves. For example, the generation unit 113 may generate stimulation sound data that includes a 6 Hz frequency component as a main component within the theta waves, with 6 Hz being the predetermined frequency. This makes it possible to stimulate the user with a specific frequency of theta waves, thereby stimulating the user with a specific frequency band including the specific frequency.

[0066] The predetermined processing performed by the generation unit 113 includes processing the predetermined sound data so that it has the characteristics of each instrument sound, such as a drum, bass, guitar, keyboard (piano), etc. For example, the processing includes processing the envelope of the predetermined sound data or synthesizing sound data having the characteristics of the instrument sound. The generation unit 113 may select a processing method corresponding to an instrument sound designated by a user or the like based on processing information in which processing methods are set for each instrument sound, and process the predetermined sound data by the selected processing method. The generation unit 113 may also synthesize multiple stimulus instrument sound data. For example, the generation unit 113 may synthesize first stimulus instrument sound data with second stimulus instrument sound data different from the first stimulus instrument sound data.

[0067] The output unit 114 outputs the stimulation sound data generated by the generation unit 113 to induce theta waves in the user's brain. For example, the output unit 114 outputs the generated stimulation sound data to a speaker and controls the speaker to output the stimulation sound. The output unit 114 may also combine the generated stimulation sound data with other sound data and output the combined data. The speaker may be provided in the information processing device 10, or may be connected to the information processing device 10 via a network.

[0068] Through the above processing, stimulation sound data that stimulates theta wave frequencies in the user's brain is generated, and by stimulating the user with this stimulation sound data, theta waves are induced in the user's brain, providing the user with a relaxation effect similar to that experienced after a sauna. Furthermore, theta wave stimulation can suppress anxiety and enhance the user's sense of well-being. Furthermore, since theta waves are said to have the effects of improving memory and inspiration, these effects can also be expected when theta waves are generated in the user's brain through theta wave stimulation.

[0069] Furthermore, the generating unit 113 can generate stimulus instrument sound data that has a frequency in a predetermined frequency band corresponding to theta waves, but has characteristics similar to those of a musical instrument sound. This allows the listener to listen to the stimulus instrument sound data without feeling any discomfort as a musical instrument sound. Below, the processing for generating stimulus instrument sound data will be explained using three examples.

[0070] (Processing A) The predetermined sound data may include first sound data including a pure tone that stimulates the user in a predetermined frequency band. For example, if the predetermined frequency band is theta waves and the specific frequency is between 4 and 8 Hz, the pure tone may be, for example, a 6 Hz sine wave, or a 10,000 Hz sine wave that oscillates at a period of 6 Hz. A pure tone is also called a single tone, a master tone, or a carrier, and refers to a sound generated by an oscillator or a vibration that becomes a master tone.

[0071] When the first sound data includes the above-mentioned pure tone, the first processing unit 113a may perform envelope processing on the first sound data, including processing related to attack and / or processing related to decay.

[0072] For example, the first processing unit 113a may process the first sound data into a sound similar to a bass drum (bass drum). In this case, the first processing unit 113a may calculate an envelope for the first sound data, and process the envelope in terms of attack, adjusting the timing at which the volume reaches a maximum, and in terms of decay, adjusting how the volume attenuates.

[0073] Through the above processing, the first sound data is envelope-processed so that it has the characteristics of a bass drum, and the first sound data becomes similar to the sound of a bass drum.

[0074] Furthermore, the first processing unit 113a may process the first sound data into a sound similar to a snare drum or a hi-hat. In this case, the first processing unit 113a may combine sound data associated with a predetermined instrument sound with the stimulation sound data. For example, the first processing unit 113a may store preset sound data for a snare and / or sound data for a hi-hat, and combine the snare sound data or the hi-hat sound data with the first sound data according to the application or user designation, thereby generating stimulation instrument sound data related to the snare and / or the hi-hat.

[0075] By performing the above process, the first sound data is synthesized with sound data that has the characteristics of a snare drum or a hi-hat, so that the first sound data resembles the sounds of a snare drum or a hi-hat. Also, by synthesizing the bass drum, snare, and hi-hat of a drum, it is possible to produce a more natural drum sound.

[0076] (Processing B) The predetermined sound data may also include instrument sound data that does not include a specific frequency (e.g., 6 Hz) in a predetermined frequency band corresponding to theta waves as a main component. For example, the instrument sound data may include sound data of drums, bass, keyboard, guitar, keyboard, etc., generated by normal playing or generation methods.

