A brain wave information processing method and system, device and medium

Abstract

The embodiment of the application discloses a brain wave information processing method and system, equipment and medium, the brain wave information processing method comprises the following steps: recording and collecting brain wave signals under different stimulation conditions and preprocessing the brain wave signals; using a signal analysis method to analyze the preprocessed brain wave signals in time domain, frequency spectrum and time-frequency domain power spectrum, extract and mark characteristic signals; using a trained classifier model to classify and identify the advanced information contained in the brain wave signals; using the classified signals as input, using a reinforcement learning algorithm for sensory enhancement, motor imagination learning and emotion evolution regulation, forming an inverse brain wave control signal; using the inverse brain wave control signal to stimulate the brain of the subject; storing the brain wave signals containing sensory information, motor imagination and emotion information, the inverse brain wave control signal and the brain wave signals after feedback stimulation, and establishing a subject advanced brain wave information database.

Description

Technical Field

[0001] This invention relates to the field of computers. More specifically, it relates to a method, system, device, and medium for processing brainwave information. Background Technology

[0002] Electroencephalography (EEG) signals are primarily formed by the summation of postsynaptic potentials generated synchronously by a large number of neurons in the cerebral cortex. They record the electrical wave changes during brain activity and are a comprehensive reflection of the electrophysiological activity of brain nerve cells on the surface of the cerebral cortex or scalp. Traditional EEG signal analysis largely relies on experts' experience to visually identify artifacts and provide evaluations based on the amplitude, frequency, and transient distribution of the EEG signal waveform. This keeps the reading and analysis of EEG information at a subjective level. In reality, EEG signals also contain higher-level brainwave information related to human physiological senses, motor imagery, emotions, and thought processes. Researching methods for recognizing and analyzing higher-level brainwave information plays a crucial role in the development of human brain development and brain-computer interface technology.

[0003] The study of sensory perception, motor association, and emotion is an interdisciplinary field involving computer science, psychology, cognitive science, and neuroscience. Compared to using facial recognition and motion analysis for sensory perception, motor association, and emotion recognition, analyzing brainwave signals can eliminate interference from human spoofing or alteration, resulting in higher recognition accuracy.

[0004] While significant improvements have been made in acquiring EEG signals, effectively extracting and selecting features to classify and identify sensory, motor, and emotional information remains a challenge in current research, given the presence of various noises, low signal-to-noise ratios, and asymmetric instability in EEG signals. Currently, artificial intelligence (AI) is used to analyze human EEG signals, analyze viewpoints, and draw conclusions based on population brain activity. However, effective methods for classifying and identifying higher-level brainwave information such as sensory, motor, and emotional information are still lacking. Summary of the Invention

[0005] The purpose of this invention is to provide a brainwave information processing method, system, device, and medium to solve at least one of the problems existing in the related technologies.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The first aspect of this invention provides a method for processing brainwave information, including...

[0008] Record and collect changes in EEG and magnetoencephalogram (MEG) signals at different brain regions under different stimulation conditions to obtain brainwave signals and preprocess the brainwave signals;

[0009] Signal analysis methods were used to analyze the time domain, spectrum, and power spectrum of the preprocessed brainwave signals, and feature signals were extracted and labeled.

[0010] A classifier model is trained using labeled feature signals, and the trained classifier model is used to classify and identify sensory information, motor imagery information, and emotional information contained in brainwave signals.

[0011] The classified signals are used as input, and the reinforcement learning algorithm in the twin brain is used to enhance sensory perception, learn motor imagery, and regulate emotional evolution, forming inverse brainwave regulation signals.

[0012] The inverse brainwave modulation signal is used to provide feedback stimulation to the subject's brain, thereby stimulating the subject's brain's potential abilities in sensory, motor imagination, and emotional regulation.

[0013] The system stores brainwave signals containing sensory information, motor imagery, and emotional information, as well as inverse brainwave modulation signals and brainwave signals after feedback stimulation, to establish a high-level brainwave information database for the subjects.

