A type 1 brain signaling safety system and its usage

Abstract

The present invention discloses a brain signal safety system and a method for using the same, relating to the field of intelligent information processing technology. The brain signal safety system includes: an information acquisition module for acquiring brain signals and safety information of brain signals; an analysis module that takes the brain signals and safety information of brain signals as input, performs analysis using a brain signal safety model, determines whether the brain signals have reached a safety limit, and derives appropriate brain signal safety measures if the safety limit has been reached; and a safety module for applying safety processing to the brain signals based on appropriate brain signal safety measures. This makes it possible to apply safety processing to the brain signals through appropriate brain signal safety measures derived from a pre-constructed brain signal safety model, thereby improving the safety of the brain interface, enhancing the effectiveness of using the brain interface, and avoiding causing safety damage to the target of the brain signals.

Description

Technical Field

[0001] This application claims the priority of a Chinese patent application with the application number 202310358178.2 and the title of the invention "A Brain-Computer Signal Security System and Its Usage Method" (A Brain-Computer Signal Security System and Its Usage Method), which was filed with the China National Intellectual Property Administration (CNIPA) on April 4, 2023, and the entire content thereof is incorporated herein by reference.

[0002] (1) Technical Field The present invention relates to the field of intelligent information processing technology, and particularly to a brain-computer signal security system and its usage method. Here, the "brain-computer signal" refers to the signal (Brain-Computer Signal, BCS) transmitted between the brain and an external device via a brain-computer interface (Brain-Computer Interface, BCI), and the same shall apply hereinafter.

Background Art

[0003] (2) Background Art A brain-computer interface (Brain-Computer Interface, BCI) refers to a direct connection established between the brain of a human or animal and an external device for realizing information exchange between the brain and the external device. BCI has an excellent effect in restoring damaged auditory, visual, and limb movement capabilities and can control implantable prosthetics in the same way as natural limbs. With the progress of current technology and knowledge, pioneers in BCI research can persuasively attempt to manufacture BCI not only for restoring human body functions but also for enhancing them. With the continuous development of science and technology, BCI has good prospects in the future and can contribute to the improvement of human life.

[0004] BCIs are mainly classified into implantable BCIs and non-implantable BCIs. Implantable BCIs are devices that are directly implanted in the human body and connected directly to the brain or nervous system. Non-implantable BCIs usually use a patch method, attaching the device to the outside of the body and stimulating the brain or nervous system using methods such as electrical current. Because BCIs primarily act on the brain, the requirements for their safety are high.

[0005] Existing BCIs lack a unified standard for the safety of brain-computer interfaces (BCS). Typically, a certain safety range is established, and the system determines whether the BCS is within this normal safety range. If it is outside the safety range, it indicates a potential problem and provides corresponding safety measures (SMs). However, the accuracy and precision of these SMs are not high, and they may cause damage to the target of the BCS. [Overview of the project] [Problems that the invention aims to solve]

[0006] (3) Content of the invention The object of the present invention is to provide a type of brain signal safety system and a method of using the same, in order to improve the safety of BCIs, enhance the effectiveness of BCI use, and avoid causing safety damage to the target of BCS. [Means for solving the problem]

[0007] To achieve the above objective, the present invention provides the following technical solutions:

[0008] (3.1) A type of brain signal safety system The system includes a Database Module (DBM), a Data Acquisition Module (DAM), an Analysis Module (AM), and a Safety Module (SM).

[0009] The DBM is for storing the Brain-Computer Interface Database (BCIDB). The BCIDB includes a Brain-Computer Signal Safety Model (BCSSM), which includes Safety Restrictions (SR) required for the BCS to correspond to different signals, sources, information channels, access methods, modes, frequencies, intensities, times, intervals, information amounts, and signal recipients / influence targets.

[0010] The aforementioned DAM is for acquiring BCS and BCS safety information.

[0011] The aforementioned AM is intended to analyze the BCS, BCS safety information, and BCSSM to derive an appropriate SM.

[0012] The aforementioned SM is intended to improve the safety of the BCS by applying safety treatment to the BCS based on an appropriate SM.

[0013] (3.2) Method of using the above-mentioned brain signal safety system The aforementioned method includes the following steps: (1) Construct a BCI database (BCIDB). The BCIDB includes a BCSSM, which includes the SRs required for the BCS corresponding to different signals, sources, information channels, access methods, modes, frequencies, intensity, time, intervals, information amounts, and signal receivers / influence targets. (2) Obtain the BCS and BCS safety information. (3) Analyze based on the BCS, BCS safety information and BCSSM to derive an appropriate SM. (4) Safe treatment of the BCS based on appropriate SMs to improve the safety of the BCS.

[0014] (3.3) Type 1 Brain-Mechanism Signal Safety System The aforementioned system includes the following: Information Acquisition Module (i.e., DAM): For acquiring BCS and BCS safety information. The BCS includes the BCS mode, type, format, content, function, neurons, information quantity, signal strength, frequency, application, target, access method, and location. The BCS safety information refers to information regarding SR necessary for the BCS.

[0015] Analysis Module (i.e., AM): This module takes the BCS and safety information of the BCS as input, performs analysis using BCSSM, determines whether the BCS has reached SR, and derives an appropriate SM if SR has been reached. The BCSSM includes the SR required for the BCS corresponding to different signals, sources, information channels, access methods, modes, frequencies, intensity, time, intervals, information amounts, and signal receivers / influence targets, and the SM required if SR has been reached.

