JP2007181709A - Sleep therapy device, electroencephalogram activation system using electroencephalogram induction method and subliminal learning system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a system for directly correcting electroencephalogram and inducing the same, effective for somnipathy, autonomic imbalance or melancholia. <P>SOLUTION: In this electroencephalogram induction system for enhancing the actual efficiency of an electroencephalogram induction device, the frequency band having a wavelength for one second to 60 seconds for adding the pause interval is compounded to the frequency for electroencephalogram induction, and the frequencies for the pause interval and the electroencephalogram induction further change comparatively largely and repeat according to the elapsed time in a specified frequency band. The specified frequency band further changes comparatively small even in every repetition. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to an electroencephalogram guidance system.

Devices that induce sleep are effective for sleep disorders, autonomic dysfunction, or depression. There are various induction methods by light stimulation and relaxation, and it is a system that induces to a sleepable state by releasing the tension of the living body to some extent, but it cannot directly correct and induce the electroencephalogram.
In modern psychiatric hospitals, there are few cases where these guidance devices are effectively used, and for sleep disorders, the use of neurological drugs such as sleeping pills is the mainstream, or depression or schizophrenia is used in stimulating devices. As a result, it was not possible to defer advanced science to psychiatric care because only electroshock therapy is generally performed in Japan. Therefore, with regard to the brain wave during sleep, a method and technique for effectively stimulating the brain wave for a long period of time have been required.
As ergonomic knowledge, there is 1 / f fluctuation theory which is said to affect the natural environment and human rhythm. This is said to have a healing effect due to subtle changes and deviations in the regular rhythm.

JP, 2005-292215, A High-speed learning system and storage medium for learning JP 2004-133362 A Learning system JP 2003-135767 A Japanese Patent Publication No. H06-85804 JP06-0642908 EEG Induction Device Special H07-012376 EEG induction device Special H06-026593 low frequency treatment device JP H04-347171 EEG induction device Special H07-90019 Sleep stage monitoring device JP2003-339674 SLEEP STAGE ESTIMATION DEVICE AND DEVICE USING SIGNAL OUTPUT FROM SLEEP STAGE ESTIMATION DEVICE JP H02-98368 SLEEP INDUCING DEVICE Japanese Patent Publication H05-056902 Sleep State Change Detection Device and Sleep State Control Device

  The bioelectric stimulation device and the low-frequency treatment device may not be effective when used for a long time because the monotonous mechanical pattern and rhythm may cause discomfort and fatigue.

  This problem seems to be related to systems and devices in general that interfere with human perception, but in particular, the mechanism of the device and frequencies up to 0.4Hz-48Hz that interfere with human brain waves are likely to be affected.

  In addition, it was necessary to mitigate the above problem by using 1 / f fluctuation theory for the device, but it is generally in the research stage and seems to be an incomplete theory.

  By the way, the electroencephalogram guidance apparatus solves the above problem by biofeedback the detected electroencephalogram and filtering a specific frequency band.

  In addition, in the subliminal learning system, it was necessary to devise a way to increase learning motivation and concentration by stimulating various brain waves. The same applies to the sleep introduction device. For this reason, it was considered difficult to put various therapeutic devices related to the mind and nerves into practical use.

  In other words, the conventional method cannot directly correct and correct the electroencephalogram. Therefore, various devices have to be applied to increase the completeness of electroencephalogram induction.

Accordingly, the present invention provides an electroencephalogram induction method for electroencephalogram induction and a system thereof.
The present inventors have surprisingly found a method for inducing a brain wave during sleep for a long time by using an electroencephalogram guidance method for an electroencephalogram guidance device.

That is, the present invention
An electroencephalogram induction method for increasing the actual efficiency of the electroencephalogram induction device,
A frequency band having a wavelength of 1 second to 60 seconds for adding a pause interval to a frequency for inducing brain waves is combined,
The frequency for the pause interval and the electroencephalogram induction further changes relatively in accordance with the elapsed time in a specific frequency band, and repeats.
The specific frequency band further relates to an electroencephalogram guidance method characterized in that the specific frequency band is relatively small even at each repetition.