[0077] In the case of the above-mentioned instrument sound data, the second processing unit 113b may process the instrument sound data using a low frequency oscillator (LFO). For example, if it is desired to stimulate the user with a specific frequency of 6 Hz, the second processing unit 113b modulates the instrument sound data by oscillating 6 Hz using the LFO so that a vibration of 6 Hz is generated.

[0078] Through the above processing, by using an LFO to oscillate a specific frequency corresponding to theta waves, it is possible to modulate normal instrument sound data and stimulate the user's brain with a specific frequency band or frequency corresponding to theta waves.

[0079] (Processing C) In addition, if the specified sound data includes instrument sound data that does not include a specific frequency (e.g., 6 Hz) in a specified frequency band corresponding to theta waves as a main component, the third processing unit 113c may include synthesizing the differential frequency between the peak frequency of the instrument sound data and the specific frequency in the specified frequency band into the instrument sound data.

[0080] Here, we will explain why, as in processing process C, synthesizing the differential frequency between the peak frequency of the instrument sound data and a specific frequency in a predetermined frequency band into the instrument sound data makes it possible to stimulate a specific frequency corresponding to theta waves in the user's brain.

[0081] First, we will explain why the LFO can stimulate a specific frequency. Hereinafter, θ1 is defined as the peak frequency of the original sound data, and θ2 is defined as the frequency at which the LFO oscillates. At this time, the frequency of the sound data modulated based on the frequency θ2 oscillated by the LFO is expressed by (Equation 1).

number

[0082] Here, in order to examine the time waveform of the modulated sound data, the modulated sound data is converted into an analytic signal by a Hilbert transform or the like, and the envelope is found by finding the absolute value (Equation 2).

number

[0083] When (Equation 2) is expanded using (Equation 3), it finally becomes √(3 / 2+2cosθ2+1 / 2cos(2θ2)).

number

[0084] As described above, the envelope of the modulated sound loses the original peak frequency θ1 and is affected only by the frequency θ2 generated by the LFO. This led the inventors to consider whether it might be possible to stimulate a specific frequency in a user listening to a synthesized sound by simply synthesizing a specific frequency with the peak frequency of the original sound data. In the following, the peak frequency θ1 is set to the specific frequency θ2. Equation 4 represents the frequency of the synthesized sound data obtained by synthesizing the peak frequency θ1 with the specific frequency θ2.

number

[0085] Here, in order to examine the time waveform of the synthetic sound data, the synthetic sound data is converted into an analytic signal by a Hilbert transform or the like, and the envelope is found by finding the absolute value (Equation 5).

number

[0086] When (Equation 5) is expanded using (Equation 6), it finally becomes √(2+2cos(θ1-θ2)).

number

[0087] Therefore, if you want to stimulate the user with a specific frequency of 6 Hz, and the peak frequency θ1 of the original sound data is, for example, 70 Hz, by setting the predetermined frequency (differential frequency) θ2 to 64 Hz, the frequency of 6 Hz can be stimulated in the user's brain, since θ1-θ2=70 Hz-64 Hz. In other words, once the specific frequency to stimulate the user's brain is determined, the differential frequency between the peak frequency of the original sound data and the specific frequency can be calculated, and this differential frequency can be synthesized into the sound data, thereby stimulating the user's brain with the specific frequency. Therefore, even a simple process such as processing process C can stimulate the user's brain with the specific frequency.

[0088] The acquiring unit 112 also acquires predetermined music data. The predetermined music data may be music data of existing music, or music data of music newly generated by a generation AI (Artificial Intelligence) or the like. The acquiring unit 112 may acquire predetermined music data selected by a user from a database in which music data is stored, or may acquire predetermined music data distributed by a music distribution service.

[0089] When the acquisition unit 112 acquires predetermined music data, the generation unit 113 analyzes the predetermined music data and extracts one or more instrument sound data. For example, the generation unit 113 separates sound source data from existing music data using a publicly known AI technology for performing sound source separation. The sound source separation AI can input music data and output each sound source data by learning the characteristics of each sound source. Each sound source data may be, for example, vocals, drums, bass, guitar, keyboard, etc. The generation unit 113 may be provided in a processing device capable of data communication with the information processing device 10 via a network, for example, a server on a cloud.

[0090] Furthermore, the generation unit 113 may select at least one piece of instrument sound data from one or more pieces of instrument sound data as the predetermined sound data, and the selected instrument sound data is then acquired by the acquisition unit 112 as the predetermined sound data.