[0014] Optionally, the step of classifying and identifying sensory information, motor imagery information, and emotional information contained in brainwave signals using a trained classifier model includes:

[0015] The sensory information is categorized into categories including visual, auditory, olfactory, and tactile.

[0016] The motor imagery information is categorized into categories including motor commands and muscle control;

[0017] Emotional information is classified using discrete emotion models or continuous emotion models, with categories including anger, fear, anticipation, sadness, disgust, surprise, acceptance, and joy.

[0018] Optionally, the step of using the classified signal as input and employing reinforcement learning algorithms in the twin brain for sensory enhancement, motor imagery learning, and emotional evolution regulation to form an inverse brainwave regulation signal includes:

[0019] By using reinforcement learning algorithms, the thought control mechanism and emotional neural regulation output from the neural circuit model in the twin brain are used as sensory, motor imagination and emotional intelligent agents to interact with specific simulated scenarios and regular simulated scenarios to carry out sensory enhancement and motor imagination learning training.

[0020] It can learn and evolve under both supervised and unsupervised conditions to achieve emotional evolution regulation, and form inverse brainwave regulation signals through sensory, transport imagination and emotional regulation stimulation.

[0021] Optionally, the method further includes: forming effective external stimuli based on olfaction, taste, vision, hearing, and touch;

[0022] The process of recording and acquiring changes in electroencephalogram (EEG) and magnetoencephalogram (MEG) signals at different brain regions under different stimulation conditions, obtaining brainwave signals, and preprocessing the brainwave signals includes:

[0023] The changes in EEG and MEG signals at different brain regions were recorded and acquired by a non-implantable EEG signal acquisition device under different effective external stimuli. The EEG and MEG signals at different brain regions were then subjected to noise reduction, filtering, artifact removal, compression of silent instantaneous signals, and amplification of special waveforms to obtain preprocessed brainwave signals.

[0024] A second aspect of the present invention provides a brainwave information processing system, comprising:

[0025] The brainwave stimulation module is configured to generate external stimulation sources;

[0026] The brainwave signal acquisition module is configured to acquire electroencephalogram (EEG) and magnetic resonance imaging (MRI) signals from different brain regions under different stimulation conditions to obtain brainwave signals.

[0027] The feature extraction and selection module is configured to perform time-domain, spectrum, and time-frequency domain power spectrum analysis on the brainwave signal using signal analysis methods to extract and label feature signals.

[0028] The artificial intelligence classifier model module is configured to train a classifier model based on labeled feature signals, and use the trained classifier model to classify and identify sensory information, motor imagery information and emotional information contained in brainwave signals.

[0029] The twin brain simulation model is configured to use reinforcement learning algorithms for sensory enhancement, motor imagery learning, and emotional evolution regulation, forming inverse brainwave regulation signals.

[0030] The brainwave signal feedback module is configured to use the inverse brainwave modulation signal to provide feedback stimulation to the subject's brain, thereby stimulating the subject's brain's potential abilities in sensory, motor imagination and emotional regulation.

[0031] The brainwave storage module is configured to store brainwave signals output by the subject's brain, including sensory information, motor imagination and emotional information, inverse brainwave modulation signals output by the twin brain model, and brainwave signals after feedback stimulation output by the brainwave signal feedback module, thereby establishing a brainwave information database for the subject.

[0032] Optionally, the external stimulus source is an effective external stimulus source including smell, taste, vision, hearing and touch;

[0033] The brainwave signal acquisition module includes wearable voltage and electromagnetic array acquisition sensors.

[0034] Optionally, it also includes,

[0035] The brainwave signal preprocessing module is configured to perform noise reduction, filtering, artifact removal, compression of silent instantaneous signals, and amplification of special waveforms on the time-domain waveform signal, and send the processed brainwave signal to the feature extraction and selection module.

[0036] Optionally, the classifier model includes at least one of decision tree, multivariate logistic regression, random forest, SVM, and deep learning.

[0037] A third aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method provided in the first aspect of the present invention.

[0038] A fourth aspect of the present invention provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect of the present invention.

