Brain-computer signal security system and method of use thereof

Abstract

The application discloses a brain-computer signal safety system and a use method thereof, and relates to the technical field of intelligent information processing. The brain-computer signal safety system comprises an information acquisition module, an analysis module and a safety module. The information acquisition module is used for acquiring brain-computer signals and safety information of the brain-computer signals. The analysis module is used for taking the brain-computer signals and the safety information of the brain-computer signals as inputs, performing analysis by using a brain-computer signal safety model, judging whether the brain-computer signals reach a safety limit, and obtaining suitable brain-computer signal safety measures when the brain-computer signals reach the safety limit. The safety module is used for performing safety processing on the brain-computer signals according to the suitable brain-computer signal safety measures. The suitable brain-computer signal safety measures are obtained by using the brain-computer signal safety model established in advance, the safety of the brain-computer interface can be improved, the use effect of the brain-computer interface can be improved, and safety damage to an object acted on by the brain-computer signals can be avoided.

Description

[0001] This application claims priority to Chinese Patent Application No. 202310358178.2, filed on April 4, 2023, entitled "A Brain-Computer Signal Security System and Its Usage Method", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This invention relates to the field of intelligent information processing technology, and in particular to a brain-computer interface signal security system and its usage method. Background Technology

[0003] Brain-computer interfaces (BCIs) are direct connections created between the brain of a person or animal and an external device, enabling information exchange between the brain and the device. BCIs have proven highly effective in restoring damaged hearing, vision, and motor skills, allowing implanted prostheses to be controlled like natural limbs. With current technological and knowledge advancements, pioneers in BCI research are convincingly attempting to create BCIs that enhance human function, going beyond simply restoring it. As science and technology continue to advance, BCIs hold a promising future, potentially contributing to a better human life.

[0004] Brain-computer interfaces (BCIs) are mainly divided into implantable BCIs and non-implantable BCIs. Implantable BCIs involve directly implanting the device into the human body, connecting it directly to the brain or nerves. Non-implantable BCIs typically use a patch method, attaching the device to the outside of the body and stimulating the brain or nerves through methods such as electrical current. Because BCIs primarily operate on the brain, their safety requirements are extremely high.

[0005] Existing brain-computer interfaces lack a unified standard for the safety of brain-computer signals. Typically, a certain safety range is established to determine whether the brain-computer signal falls within a normal, safe range. If it falls outside this range, a potential problem is indicated, and corresponding safety measures are provided. However, the accuracy and precision of these safety measures are not high, and they may potentially cause harm to the recipient of the brain-computer signal. Summary of the Invention

[0006] The purpose of this invention is to provide a brain-computer interface (BCI) signal security system and its usage method, which can improve the security of the BCI, enhance the effectiveness of the BCI, and avoid causing safety damage to the target of the BCI signal.

[0007] To achieve the above objectives, the present invention provides the following solution:

[0008] A brain-computer interface security system, the system comprising: a database module, a data acquisition module, an analysis module, and a security module.

[0009] The database module is used to store a brain-computer interface database. The brain-computer interface database includes a brain-computer signal security model. The brain-computer signal security model includes the security restrictions required for brain-computer signals of different signals, information sources, information channels, access methods, modes, frequencies, strengths, times, intervals, information content, and the objects receiving / affecting the signals.

[0010] The information acquisition module is used to acquire brain-computer signals and brain-computer signal security information;

[0011] The analysis module is used to analyze brain-computer signals, brain-computer signal security information, and brain-computer signal security models to derive suitable brain-computer signal security measures.

[0012] The security module performs security processing on brain-computer signals according to appropriate brain-computer signal security measures, thereby improving the security of brain-computer signals.

[0013] A method of using the above-mentioned brain-computer interface security system, the method comprising:

[0014] Establish a brain-computer interface database, which includes a brain-computer signal security model. The brain-computer signal security model includes the security restrictions required for brain-computer signals of different signals, information sources, information channels, access methods, modes, frequencies, strengths, times, intervals, information content, and the objects receiving / affecting the signals.

[0015] Acquiring brain-computer interface signals and their security information;

[0016] By analyzing brain-computer signals, their security information, and security models, suitable brain-computer signal security measures can be derived.

[0017] By implementing appropriate brain-computer interface (BCI) signal security measures, the security of BCI signals can be improved.

[0018] A brain-computer interface security system, comprising:

[0019] The information acquisition module is used to acquire brain-computer signals and their security information; the brain-computer signals include: the pattern, type, form, content, function, neurons, information content, signal strength, frequency, purpose, target, access method, and location of the brain-computer signals; the security information of the brain-computer signals refers to information related to the security restrictions required for the brain-computer signals.

[0020] The analysis module is used to analyze the brain-computer signals and their security information as input using a brain-computer signal security model, determine whether the brain-computer signals have reached security limits, and derive appropriate brain-computer signal security measures when the security limits are reached. The brain-computer signal security model includes the security limits required for brain-computer signals of different signals, information sources, information channels, information forms, information content, access methods, modes, frequencies, strengths, times, intervals, information amounts, and the objects receiving / affecting the signals, as well as the brain-computer signal security measures required when the security limits are reached.

[0021] A security module is used to perform secure processing of the brain-computer signals according to appropriate brain-computer signal security measures.