-The therapeutic effects of bioelectric stimulation devices and low frequency treatment devices can be applied to the head for a long time.
-In the electroencephalogram induction device, the actual efficiency can be sustained for a long time. In addition, by correcting brain waves in the sleep state, it becomes possible to treat all illnesses related to the mind and nerves.
-It is possible to activate the brain as a result by performing the electroencephalogram guidance over the entire electroencephalogram band.
-It can be applied as a substitute for the conventional 1 / f fluctuation effect.
-The subliminal effect can be amplified in the subliminal system.

Specific examples of the present invention will be described below.

  FIG. 1 shows an example used in a system or the like that requires a time interval related to perception. In each step a-e and rest period f, it is shown that the interval is gradually increased in order to activate the electroencephalogram. The effect of this method is similar to the 1 / f fluctuation theory, but it is possible to reduce the stress compared to the conventional monotonous stimulus by incorporating it not only in the time interval but also in various scenes. For example, when applied to an α wave in an electroencephalogram induction device, it is considered that the actual efficiency of electroencephalogram induction is increased by changing and changing the place where only 10 Hz is normally used within a band from 8 to 13 Hz. Furthermore, it is possible to activate the brain as a result by performing the electroencephalogram guidance over the entire electroencephalogram band.

In addition, the rest interval is preferably equal to or less than the respiration rate predicted according to the electroencephalogram. Since the respiration rate per minute is generally 12 to 20 in normal times, the composite wave is 1/5 Hz to 1/3 Hz or less. Is used. In video and the like, it seems to be effective for amplifying continuous subliminal effects. As a specific example, in the learning process in the learning system, even when the pause time f continues five times due to the subliminal effect (amplification) of the learning process ae, the actual efficiency seems to be sustained. Furthermore, by implementing the above-described electroencephalogram induction, three synergistic effects such as electroencephalogram induction, subliminal effect, and 1 / f fluctuation effect (electroencephalogram induction method) can be expected. (See Example 3)

  Similarly, when the process of about 20 seconds shown in FIG. 1 is continuously repeated for 90 minutes, it is desirable to have a different change every time.

  For example, in order to bring out an effect of 20%, if the first is 20 seconds (because 20 seconds is the maximum value in the electroencephalogram induction method), 90 seconds later is 16 seconds, in proportion to the passage of time. An effect that activates the electroencephalogram can be obtained by subtle changes. The most appropriate amount of effect is that it does not change too much, is monotonous, and is not easily conscious. (See Example 7) In FIGS. 3 and 6, it is shown that the frequency band changes with each iteration.

Next, a specific example in which the electroencephalogram guidance method is used in a subliminal learning system will be described.
For example, for the purpose of learning English words, in the learning process ae shown in FIG. 1, ab is a translated sentence, c is an English word, d is an example, and e is a display of all images displayed up to ad. Do. When the English word is “speed”, a and b are “speed, speed, and quick”, c is “speed”, d is “speed up speed increases”, and e is all of these.

  Here, ab is the first stage for displaying the associative source image, cd is the second stage for displaying the associative destination image and its additional information, e is a display for review, and each of these steps Is to amplify the subliminal effect by providing the pause time f.

  Further, in the learning step a-d, brain wave induction is performed by blinking the background together with the image display.

  As long as the format of the image can be expressed, it can be an image in addition to a character string, or it can be added with sound, and when there are multiple association source images or association destination images for one learning image May be continuously displayed in synchronism with the electroencephalogram induction frequency.