[0016] Safety module (i.e., SM): This module is used to apply safety treatment to the BCS based on an appropriate SM.

[0017] (4) Additional features in some embodiments (4.1) Additional database modules In some embodiments, the system further includes a DBM for storing BCIDBs. The BCIDB includes BCSSMs for each user type or each individual user.

[0018] The aforementioned BCSSM is obtained by simultaneously personalizing settings for at least one of the following information: the target population, genetic factors, physiological factors, psychological factors, physical factors, lifestyle / life history, exercise status, living situation, learning situation, work situation, disease status, medications, medical devices, diet, physiological / psychological trauma, surgery, radiation, physiotherapy, rehabilitation, operation, examination, and psychological intervention, based on the potential impact that the BCS may have on the user, corresponding to different signals, information sources, information channels, access methods, modes, frequencies, intensity, time, intervals, amount of information, and the target of signal reception / affect.

[0019] The above-mentioned AM is further used to determine an appropriate BCSSM based on the BCS and the safety information of the BCS before performing an analysis using the BCSSM. The appropriate BCSSM is either one BCSSM in the BCIDB or a model obtained by adjusting one BCSSM in the BCIDB based on the actual situation of the BCS.

[0020] (4.2) Consideration of Interaction In some embodiments, in the process of deriving an appropriate SM, it is necessary to further consider the interaction between different dimensions of the BCS or between one or more BCSs. Furthermore, by this consideration, the safety risk caused by the interaction can be avoided.

[0021] (4.3) Evaluation of Safety Measures In some embodiments, the above-mentioned AM is further used to set corresponding levels / scores for different analysis results in at least one analysis item among riskiness, harmfulness, complexity, limitations, effects, time, cost, convenience, economy, information completeness / authenticity / accuracy, user merits, and user damages for the SM. Based on the levels / scores corresponding to each analysis item of different SMs and the BCSSM, the overall level / score of the SM is calculated and used to determine an appropriate SM based on the overall level / score.

[0022] (4.4) Expansion of Brain-Machine Signal Safety Model In some embodiments, the BCSSM further includes the SR required for BCSs with different contents, actions, and effects, and the SM required when the SR is reached. The effects include effects occurring on at least one of knowledge, cognition, values, interests, methods, goals, emotions, states, physical abilities, skills, behaviors, intelligence, memory, physiological functions, expressions, immune capabilities, excitability, and establishment / disconnection of brain synapses / connections.

[0023] <000009�>(4.5) Additional Functional Modules In some embodiments, the system further includes the following: - Processing Module (PM): It is used to perform at least one of the operations of translation, matching, conversion, and recognition on the BCS to obtain the feature information of the BCS. Based on the feature information of the BCS and an appropriate SM, before performing security processing on the BCS based on the appropriate SM, pre-processing is performed on the BCS to ensure the functionality of the BCS. - Adjustment & Optimization Module (AOM): After performing security processing on the BCS based on an appropriate SM, it obtains the actual effect of the security processing on the BCS, analyzes the actual effect to obtain an analysis result, and is used to adjust and optimize an appropriate BCSSM based on the analysis result. Specifically, by means of a method based on Big Data (BD) or evidence, it tracks and analyzes the application results of different users, different signals, and different models, continuously accumulates the relationships between data, discovers parts that need improvement or are possible to improve, and dynamically optimizes them. - Signal Adjustment Module (SAM): It is used to adjust the BCS by adopting at least one of the measures of deletion, reduction, decrease, adjustment, change, translation, conversion, reminder, warning, closure, enhancement, and increase. Thereby, the effect of the BCS (including improvement, change, increase, and decrease of the effect of the BCS) is adjusted.

Advantages of the Invention

[0024] (5) Technical effects According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: The present invention provides a type of brain signal safety system and a method for using the same. The system includes a DAM that acquires a BCS and safety information of the BCS, an AM that takes the BCS and safety information as input, analyzes them using an appropriate BCSSM to determine whether the BCS has reached SR, and derives an appropriate SM if SR has been reached, and an SM that applies safety processing to the BCS based on the appropriate SM. In this way, safety processing can be applied to the BCS through an appropriate SM derived from a pre-constructed BCSSM, improving the safety of the BCI, enhancing the effectiveness of using the BCI, and avoiding causing safety damage to the target of the BCS. [Brief explanation of the drawing]

[0025] (6) Brief description of the drawings To more clearly illustrate embodiments of the present invention or prior art solutions, the necessary drawings for the embodiments are briefly introduced below. Clearly, the drawings in the following description represent only some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these without any creative effort.

[0026] [Figure 1] Figure 1 is a system configuration diagram of a type of brain signal safety system provided by Embodiment 3 of the present invention. [Modes for carrying out the invention]

[0027] (7) Specific embodiments Hereinafter, the technical solutions in embodiments of the present invention will be clearly and completely described with reference to the drawings of the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments that a person skilled in the art could obtain without creative effort based on embodiments of the present invention are within the scope of protection of the present invention.

[0028] The object of the present invention is to provide a type of brain signal safety system and a method of using the same, in order to improve the safety of BCIs, enhance the effectiveness of BCI use, and avoid causing safety damage to the target of BCS. In order to make the above object, features and advantages of the present invention clearer and easier to understand, the present invention will be described in more detail below with reference to the drawings and specific embodiments.

[0029] (7.1) Example 1 A type of brain signal safety system, the system includes DBM, DAM, AM, and SM.

