CN115335102B

CN115335102B - Method, system and brain keyboard for generating feedback in brain

Info

Publication number: CN115335102B
Application number: CN202180016100.1A
Authority: CN
Inventors: 张鸿勋
Original assignee: Individual
Current assignee: Individual
Priority date: 2021-02-08
Filing date: 2021-02-08
Publication date: 2024-06-04
Anticipated expiration: 2041-02-08
Also published as: WO2022165832A1; CN115335102A

Abstract

A method, system and brain keyboard for generating feedback in the brain, the method comprising: determining a sequence having a length of time, wherein the sequence includes one or more concepts over a plurality of time periods of the length of time; and perceiving the sequence by the brain in a natural manner over the length of time to produce a desired feedback in the brain. The system comprises a generating device and a transmitting device, wherein the brain keyboard comprises a keyboard and a processor, the keyboard comprises a plurality of keys, and at least one or more keys correspond to one or more concepts; the processor is configured to receive key presses from the keyboard to form a sequence having a length of time, wherein the sequence includes the one or more concepts over a plurality of periods of the length of time. The method and the system have low cost, are easy to operate and have no harm to the subjects.

Description

Method, system and brain keyboard for generating feedback in brain

Technical Field

The present invention relates to brain science, and in particular to a method, system and brain keyboard for generating feedback in the brain.

Background

The brain is a major component of the human central nervous system and includes approximately 860 billions of glial cells, the neurons, of which there are billions. Each neuron typically has hundreds to thousands of nerve synapses, and the number of synapses in the brain is estimated to be as many as about 10 ¹⁵. An adult human brain weighs about 1.2-1.6 kg and the major component is blood. Although the weight of the brain is only 2% -4% of the weight of the human body, the oxygen consumption can be 1/4 of the total oxygen consumption. The cerebral blood flow amounts to 15% of the total blood output to the heart, which consumes approximately 25W of power.

The brain is the most complex organ of the human body, and is the "command part" which receives external stimulus, produces sensation, forms consciousness and thinking, and gives instructions and drives actions. The cerebral cortex is the material basis for advanced neural activity and is the tissue that produces thinking. The left and right hemispheres of the cortex can be divided into 5 lobes: frontal lobe, temporal lobe, parietal lobe, occipital lobe, and island lobe; among them, the frontal and temporal lobes are traditionally thought to be related to language, emotion, memory. However, studies on the physiological basis of the formation and retrieval of the appearance of the language center and the memory, the formation and influence of emotion, etc. are still in an early stage.

Currently, three main directions of brain science research are structural and functional studies of the brain (in particular, advanced functions of the brain), brain disease studies, and brain application studies. The research of brain application is an important field in the brain science field, and aims to analyze the material basis of the nervous system structure and psychological activities of the brain, and simultaneously develop algorithms or models of advanced brain functions by utilizing methods and means such as information science, computer science and the like, so as to promote the development of fields such as artificial intelligence, robots and the like.

However, cross-domain research and application results for solving problems related to the brain disease field by means of brain application research methods and means using the physiological basis of brain structural and functional studies have not been reported in the art.

Disclosure of Invention

In view of the technical problems in the prior art, the present invention provides a method, system and brain keyboard for generating feedback in the brain to generate desired feedback in the brain.

In order to solve the above technical problem, according to one aspect of the present invention, there is provided a method of generating feedback in a brain, comprising: determining a sequence having a length of time, wherein the sequence includes one or more concepts (concepts) over a plurality of time periods of the length of time; and perceiving the sequence by the brain in a natural manner over the length of time to produce a desired feedback (desired feedback) in the brain.

According to another aspect of the present invention there is provided a system for generating feedback in the brain comprising generating means and transmitting means, the generating means being configured to generate a sequence of concepts having a length of time, wherein the one or more concepts are included over a plurality of periods of the length of time; the delivery device is configured to perceive the sequence in a natural manner by the brain of the subject over the length of time, producing the desired feedback in the brain of the subject.

According to another aspect of the present invention there is provided a brain keyboard comprising a keyboard and a processor, the keyboard comprising a plurality of keys, at least one or more keys corresponding to one or more concepts; the processor is configured to receive key operations from the keyboard to form a sequence having a length of time, wherein the sequence includes the one or more concepts over a plurality of periods of the length of time; wherein the sequence is perceived by the brain in a natural manner over the length of time to produce the desired feedback in the brain.

The present invention, through the foregoing methods, can in a natural manner cause the subject's brain to perceive a sequence of concepts to produce a desired feedback in the subject's brain that corresponds to a desired experience, such as relaxation, calm, confidence, pleasure, satisfaction, courage, health, excitement, success, beauty, and the like. The method and the system provided by the invention not only enable the average person to feel various experiences expected by the average person, but also can be used for treating or preventing the psychological diseases or the mental diseases. The method and the system provided by the invention have the advantages of low cost, no harm to the subject, easy operation and remarkable effect.

Drawings

Preferred embodiments of the present invention will be described in further detail below with reference to the attached drawing figures, wherein:

FIG. 1 is a schematic structural diagram of an MRI apparatus in accordance with an embodiment of the present invention;

FIG. 2 is a schematic representation of the morpheme aspect disclosed in Huth et al, article Natural Speech disclosure semantic map tiling human cerebral cortex;

FIG. 3 is a principal component analysis schematic of a semantic model of the morpheme aspect disclosed in Huth et al, article Natural Speech disclosure semantic map tiling human cerebral cortex;

FIG. 4 is a flow chart of a method of generating feedback in the brain according to one embodiment of the invention;

FIG. 5 is a flow chart of a method for time division of target feedback into multiple activation modes according to one embodiment of the invention;

FIG. 6 is a schematic representation of time slicing of target feedback over time according to one embodiment of the invention;

FIG. 7 is a schematic diagram of time slicing obtained after identifying and merging time slices according to an embodiment of the invention;

FIG. 8 is a schematic diagram of acquiring one or more concepts corresponding to an activation pattern of a time slice, according to one embodiment of the invention;

FIG. 9 is a timing diagram of various concepts according to one embodiment of the invention;

FIG. 10 is a schematic block diagram of a system for generating feedback in the brain according to one embodiment of the invention;

FIG. 11 is a schematic block diagram of a system for generating feedback in the brain according to another embodiment of the invention;

FIG. 12 is a schematic block diagram of a system for generating feedback in the brain according to yet another embodiment of the invention;

FIG. 13 is a schematic block diagram of a system for generating feedback in the brain in accordance with a further embodiment of the invention;

FIG. 14 is a schematic block diagram of a system for generating feedback in the brain according to yet another embodiment of the invention;

FIG. 15 is a graphical representation of the first round test subject's physical fatigue recovery assessment score according to one embodiment of the invention;

FIG. 16 is a graphical representation of the target test group physical fatigue recovery assessment score at the second round of testing in accordance with one embodiment of the present invention;

FIG. 17 is a graph of evaluation scores of physical fatigue recovery with reference to a group in a second round of testing according to one embodiment of the invention;

FIG. 18 is a graph showing the ratio of the average depth relaxation of brain waves of a target test group during a second test cycle according to one embodiment of the present invention;

FIG. 19 is a graph showing the ratio of the average depth relaxation of brain waves in a reference group during a second test run according to one embodiment of the present invention;

FIG. 20 is a schematic diagram of a first round of test subject sleep quality score values, according to one embodiment of the invention;

FIG. 21 is a graphical representation of sleep quality score values for a second round of testing for a target test group in accordance with one embodiment of the present invention;

FIG. 22 is a graphical representation of sleep quality score values for a reference group during a second round of testing in accordance with one embodiment of the present invention;

FIG. 23 is a graphical representation of reverse scoring averages of the STAI Spierbie anxiety scale for subjects at the first round of testing according to one embodiment of the present invention;

FIG. 24 is a diagram of the reverse scoring averages of the STAI Siderurg anxiety scale for the second round of test target test groups according to one embodiment of the invention;

FIG. 25 is a graphical representation of the reverse scoring averages of the STAI Szechwan anxiety scale for the second round of test reference groups in accordance with one embodiment of the present invention;

FIG. 26 is a reverse scoring average schematic of a physical problem assessment table for a first round of test subjects according to one embodiment of the invention;

FIG. 27 is a schematic diagram of a forward scoring average for a physical problem improvement assessment table for a second round of test objective test groups according to one embodiment of the present invention; and

FIG. 28 is a schematic of a forward scoring average for a physical problem improvement assessment table for a second round of test reference groups according to one embodiment of the invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

In the following detailed description, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration specific embodiments of the application. In the drawings, like reference numerals describe substantially similar components throughout the different views. Various specific embodiments of the application are described in sufficient detail below to enable those skilled in the art to practice the teachings of the application. It is to be understood that other embodiments may be utilized or structural, logical, or electrical changes may be made to embodiments of the present application.

Billions of years of life evolutionary history on earth can be summarized as two basic evolutionary modes: a biological evolution mode for realizing gene information transmission, namely, generating proteins and cell tissues according to a certain rule by a genome consisting of nucleotide sequences, thereby generating living organisms with infinite diversity; the other is an evolutionary way of realizing non-genetic information transfer through language. The latter evolutionary approach distinguishes humans from other life forms, has thought, and has prompted cultural advances and prosperity.

The evolution of mental stress from low to high from the current genetic science also determines the evolution of life from low to high according to Cai Shushan, summarized in the book "mental and cognitive of humans" published by people publishers in 2015. The mental evolution of life involves five levels from low to high: neural, psychological, linguistic, thought, and cultural levels; the neural and psychological levels of which are common to humans and animals and may be referred to as the low-level cognitive levels; language, thought and cultural levels are human-specific, also known as high-level cognitive levels. The language is the basis of human cognition, human thinking is formed on the basis of abstract concept language, the language and the thinking together construct a human knowledge system, even the whole human society is defined, and the accumulation of knowledge forms culture, and the development of the human society is promoted in turn.

From the beginning of the 20 th century, with the development of brain science, people developed comprehensive research on human cognition from various levels of nerves, psychology, language, thinking and culture, so that branches of the brain science system with different research emphasis are presented. Among them, neuro-linguistics is a very important research field. Neuro-linguistics study the relationship between language and brain function with the aim of interpreting the neural and psychological mechanisms of understanding, generating, learning and learning of human language, the subject of which is the interactive relationship between the human nervous system and human language, understanding how the human brain receives, stores, processes and extracts language information.