  The learning system according to the above is also possible by a general method. However, it is necessary to give subtle changes to the a-f time and the electroencephalogram frequency little by little because it is easy to get bored with a monotonous rhythm only by repeating these learning processes. It is possible with the 1 / f fluctuation theory, but especially in the EEG guidance method, the learning process ab is for recognizing an image, and the learning process cd is for learning. Should be decided. That is, regardless of the order of the process ae of one learning image, the learning process cd is at least in the middle of the alpha wave with the highest wavelength, and the learning process ab is for transitioning to a lower wavelength. It seems to be more appropriate as a learning effect. At this time, the relational expression between each process and the frequency becomes c> d> e> a> b, but especially e may be ignored because it is a review process.

  In addition, when determining the frequency for inducing brain waves, the use of θ waves may make you sleepy. Therefore, it is possible to use the δ wave or the α wave low band instead, but this problem can be avoided if the user sufficiently sleeps. Further, there is a problem that flickering occurs depending on the specifications of the display terminal.

FIG. 2 is an example of a waveform of an electroencephalogram induction method, and shows a composite wave of an electroencephalogram and an electroencephalogram induction execution time. In the subliminal learning system, the non-execution time of the electroencephalogram induction also serves as an amplification of the subliminal effect.

  The following is a description of problems in conducting brain wave guidance during sleep and the brain wave guidance system.

  The state of brain waves during sleep from sleep to wake up is a stage where deep sleep and shallow sleep are repeated about 5 times, and one stage takes about 90 minutes, and REM sleep for shallow sleep Non-REM sleep for deep sleep is configured at a ratio of about 1: 4, and in the REM sleep stage that appears during stage repetition, physical activity increases rapidly, and the respiratory rate increases with EEG in general Known to.

  In mental illness, recovery may be delayed or worsened due to poor sleep state, which may contribute to the etiology. It is considered that treatment can be performed by correcting the transition of the electroencephalogram during sleep to a normal range using the device.

  Specifically, it is necessary to devise an ergonomic rhythm for the pulse wave for brain wave induction, not only using the frequency for stimulating the brain wave.

  Now, an ergonomic rhythm is shown as a specific example.

  First, regarding the duration of EEG induction, if the pause time is fixed or if there is no pause time, it may be perceived as an unbearable rhythm immediately after EEG induction begins, and the 1 / f fluctuation theory is used. Even if it exists, the effect required for ideal sleep induction cannot be obtained because it is theoretically incomplete in the first place.

  Therefore, the interval between EEG induction pauses is determined by combining frequencies that are less than or equal to the approximate value of the respiratory rate that is related to the EEG frequency. The execution time of the electroencephalogram induction becomes more fatigued as the frequency for the electroencephalogram becomes higher. Therefore, the execution interval should be determined so as not to cause continuous stimulation in addition to the above condition of the respiration rate or less. In addition, the respiratory rate during sleep is about 0.2 Hz during non-REM sleep and about 0.4 Hz during REM sleep, but it can be further reduced to 1/2 if it is applied to each of EEG induction time and rest time. good. (See Example 7)

  Next, problems during non-REM sleep are shown.

  In general, the average ratio of REM sleep to non-REM sleep is about 1: 4, but in REM sleep, REM sleep suddenly starts from the deepest sleep state of non-REM sleep. The sleep induction time must actually vary. Rather, in non-REM sleep, it is desirable that the effect amount of electroencephalogram induction be minimized and taken longer to induce REM sleep naturally.

  In addition, at the start of EEG induction of REM sleep, there is a possibility of sleep disturbance due to random timing. Therefore, it is possible to ergonomically reduce the transition of the previous electroencephalogram frequency by inheriting it directly to the pause interval or the composite wave. That is, since the brain wave immediately before REM sleep is about 0.5 Hz (delta wave low band), the frequency for the composite wave immediately after REM sleep is preferably about 0.5 Hz or about 0.25 Hz.

  Therefore, it can be considered that the brain wave induction during substantial REM sleep may be 1: 7 ± 2 assuming the first half of the lag.

In addition, as the sleep time elapses, the arousal level gradually increases, so the transition of the electroencephalogram frequency and the respiratory frequency must increase as a whole.
FIG. 3 shows the transition of the dominant brain wave for actually inducing the brain wave during sleep and the pause interval, and the band of the pause interval is about 0.01-0.3 Hz.