[0022] In some embodiments, the system further includes: a database module for storing a brain-computer interface database, wherein the brain-computer interface database includes a brain-computer signal security model for each type of user or for each user; the brain-computer signal security model is obtained by personalizing settings based on the potential impact of brain-computer signals on users from different signals, information sources, information channels, information forms, information content, access methods, modes, frequencies, intensities, times, intervals, information amounts, and the objects receiving / affecting the signals, while also considering at least one of the following information regarding the target population, genetic factors, physiological factors, psychological factors, physical factors, lifestyle / life history, exercise, sleep patterns, learning conditions, work conditions, disease conditions, medications, medical devices, diet, physiological / psychological trauma, surgery, radiation, physiotherapy, rehabilitation, operation, examination, and psychological intervention.

[0023] The analysis module is also used to determine a suitable brain-computer signal security model based on the brain-computer signal and its security information before performing analysis using the brain-computer signal security model; the suitable brain-computer signal security model is a brain-computer signal security model in the brain-computer interface database, or a model obtained by adjusting a brain-computer signal security model in the brain-computer interface database according to the actual situation of the brain-computer signal.

[0024] In some embodiments, in the process of deriving suitable brain-computer signal security measures, it is also necessary to consider the mutual influence between different dimensions of brain-computer signals or between more than one brain-computer signal.

[0025] In some embodiments, the analysis module is further configured to assign corresponding levels / scores to the different analysis results of brain-computer signal security measures in at least one of the following analysis items: risk, harm, complexity, limitations, effectiveness, time, cost, convenience, economy, information integrity / authenticity / accuracy, user benefits, and user harm. Based on the levels / scores corresponding to different brain-computer signal security measures in each analysis item and the brain-computer signal security model, the module calculates the comprehensive level / score of the brain-computer signal security measures and determines suitable brain-computer signal security measures based on the comprehensive level / score.

[0026] In some embodiments, the brain-computer signal security model further includes security restrictions required for brain-computer signals with different contents, functions, and effects, as well as brain-computer signal security measures required to achieve the security restrictions. The effects include the effects on knowledge, cognition, values, interests, methods, goals, emotions, states, physical fitness, skills, behavior, intelligence, memory, physiological functions, expression, immunity, excitability, and the establishment / connection / disconnection of brain synapses.

[0027] In some embodiments, the system further includes: a processing module, configured to perform at least one of the following operations on the brain-computer signal: translation, matching, conversion, and recognition, to obtain feature information of the brain-computer signal; and to preprocess the brain-computer signal in advance, based on the feature information of the brain-computer signal and suitable brain-computer signal security measures, before performing security processing on the brain-computer signal according to suitable brain-computer signal security measures, to ensure the functionality of the brain-computer signal.

[0028] In some embodiments, the system further includes an adjustment and optimization module, configured to obtain the actual effect of the security processing of the brain-computer signal after the brain-computer signal has been security processed according to suitable brain-computer signal security measures, analyze the actual effect to obtain analysis results, and adjust and optimize the suitable brain-computer signal security model based on the analysis results.

[0029] In some embodiments, the system further includes a signal adjustment module for adjusting the brain-computer interface signal by taking at least one of the following measures: deletion, reduction, decrease, adjustment, alteration, translation, conversion, reminder, warning, shutdown, enhancement, and increase.

[0030] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0031] This invention provides a brain-computer interface (BCI) security system and its usage method. The BCI security system includes: an information acquisition module for acquiring BCI signals and their security information; an analysis module for analyzing the BCI signals and their security information using a suitable BCI security model, determining whether the BCI signals meet security limits, and deriving suitable BCI security measures when security limits are met; and a security module for performing security processing on the BCI signals according to the suitable security measures. By using the suitable BCI security measures derived from the pre-established BCI security model to perform security processing on the BCI signals, the security of the BCI interface can be improved, the effectiveness of the BCI interface can be enhanced, and safety damage to the target of the BCI signals can be avoided. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a system block diagram of a brain-computer signal security system provided in Embodiment 3 of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] The purpose of this invention is to provide a brain-computer interface (BCI) signal security system and its usage method, which can improve the security of the BCI, enhance the effectiveness of the BCI, and avoid causing safety damage to the target of the BCI signal.

[0036] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] Example 1

[0038] A brain-computer interface security system, the system comprising: a database module, a data acquisition module, an analysis module, and a security module.

[0039] The database module is used to store a brain-computer interface database. The brain-computer interface database includes a brain-computer signal security model. The brain-computer signal security model includes the security restrictions required for brain-computer signals of different signals, information sources, information channels, access methods, modes, frequencies, strengths, times, intervals, information content, and the objects receiving / affecting the signals.

[0040] The information acquisition module is used to acquire brain-computer signals and brain-computer signal security information;

[0041] The analysis module is used to analyze brain-computer signals, brain-computer signal security information, and brain-computer signal security models to derive suitable brain-computer signal security measures.

[0042] The security module is used to perform secure processing of brain-computer signals according to appropriate brain-computer signal security measures, thereby improving the security of brain-computer signals.

[0043] Example 2

[0044] A method of using the brain-computer interface security system as described in Example 1, the method comprising:

[0045] Establish a brain-computer interface database, which includes a brain-computer signal security model. The brain-computer signal security model includes the security restrictions required for brain-computer signals of different signals, information sources, information channels, access methods, modes, frequencies, strengths, times, intervals, information content, and the objects receiving / affecting the signals.

[0046] Acquiring brain-computer interface signals and their security information;

[0047] By analyzing brain-computer signals, their security information, and security models, suitable brain-computer signal security measures can be derived.