[0030] The DBM is for storing the BCIDB. The BCIDB includes a BCSSM, which includes the SRs required for the BCS corresponding to different signals, sources, information channels, access methods, modes, frequencies, intensity, time, intervals, amount of information, and the recipient / influence target of the signal.

[0031] The aforementioned DAM is for acquiring BCS and BCS safety information.

[0032] The aforementioned AM is intended to analyze the BCS, BCS safety information, and BCSSM to derive an appropriate SM.

[0033] The aforementioned SM is intended to improve the safety of the BCS by applying safety treatment to the BCS based on an appropriate SM.

[0034] (7.2) Example 2 A method for using a type of brain signal safety system described in Example 1, the method comprising the following steps: (1) Construct a BCIDB. The BCIDB includes a BCSSM, which includes the SRs required for the BCS corresponding to different signals, sources, information channels, access methods, modes, frequencies, intensity, time, intervals, information amounts, and the recipients / influences of the signals. (2) Obtain the BCS and BCS safety information. (3) Analyze based on the BCS, BCS safety information and BCSSM to derive an appropriate SM. (4) Safe treatment of the BCS based on appropriate SMs to improve the safety of the BCS.

[0035] (7.3) Example 3 A type of brain signal safety system, as shown in Figure 1, includes the following:

[0036] Information Acquisition Module (i.e., DAM): This module is used to acquire BCS and BCS safety information. BCS includes the mode, type, format, content, function, neurons, information volume, signal strength, frequency, application, target, access method, and location of the BCS. BCS safety information refers to information regarding SR required for the BCS.

[0037] The analysis module (i.e., AM) takes the BCS and safety information of the BCS as input, performs analysis using BCSSM, determines whether the BCS has reached SR, and derives an appropriate SM if SR has been reached. The BCSSM includes the SR required for the BCS corresponding to different signals, sources, information channels, access methods, modes, frequencies, intensity, time, intervals, information amounts, and signal receivers / influence targets, and the SM required if SR has been reached.

[0038] Safety module (i.e., SM): This module is used to apply safety treatment to the BCS based on an appropriate SM.

[0039] One type of brain signal safety system in this embodiment is applicable to implanted BCS (i.e., BCS generated by an implanted BCI) and / or non-implanted BCS (i.e., BCS generated by a non-implanted BCI).

[0040] One type of brain signal safety system in this embodiment further includes a DBM, which is for storing BCIDBs. The BCIDBs include BCSSMs for each user type or each individual user.

[0041] The BCSSM is obtained by simultaneously personalizing settings for at least one of the following: the target population, genetic factors, physiological factors, psychological factors, physical factors, living conditions / life history, exercise status, living conditions, learning status, work status, disease status, medications, medical devices, diet, physiological / psychological trauma, surgery, radiation, physiotherapy, rehabilitation, manipulation, examination, and psychological intervention, based on different signals, information sources, information channels, access methods, modes, frequencies, intensity, time, intervals, amount of information, and the potential effects that the BCS corresponding to the signal recipient / affected target may have on the user (these effects include effects on the user's health, behavior, sensitivity, emotions, social status, thoughts, feelings, memory, physiological function, skills, mental appearance, rest, body parts, organs, systems, functions, needs, immunity, metabolism, abilities, temperament, excitability, etc.).

[0042] In this embodiment, a BCIDB is first constructed. The BCIDB includes a BCSSM, which includes SRs required for a BCS corresponding to different signals, sources, information channels, access methods, modes, frequencies, intensities, times, intervals, information content, signal receivers / influence targets, and SMs required when an SR is reached. Preferably, the BCSSM in this embodiment further includes SRs required for a BCS having different content, actions, and effects, and SMs required when an SR is reached. The effects include effects on at least one of knowledge, cognition, values, interests, methods, goals, emotions, states, physical abilities, skills, behaviors, intelligence, memory, physiological functions, expression, immune function, excitability, and the establishment / disconnection of brain synapses / connections.

[0043] For example, a single BCS (Blockchain Computing System) containing atheistic content may not be highly stimulating to atheist users, but it could be highly stimulating to other users, potentially leading to safety risks.

[0044] The human brain is divided into different regions, each corresponding to a different function (for example, the motor cortex is used to control various movements, the sensory cortex is used to sense temperature, humidity, touch, etc., and the visual cortex is used to form vision). Therefore, the brain cell system (BCS) employs different modes based on different functions and uses, acts on different neurons, has different signal intensities and frequencies, and transmits different amounts of information. Consequently, it is necessary to construct a BCSSM (Brain-Critical System-Specific Memory).

[0045] Specifically, based on the potential impact that the BCS may have on the user, corresponding to different signals, sources, information channels, access methods, modes, frequencies, intensity, time, intervals, information content, and the receiving / affected object of the signal, the required SR for the BCS and the SM required when the SR is reached are determined, and the BCSSM is obtained.

[0046] If the BCSSM includes SRs and SMs required when the SR is reached for a BCS with further different content, actions, and effects, the BCSSM is obtained by determining the SRs and SMs required when the SR is reached for a BCS based on further different signals, sources, information channels, access methods, modes, frequencies, intensity, time, intervals, amount of information, signal reception / influence targets, and the potential impact that the BCS may have on the user corresponding to the content, actions, and effects of the BCS.

[0047] Among these, BCSSM can be constructed by at least one of the following methods: manual configuration, artificial intelligence analysis using machine learning, or large-scale data analysis. The potential impact of BCS on the user includes effects on the physiology, psychology, mental state, function, and memory of the human body, and some of these effects may cause damage, so it is essential to set up SR (Sensitivity Restriction). For example, SR can be implemented for electrical signals by limiting the voltage, current, frequency, etc. of the signal.