  An electroencephalogram treatment apparatus may be accompanied by peculiar fatigue by using it too frequently. This is the same for music, as if you get tired of listening to the same music every day.

  Therefore, in order to prevent the sleep effect from remarkably deteriorating, it is necessary to incorporate a slightly different change from day to day and refrain from daily use.

  Tables 1 and 2 below show the EEG start point frequency, EEG arrival frequency, pause interval start point frequency, pause interval end point frequency, stage time (REM Sleep time).

The electroencephalogram induction uses a frequency determined by an electroencephalogram induction method, but the means should not specify any one of electrical stimulation, optical stimulation, and acoustic stimulation.
For example, in an electroencephalogram treatment apparatus, the electroencephalogram induction medium and the part to be applied to the human body should be determined according to the symptom and diseased part within the effective range of the electroencephalogram induction apparatus, and head and light stimulation induction are not necessarily suitable. Considering the case of chronic diseases and neurological symptoms, it can be recovered from an abnormal state by performing it around the most suitable living body part.

  Moreover, since the response of the light stimulus to the living body is slow, the actual efficiency seems to be significantly reduced when the frequency becomes a high frequency such as a β wave.

  By the way, there are α waves, β waves, and γ waves in the brain waves at awakening. Of these, α waves are in a relaxed and concentrated state, β waves are in a normal living state or in a killed state, and γ waves are excited.・ It is generally known that the situation is frustrating.

  Among them, the γ wave has the highest arousal level. Therefore, when waking up, it is possible to activate the electroencephalogram as a result by stimulating all the way up to γ wave to α wave. FIG. 6 shows an execution interval for activating the electroencephalogram when the electroencephalogram is about 47.8 Hz-7.4 Hz and the frequency for the respiration rate is about 0.4-0.1 Hz. (In addition, the overall frequency decreases with each iteration, and the highest value after 3 minutes is because the frequency transition is repeated.)

  In an actual electroencephalogram induction device, the characteristics and voltage used of each means are different. Therefore, when the electroencephalogram induction device is directly controlled by a pulse wave from a computer, it is necessary to devise the waveform or pulse width.

  Next, points to be noted when using each means will be described.

  1. The treatment site when electrical stimulation is used for electroencephalogram induction seems to be effective on the neck and back, or the nasal bone and its surroundings. The effect of attaching the conductors symmetrically to the human body is relatively low. Since the effective voltage of electrical stimulation is 40 volts ± 20 volts, a bias voltage of about 20 volts is added, or 1/3 of the maximum output is added to the height of the waveform. The pulse width is 100 ± 50 μsec. The actual efficiency of EEG induction is the highest.

  2. The effect of light stimulation may be low because it cannot be seen that the high frequency of the β wave or higher is blinking. At low frequencies, the effect is almost lost when the light lighting time is constant, so the lighting time must be determined from the ratio to the rest time. When using an LED, the effective voltage is about 4 ± 2 volts, so a bias voltage of about 2 volts is added, or 1/3 of the maximum output is added to the height of the waveform. The actual efficiency of EEG induction is the second highest if it is less than β waves.

  3. When sonic stimulation is used for brain wave induction, one pulse wave corresponds to two sound wave waveforms, and therefore, a half pulse wave or a sawtooth wave is used. Other than that, there is a method of generating an interference frequency within an effective electroencephalogram induction range by causing interference between two different relatively high frequencies. This method can also be realized by program software of a general-purpose computer. Moreover, it is appropriate that the sound volume is about the ambient sound.

  4). Light flashing stimulation using a display terminal is generally designed to have a display update frequency of around 60 Hz, so that there is a problem that flickering occurs depending on the frequency for flashing in the background. To solve this problem, select the frequency list that is divisible from the update frequency (30, 20, 15, 12, 10, .. for 60Hz) and the one closest to the frequency required for EEG induction. There is a way. As other methods, it is necessary to tune the update frequency itself as in the ninth embodiment or to provide a separate electroencephalogram induction device.