In 1987, kaplan (d.) "neuro-linguistic and linguistic language disorders: the treatise (published by Cambridge university Press in 1987) is a representative work in the early stages of neuro-linguistics development and discusses the relationship between different regions of the brain and language. Shi Temo (Stemmer, b.) and Whitaker (Whitaker, HA) published in 1998 a book of neuro-linguistics handbook (1998 press from massachusetts, usa) summarizing the progress of neuro-linguistics, and studied the brain mechanisms of language processing. Representative works in the neuro-linguistic field are professor illisha white, elsen (Elisabeth Ahls en) and john, benjamin (John Benjamins), published in 2006, neuro-linguistic guides (ahlsen, e. (2006), introduction to neurolinguistics. In this book two authors summarize the latest developments in brain mechanism research for language processing and language learning using various methods and techniques. For example, the process that the brain would follow when processing language information, the interactions of different brain regions in language processing, the location of brain activation when a subject generates or perceives other languages than his native language, and so forth. Although many advances have been made in neuro-linguistics, understanding of the memory formation and search processes and the mechanisms of language formation remain at a relatively macroscopic stage and do not form an efficient description of the neural network level.

The cerebral cortex in the human brain is the material basis for higher neural activity, which is the organ that produces thinking, which dominates all activities inside the body and coordinates the balance of the body with the external environment. The brain hemisphere is divided into five parts, namely frontal lobe, temporal lobe, occipital lobe, parietal lobe and island lobe, by means of the furrows and ravines presented by the surface of the brain cortex, each of which has a certain functional division, for example frontal lobe is related to reasoning, planning, emotion, partial language and movement; temporal lobes are associated with perception, hearing and memory; occipital lobe is related to vision; parietal leaves are associated with touch, temperature, pressure, pain, etc.; the islets are involved in the autonomous function of the brain stem, while also processing taste information.

During research on language processing and brain, one of the earliest people who established a link between a particular brain region and language processing was the french surgeon-Bao Luobu rocard. Bromocards have found that most human brains are damaged (or diseased) on the left frontal lobe by necropsy on many people with speech defects, now known as the Broca region. The left posterior temporal gyratory region in the brain was designated as the weinik region (WERNICKE DISTRICT) by the german decsarian, neuropathologist cals weinik (CARL WERNICKE), and it was proposed that different regions of the brain were dedicated to different language tasks. The surface of the brain is divided into differently numbered regions, called Brodmann regions, by the cellular structure and function of each region, of colbinia Brodmann (Korbinian Brodmann) at the beginning of the twentieth century. The Brodmann (Brodmann) region is widely used in the neuro-linguistic field for research into the location of brain-specific language "modules," e.g., the Broca region handles the generation of motion of speech, while the wernike region handles auditory speech understanding.

After continuous split brain human experiments for more than one year after the 60 s of the 20 th century, the mental biologist spell of the united states proposed specialization and division of left and right brains: the left brain is responsible for five senses of memory, time, language, judgment, arrangement, classification, logic, analysis, writing, reasoning, inhibition and the like, and the thinking mode has the characteristics of continuity, analysis and the like. Thus, the left brain is referred to as the "conscious brain", "academic brain", "linguistic brain" or "logical brain". The right brain is mainly responsible for spatial image memory, intuition, emotion, physical coordination, visual perception, art, music rhythm, imagination, inspiration, insight and the like, and the thinking mode has the characteristics of disorder, jumping, intuitiveness and the like. Therefore, the right brain is also called "instinct brain", "subconscious brain", "creative brain", "musical brain" or "artistic brain".

Another important task in neuro-linguistics is the testing and evaluation of theories presented by psycho-and theoretical linguists. In general, a theoretical linguist presents a model to explain the structure of a language and the organization of language information; psychological linguists propose models and algorithms to explain the way language information is processed in the mind; the manner in which a neuro-linguist analyzes brain activity to infer biological structures (populations and individuals), e.g., the neural network performs these psycho-linguistic processing algorithms. For example, the "sequence" model of jetty-ford (Janet Fodor) and Lyn Frazier (Lyn Frazier) and the "unified model" of sierozem (Theo Vosse) and jerad kent (GERARD KEMPEN) are both different models for sentence processing. Neuro-linguistics utilize sentence processing experiments, ELAN, N400 and P600 brain responses obtained according to ERP techniques to examine physiological brain responses, and then compare the results of the physiological brain responses with the predicted results of sentence processing models proposed by psycho-linguists to reflect the rationality of different sentence processing models. On the other hand, neuro-linguistics may also guide psycho-linguistics to put forward new theories on the structure and organization of language by "generalizing knowledge of neural structure into linguistic structure" based on knowledge of brain physiology.

Research methods and related techniques applied in the field of neurolinguistics are also continuously developing with the progress of modern technology. The following is a detailed description of the technical means of brain science research.

Initially, the actual linguistic process, the language mechanism of the presumed brain, was studied by using a study of the etiology and pathology of the experiment. Taking a pathological experimental method as an example, the language status of a brain injury patient is analyzed from the neuropsychological point of view, and the analysis of brain injury areas is utilized to understand the language generation process and the neuropsychological mechanism thereof.

With advances in science and technology, brain science has emerged as a new research approach in which brain signals and brain information are mainly acquired. Acquisition of brain signals includes Electroencephalogram (EEG) techniques for acquiring scalp brain electrical signals, electroencephalogram (Electrocorticography, ECoC) techniques for acquiring cortex brain electrical signals, magnetoencephalography (Magnetoencephalography, MEG) techniques for acquiring magnetoencephalography signals, event-related potential (ERP) techniques, and the like. Electroencephalogram technology uses atraumatic EEG electrodes to record potential changes at different craniocerebral locations. The electroencephalogram technique is deeper, and the ECoC electrodes built in the cerebral cortex are used for collecting the potential change of deep cortex activities. The electroencephalogram technique is an invasive brain signal acquisition technique, although more accurate. The magnetoencephalography technology uses a particularly sensitive supercooled electromagnetic measuring device to measure extremely weak magnetoencephalography in the brain, so as to obtain the change of electric field distribution in the brain. The principle of the event-related potential technique is that when the sensory afferent system of a person is stimulated specifically, a specific potential is induced in the central nervous system, and this induced potential can explain the neural mechanism of a certain function.

Another method of obtaining brain information is to image the human brain directly to observe the condition of the living human brain processing language information. The brain imaging technique includes computed tomography (Computed Tomography, CT), such as X-ray CT, ultrasound CT, gamma-ray CT; positron emission computed tomography (Position Emission Computed Tomography, PET); single Photon Emission Computed Tomography (SPECT), single-Photo Emission Computed Tomography; magnetic resonance imaging (Magnetic Resonance Imaging, MRI); functional magnetic resonance imaging (functional Magnetic Resonance Imaging, fMRI); near infrared Spectroscopy (NEAR INFRARED spectra, NIRS); functional near infrared Spectroscopy (functional NEAR INFRARED Spectroscopy, fNIRS); cerebral angiography; photoacoustic imaging (Photoacoustic Imanging, PAI); fast-functioning photoacoustic microscopy (fast-functional RAM), and so forth.

In addition to the above methods, there are methods for testing the speech function of the brain hemisphere, such as anesthetizing one brain hemisphere, and studying the speech function of the other brain hemisphere; or presenting words to a semi-side visual field of a person by using a shorthand instrument to study brain functions of speech vision; or providing audible language information to both ears of a person to study the hearing function of the two hemispheres of the brain; or cutting off the ploughing and grinding wheel between the two hemispheres of the brain, researching the speech functions of the two hemispheres of the schizophrenic brain, and the like.

Due to the progress of brain signal and brain information acquisition and analysis techniques, research areas and depths in the neuro-linguistics field have also been greatly expanded and achievements brilliant. Two non-invasive important brain information acquisition techniques, functional magnetic resonance imaging fMRI and functional near infrared spectroscopy fNIRS, are described in more detail below.

FMRI belongs to an imaging method in MRI, and the main principle is to measure the change condition of hemodynamic force caused by neuron activity by using magnetic resonance imaging, namely, to realize brain engineering function imaging by detecting the magnetic field change of blood entering brain cells. As shown in fig. 1, a schematic structural diagram of the MRI apparatus is shown. The MRI device mainly comprises a magnet system, a radio frequency system and a computer image reconstruction system. The magnet system is mainly used for generating two magnetic fields, one is a static magnetic field, and the other is also called a main magnetic field; the other is a gradient field (gradient coils). Most of the current devices use superconducting magnets to generate the main magnetic field with a field strength of 0.2T-7.0T, typically 1.5T and 3.0T, and further a shim coil (shim coil) to assist the main magnetic field to achieve high homogeneity. Spatial encoding of NMR signals is achieved using gradient field coils to generate and control gradients in the magnetic field to generate gradient fields. The magnetic field gradient device comprises three groups of coil groups, wherein gradient fields in the x, y and z directions are generated, and the magnetic fields of the coil groups are superposed to obtain gradient fields in any direction. A radio frequency system includes a Radio Frequency (RF) generator and a Radio Frequency (RF) receiver. The RF generator is used to generate short and strong RF field, which is applied to the sample in pulse mode to make the hydrogen nucleus in the sample produce NMR phenomenon. The radio frequency receiver is used for receiving the NMR signals, amplifying the NMR signals and sending the NMR signals into the computer image reconstruction system. The computer image reconstruction system converts the analog signal into digital signal through A/D converter, processes the digital signal according to the corresponding relation with each voxel of the observation layer to obtain layer image data, and then adds the layer image data to the image display through D/A converter, and displays the image of the layer to be observed with different gray scales according to the NMR size. With the progress of technology, various improved techniques for solving the problems of increasing imaging speed and increasing spatial resolution have also appeared. For example, using the product of the temporal basis function and the spatial basis function independent of each other and representing the magnetic resonance signals to obtain a functional magnetic resonance imaging image with high spatial resolution and high temporal resolution; for another example, a scanning method under EPI (echo planar imaging) in which a plurality of echo signals are sequentially collected by sequentially reversing a gradient magnetic field at a high speed after exciting nuclear magnetism once is adopted to solve the problem of image distortion caused by image position shift, so that fMRI can be better applied to brain function research.

The fNIRS technology is based on that the brain nerve activity can cause local hemodynamic changes, and utilizes the difference characteristic of oxyhemoglobin and deoxyhemoglobin in brain tissues on the near infrared light absorptivity with the wavelength in the range of 600-900nm to directly detect the hemodynamic activity information of the cerebral cortex in real time. The mental activity of the brain can be deduced by the change of the hemodynamic activity information. The devices involved may include fNIRS detectors, light sources, probes, and computer systems. fNIRS detectors include forehead region detectors and whole brain detectors. The light source and detector are placed on a probe that is connected to a computer system by wires and in contact with the living being to be tested, such as the brain. The computer system controls the light source to be turned on and off by sending out control signals, and the detector inputs the measurement data of the light source to the computer system, and the brain function image is obtained through AD conversion, processing and the like of the signals. Along with the improvement of hardware equipment in manufacturing and data processing methods, fNIRS technology provides a powerful monitoring means for monitoring brain activities.