[0039] The beneficial effects of this invention are as follows:

[0040] The brainwave information processing method provided in this embodiment collects EEG and MEG signals from subjects under different external stimuli, preprocesses them, and uses a trained classifier model to identify the preprocessed brainwave information to obtain high-level information. Reinforcement learning is performed based on a reinforcement learning algorithm in a twin brain, and the inverse brainwave modulation signal is used to provide feedback stimulation to the subject's brain, stimulating the subject's brain's potential abilities in sensory perception, motor imagination, and emotional regulation, forming a more favorable mode of thinking control and emotional regulation based on environmental conditions, thus realizing the identification of high-level information in EEG and MEG signals. Attached Figure Description

[0041] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0042] Figure 1 A flowchart illustrating a brainwave information processing method according to an embodiment of this application is shown.

[0043] Figure 2 A flowchart illustrating a brainwave information processing method according to another embodiment of this application is shown.

[0044] Figure 3 This diagram illustrates a twin brain simulation model for reinforcement learning, as proposed in one embodiment of this application.

[0045] Figure 4 This illustration shows a schematic diagram of the structure of a computer device according to an embodiment of this application. Detailed Implementation

[0046] To more clearly illustrate the present invention, the following description, in conjunction with embodiments and accompanying drawings, further explains the invention. Similar components in the drawings are indicated by the same reference numerals. Those skilled in the art should understand that the specific description below is illustrative rather than restrictive and should not be construed as limiting the scope of protection of the present invention.

[0047] In extreme working environments, such as those of astronauts, pilots, and soldiers, in order to obtain subtle changes in their senses, motor imagination, and emotions while in a silent state, and to regulate their emotions and thought processes, it is necessary to collect and identify high-level information from their electroencephalogram (EEG) and magnetoencephalogram (MEG) signals. This high-level information includes sensory information, motor imagination information, and emotional information.

[0048] Based on the above considerations, one embodiment of the present invention provides a brainwave information processing method, such as... Figure 1 As shown, the method includes:

[0049] S10: Record and collect changes in EEG and magnetoencephalogram (MEG) signals at different brain regions under different stimulation conditions, obtain brainwave signals, and preprocess the brainwave signals;

[0050] S20: Analyze the preprocessed brainwave signals in the time domain, spectrum, and time-frequency domain power spectrum using signal analysis methods, and extract and label the feature signals;

[0051] S30: Train a classifier model using labeled feature signals, and use the trained classifier model to classify and identify the sensory information, motor imagery information, and emotional information contained in brainwave signals.

[0052] S40: The classified signal is used as input, and the reinforcement learning algorithm in the twin brain is used to enhance sensory perception, learn motor imagery and regulate emotional evolution, forming an inverse brainwave regulation signal;

[0053] S50: Use the inverse brainwave modulation signal to provide feedback stimulation to the subject's brain, thereby stimulating the subject's brain's potential abilities in sensory, motor imagination and emotional regulation.

[0054] S60: Stores brainwave signals including sensory information, motor imagery and emotional information, inverse brainwave regulation signals, and brainwave signals after feedback stimulation.

[0055] This embodiment collects EEG and MEG signals from subjects under different external stimuli, preprocesses them, and uses a trained classifier model to identify the preprocessed brainwave information to obtain high-level information. Reinforcement learning is then performed based on a reinforcement learning algorithm in the twin brain, and the inverse brainwave modulation signal is used to provide feedback stimulation to the subject's brain, stimulating the subject's brain's potential abilities in sensory perception, motor imagination, and emotional regulation, forming a more favorable mode of thought control and emotional regulation based on environmental conditions, thus realizing the identification of high-level information in EEG and MEG signals.

[0056] In one specific embodiment, the classification and identification of sensory information, motor imagery information, and emotional information contained in brainwave signals using a trained classifier model includes:

[0057] The sensory information is classified using a trained classifier model, which includes visual, auditory, olfactory, and tactile categories. The trained classifier model is a classifier model that includes decision tree, multivariate logistic regression, random forest, SVM, and deep learning methods.

[0058] The motor imagery information is categorized into categories including motor commands and muscle control;

[0059] Emotional information is classified using discrete emotion models or continuous emotion models, with categories including anger, fear, anticipation, sadness, disgust, surprise, acceptance, and joy.