[0048] Based on this, a general-purpose BCSSM is constructed in this embodiment. After obtaining safety information for BCS and BCS, AM takes the safety information for BCS and BCS as input, performs analysis using BCSSM, determines whether BCS has reached SR, and derives an appropriate SM if SR has been reached.

[0049] Due to differences in human physical conditions or the state in which they are located, different people have different tolerance levels for BCS. Therefore, in this embodiment, BCSSM can be further individualized for at least one piece of information from the target population, genetic factors, physiological factors, psychological factors, physical factors, lifestyle / life history, exercise status, living situation, learning situation, work situation, disease status, medications, medical devices, diet, physiological / psychological trauma, surgery, radiation, physiotherapy, rehabilitation, manipulation, examination, and psychological intervention, thereby allowing BCSSM to be applied individually to different targets.

[0050] Factors that may affect the required SR for BCS include: - Population factors: nationality, race, gender, age, etc.; - Genetic elements: genes, gene mutations / changes, genetic defects, history of genetic diseases, family history of diseases, etc. - Physiological elements: physical condition, nutritional status, hearing, vision, taste, smell, touch, respiration, motor coordination, digestion, absorption, excretion, sexual function, growth capacity, and related abilities, etc. - Psychological elements: psychological disorders, emotions, sensitivity, intelligence, attention, memory, perception, communication skills, expressive abilities, etc. - Physical elements: height, weight, strength, speed, physical development status, etc.; - Living conditions / life history elements: work information, diet-related information, learning information, exercise information, entertainment information, daily living information, etc.; - Disease elements: Diagnosis results, symptoms, etc.; - Time elements: year / season / month / rhythm / season / week / day, morning / forenoon / noon / afternoon / evening / night, after waking up / before going to bed / after exercise / before meals, etc. - Environmental factors: temperature, humidity, atmospheric pressure, season, longitude, latitude, altitude, air quality, topography, terrain, oxygen content, sunlight, ultraviolet radiation, radiation, electromagnetic waves, noise, epidemics, plants, etc. Furthermore, it includes elements such as drugs, medical devices, diet, physiological / psychological trauma, surgery, radiation, physiotherapy, rehabilitation, manipulation, testing, psychological interventions, and other possible elements.

[0051] In this embodiment, BCSSM is obtained for each user type or each individual user by simultaneously applying individual settings to at least one piece of information from the following: the target population, genetic factors, physiological factors, psychological factors, physical factors, lifestyle / life history, exercise status, living situation, learning status, work status, disease status, medications, medical devices, diet, physiological / psychological trauma, surgery, radiation, physiotherapy, rehabilitation, operation, examination, and psychological intervention, based on the potential impact that the BCS corresponding to different signals, information sources, information channels, access methods, modes, frequencies, intensity, time, intervals, amount of information, and the potential impact that the BCS corresponding to the signal recipient / affected object may have on the user.

[0052] Based on this, multiple individualized BCSSMs are constructed in this embodiment. After obtaining safety information for the BCS and BCS, the AM further determines an appropriate BCSSM based on the BCS and BCS safety information before performing analysis using the BCSSM. An appropriate BCSSM is either one of the BCSSMs in the BCIDB, ​​or a model obtained by adjusting one of the BCSSMs in the BCIDB based on the actual conditions of the BCS.

[0053] To improve the efficiency of constructing multiple individualized BCSSMs, in this embodiment, existing data is classified based on different user groups, a model is first constructed for one user group, and then the classification of user groups is gradually refined, ultimately leading to the construction of a BCSSM for each individual user.

[0054] Through observation, testing, and measurement methods, we collect user indicators under various BCS conditions, as well as real-time, short-term, and long-term effects on the whole body, various systems, organs, parts, tissues, various cells, various molecular levels, various physiological functions, and psychological state when the user is placed within different BCS ranges. Using this data, we analyze the potential impact that different BCS ranges may have on the user, determine the required SR and, if the SR is reached, the required SM, and construct the BCSSM.

[0055] Furthermore, the BCSSM can be constructed by combining knowledge from academic fields such as physiology, pathology, genetics, pharmacology, psychology, materials science, and environmental science, and by constructing, estimating, and adjusting for relevant influences and limitations.

[0056] The analysis required for BCS includes: which elements of BCS affect the user, what kind of effect they have, and to what extent; the relationship between the value of each element and the effect; what kind of effect / degree of effect is detrimental to the user; and the SM necessary to avoid BCS causing harm to the human body under different conditions.

[0057] After obtaining combinations of elements from each dimension, the data is aggregated and evaluated for common features. For combinations of elements with common features, a comprehensive evaluation, monitoring, and control are performed, and a BCSSM is constructed using various relevant statistical methods and large-scale data monitoring techniques.

[0058] In this embodiment, BCSSM is a model for a single user or a group of users; for example, it may be a model for a specific user or a model for a group of people (e.g., a group of patients with mental disorders, a group of minors aged 12-18, etc.), and may also be a regional model, an industry model, an ethnic model, etc. The model may be a static model or a dynamic model (e.g., a model after being influenced / stimulated, a model after taking medication, a model after eating, a model before going to sleep, etc.).