The paper "Natural speech reveals a semantic map (Natural SPEECH REVEALS THE SEMANTIC MAPS THAT TILE human cerebral cortex) of the human cerebral cortex tiled," journal of Nature "published by the first author of Alexander Hu Sai (Alexander G. Huth), in year 2016, month 27. Huth et al used brain imaging techniques to map a semantic map of the brain from which it was clear how different areas of the brain represented 985 common english words and their meanings.

Fig. 2 is a schematic diagram of a model of the morpheme aspect disclosed in the article by Huth et al. Huth et al measured Brain BOLD (Brain Blood-Oxygen LEVEL DEPENDENT) feedback during a story in which seven subjects heard a2 hour natural language statement using the fMRI imaging method. Each Word (Word) is projected into a 985-dimensional space created based on statistics of Word co-occurrence. The model in terms of morphemes reflects how the occurrence of words affects BOLD feedback. FIG. 3 is a schematic representation of an principal component analysis of a semantic model of the morpheme aspect disclosed in the article by Huth et al. Principal component analysis reflects 4 important semantic dimensions in the brain, and a color map is obtained after RGB coloring. Huth et al work has reached some important conclusions: first, there is a clear correlation between words and the distribution of semantically selected regions in the cortex. On the other hand, the distribution of semantically selected regions corresponding to the same word is also highly uniform among different individuals.

On the basis of the basic research results, people have further research results and have deeper knowledge of language. Natural communication (Nature Communications) published an article entitled "connecting concepts in the brain by mapping cortical representations of semantic relationships (Connecting concepts in the brain by mapping cortical representations of semantic relations) on month 4 and 20 of 2020. Through a morpheme aspect prediction model and brain imaging technique similar to Huth et al, the article concludes: the semantic categories and relationships are both represented by spatially overlapping cortical patterns rather than anatomically separated regions, and the human brain encodes not only concepts using a distributed network, but also relationships between concepts.

The above advances in brain science have advanced people's understanding of advanced functions of the brain to a new stage. As suggested by nomm-chomski (Noam Cginsky) et al, published in journal of nature under the name "language, ideas and brain" at 18, 9, 2017, the language should not be equated with "speech" or "communication", but should be best described as a biologically defined computational cognitive mechanism. As demonstrated by the research of the inventor of the application, the language can be made into a tool by using the determined cognitive mechanism, and specific physiological feedback is generated in the brain, so that the mental mind of a human is improved, the thinking transformation is promoted, and the progress of culture is promoted.

In the description herein below, the following definitions of terms are provided to aid in the understanding of the present invention.

As used herein, a "concept" refers to a unit of mental activity that has a definite meaning. For example, a concept may be a Chinese character (character) or word (word); or may be a word or phrase (phrase) in english. But strokes of Chinese characters, foreign letters, japanese katakana, etc. without specific meanings cannot be a unit of thinking activity, i.e., are not concepts herein referred to. As found, there is a clear association of the concepts herein with language. Concepts represented by different languages may have the same meaning; however, the meanings of both are not the same in most cases. For example, chinese "tables" are not identical to English "tables" in terms of their concept, and therefore can be considered as different concepts. In the case of foreign language, the difference between "computer" and "computer" is very small, and the same concept can be considered. In some embodiments, similar feedback is more likely to form in the brain between people in the same language due to the large difference in concepts in different languages.

Similar concepts may form similar feedback in the brain. Based on the existing linguistic classification and the frequency of simultaneous occurrence of statistically identical concepts, a subset of concepts may be derived. Included in this subset of concepts are a number of representative concepts. These representative concepts can form a space in terms of morphemes. Other concepts may be expressed as projections of one or more representative concepts in space of the morpheme aspect of the subset. In other words, the subset can express other concepts that do not appear in the subset representative concepts. For a particular language, the subset under that language can express the meaning of the concepts of that language, thereby reducing that language to a sequence of subsets of representative concept formations that can be processed quickly.

Further, the subset of representative concepts can also be categorized into different empirical subsets based on the expressed experiences. "experience" herein refers to feedback formed in the brain as a result of experience, which may be fixed feedback formed by multiple practices; or may be a single feedback formed by a single experience. Experience includes, but is not limited to, relaxation, calm, confidence, pleasure, satisfaction, brave, health, excitement, success, beauty, and the like. The subset of experiences formed by the classified representative concepts can more easily form feedback in the brain corresponding to the experiences it represents.

As used herein, "feedback" in the brain refers to a response in the cortex of the brain to external stimuli. As is known, under external stimulus, different areas in the cerebral cortex are in an active state, and further cause metabolic changes of various active substances, such as oxygen, glucose, etc., to be sensed by the electroencephalogram signal detection means. The activation pattern of the cortex represents the spatial distribution of the areas of cortical activity. Different activation patterns indicate that different regions of the cerebral cortex are active. Even if a person is in a sleep state, a plurality of areas in the cerebral cortex are in an active state, although the areas in an active state are reduced as compared to an awake state. If one looks at changes in a number of areas of the cortex that are active over time, such changes are in fact indicative of changes in the activation pattern of the cortex.

The cortical activation pattern is rapidly time-varying. The duration of the different activation modes is very short, e.g. several hundred milliseconds to several seconds. The different activation patterns may be generated in response to different external stimuli or may be generated spontaneously in response to advanced brain functions. Thus, the different activation patterns of the cerebral cortex are independent of each other. On the contrary, the mutually independent activation modes also correspond to different external stimuli or results of advanced brain functions.

Further, since the duration of the activation pattern is limited, the cortex may experience a number of different activation patterns over a period of time, e.g., tens of seconds. Thus, the change in the plurality of regions of the cortex in an active state during the period of time may be sliced in time (e.g., 100 milliseconds) with the plurality of regions of the cortex in an active state during each time slice as an activation pattern. The active modes within adjacent time slices may be a continuation of the same active mode and thus be mutually non-independent. Or the activation patterns within adjacent time slices are different activation patterns and are independent of each other. Thus, by the independent relationship between the activation patterns within each time slice, the change in activation pattern of the cerebral cortex over that period of time can be derived.

As used herein, "desired feedback" refers to the formation of feedback in the brain that is obtained by simulation of the target feedback. For target feedback, the analog feedback will be as consistent as possible with the target feedback, although both need not be completely consistent. There may be multiple similar activation patterns and/or variations in activation patterns over time between the target feedback and the desired feedback. The change in activation pattern over time includes sequential, continuous, intermittent, etc. In some embodiments, the targeted feedback represents a desired experience including, but not limited to, relaxation, calm, confidence, pleasure, satisfaction, brave, health, excitement, success, beauty, and the like. In some embodiments, the simulated feedback transfers the desired experience from the other cortex into the subject's cortex through simulation of the target feedback.

As used herein, "perceived by the brain in a natural manner" refers to a manner of perception by the human body itself, including: auditory, visual, tactile, olfactory, and gustatory, input concepts into the brain from the outside. For example, listening to a piece of text, reading a piece of text, tactilely perceiving a piece of text in braille, smelling a smell or tasting a taste. As understood, "perceived by the brain in a natural manner" does not include communication with the brain by invasive or non-invasive means such as by needle punching, electric shock, surgery, and the like. For a brain-computer interface, it is also within the scope of this term if the brain-computer interface communicates with the brain in the same manner as the human body itself perceives. On the other hand, if the brain-computer interface employs a non-human body-aware manner, such as applying a voltage through electrodes in a certain area of the brain, it would not be within the scope of the present invention. It will be appreciated that communication with the brain in a natural manner is more efficient, less adverse effects and is more acceptable.

As used herein, a "brain keyboard" refers to an input device that includes a plurality of keys. At least one or more keys correspond to one or more concepts. In some embodiments, the plurality of keys corresponds to a plurality of concepts of the representative subset of concepts. In other embodiments, the plurality of keys corresponds to a plurality of concepts of the experience subset. The keys may be physical keys or virtual keys on the input interface. When a key of the brain keyboard is "pressed," the concept corresponding to the key is entered. A sequence of time periods results when a plurality of keys are "pressed" consecutively. The sequence includes the one or more concepts entered by the keys of the brain keyboard over a plurality of time periods of the length of time. Thus, in some embodiments, the brain keyboard may be considered as a dedicated device capable of applying the method of the present invention. In some embodiments, the brain keyboard may also include other components such as a processor, memory, display, speakers, etc., to become a device capable of interacting with the brain of a user and subject.

As used herein, "psychological disorders" refer to disorders that deviate in thinking, emotion, and behavior from normal social life norms. Psychological disorders include, but are not exhaustive: depression, macro depression, treatment of resistant depression and treatment of resistant bipolar depression, bipolar disorder, seasonal affective disorder, mood disorder, chronic depression, psychotic depression, postpartum depression, premenstrual dysphoric disorder (PMDD), contextual depression, atypical depression, mania, anxiety, attention Deficit Disorder (ADD), attention deficit disorder with hyperactivity (ADD h) and attention deficit/hyperactivity disorder (AD/HD), bipolar and manic disorders, obsessive compulsive disorder, bulimia, premenstrual syndrome, substance addiction or abuse, nicotine addiction, psycho-sexual dysfunction and pseudobulbar disorder.

As used herein, "mental disorder" refers to a disorder of cognitive, affective, will or behavioral appearance resulting from a brain dysfunction. Mental disorders include, but are not exhaustive: schizophrenia, schizoaffective disorder, bipolar disorder, obsessive-compulsive disorder, parkinson's disease, oppositional defiant disorder, charles Bonnet syndrome, autism, and Tourette's disease.

As used herein, "non-disease psychological aberration state" refers to a psychological state of abnormal appearance of cognition, emotion, mind, or behavior that does not have a pathological basis. Non-exhaustive states of disease psychological aberrations include: lack of one or more of confidence, small gall, sensitivity, distraction, mental weakness, compulsive behavior, examination fear, lecture fear.

The present invention aims to achieve the transplantation of experience in another brain into the brain by simulating feedback in the other brain by means of which multiple concepts are perceived by the brain in a natural way. After perceiving the same concept, the high consistency of feedback formed between different human individuals becomes the basis for experience transplantation in a natural way; deep learning neural network models, in turn, enable such simulations. While the feedback formed by the simulation is not exactly the same as the target feedback, similar experience can be formed in the brain as well. Such low cost, harmless and acceptable empirical transplantation in the brain would undoubtedly be a significant advance in the brain science field.

The technical scheme of the invention is described in detail below through specific examples.