[0048] By implementing appropriate brain-computer interface (BCI) signal security measures, the security of BCI signals can be improved.

[0049] Example 3

[0050] A brain-computer interface security system, such as Figure 1 As shown, it includes:

[0051] The information acquisition module is used to acquire brain-computer signals and their security information. Brain-computer signals include: the pattern, type, form, content, function, neurons, information content, signal strength, frequency, purpose, target, access method, and location of the brain-computer signals. The security information of brain-computer signals refers to information related to the security restrictions required for brain-computer signals.

[0052] The analysis module takes brain-computer signals and their security information as input, analyzes them using a brain-computer signal security model, determines whether the brain-computer signals have reached security limits, and derives appropriate brain-computer signal security measures when the limits are reached. The brain-computer signal security model includes the security limits required for brain-computer signals of different signals, information sources, information channels, information forms, information content, access methods, modes, frequencies, strengths, times, intervals, information volumes, and the objects receiving / affecting the signals, as well as the brain-computer signal security measures required when the security limits are reached.

[0053] The safety module is used to safely process brain-computer signals according to appropriate brain-computer signal safety measures.

[0054] The brain-computer signal security system of this embodiment is applicable to implantable brain-computer signals (i.e., brain-computer signals generated by implantable brain-computer interfaces) and / or non-implantable brain-computer signals (i.e., brain-computer signals generated by non-implantable brain-computer interfaces).

[0055] The brain-computer interface (BCI) security system in this embodiment also includes a database module for storing a BCI database. The BCI database includes a BCI security model for each type of user or for each user. The BCI security model is obtained by personalizing settings based on the potential impact of different signals, information sources, information channels, information forms, information content, access methods, modes, frequencies, intensities, times, intervals, information amounts, and the BCI signals of the receiving / affected objects on the user. The potential impact on the user includes the impact on the user's health, behavior, feelings, emotions, social status, thinking, mood, memory, physiological functions, skills, mental state, rest, body parts, organs, systems, functions, needs, immunity, metabolism, abilities, temperament, and excitement levels. It also takes into account at least one of the following information for the target population: genetic factors, physiological factors, psychological factors, physical factors, lifestyle / life history, exercise, rest, learning, work, disease, drugs, medical devices, diet, physiological / psychological trauma, surgery, radiation, physiotherapy, rehabilitation, operation, examination, and psychological intervention.

[0056] This embodiment first establishes a brain-computer interface (BCI) database, which includes a BCI signal security model. This model includes the security restrictions required for different signals, information sources, information channels, information forms, information content, access methods, modes, frequencies, strengths, times, intervals, information volumes, and the objects receiving / affecting the signals, as well as the BCI signal security measures required to meet these restrictions. Preferably, the BCI signal security model in this embodiment may also include the security restrictions required for different content, functions, and effects of BCI signals, as well as the BCI signal security measures required to meet these restrictions. Effects include the impact on knowledge, cognition, values, interests, methods, goals, emotions, states, physical fitness, skills, behavior, intelligence, memory, physiological functions, expression, immunity, excitability, and the establishment / disconnection of at least one of the following: brain synapses. For example, the same BCI signal containing atheistic content might not be very stimulating for an atheist user, but it could be very stimulating for other users, potentially posing a security risk.

[0057] Because the human brain is divided into different regions, each corresponding to a different function—for example, the motor cortex controls various movements, the sensory cortex senses temperature, humidity, and touch, and the visual cortex forms vision—brain-computer signals, depending on their function and purpose, employ different modes, act on different neurons, and have different signal intensities, frequencies, and transmit different amounts of information. Therefore, it is necessary to establish a brain-computer signal security model. Specifically, this model examines the potential impacts of different brain-computer signals on users, including their sources, channels, forms, content, access methods, modes, frequencies, intensities, durations, intervals, information amounts, and the objects receiving / affecting the signals. It aims to determine the necessary security limits for brain-computer signals and the security measures required to meet these limits, thus obtaining a brain-computer signal security model. The brain-computer interface (BCI) signal safety model further includes the safety restrictions required for BCI signals with different content, functions, and effects, as well as the BCI signal safety measures required to achieve these restrictions. This is achieved by further analyzing the potential impacts of different BCI signals, information sources, information channels, information forms, information content, access methods, modes, frequencies, strengths, times, intervals, information volumes, signal receivers / affected objects, content, functions, and effects on users. This process determines the necessary safety restrictions for BCI signals and the required safety measures to achieve these restrictions, thus obtaining the BCI signal safety model. The BCI signal safety model can be established using at least one method: manual setting, artificial intelligence through machine learning analysis, or big data analysis. The potential impacts of BCI signals on users can affect their physiology, psychology, mental state, function, and memory; some of these impacts may cause harm, thus requiring safety restrictions. For example, for electrical signals, voltage, current, and frequency can be restricted to limit the safety of the electrical signal.

[0058] Based on this, this embodiment establishes a general brain-computer signal security model. After acquiring the brain-computer signal and its security information, the analysis module uses the brain-computer signal and its security information as input, and uses the brain-computer signal security model to analyze it, determine whether the brain-computer signal has reached the security limit, and derive appropriate brain-computer signal security measures when the security limit is reached.

