CN115335102A - Method and system for generating feedback in brain and brain keyboard - Google Patents

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 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 operations from the keyboard forming 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 system have low cost, easy operation, and no harm to the subject.

Description

Method and system for generating feedback in brain and brain keyboard Technical Field

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

Background

The brain is human bodyThe major components of the central nervous system, including approximately 860 billion nerve cells (neurons) and billions of glial cells. Each neuron usually has hundreds to thousands of synapses, and the number of synapses in the brain is estimated to be about 10 ¹⁵ A plurality of. The brain of an adult human weighs about 1.2-1.6 kg and the main component is blood. Although the weight of brain is only 2% -4% of human body weight, its oxygen consumption can account for 1/4 of total oxygen consumption. The blood flow of the brain accounts for 15% of the total blood output of the heart, and the power consumed by the brain is about 25W.

The brain is the most complex organ of the human body, and is the 'command part' for receiving external stimulation, generating feeling, forming consciousness and thinking, giving instructions and driving action. The cerebral cortex is the material basis for the activity of the higher nerves and the tissue that generates the thought. The left and right hemisphere of the cerebral cortex can be divided into 5 lobes: frontal, temporal, parietal, occipital and islet leaves; among them, the frontal and temporal lobes have traditionally been considered to be related to language, emotion, memory. However, studies on the expression of the language center and the physiological basis of the formation and retrieval of memory, the formation and influence of emotion, and the like are still in the early stages.

Currently, the three main directions of brain science research are structural and functional studies of the brain (particularly the higher functions of the brain), studies of brain diseases, and studies of brain applications. The research of brain application is a key field in the field of brain science, and aims to develop algorithms or models of high-level functions of the brain by using methods and means such as information science and computer science while analyzing the material basis of the nervous system structure and psychological activities of the brain, so as to promote the development of the fields such as artificial intelligence and robots.

However, the research and application results of the cross-domain research and application to solve the related problems in the field of brain diseases by means of the methods and means of brain application research using the physiological basis of the structural and functional research of the brain 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, a system and a brain keyboard for generating feedback in the brain, so as to generate the desired feedback in the brain.

In order to solve the above technical problems, 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 periods of the length of time; and perceiving said sequence by the brain in a natural manner over said length of time to produce a desired feedback (desired feedback) in said brain.

According to another aspect of the 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 communication device is configured to perceive the sequence 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.

According to another aspect of the 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 of the keys corresponding to one or more concepts; the processor is configured to receive key operations from the keyboard forming 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 aforementioned methods, can cause the subject's brain to perceive a conceptual sequence in a natural manner to produce desired feedback in the subject's brain that corresponds to a desired experience, such as relaxation, calm, confidence, pleasure, satisfaction, brave, health, excitement, success, beauty, and the like. The method and system provided by the invention can not only make ordinary people feel various expected experiences, but also be used for treating or preventing psychological diseases or mental diseases. The method and the system provided by the invention have the advantages of low cost, no harm to a subject, easy operation and obvious effect.

Drawings

Preferred embodiments of the present invention will be described in further detail below with reference to the accompanying drawings, in which:

FIG. 1 is a schematic structural view of an MRI apparatus according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a morpheme aspect model disclosed in article "Natural Speech reveals semantic map of tiled human cerebral cortex" by Huth et al;

FIG. 3 is a principal component analysis diagram of a semantic model in morphemes disclosed in article "Natural Speech reveals semantic map of tiled human cerebral cortex" by Huth et al;

fig. 4 is a flow diagram of a method of generating feedback in the brain, according to an embodiment of the present invention;

FIG. 5 is a flow diagram of a method of time-dividing target feedback into a plurality of active modes, according to one embodiment of the invention;

FIG. 6 is a schematic time slicing diagram after slicing the target feedback in time according to one embodiment of the present invention;

FIG. 7 is a schematic diagram of identifying, merging, and then obtaining time slices according to an embodiment of the invention;

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

FIG. 9 is a timing diagram illustrating concepts in accordance with an embodiment of the invention;

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

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

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

FIG. 13 is a functional block diagram of a system for generating feedback in the brain according to yet another embodiment of the present invention;

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

FIG. 15 is a graphical representation of physical fatigue recovery ability assessment scores for a first round of test subjects according to one embodiment of the invention;

FIG. 16 is a graphical representation of the body fatigue recovery ability assessment scores for the target test group during the second round of testing, in accordance with one embodiment of the present invention;

FIG. 17 is a graphical illustration of a reference group physical fatigue recovery ability assessment score during a second round of testing according to an embodiment of the invention;

FIG. 18 is a graphical illustration of brain wave mean depth relaxation ratio for a target test group during a second round of testing according to an embodiment of the present invention;

FIG. 19 is a graphical illustration of brain wave mean depth relaxation ratio for a reference group during a second round of testing according to an embodiment of the present invention;

figure 20 is a graph illustrating sleep quality score values for test subjects in a first round according to one embodiment of the present invention;

FIG. 21 is a graphical illustration of sleep quality score values for a target test group during a second round of testing according to one embodiment of the invention;

FIG. 22 is a graphical illustration of sleep quality score values for a reference group during a second round of testing according to one embodiment of the invention;

FIG. 23 is a schematic representation of the inverse score mean of the STAI Pierberg anxiety Scale for subjects during a first round of testing according to one embodiment of the present invention;

figure 24 is a graphical representation of the STAI spear anxiety table reverse scoring averages for the second round test target test group in accordance with one embodiment of the present disclosure;

FIG. 25 is a schematic representation of the inverse score mean of the STAI Pierberg anxiety Scale for the second round of test reference group according to an embodiment of the present invention;

figure 26 is a schematic representation of the reverse scoring averages of the physical problem assessment table for the first round of test subjects according to one embodiment of the present invention;

fig. 27 is a schematic diagram of the forward scoring averages of physical problem improvement assessment tables for the second round of test target test group according to one embodiment of the present invention; and

fig. 28 is a graph showing the forward-scoring averages of physical problem improvement assessment tables for the second round test reference group according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

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

The life evolutionary processes of billions of years on earth can be summarized into two basic evolutionary modes: a biological evolutionary approach to gene information transfer, which is the generation of proteins and cellular tissues from a genome consisting of nucleotide sequences according to a certain rule, resulting in an unlimited diversity of living organisms; another is an evolutionary approach to non-genetic information transfer through language. The latter evolution distinguishes human beings from other life bodies, has thought, and promotes cultural progress and prosperity.

According to the summary of Chua eosin, 2015 book of mental and cognition in human published by people's publisher, from the viewpoint of contemporary genetic science, the evolution of mental from low level to high level also determines the evolution of life from low level to high level. The evolution of life in mind includes five levels from low-level to high-level: neural, psychological, linguistic, thought, and cultural; the nervous hierarchy and the psychological hierarchy are common to human and animals and can be called a low-level cognitive hierarchy; the language, thinking, and cultural hierarchies are human-specific, also referred to as high-level cognitive hierarchies. The language is the basis of human cognition, human thinking is formed on the basis of abstract concept language, the language and the thinking jointly construct a human knowledge system, even the whole human society is defined, the accumulation of knowledge forms culture, and the culture flourishing also promotes the development of the human society.

With the development of brain science from the beginning of the 20 th century, people have developed comprehensive research on human cognition from various levels of nerves, psychology, language, thinking and culture, and thus various brain science system branches with different research emphasis appear. Among them, neuro-linguistic is a very important research field. Neuro-linguistic studies the relationship between language and brain function, with the aim of interpreting the neural and psychological mechanisms of understanding, production, acquisition 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 linguistic information.

In 1987, kaplan (Caplan, d.) "neuro-and linguistic language disorders: introduction (published 1987 by Cambridge university Press) is a representative work in the early stages of neuro-linguistic development, discussing the relationship between different regions of the brain and language. Schtemmol (Stemmer, b.) and whittaker (HA) published a book in the neuro-linguistic handbook (published by 1998, massachusetts), 1998, summarizing the progress of neuro-linguistic, and discussing brain mechanisms of speech processing. Representative works in the field of neurolinguistics are "guidance on neurolinguistics" (ahlsen. E. (2006) introductions to neurolinguistics ", issued in 2006 by professor yirsaberg university of netherlands elisaburg, elisabeth (Elisabeth aels en) and John Benjamins (John Benjamins). Two authors in this book summarized the recent development of brain mechanism research for language processing and language learning using various methods and techniques. For example, the process that the brain follows when processing linguistic information, the interaction of different brain regions in linguistic processing, and the location of brain activation when a subject produces or perceives a language other than his native language, among others. Although many advances are made in neuro-linguistic, the understanding of the memory formation and search process and the language formation mechanism still remains at a relatively macroscopic stage, and no effective description at the neural network level is formed.