FIG. 1 is a flow diagram of a method for generating feedback in the brain, according to one embodiment of the invention. As shown, at step 110, a desired experience is determined. The desired feedback developed in the brain is a simulation of the target feedback. The target feedback is a temporal change in activation pattern in the cortex of the other brain, which represents an experience. Such experience includes, but is not limited to, relaxation, calm, confidence, pleasure, satisfaction, brave, health, excitement, success, beauty. Thus, determining the desired experience may also be understood as determining the target feedback to be simulated. The desired feedback, while not necessarily identical to the target feedback, may be as close as possible to the target feedback. In this way, a desired experience can be obtained in the brain.

For example, for a very tired person, a mere rest (such as sleep) is not enough to be relaxed. Because the brain is still running at high speed while at rest, it is still busy. After recovering from rest, the person does not feel relaxed and may feel tired at some time. The mind of the person cannot change this experience and some have to resort to drugs. For a person in need of this, the desired feedback may be determined as relaxed or deeply relaxed experience.

As yet another example, some people have severe examination phobia. Even if the review is very good, there is no confidence before the examination and the examination performance is seriously affected. Such test phobia is also difficult to change by the mind of a person. For those in need thereof, the desired feedback may be determined as confidence or experience of examination confidence.

Next, at step 120, a target feedback is determined based on the desired experience. As understood, the target feedback is feedback formed in the brain of a person with a certain experience. In some embodiments, the target feedback is obtained by collecting feedback on a desired experience in the brain of an individual having the desired experience. Feedback formed in the cortex of the brain over time is recorded by brain signal detection techniques and can be used as target feedback. Of course, as will be appreciated, even for experience with deep relaxation, different individuals may be selected, or feedback in the cortex after different activities of the individual may be selected as the target feedback. The invention is not limited in any way herein.

For another example, if confidence experience is desired, individuals with confidence experience may be selected as athletes with excellent athletic performance, and feedback on the desired experience may be selected as feedback that the individuals develop in the brain some time before attending a game item that they are good at. For example, playing a sound recording or video before a race creates a stressful atmosphere before the race, so that these individuals create a feeling of being about to attend the race. Of course, as will be appreciated, even for confident experience, different individuals may be selected, or feedback in the cortex following different activities of the individual may be selected as the target feedback. The invention is not limited in any way herein.

For another example, if it is desired to obtain calm experience, individuals with calm experience may be selected as participants who have experienced multiple events, and feedback on the desired experience may be selected as feedback that the individuals develop in the brain for a period of time before learning to participate in an event. For example, aircraft repair personnel are notified that a component of an aircraft that is in flight is malfunctioning, and for aircraft repair personnel such a malfunction is frequent and does not have serious consequences. Feedback is recorded that is developed in the brain after the aircraft repair personnel learn of the fault. Of course, as will be appreciated, even for calm experience, different individuals may be selected, or feedback in the cortex following different activities of the individual may be selected as the target feedback. The invention is not limited in any way herein.

Many such examples are possible. Such examples, in fact, illustrate that in order to obtain target feedback of a desired experience, an individual having the desired experience may be selected and the activities of the individual reproducing the desired experience may be reproduced. Feedback in the cerebral cortex is recorded as target feedback in individuals with desired experience before, during and after these activities. In some embodiments, an activity that reproduces a desired experience may be selected according to an application scenario of the grafted experience. The higher the degree of similarity between the two, the better the effect that the transplanted experience can exert.

In some embodiments, target feedback is recorded using fMRI data obtained from a functional magnetic resonance scanner scanning its brain. However, due to the device limitations of fMRI, the range of options for reproducing the activity of the desired experience may be small, typically only through wearable devices, such as VR glasses, etc., such that the person with the desired experience creates a sensation of participating in the activity of reproducing the desired experience. MRI image data, CT image data, SEPECT image data, and PAI image data, like fMRI image data, all impose a number of limitations on the activity of reproducing the desired experience.

In contrast, the wearable detection device used for fNIRS image data or NIRS image data obtained by forehead region fNIRS detector, whole brain fNIRS detector is more suitable for more complex activities. Of course, electroencephalogram or magnetoencephalography data and the like obtained by invasive or non-invasive electrodes also have little limitation on complex activities since it is not necessary to put the brain in a large-scale device.

In the following section of the present invention, fMRI data is taken as an example to illustrate the technical solution of the present invention. As will be appreciated, other brain signals (e.g., magnetoencephalography) or data obtained by brain information detection techniques may also be used in the present invention. The invention is not limited in any way herein.

The experience possessed will vary from individual to individual and experience of experiencing the same activity rendition will also vary. Thus, there is a large variance between individuals even for feedback for the same experience. In some embodiments, common features for the same experience between different individuals are obtained by deep learning the neural network model, thereby reducing noise reflected in the target feedback due to differences between individuals.

Since fMRI is capable of obtaining three-dimensional images, a 3D convolutional neural network model (CNN) can be selected to learn feedback of the cortex of the brain for which experience is desired. In order to reduce the amount of calculation, CNN using a two-dimensional image may also be selected. fMRI data recorded after reproduction of a desired empirical activity by an individual having a desired experience is used as a training sample. These parameters related to the person are taken as training parameters because the language, age, sex, religion belief, education level, occupation or once occupation among individuals have a great influence on the desired experience. In some embodiments, construction of the neural network model is performed using Convolutional Sparse Coding (CSC). The convolution sparse coding is a linear convolution unsupervised learning method, and the model is simpler and more visual and is easy to analyze and understand.

In some embodiments, for an individual desiring to obtain a certain experience, the individual's language, age, gender, religion belief, education level, occupation, or once occupation is entered as parameters into the trained neural network model. The neural network model outputs a target feedback that best matches the experience of the individual based on the training results. Therefore, the method can solve the problems that training samples are fewer and a neural network model with accurate construction cannot be obtained, and can reduce the influence of individual differences among the training samples on target feedback.

As will be appreciated, the neural network model applied to the present invention is not limited to CNN, and other feature classification models may be used; nor is it limited to three-dimensional data obtained by fMRI as training samples. Other brain signals or brain information data may also be applied thereto. For example, processing the data of a Magnetoencephalogram (MEG) with a model built by CSC can also achieve a very good effect.

In some embodiments, a database of target feedback for each experience is built. The multiple target feedback in the database is categorized by the different experiences targeted. Further, parameters related to people such as language, age, gender, religion belief, education level, occupation or once occupation can also be used as the basis for further classification of target feedback. The matching degree of the target feedback that can be obtained is higher if parameters related to people such as language, age, sex, religion belief, education level, occupation or once occupation are further considered. Further, the neural network model constructed as above can be used to update the target feedback stored in the database. Thus, the target feedback matched to the desired feedback can be obtained by using the database based on the desired feedback, without generating the latest target feedback each time by using the trained neural network model. Although the accuracy of the target feedback database is not as good as the trained neural network model, it is more convenient and faster to use and the previous steps can be omitted.

At step 130, the target feedback is time-divided into a plurality of activation modes. The target feedback in the cortex includes changes in activation patterns in the cortex over time. For each moment, the activation pattern includes a spatial distribution of the activated portions in the cortex. Thus, the target feedback may be considered as a sequence of time-varying multiple activation patterns in the cerebral cortex.

In some embodiments, as shown in fig. 5, the step of time-dividing the target feedback into a plurality of activation modes includes:

Step S1301 to slice the target feedback in time. Each time slice has a length of 10-50ms. As shown in the schematic diagram of fig. 6, each time slice has a length of 20ms and is numbered a00001, a00002, etc. for subsequent processing. Although smaller time slices can improve the accuracy of the simulation, too small a time length can result in a large amount of data to be processed, resulting in a large amount of computation, and too small a time slice can also be mismatched with the perception of the natural mode, resulting in a large amount of disturbance data. The step is performed to obtain a set of time slices, wherein each time slice has a unique number.

In step S1302, the activation pattern of the cerebral cortex, i.e. the spatial distribution and signal intensity of the activated parts, in each time slice is determined. In some embodiments, an activation pattern representative of the time slice is obtained as an activation pattern for the time slice. There are various ways in which a representative activation pattern can be obtained, for example, selecting the spatial distribution of the active portions at the middle of the time slice as the representative activation pattern; or calculating the average spatial distribution of the time slices after the superposition of the active parts at each moment as a representative active mode. Of course, other approaches may be applied thereto.

Step S1303, calculating whether the activation modes of the cerebral cortex in the adjacent time slices are independent of each other. If the activation pattern of the cortex in two adjacent time slices is non-independent, indicating that the activation pattern has continued for at least two time slices, then the two time slices are combined at step S1304, and if the activation patterns of the cortex in the two time slices are mutually independent, the two activation patterns are recorded as different activation patterns at step S1305.

Step S1306, judging whether all adjacent time slices are processed, if not, repeating the steps until the adjacent time slices are independent. If all adjacent time slices have been processed, then the target feedback has been partitioned into a plurality of active modes that extend over an integer number of time slices. As shown in fig. 7, each time slice, such as B0001 and B0002, has a certain time length, and the activation mode of each time slice is different from that of the other time slices, that is, another time slice set is obtained at this time, where each time slice includes an activation mode and has a corresponding time period, and all time slices in the set form a sequence with temporal continuity.

In some embodiments, the difference in activation patterns of the cortex in adjacent time slices is calculated when calculating whether the activation patterns of the cortex in adjacent time slices are independent of each other. If the difference exceeds a predetermined range, the activation patterns in the two time slices are considered to be independent of each other. In other embodiments, correlation coefficients of activation patterns of the cerebral cortex in adjacent time slices are calculated. If the correlation coefficient exceeds a predetermined threshold, the activation patterns in the two time slices are considered to be independent of each other. Of course, other approaches may be applied thereto.

Taking fMRI data processing as an example, after time slicing is performed on fMRI data fed back by a target at a certain interval (for example, 10-50 ms), an activated region of cerebral cortex and signal intensity thereof at a plurality of moments can be obtained, wherein the signal intensity is reflected as the color shade of the activated region. The time-dependent change of the plurality of activation modes can be determined from an independent analysis of the plurality of time-dependent activation modes. Thus, the target feedback is divided into a plurality of time periods, each corresponding to an activation mode.

Next, at step 140, one or more concepts corresponding to each of a plurality of activation patterns of the target feedback are identified.

According to the response of the cerebral cortex to the concept perceived in a natural way, the response activation areas of the cerebral cortex to the same concept among different individuals and the distribution and signal intensity thereof have high consistency; the signal strength is also different for different concepts, in response to different distributions of active areas.