[0091] The generation unit 113 may also process at least one of the sound source-separated instrument sound data into stimulating instrument sound data. For example, the generation unit 113 performs the above-described processing B or processing C on one piece of instrument sound data.

[0092] Through the above processing, it becomes possible to, for example, separate existing instrument data into sound sources, extract each sound source data, and generate stimulating instrument sound data from the extracted sound source data that stimulates a specific frequency corresponding to theta waves.

[0093] Furthermore, the generating unit 113 may synthesize the stimulating instrument sound data with predetermined music data. For example, the generating unit 113 may replace sound source data separated from existing music with the stimulating instrument sound data or superimpose the sound source data on the original sound source data to generate music data.

[0094] The output unit 114 may also output synthesized music data including stimulating instrument sound data. This makes it possible to extract sound source data of a predetermined instrument sound from existing music data, replace this sound source data with stimulating instrument sound data, or superimpose the stimulating instrument sound data on the original sound source data, thereby generating and outputting music data that stimulates the user's brain with a predetermined frequency band or a specific frequency corresponding to theta waves.

[0095] By using the above-mentioned processing, it is possible to realize remixes that create derivative music by reconstructing the structure of the sound source using the sound source separation technology, which is a well-known technology.In addition, by using the sound source separation technology, it is possible to easily collect sound source materials to be processed.

[0096] Furthermore, the generation unit 113 may identify the type of predetermined sound data acquired by the acquisition unit 112 and perform processing according to the type of predetermined sound data. For example, the generation unit 113 performs frequency analysis on the predetermined sound data and / or determines similarity with instrument sound data to identify a pure tone that does not stimulate the user in a predetermined frequency band, a stimulating sound, or an instrument sound. The generation unit 113 may perform processing A if the predetermined sound data is pure tone data or first sound data, or may perform processing B or C if the predetermined sound data is instrument sound data. This makes it possible to automatically generate appropriate stimulating instrument sound data by inputting predetermined sound data.

[0097] <Example> Next, a specific example of processing will be described. FIG. 4 is a diagram showing an example of generating a sine wave according to the first embodiment. The example shown in FIG. 4 shows an example of generating a sine wave, which is a pure tone. "Brain Wave" shown in FIG. 4 is a specific frequency that is desired to stimulate the user, and indicates 6 Hz. For example, if "Brain Wave" is set to 6 Hz, it becomes possible to generate a 6 Hz sine wave pure tone. That is, the generation unit 113 accepts a user operation, acquires the frequency input to "Brain Wave," and generates a sine wave pure tone of this frequency. "Interval" indicates the interval between sounds, and indicates 1 second / 6 Hz = 167 ms.

[0098] "Interval from Br" indicates the "interval" adjusted by "Brain Wave", and the interval between sounds is adjusted by 6Hz. "Length" indicates the length of the sound, for example, 167ms. The original sound carrier is 10KHz.

[0099] In the example shown in Fig. 4, the basic setting is to repeat ON / OFF every 167 ms for the original sound carrier frequency of 10 kHz to generate 6 Hz, but the generation unit 113 may change the ON / OFF interval to "interval from Br" so that the specific frequency of 6 Hz is generated from the sound. According to the above-mentioned process, the interval between sounds varies in accordance with the fluctuation of the specific frequency, and is adjusted so that the interval between sounds becomes the specific frequency. According to the example shown in Fig. 4, the generation unit 113 can generate stimulation sound data that stimulates the specific frequency from scratch, and generate stimulation instrument sound data based on this stimulation sound data.

[0100] FIG. 5 is a diagram showing an example of processing A according to the first embodiment. In the example shown in FIG. 5, the first processing unit 113a performs envelope processing P10 on the first sound data W10, thereby making it resemble the sound of a bass drum. The first sound data W10 is a pure tone that itself generates fluctuations. The first processing unit 113a can automatically perform envelope processing by storing or having a machine learning model learn the magnitude and timing of the attack, the amount of decay, and other characteristics of the bass drum in advance.

[0101] The first processing unit 113a also synthesizes sound data N10 from the first sound data W10 to resemble the characteristics of a snare or hi-hat. The first processing unit 113a may also perform both envelope processing and sound data synthesis to resemble the sound of a specific instrument such as a bass drum, snare, or hi-hat.

[0102] Fig. 6 is a diagram showing an example of processing B according to the first embodiment. In the example shown in Fig. 6, the second processing unit 113b performs LFO processing on the instrument sound data W12, modulating it using a low frequency so as to emit a specific frequency W14. As a result, stimulating instrument sound data that stimulates the user with the specific frequency W14 is generated.