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

During research on language processing and brain, one of the earliest persons who established a connection between a specific brain region and language processing was the french surgeon-paul brouca. Bromoka found most of the human brain damaged (or diseased) on the left frontal lobe, now called the Broca region, by autopsy of many speakers deficient. German anatomists, neuropathologists karl wenick (Carl wennicke) named the left posterior temporal superior region in the brain as wenick region (wennicke disconnect) and proposed that different regions of the brain were dedicated to different language tasks. Korbinia Brodmann in the early twentieth century divides the surface of the brain into differently numbered regions based on the cellular structure and function of each region, which are called Brodmann regions. The Brodmann region is widely used in the neuro-linguistic field for studying the location of brain specific language "modules," e.g., the Broca region deals with speech motor production, while the Wernicke region deals with auditory speech understanding.

After the split brain experiments were performed for many consecutive years after the 60 s of the 20 th century, the american psychobiologist spipe proposed specialization and division of the left and right brain: 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, analyticity and the like. Thus, the left brain is referred to as the "conscious brain", the "academic brain", the "language brain" or the "logical brain". The right brain is mainly responsible for spatial image memory, intuition, emotion, body coordination, visual perception, art, music rhythm, imagination, inspiration, indulgence and the like, and the thinking mode has the characteristics of disorder, jumping, intuition and the like. Therefore, the right brain is also called "instinctive brain", "subconscious brain", "creative brain", "music brain" or "artistic brain".

Another important task of neuro-linguistics is the testing and evaluation of theories proposed by psycholinguists and theoretical linguists. In general, theoretical linguists propose models to explain the structure of the language and the way in which the linguistic information is organized; a psycholinguist proposes a model and an algorithm to explain the processing mode of language information in the mind; the way that neurolinguists analyze brain activity to infer biological structures (populations and individuals), for example, neural networks perform these psycho-linguistic processing algorithms. For example, the "sequence" models of Janet ford (Janet Fodor) and ryan Frazier (Lyn Frazier) and the "unified model" of sieo waters (the Vosse) and jerad ken (Gerard Kempen) are different models for sentence processing. Neurolinguistics can reflect the rationality of different sentence processing models by using sentence processing experiments, checking physiological brain responses according to ELAN, N400 and P600 brain responses obtained by ERP techniques, and comparing the results of the physiological brain responses with the predicted results of the sentence processing models proposed by psycholinguists. On the other hand, neuro-linguistic may also guide psycho-linguistic to put new theories on the structure and organization of language by "generalizing the knowledge of neural structures into linguistic structures" based on the knowledge of brain physiology.

Research methods and related technologies applied in the field of neuro-linguistic have been continuously developed with the progress of modern technologies. The following describes the technical means of brain science research in detail.

Initially, both genetic and pathological approaches were used to study the actual language process and to infer the language mechanisms of the brain. Taking a pathology experimental method as an example, it analyzes the language condition of a brain injury patient from the neuropsychological perspective, and utilizes the analysis of the brain injury area to understand the language generation process and the neuropsychological mechanism thereof.

With the advancement of scientific technology, new research methods have emerged in brain science, where the methods are based primarily on acquired brain signals and brain information. The acquisition of brain signals includes an Electroencephalogram (EEG) technique for acquiring scalp electroencephalograms, an electrocorticogram (ECoC) technique for acquiring cortex electroencephalograms, an MEG (magnetoencephalogram) technique for acquiring magnetoencephalogram signals, an Event-related potential (ERP) technique, and the like. Electroencephalography uses noninvasive EEG electrodes to record the change in potential at different cranial brain locations. The electrical map technology of the cerebral cortex is deeper, and ECoC electrodes arranged in the cerebral cortex are adopted to collect potential changes of deep cortex activity. Although the electroencephalogram technology is more accurate, the technology is an invasive brain signal acquisition technology. The magnetoencephalogram technique measures the extremely weak magnetoencephalo-magnetic wave in the cranium by using a particularly sensitive ultra-cold electromagnetic measuring device, so as to obtain the change of electric field distribution in the brain. Event-related potential technology belongs to an evoked potential method, and the principle of the method is that when a sensory afferent system of a human is stimulated specially, a special potential is evoked in a central nervous system, and the evoked potential can explain a certain functional neural mechanism.

Another method of obtaining brain information is to directly image the human brain to observe the situation in which the living human brain processes speech information. The brain imaging techniques include Computed Tomography (CT), such as X-ray CT, ultrasound CT, gamma ray CT; positron Emission Computed Tomography (PET); single-photon Emission Computed Tomography (SPECT); magnetic Resonance Imaging (MRI); functional Magnetic Resonance Imaging (fMRI); near Infrared Spectroscopy (NIRS); functional Near Infrared Spectroscopy (fNIRS); cerebrovascular angiography; photoacoustic imaging (PAI); fast-functional photoacoustic microscopy (fast-functional RAM) and the like.

In addition to the above methods, there are also methods of testing the speech function of the cerebral hemisphere, for example, anesthetizing one cerebral hemisphere and studying the speech function of the other cerebral hemisphere; or the words are shown to the half-side visual field of the human by a quick-speed instrument to research the brain function of speech and vision; or providing voice language information for the two ears of the human, and researching the speech and hearing functions of the two hemispheres of the brain; or cutting off the grinding between the two hemispheres of the brain, researching the speech function of the two hemispheres of the schizont, and the like.

Due to the progress of brain signal and brain information acquisition technology and analysis technology, the research surface and depth in the field of neuro-linguistic is greatly expanded and the performance is very good. Two non-invasive and 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 of blood dynamics caused by neuron activity by using magnetic resonance imaging, namely, the brain work function imaging is realized by detecting the change of a magnetic field of blood entering brain cells. As shown in fig. 1, a schematic structural diagram of an 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 of which is a static magnetic field and is also called a main magnetic field; the other is a gradient field (gradient coils). Most current devices use superconducting magnets to generate the main magnetic field at a strength of 0.2T-7.0T, commonly 1.5T and 3.0T, and further shim coils (shim coils) to assist the main magnetic field to achieve high homogeneity. Spatial encoding of NMR signals is achieved using gradient field coils for generating and controlling gradients in the magnetic field to generate gradient fields. The gradient magnetic field generating device comprises three groups of coil groups, gradient fields in x, y and z directions are generated, and the gradient fields in any direction can be obtained by superposing the magnetic fields of the coil groups. The radio frequency system includes a Radio Frequency (RF) generator and a Radio Frequency (RF) receiver. The radio frequency generator is used for generating a short and strong radio frequency field which is applied to the sample in a pulse mode to enable the hydrogen nuclei in the sample to generate an NMR phenomenon. The radio frequency receiver is used for receiving NMR signals, and sending the signals to the computer image reconstruction system after amplification. The computer image reconstruction system converts the analog signal into digital signal via A/D converter, and processes the digital signal via computer according to the corresponding relation with each voxel in the observation layer to obtain image data, which is then applied to the image display via D/A converter to display the image in different grey scales according to NMR size. With the advance of technology, various improved techniques for solving the problems of increasing imaging speed and increasing spatial resolution have also appeared. For example, the magnetic resonance signals are represented by the product sum of the time basis function and the space basis function which are independent of each other 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 after nuclear magnetic excitation is performed once, gradient magnetic fields are sequentially reversed at a high speed, and a plurality of echo signals are sequentially collected is employed to solve the problem of image distortion due to image position shift, so that fMRI can be better applied to brain function studies.

The fNIRS technology is based on that the brain nerve activity can cause local hemodynamic changes, and the hemodynamic activity information of cerebral cortex is directly detected in real time by utilizing the difference characteristic of the oxyhemoglobin and the deoxyhemoglobin in brain tissues on the absorption rate of near infrared light with the wavelength in the range of 600-900 nm. The change condition of the hemodynamic activity information can be used for reversely deducing the mental activity condition of the brain. The apparatus involved may include a fNIRS detector, a light source, a probe head, and a computer system. The fNIRS detectors include a forehead region detector and a whole brain detector. The light source and the detector are arranged on the probe and are in contact with the tested organism, such as the brain, and the probe is connected to the computer system through a wire. The computer system sends out control signals to control the light source to be on or off, the detector inputs the measurement data into the computer system, and brain function images are obtained through AD conversion, processing and the like of the signals. With the improvement of hardware equipment in manufacturing and the improvement of a data processing method, the fNIRS technology provides a powerful monitoring means for monitoring brain activities.

2016, 4, 27 th, nature (Nature) journal published a first article by Alexander, huth, which discloses a semantic map of the cerebral cortex of tiled human beings (Natural speech), and disclosed an important research result. Huth et al used brain imaging techniques to map a semantic map of the brain from which it can be clearly seen how different regions of the brain represent 985 common english words and their meanings.