In some embodiments, a distribution of activation regions in the cerebral cortex and their signal strengths corresponding to each concept in a group comprising a plurality of concepts is obtained. The distribution of activated areas in the cerebral cortex corresponding to a concept and its signal intensity are defined as a "concept pattern" corresponding to the concept. In some embodiments, a concept pattern database is established storing concept patterns for a plurality of concepts. Taking fMRI data as an example, fMRI data is recorded for feedback formed in the cortex of the brain when a subject listens to a piece of text in a natural language expression. After data processing, fMRI data is associated with concepts in the text, so that a concept mode reflected by fMRI data corresponding to the concepts can be obtained. These concepts and the corresponding concept patterns are stored, whereby a concept pattern database can be built. Those skilled in the art will appreciate that other types of brain signals or data from brain detection techniques can also be used to build the conceptual pattern database. By other means, the concept pattern database can be obtained by associating the concept with the corresponding concept pattern. The invention is not limited in any way herein.

Correspondingly, from an active region and its signal strength included in the time-sliced time length of an active pattern, as shown in fig. 8, a conceptual pattern corresponding thereto, such as conceptual pattern 1, can be determined from the active region and its distribution and signal strength. And comparing the concept pattern 1 with a plurality of concept patterns stored in a concept pattern database to find out one concept or a combination of a plurality of concepts. The corresponding activation patterns of the concept combinations are superposition of one or more concept patterns in the concept combinations, and the activation patterns of the concept combinations are close to the activation patterns of the target feedback. Thus, one activation pattern in the target feedback is associated with a combination of one or more concepts, or is decomposed into concept patterns of one or more concepts in the combination of one or more concepts.

In some embodiments, a conceptual combination of one activation pattern in the target feedback is obtained using a deep learning neural network model. For example, a convolutional neural network model (CNN) recognition concept combination may be selected. Other neural network models for pattern recognition, such as deep belief network DBN models, recurrent neural network RNN models, etc., may also be applied herein.

In some embodiments, taking fMRI data as an example, two or more different words (corresponding to different concepts) are read out to the subject and fMRI data fed back in the cerebral cortex of the subject is recorded. And taking a data set formed by different words and corresponding fMRI data as a training set to train the neural network model. The trained neural network mode takes fMRI data as input, outputs a plurality of different combinations of concepts, and the plurality of different combinations of concepts are ordered from high to low in matching with the fMRI data as input. Multiple conceptual combinations corresponding to one activation pattern in the target feedback can be obtained using the trained neural network model. In some embodiments, only the highest degree of matching concept combinations are typically used. When the matching degree of the concept combinations is not quite different, the concept combinations are reserved for later selective use.

At step 150, a sequence having a length of time is determined based on the target feedback, wherein the sequence includes one or more concepts over a plurality of time periods of the length of time. As previously described, the target feedback is divided into a plurality of active modes, each active mode extending for an integer number of time slices of time length. Further, each activation pattern may correspond to one or more conceptual combinations. The duration of the conceptual combination is the same as the duration of the active mode and is also the time length of an integer number of time slices. Thus, the target feedback can be decomposed into a sequence of conceptual combinations of multiple lengths of time. Further, one of the plurality of time-length concept combination sequences is selected as a concept combination sequence corresponding to the target feedback. As shown in fig. 9, which is a timing diagram of a plurality of concepts according to one embodiment of the present invention, concepts corresponding to three consecutive activation modes are shown. In this embodiment, the activation mode 1 includes three concepts: concept 1-concept 3; the active mode 2 includes a concept: concept 4; the active mode 3 includes a concept: concept 5. The starting moments corresponding to the five concepts are respectively 0, T1, T2, T3, T4 and T5. The corresponding duration of the activation mode 1 is t1=t3-0; the corresponding duration of the activation mode 2 is t2=t4-T3; the active mode 3 corresponds to a time period of t3=t5-T4. Thus, each concept includes both a start time and a duration time.

In some embodiments, the criteria for selecting the concept combination sequence may be varied. For example, the selection may be made based on correspondence between desired experiences and concept combinations, and concepts that are not frequently found in a certain experience may be eliminated. For example, the desired experience is "calm", and then combinations of concepts including, for example, "violent", etc., are as little selected as possible. For another example, the selection may be performed according to the association relationship between the concept combinations, so that the concept combinations with poor association or difficult to be associated in the front-back direction are removed. For example, the concept combinations before and after the sequence are related to "flying", and then the concept combinations in the middle are selected as the concept combinations related to "flying". For another example, the selection may be made according to the overall theme of the concept combination, and the concept combination with poor relevance may be removed. For example, where the combination of concepts in the sequence are all "sea" related, other combinations of concepts may be eliminated when there is a choice of sea related combination of concepts. In some embodiments, if there are multiple better combinations of concepts that are difficult to trade off, they may all be reserved for subsequent steps.

In step 160, a word is formed from the time instants and durations of the occurrence of the conceptual combinations within the time period.

As described above, the combination of concepts within different time periods within a time period forms a sequence. The sequence includes a plurality of concepts having temporal properties, one of which is a duration of the concept, different concepts, the duration of which in the sequence is different. In order to get a certain feedback in the brain, the duration of the concept in the sequence does not depend entirely on how many syllables are, but on the association of the concept with the feedback desired in the brain. Thus, in a text segment formed by a combination of concepts, each concept has a defined start time and duration. This is also called the time attribute of the concept in the sequence.

For example, in order to evoke the experience of "confidence" of the player, two concepts of "applause" and "jump" appear in the concept combination. The time slices determined according to the activation mode may differ in duration. For example, the duration of "applause" is greater than the duration of "take off". In a more specific example, between the two concepts of "elegance" and "landing", the starting moment of "elegance" is the 2 nd second from the start of the sequence play, with a duration of 0.5 seconds; the start time of the "landing" is 2.5 seconds of the start of the sequence playing, and the duration is 0.2 seconds.

At step 170, text is added to the segment of text to form content having consistent semantics. Since the perception is in a natural way, the effect achieved in a way that is more favourable for brain acceptance is also better, and it is therefore necessary to form a coherent semantic, in some embodiments a definite theme, which is a more favourable solution.

In some embodiments, a fluxing word or the like is added to the concept combination to link semantics. For example, concepts may correspond to one or more phrases (or phrases). For example, "graceful" and "jump" are combined into "gracefully jump"; the 'dancing' and the 'fairy' are combined into the 'dancing fairy' and other phrases. If two words of the same nature are in a conceptual combination, they may also be directly juxtaposed. For example, "brave" and "firm" are directly combined into "firm brave". If the concept combination is far apart from two words, the combination may be based solely on the attributes of the words. Although difficult to understand, semantics are still coherent. For example, combining "red" and "sitting" directly by adjective preceded by the physical word "sitting in red" followed by "sitting in red"; the 'temple' and the 'flying' are directly combined into the 'flying temple' by nouns.

In some embodiments, adverbs, prepositions, etc. are added to the front-to-back concept combination to have consistent semantics. The front and back concept combinations may differ significantly, so that adverbs, prepositions, etc. need to be added to make the speech coherent. For example, the former combination of concepts is "applause start" and the latter combination of concepts is "gracefully landing", and then the modified tandem semantics may be "gracefully landing with applause start".

In some embodiments, a small number of entity words are added to the front-to-back concept combination to have consistent semantics. The front-back concept combinations may differ significantly, so that it is necessary to add entity words or the like to make the speech coherent. For example, when "sit down in red", "fly-in sprite", and "fly-in temple" are combined, a lower modification "sit down in red", see "fly-in sprite" and "fly-in temple" may be made; where "in" and "in" are added prepositions and "see" is an added entity word. In some embodiments, the added co-words, prepositions, adverbs and entity words should be as short in duration in the sequence as possible to minimize the impact on feedback of the original sequence definition. Moreover, the added entity words should be as common, commonplace actions or things as possible.

In some embodiments, the text may be modified in a manual proofing manner. In particular, text may be modified to approximate the subject matter associated with the desired experience as closely as possible.

In step 180, the time at which one or more concepts occur and/or the duration within the time period is adjusted based on the content having consistent semantics. In some cases, a piece of text may sound to be strange if multiple concepts, defined entirely by the target feedback, appear at times and/or durations within the time period, affecting the feedback effect in the brain. Thus, in some embodiments, it may be desirable to adjust the time at which one or more concepts occur and/or the duration of the time period so that the entire text sounds more natural. However, the adjustment cannot exceed the preset range, otherwise feedback in the cortex may be altered, failing to replicate the experience. In general, the adjustment of the starting moment does not exceed 20ms, while the adjustment of the duration does not exceed 50ms. Thus, the method is beneficial to adjusting the reading rhythm of the characters and cannot deviate from the target feedback too much.

After adjustment, a defined sequence will be obtained. The sequence includes a plurality of concepts, each having its determined start time and duration. Such text is suitable for human or machine reading or otherwise being perceived by the brain in a natural manner.

At step 190, the sequence is perceived by the brain in a natural manner over the length of time to produce the desired feedback in the brain.

The sequence may be perceived by the brain in a natural way, e.g. in an audible, visual, tactile way, so that feedback may be generated in the brain. For example, the sequence is played in a speech output device, or presented as video, or presented in braille. In a natural way, the brain is able to perceive multiple concepts in the sequence at defined moments in time and duration and to generate the desired feedback at the brain. The desired feedback is a simulation of the conceptual combined sequence pattern of the target feedback, thereby enabling the transfer of the target experience from one human individual to another.

It is envisioned that the methods of the present invention can be used in methods of treating or preventing a psychological or mental disorder comprising: depression, macro depression, treatment of resistant depression and treatment of one or more of resistant bipolar depression, bipolar disorder, seasonal affective disorder, mood disorder, chronic depression, psychotic depression, postpartum depression, premenstrual dysphoric disorder (PMDD), contextual depression, atypical depression, mania, anxiety disorders, attention Deficit Disorder (ADD), attention deficit disorder with hyperactivity (ADD h) and attention deficit/hyperactivity disorder (AD/HD), bipolar and manic disorders, obsessive compulsive disorder, bulimia, premenstrual syndrome, substance addiction or abuse, nicotine addiction, psycho-sexual dysfunction, and pseudobulbar disorder. The mental disorders include: schizophrenia, schizoaffective disorder, bipolar disorder, obsessive-compulsive disorder, parkinson's disease, oppositional defiant disorder, charles Bonnet syndrome, autism, and Tourette's disease.

According to another aspect of the invention, the invention also provides a system for generating feedback in the brain. Fig. 10 is a schematic block diagram of a system for generating feedback in the brain according to one embodiment of the invention. As shown in fig. 10, the system comprises a brain keyboard 1 and a transmission device 2, the brain keyboard 1 being configured to generate a sequence of concepts having a length of time, wherein the one or more concepts are included over a plurality of time periods of the length of time; the transfer device 2 is configured to send the sequence to the brain of the subject over the length of time, producing the desired feedback in the brain of the subject.