[0103] 7 is a diagram showing an example of processing C according to the first embodiment. In the example shown in FIG. 7, the third processing unit 113c identifies a peak frequency FP at a peak P12 for a graph G12 of frequency components of the musical instrument sound data W12. Next, the third processing unit 113c calculates a differential frequency F1 between the peak frequency FP and a specific frequency FT that is desired to stimulate the user, and adds the differential frequency F1 in the graph G14 to the frequency components of the musical instrument sound data W12 in the graph G12. As a result, after the addition of the differential frequency F1, stimulating musical instrument sound data that stimulates the user at the specific frequency FT is generated.

[0104] FIG. 8 is a diagram illustrating an example of remix processing according to the first embodiment. In the example illustrated in FIG. 8, music data M20 of an original song is music data that does not have, as its main components, components in a predetermined frequency band that stimulate the user. The generation unit 113 separates the sound source data for each instrument using a known sound source separation technique. For example, sound source data W20 represents a guitar waveform 1, sound source data W22 represents a bass waveform 2, and sound source data W24 represents a drum waveform 3.

[0105] For example, if it is difficult to convert the waveform 1 of the sound source data W20 into MIDI (Musical Instrument Digital Interface) information, the generation unit 113 processes the sound source data W20 using LFO technology, thereby generating sound source data WS2 that can enhance specific frequencies.

[0106] Furthermore, the generation unit 113 converts, for example, waveform 2 of the sound source data W22 into MIDI1 information. For example, the generation unit 113 processes the MIDI1 information to produce a specific frequency (e.g., 6 Hz). For example, the generation unit 113 performs LFO processing to oscillate at 6 Hz, and generates sound source data WS4 that maintains the waveform of the bass sound as much as possible.

[0107] The generator 113 also converts, for example, waveform 3 of the sound source data W24 into MIDI2 information. For example, the generator 113 uses the MIDI2 information to generate sound source data WS6 that maintains the waveform of the drum sound as much as possible through envelope processing and sound data synthesis.

[0108] The generation unit 113 synthesizes the sound source data WS2, WS4, and WS6 to generate music data MS2 in which a predetermined frequency band or a specific frequency is enhanced. This allows the generation unit 113 to reconstruct, from existing music data, music data that can stimulate a user in a predetermined frequency band or a specific frequency. Note that the instrument sounds corresponding to the above-described instrument sounds are merely examples, and other processing may be performed.

[0109] <Operation> Next, a description will be given of the operation of the information processing device 10 according to the first embodiment. Fig. 9 is a flowchart showing an example of processing by the information processing device 10 according to the first embodiment.

[0110] In step S102, the acquisition unit 112 acquires predetermined sound data. The predetermined sound data may be sound data, first sound data, or instrument sound data. The acquisition unit 112 may also acquire pure sound data that does not stimulate the predetermined frequency band.

[0111] In step S104, the generation unit 113 processes the acquired predetermined sound data into stimulation sound data that stimulates the user in a predetermined frequency band corresponding to theta waves. Furthermore, the generation unit 113 may select and apply any one of the processing processes A to C depending on the characteristics of the predetermined sound data. Furthermore, the generation unit 113 may start with a process for generating pure tones (e.g., FIG. 4) and generate stimulation sound data from the beginning.

[0112] In step S106, the output unit 114 outputs the stimulation sound data generated or processed by the generation unit 113 via a speaker or the like.

[0113] Fig. 10 is a flowchart showing an example of processing related to the first embodiment. In the example shown in Fig. 9, the processing is divided depending on the type of acquired predetermined sound data, but the acquisition unit 112 or the generation unit 113 may use the characteristics of the predetermined sound data to determine whether it is first sound data or instrument sound data, and automatically select and execute processing.

[0114] In step S202, the acquisition unit 112 acquires the first sound data.

[0115] In step S204, the first processing unit 113a performs envelope processing on the acquired first sound data, the envelope processing being set to the characteristics of a predetermined musical instrument sound (drums, bass, guitar, keyboard, etc.).

[0116] In step S206, the first processing unit 113a synthesizes sound data with the acquired first sound data to produce a predetermined musical instrument sound. Note that the processing of steps S204 and S206 may be performed in either order, or both processes may be performed in two stages.

[0117] In step S302, the acquisition unit 112 acquires predetermined instrument sound data.