FIG. 2 is a schematic diagram of a morpheme-based model disclosed in Huth et al. Huth et al measured Brain BOLD (Brain Blood-Oxygen Level dependency) feedback during a story presented in 2 hours natural language by seven subjects using fMRI imaging. Each Word (Word) is projected into a 985-dimensional space created based on statistics of Word co-occurrences. The morpheme-wise model reflects how the appearance of a word affects BOLD feedback. FIG. 3 is a schematic diagram of principal component analysis of semantic models in terms of morphemes disclosed in Huth et al. The principal component analysis reflects 4 important semantic dimensions in the brain, and a color map is obtained after RGB coloring. The work of Huth et al leads to several important conclusions: first, there is a clear association of words with the distribution of semantically selected regions in the cerebral cortex. On the other hand, the distribution of the semantic selection regions corresponding to the same word is highly uniform among different individuals.

On the basis of the basic research results, people obtain further research results and have deeper understanding on languages. Natural Communications (Nature Communications) published at 20/4/2020 entitled Connecting concepts in the brain by mapping cortical representations of semantic relationships (Connecting concepts in the brain by mapping diagnostic representations) an article. Through a morpheme-aspect predictive model and brain imaging technology similar to Huth et al, the article concludes: semantic classes and relationships are 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 the understanding of the higher functions of the brain to a new stage. As suggested by nam joimsky (Noam Cginsky), entitled "language, thought, and brain" in nature journal, 9/18 2017, language should not be equated with "speech" or "communication," but rather should be best described as a biologically defined computational cognitive mechanism. As proved by the research of the inventor of the present application, with this determined cognitive mechanism, the language can be made a tool to generate specific physiological feedback in the brain, thereby improving the mental intelligence of human, promoting the transition of thinking and promoting the progress of culture.

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

As used herein, a "concept" refers to a unit of mental activity having a defined meaning. For example, a concept may be a word (character) or a word (word) in Chinese; or an english word (word) or phrase (phrase). However, strokes of chinese characters, foreign letters, japanese katakana, etc. that do not have specific meanings cannot be units of thought activities, which is not the concept referred to herein. As found, there is a clear association of concepts herein with language. Concepts represented by different languages may have the same meaning; however, in most cases, the meanings of both are different. For example, the "table" in chinese and the "table" in english are not consistent in terms of extension of concept, and thus can be considered as different concepts. In the case of foreign language, for example, "computer" and "computer" have very small differences and can be regarded as the same concept. In some embodiments, it is easier for people in the same language to develop similar feedback in the brain, as concepts under different languages are very different.

Similar concepts may form similar feedback in the brain. A subset of concepts can be derived based on existing linguistic classifications and statistically the frequency with which the same concept occurs at the same time. A number of representative concepts are included in this subset of 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 in terms of morphemes of the subset. In other words, the subset is capable of expressing other concepts that are not present 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 concepts that can be quickly processed.

Further, the subset of representative concepts can also be categorized into different subsets of experience based on the expressed experience. "experience" herein refers to feedback that develops in the brain due to experience, which may be fixed feedback developed from multiple practices; or may be a single feedback resulting from a single experience. Experience includes, but is not limited to, relaxation, calm, confidence, pleasure, satisfaction, brave, healthy, excited, successful, beautiful, and the like. The subset of experiences formed from the sorted representative concepts can more easily form feedback in the brain corresponding to the experience that it represents.

As used herein, "feedback" in the brain refers to the response in the cerebral cortex to external stimuli. As is known, under external stimulation, different areas in the cerebral cortex are active and further cause a change in the metabolism 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 different multiple regions in the cerebral cortex are active. Even if a person is asleep, many areas of the cerebral cortex are still active, although the areas that are active are reduced compared to awake. If changes in multiple areas of the cortex that are active over time are examined, such changes are indicative of a change in the activation pattern of the cortex.

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

Further, since the duration of the activation pattern is limited, the cerebral cortex experiences a number of different activation patterns over a period of time, such as ten seconds. Thus, changes in the active areas of the cortex during that time period can be sliced over time (e.g., 100 milliseconds) to treat the active areas of the cortex as an activation pattern for each time slice. The activation patterns in adjacent time slices may be a continuation of the same activation pattern and, therefore, are not independent of each other. Alternatively, the activation patterns in adjacent time slices are different activation patterns, but independent of each other. Therefore, by the independence between the activation patterns in the respective time slices, the change in the activation pattern of the cerebral cortex during that time period can be derived.

As used herein, "desired feedback" refers to forming feedback in the brain that is obtained by simulating a target feedback. For the target feedback, the analog feedback will be as consistent as possible with the target feedback, although it is not necessary that the two be completely consistent. There may be a plurality of similar activation patterns and/or temporal variations in activation patterns between the target feedback and the desired feedback. The change in the activation pattern over time includes sequence, duration, interval, etc. In some embodiments, the target 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 a desired experience from another cerebral cortex into the cerebral cortex of the subject through simulation of the target feedback.

As used herein, "perceived by the brain in a natural manner" refers to the manner of perception by the human body itself, including: auditory, visual, tactile, olfactory and gustatory, which input concepts from the outside into the brain. For example, listening to a piece of text, reading a piece of text, perceiving a piece of text in braille by the sense of touch, smelling a certain smell or tasting a certain taste. As understood, "perceived by the brain in a natural manner" does not include communicating with the brain in an invasive or non-invasive manner, such as by acupuncture, electrical shock, surgery, and the like. For brain-computer interfaces, it is also within the scope of this term if they communicate with the brain in the same way as the human body perceives itself. On the other hand, if the brain-computer interface employs a non-human body's own sensing means, such as applying a voltage across an electrode in a certain region 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 can be more efficient, less side-effects, and more acceptable.

As used herein, "brain keyboard" refers to an input device that includes a plurality of keys. At least one or more of the keys corresponds to one or more concepts. In some embodiments, the plurality of keys correspond to a plurality of concepts of the representative subset of concepts. In other embodiments, the plurality of keys correspond to a plurality of concepts of the experience subset. The keys may be a plurality of physical keys or a plurality of virtual keys on the input interface. When a key of the brain keyboard is "pressed," the concept corresponding to the key is entered. Multiple keys are "pressed" in succession to obtain a sequence having a length of time. The sequence includes the one or more concepts of key input by the brain keyboard over a plurality of periods of the length of time. Thus, in some embodiments, a brain keyboard may be considered 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, speaker, etc., to become a device capable of interacting with the user and the subject's brain.

As used herein, "psychological disorders" refers to disorders that deviate from the normal social lifestyle norms in thinking, emotion, and behavior. Psychological disorders non-exclusively include: depression, major depression, treatment resistant depression and treatment resistant bipolar depression, bipolar disorder, seasonal affective disorder, mood disorder, chronic depression, psychotic depression, postpartum depression, premenstrual dysphoric disorder (PMDD), situational depression, atypical depression, mania, anxiety disorders, attention Deficit Disorder (ADD), attention deficit disorder with hyperactivity (ADDH) 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 pseudobulbus.

As used herein, "psychiatric disorders" refers to disorders in which the cognition, emotion, will or behavior is impaired as a result of a dysfunction in the brain. Non-exhaustive list of psychiatric disorders includes: one or more of 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 mental state" refers to a mental state of abnormality in the appearance of cognition, emotion, will, or behavior that does not have a pathological basis. Non-disease mental disorder states non-exclusively include: lack of one or more of confidence, timid, sensitivity, distraction, weak will, compulsive behavior, fear of examination, fear of speech.

The present invention aims to realize the transplantation of experience in another brain into the brain by simulating feedback in the other brain by means of which concepts are perceived by the brain in a natural manner. After perceiving the same concept, the high consistency of feedback developed between different human individuals becomes the basis for experience transplantation in a natural way; deep learning neural network models make such simulations realistic. Although the feedback formed by simulation will not be exactly the same as the target feedback, similar experience can be formed in the brain as well. This low cost, non-injurious and well-accepted transplantation of experience in the brain will undoubtedly be a significant advance in the field of brain science.

The technical solution of the present invention will be described in detail by specific examples below.

Fig. 1 is a flow diagram of a method of generating feedback in the brain, according to an embodiment of the present 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 the temporal change in the activation pattern in the cerebral cortex in another brain, which represents an experience. Such experiences include, but are not limited to, relaxation, calm, confidence, pleasure, satisfaction, brave, healthy, excited, successful, beautiful. Thus, determining the desired experience may also be understood as determining the target feedback being simulated. The desired feedback, although not necessarily exactly identical to the target feedback, may be as close to the target feedback as possible. In this way, a desired experience can be obtained in the brain.

For example, for a very tired person, simple rest (e.g., sleep) may not be able to relax. Because the brain is still running at high speed and still busy when resting. After returning from rest, the person does not feel relaxed and at some point feels more tired. The mind of the person cannot alter this experience and some have to resort to medication. For persons with this need, the desired feedback may be determined as a relaxation or a deep relaxation experience.

As yet another example, some people have severe examination phobia. Even if well learned, there is still no confidence before the exam and the exam performance can be severely impacted. Such test phobias are also difficult to change by the will of a person. For persons with this need, the desired feedback may be determined as a confident or exam confident experience.