Wherein the brain keyboard 1 comprises said keyboard 11 and a processor 12. The keyboard 11 comprises a plurality of keys, at least one or more keys corresponding to one or more concepts. The keyboard 11 may be a physical keyboard, such as a keyboard similar to a conventional computer keyboard, which includes various letters, numbers, characters, etc., or a virtual keyboard, such as a virtual keyboard application running in a computer, which has a display interface with a plurality of keys for a user to input one or more concepts. In one embodiment, the keyboard further comprises keys having a length of time. When inputting a concept, the corresponding time, such as the starting time and the duration, can also be input at the same time. The processor 12 is connected to the keyboard 11 and receives key operations from the keyboard, and forms a conceptual sequence having a time length according to the key operations. The processor 12 is connected to the transmitting means 2, which transmitting means 2 receives the sequence of concepts generated by the processor 12 and transmits it to a user, so that the sequence of concepts is perceived in a natural way by the user's brain during its time span, thereby producing the desired feedback in the user's brain.

The transmitting means 2 may be a means for transmitting visual, audible or tactile information, for example a display, which converts the sequence of concepts into video information, such that the user's brain perceives the sequence of concepts in the video information. Alternatively the transmitting means 2 are audio playback means which convert the sequence of concepts into audio information, in particular speech information, so that the user's brain perceives the sequence of concepts in the audio information or speech information. In another way, the conveyor 2 can be made as a braille reader readable by a blind person, through which the blind user can perceive the sequence of concepts, thereby producing the desired feedback in his brain. A switch is provided in the transfer device 2 to control the emission time of the sequence of concepts.

Fig. 11 is a schematic block diagram of a system for generating feedback in the brain according to another embodiment of the invention. On the basis of the illustration in fig. 10, the present embodiment further comprises a data center 3 and a sample processing system 4, wherein the data center 3 stores various data, such as brain feedback sample data and corresponding conceptual sequences, and various sample data, such as brain cortex data from different people and experiences. The data center 3 comprises one or more databases, such as a database storing data of cerebral cortex of a person from various experiences and corresponding parameters related to the person, a conceptual model database, etc. The data of the cerebral cortex of each experienced person may be a plurality of persons, a plurality of types of data. The data is obtained by various means when the brain information is collected while the person has a certain experience. The experience or experience includes, but is not limited to, relaxation, calm, confidence, pleasure, satisfaction, brave, health, excitement, success, beauty. The parameters related to the person include, but are not limited to, language, age, gender, religion belief, education level, occupation or ever occupation, etc. The cerebral cortex data may be fMRI data, but may also be MRI image data, CT image data, SEPECT image data, NIRS image data, fNIRS image data, PAI image data, electroencephalogram or magnetoencephalography data, or the like.

The sample processing system 4 at least comprises a deep learning neural network model module 41 and a pattern recognition model module 42, wherein the deep learning neural network model module 41 is used for processing cerebral cortex data obtained from different people in the same experience so as to obtain the time change of an activation pattern in the cerebral cortex; the form of the data of the change in the activation pattern over time varies from one original data to another. The pattern recognition model module 42 is configured to compare the change in activation pattern of the cortex with the activation pattern of the plurality of concepts in the cortex over the one time period to obtain one or more concepts corresponding to the change in activation pattern in the cortex over time, and to rank the concepts by their occurrence time and duration to obtain a sequence of concepts having a time length.

The models used by the two model modules in this embodiment may be one or more of a neural network CNN model, a deep belief network DBN model, and a recurrent neural network RNN model, and may also be other various types of models that adopt various other algorithms, and those skilled in the art may refer to modeling methods in related fields to implement the deep learning neural network model and the pattern recognition model in the present invention, and since the establishment of the models is not an important point of the present invention, the description is omitted here.

According to another aspect of the present invention, there is provided a method for treating or preventing a psychological or mental disorder, which can be treated or prevented by using the system provided in fig. 10 or 11, using the aforementioned method for generating feedback in the brain. The psychological disorders include: depression, macro depression, treatment of resistant depression and treatment of one or more of resistant bipolar depression, bipolar disorder, seasonal affective disorder, mood disorder, chronic depression, psychotic depression, postpartum depression, premenstrual dysphoric disorder (PMDD), contextual depression, atypical depression, mania, anxiety disorders, attention Deficit Disorder (ADD), attention deficit disorder with hyperactivity (ADD h) and attention deficit/hyperactivity disorder (AD/HD), bipolar and manic disorders, obsessive compulsive disorder, bulimia, premenstrual syndrome, substance addiction or abuse, nicotine addiction, psycho-sexual dysfunction, and pseudobulbar disorder. The mental disorders include: schizophrenia, schizoaffective disorder, bipolar disorder, obsessive-compulsive disorder, parkinson's disease, oppositional defiant disorder, charles Bonnet syndrome, autism, and Tourette's disease.

According to another aspect of the present invention, a system for generating feedback in the brain is presented, as shown in fig. 12, comprising generating means 1a and transmitting means 2a, said generating means 1a being configured to generate a sequence of concepts having a length of time, wherein said one or more concepts are included within a plurality of periods of said length of time; the delivery device 2a is configured to perceive the sequence in a natural manner by the brain of the subject over the length of time, producing the desired feedback in the brain of the subject. In some embodiments, the generating means 1a comprises target feedback determining means 11a and target feedback analyzing means 12a. The target feedback determination means 11a determines target feedback based on experience to be obtained by the subject. Such experiences include, but are not limited to, relaxation, calm, confidence, pleasure, satisfaction, brave, health, excitement, success, beauty, and the like. Wherein in some embodiments the generating means 1a comprises a target feedback database 16a comprising target feedback data corresponding to any of the experiences described above. The feedback data is feedback data recorded by brain signal detection techniques formed in the cortex of the brain over a period of time. For another example, for confidence experience, the target feedback database 16a records feedback data that is developed in the brain for a period of time before an athlete with excellent performance engages in his or her own good game. The feedback data includes, but is not limited to, MRI image data, fMRI image data, CT image data, SEPECT image data and PAI image data, fNIRS image data, and the like.

In some embodiments, the target feedback determination means 11a comprises a target feedback deep learning neural network model. The neural network model outputs target feedback that most closely matches the person's experience of the individual (subject) at the input of the person's related parameters and experience based on the training results. Wherein the human-related parameters include the individual's language, age, gender, religion belief, education level, occupation or ever occupation, and the experience includes any of the aforementioned relaxation, calm, confidence, pleasure, satisfaction, brave, health, excitement, success, beauty, etc.

In some embodiments, the target feedback analysis device 12a includes a target feedback time-slicing module 120a and an activation pattern association analysis module 121a. After obtaining the target feedback, the target feedback determination device 11a sends the target feedback to the target feedback analysis device 12a. The target feedback time slicing module 120a of the target feedback analysis device 12a acquires the target feedback data and slices it for a certain period of time to obtain a plurality of time slices of the same period of time, as shown in fig. 6. Wherein the time period of the time slice cannot be too large nor too small, preferably 10-50ms in one embodiment. The activation pattern association analysis module 121a performs association analysis of the activation patterns, for example, determines the activation patterns of the cerebral cortex in each time slice, including the spatial distribution and the signal intensity of the activation portions, and then recalculates whether the activation patterns of the cerebral cortex in the adjacent time slices are independent of each other. For example, whether the spatial distribution of the activated portions of the cerebral cortex in the two time slices is the same or whether the difference is within an allowable range is compared, and then the signal intensities of the corresponding activated portions are compared, and when the difference of the spatial distribution of the activated portions and the difference of the signal intensities are within the allowable range, the two are determined to be independent, otherwise, the two are determined to be independent. After the correlation analysis of the activation modes, a plurality of corresponding time slices are obtained, and each time slice corresponds to one activation mode, as shown in fig. 7.

In some embodiments, the target feedback analysis device 12a includes a concept combination identification module 122a. In some embodiments, the generating means 1a comprises a conceptual pattern database 17a in which feedback data formed in the brain by one or more conceptual combinations, i.e. corresponding to the distribution of active areas in the cortex of the brain and their signal strengths, are stored. Included in the concept-combination-recognition module 122a is a concept-recognition neural network model. The neural network model inputs an activation pattern of the target feedback, such as data corresponding to a time slice in fig. 7, based on the training results, and outputs one or more conceptual combinations that match the activation pattern.

In some embodiments, the generating device 1a further comprises a semantic module 13a configured to modify the sequence of concepts to form coherent semantics. The concept set identification module 122a obtains a time-ordered concept set, each concept having temporal properties, i.e., time and duration, and adds additional words, such as co-words, prepositions, adverbs, real words, etc., to the concept set to be consistent with the semantics of the concept set in order to better be accepted by the brain to achieve the desired effect. For example, adding "please", "to" the concept "listen", "rippling", and "running" to get the words "please listen to the running" with consecutive semantics. The time at which the concepts occur and/or the duration within the time period is adjusted after the addition of the additional vocabulary. For example, the concept of "please" is added to "listen" and the start time of "listen" is delayed such that the adjusted duration of "please listen" is the same as the duration of "listen" before adjustment. The processing of the concept sequence by the semantic module 13a results in text with consistent semantics.

In some embodiments, the delivery means 2a comprises one or more means for one or more of hearing, vision or touch, or a combination thereof, to cause the brain to perceive the sequence in a natural way, and correspondingly the system further comprises conversion means 3a. As shown in the system block diagram of fig. 13, the conversion device 3a includes an audio conversion module 30a, a video conversion module 31a, a braille conversion module 32a, and the like. The audio conversion module 30a is configured to convert text with consistent semantics representing a target feedback of experience into corresponding audio information. For example, speech information uttered by a human or machine reading. Further, corresponding background music, such as classical music of the middle and outer, various natural sounds, etc., may be configured for the voice information according to desired experience. The video conversion module 31a selects one or more video segments corresponding to the text in the material library according to the text with continuous semantics which represents the target feedback of experience, connects the video segments into a whole video image, and stores the whole video image into a corresponding format according to the system of the transmission device. The braille conversion module 32a may convert text with consistent semantics representing a target feedback of experience into outputtable braille. Of course, the conversion device 3a described above may be integrated in the generation device 1a as one conversion module of the generation device 1 a.

The transmitting device 2a may be varied, for example, the audio playing device 20a for hearing includes, but is not limited to, a speaker, a headphone, an audio player, and the like; among them, the video playing device 21a for vision includes, but is not limited to, various displays such as a desktop computer display, a laptop computer display; various mobile terminal display screens, such as mobile phone display screens, flat panel display screens, etc.; various large screen displays; the tactile braille reader 22a devices include, but are not limited to, printers for printing braille, electronic braille readers, and the like.

In some embodiments, the transmitting device 2a may also be a device with both audio and video playback, such as a VR/AR device, including but not limited to VR/AR glasses, VR/AR head displays, and the like.