[0059] In this embodiment, BCSSM can be constructed based on various medical manuals, guidelines, research reports, literature, expert consensus, meeting minutes and consensus within medical organizations / hospitals / departments, industry standards, textbooks, papers, publications, inventions, scientific reasoning, experimental reports, test reports, data analysis reports, test reports, measurement reports, approval documents, relevant laws and regulations, relevant guidance opinions, relevant policies, relevant systems, evaluations / reports by relevant physicians / nurses / pharmacists / caregivers / patients, and other research results with expertise / authority.

[0060] It can be manually configured by experts based on research results, literature, materials, experience, etc., in which they possess expertise / authority; it can be constructed by acquiring and reorganizing information through data mining; it can be constructed through large-scale data analysis; it can be constructed through deep learning of artificial intelligence; it can be constructed by combining existing knowledge graphs; or it can be constructed by continuously acquiring new data during the process of use.

[0061] The information acquisition module (i.e., DAM) is used to acquire BCS and BCS safety information.

[0062] Among these, the DAM acquires the BCS from the BCI or other related system. The BCS includes information such as the mode, type, format, content, function, neurons, information content, signal strength, frequency, application, target, access method, and location of the BCS.

[0063] In this context, BCS safety information refers to information on SR necessary for BCS, and may include basic information on the target, genetic information, family health information (e.g., family medical history), medical history, allergy history, history of local epidemics, drug use history, surgical history, surgical procedure use history, learning status, work status, exercise status, home situation, living environment, hobbies, compliance status, tolerance status, medical insurance status, as well as information on physiological / psychological / learning / work / physical condition / sleep / exercise / emotions / metabolism / vision / hearing / intelligence / attention / diet / immunity / growth / development / memory / growth ability and wake-up times.

[0064] Furthermore, it may include information such as temperature, humidity, atmospheric pressure, season, longitude, latitude, altitude, air quality, topography, terrain, oxygen content, light, ultraviolet radiation, radiation, electromagnetic waves, noise, epidemics, and plants.

[0065] Furthermore, it may include physical examination-related indicators, hematological indicators, thrombosis and hemostasis-related indicators, excretory / secretory / fluid-related indicators, renal function-related indicators, hepatic function-related indicators, biochemical indicators, immunological indicators, genetic indicators, pathogen-related indicators, cardiac function-related indicators, pulmonary function-related indicators, psychological state-related indicators, mental state-related indicators, athletic performance-related indicators, imaging-related indicators, and acoustics-related indicators.

[0066] Specifically, this includes medical indicators such as height, weight, body temperature, vision, hearing, blood glucose, red blood cells, white blood cells, platelets, uric acid, cholesterol, transaminase, calcium, iron, potassium, triglycerides, heart rate, body fat percentage, lung capacity, imaging results, acoustic results, intelligence, psychological state, and athletic ability; ratios of various indicators (e.g., height / weight, height / waist size, etc.); various health expressions, perceptions, and standards (e.g., skin color, tongue coating, fundus, body fat percentage, skin, hair quality, strength, etc.); and may also include other information related to SR necessary for BCS.

[0067] The sources from which BCS safety information is obtained include: The target personal information databases, health records, physician's orders, medical records / electronic medical records, diagnostic reports, test / measurement results, monitoring results, nutritional assessment reports, health records of family members or family members, physician's orders, medical records, medication records, prescriptions, electronic medical records, healthcare information systems, pharmacy / medical equipment store information systems, consultation records, treatment records, evaluation reports, consultation records, survey records, living records / plans, meal records / plans, purchase records / plans, medication records / plans, treatment records / plans, exercise records / plans, work records / plans, learning records / plans, rehabilitation records / plans, health records / plans, tests / examination forms, surgical plans / records, health management plans, invoices, clinical treatment pathways, test / measurement results, surgical settings / records, genetic test results.

[0068] Furthermore, it can be obtained from user / physician / nurse / caregiver usage / prescription / recommendation records; and from information provided by devices or systems such as various wearable devices, sensors, electronic devices, electronic positioning systems, weather forecasting systems, electronic temperature / humidity / barometric pressure measuring devices, smart speakers, smart home systems, smart monitoring systems, smart glasses, smart toilets, smart floors, smart scales, smart measurement / analysis devices, electronic infusion systems, surgical robots, facial recognition analysis, fingerprint recognition, voice recognition, gait recognition, positioning systems, and social platforms.

[0069] Furthermore, this information can be obtained through large-scale data analysis of information related to lifestyle, learning, work, exercise, going out, socializing, shopping, eating, daily routines, and entertainment; and through analysis of related information such as race, family, region, age, marriage, and upbringing.

[0070] Missing information can be provided or supplemented by the user; or, by utilizing highly correlated information, users / physicians can proactively present whether a relevant situation has occurred, prompting them to observe, monitor, examine, inquire, analyze, confirm, record, or obtain relevant indicators / expressions / sensations / symptoms / physiological changes.

[0071] The evaluation report includes reports on physiology, psychology, economics, creditworthiness, athletic ability, etc.; the smart measurement / analysis devices include odor, image, sound, pulse, X-ray, CT, magnetic resonance, ultrasound, electroencephalography, mass spectrometer, tongue diagnosis analysis, fundus examination, gastroscopy, colonoscopy, catheter, laparoscopy, heart rate, blood oxygen saturation, blood pressure, blood glucose, blood lipids, body temperature, blood tests, urine tests, stool tests, pulse measurement / analysis devices, weight / body fat scales, etc.