Next, at step 120, a target feedback is determined based on the desired experience. As is understood, the target feedback is feedback developed in the brain of a person with a certain experience. In some embodiments, the target feedback is obtained by collecting feedback for a desired experience in the brain of an individual with the desired experience. For example, if a deep relaxation experience is desired, individuals with a deep relaxation experience may be selected as religious plutellers, and feedback for the desired experience may be selected as feedback developed in the brains after these individuals have performed religious activities such as meditation, prayer, etc. Feedback developed in the cerebral cortex over time can be recorded as targeted feedback by brain signal detection techniques. Of course, as will be appreciated, even with the experience of deep relaxation, different individuals may be selected, or feedback in the cerebral cortex after different activities of the individual may be selected as the target feedback. The invention is not limited in any way here.

As another example, if confident experiences are desired, individuals with confident experiences may be selected as athletes having superior athletic performance, and feedback for the desired experiences may be selected as feedback developed in the brain of these individuals over a period of time before they participate in their good game. For example, playing a recording or video prior to the game creates a stressful atmosphere prior to the game, causing the individuals to feel about to play in the game. Of course, as will be appreciated, even with a confident experience, different individuals may be selected, or feedback in the cerebral cortex after different activities of the individual may be selected as the target feedback. The invention is not limited in any way here.

As another example, if a calm experience is desired, individuals with calm experience may be selected as participants who have experienced multiple events, and feedback for the desired experience may be selected as feedback that the individuals develop in the brain for a period of time before learning that they are about to participate in an event. For example, an aircraft repair crew is notified that a component of an aircraft in flight is malfunctioning, which is a frequent occurrence and without serious consequences for the aircraft repair crew. Feedback developed in the brain after the flight crew learned the fault is recorded. Of course, as will be appreciated, even for a calm experience, different individuals may be selected, or feedback in the cerebral cortex after different activities of the individual may be selected as the target feedback. The invention is not limited in any way here.

There are many examples of this. Such examples illustrate that, in order to obtain targeted feedback of a desired experience, the desired experience of an individual may be reproduced by selecting individuals with the desired experience and those individuals. Feedback in the cerebral cortex of individuals with desired experience is recorded as target feedback before, during and after these activities. In some embodiments, the activity that reproduces the desired experience may be selected according to the application scenario of the experience being transplanted. The higher the similarity between the two, the better the effect that the experience of being transplanted can exert.

In some embodiments, the target feedback is recorded using fMRI data obtained from a scan of the brain with a functional magnetic resonance apparatus. However, due to the device limitations of fMRI, the selectable range of activities that reproduce the desired experience may be small, typically only through wearable devices, such as VR glasses, etc., such that the individual with the desired experience has a sensation of participating in the activities that reproduce the desired experience. MRI image data, CT image data, SEPECT image data, and PAI image data, like fMRI image data, all can impose a number of limitations on the ability to reproduce a desired experience.

In contrast, a wearable detection device for fNIRS image data obtained by a forehead region fNIRS detector, a whole brain fNIRS detector, or NIRS image data is more suitable for more complex activities. Of course, electroencephalography or magnetoencephalography data obtained by invasive or non-invasive electrodes, etc., have little limitation on complex activities because the brain does not need to be placed in a large apparatus.

In the following sections of the present invention, the technical solutions of the present invention are described taking fMRI data as an example. As will be appreciated, data obtained from other brain signals (e.g., magnetoencephalograms) or brain information detection techniques may also be used in the inventive arrangements. The invention is not limited in any way here.

Experience varies from individual to individual and experience with the same rendition of the activity may vary. Thus, even for feedback to the same experience, there may be large differences between individuals. In some embodiments, common features for the same experience between different individuals are obtained through deep learning neural network models, thereby reducing noise reflected in the target feedback due to differences between individuals.

Since fMRI can obtain three-dimensional images, a 3D convolutional neural network model (CNN) can be selected to learn the feedback of the cortex that one wishes to experience. To reduce the amount of computation, CNN using a two-dimensional image may also be selected. The fMRI data recorded after the individual with the desired experience reproduces the desired experience activity is used as a training sample. These human-related parameters are used as training parameters because the language, age, sex, religious beliefs, education, occupation, or once occupation among individuals have a great influence on the desired experience. In some embodiments, the construction of the neural network model is performed using Convolutional Sparse Coding (CSC). The convolution sparse coding is a method for unsupervised learning of linear convolution, and a model is simpler, more intuitive and easy to analyze and understand.

In some embodiments, for an individual who wishes to gain some experience, the individual's language, age, gender, religious beliefs, education, occupation, or once occupation is entered as a parameter into the trained neural network model. The neural network model outputs a target feedback that most closely matches the experience of the individual based on the training results. Therefore, the problems that training samples are few and accurate neural network models cannot be constructed can be solved, and the influence of individual difference among the training samples on target feedback can be reduced.

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

In some embodiments, a database of target feedback for individual experiences is established. The multiple target feedbacks in the database are categorized according to the different experiences targeted. Furthermore, parameters related to human, such as language, age, sex, religious belief, education level, occupation or ever occupation, can also be the basis for further classification of the target feedback. If parameters related to the person, such as language, age, sex, religious beliefs, education, occupation or ever occupation, are further considered, a higher degree of matching of the target feedback can be obtained. Further, the neural network model constructed as above can be used to update the target feedback stored in the database. Thus, a database is used to obtain a matching target feedback based on the desired feedback without using a trained neural network model to generate the latest target feedback each time. 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 active modes. Targeted feedback in the cortex includes temporal changes in the activation pattern in the cortex. For each instant of time, the activation pattern includes a spatial distribution of the active parts in the cerebral cortex. Thus, the target feedback can be thought of as a temporally varying sequence of 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 active modes comprises:

step S1301, slicing the target feedback by time. Each time slice is 10-50ms in length. 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 slice may not only result in a large amount of data to be processed and cause a huge amount of computation, but also may not match with the perception of natural modes and result in a large amount of interference data. Through the steps, a time slice set is obtained, 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 part, in each time slice is determined. In some embodiments, a representative activation pattern in the time slice is obtained as the activation pattern for the time slice. There are various ways to obtain a representative activation pattern, such as choosing the spatial distribution of the activated portions at the time instant in the time slice as the representative activation pattern; or, calculating the average spatial distribution of the overlapped activation parts at each moment in the time slice as a representative activation mode. Of course, other means may be applied thereto.

Step S1303, it is calculated whether the activation patterns of the cerebral cortex in adjacent time slices are independent from each other. If the activation patterns of the cortex in two adjacent time slices are dependent, indicating that the activation pattern lasts for at least two time slices, the two time slices are merged in step S1304, and if the activation patterns of the cortex in two time slices are independent of each other, the two activation patterns are recorded as different activation patterns in step S1305.

In step S1306, after determining whether all the adjacent time slices have been processed, if not, the foregoing steps are repeated until all the adjacent time slices are independent. If all neighboring time slices have been processed, the target feedback has now been divided into a number of active modes extending over an integer number of time slices. As shown in fig. 7, each time slice, e.g. B0001, B0002, has a certain time length, and the activation pattern of the time slice is different from the activation patterns of other time slices, i.e. another time slice set is obtained, wherein each time slice includes one activation pattern and has a corresponding time period, and all time slices in the set form a time-continuous sequence.

In some embodiments, the difference in the 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 for activation patterns of the cerebral cortex in adjacent time slices are calculated. The activation patterns in the two time slices are considered to be independent of each other if the correlation coefficient exceeds a predetermined threshold. Of course, other approaches may be applied here.

Taking fMRI data processing as an example, after time slicing is performed on fMRI data fed back by a target at certain intervals (e.g., 10-50 ms), active regions of cerebral cortex and signal intensities thereof at multiple moments can be obtained, wherein the signal intensities are reflected as the shades of the active regions. The variation of the plurality of activation patterns over time can be determined from an analysis of the independence of the activation patterns at a plurality of times. Thereby, the target feedback is divided into a plurality of time periods, each time period corresponding to one active 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 cerebral cortex has high consistency with the response activation region of the same concept and the distribution and signal intensity of the cerebral cortex among different individuals; for different concepts, the signal strength varies in response to different distributions of activation regions.

In some embodiments, the distribution of activation regions in the cerebral cortex and their signal strength corresponding to each concept in a group comprising a plurality of concepts is obtained. The distribution of the activation region in the cerebral cortex corresponding to a concept and its signal intensity are defined as the "concept pattern" corresponding to the concept. In some embodiments, a concept pattern database is established storing concept patterns for a plurality of concepts. Take fMRI data as an example, by recording feedback fMRI data that forms in the cerebral cortex when the subject listens to a piece of textual content expressed in natural language. By performing data processing to associate fMRI data with the concept in the text, a concept pattern reflected by fMRI data corresponding to the concept can be obtained. These concepts and corresponding concept patterns are stored, whereby a concept pattern database can be built. It will be appreciated by those skilled in the art that other types of brain signals or data from brain detection techniques can be used to build the conceptual pattern database. In other ways, the concept pattern database can be obtained by associating the concept with the concept pattern corresponding to the concept. The invention is not limited in any way here.