The transfer device 2a may also comprise professional and non-professional audiovisual rooms, film studios. For example, the audio-visual room may be a professional or home audio-visual room including an audio playing device, a video playing device, a signal source device, etc., and the generating apparatus 1a transmits the converted audio-video information to the signal source device of the video room, and then the subject may be caused to perceive the concept in the sequence in an audio-visual manner in the audio-visual room, thereby generating desired feedback in his brain and obtaining desired experience. The studio may be a professional or non-professional studio including cameras (e.g. full-frame or digital back), lenses, lights, curtains, background props, etc. In a studio, subjects are audibly and visually aware of concepts in the sequence by way of actor performance, thereby producing desired feedback in their brains and obtaining desired experience.

In some embodiments, the system for generating feedback in the brain further comprises a storage device 4a, as shown in the system block diagram of fig. 14. The storage device 4a is configured to store brain feedback sample data and corresponding conceptual sequences, which may be one or more of a local memory, a local database, and a cloud database. In one embodiment, the stored brain feedback sample data and the corresponding concept sequence, the target feedback database, the concept pattern database and the like are all located in the cloud database, and the generating device 1a obtains the required data from the cloud database through any existing communication mechanism when needed, so that the local storage space can be saved. In some embodiments, some modules in the generating device 1a, such as the target feedback analysis device, the conversion device, and the like, may be placed in the cloud, and the powerful cloud computing power is utilized to obtain the required audio and video information that may be executed by the specific transmitting device.

The system for generating feedback in the brain also comprises a sample processing device 5a configured to obtain corresponding brain feedback sample data and corresponding sequence of concepts using as samples the cortical data obtained from different people with the same experience. The resulting brain feedback sample data and the corresponding sequence of concepts are stored in the storage means 4 a. In some embodiments, the sample processing system 5a includes a deep-learning neural network model module 51a and a pattern recognition model module 52a, the deep-learning neural network model module 51a configured to process cortical data obtained from different people with the same experience to derive changes in activation patterns in the cortex over time; the pattern recognition model module 52a compares the change in activation pattern of the cortex with the activation pattern of the plurality of concepts in the cortex over the one time period to obtain one or more concepts corresponding to the change in activation pattern in the cortex over time and sequences the concepts in order of occurrence and duration of the concepts to obtain a sequence of concepts having a length in time.

According to another aspect of the present invention, a brain keyboard is presented, as shown in fig. 10, comprising a keyboard 11 and a processor 12, the keyboard comprising a plurality of keys, at least one or more keys corresponding to one or more concepts; a processor to receive key operations from the keyboard to form a sequence having a length of time, wherein the sequence includes the one or more concepts over a plurality of time periods of the length of time; wherein the sequence is perceived by the brain in a natural manner over the length of time to produce the desired feedback in the brain.

The application effect of the present invention will be described below by way of specific examples.

Application example one

The "deep relaxation" sequence obtained in the manner of the present invention was repeated in speech manner to the subject using the speaker for 13 minutes by taking the brain feedback after the meditation of the Buddhist as the target feedback of the "deep relaxation" experience. Determining whether a desired feedback is formed with the subject depth relaxation assessment index; wherein the evaluation index comprises physical fatigue recovery capability, sleep quality and deep relaxation duty ratio of brain wave presentation.

Two additional tests were performed:

First round test:

The concept sequence was relaxed audibly at a fixed time live depth for 13 minutes each day, live for 7 days continuously, and a questionnaire was given to the subject to evaluate his physical fatigue recovery. A total of 120 valid questionnaires were harvested after the end of 7 days. The male and female sex ratio of the subjects was 5:1, a step of; 77% of subjects are adults between 30-50 years of age; the above academy of the family accounts for 40%, 6 of which are the major, and 2 of which are the doctor; the first line city user accounts for 21.67%.

Second round of test:

42 people are selected to participate in the test, and the test time is 14 days. 32 enter the target test group; of these, 17 were tested via a depth relaxation sequence of audio of about 40 minutes per day (three consecutive plays) and another 15 were tested via a depth relaxation sequence of audio of about 80 minutes per day (six consecutive plays). Another 10 experienced meditation were used as reference group. The 10 persons of the reference group perform meditation relaxation for 40 minutes per day in a self-habitual way.

During the second round of testing, 42 participants recorded sleep data daily with snail sleep software and were tested daily for 4 minutes of brain waves using Brainlink Lite smart hair rings after the test. As will be appreciated by those skilled in the art, snail sleep software is an intelligent sleep monitoring software manufactured by the company of the scion technology (beijing). The software learns the measurement by the professional sleep monitoring device PSG (polysomnography) by using a deep learning algorithm, and the accuracy and the correctness of the measurement data approach to scientific research and clinical measurement results. Brainlink Lite is an intelligent brain wave EEG acquisition device manufactured by Shenzhen macrointellectual technology Co., ltd. Which is a 512 sample rate single channel headband with 3 dry electrodes on the forehead.

Physical fatigue recovery performance index:

In the questionnaire of the first round of test, subjects were asked to evaluate their own fatigue recovery ability for two periods of time, before and on the seventh day of the test, on a scale of 10 from 1 to 10. The average score obtained by calculating 120 data is shown in fig. 15, and is a schematic diagram of evaluating the average score of the physical fatigue recovery ability according to an embodiment of the present invention. The average of the time period was increased from 5.53 minutes before the test to 7.41 minutes on the seventh day of the test, and the time period was increased by 34.00%.

Four statistics were taken during the second 14 days of the test run, and figure 16 is a graph showing the average score of the test group's assessment of physical fatigue recovery at 4 statistics. As can be seen from the figure, after the test on the first day, the physical fatigue recovery capacity of 32 test group members increased from the previous average of 4.50 minutes to 5.19 minutes, by 15.33%; the physical fatigue recovery capacity after 7 days is increased from 4.50 minutes to 6.56 minutes, and is improved by 45.78 percent; the physical fatigue recovery capacity after 14 days is increased from 4.50 minutes to 7.63 minutes, and is improved by 69.56 percent.

Figure 17 is an average score of the assessment of physical fatigue recovery in the second round of testing for the reference group at 4 statistics. Rising to 5.5 after meditation of the first day by 5.77% compared with 5.2 of the previous day; after 7 days continuously, the physical fatigue recovery capacity is increased from the previous 5.2 to 6.1, and 17.31% is improved; after 14 consecutive days, the physical fatigue recovery capacity is increased from the previous 5.2 to 6.8, which is improved by 30.77%.

By comparing the data of the target test group with the data of the reference group, the recovery capability of the target test group on physical fatigue is improved more remarkably. Since the individuals of the target test group do not have experience of meditation depth relaxation, it is quite unexpected that the sequence of depth relaxation from meditation individuals according to the present invention can produce physical fatigue recovery data exceeding those with meditation experience.

Depth relaxation duty cycle index:

FIG. 18 is a graph showing the ratio of the average brain wave depth relaxation degree calculated from the brain wave data of the target test group. The brain wave average depth relaxation duty cycle of the day before the test is 7.53%; the brain wave average depth relaxation ratio after the first day test is 10.75%; 11.66% on day 7. The deep relaxation duty cycle of brain waves as a whole shows a tendency to be higher and higher with time.

Fig. 19 is a ratio of brain wave mean depth relaxation calculated from brain wave data of a reference group. The average depth relaxation ratio of before-meditation and brain waves on the first day was 1.50%, and after meditation on the first day was 4.20%; after 7 days, 1.9% and no increasing trend was seen.

As can be seen by comparing the brain wave data of the two groups, the subject's brain through this example has a better degree of relaxation and better effect. Brain wave data is the most direct data reflecting deep relaxation. The brain wave data obtained after application of the method of the invention reflects the successful transplantation of meditation depth relaxation experience. Moreover, the increasing trend suggests that this experience becomes increasingly an experience of the subject itself as feedback is formed in the brain by multiple applications of the method of the present invention.

Sleep quality index:

Fig. 20 is a schematic diagram of 120 person sleep quality scores in the first round of testing. The 7-day sleep quality score increased from the previous average of 5.8 minutes to an average of 7.58 minutes, 30.69% improvement.

Fig. 21 is a graph showing the sleep quality score values of 32 persons in the target test group in the second round of testing. The average sleep quality of the day before the test is started is 5.25 minutes, and the sleep quality is improved to 5.88 minutes after the test of the first day, so that the sleep quality is improved by 12.00 percent; after 7 days, the water is lifted to 6.75 minutes on average, and the water is lifted by 28.57 percent; after 14 days, the lifting is up to 7.5 minutes on average, and the lifting is up to 42.86 percent. The sleep quality of the target test group gradually increased over time.

Fig. 22 is a schematic diagram of 10 person sleep quality scores of the reference group in the second round of test. The sleep quality before meditation is averagely divided into 6.2 points, and the sleep quality is improved to 6.5 points after meditation on the first day, so that the sleep quality is improved by 4.84 percent; after 7 days, the water is lifted to 6.5 minutes on average, and the water is lifted by 16.13 percent; after 14 days, the average of the improvement is 7.4 minutes, and the improvement is 19.35 percent.

By comparing the sleep quality scoring values of the target test group and the reference group, the target test group adopting the method of the invention can better improve the sleep quality. Sleep quality reflects, on the one hand, the degree of deep relaxation and, on the other hand, the overall change to the subject after the experience transplantation. Due to the wide variety of sleep problems, it is very common for subjects to have sleep problems, and one of the reasons for willingness to participate in the test. The effect of the application of the method reflects that such empirical transplantation is not short-term and evanescent, but rather can produce changes to the subject as a whole. This is almost the same as the subject itself gets a similar experience. Further, such global changes are also clear for the effect of treating and preventing psychological or mental disorders in a subject.

Application example II

The brain feedback after the aircraft in the cabin is listened to by a pilot to take off and record is used as target feedback of 'calm' experience, a 'calm' experience sequence is sent to a subject in a voice mode by using a loudspeaker, and the repeated playing time is 15 minutes. The desired feedback is manifested in that the subject is able to reduce anxiety, increase the calm of the heart, and reduce physical problems. Using the reverse score value of STAI Szechwan anxiety scale as the corresponding evaluation index of the calm and safe degree; the severity score (reverse score) of the physical problem and the mountain change score (forward score) value of the identity problem are used as indicators of the physical problem.

Two additional tests were performed:

First round test:

In audio mode, a sequence of calm concepts was played for 15 minutes at a fixed time each day, live for 7 days continuously, and 120 subjects were given a questionnaire to evaluate their anxiety level.