[0072] If the BCSSM is a general-purpose BCSSM, the AM (Analysis Manager) will directly use the BCSSM for analysis. Specifically, the BCS (Body Condition Score) and BCS safety information will be used as input, the analysis will be performed using the appropriate BCSSM, it will be determined whether the BCS has reached SR (Surface Resistance), and if SR has been reached, the appropriate SM (Surface Resistance) will be derived.

[0073] If the BCSSM consists of multiple individualized BCSSMs, the AM is used to determine the appropriate BCSSM based on the BCS and BCS safety information. Specifically, it either directly matches the appropriate BCSSM within the BCIDB based on the BCS and BCS safety information, or selects the most appropriate BCSSM within the BCIDB based on the BCS and BCS safety information, further considers the difference between the actual BCS situation and the existing model (i.e., the most appropriate BCSSM), makes appropriate adjustments to the most appropriate BCSSM, and further adapts the model to the actual situation to obtain an appropriate BCSSM. Therefore, in this embodiment, the appropriate BCSSM is either one BCSSM within the BCIDB, ​​or a model obtained by adjusting one BCSSM within the BCIDB based on the actual BCS situation.

[0074] The adjustment can be performed manually, automatically by a brain signal safety system, or automatically by automatic learning using artificial intelligence.

[0075] After obtaining an appropriate BCSSM, the BCS and BCS safety information are used as input, and analysis is performed using the appropriate BCSSM to determine whether the BCS has reached SR. If SR has been reached, an appropriate SM is derived. This allows for analysis based on an appropriate BCSSM, BCS, and BCS safety information, and the SM can be derived; the SM may include restricting the voltage, current, frequency, site of action, acting neurons, etc., of the BCS.

[0076] In this embodiment, in the process of deriving an appropriate SM, it is necessary to further consider the interactions between different dimensions of the BCS or between one or more BCSs. These interactions may include strengthening or weakening of the BCS, changes in information format or information content, effects on receiving channels / locations, and effects on effectiveness. By considering these interactions, an even more appropriate SM can be obtained.

[0077] The BCSSM may further include at least one of the following: mutual limitations, contraindications, or interactions between different BCSs; including mutual limitations / contraindications / interactions between BCSs of various modes, functions, neurons, information content, signal intensity, frequency, and applications.

[0078] These mutual limitations / contraindications / interactions may be caused by temporal, psychological, physiological, signal intensity, signal frequency, etc. Relevant mutual limitations / contraindications / interactions may include materials, types, methods of use, usage conditions, time, intensity, frequency, population, genetic factors, diseases, medical history, allergy history, physical indicators, physiological development, marital development, physiological state, psychological / intellectual state, lifestyle / work / learning / exercise / recreational state, temperature / humidity / pressure / season / altitude / air quality / oxygen content / light / ultraviolet rays / noise, emotions, electricity, magnetism, light, heat, radiation, stimuli, etc.

[0079] In this embodiment, in the process of deriving a suitable SM, the AM obtains a plurality of elementary SMs and further selects a suitable SM from the plurality of elementary SMs.

[0080] The specific selection method is as follows: For each SM, the AM sets a corresponding level / score for different analysis results in at least one of the following analysis items: risk, hazard, complexity, limitations, effectiveness, time, cost, convenience, economics, completeness / truth / accuracy of information, user benefits, and user harm.

[0081] When analyzing a Service Model (SM), the overall level / score of the SM is calculated based on the levels / scores corresponding to each analysis item of different SMs and the BCSSM, making it easier to determine the feasibility of the SM or to recommend it.

[0082] In other words, in this embodiment, AM further assigns corresponding levels / scores to different analysis results for each SM in at least one of the following analysis items: riskiness, hazard, complexity, limiting conditions, effectiveness, time, cost, convenience, economics, completeness / truth / accuracy of information, user benefits, and user harm. Based on the levels / scores corresponding to each analysis item of different SMs and the BCSSM, an overall level / score for the SM is calculated; and the appropriate SM is determined based on the overall level / score and used to select an appropriate SM from several preliminary SMs.

[0083] SM is intended to improve the safety of the BCS by applying safety treatment to the BCS based on the appropriate SM.

[0084] One type of brain signal safety system in this embodiment further includes a PM; the PM performs at least one of the operations of translation, matching, transformation, and recognition on the BCS to obtain characteristic information of the BCS. Based on the characteristic information of the BCS and an appropriate SM, the PM is used to pre-process the BCS before performing safety processing on the BCS based on an appropriate SM, in order to ensure the functionality of the BCS.

[0085] Specifically, in this embodiment, the BCS is pre-treated with PM before undergoing safety treatment based on an appropriate SM, thereby ensuring the functionality of the BCS as much as possible.

[0086] For example, if the BCS voltage exceeds the standard, the appropriate SM (Service Manager) should limit the voltage. In this case, directly reducing the BCS voltage would meet the safety standard, but it could cause a significant loss of functionality, preventing the BCS from performing its intended function.

[0087] To realize the pre-processing process, the PM in this embodiment is for performing at least one operation on the BCS, such as translation, matching, transformation, or recognition. The PM performs operations such as translation, matching, transformation, and recognition on the BCS, analyzes the characteristics of the BCS, and further pre-processes the BCS based on the characteristics of the BCS and an appropriate SM, thereby guaranteeing the functionality of the BCS as much as possible.

[0088] In other words, before applying safety treatment to the BCS based on appropriate SMs, the PM pre-treats the BCS based on its characteristics. This pre-treating can target groups of people or individuals. That is, before entering the BCI, known / general safety risks are intervened in advance, but after pre-treating, the risks can be reduced in advance, preventing general / ordinary safety damage to users who do not use SMs or have limited SM functionality, or providing collective protection to users receiving the same signal together, reducing the total cost consumed by each user taking individual measures, and ensuring the functionality of the BCS as much as possible.