[0059] Different people, due to differences in their physical conditions or states, have different acceptable ranges for brain-computer signals. Therefore, in this embodiment, the brain-computer signal safety model can also be personalized based on at least one of the following information for the target population: genetic factors, physiological factors, psychological factors, physical factors, lifestyle / life history, exercise, daily routine, learning status, work status, disease status, medications, medical devices, diet, physiological / psychological trauma, surgery, radiation, physiotherapy, rehabilitation, operation, examination, and psychological intervention. This allows the brain-computer signal safety model to be applied to different target groups in a personalized manner.

[0060] Factors that may affect the safety restrictions required for brain-computer interfaces can include: population factors, such as nationality, race, gender, and age; genetic factors, such as genes, genetic variations / alterations, genetic defects, family history of diseases, etc.; physiological factors, such as physical fitness, nutritional status, hearing, vision, taste, smell, touch, respiration, motor coordination, digestion, absorption, excretion, sexual function, fertility, and related abilities; psychological factors, such as mental illness, emotions, feelings, intelligence, attention, memory, perception, communication skills, and expressive abilities; physical factors, such as height, weight, strength, speed, and physical development; and lifestyle / life history factors. Examples of factors include: work-related information, diet-related information, study-related information, exercise-related information, entertainment-related information, and daily routine information; disease-related factors, such as diagnosis and symptoms; time-related factors, such as year / season / month / circadian rhythm / ten-day period / week / day, morning / morning / noon / afternoon / evening / night, after waking up / before going to bed / after exercising / before eating; environmental factors, such as temperature, humidity, air pressure, season, longitude, latitude, altitude, air quality, topography, landforms, oxygen content, light, ultraviolet radiation, radiation, electromagnetic waves, noise, epidemics, and plants; and may also include factors such as drugs, medical devices, diet, physiological / psychological trauma, surgery, radiation, physiotherapy, rehabilitation, operation, examination, psychological intervention, and other possible elements.

[0061] This embodiment considers the potential impact of brain-computer signals on users based on different signals, information sources, information channels, information forms, information content, access methods, modes, frequencies, strengths, times, intervals, information amounts, and the brain-computer signals of the recipients / affected objects. It also personalizes settings based on at least one of the following factors: the target population, genetic factors, physiological factors, psychological factors, physical factors, lifestyle / life history, exercise, sleep patterns, learning and working conditions, disease conditions, medications, medical devices, diet, physiological / psychological trauma, surgery, radiation, physiotherapy, rehabilitation, operation, examination, and psychological intervention, to obtain a brain-computer signal safety model for each type of user or for each individual user.

[0062] Based on this, this embodiment establishes multiple personalized brain-computer interface (BCI) signal security models. After acquiring the BCI signal and its security information, the analysis module is further used to determine a suitable BCI signal security model based on the BCI signal and its security information before performing analysis using the BCI signal security model. The analysis is then performed using the suitable BCI signal security model to determine whether the BCI signal has reached the security limit. If the security limit is reached, suitable BCI signal security measures are derived. The suitable BCI signal security model is either a BCI signal security model in the BCI interface database or a model obtained by adjusting a BCI signal security model in the BCI interface database according to the actual situation of the BCI signal.

[0063] To improve the efficiency of establishing multiple personalized brain-computer interface (BCI) signal safety models, this embodiment can first classify existing data based on different user groups to establish a model for one type of user. This classification can then be gradually refined, making the user group classification increasingly detailed, ultimately establishing a BCI signal safety model for each individual user. Through observation, inspection, and detection methods, indicators of users under various BCI signal conditions are collected, along with the real-time, short-term, and long-term impacts on the body as a whole, its systems, organs, parts, tissues, cells, molecular levels, physiological functions, and psychological states at different BCI signal ranges. This data is used to analyze the potential impact of BCI signals on users at different ranges, determine the necessary safety limits and the BCI signal safety measures required to reach those limits, and thus establish a BCI signal safety model. Furthermore, it is possible to combine various scientific disciplines, including physiology, pathology, genetics, pharmacology, psychology, materials science, and environmental science, to establish, predict, and adjust relevant influences and limitations to build a BCI signal safety model. The analysis required for brain-computer interfaces (BCIs) includes: which factors of BCIs affect users, what kind of impact they have, and the extent of their impact; the relationship between the values ​​of each factor and their impact; which impacts / to what extent are detrimental to users; and the BCI safety measures required to prevent BCIs from causing harm to the human body under different conditions.

[0064] After obtaining the combinations of elements from various dimensions, a dataset is created to assess whether there are common characteristics. For combinations of elements with common characteristics, a comprehensive evaluation, monitoring, and control are conducted. Various relevant statistical methods and big data monitoring techniques are used to establish a brain-computer interface (BCI) signal safety model. In this embodiment, the BCI signal safety model is a model of a user or a group of users. For example, it can be a model of a specific user, or a model of a population group, such as a model of people with mental illness, a model of minors aged 12-18, etc. It can also be a regional model, an industry model, an ethnic model, etc. The model can be a static model or a dynamic model, such as a model after being affected / stimulated, after taking medication, after eating, or before going to sleep.

[0065] In this embodiment, the brain-computer interface signal security model can be established based on various medical manuals, guidelines, research reports, literature, expert consensus, meeting minutes and consensus within medical alliances / hospitals / departments, industry standards, textbooks, papers, books, inventions, scientific inferences, experimental reports, test reports, data analysis reports, test reports, inspection reports, approval documents, relevant regulations, relevant guidelines, relevant policies, relevant systems, evaluations / reports from relevant doctors / nurses / pharmacists / nursing staff / patients, and other professional / authoritative research results. It can be manually set by experts based on professional / authoritative research results, literature, data, experience, etc., or it can be established based on information obtained and reorganized through data mining, or it can be established through big data analysis, or it can be established through artificial intelligence deep learning, or it can be established by combining existing knowledge graphs, or it can be established by continuously acquiring new data during use.