Correspondingly, from an activation pattern comprising the activation areas and their signal strengths within the time length of the time slice, as shown in fig. 8, a conceptual pattern corresponding thereto, such as conceptual pattern 1, may be determined from the activation areas and their distribution and signal strengths. Then, the concept pattern 1 is compared 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 activation pattern corresponding to the concept combinations is a superposition of one or more concept patterns in the concept combinations, and the activation pattern of the concept combinations is close to the activation pattern of the target feedback. Thereby, one activation pattern in the target feedback is corresponding to or 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) may be selected to identify concept combinations. Other neural network models for pattern recognition, such as a Deep Belief Network (DBN) model, a Recurrent Neural Network (RNN) model, etc., may also be applied thereto.

In some embodiments, taking fMRI data as an example, two or more different words (corresponding to different concepts) are read out of the subject and fMRI data fed back in the cerebral cortex of the subject is recorded. And (3) training the neural network model by using a data set formed by different words and corresponding fMRI data as a training set. The trained neural network model takes fMRI data as input, outputs a plurality of different concept combinations, and the plurality of different concept combinations are ranked from high to low in matching degree with the fMRI data as input. A plurality of concept combinations corresponding to one activation pattern in the target feedback can be obtained by using the trained neural network model. In some embodiments, only the concept combinations with the highest degree of match are typically used. When the matching degrees of the concept combinations are not large, the concept combinations are reserved for later selective use.

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

In some embodiments, the criteria for selecting the concept combination sequence may be varied. For example, a selection may be made based on a desired correspondence between experience and a combination of concepts, and concepts that are not frequently found in a certain experience may be eliminated. For example, if the desired experience is "calm", then a combination of concepts, including concepts such as "fierce", etc., is to be chosen as little as possible. For another example, the concept combinations may be selected according to the association relationship between the concept combinations, and the concept combinations with relatively poor association or difficult association may be eliminated. For example, if the concept combinations before and after the sequence are all related to "flying", the concept combination in the middle is also selected to be related to "flying". For another example, the selection may be performed according to the overall theme of the concept combination, and the concept combination with poor relevance is eliminated. For example, concept combinations in the sequence are all related to the sea, and when a concept combination related to the sea can be selected, other concept combinations can be eliminated. In some embodiments, if there are multiple better combinations of concepts that are difficult to trade off, they may all be retained for use in subsequent steps.

At step 160, a segment of text is formed according to a conceptual combination of the time of occurrence and the duration of the time segment.

As described above, the combination of concepts over different time periods within a time length forms a sequence. The sequence comprises a plurality of concepts having temporal attributes, one of said temporal attributes being a duration of the concept, different concepts differing in duration in the sequence. 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 that is desired in the brain. Thus, in a text segment formed by combining concepts, each concept has a certain starting time and duration. This is also referred to as the temporal nature of the concept in the sequence.

For example, in order to evoke the experience of "confidence" of a player, two concepts of "applause" and "take-off" appear in the concept combination. The time duration of the two may be different depending on the time slice determined by the activation mode. For example, the duration of the "applause" is greater than the duration of the "take-off". In a more specific example, between the two concepts of "graceful" and "landing," the starting time of "graceful" is 2 seconds from the beginning of the sequence playing, and the duration is 0.5 seconds; and the starting time of landing is 2.5 seconds of the beginning of the sequence playing and the duration is 0.2 seconds.

In step 170, words are added to the segment of words to form content with coherent semantics. Since the perception is in a natural way, the effect obtained in a way more favorable for the brain to accept is also better, so that it is necessary to form coherent semantics, and in some embodiments, to form a certain theme, which is a more favorable technical solution.

In some embodiments, auxiliary words or the like are added to the concept combinations to have coherent semantics. For example, concepts are mapped to one or more phrases (or phrases). For example, "graceful" and "jump up" are combined into "gracefully jumping up"; the 'dancing' and the 'sprite' are combined into a phrase 'dancing sprite' and the like. Two words of the same nature can also be directly juxtaposed if they are in a conceptual combination. For example, "brave" and "strong" are combined directly into "strong brave". If the concept combines two words far apart, the combination can be based on the attributes of the words only. Although difficult to understand, the semantics are still coherent. For example, "red" and "sit down" are combined directly by adjective into "red sit down" behind the front entity word; the temple and the flying are directly combined into the flying temple according to terms.

In some embodiments, adverbs, prepositions, etc. are added in a contextual concept combination to have coherent semantics. There may be a large difference between the concept combinations before and after, so that adverbs, prepositions, etc. need to be added to make the speech coherent. For example, if the preceding combination of concepts is "applause onset" and the following combination of concepts is "falls gracefully," then the modified contextual semantics may be "falls gracefully as applause commences.

In some embodiments, a small number of entity words are added in a contextual concept combination to have a coherent semantic meaning. The difference between the concept combinations may be large, so that physical words and the like need to be added to make the voice coherent. For example, let 'sit down' red,When the flying spirit and the flying temple are combined, the modification can be made "In thatRed colourInTo sit down,see through"flying fairy" and "flying temple"; where "in" and "is an added preposition 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 as possible in the sequence to minimize the impact on feedback of the original sequence definition. Also, the added entity words should be as common, familiar, as common actions or things as possible.

In some embodiments, the text may be modified by manual proofing. In particular, the text may be modified to be as close as possible to the subject matter associated with the desired experience.

In step 180, the time of day and/or duration of the occurrence of one or more concepts within the time period is adjusted based on the content having coherent semantics. In some cases, a text may sound odd in rhythm if multiple concepts defined purely in terms of targeted feedback occur at the time and/or for the duration of the time period, affecting the feedback effect in the brain. Thus, in some embodiments, it may be desirable to adjust the time of day and/or duration that one or more concepts appear within the time period such that the entire text sounds more natural. However, the adjustment cannot exceed a predetermined range, or otherwise the feedback in the cerebral cortex may be altered, failing to achieve the effect of the replication experience. In general, the start time is adjusted by no more than 20ms and the duration is adjusted by no more than 50ms. Therefore, the method is favorable for adjusting the reading rhythm of the characters and cannot excessively deviate from the target feedback.

After adjustment, a defined sequence will be obtained. A plurality of concepts are included in the sequence, each concept having its determined start time and duration. Such text is suitable for human or machine reading or otherwise natural perception by the brain.

In 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. auditory, visual, tactile, so that feedback may be generated in the brain. For example, the sequence is played in a voice output device, or presented in video, or braille. In a natural way, the brain is able to perceive a number of concepts in the sequence at defined moments in time and durations and to generate the desired feedback at the brain. The desired feedback is a simulation of the conceptual combined sequence approach of the target feedback, enabling the transfer of the target experience from one human individual to another.

It is envisioned that the methods of the invention can be used in methods of treating or preventing a psychological or psychiatric disorder, including: depression, macrodepression, treatment resistant depression and treatment resistant bipolar depression, bipolar disorder, seasonal affective disorder, mood disorder, chronic depression, psychotic depression, postpartum depression, premenstrual dysphoric disorder (PMDD), situational depression, atypical depression, mania, anxiety, attention Deficit Disorder (ADD), attention deficit disorder with hyperactivity (ADDH) 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 diseases include: one or more of 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, there is also provided a system for generating feedback in the brain. Fig. 10 is a functional block diagram of a system for generating feedback in the brain according to one embodiment of the present invention. As shown in fig. 10, the system comprises a brain keyboard 1 and a transmission device 2, the brain keyboard 1 is configured to generate a concept sequence with a time length, wherein the one or more concepts are included in a plurality of periods of the time length; the transmission device 2 is configured to send the sequence to the brain of the subject over the length of time, where it produces the desired feedback.

The brain keyboard 1 comprises the keyboard 11 and a processor 12. The keyboard 11 includes a plurality of keys, at least one or more of which corresponds 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, whose display interface has a plurality of keys for a user to input one or more concepts. In one embodiment, the keyboard further comprises keys having a time duration. When a concept is input, corresponding time, such as starting time and duration, can also be input simultaneously. The processor 12 is connected to the keyboard 11, receives key operations from the keyboard, and forms a conceptual sequence having a time duration according to the key operations. Said processor 12 is connected to the transmission means 2, and the transmission means 2 receives said concept sequence generated by said processor 12 and transmits it to a user, so that said concept sequence is perceived by the user's brain in a natural way 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, auditory or tactile information, such as a display, which converts the sequence of concepts into video information, so that the user's brain perceives the sequence of concepts in the video information. Alternatively, the transmitting device 2 is an audio playing device, which converts the concept sequence into audio information, especially voice information, so that the user feels the concept sequence in the audio information or the voice information. In another way, the conveyor 2 can be made as a braille reader that can be read by the blind, by means of which the blind user can perceive the sequence of concepts and thus produce the desired feedback in his brain. A switch is provided in the conveying device 2 to control the timing of the emission of the concept sequence.