[0089] In this embodiment, the PM can analyze the characteristics of the BCS using methods such as the time-domain method, the frequency-domain method, and the time-frequency method.

[0090] (7.3.1) Time domain method Waveform feature parameters are directly extracted through time-domain analysis using methods such as zero-crossing analysis, histogram analysis, analysis of variance, correlation analysis, peak detection and waveform parameter analysis, coherent averaging, and waveform recognition. These waveform feature parameters are then used for classification, recognition, tracking, and transient analysis of the BCS.

[0091] Time-domain feature extraction methods combine specific filtering and sampling techniques to remove time-domain noise from the BCS and improve the signal-to-noise ratio of the BCS. Among these, amplitude features and amplitude energy features are the most frequently extracted.

[0092] Possible filtering methods include bandpass filtering, Laplace filtering, all-inducted mean reference method, Kalman filtering, and moving average filtering. Additionally, continuous or discrete wavelet transforms can be used to extract time-varying features of the BCS.

[0093] (7.3.2) Frequency Domain Method The characteristics of the BCS can be analyzed using power spectrum estimation and parameter modeling methods.

[0094] Power spectrum estimation is a frequency domain analysis method that can reflect the frequency components and relative intensity of a signal; by using this method to analyze the power and coherence in each frequency band of the BCS, the regularity of the signal can be obtained.

[0095] The parameter model method is one of the most widely used methods in modern spectral estimation; it features high frequency resolution and a smooth spectral diagram, enabling automated parameter extraction and quantitative analysis, making it particularly suitable for processing short data sets and applicable to dynamic analysis of BCS.

[0096] Frequency-domain features are typically measured using power spectral density (PSD), adaptive autoregressive (AAR) model parameters, or wavelet frequency band energy; corresponding extraction methods mainly include fast Fourier transform (FFT), AAR models, and wavelet transforms.

[0097] (7.3.3) Time-Frequency Method Because BCS has complex and non-stationary characteristics, and conventional time-domain and frequency-domain analyses also have uncertainty in signal processing, a combination of time-domain features and frequency-domain power spectra is used to extract features from BCS. Among these, the Wigner distribution (WD) and wavelet transform (WT) are preferred time-frequency analysis methods.

[0098] One type of brain signal safety system in this embodiment further includes an AOM; the AOM applies safety processing to the BCS based on an appropriate SM, obtains the actual effect of the safety processing on the BCS, performs an analysis on the actual effect to obtain analysis results, and adjusts and optimizes an appropriate BCSSM based on the analysis results. Specifically, it tracks and analyzes the application results for different users, different signals, and different models using large-scale data or evidence-based methods, continuously accumulates relationships between data, and dynamically optimizes areas that need or can be improved.

[0099] One type of brain signal safety system in this embodiment further includes a SAM; the SAM is for adjusting the BCS by taking at least one of the following measures: deletion, reduction, decrease, adjustment, modification, translation, conversion, reminder, warning, closure, enhancement, or increase, if the BCS is ineffective. This adjusts the effect of the BCS (including improvement, modification, increase, and decrease of the BCS's effect). Here, "ineffective BCS" refers to a case where the effect of the original acquired BCS is insufficient, or where the effect of the BCS after safety processing is insufficient; in this case, the BCSSM can be used to determine whether the effect is insufficient or not.

[0100] One type of brain signal safety system in this embodiment can be used standalone, or provided to users as access-type external hardware such as a mobile hard disk, box, or card; it can be installed on a local server to support local users, installed on a private cloud server to support private cloud users, or installed on the internet to provide services to internet users.

[0101] The brain signal safety system provided in this embodiment acquires BCS and BCS safety information, performs analysis based on BCS, BCS safety information, and BCSSM, determines whether BCS has reached SR, derives an appropriate SM if SR has been reached, and improves BCS safety by applying safety processing to BCS based on the appropriate SM. In this embodiment, BCSSM is constructed using a large data sample based on BCS corresponding to different signals, information sources, information channels, access methods, modes, frequencies, intensity, time, intervals, information volume, and signal recipients / affected targets. Furthermore, in the BCSSM construction process, the actual circumstances of individuals are fully considered, target populations / application scenarios and related parameter criteria are subdivided, and individualized user information is fully grasped to obtain a BCSSM for a single user group or a single user. In addition, an appropriate SM is presented based on the individualized status of the target of the BCS, safety processing is applied to the BCS based on the appropriate SM, and pre-processing is applied to the BCS to guarantee its functionality as much as possible. This improves the safety of BCI, enhances the effectiveness of BCI use, and avoids causing safety damage to the target of BCI.

[0102] The technical features of each of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described, but as long as these combinations of technical features are not contradictory, they are all considered to fall within the scope described herein.

[0103] This specification describes the principles and implementations of the present invention using specific examples. The above descriptions of examples are merely for the purpose of understanding the methods and core concepts of the present invention. At the same time, those skilled in the art may modify the specific implementations and scope of application based on the concept of the present invention. In summary, the contents of this specification should not be understood as limitations on the present invention.