[0066] The information acquisition module is used to acquire brain-computer signals and their security information.

[0067] The information acquisition module acquires brain-computer signals from the brain-computer interface or other related systems. The brain-computer signals include information such as the pattern, type, form, content, function, neurons, information content, signal strength, frequency, purpose, target, access method, and location of the brain-computer signals.

[0068] Among them, the safety information of brain-computer signals refers to information related to the safety restrictions required for brain-computer signals, which may include: basic information of the target, genetic information, family health information (such as family medical history), medical history, allergy history, regional epidemiological history, medication history, surgical history, surgical protocol history, learning status, work status, exercise status, family situation, living environment, hobbies, compliance status, tolerance status, medical insurance status, as well as physiological / psychological / learning / work / physical fitness / sleep / exercise / emotion / metabolism / vision / hearing / intelligence / attention / diet / immunity / growth and development / memory / fertility status and work and rest schedules; it may also include: temperature, humidity, air pressure, season, longitude, latitude, altitude, air quality, topography, landforms, oxygen content, light, ultraviolet radiation, radiation, electromagnetic waves, noise, epidemiology, plants, etc.; it may also include: physical examination related indicators, hematological related indicators, blood The indicators include those related to thrombosis and hemostasis, excretion / secretion / body fluids, kidney function, liver function, biochemistry, immunology, genetics, pathogens, heart function, lung function, psychological state, mental state, athletic ability, imaging, and acoustics. Specifically, these include: height, weight, body temperature, vision, hearing, blood glucose, red blood cells, white blood cells, platelets, uric acid, cholesterol, transaminases, calcium, iron, potassium, triglycerides, heart rate, body fat, vital capacity, and medical indicators related to imaging, acoustics, intelligence, psychology, and athletic ability; ratios of various indicators, such as height / weight and height / waist circumference; various health manifestations, feelings, and standards, such as skin color, tongue coating, fundus examination, body fat, skin texture, hair quality, and strength; and may also include other information related to the safety limitations required for brain-computer interfaces.

[0069] Sources for obtaining safe information from brain-computer interfaces can include: the target's personal information database, health records, medical orders, medical records / electronic medical records, diagnostic reports, examination / test results, monitoring results, nutritional assessment reports, family or clan member health records, medical orders, medical records, medication records, prescriptions, electronic medical records, medical institution information systems, pharmacy / medical device store information systems, medical records, treatment records, assessment reports, consultation records, survey records, daily routine records / plans, dietary records / plans, shopping records / plans, medication records / plans, treatment records / plans, exercise records / plans, work records / plans, study records / plans, rehabilitation records / plans, health care records / plans, test / examination reports, surgical plans / records, health management plans, bills, clinical treatment pathways, examination / test results, and surgical settings. Records and genetic testing results can be obtained from user / doctor / nurse / caregiver usage / prescription / recommendation records, or from various wearable devices, sensors, electronic devices, electronic positioning systems, weather forecasting systems, electronic temperature / humidity / barometric pressure detection devices, smart speakers, smart home systems, smart monitoring / monitoring systems, smart glasses, smart toilets, smart floors, smart scales, smart detection / analysis devices, electronic infusion systems, surgical robots, facial recognition analysis, fingerprint recognition, voice recognition, gait recognition, positioning systems, social platforms, etc. They can also be obtained through big data analysis of information related to life, learning, work, exercise, travel, social interaction, shopping, diet, rest, and entertainment, or from analysis of information related to race / family / region / age / marital status / fertility. Missing information can be provided or supplemented by the user, or highly relevant information can be proactively prompted to the user / doctor for observation, monitoring, examination, inquiry, analysis, confirmation, and recording of relevant situations or acquisition of relevant indicators / performance / feelings / symptoms / physiological changes. The assessment report includes physiological, psychological, economic, credit, and athletic abilities. Intelligent detection / analysis equipment includes: odor, image, sound, pulse, X-ray, CT, MRI, ultrasound, electroencephalogram, mass spectrometry, tongue diagnosis analysis, fundus examination, gastroscopy, colonoscopy, catheterization, minimally invasive endoscopy, heart rate, blood oxygen saturation, blood pressure, blood glucose, blood lipids, body temperature, blood tests, urine tests, stool tests, pulse measurement / analysis equipment, weight / body fat scales, etc.

[0070] When the brain-computer signal security model is a general brain-computer signal security model, the analysis module directly uses the brain-computer signal security model for analysis. Specifically, it takes brain-computer signals and their security information as input, uses a suitable brain-computer signal security model for analysis, determines whether the brain-computer signals have reached the security limits, and derives appropriate brain-computer signal security measures when the security limits are reached.

[0071] When there are multiple personalized brain-computer interface (BCI) security models, the analysis module determines a suitable BCI security model based on the BCI signals and their security information. Specifically, it directly matches the BCI signals and their security information to the BCI database to find a suitable model. Alternatively, it selects the most suitable BCI security model from the database based on the BCI signals and their security information, then considers the differences between the actual situation of the BCI signals and the existing model (i.e., the most suitable BCI security model), and makes appropriate adjustments to make the model more suitable for the actual situation. Therefore, in this embodiment, the suitable BCI security model is either one from the BCI database or a model obtained by adjusting one from the database based on the actual situation of the BCI signals. The adjustment can be done manually, automatically by the BCI security system, or automatically by artificial intelligence through self-learning.