[0118] In step S304, the second processing unit 113b executes LFO processing on the acquired instrument sound data.

[0119] In step S402, the acquisition unit 112 acquires predetermined instrument sound data.

[0120] In step S404, the third processing unit 113c identifies the peak frequency of the acquired musical instrument sound data.

[0121] In step S406, the third processing unit 113c calculates the differential frequency between the peak frequency and the specific frequency (target frequency).

[0122] In step S408, the third processing unit 113c combines the difference frequency with the frequency component of the original instrument sound data.

[0123] At least one process may be executed in steps S200, S300, and S400, or multiple processes may be executed in parallel or in time series.

[0124] Through the above processing, stimulation sound data that stimulates the user's brain with theta wave frequencies is generated, and by stimulating the user with this stimulation sound data, theta waves are induced in the user's brain, giving the user a relaxation effect similar to that experienced after a sauna. Furthermore, theta wave stimulation can suppress anxiety and increase the user's sense of well-being. It is also possible to generate the above-mentioned stimulation instrument sound data so that the user does not feel uncomfortable.

[0125] [Second embodiment] In the second embodiment, the information processing device 10 may acquire an electroencephalogram signal of the user measured by an earphone set 20 described below, and execute the processing disclosed below using the electroencephalogram signal of the user. <Earphone set composition> 11 and 12 provide an overview of the earphone set 20 according to the second embodiment. Note that the earphone set 20 is not limited to the examples shown in FIGS. 11 and 12, and any earphones that can sense brain waves from the ear canal and output them to an external device can be applied to the technology of the present disclosure.

[0126] Fig. 11 is a diagram showing an example of an earphone set 20 according to the second embodiment. The earphone set 20 shown in Fig. 11 has a pair of earphones 200R, 200L and a neck strap 210. Each earphone 200R, 200L is connected to the neck strap 210 using a cable capable of signal communication, but may also be connected using wireless communication. Hereinafter, RL will be omitted when there is no need to distinguish between left and right.

[0127] The neck strap part 210 has a central member that fits along the back of the neck, and rod-shaped members (arms) 212R, 212L that curve along both sides of the neck. Electrodes 222, 224 that sense EEG signals are provided on the surface of the central member that comes into contact with the neck on the back side. Electrodes 222, 224 are an electrode that is connected to earth and a reference electrode, respectively. This allows the electrodes 222, 224 to be spaced apart from the elastic electrodes provided on the ear tips of the earphones, as will be described later, making it possible to acquire EEG signals with high accuracy. The neck strap part 210 may also have a processing unit that processes EEG signals and a communication device that communicates with the outside, but these processing units and communication units may be provided in the earphones 200.

[0128] Furthermore, the tip sides of the rod-shaped members 212R, 212L on both sides of the neck strap 210 are heavier than the base sides (center member sides), which allows the electrodes 222, 224 to be properly pressed against the neck of the wearer. For example, weights are provided on the tip sides of the rod-shaped members 212R, 212L. However, the positions of the electrodes 222, 224 are not limited to these positions.

[0129] Fig. 12 is a diagram showing an example of a schematic cross section of earphone 200R according to an embodiment. Earphone 200R shown in Fig. 12 may have elastic member 208 (e.g., urethane) between speaker 202 and nozzle 204, for example. Providing this elastic member 208 makes it difficult for vibrations from speaker 202 to be transmitted to the elastic electrode of ear tip 206, preventing sound interference between the elastic electrode of ear tip 206 and speaker 202.

[0130] Furthermore, ear tip 206, which includes an elastic electrode, is located at the sound guide port, but the elasticity of the elastic electrode itself makes it possible to prevent interference from sound vibrations. Also, by using an elastic material for the housing, this elastic material makes it difficult for sound vibrations to be transmitted to the elastic electrode of ear tip 206, making it possible to prevent interference from sound vibrations.

[0131] The earphone 200 includes an audio sound processor.

[0132] Furthermore, the ear tip 206 conducts the brain wave signal sensed from the ear canal to the contact of an electrode provided in the nozzle 204. The brain wave signal is transmitted from the ear tip 206 via the contact to a biosensor (not shown) inside the earphone 200. The biosensor outputs the sequentially acquired brain wave signals via a cable to a processing device provided in the neck strap 210 or transmits them to an external device. Furthermore, the ear tip 206 may be insulated from the housing containing the biosensor and audio sound processor.