Claims

1. A type of brain signal safety system, characterized in that the system includes a database module, a data acquisition module, an analysis module, and a safety module. The database module is for storing a brain-machine interface database, the brain-machine interface database includes a brain-machine signal safety model, the brain-machine signal safety model includes safety constraints required for brain-machine signals corresponding to different signals, sources, information channels, access methods, modes, frequencies, intensity, time, intervals, amount of information, and signal recipients / influence targets. The aforementioned data acquisition module is for acquiring brain signals and safety information related to brain signals. The aforementioned analysis module performs analysis based on brain signals, safety information of brain signals, and brain signal safety models to derive appropriate brain signal safety measures. The aforementioned safety module is designed to improve the safety of brain signals by applying safety processing to brain signals based on appropriate brain signal safety measures.

2. A method for using a brain signal safety system according to claim 1, characterized in that the method includes the following steps: A brain-machine interface database is constructed, the brain-machine interface database includes a brain-machine signal safety model, the brain-machine signal safety model includes safety constraints required for brain-machine signals corresponding to different signals, sources, information channels, access methods, modes, frequencies, intensity, time, intervals, amount of information, and signal recipients / influence targets. We acquire brain signals and safety information related to those brain signals. Based on brain signals, safety information regarding brain signals, and brain signal safety models, we conduct analysis to derive appropriate brain signal safety measures. The safety of brain signals is improved by applying safety processing to brain signals based on appropriate brain signal safety measures.

3. It is a type of brain signal safety system, and its characteristics include the following: Information acquisition module: For acquiring brain signals and safety information of brain signals, the brain signals include the mode, type, format, content, function, neurons, amount of information, signal intensity, frequency, application, target, access method, and location of the brain signals, and the safety information of the brain signals refers to information regarding safety restrictions necessary for the brain signals. Analysis Module: This module takes the brain signal and safety information of the brain signal as input, performs analysis using a brain signal safety model, determines whether the brain signal has reached a safety limit, and derives appropriate brain signal safety measures if the safety limit has been reached. The brain signal safety model includes safety limits required for brain signals corresponding to different signals, information sources, information channels, information formats, information content, access methods, modes, frequencies, intensity, time, intervals, information volume, signal reception / influence targets, and brain signal safety measures required if the safety limit has been reached. Safety module: This module is used to apply safety processing to brain signals based on appropriate brain signal safety measures.

4. A brain-device signal safety system according to claim 3, characterized in that it further includes a database module, the database module for storing a brain-device interface database, the brain-device interface database includes a brain-device signal safety model for each user type or each individual user, The aforementioned brain signal safety model is obtained by simultaneously individualizing settings for at least one of the following: the target population, genetic factors, physiological factors, psychological factors, physical factors, lifestyle / life history, exercise status, living situation, learning status, work status, disease status, medications, medical devices, diet, physiological / psychological trauma, surgery, radiation, physical therapy, rehabilitation, operation, examination, and psychological intervention, based on the potential impact that brain signals corresponding to different signals, information sources, information channels, information formats, information content, access methods, modes, frequencies, intensity, time, intervals, amount of information, and the potential impact that brain signals corresponding to the signal recipient / affected target may have on the user. The analysis module further determines an appropriate brain signal safety model based on the brain signal and safety information of the brain signal before performing analysis using the brain signal safety model. The appropriate brain signal safety model is either one of the brain signal safety models in the brain interface database, or a model obtained by adjusting one of the brain signal safety models in the brain interface database based on the actual situation of the brain signal.

5. A type of brain signal safety system according to claim 3, characterized in that, in the process of deriving appropriate brain signal safety measures, it is necessary to further consider the mutual influence between different dimensions of brain signals or between one or more brain signals.

6. A type of brain signal safety system according to claim 3, characterized in that the analysis module further sets corresponding levels / scores for different analysis results in at least one analysis item among riskiness, hazard, complexity, limiting conditions, effectiveness, time, cost, convenience, economics, completeness / truth / accuracy of information, user benefits, and user harm for brain signal safety measures, calculates an overall level / score for brain signal safety measures based on the levels / scores corresponding to each analysis item of different brain signal safety measures and a brain signal safety model, and determines an appropriate brain signal safety measure based on the overall level / score.

7. A brain signal safety system according to claim 3, characterized in that the brain signal safety model further includes safety limits required for brain signals having different content, functions, and effects, and brain signal safety measures required when the safety limits are reached, wherein the effects include effects occurring on at least one of knowledge, cognition, values, interests, methods, goals, emotions, states, physical abilities, skills, behavior, intelligence, memory, physiological functions, expression, immune function, excitability, and the establishment / disconnection of brain synapses / connections.

8. A type of brain signal safety system according to claim 3, characterized in that it further includes a processing module, the processing module performs at least one operation on the brain signal, such as translation, matching, conversion, and recognition, to acquire characteristic information of the brain signal, and pre-processes the brain signal based on the characteristic information of the brain signal and appropriate brain signal safety measures, before performing safety processing on the brain signal based on appropriate brain signal safety measures, in order to guarantee the functionality of the brain signal.

9. A brain signal safety system according to claim 3, characterized in that it further includes an adjustment and optimization module, which, after applying safety processing to the brain signal based on appropriate brain signal safety measures, obtains the actual effect of the safety processing on the brain signal, performs an analysis on the actual effect to obtain an analysis result, and adjusts and optimizes an appropriate brain signal safety model based on the analysis result.

10. A brain-machine signal safety system according to claim 3, further comprising a signal adjustment module, the signal adjustment module for adjusting the brain-machine signal by employing at least one of the following measures: deletion, reduction, decrease, adjustment, modification, translation, conversion, reminder, warning, closure, enhancement, and increase.

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