[0072] After obtaining a suitable brain-computer interface (BCI) signal safety model, the BCI signal and its safety information are used as inputs. The model is then used to analyze the BCI signal to determine whether it meets safety limits. If it does, appropriate BCI signal safety measures are derived. Based on the suitable BCI signal safety model, the BCI signal, and its safety information, appropriate BCI signal safety measures are obtained. These measures may include limiting the voltage, current, frequency, site of action, and neurons involved in the BCI signal.

[0073] In this embodiment, in deriving suitable brain-computer interface (BCI) signal security measures, it is also necessary to consider the mutual influence between different dimensions of BCI signals or between more than one BCI signal. This mutual influence may involve the enhancement or weakening of BCI signals, changes in information form or content, impacts on the receiving channel / location, and effects on the overall performance. By considering these mutual influences, more suitable BCI signal security measures can be obtained. The BCI signal security model may also include at least one of the following: mutual restrictions, taboos, and mutual influences between different BCI signals. This includes mutual restrictions / taboos / mutual influences of BCI signals of various modes, functions, neurons, information content, signal strength, frequency, and applications. These mutual restrictions / taboos / mutual influences may be caused by time factors, psychological factors, physiological factors, signal strength factors, signal frequency factors, etc. Related mutual restrictions / contraindications / mutual influences may include: materials, type, method of use, conditions of use, time, intensity, frequency, population, genetic factors, diseases, medical history, allergy history, physical indicators, physiological development, marriage and childbearing, physiological condition, psychological / intellectual condition, living / working / studying / exercising / recreational conditions, temperature / humidity / air pressure / season / altitude / air quality / oxygen content / light / ultraviolet radiation / noise, emotions, electricity, magnetism, light, heat, radiation, stimulation, etc.

[0074] In this embodiment, during the process of deriving suitable brain-computer interface (BCI) security measures, the analysis model obtains multiple preliminary BCI security measures. Further, a suitable BCI security measure is selected from these preliminary measures. Specifically, the analysis module assigns corresponding levels / scores to the different analysis results of the BCI security measures in at least one of the following categories: risk, harm, complexity, limitations, effect, time, cost, convenience, economy, information integrity / authenticity / accuracy, user benefit, and user harm. When analyzing BCI security measures, the module can calculate the comprehensive level / score of each BCI security measure based on its corresponding level / score for each analysis category and the BCI security model. This allows for the determination of the feasibility of the BCI security measure or for recommending it. In this embodiment, the analysis module is also used to set corresponding levels / scores for different analysis results of brain-computer signal security measures in at least one of the following analysis items: risk, harm, complexity, limitations, effect, time, cost, convenience, economy, information integrity / authenticity / accuracy, user benefit, and user harm. Based on the level / score corresponding to each analysis item of different brain-computer signal security measures and the brain-computer signal security model, the module calculates the comprehensive level / score of the brain-computer signal security measures. Based on the comprehensive level / score, the module determines suitable brain-computer signal security measures to select suitable brain-computer signal security measures from multiple preliminary brain-computer signal security measures.

[0075] The security module is used to process brain-computer signals securely according to appropriate brain-computer signal security measures, thereby improving the security of brain-computer signals.

[0076] The brain-computer signal security system of this embodiment further includes: a processing module, used to perform at least one of the following operations on the brain-computer signal: translation, matching, conversion, and recognition, to obtain the feature information of the brain-computer signal, and to preprocess the brain-computer signal in advance according to the feature information of the brain-computer signal and suitable brain-computer signal security measures, before performing security processing on the brain-computer signal according to the suitable brain-computer signal security measures, to ensure the functionality of the brain-computer signal.

[0077] Specifically, in this embodiment, before performing safety processing on the brain-computer interface (BCI) signal according to appropriate BCI signal safety measures, the processing module preprocesses the BCI signal to ensure its functionality as much as possible. For example, if the voltage of the BCI signal exceeds the standard, and the appropriate BCI signal safety measure is to limit the voltage, then directly reducing the voltage of the BCI signal would meet the safety requirements, but it might cause significant functional loss, rendering the BCI signal unable to perform its original function. To implement the preprocessing process, the processing module in this embodiment performs at least one of the following operations on brain-computer interfaces: translation, matching, conversion, and recognition. By performing these operations, the module analyzes the characteristics of the brain-computer signals. Based on these characteristics and appropriate brain-computer interface security measures, the signals are preprocessed to ensure their functionality as much as possible. Specifically, before performing security processing based on appropriate brain-computer interface security measures, the processing module preprocesses the signals according to their characteristics. This preprocessing can be either population-oriented or individual-oriented. It involves intervening in known / general security risks before they enter the brain-computer interface. Preprocessing can reduce risks in advance, preventing users without or with limited security measures from suffering general / conventional security harm. Alternatively, it can provide collective protection for users receiving the same signals, reducing the total cost of individual measures for each user and ensuring the functionality of the brain-computer interface as much as possible.

[0078] In this embodiment, the processing module can analyze the characteristics of brain-computer signals using methods such as time-domain method, frequency-domain method, and time-frequency method.