[0133] By using the above-described earphone set 20, it is possible to measure the user's electroencephalogram signal in real time, modulate the personal frequency identified from this real-time electroencephalogram signal, and output stimulation sound data from the earphone set 20.

[0134] The user may wear, for example, headgear that measures electroencephalograms using the International 10 / 20 system as the electroencephalogram measuring device. The electroencephalogram measuring device may include devices capable of measuring electroencephalograms, such as a measuring device that uses scalp electrodes, a measuring device that measures brain activity using intracranial electrodes, a measuring device that measures brain activity using functional magnetic resonance imaging (fMRI), or a measuring device that measures brain activity using near-infrared spectroscopy (NIRS). In this case, the acquiring unit 112 may acquire a personal frequency based on the electroencephalogram signal acquired from the electroencephalogram measuring device, and the output unit 114 may output the modulated stimulating instrument sound data via a speaker or the like.

[0135] The acquisition unit 112 of the information processing device 10 acquires an electroencephalogram signal from an electroencephalogram measuring device (for example, an earphone set 20) worn by a user.

[0136] The determination unit 115 determines whether the user's brain is in a state of relaxation or a non-sensation state based on the theta waves included in the EEG signal acquired by the acquisition unit 112. For example, by utilizing the learning model of the AI ​​decoding algorithm used in the first experiment, the determination unit 115 determines whether the user is in a "sensation" state or a "non-sensation" state based on the EEG signal. Here, the learning model is a learning model trained to classify the state before and after sauna using the EEG signal after sauna and the EEG signal before sauna as training data.

[0137] The output unit 114 outputs to the user the state determined by the determination unit 115. For example, the output unit 114 outputs to a display unit or the like of the information processing device 10 whether the user is in a "tonotonous" state or a "non-tonotonous" state based on the electroencephalogram signal of the user stimulated by the stimulation sound data.

[0138] Through the above process, the user can understand his or her own state of being stimulated by the stimulating sound data.

[0139] Furthermore, the "toning" state identified by the learning model includes a state in which the absolute value of P300 decreases and the absolute value of MMN (mismatch negativity) increases compared to before stimulation with the sound stimulus data, as shown in Experiment 1. This suggests that stimulation with theta waves causes the user's brain activity to shift to a state in which less attention is required, as after a sauna, as P300 is closely related to attention, and the increase in the absolute value of MMN makes the user more sensitive to auditory stimuli.

[0140] The electroencephalogram measuring device is included in the earphone 200, and the output unit 114 outputs the stimulation sound data from the earphone 200 via a wired or wireless connection.

[0141] <Operation> Next, a description will be given of the operation of the information processing device 10 according to the second embodiment. Fig. 13 is a flowchart showing an example of the processing of the information processing device 10 according to the second embodiment.

[0142] In step S302, an electroencephalogram signal is acquired from an electroencephalogram measuring device (for example, earphone set 20) worn by the user.

[0143] In step S304, the determination unit 115 determines whether the user's brain is in a toned state or a non-toned state based on the theta waves included in the electroencephalogram signal acquired by the acquisition unit 112. The determination unit 115 may determine the state as a toned state if the absolute value of P300 decreases and the absolute value of MMN (mismatch negativity) increases compared to before stimulation with the stimulation sound data, and may determine any other state as a non-toned state.

[0144] In step S306, the output unit 114 outputs to the user the state determined by the determination unit 115. For example, the output unit 114 outputs to a display unit or the like of the information processing device 10 whether the user is in a "toned" state or a "non-toned" state based on the electroencephalogram signal of the user stimulated by the stimulation sound data.

[0145] <Modification> The above-described embodiments are examples for explaining the technology of the present disclosure, and are not intended to limit the technology of the present disclosure to only the embodiments and examples. The technology of the present disclosure can be modified in various ways without departing from the spirit of the present disclosure. Note that each sound data, such as the predetermined sound data, instrument sound data, stimulating instrument sound data, and sound source data, may be referred to as nth sound data (n is an integer equal to or greater than 1). Each processing step in the generation unit 113 of the present disclosure may be independently implemented as a program to be executed by a computer, thereby enabling only the program corresponding to the required processing step to be installed and executed on the computer. <Variation 1> In the above embodiment, it has been explained that the frequency to stimulate the user (for example, theta waves of 4 to 8 Hz) can be set as appropriate, but in variant example 1, a personally characteristic frequency (also called an "individual specific frequency") that stimulates each user appropriately is identified, and this individual specific frequency is set as the target frequency.