(The artificial intelligence system (extended) shown below is a medium for data analysis and calculation, and is a construction method that can be reproduced on a computer. Is also feasible.)

  FIG. 4 is a method for calculating effective brain wave guidance from the change in dominant brain waves necessary for the living body in brain wave guidance. When the artificial intelligence system learns, the relationship between the brain wave and the time axis is actually easy to understand. Each process is shown in FIG.

  First, input the change a of the dominant brain wave over time within a range that can be understood from general knowledge, and make up the unclear part by connecting the dominant brain waves before and after smoothly with a curve to create a prototype b of the brain wave induction pattern .

  Next, the electroencephalogram guidance device is tried during sleep, and when there is discomfort or awakening during this time, the time d1 is recorded as failure information d, and the wavelength at that time is changed and the process is temporarily terminated. Furthermore, when failure information d2 is generated by the next trial, the wavelength is appropriately changed to d1 if it is earlier than failure information d1, and to d2 if it is later. By repeating this method, it is possible to induce a change in dominant brain wave that is necessary for the living body for a long time.

  In addition, the electroencephalogram immediately after waking up is still asleep, and a final induction step with a wavelength level higher than the α wave should be provided in order to promote an appropriate arousal state. In the final induction stage, it is desirable that γ waves, β waves, and α waves are stimulated uniformly.

  In addition, continuous stimulation of the brain wave by the brain wave guidance device causes a burden on the nerve and causes discomfort, so this problem can be solved by providing a resting time in synchronization with the interval of breathing movement so as not to cause much stress. It is necessary to avoid it. More strictly speaking, if the breathing motion cannot be predicted, an interval equal to or less than the breathing rate is used. FIG. 6 shows an execution interval for activating the electroencephalogram by uniformly performing electroencephalogram induction when waking up. During sleep, it must also be considered that breathing and heart rate change greatly during REM sleep and non-REM sleep. FIG. 7 shows the transition of the electroencephalogram and the pause interval actually derived using this method.

  When using the artificial intelligence system using the above method, after learning the dominant brain wave during sleep and its time from the subject at the basic system stage, it moves to the artificial intelligence system expansion type and actually If the subject feels uncomfortable or awakens during guidance using the guidance device, the switch such as a push button is expanded as guidance failure information (which is considered to be an action error in brain wave observation and brain wave guidance). It must be self-correcting in preparation for the observation equipment on the side. Further, when learning sufficient for prediction is completed, it is possible to simulate one or more types of long-term ideal electroencephalogram guidance patterns, and the system can be shifted to a small computer with a simplified system.

Further, in order to further improve the guidance accuracy, for example, there is a method of adjusting the actual efficiency disparity due to individual differences in advance by adding attribute information for each subject such as age, average sleep time, and usage frequency to the learning element. is there.

  The appropriate rest interval of EEG induction is originally best synchronized with the biorhythm of the human body, so instead of simulating the respiratory frequency and EEG transition, a means to detect the respiratory rate (or respiratory movement) It is more desirable to use to predict the most appropriate electroencephalogram. In addition, since it is possible to predict the timing at which REM sleep starts from the detected respiratory rate, by incorporating this into the electroencephalogram treatment apparatus, for example, a method without the lag of Example 4 is also possible. It is done.

  In multimedia, it is produced based on a frequency that humans can perceive. For example, in the case of movie film, 24 Hz, and in the case of music, if the quarter note is 1 second, the 32nd note is 8 Hz.

  However, it can be said that the actual update interval slightly exceeds the perceivable range because it is difficult to withstand flicker and fatigue when the frequency is close to the electroencephalogram. This is because the brain wave is temporarily corrected by interfering with the brain wave, but the update frequency is constant, resulting in fatigue.