[0079] Time-domain methods: These methods extract waveform feature parameters directly through zero-crossing analysis, histogram analysis, variance analysis, correlation analysis, peak detection, waveform parameter analysis, coherent averaging, and waveform recognition. These feature parameters are then used for brain-computer interface (BCI) signal classification, recognition, tracking, and transient analysis. Time-domain feature extraction combines specific filtering methods with sampling methods to remove temporal noise from BCI signals and improve their signal-to-noise ratio. Amplitude and amplitude energy features are the most frequently extracted. Filtering methods include bandpass filtering, Laplace filtering, all-lead average reference filtering, Kalman filtering, and moving average filtering. Furthermore, continuous or discrete wavelet transforms can also be used to extract time-varying features of BCI signals.

[0080] Frequency domain methods: Power spectrum estimation and parametric modeling can be used to analyze the characteristics of brain-computer interfaces (BCIs). Power spectrum estimation is a frequency domain analysis method that reflects the frequency components and relative strengths of a signal. By analyzing the power and coherence of each frequency band of the BCI signal using this method, the signal's patterns can be obtained. Parametric modeling is the most widely used method in modern spectrum estimation. Due to its high frequency resolution and smooth spectrum, it can automatically extract and quantitatively analyze parameters, making it particularly suitable for short data processing and applicable to the dynamic analysis of BCI signals. Frequency domain features are typically measured using power spectral density (PSD), adaptive autoregressive (AAR) model parameters, or wavelet band energy. Corresponding extraction methods include Fast Fourier Transform (FFT), AAR models, and wavelet transforms.

[0081] Time-frequency method: Brain-computer signals have complex and non-stationary characteristics, and traditional time-domain and frequency-domain analysis also have signal processing uncertainties. Therefore, combining time-domain feature values ​​with frequency-domain power spectrum is used for feature extraction of brain-computer signals. Among them, Wigner distribution and wavelet transform are preferred time-frequency analysis methods.

[0082] The brain-computer interface (BCI) security system of this embodiment can also analyze the actual effects of security processing of BCI signals during use. The analysis results can be used to adjust and optimize the BCI security model. Specifically, the BCI security system of this embodiment also includes an adjustment and optimization module, which is used to obtain the actual effects of security processing of BCI signals after processing them according to suitable BCI security measures, analyze the actual effects, obtain analysis results, and adjust and optimize the suitable BCI security model based on the analysis results. Specifically, through big data or evidence-based methods, the application results of different users, different signals, and different models are tracked and analyzed, continuously accumulating data relationships, identifying areas that need / can be improved, and performing dynamic optimization.

[0083] The brain-computer signal security system in this embodiment further includes a signal adjustment module, used to adjust the brain-computer signal by taking at least one of the following measures when the brain-computer signal effect is poor: deletion, reduction, decrease, adjustment, alteration, translation, conversion, reminder, warning, shutdown, enhancement, and addition, in order to adjust the effect of the brain-computer signal, including improving, altering, increasing, and decreasing the effect of the brain-computer signal. Poor effect of the brain-computer signal refers to poor effect of the originally acquired brain-computer signal, or poor effect of the brain-computer signal after security processing. In this case, the brain-computer signal security model can be used to determine whether the effect is poor.

[0084] The brain-computer signal security system of this embodiment can be used as a standalone device, or it can be used by users through external hardware such as mobile hard drives, boxes, or cards. It can also be installed on a local server to support local users, or it can be installed on a private cloud server to support private cloud users, or it can be installed on the Internet to provide services to Internet users.

[0085] This embodiment provides a brain-computer interface (BCI) signal security system that acquires BCI signals and their security information. It analyzes the BCI signals, their security information, and a BCI signal security model to determine if they meet security limits. If limits are met, it determines appropriate security measures and performs security processing on the BCI signals based on these measures, thereby improving their security. This embodiment establishes a BCI signal security model based on a large-scale data sample, considering different signals, information sources, information channels, information formats, information content, access methods, modes, frequencies, strengths, times, intervals, information volumes, and the objects receiving / affecting the signals. The brain-computer interface (BCI) signal security model is developed, taking into full account the actual situation of individuals during its establishment. The model is segmented according to the target population / application scenario and relevant parameter standards, fully grasping the individualized information of users to obtain a BCI signal security model for a class of users or a single user. Furthermore, suitable BCI signal security measures are proposed based on the personalized circumstances of the objects affected by the BCI signal. The BCI signal is then processed securely according to these appropriate security measures, and preprocessing can be performed on the BCI signal in advance to ensure its functionality as much as possible. This improves the security of the BCI, enhances its effectiveness, and avoids causing safety damage to the objects affected by the BCI.

[0086] The technical features 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. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0087] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A brain-computer interface security system, characterized in that, The system includes: a database module, an information acquisition module, an analysis module, and a security module. The database module is used to store a brain-computer interface database. The brain-computer interface database includes a brain-computer signal safety model. The brain-computer signal safety model includes the safety restrictions required for brain-computer signals of different signals, information sources, information channels, access methods, modes, frequencies, strengths, times, intervals, information amounts, and the objects receiving / affecting the signals. The brain-computer signal safety model performs personalized analysis on at least one of the following factors: population factors, genetic factors, physiological factors, psychological factors, and physical factors of the target object. By collecting indicators of users under various brain-computer signal conditions, as well as the impact of users on their bodies, physiological functions, and psychological conditions in different ranges of brain-computer signals, the model analyzes the possible impact of brain-computer signals on health conditions in different ranges and determines the required safety restrictions. The information acquisition module is used to acquire brain-computer signals and brain-computer signal security information; The analysis module is used to analyze brain-computer signals, brain-computer signal security information, and brain-computer signal security models to derive suitable brain-computer signal security measures. The security module performs security processing on brain-computer signals according to appropriate brain-computer signal security measures, thereby improving the security of brain-computer signals.