Fig. 11 is a functional block diagram of a system for generating feedback in the brain according to another embodiment of the present invention. On the basis of fig. 10, the present embodiment further includes a data center 3 and a sample processing system 4, where the data center 3 stores various data, such as brain feedback sample data and corresponding concept sequences, and various sample data, such as cortical data from different people and different experiences. The data center 3 includes one or more databases, such as a database storing cortical data from various experienced persons and corresponding parameters associated with the persons, a conceptual pattern database, and the like. Cortical data for each experienced person may be multiple persons, multiple types of data. The data is obtained in various ways when people acquire their brain information with some experience. Including but not limited to relaxation, calm, confidence, pleasure, satisfaction, brave, healthy, excited, successful, beautiful. The parameters related to the person include, but are not limited to, language, age, gender, religious beliefs, education, occupation or ever occupation, etc. The cortical 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 magnetoencephalogram data, and 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 cortical data obtained from different people in the same experience so as to obtain the change of an activation pattern in a cortical brain in time; the time-varying data form of the activation pattern differs depending on the original data. The pattern recognition model module 42 is configured to compare the change of the activation pattern of the cortex in the time period with the activation patterns of the concepts in the cortex to obtain one or more concepts corresponding to the change of the activation pattern in the cortex in time, and to obtain a concept sequence with a time length according to the occurrence time and duration of the concepts.

The model used by the two model modules in this embodiment may be a combination of one or more of a neural network CNN model, a deep belief network DBN model, and a recurrent neural network RNN model, or may be a model of other various types, which adopts various other algorithms.

According to another aspect of the present invention, there is provided a method for treating or preventing a psychological or psychiatric disorder which can be treated or prevented using the aforementioned method of generating feedback in the brain using the system provided in fig. 10 or 11. The psychological diseases comprise: depression, major depression, treatment resistant depression and treatment resistant bipolar depression, bipolar disorder, seasonal affective disorder, mood disorder, chronic depression, psychotic depression, postpartum depression, premenstrual dysphoric disorder (PMDD), situational depression, atypical depression, mania, anxiety disorders, attention Deficit Disorder (ADD), attention deficit disorder with hyperactivity (ADDH) 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 diseases include: schizophrenia, schizoaffective disorder, bipolar disorder, obsessive-compulsive disorder, parkinson's disease, contra-depressive disorder, charles Bonnet syndrome, autism and Tourette's disease.

According to another aspect of the present invention, there is provided a system for generating feedback in the brain, 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 in a plurality of periods of said length of time; the transmission device 2a is configured to cause the sequence to be perceived by the brain of the subject in a natural manner over the length of time, producing the desired feedback in the brain of the subject. In some embodiments, the production apparatus 1a includes a target feedback determination apparatus 11a and a target feedback analysis apparatus 12a. The target feedback determination means 11a determines the target feedback based on experience to be obtained by the subject. Including but not limited to relaxation, calm, confidence, pleasure, satisfaction, brave, healthy, excited, successful, beautiful, etc. In some embodiments, a target feedback database 16a is included in the production device 1a, which includes target feedback data corresponding to any of the aforementioned experiences. For example, for deep relaxation experience, the target feedback database 16a stores therein feedback data formed in the brain of a religious discipline sincere who performed religious activities such as meditation, prayer, etc. The feedback data is feedback data formed in the cerebral cortex over a period of time recorded by a brain signal detection technique. For example, for a confident experience, the goal feedback database 16a records feedback data that is developed in the brain of an excellent athlete for a period of time before the athlete participates in a good game. The feedback data includes, but is not limited to, MRI image data, fMRI image data, CT image data, sepict 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 a target feedback that most closely matches the experience of an individual (subject) in inputting the individual's human-related parameters and experience based on the results of the training. Wherein the human-related parameter comprises the individual's language, age, gender, religious belief, educational level, occupation, or past professional, and the experience comprises any of the foregoing relaxation, calmness, confidence, pleasure, satisfaction, branchiness, health, excitement, success, beauty, and the like.

In some embodiments, target feedback analysis device 12a includes a target feedback time slicing module 120a and an activation pattern association analysis module 121a. The target feedback determination means 11a sends the target feedback to the target feedback analysis means 12a after obtaining the target feedback. The target feedback time slicing module 120a of the target feedback analysis device 12a acquires target feedback data, and slices the target feedback data according to a certain time period to obtain a plurality of time slices with the same time period, as shown in fig. 6. Wherein the time slicing period must not be too large or too small, and in one embodiment, is preferably 10-50ms. The activation pattern correlation analysis module 121a performs correlation analysis of the activation patterns for each time slice, for example, determines the activation pattern of the cortex in each time slice, the activation pattern includes the spatial distribution and signal strength of the activated portion, and then calculates whether the activation patterns of the cortex in adjacent time slices are independent from each other. For example, whether the spatial distribution of the activated portions of the cerebral cortex in two time slices is the same or 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 activated portions are determined to be not independent, otherwise, the two activated portions are independent. After the correlation analysis of the activation patterns, a plurality of corresponding time slices are obtained, and each time slice corresponds to one activation pattern, 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 production apparatus 1a includes a concept pattern database 17a in which feedback data formed in the brain by a combination of one or more concepts, i.e., distribution of activation regions in the cerebral cortex and signal intensity thereof, is stored. The concept combination recognition module 122a includes a concept recognition neural network model therein. The neural network model inputs an activation pattern of target feedback according to the training result, such as data corresponding to a time slice in fig. 7, and outputs one or more concept combinations matched with the activation pattern.

In some embodiments, the production device 1a further comprises a semantics module 13a configured to modify the sequence of concepts to form coherent semantics. The concept combination recognition module 122a obtains a chronological concept combination, each concept having time attributes such as time and duration, and adds additional words such as auxiliary words, prepositions, adverbs, real words, etc. to the concept combination to link the semantics of the concept combination for better brain acceptance and desired effect. For example, the concept "listen" or "rippled" is augmented with "please" or "rippled" to obtain the text "please listen to the rippled running water" with coherent semantics. The time instants and/or durations at which the plurality of concepts occur within the time period are adjusted after adding the additional vocabulary. For example, adding "please" to the concept of "listen" and delaying the start time of "listen" makes the duration period of "please listen" adjusted the same as the duration period of "listen" before adjustment. The semantic module 13a processes the concept sequence to obtain words with coherent semantics.

In some embodiments, the delivery device 2a comprises one or more devices or combinations thereof directed to one or more of auditory, visual or tactile, so that the brain perceives the sequence in a natural way, and correspondingly the system further comprises a conversion device 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 used to convert text with coherent semantics representing an empirical target feedback into corresponding audio information. For example, spoken voice information is spoken manually or by machine. Further, according to the desired experience, corresponding background music, such as Chinese and foreign classical music, various natural sounds, etc., can be configured for the voice information. The video conversion module 31a selects a video or a plurality of videos corresponding to the text with coherent semantics in the material library according to the text which represents the target feedback of an experience, connects the plurality of videos 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 can convert text having a coherent semantic meaning representing a target feedback of experience into output braille. Of course, the conversion device 3a described above can also be integrated in the production device 1a as a conversion module of the production device 1a.

The transmission device 2a may be various, 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; the video playing device 21a for vision includes, but is not limited to, various displays, such as desktop computer displays, laptop computer displays; various mobile terminal display screens, such as a mobile phone display screen, a flat panel display screen and the like; various large screen displays; the braille reader 22a device for the tactile sense includes, but is not limited to, a printer for printing braille, an electronic braille reader, etc.

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 display, etc.

The conveyor 2a may also comprise professional and non-professional audiovisual rooms, studios. For example, the audiovisual room may be a professional or home audiovisual room including an audio playing device, a video playing device, a signal source device, etc., and the production apparatus 1a transmits the converted audiovisual information to the signal source device of the video room, so that the subject can sense the concepts in the sequence in an auditory and visual manner in the audiovisual room, thereby generating the desired feedback in his brain and obtaining the desired experience. The studio may be a professional or non-professional studio that includes cameras (e.g., full-line or digital back), lenses, lights, curtains, background props, etc. In the studio, the subject is caused to perceive the concepts in the sequence in an audible, visual manner by way of an actor performing, thereby producing the desired feedback in his brain and obtaining the desired experience.