[0060] This embodiment uses an artificial intelligence classifier model to identify and classify sensory information, motor imagery information, and emotional information contained in brainwave signals, thereby achieving feature extraction and classification of advanced information in brainwave signals.

[0061] Neural circuit models are one of the models used in twin brains to simulate the regulatory feedback mechanism of the human brain. They are a basic type of model that constitutes twin brain models. Based on this model, twin brains can generate various types of simulated brain signals, which can be used as input signals for reinforcement learning training.

[0062] To simulate the human brain's ability to learn and evolve on its own, the twin brain model incorporates reinforcement learning algorithms from artificial intelligence. These algorithms continuously optimize the twin brain's simulated signals based on current environmental information, making them more closely resemble the signals of the human brain.

[0063] In one specific embodiment, the step of using reinforcement learning algorithms in twin brains for sensory enhancement, motor imagery learning, and emotional evolution regulation to form inverse brainwave regulation signals includes:

[0064] The reinforcement learning algorithm in the twin brain uses the thought control mechanism and emotional neural regulation output by the neural circuit model in the twin brain as a sensory, motor imagination and emotional intelligent agent to interact with specific simulated scenarios and regular simulated scenarios to carry out sensory enhancement and motor imagination learning training.

[0065] It can learn and evolve under supervised and unsupervised conditions to achieve emotional evolution regulation, and obtain inverse brainwave regulation signals through sensory, transport imagination and emotional regulation stimulation.

[0066] Specifically, the specific simulation scenarios are scenarios set for specific groups of people under specific conditions, such as scenarios of pilots in their combat situations. Specifically, the combat situations include flight combat, combat command, and extravehicular activities on the space station; the regular simulation scenarios are daily scenarios of specific groups of people, such as daily scenarios of pilots in their non-wartime situations.

[0067] In one specific embodiment, the method further includes: forming effective external stimuli based on olfaction, taste, vision, hearing, and touch, wherein the effective external stimuli include odors, music, images, and videos.

[0068] In a specific embodiment, the process of recording and acquiring changes in EEG and magnetoencephalogram (MEG) signals at different brain regions under different stimulation conditions, obtaining brainwave signals, and preprocessing the brainwave signals includes:

[0069] Wearable voltage and electromagnetic array sensors are used to collect changes in EEG and magnetoencephalogram (MEG) signals at different brain regions under different effective external stimuli. The EEG and MEG signals at different brain regions are then processed by noise reduction, filtering, artifact removal, compression of silent instantaneous signals, and amplification of special waveforms to obtain preprocessed brainwave signals.

[0070] The following explanation uses the brainwave information processing methods described by astronauts as examples:

[0071] like Figure 2 As shown, the EEG information processing method includes, in a training environment, using a variety of sensory and intuitive stimulation methods to provide divergent multi-stimulation to the test astronauts;

[0072] Wearable voltage and electromagnetic array sensors were used to record and collect changes in electroencephalogram (EEG) and magnetic resonance imaging (MRI) signals in different brain regions of the test astronauts under different stimulation conditions.

[0073] The collected EEG and MRI signals are preprocessed, including noise reduction, filtering, artifact removal, compression of silent instantaneous signals, and amplification of special waveforms. The EEG and MRI signals are time-domain waveform signals.

[0074] Multiple signal analysis methods were used to analyze the time domain, spectrum, and power spectrum of the preprocessed brainwave signals, and feature signals were extracted and labeled.

[0075] A classifier model was trained using labeled feature signals, and the trained classifier model was used to identify sensory information, motor imagery information, and emotional information contained in the brainwave signals of the test astronauts.

[0076] Specifically, the sensory information includes vision, hearing, smell and touch; the motor imagery information includes motor commands and muscle control information; and the emotional information includes discrete emotion models and continuous emotion models.

[0077] Using the signal output by the classifier model as input, and utilizing the twin brain simulation model and reinforcement learning algorithms in the twin brain, sensory enhancement, motor imagination learning, and thought and emotion evolution regulation are carried out in specific environmental scenarios to form inverse brainwave regulation stimulation signals.