[0146] For example, the band of theta waves is said to be 4 Hz to 8 Hz, and the inventors have found through the above-mentioned experiment that the theta waves that are appropriately stimulated vary from user to user. For example, when each user is asked to listen to a series of sounds containing theta waves in the range of 4 Hz to 8 Hz, for example, the EEG signal of user A may peak at 5 Hz, while the EEG signal of user B may peak at 6 Hz.

[0147] In light of the above, in Modification 1, as a pre-processing step, the user is asked to listen to sounds in a predetermined frequency band to which stimulation is to be applied while the frequencies are changed in sequence, and the frequency within the predetermined frequency band at which the user receives an appropriate stimulation is identified. For example, in the case of the theta wave frequency band, sounds having frequencies from 4 Hz to 8 Hz are output from the earphone set 20 to the user and the user is asked to listen to the sounds. While the user is listening to the sounds, the earphone set 20 measures the user's electroencephalogram (EEG) signals and outputs the results to the information processing device 10.

[0148] The information processing device 10 frequency-converts the EEG signals sequentially acquired from the earphone set 20, and identifies, for example, frequencies between 4 Hz and 8 Hz at which the spectral power value peaks as individual-specific frequencies. For example, the acquisition unit 112 sequentially acquires EEG signals from the earphone set 20, and the generation unit 113 frequency-converts the sequentially generated EEG signals, and identifies individual-specific frequencies based on each frequency signal in a predetermined frequency band that has been frequency-converted. The generation unit 113 may use a band-pass filter to extract each frequency signal in the predetermined frequency band and then identify the individual-specific frequencies. The generation unit 113 sets the identified individual-specific frequencies to target frequencies that are desired to stimulate the user.

[0149] Once the target frequency is set by the generation unit 113, other processes are the same as those disclosed in the embodiment. For example, the generation unit 113 generates stimulating instrument sound data related to an instrument sound that stimulates the user using the above-described processing process based on the individual-specific frequency in the predetermined frequency band.

[0150] Furthermore, the generation unit 113 generates stimulation sound data to stimulate the user using the above-mentioned processing, etc., based on the individual specific frequencies extracted using the spectral power values ​​of the electroencephalogram signals acquired in sequence. As a specific example, the generation unit 113 may perform envelope processing, including processing related to attack and / or processing related to decay, on the first sound data, as described above.

[0151] The generation unit 113 may also synthesize sound data associated with a predetermined musical instrument sound with the first sound data. The generation unit 113 may also generate the first sound data using a low-frequency oscillator.

[0152] In addition, when the acquisition unit 112 includes acquiring predetermined sound data including instrument sound data, the generation unit 113 may include synthesizing the differential frequency between the frequency acquired by converting the predetermined sound data and the individual specific frequency into the predetermined sound data.

[0153] This makes it possible to identify a characteristic frequency to which the user's brain responds appropriately within the frequency band corresponding to theta waves to be stimulated in the user, and to stimulate the user using that frequency. Also, the EEG measuring device is not limited to the earphone set 20.

[0154] <Variation 2> In Modification 2, the acquisition unit 112 may acquire the user's electroencephalogram signal, and the generation unit 113 may identify a characteristic frequency from the user's electroencephalogram signal and process the characteristic frequency to become a target frequency at which it is desired to stimulate the user. For example, the acquisition unit 112 may acquire the electroencephalogram signal acquired from the above-described earphone set 20, and the generation unit 113 may identify a characteristic frequency from the acquired electroencephalogram signal and acquire the identified characteristic frequency of the user (hereinafter also referred to as a "personal frequency"). Note that the acquisition unit 112 may acquire a personal frequency identified by an external device. In this case, the generation unit 113 may modulate the personal frequency acquired by the acquisition unit 112 to become the target frequency.

[0155] <Modulation processing example> The acquisition unit 112 sequentially acquires electroencephalogram signals measured by an electroencephalogram measuring device worn by a predetermined user, for example, a bioelectrode included in the earphone set 20. The electroencephalogram measuring device is not limited to the earphone set 20.

[0156] The generation unit 113 sequentially acquires the EEG signals measured by the EEG measuring device worn by the predetermined user and acquired by the acquisition unit 112, and performs frequency conversion on the EEG signals. The frequency conversion converts time component signals into frequency component signals, for example, by FFT conversion or wavelet conversion. The generation unit 113 also applies a band-pass filter to the frequency components, extracts frequency signals in a predetermined frequency band, and identifies characteristic frequency signals from among the frequency components.