In some embodiments, the system for generating feedback in the brain further comprises a storage means 4a, as shown in the system block diagram of fig. 14. The storage 4a is configured to store brain feedback sample data and corresponding concept 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 production apparatus 1a obtains the required data from the cloud database through any one of the existing communication mechanisms when necessary, so that the local storage space can be saved. In some embodiments, some modules in the production apparatus 1a, such as the aforementioned target feedback analysis apparatus, conversion apparatus, etc., may be placed in the cloud, and the communication power may be calculated by using the strong cloud to obtain the required audio and video information that can be executed by the specific transmission apparatus.

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

According to another aspect of the present invention, there is provided a brain keyboard, 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 of the 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 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 a desired feedback in the brain.

The following examples are intended to illustrate the effects of the present invention.

Application embodiment 1

The sequence of "deep relaxation" obtained in the manner of the present invention was repeated in voice for 13 minutes using a speaker to the subject by using the brain feedback after budding belief meditation as the target feedback of the "deep relaxation" experience. Determining whether the desired feedback is formed in a subject deep relaxation assessment metric; wherein the evaluation indexes comprise physical fatigue recovery capacity, sleep quality and deep relaxation ratio of brain wave presentation.

Two runs were performed:

the first round of testing:

a deep relaxation concept sequence was live for 13 minutes at a fixed time each day, live for 7 consecutive days, and questionnaires were issued to the subjects to assess their physical fatigue recovery abilities, in an audio manner. A total of 120 available questionnaires were harvested at the end of 7 days. The male-female sex ratio of the subjects was 5:1;77% of subjects are adults between 30-50 years of age; the academic calendars account for 40 percent, wherein 6 Master academic calendars and 2 doctor academic calendars are included; first-line urban users account for 21.67%.

And (3) testing for the second round:

42 persons were selected to participate in the test for a total of 14 days. 32 into the target test group; of these, 17 people tested about 40 minutes (three plays in succession) of deep relaxation sequence audio per day, and another 15 people tested about 80 minutes (six plays in succession) of deep relaxation sequence audio per day. There were another 10 experienced meditatins as reference groups. The reference group of 10 persons underwent 40 minutes meditation relaxation each day with their own habitual method.

During the second round of testing, 42 participants recorded sleep data with snail sleep software each day and tested 4 minutes of brain waves each day after the test using the brain Lite smart head ring. As will be appreciated by those skilled in the art, the snail sleep software is an intelligent sleep monitoring software manufactured by seebo dragon technology (beijing) limited. The software uses deep learning algorithm to learn measurement by special sleep monitoring equipment PSG (polysomnography), and the accuracy and correctness of the measurement data approach scientific research and clinical measurement results. Brain link Lite is an intelligent electroencephalogram EEG acquisition device produced by Intelligent head Ring Shenzhen Intelligence science and technology, inc., which is a 512-sampling-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 rate their own fatigue recovery capacity on 10 grades of the fatigue recovery capacity from 1 to 10, both before and on the seventh day of the test. The average score calculated from 120 data is counted, and as shown in fig. 15, it is a schematic diagram of the average score of the physical fatigue recovery capability assessment according to an embodiment of the present invention. The average score increased from 5.53 before the test to 7.41 on the seventh day, which was 34.00%.

Four statistics were performed during the second round of 14 days of testing, and fig. 16 is a graph showing the mean scores of the test groups evaluated for physical fatigue recovery at 4 statistics. As can be seen from the figure, after the first day of testing, the fatigue recovery capacity of the body of 32 test group members is increased from the previous average 4.50 points to 5.19 points, which is increased by 15.33%; the physical fatigue recovery capacity after 7 days is increased from the previous 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 the previous 4.50 minutes to 7.63 minutes, and is improved by 69.56 percent.

Fig. 17 is the average score of the evaluation of the recovery ability from physical fatigue of the reference group at 4 statistics in the second round of test. The ratio of 5.2 after meditation on the first day to 5.5 after meditation on the previous day is increased by 5.77%; after 7 continuous days, the physical fatigue recovery capacity is increased from the previous 5.2 to 6.1, which is improved by 17.31 percent; after 14 consecutive days, the physical fatigue recovery capacity rose from the previous 5.2 to 6.8, which was 30.77% higher.

By comparing the data of the target test group and the data of the reference group, the recovery capability of the target test group to the body fatigue is improved more obviously. Since the individuals of the target test group do not have experience in meditation deep relaxation, it is highly surprising that the sequence of deep relaxations from meditation individuals that enables the generation of physical fatigue recovery data that exceeds that of the experienced meditation through the present invention.

Deep relaxation proportion index:

fig. 18 is a schematic diagram of a mean depth relaxation fraction of brain waves calculated from brain wave data of a target test group. The mean depth relaxation of brain waves on the day before the test was 7.53%; the mean depth relaxation of brain waves after the test on the first day is 10.75%; day 7 was 11.66%. The deep relaxation of brain waves tends to be higher and higher over time than it is in general.

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

By comparing the two groups of brain wave data, the brain of the subject is relaxed more and the effect is better. Brain wave data is the most direct data reflecting deep relaxation. The brain wave data obtained by applying the method of the invention reflects the successful transplantation of the deep relaxation experience of meditation. Moreover, the increasing trend indicates that this experience is becoming more and more an experience of the subject himself as the method of the invention is applied multiple times to form feedback in the brain.

The sleep quality index is as follows:

fig. 20 is a graph of the sleep quality score of 120 people in the first round of testing. The 7-day sleep quality score increased from the previous average of 5.8 points to an average of 7.58 points by 30.69%.

Fig. 21 is a graph showing the score of the quality of sleep of 32 persons in the target test group in the second round of testing. The sleep quality of the first day before the test is started is averagely divided into 5.25 minutes, and the sleep quality of the first day after the test is improved to 5.88 minutes, which is improved by 12.00 percent; after 7 days, the average score is increased to 6.75, which is increased by 28.57%; after 14 days, the average score is increased to 7.5, and the average score is increased by 42.86%. A trend of gradually increasing sleep quality of the target test group over time.

Fig. 22 is a graph showing the sleep quality score of 10 persons in the reference group in the second round of test. The average sleep quality before meditation is 6.2 minutes, and the sleep quality after meditation on the first day is improved to 6.5 minutes, which is improved by 4.84 percent; after 7 days, the average score is increased to 6.5, which is increased by 16.13%; after 14 days, the average score was increased to 7.4, which is 19.35%.

By comparing the sleep quality scores 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. The quality of sleep reflects on the one hand the degree of deep relaxation and on the other hand the overall changes to the subject after the experience transfer. Because of the wide range of sleep problems, it is very common for subjects to have sleep problems, and it is one of the reasons for their willingness to participate in this test. The utility of the present method reflects that such empirical transfer is not short-term and evanescent, but can be altered for the subject as a whole. This is almost the same as the subject itself gained similar experience. Further, such global changes are also clear for the effect of treating and preventing psychological or psychiatric disorders in a subject.

Application example two

The brain feedback of the pilot listening to the aircraft in the cabin after taking off and recording is used as the target feedback of the 'calm' experience, the 'calm' experience sequence is sent to the subject in a voice mode by a loudspeaker, and the time length of the repeated playing sequence is 15 minutes. Desirable feedback is manifested in the ability of the subject to reduce anxiety, improve mental tranquility, and reduce physical problems. The reverse scoring value of the STAI spear anxiety scale is used as an evaluation index corresponding to the calmness and security degree; the severity score (reverse score) of the physical problem and the hill change score (forward score) value of the identity problem are used as indicators of the physical problem.

Two runs were performed:

the first round of testing:

a sequence of calm concepts was played in audio form for 15 minutes at a fixed time each day for 7 consecutive days, and questionnaires were issued to 120 subjects to assess their anxiety.

And (3) testing in a second round:

42 persons were selected to participate in the test for a total of 14 days. 32 enter target test group; of these, 17 persons were tested with a calm concept sequence audio of about 45 minutes (played three times in succession) per day, and another 15 persons were tested with a calm concept sequence audio of about 90 minutes (played six times in succession) per day. Another 10 experienced meditatins were used as reference group. The reference group of 10 persons underwent 40 minutes meditation relaxation each day with their own habitual method.

Indexes of calm and ease degree:

figure 23 is a graph of the STAI spear anxiety scale reverse score mean for 120 subjects in the first round of testing. After 7 consecutive days of testing, the degree of calm-quietness changed from the previous average of 2.63 points to 1.84 points, an improvement of 30.04%.

Figure 24 is the mean of the STAI spear anxiety scale reverse scores for subjects in the 32 target test groups in the second round of testing. After the test of the first day, the degree of calm and tranquility is changed from the previous 2.53 to 2.47, which is improved by 2.37%; after 7 days, the calmness and reassurance degree is changed from the previous average 2.53 points to 2.06 points, which is improved by 18.58 percent; after 14 days, the degree of calm and reassurance changed from the previous average 2.53 points to 1.70 points, an improvement of 32.81%. The degree of calm and reassurance shows a tendency to be higher and higher with time.