[0078] Specifically, the specific environmental scenarios are flight combat, combat command, and space station extravehicular activity.

[0079] By using inverse brainwave modulation signals to provide feedback stimulation to the brains of test astronauts, the potential abilities of the test brains in sensory, motor imagination, and emotional regulation are stimulated. The advanced brainwave signals of the test subjects' brains, the self-learning and self-evolving inverse brainwave modulation signals of the twin brain model, and the brainwave signals collected after feedback stimulation are stored to establish a database of advanced brainwave information of the test subjects.

[0080] A second embodiment of the present invention provides a brainwave information processing system, including...

[0081] The brainwave stimulation module is configured to generate external stimulation sources;

[0082] The brainwave signal acquisition module is configured to record and acquire electroencephalogram (EEG) and magnetic resonance imaging (MRI) signals from different brain regions under different stimulation conditions to obtain the subject's brainwave signals.

[0083] The feature extraction and selection module is configured to perform time-domain, spectral, and time-frequency domain power spectrum analysis on brainwave signals using signal analysis methods to extract and label feature signals.

[0084] The artificial intelligence classifier model module is configured to train a classifier model based on labeled feature signals, and use the trained classifier model to classify and identify sensory information, motor imagery information, and emotional information contained in brainwave signals.

[0085] The twin brain simulation model is configured to use reinforcement learning algorithms for sensory enhancement, motor imagery learning, and emotional evolution regulation, forming inverse brainwave regulation signals.

[0086] The brainwave signal feedback module is configured to use the inverse brainwave modulation signal to provide feedback stimulation to the subject's brain, thereby stimulating the subject's brain's potential abilities in sensory, motor imagination and emotional regulation.

[0087] The brainwave storage module is configured to store brainwave signals output by the subject's brain, including sensory information, motor imagination and emotional information, inverse brainwave modulation signals output by the twin brain model, and brainwave acquisition signals after feedback stimulation output by the brainwave signal feedback module, in order to establish a brainwave information database of the subject.

[0088] This embodiment uses an artificial intelligence classifier model to identify sensory, motor imagery, and emotional information contained in brainwave signals. It uses a twin brain simulation model to achieve reinforcement learning training of advanced brainwave information, generating inverse brainwave stimulation signals for thought control and emotion regulation under specific environmental conditions. This better realizes the development, enhancement, and regulation of human brain capabilities and has broad application prospects.

[0089] In one specific embodiment, the external stimulus source is an effective external stimulus source including olfactory, gustatory, visual, auditory, or tactile stimuli;

[0090] The brainwave signal acquisition module includes wearable voltage and electromagnetic array acquisition sensors.

[0091] In one specific embodiment, the brainwave signal preprocessing module is configured to perform noise reduction, filtering, artifact removal, compression of silent instant signals, and amplification of special waveforms on the time-domain waveform signal, and send the processed brainwave signal to the feature extraction and selection module.

[0092] In one specific embodiment, the classifier model includes at least one of decision tree, multivariate logistic regression, random forest, SVM, and deep learning.

[0093] In one specific embodiment, the schematic diagram of the twin brain simulation model includes simulated neurons, simulated synapses, and connection components. The twin brain simulation model has different brain state working modes, different brain wave output characteristics, and different synaptic transmission characteristics, and can realize the simulation functions of memory formation and retrieval, long-term memory and long-term memory conversion, and motor closed-loop control.

[0094] In a specific embodiment, such as Figure 3 The diagram shown is a simulation diagram of the twin brain simulation model performing reinforcement learning. The twin brain simulation model uses specific environmental simulation scenarios and conventional environmental simulation scenarios to perform sensory enhancement, motor thinking learning, and regulation of thought and emotion evolution.

[0095] It should be noted that the process and principle of the brainwave information processing system provided in this embodiment are similar to the process and principle of the brainwave information processing method provided in the above embodiments. The relevant parts can be referred to, and will not be repeated here.