[0157] For example, the generation unit 113 applies a band-pass filter to the frequency-converted signal to extract a signal in a frequency band corresponding to θ (theta) waves (4 to 8 Hz). Next, the generation unit 113 identifies a characteristic frequency signal from the signals in the extracted frequency band. The characteristic frequency signal is, for example, a signal with a frequency corresponding to a peak of the spectral power value, and this characteristic frequency signal (personal frequency signal) may vary for each user or each time the electroencephalogram signal is measured.

[0158] The generation unit 113 modulates the personal frequencies indicated by the sequentially acquired personal frequency signals so that they become target frequencies that are intended to stimulate the predetermined user. The generation unit 113 modulates the personal frequencies using, for example, the above-mentioned processing B or processing C as a modulation method, and generates a signal having the target frequency (an example of stimulation sound data).

[0159] The generating unit 113 processes the modulated target frequency signal (an example of stimulation sound data) into stimulation instrument sound data relating to an instrument sound that stimulates the user in a predetermined frequency band using the processing A described above.

[0160] According to the above process, modulation processing (for example, processing processing B or processing C) is performed on the personal frequency based on the user's electroencephalogram signal acquired in real time so that it becomes a target frequency, and processing processing A is performed on the stimulation sound data having the target frequency so that it becomes stimulation instrument sound data. This makes it possible to induce the user's current electroencephalogram state to a state in which the brain generates a predetermined frequency band to be stimulated.

[0161] <Variation 3> In Modification 3, the generation unit 113 may store the stimulus instrument sound data as MIDI data. The MIDI data includes numerical data such as pitch, volume, duration, and timbre of sounds that are elements of musical instrument performance. The generation unit 113 can easily process the stimulus sound data or sound source data of the MIDI data.

[0162] The output unit 114 may also output the stimulus instrument sound data as MIDI data to a MIDI-equipped electronic device, such as an electronic musical instrument (such as a synthesizer), so that the stimulus instrument sound data is output from such a device. This allows, for example, an electronic musical instrument to be played using the stimulus instrument sound data. As a result, the user can stimulate a predetermined frequency band while playing the electronic musical instrument. The information processing device 10 may also be a MIDI-equipped electronic device. [Explanation of symbols]

[0163] 10. Information processing equipment 20 earphone sets 110 processors 111 Control Unit 112 Acquisition Department 113 Generation part 113a~c 1st~3rd processing section 114 Output section 115 Judgment section 120 Network Communication Interface 130 Storage device 150 User Interface

Claims

1. A processor included in the information processing device Acquiring sound data; generating stimulation sound data that stimulates the user's brain with a frequency in the theta wave frequency band based on the sound data; outputting the stimulation sound data to induce the theta waves in the user's brain; An information processing method that performs the above.

2. The generating step comprises:

2. The information processing method according to claim 1, further comprising: synthesizing a differential frequency between a peak frequency among frequencies obtained by frequency-converting the sound data and a frequency of the theta wave into the sound data; and modulating the frequency peak of the sound data to the theta wave.

3. The obtaining includes: acquiring sound data including a pure tone that stimulates the user's brain in the frequency band of theta waves; The generating step comprises: generating stimulus instrument sound data by synthesizing the sound data including the pure tones with envelope processing set to characteristics of a predetermined instrument sound and / or sound data associated with the predetermined instrument sound; The outputting step includes: The information processing method according to claim 1 , further comprising outputting the stimulus instrument sound data.

4. the processor: acquiring an electroencephalogram signal from an electroencephalogram measuring device worn by the user; determining whether the user's brain is in a relaxed state or a non-relaxed state based on theta waves included in the electroencephalogram signal; The outputting step includes: The information processing method according to claim 1 , further comprising outputting the determined state to the user.

5. 5. The information processing method according to claim 4, wherein the state of being in a ...

6. the electroencephalogram measuring device is included in an earphone; The outputting step includes: The information processing method according to claim 4 , further comprising outputting the stimulation sound data from the earphone.

7. A processor included in the information processing device Acquiring sound data; generating stimulation sound data that stimulates the user with a frequency in the theta wave frequency band based on the sound data; outputting the stimulation sound data to induce the theta waves in the user's brain; A program that executes.

8. An information processing device including a processor, the processor: Acquiring sound data; generating stimulation sound data that stimulates the user with a frequency in the theta wave frequency band based on the sound data; outputting the stimulation sound data to induce the theta waves in the user's brain; An information processing device that executes the above.

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