Figure 25 is a table of STAI spear anxiety values for 10 subjects in the reference group in the second round of testing, back-scored. The subjects in the reference group changed from 2.6 to 2.5 on the day before meditation, an improvement of 3.85%; after 7 days, the degree of calm and tranquility is changed from 2.6 to 2.4, which is improved by 7.69%; after 14 days, the degree of calm and reassurance changed from 2.6 to 2.3, an improvement of 11.54%.

By contrast, although the reference group also showed a tendency of increasing the degree of calm and reassurance with time, the target test group showed a higher degree of calm and reassurance than the reference group, and the calm and anxiety reduction effects were better.

Physical problem index:

fig. 26 is a graph showing the reverse score average of the physical problem assessment table of 120 subjects in the first round of testing. In the first round of testing, 120 subjects had completed the test and had a 41.12% improvement in the severity (reverse score) score of the physical problem from the previous 7.32 to 4.31.

Fig. 27 is a graph showing the mean positive scores of the physical problem improvement assessment table of 32 subjects in the target test group in the second round of testing. In the second round of testing, the physical problem improvement degree of 32 subjects in the target test group increased to 4.56 from 3.90 minutes on the day before the start of the test to 4.56 after the test on the first day, which is 16.92% higher; after 7 days, the temperature rises to 5.78 percent, which is increased by 48.21 percent; after 14 days, it rose to 7.59, a 94.12% increase.

Fig. 28 is a graph showing the mean positive scores of the physical problem improvement assessment tables of 10 subjects in the reference group in the second round of testing. The degree of improvement in physical problems in the subjects of the reference group increased from 4.8 points on the day before the start to 5.4 points on the first day, which was 12.50% higher; after 7 days, the temperature rises to 5.5 percent, which is improved by 14.58 percent; after 14 days, the increase was 6.4, which is a 33.33% increase.

By comparison, the method provided by the invention can be used for more effectively improving the physical problem. The significant improvement in anxiety psychology and physical problems due to anxiety illustrate the high efficacy of the methods of the present invention in treating psychological and psychiatric disorders. Moreover, 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 from occurring. 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 only for illustrating the present invention and not for limiting the present invention, and those skilled in the art can make various changes and modifications without departing from the scope of the present invention, and therefore, all equivalent technical solutions should fall within the scope of the present invention.

Claims (41)

1. A method of generating feedback in the brain, comprising:

   determining a sequence having a length of time, wherein the sequence includes one or more concepts (concepts) over a plurality of periods of the length of time; and

   the sequence is perceived by the brain in a natural manner over the length of time to produce a desired feedback (desired feedback) in the brain.
2. The method of claim 1, wherein the activation pattern comprises a spatial distribution of active parts in the cerebral cortex.
3. The method of claim 2, wherein the desired feedback includes temporal changes in activation patterns in the cerebral cortex.
4. The method of claim 1, wherein the desired feedback simulates temporal changes in activation patterns in another cerebral cortex.
5. The method of claim 4, wherein the temporal change in activation pattern in another cerebral cortex as a simulated subject represents an experience.
6. The method of claim 4, wherein the temporal changes in activation patterns in another cerebral cortex as a simulated subject are from a deep learning neural network model.
7. The method of claim 6, further comprising: cortical data obtained from different people at the same experience is processed by the deep learning neural network model to derive temporal changes in their activation patterns.
8. The method of claim 7, wherein the cortical data is one or more of fMRI data, MRI data, CT data, SEPECT data, NIRS data, fNIRS data, PAI data.
9. The method of claim 6, wherein the change in the activation pattern in the other cerebral cortex over time corresponds to a desired experience and a parameter associated with the person.
10. The method of claim 9, wherein the human-related parameters comprise: one or more of language, age, gender, religious beliefs, education, occupation, or once occupation.
11. The method of claim 9, wherein the desired experience comprises: one or more of relaxed, calm, confident, happy, satisfied, brave, healthy, excited, successful, beautiful.
12. The method of claim 4, further comprising: the temporal change of the activation pattern in the other cerebral cortex as a simulation object is divided into a plurality of time segments.
13. The method of claim 12, wherein the patterns of activation of the cerebral cortex are independent of each other between the plurality of time periods.
14. The method of claim 12, wherein the pattern of activation of the cerebral cortex during the one time period corresponds to one or more concepts.
15. The method of claim 14, further comprising: based on changes in the activation pattern of the cerebral cortex over the period of time, the times and durations of the occurrences of the one or more concepts over the period of time are obtained.
16. The method of claim 15, further comprising: by comparing the change in the activation pattern of the cortex during the period of time with the activation pattern of the concepts in the cortex.
17. The method of claim 16, further comprising: and determining one or more concepts corresponding to the activation pattern of the cerebral cortex in the one time period through the pattern recognition model.
18. The method of claim 17, further comprising: the pattern recognition model is one or more of a Convolutional Neural Network (CNN) model, a Deep Belief Network (DBN) model and a Recurrent Neural Network (RNN) model.
19. The method of claim 15, further comprising: forming a segment of text according to the time and duration of occurrence of the one or more concepts within the time segment.
20. The method of claim 19, further comprising: adding words in the text segment to form content with coherent semantics.
21. The method of claim 20, further comprising: the time of day and/or duration of the occurrence of one or more concepts within the time period is adjusted based on the content having coherent semantics.
22. The method of claim 21, wherein the time of day and/or duration of one or more concepts occurring within the time period is adjusted within a preset range.
23. The method of claim 1, wherein the concepts correspond to one or more phrases, words, or morphemes.
24. The method of claim 23, wherein the same phrase, word, or morpheme in different languages corresponds to the same concept or different concepts.
25. The method of claim 1, wherein the natural manner is an auditory manner.
26. The method of claim 1, wherein the natural manner is a visual manner.
27. The method according to claim 1, wherein the sequence is repeatedly perceived by the brain in a natural manner, at preset time intervals and preset times within the length of time.
28. A method for treating or preventing a psychological or psychiatric disorder comprising a method of generating feedback in the brain as claimed in any of claims 1-26.
29. The method of claim 28, wherein the psychological disorder comprises: depression, macrodepression, treatment resistant depression and treatment resistant bipolar depression, bipolar disorder, seasonal affective disorder, mood disorder, chronic depression, psychotic depression, postpartum depression, premenstrual dysphoric disorder (PMDD), situational depression, atypical depression, mania, anxiety, attention Deficit Disorder (ADD), attention deficit disorder with hyperactivity (ADDH) 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.
30. The method of claim 28, wherein the psychiatric disorder comprises: one or more of schizophrenia, schizoaffective disorder, bipolar disorder, obsessive compulsive disorder, parkinson's disease, oppositional defiant disorder, charles Bonnet syndrome, autism and Tourette's disease.
31. 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 periods of the length of time; and

    a transmission device configured to cause the sequence to be perceived by the brain of the subject in a natural manner over the length of time, producing a desired feedback in the brain of the subject.
32. The system of claim 31, further comprising a conversion device configured to convert the sequence into audio information or video information.
33. The system of claim 31, further comprising a storage device configured to store brain feedback sample data and corresponding concept sequences.
34. The system of claim 33, further comprising a sample processing device configured to obtain corresponding brain feedback sample data and corresponding concept sequences using cortical data obtained from different people in the same experience as a sample.
35. The system of claim 34, wherein the sample processing device comprises:

    a deep learning neural network model module configured to process cortical data obtained from different persons at the same experience to derive temporal changes in activation patterns in the cortex; and

    a pattern recognition model module configured to compare the change of the activation pattern of the cortex during the period of time with the activation patterns of a plurality of concepts in the cortex to obtain one or more concepts corresponding to the change of the activation pattern in the cortex in time, and to obtain a concept sequence with a time length according to the appearance time and duration of the concepts.
36. The system of claim 31, wherein the communication device further comprises a concept conversion module configured to convert the sequence of concepts into one or more of text, video information, and audio information.
37. The system of claim 31, wherein the production device comprises:

    a target feedback determination device configured to determine a target feedback according to experience to be obtained by the subject; and

    a target feedback analyzing device 12a configured to analyze the target feedback to obtain the sequence of concepts having a length of time.
38. The system of claim 37, wherein the target feedback determination means comprises a target feedback deep learning neural network model that outputs a target feedback that best matches the experience of the individual based on the input human-related parameters associated with the individual and the experience.
39. The system of claim 37, the target feedback analysis device comprising:

    a target feedback time slicing module configured to slice target feedback data by a time period to obtain a plurality of time slices;

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

    a concept combination identification module configured to match the plurality of activation patterns with one concept or a plurality of concept combinations corresponding thereto.
40. The system of claim 31, the delivery device comprising one or more of the following in combination: audio playback devices, video playback devices, braille readers, VR/AR devices, audio-visual rooms, and studios.
41. A brain keyboard, comprising:

    a keyboard comprising a plurality of keys, at least one or more keys corresponding to one or more concepts; and

    a processor configured to receive key operations from the keyboard forming 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.

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