2. A method of using the brain-computer interface security system as described in claim 1, characterized in that, The method includes: Establish a brain-computer interface database, which includes a brain-computer signal security model. The brain-computer signal security model includes the security restrictions required for brain-computer signals of different signals, information sources, information channels, access methods, modes, frequencies, strengths, times, intervals, information content, and the objects receiving / affecting the signals. Acquiring brain-computer interface signals and their security information; By analyzing brain-computer signals, their security information, and security models, suitable brain-computer signal security measures can be derived. By implementing appropriate brain-computer interface (BCI) signal security measures, the security of BCI signals can be improved.

3. A brain-computer interface security system, characterized in that, include: The information acquisition module is used to acquire brain-computer signals and their security information. The brain-computer signals include: the pattern, type, form, content, function, information content, signal strength, frequency, purpose, target, access method, and location of the brain-computer signals; the security information of the brain-computer signals refers to information related to the security restrictions required for the brain-computer signals. The analysis module is used to analyze the brain-computer signals and their security information as input using a brain-computer signal security model, determine whether the brain-computer signals have reached security limits, and derive appropriate brain-computer signal security measures when the security limits are reached. The brain-computer signal security model includes the security limits required for brain-computer signals of different signals, information sources, information channels, information forms, information content, access methods, modes, frequencies, strengths, times, intervals, information amounts, and the objects receiving / affecting the signals, as well as the brain-computer signal security measures required when the security limits are reached. A security module is used to perform secure processing of the brain-computer signals according to appropriate brain-computer signal security measures; The database module is used to store the brain-computer interface database, which includes a brain-computer signal security model for each type of user or for each user.

4. The brain-computer interface signal security system according to claim 3, characterized in that, Also includes: The database module stores a brain-computer interface database, which includes a brain-computer signal safety model for each type of user or for each user. The brain-computer signal safety model is obtained by personalizing settings based on the potential impact of different signals, information sources, information channels, information forms, information content, access methods, modes, frequencies, strengths, times, intervals, information volumes, and the brain-computer signals of the receiving / affected objects on the user. It also considers at least one of the following factors related to the target population: demographic factors, genetic factors, physiological factors, psychological factors, physical factors, lifestyle / history, exercise, sleep patterns, learning habits, work conditions, disease conditions, medications, medical devices, diet, physiological / psychological trauma, surgery, radiation, physiotherapy, rehabilitation, operation, examination, and psychological intervention. The analysis module is also used to determine a suitable brain-computer signal security model based on the brain-computer signal and its security information before performing analysis using the brain-computer signal security model; the suitable brain-computer signal security model is a brain-computer signal security model in the brain-computer interface database, or a model obtained by adjusting a brain-computer signal security model in the brain-computer interface database according to the actual situation of the brain-computer signal.

5. A brain-computer interface security system according to claim 3, characterized in that, In the process of determining appropriate brain-computer interface (BCI) safety measures, it is also necessary to consider the mutual influence between different dimensions of BCI or between more than one BCI.

6. A brain-computer interface security system according to claim 3, characterized in that, The analysis module is also used to set corresponding levels / scores for different analysis results of brain-computer signal security measures in at least one of the following analysis items: risk, harm, complexity, limitations, effect, time, cost, convenience, economy, information integrity / authenticity / accuracy, user benefit, and user harm. Based on the level / score corresponding to each analysis item of different brain-computer signal security measures and the brain-computer signal security model, the module calculates the comprehensive level / score of the brain-computer signal security measures and determines the appropriate brain-computer signal security measures based on the comprehensive level / score.

7. A brain-computer interface security system according to claim 3, characterized in that, The brain-computer signal safety model also includes the safety restrictions required for brain-computer signals with different contents, functions, and effects, as well as the brain-computer signal safety measures required to achieve the safety restrictions. The effects include the effects on at least one of knowledge, cognition, values, interests, methods, goals, emotions, states, physical fitness, skills, behavior, intelligence, memory, physiological functions, expression, immunity, and excitability.

8. A brain-computer interface security system according to claim 3, characterized in that, Also includes: The processing module is used to perform at least one of the following operations on the brain-computer signal: translation, matching, conversion, and recognition, to obtain the feature information of the brain-computer signal; Based on the characteristic information of the brain-computer signals and appropriate brain-computer signal security measures, the brain-computer signals are preprocessed before being processed for security purposes according to the appropriate brain-computer signal security measures, so as to ensure the functionality of the brain-computer signals.

9. A brain-computer interface security system according to claim 3, characterized in that, Also includes: The adjustment and optimization module is used to obtain the actual effect of the brain-computer signal security processing after the brain-computer signal is security processed according to suitable brain-computer signal security measures, analyze the actual effect, obtain the analysis results, and adjust and optimize the suitable brain-computer signal security model based on the analysis results.

10. A brain-computer interface security system according to claim 3, characterized in that, Also includes: The signal adjustment module is used to adjust the brain-computer signal by taking at least one of the following measures: deletion, reduction, decrease, adjustment, change, translation, conversion, reminder, warning, shutdown, enhancement, and increase.

Images