Second round of test:

42 people are selected to participate in the test, and the test time is 14 days. 32 enter the target test group; of these, 17 persons tested via calm conceptual sequential audio of about 45 minutes (three consecutive plays) per day, and another 15 persons tested via calm conceptual sequential audio of about 90 minutes (six consecutive plays) per day. Another 10 experienced meditation were used as reference group. The 10 persons of the reference group perform meditation relaxation for 40 minutes per day in a self-habitual way.

Calm and calm the heart degree index:

Figure 23 is a graphical representation of the reverse scoring averages of the STAI spell-berg anxiety scale for 120 subjects in the first round of testing. After 7 consecutive days of testing, the degree of calm and restlessness changed from the previous average of 2.63 minutes to 1.84 minutes, improving by 30.04%.

Figure 24 is a reverse scoring average of the STAI spell-berg anxiety scale for subjects in the 32 target test group in the second round of testing. After the test on the first day, the calm and calm degree is changed from the previous 2.53 to 2.47, and the improvement is 2.37%; after 7 days, the calm and calm degree is changed from the average 2.53 to 2.06, and the improvement is 18.58%; after 14 days, the degree of calm and restlessness was changed from the previous average of 2.53 minutes to 1.70 minutes, which was improved by 32.81%. The degree of calm and peace presents a trend over time to increase.

Figure 25 is a reverse scoring average of the STAI spell-berg anxiety scale for 10 subjects in the reference group in the second round of testing. Subjects in the reference group changed from 2.6 to 2.5 the day before meditation, 3.85% improvement; after 7 days, the calm and calm degree is changed from 2.6 to 2.4, and the improvement is 7.69%; after 14 days, the calm and calm degree was changed from 2.6 to 2.3, and the improvement was 11.54%.

By contrast, although the calm and calm degree of the reference group also showed a tendency to gradually increase with time, the increase in calm and calm degree of the target test group was higher than that of the reference group in terms of the contemporaneous comparison, and the effects of calm and reducing anxiety were better.

Physical problem index:

Fig. 26 is a graphical representation of the reverse scoring averages of the physical problem assessment table for 120 subjects in the first round of testing. In the first round of testing, 120 subjects, after the test was completed, scored the severity of the physical problem (reverse scoring) from the previous 7.32 points to 4.31 points, improving by 41.12%.

Figure 27 is a graphical representation of the forward scoring averages of the physical problem improvement assessment table for 32 subjects in the subject test group in the second round of testing. In the second round of testing, the improvement degree of physical problems of 32 subjects in the target test group is increased by 16.92% from 3.90 minutes of the day before the start to 4.56 after the test of the first day; after 7 days, the water rises to 5.78, and the water is lifted by 48.21%; after 14 days, the rise was 7.59 and 94.12% of the rise.

Fig. 28 is a graphical representation of the forward scoring averages of the physical problem improvement assessment table for 10 reference group subjects in the second round of testing. The improvement of physical problems in the subjects in the reference group increased by 12.50% from 4.8 points on the day before the start, to 5.4 on the first day; after 7 days, the temperature rises to 5.5 percent, and the temperature is improved by 14.58 percent; after 14 days, the rise was 6.4, which was 33.33% improvement.

It can be seen by comparison that the method provided by the invention can more effectively improve the physical problems. The marked improvement in anxiety and physical problems caused by anxiety illustrates the high efficacy of the methods of the invention in the treatment of psychological and psychiatric disorders. Furthermore, without any pharmaceutical assistance, and without any side effects, the method of the invention can of course also be better applied to prevent these problems. Of course, the methods of the present invention are expected to produce better therapeutic and prophylactic effects if used in combination with other drugs.

The above embodiments are provided for illustrating the present invention and not for limiting the present invention, and various changes and modifications may be made by one skilled in the relevant art without departing from the scope of the present invention, therefore, all equivalent technical solutions shall fall within the scope of the present disclosure.

Claims

1. A method of non-therapeutic use to generate feedback in the brain, comprising:

Determining a sequence of concepts having a length of time, wherein the sequence of concepts includes one or more concepts over a plurality of time periods of the length of time; wherein the concept refers to a unit of mental activity having a determined meaning;

Sensing the sequence of concepts by the brain in a natural manner over the length of time to produce a desired feedback in the brain;

Storing brain feedback sample data and corresponding conceptual sequences; and

Using cerebral cortex data obtained from different people in the same experience as a sample to obtain corresponding cerebral feedback sample data and a corresponding concept sequence;

wherein the desired feedback is a simulation of the change in activation pattern over time in another cerebral cortex;

wherein the change in activation pattern over time in another cerebral cortex as a simulation object comes from a deep learning neural network model;

Wherein further comprising: processing cerebral cortex data obtained from different people in the same experience through the deep learning neural network model to obtain the time change of the activation mode;

Further comprises: by comparing the change in activation pattern of the cortex over a period of time with activation patterns of multiple concepts in the cortex;

Further comprises: one or more concepts corresponding to the activation pattern of the cerebral cortex over the one time period are determined by the pattern recognition model.

2. The method of claim 1, wherein the desired feedback comprises a temporal change in activation patterns in the cerebral cortex.

3. The method of claim 2, wherein the activation pattern comprises a spatial distribution of activated portions in the cerebral cortex.

4. The method of claim 1, wherein the change in activation pattern over time in another cerebral cortex as a simulated subject represents an experience.

5. The method of claim 1, wherein the cerebral cortex data is one or more of fMRI data, MRI data, CT data, SEPECT data, NIRS data, fNIRS data, PAI data.

6. The method of claim 1, wherein the change in activation pattern in the other cerebral cortex over time corresponds to a desired experience and parameters related to the person.

7. The method of claim 6, wherein the human-related parameter comprises: one or more of language, age, gender, religion belief, educational level, or occupation.

8. The method of claim 6, wherein the parameters related to the person include a once occupational.

9. The method of claim 6, wherein the desired experience comprises: one or more of relaxation, calm, confidence, pleasure, satisfaction, brave, health, excitement, success, beauty.

10. The method of claim 1, further comprising: the change in activation pattern over time in another cerebral cortex as a simulation object is divided into a plurality of time periods.

11. The method of claim 10, wherein the activation patterns of the cerebral cortex are independent of each other between the plurality of time periods.

12. The method of claim 10, wherein the activation pattern of the cerebral cortex during the one time period corresponds to one or more concepts.

13. The method of claim 12, further comprising: based on the change in activation pattern of the cortex over the one time period, the time and duration of the occurrence of the one or more concepts over the one time period is obtained.

14. The method of claim 1, further comprising: the pattern recognition model is one or a combination of a convolutional neural network CNN model, a deep belief network DBN model and a recurrent neural network RNN model.

15. The method of claim 13, further comprising: a word is formed based on the time and duration of occurrence of the one or more concepts within the one time period.

16. The method of claim 15, further comprising: adding characters in the length of characters to form content with coherent semantics.

17. The method of claim 16, further comprising: the time and/or duration of the occurrence of one or more concepts within the one time period is adjusted based on the content having consecutive semantics.

18. The method of claim 17, wherein the time of occurrence and/or duration of one or more concepts within the one time period is adjusted within a preset range.

19. The method of claim 1, wherein the concepts correspond to one or more phrases, words, or morphemes.

20. The method of claim 19, wherein the same phrase, word, or morpheme in different languages corresponds to the same concept or different concepts.

21. The method of claim 1, wherein the natural mode is an auditory mode.

22. The method of claim 1, wherein the natural manner is a visual manner.

23. The method of claim 1, wherein the sequence of concepts is perceived repeatedly by the brain in a natural manner, at preset time intervals and preset times over the length of time.

24. A system for generating feedback in the brain, comprising:

Generating means configured to generate a sequence of concepts having a length of time, wherein the one or more concepts are included within a plurality of time periods of the length of time; wherein the concept refers to a unit of mental activity having a determined meaning;

A transmitting device configured to perceive the sequence of concepts in a natural manner by the brain of the subject over the length of time, producing a desired feedback in the brain of the subject;

a storage device configured to store brain feedback sample data and corresponding conceptual sequences; and

Sample processing means configured to obtain corresponding brain feedback sample data and corresponding conceptual sequences using as samples brain cortex data obtained from different persons with the same experience;

wherein the sample processing device comprises:

a deep learning neural network model module configured to process cortical data obtained from different people with the same experience to obtain a temporal change in activation patterns in the cortex; and

A pattern recognition model module configured to compare changes in activation patterns of the cortex with activation patterns of a plurality of concepts in the cortex over a period of time to obtain one or more concepts corresponding to changes in activation patterns in the cortex over time, and to rank the concepts by occurrence time and duration of the concepts to obtain a sequence of concepts having a length of time;

wherein the transfer device also includes a concept conversion module configured to convert the concept sequence into one or more of text, video information, and audio information.

25. The system of claim 24, further comprising a conversion device configured to convert the sequence of concepts into audio information or video information.

26. The system of claim 24, wherein the generating means comprises:

A target feedback determination device configured to determine target feedback based on experience to be obtained by the subject; and

A target feedback analysis device configured to analyze the target feedback to obtain the sequence of concepts having a length of time.

27. The system of claim 26, wherein the target feedback determination means comprises a target feedback deep learning neural network model that outputs target feedback that most closely matches an individual's experience based on input parameters and experience associated with the individual.

28. The system of claim 26, the target feedback analysis device comprising:

The target feedback time slicing module is configured to slice target feedback data according to a certain time period to obtain a plurality of time slices;

an activation pattern association analysis module configured to perform association analysis of activation patterns on the plurality of time slices to obtain a plurality of time-ordered activation patterns; and

A concept combination identification module configured to match the plurality of activation patterns with its corresponding concept or concepts.

29. The system of claim 24, the transfer device comprising one or a combination of: audio player, video player, braille reader.

30. The system of claim 24, the transfer device comprising one or a combination of: VR/AR devices, audiovisual cabins and film studios.

31. A brain keyboard, comprising:

a keyboard comprising a plurality of keys, at least one or more keys corresponding to one or more concepts; wherein the concept refers to a unit of mental activity having a determined meaning;

A processor configured to receive key operations from the keyboard to form a sequence of concepts having a length of time, wherein the sequence of concepts includes the one or more concepts over a plurality of periods of the length of time; wherein the sequence of concepts is perceived by the brain in a natural manner over the length of time to produce a desired feedback in the brain; and

A storage device configured to store brain feedback sample data and corresponding conceptual sequences;

Wherein the processor is further configured to utilize cortical data from different people obtained with the same experience as a sample, obtaining corresponding brain feedback sample data and a corresponding sequence of concepts;

Wherein the processor further comprises:

A pattern recognition model module configured to compare changes in activation patterns of the cortex with activation patterns of a plurality of concepts in the cortex over a period of time to obtain one or more concepts corresponding to changes in activation patterns in the cortex over time, and to rank the concepts by their time of occurrence and duration to obtain a sequence of concepts having a length in time.