[0096] like Figure 4 The diagram illustrates the structure of a computer device according to a third embodiment of the present invention. Suitable for implementing the brainwave information processing method provided in the above embodiments, it includes a central processing module (CPU), which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) or a program loaded from a storage portion into a random access memory (RAM). The RAM also stores various programs and data required for the operation of the computer device. The CPU, ROM, and RAM are interconnected via a bus. An input / output (I / O) interface is also connected to the bus.

[0097] The following components are connected to the I / O interface: input sections including keyboards, mice, etc.; output sections including liquid crystal displays (LCDs) and speakers, etc.; storage sections including hard disks, etc.; and communication sections including network interface cards such as LAN cards and modems. The communication sections perform communication processing via networks such as the Internet. Drives are also connected to the I / O interface as needed. Removable media, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on the drive as needed so that computer programs read from them can be installed into the storage section as required.

[0098] Specifically, according to this embodiment, the process described in the flowchart above can be implemented as a computer software program. For example, this embodiment includes a computer program product comprising a computer program tangibly embodied on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium.

[0099] A fourth embodiment of this application provides a computer-readable storage medium having a computer program stored thereon, which is implemented when executed by a processor:

[0100] S10: Record and collect changes in EEG and magnetoencephalogram (MEG) signals at different brain regions under different stimulation conditions, obtain brainwave signals, and preprocess the brainwave signals;

[0101] S20: Analyze the preprocessed brainwave signals in the time domain, spectrum, and time-frequency domain power spectrum using signal analysis methods, and extract and label the feature signals;

[0102] S30: Train a classifier model using labeled feature signals, and use the trained classifier model to classify and identify the sensory information, motor imagery information, and emotional information contained in brainwave signals.

[0103] S40: The classified signal is used as input, and the reinforcement learning algorithm in the twin brain is used to enhance sensory perception, learn motor imagery and regulate emotional evolution, forming an inverse brainwave regulation signal;

[0104] S50: Use the inverse brainwave modulation signal to provide feedback stimulation to the subject's brain, thereby stimulating the subject's brain's potential abilities in sensory, motor imagination and emotional regulation.

[0105] S60: Stores brainwave signals of the subject's brain, including sensory information, motor imagination and emotional information, inverse brainwave regulation signals and brainwave signals collected after feedback stimulation.

[0106] In practical applications, the computer-readable storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0107] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit programs for use by or in connection with an instruction execution system, apparatus, or device.

[0108] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0109] It should be noted that the flowcharts and schematic diagrams in the accompanying drawings illustrate the architecture, functions, and operations of possible implementations of the system, method, and computer program product of this embodiment. In this regard, each block in the flowchart or schematic diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the schematic diagram and / or flowchart, and combinations of blocks in the schematic diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0110] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is impossible to exhaustively list all the implementation methods here. All obvious variations or modifications derived from the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A brainwave information processing method, characterized in that, include Record and collect changes in EEG and magnetoencephalogram (MEG) signals at different brain regions under different stimulation conditions to obtain brainwave signals and preprocess the brainwave signals; Signal analysis methods were used to analyze the time domain, spectrum, and power spectrum of the preprocessed brainwave signals, and feature signals were extracted and labeled. A classifier model is trained using labeled feature signals, and the trained classifier model is used to classify and identify sensory information, motor imagery information, and emotional information contained in brainwave signals. The classified signals are used as input, and the reinforcement learning algorithm in the twin brain is used to enhance sensory perception, learn motor imagery, and regulate emotional evolution, forming inverse brainwave regulation signals. The inverse brainwave modulation signal is used to provide feedback stimulation to the subject's brain, thereby stimulating the subject's brain's potential abilities in sensory, motor imagination, and emotional regulation. Store brainwave signals containing sensory information, motor imagination and emotional information, inverse brainwave regulation signals and brainwave signals after feedback stimulation, and establish a high-level brainwave information database of subjects; The process of using the classified signals as input and employing reinforcement learning algorithms in the twin brain for sensory enhancement, motor imagery learning, and emotional evolution regulation to form inverse brainwave regulation signals includes: By using reinforcement learning algorithms, the thought control mechanism and emotional neural regulation output from the neural circuit model in the twin brain are used as sensory, motor imagination and emotional intelligent agents to interact with specific simulated scenarios and regular simulated scenarios to carry out sensory enhancement and motor imagination learning training. It can learn and evolve under both supervised and unsupervised conditions to achieve emotional evolution regulation, and form inverse brainwave regulation signals through sensory, transport imagination and emotional regulation stimulation.