  As another method for preventing interference with the electroencephalogram, it is also possible to synchronize (feed back) the dominant electroencephalogram with the detected electroencephalogram (electroencephalogram filter). For example, in the case of a television set, it is possible to achieve power saving and high luminance by detecting and synchronizing a dominant brain wave where it is currently updated at 60 Hz. In the case of classical music as well, it seems that there is a relaxation effect by changing the tempo appropriately to bring about changes in the brain waves.

  In addition, there is a problem that it is theoretically difficult to completely synchronize the low frequency with the electroencephalogram (biofeedback). Therefore, for example, when a dominant brain wave is detected every other minute, an intermediate technical method that encompasses human perception and theoretical synchronization by using a brain wave induction method to widen the frequency band. Is the method. For example, when the dominant electroencephalogram is 10 Hz, it has an update frequency that changes at 11-9 Hz using the electroencephalogram guidance method.

  A device using this technical method is useful not only for a display terminal having an update frequency but also for detecting a dominant brain wave with a music playback device or the like to change the playback speed. Moreover, it can also be used for an artificial vision device that converts an image into sound waves for obtaining from the auditory sense.

  The artificial perception apparatus receives the “conversion source information” on the video screen, performs the “conversion to frequency band” process, and outputs the “sound wave waveform”, thereby obtaining video information from the auditory sense.

  The feature of the present invention is mainly a conversion processing method, which is an artificial visual device that is applied to an internal program of a computer, and an artificial hearing device that can obtain sound wave information from vision by reversing the flow of the processing There is.

Therefore, the present invention provides a waveform conversion algorithm and an apparatus thereof that have the effects of the invention by acquiring a video screen, converting it to a low-frequency-high frequency (formula AC), and outputting it. Specifically, a waveform (1/20 milliseconds = 20 kHz, 1 millisecond = 1 kHz) with a high frequency corresponding to the color information in FIG. 10 is generated and output, and the number of the color information in FIG. Correspondingly, continuous processing is performed at a mid-range frequency (about 120 Hz in the case of 0.1 / 12 seconds) assuming the pitch (16-1.6 kHz) in FIG. 11 and is actually applied to an artificial visual device. The update interval is set as a low frequency (0.1 second = 10 Hz).

(Formula A) Low frequency ≒ EEG band for perception (0.5-48Hz, preferably 4-23Hz)
(Formula B) Mid frequency ≒ Band for pitch (16-1.6kHz)
(Formula C) High frequency <audible band (20-20KHz)

  Further, in the waveform conversion for a single color at a high frequency shown in FIG. 10, the saturation is generated as the number of waves, the hue is generated as the level of the wave (strength), and the first part of the waveform height is according to the lightness It is possible to generate only with a specific saturation or hue, and furthermore, the three elements for waveform conversion should not specify color information or color space, but human beings. This is because it should be determined engineeringly, and color space cannot be said to be in a complete research stage. This waveform conversion corresponds to waveform synthesis by an oscillator and its filter in a synthesizer, or a pedal portion of a piano.

  The reason why the middle frequency is a band having a change in pitch is to devise color information (especially the horizontal axis position) to make it easier to perceive, and the low frequency is just an update interval. Therefore, it can be said that the core part of the present invention is a waveform conversion method for converting color information into a waveform at a high frequency.

  Waveform conversion in the artificial visual device is, for example, if the color information is 12 on both the left and right sides, the mid-range frequency is 12 gradations. If the deviation difference at this time is doubled, it becomes 12 scales for one octave just like a keyboard.

  By the way, the synthesizer is a device that synthesizes and outputs a frequency of a pitch corresponding to 12 scales, but has a function that can perform timbre processing in addition to automatic performance and sound intensity. Specifically, the timbre is determined by synthesizing a waveform that is an element of the timbre with the frequency of the pitch. In other words, even if an arbitrary waveform is synthesized with a frequency of 16-1.6 kHz used for the pitch, it can be devised so as not to affect the pitch. This kind of tone processing can be obtained in the same way by pressing a piano pedal.