2. The brainwave information processing method according to claim 1, characterized in that, The process of classifying and recognizing sensory, motor imagery, and emotional information contained in brainwave signals using a trained classifier model includes: The sensory information is categorized into categories including visual, auditory, olfactory, and tactile. The motor imagery information is categorized into categories including motor commands and muscle control; Emotional information is classified using discrete or continuous emotion models, with categories including anger, fear, anticipation, sadness, disgust, surprise, acceptance, and joy.

3. The brainwave information processing method according to claim 1, characterized in that, The method further includes: generating effective external stimuli based on olfaction, taste, vision, hearing, and touch; The process of recording and acquiring changes in electroencephalogram (EEG) and magnetoencephalogram (MEG) signals at different brain regions under different stimulation conditions, obtaining brainwave signals, and preprocessing the brainwave signals includes: The changes in EEG and MEG signals at different brain regions were recorded and acquired by a non-implantable EEG signal acquisition device under different effective external stimuli. The EEG and MEG signals at different brain regions were then subjected to noise reduction, filtering, artifact removal, compression of silent instantaneous signals, and amplification of special waveforms to obtain preprocessed brainwave signals.

4. A brainwave information processing system, characterized in that, include The brainwave stimulation module is configured to generate external stimulation sources; The brainwave signal acquisition module is configured to acquire electroencephalogram (EEG) and magnetic resonance imaging (MRI) signals from different brain regions under different stimulation conditions to obtain brainwave signals. The feature extraction and selection module is configured to perform time-domain, spectrum, and time-frequency domain power spectrum analysis on the brainwave signal using signal analysis methods to extract and label feature signals. The artificial intelligence classifier model module is configured to train a classifier model based on labeled feature signals, and use the trained classifier model to classify and identify sensory information, motor imagery information and emotional information contained in brainwave signals. The twin brain simulation model is configured to utilize reinforcement learning algorithms for sensory enhancement, motor imagery learning, and emotional evolution regulation, forming inverse brainwave regulation signals. This includes: using reinforcement learning algorithms to treat the thought control mechanism and emotional neural regulation output from the neural circuit model in the twin brain as sensory, motor imagery, and emotional intelligent agents, interacting with specific and regular simulated scenarios to conduct sensory enhancement and motor imagery learning training; and performing self-learning and self-evolution under supervised and unsupervised conditions to achieve emotional evolution regulation, and forming inverse brainwave regulation signals through sensory, motor imagery, and emotional regulation stimuli. The brainwave signal feedback module is configured to use the inverse brainwave modulation signal to provide feedback stimulation to the subject's brain, thereby stimulating the subject's brain's potential abilities in sensory, motor imagination and emotional regulation. The brainwave storage module is configured to store brainwave signals output by the subject's brain, including sensory information, motor imagination and emotional information, inverse brainwave modulation signals output by the twin brain simulation model, and brainwave signals after feedback stimulation output by the brainwave signal feedback module, thereby establishing a database of subject brainwave information.

5. The brainwave information processing system according to claim 4, characterized in that, The external stimuli are effective external stimuli including olfaction, taste, vision, hearing, and touch; The brainwave signal acquisition module includes wearable voltage and electromagnetic array acquisition sensors.

6. The brainwave information processing system according to claim 4, characterized in that, It also includes, The brainwave signal preprocessing module is configured to perform noise reduction, filtering, artifact removal, compression of silent instantaneous signals, and amplification of special waveforms on the time-domain waveform signal, and send the processed brainwave signal to the feature extraction and selection module.

7. The brainwave information processing system according to claim 4, characterized in that, The classifier model includes at least one of decision tree, multivariate logistic regression, random forest, SVM, and deep learning.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-3.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-3.

Images