  Now, in the artificial visual apparatus, the sound and the timbre can be discriminated in this way, thereby converting the sound into an audible sound wave. For example, when converting the color information for 8 dots on the video screen into sound, the player plays in order from the sound of the sound to the sound one octave above (assuming the black keyboard is ignored).

  At this time, Doremifasolaside corresponds to eight pieces of color information. Furthermore, the intensity of the sound changes according to the hue, and can be expressed by making it as small as pianissimo if it is blue, or as strong as an accent if it is white or yellow. The timbre processing described above can be used to express brightness, saturation, and the like. Specifically, a waveform that can be synthesized as a timbre is generated at a high frequency, and the waveform is output based on the frequency for the pitch. That is, the output of the middle frequency is not simply a sine wave or a pulse wave, but an output interval of a waveform generated by a high frequency. (For example, if only lightness such as a monochrome image is converted instead of hue and tone, it is not necessary to generate a waveform with a high frequency.)

In the same way, it means an update interval for the above-described conversion process even at a low frequency. For example, it is a frequency for continuously converting video to sound waves by providing an update interval of 60 Hz as in a television.
Therefore, the relational expression 1-2 on the time axis of each frequency is theoretically established. For example, if the mid-frequency is a 12-tone (12) keyboard, the low-frequency must be at least the required length x 12 times to distinguish one pitch, and this is converted to frequency (Hertz) Then, formula 1 is inevitably established. The relational expression between the middle frequency and the high frequency can be expressed by Equation 2.

(Formula 1) Low frequency ≤ Average value of mid frequency ÷ Number of color information (Formula 2) Maximum value of mid frequency ≤ High frequency ÷ Maximum value of brightness (resolution)

  That is, the number of color information can be added by lowering the low frequency and resolution. Actually, it is necessary to set the update interval low in order to convert more color information in consideration of the audible range, and it seems that the α wave low range of the electroencephalogram is suitable as an artificial perception device. In addition, convenience is enhanced by automatically increasing or decreasing according to the situation. In the future, users will be able to hear the scales in a capacity-like manner (like a musician), so the conversion range is expanded by including the vertical axis so that the keyboard increases by one octave. It can be expected that the visual information can be recognized.

The artificial hearing device is an inverse application of the waveform conversion algorithm of the present invention. In other words, if the law for converting light to sound waves is ergonomic based on artificial intelligence reasoning, it can be reversibly adapted to other uses.
In addition, an artificial hearing device specialized in the formant frequency for analyzing voiceprints is considered to be able to distinguish human voices from information obtained from vision.

For example, in order to display 12 full-color LEDs, the pitch of 12 gradations is analyzed from the input sound wave. At this time, since human perception is low frequency, the information of the dimension which was not heard at all appears in front of the eye by updating it with the frequency of the low band and further corresponding to the left and right sound waves. If a total of 24 full-color LEDs are attached to the edge of the glasses, it is expected that it will be closer to practical use, but it may be a color liquid crystal screen. (In a CPU medium with low waveform analysis accuracy, it is necessary to omit waveform analysis at high frequencies and replace the hue of the mid frequency with lightness.)
In addition to the method using a spectrum analyzer and an oscilloscope, there is a method for realizing the analysis method by calculating from sound data using a computer. For example, it can be calculated as a wavelength component from the number of waves included in a certain time, an average value, and a total value. In the color output stage, it is desirable to convert to an ergonomic color space, for example, Munsell color space.

  Since the low frequency for the update interval shown in Example 11-12 is based on the brain wave, if the low frequency is modulated in order to detect and synchronize the dominant brain wave, the brain wave of the user is obtained. Accordingly, the most appropriate low frequency can be derived.

  In addition, as this related technology, there is an electroencephalogram guidance device that filters a detected electroencephalogram with a specific frequency for an α wave, and performs bioconversion by optical conversion. In other words, it is considered technically possible to apply biofeedback technology and an electroencephalogram guidance method for activating electroencephalogram to the update interval of the artificial visual device and the artificial auditory device.

The electroencephalogram guidance method can be used by being used in a system and apparatus that require a time interval related to perception.

It is a conceptual diagram of the element in a subliminal learning system. It is a composite waveform diagram of an electroencephalogram frequency and a respiratory frequency. It is a transition figure of the rest interval by the electroencephalogram in a sleep treatment device, and a compound wave. It is a line diagram showing a method for calculating EEG during sleep. It is a conceptual diagram of the element in a subliminal learning system. This is an execution interval for activating the electroencephalogram. It is an imaginary view of an artificial visual device. It is an imaginary view of a handy type artificial visual device. It is the figure which showed the state of the conversion range in a video screen. It is a figure which converts color information into the waveform of a high frequency band. It is a figure of the modulation frequency of the mid range distributed to right and left.

Explanation of symbols

10 Subliminal learning system
11 Dictionary database
12 EEG induction (and EEG activation method)
13 Subliminal effect
14 1 / f fluctuation effect (and EEG activation method)
20 Image learning (by subliminal display and electroencephalograph)
21 Data selection (learned input, reset, and split dictionary database)
DESCRIPTION OF SYMBOLS 1 Main body 2 Small camera 3 Small speaker 4 Pointing device 5 Pad 6 Small terminal device 010 Video screen 011 Waveform conversion range 012 Center of waveform conversion range AC Color information arranged on the left side ac Color information arranged on the right side

Claims (5)

In a device that affects human perception,
An electroencephalogram activation method applied to an internal cycle to activate an arbitrary electroencephalogram band of a user,
The frequency of EEG induction is a means of repeating at least one type of EEG band from δ wave, θ wave, α wave, β wave, γ wave,
Means for taking transition changes into the electroencephalogram band in order to make the transition path different for each iteration,
A means for taking into account the rest period using a respiratory cycle corresponding to maintaining the affinity to the human body for the electroencephalogram induction, or a cycle less than that,
EEG activation method characterized by comprising The brain wave guiding means includes an information output terminal that affects perception,
3. The electroencephalogram activation method according to claim 2, wherein the contents of the information output terminal and the pause time are made to synchronize with each other. In an electroencephalogram guidance device and an information output terminal that affects perception,
An electroencephalogram induction method applied to an internal cycle to activate an arbitrary electroencephalogram band of a user,
The frequency of brain wave induction is a step of repeating at least one kind of brain wave band from δ wave, θ wave, α wave, β wave, γ wave, and repeating,
Adding a transition change to the electroencephalogram band so as to make the transition path different for each iteration; and
In consideration of the electroencephalogram induction, taking into account a breathing cycle synchronized with the content of the information output terminal, or a rest time less than that,
An electroencephalogram induction method comprising: A subliminal image learning system including an information output terminal and an electroencephalogram guidance means,
The frequency of EEG induction is a means of repeating at least one type of EEG band from δ wave, θ wave, α wave, β wave, γ wave,
Means for taking transition changes into the electroencephalogram band in order to make the transition path different for each iteration,
A means for taking into account the rest period by using a respiration cycle corresponding to the content of the information output terminal to the brain wave induction, or a cycle less than that,
Subliminal learning system characterized by having In the electroencephalogram induction device, the sleep treatment device corrects the electroencephalogram of all sleep stages,
The frequency of EEG induction is a means of repeating the β-δ wave band corresponding to the sleep stage,
Means for taking transition changes into the electroencephalogram band in order to make the transition path different for each iteration,
In addition to the electroencephalogram induction, a resting time is added using a respiratory cycle corresponding to maintaining affinity to the human body, and the resting time induces REM sleep induction of the user at the time of non-REM sleep-δ wave induction. The time lag for prompting includes a respiratory cycle or less, and the transition end point of the electroencephalogram induction frequency and the transition start point of the respiratory cycle of the REM sleep-α / β wave induction are approximated means,
A sleep treatment apparatus comprising: