Closed-loop neural feedback method and device based on tactile memory electroencephalogram signal adjustment

Abstract

The invention relates to the technical field of human-computer interaction and brain informatics, in particular to a closed-loop neural feedback method and device based on tactile memory electroencephalogram signal adjustment, and the method comprises the steps: collecting electroencephalogram signals of a testee, and carrying out real-time online data preprocessing; designing a tactile memory stimulation task, activating tactile memory of the testee, and recording corresponding electroencephalogram signal response; feature extraction and analysis are conducted on the corresponding electroencephalogram signals, and electroencephalogram features related to tactile memory are obtained; and designing a closed-loop neural feedback system based on the electroencephalogram characteristic signal of the tactile memory, monitoring the electroencephalogram signal of the testee in real time by the closed-loop neural feedback system, performing real-time stimulation according to a preset tactile stimulation task, and recording the electroencephalogram response of the testee. According to the technical scheme, the tactile memory electroencephalogram characteristic signals are combined with a closed-loop neural feedback system, and a new thought and method are provided for diagnosis and treatment of tactile memory related diseases.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of human-computer interaction and brain information technology, and in particular, relates to a closed-loop neurofeedback method and device based on tactile memory electroencephalogram signal regulation. BACKGROUND

[0002] With the development of neuroscience and artificial intelligence technology, people's understanding of the brain is deepening, which promotes the research and application of closed-loop neurofeedback systems. However, traditional closed-loop neurofeedback systems have some limitations. One of the main problems is the use of single signal source, usually only based on visual or auditory electroencephalogram signal feedback system. In addition, the existing technology may not be flexible enough in information processing, limiting the applicability and effectiveness of the system.

[0003] Currently, existing implementation schemes mainly focus on using electroencephalogram (EEG) signals for simple feedback training, such as through visual or auditory signal feedback to help users improve attention or reduce stress. These systems usually rely on specific brain wave frequency bands (such as alpha waves, beta waves) to evaluate the user's relaxation or concentration state, and provide feedback accordingly. However, these schemes have certain limitations in dealing with complex cognitive functions. They often ignore more subtle features in the electroencephalogram signal, such as changes in electroencephalogram patterns related to specific memory tasks, and how to provide personalized and dynamic feedback based on these changes.

[0004] And tactile memory as an important physiological indicator can be used to evaluate and regulate the cognitive state of users. Tactile memory involves the encoding, storage and retrieval process of tactile information by the brain, and its related electroencephalogram feature signals can reflect the memory and cognitive function state of users. Therefore, a new type of closed-loop neurofeedback system is needed, which can combine tactile brain network signals to achieve more accurate and effective neuroregulation and intervention.

[0005] However, the existing technology has several major drawbacks in the application of closed-loop neurofeedback systems:

[0006] Poor generalization: Many existing systems mainly rely on general visual or auditory brain wave bands (such as alpha waves, beta waves) to evaluate the user's psychological state, which lacks pertinence and is difficult to adapt to large individual differences. Lack of real-time and dynamic adjustment capability: Existing solutions often lack sufficient real-time when processing electroencephalogram signals, and cannot provide dynamic adjustment feedback according to the immediate changes in user state. Focus on single sensory feedback: Most systems only provide feedback through visual or auditory signals, ignoring the potential of other senses such as touch, which limits the diversity and effectiveness of feedback. Lack of support for complex cognitive functions: Although some systems attempt to improve cognitive functions through electroencephalogram signal feedback, they often fail to effectively process signals related to complex cognitive tasks such as memory and learning. SUMMARY

[0007] The embodiment of the present application provides a closed-loop neurofeedback method and device based on haptic memory electroencephalogram signal adjustment, which at least solves the technical problem of low efficiency of existing closed-loop neurofeedback systems.

[0008] According to an embodiment of the present application, a closed-loop neurofeedback method based on haptic memory electroencephalogram signal adjustment is provided, comprising the following steps:

[0009] S101: Collecting the electroencephalogram signals of the subject and performing real-time online data preprocessing;

[0010] S102: Designing a haptic memory stimulation task to activate the subject's haptic memory and recording the corresponding electroencephalogram signal response;

[0011] S103: Extracting and analyzing the corresponding electroencephalogram signals to obtain electroencephalogram features related to haptic memory;

[0012] S104: Based on the electroencephalogram feature signals of haptic memory, designing a closed-loop neurofeedback system, real-time monitoring the electroencephalogram signals of the subject by the closed-loop neurofeedback system, and real-time stimulation according to the preset haptic stimulation task, and recording the electroencephalogram response.

[0013] Further, the method further comprises:

[0014] S105: Analyzing the electroencephalogram response of the subject and adjusting the stimulation parameters according to the preset rules, continuously monitoring the electroencephalogram signals and adjusting the stimulation parameters to close-loop regulate the brain neural activity of the subject.

[0015] Further, the method further comprises:

[0016] S106: Experimental verification, evaluation of the effect and feasibility of the closed-loop neurofeedback system, and adjustment and improvement of the closed-loop neurofeedback system.

[0017] Further, in step S101, the electroencephalogram of the subject is collected by an electroencephalogram device.

[0018] Further, in step S101, the data preprocessing includes filtering and denoising.

[0019] Further, in step S102, the tactile memory stimulation task includes a tactile memory task.

[0020] Further, in step S103, the extracted features of the electroencephalogram include time domain features, frequency domain features and spatial domain features.

[0021] According to another embodiment of the present application, a closed-loop neurofeedback device based on tactile memory electroencephalogram regulation is provided, comprising:

[0022] A data acquisition unit is configured to collect the electroencephalogram of the subject and perform real-time online data preprocessing;

[0023] A task design unit is configured to design a tactile memory stimulation task, activate the tactile memory of the subject, and record the corresponding electroencephalogram response;

[0024] A feature acquisition unit is configured to extract and analyze the corresponding electroencephalogram, and obtain the electroencephalogram features related to the tactile memory;

[0025] A real-time stimulation unit is configured to design a closed-loop neurofeedback system based on the electroencephalogram features of the tactile memory, monitor the electroencephalogram of the subject in real time by the closed-loop neurofeedback system, stimulate in real time according to the preset tactile stimulation task, and record the electroencephalogram response.

[0026] Further, the device further comprises:

[0027] A closed-loop regulation unit is configured to analyze the electroencephalogram response of the subject, adjust the stimulation parameters according to the preset rules, and continuously monitor the electroencephalogram and adjust the stimulation parameters to regulate the brain neural activity of the subject in a closed loop.

[0028] Further, the device further comprises:

[0029] An adjustment and improvement unit is configured to perform experimental verification, evaluate the effect and feasibility of the closed-loop neurofeedback system, and adjust and improve the closed-loop neurofeedback system.

[0030] A storage medium stores a program file capable of implementing any one of the above-mentioned closed-loop neurofeedback methods based on tactile memory electroencephalogram regulation.

[0031] A processor is configured to run a program, wherein the program performs any one of the above-mentioned closed-loop neurofeedback methods based on tactile memory electroencephalogram regulation when running.

[0032] The closed-loop neurofeedback method and device based on tactile memory EEG signal regulation in the embodiment of the application, by in-depth analysis of the EEG characteristic signal related to tactile memory, aims to provide more personalized neurofeedback related to somatosensory. The application is committed to realizing real-time monitoring and analysis of EEG signals, and dynamically adjusting the feedback strategy according to the changes of these signals, so as to provide more effective cognitive function training and rehabilitation treatment. The application focuses on developing technology that can accurately identify and utilize EEG characteristic signals related to complex cognitive tasks to support the training and rehabilitation of high-level cognitive functions such as memory and learning. The technical scheme of the application combines tactile memory EEG characteristic signals with a closed-loop neurofeedback system, providing new ideas and methods for the diagnosis and treatment of tactile memory-related diseases. BRIEF DESCRIPTION OF DRAWINGS

[0033] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and serve to explain the principles of the application, and do not limit the application. In the drawings:

[0034] Figure 1 Flowchart of the closed-loop neurofeedback method based on tactile memory EEG signal regulation of the application;

[0035] Figure 2 Preferred flowchart of the closed-loop neurofeedback method based on tactile memory EEG signal regulation of the application;

[0036] Figure 3 Preferred flowchart of the closed-loop neurofeedback method based on tactile memory EEG signal regulation of the application;

[0037] Figure 4 Overall flowchart of the application;

[0038] Figure 5 Working memory flowchart in the application;

[0039] Figure 6 Closed-loop neurofeedback training flowchart in the application;

[0040] Figure 7 Pre-test task accuracy statistical chart in the application;

[0041] Figure 8 Neurofeedback visual control chart in the application;

[0042] Figure 9 Module chart of the closed-loop neurofeedback device based on tactile memory EEG signal regulation of the application;

[0043] Figure 10A preferred module diagram of the closed-loop neurofeedback device based on the haptic memory EEG signal adjustment of the present application;

[0044] Figure 11 A preferred module diagram of the closed-loop neurofeedback device based on the haptic memory EEG signal adjustment of the present application. DETAILED DESCRIPTION

[0045] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the embodiment of the present application will be described clearly and completely in the following by combining the drawings in the embodiment of the present application. Obviously, the described embodiment is only a part of the embodiment of the present application, not all. Based on the embodiment in the present application, all other embodiments obtained by the person skilled in the art without creative labor should belong to the scope of protection of the present application.

[0046] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0047] Embodiment 1

[0048] According to an embodiment of the present application, a closed-loop neurofeedback method based on haptic memory EEG signal adjustment is provided, referring to Figure 1 , comprising the following steps:

[0049] S101: collecting the EEG signal of the subject and performing real-time online data preprocessing;

[0050] S102: design a haptic memory stimulation task, activate the haptic memory of the subject, and record the corresponding EEG signal response;

[0051] S103: feature extraction and analysis of the corresponding EEG signal, obtaining the EEG feature related to the haptic memory;

[0052] S104: based on the EEG feature signal of the haptic memory, design a closed-loop neurofeedback system, which monitors the EEG signal of the subject in real time, stimulates in real time according to the preset haptic stimulation task, and records the EEG response.

[0053] The closed-loop neurofeedback method based on haptic memory EEG signal regulation in the embodiment of the application, by in-depth analysis of the EEG characteristic signals related to haptic memory, aims to provide more personalized neurofeedback related to somatosensory aspects. The application is committed to realizing real-time monitoring and analysis of EEG signals, and dynamically adjusting the feedback strategy according to the changes of these signals, so as to provide more effective cognitive function training and rehabilitation treatment. The application focuses on developing technologies that can accurately identify and utilize EEG characteristic signals related to complex cognitive tasks to support the training and rehabilitation of high-level cognitive functions such as memory and learning. The technical scheme of the application combines haptic memory EEG characteristic signals with closed-loop neurofeedback systems, providing new ideas and methods for the diagnosis and treatment of haptic memory-related diseases.

[0054] Among them, referring to Figure 2 , the method further comprises:

[0055] S105: Analyze the EEG response of the subject, and adjust the stimulation parameters according to the preset rules, continuously monitor the EEG signals and adjust the stimulation parameters, and close-loop regulate the brain neural activity of the subject.

[0056] Among them, referring to Figure 3 , the method further comprises:

[0057] S106: Perform experimental verification to evaluate the effectiveness and feasibility of the closed-loop neurofeedback system, and adjust and improve the closed-loop neurofeedback system.

[0058] Specifically, the application is committed to solving the problems encountered in using haptic memory-related EEG characteristic signals to improve the application efficiency and effectiveness of closed-loop neurofeedback systems in the fields of cognitive function training, rehabilitation treatment, and human-computer interaction. Therefore, based on the technical background and existing implementation schemes, the application proposes a more refined closed-loop neurofeedback technology based on haptic signals. It focuses on using EEG characteristic signals related to haptic memory to accurately extract and analyze these signals through advanced signal processing and machine learning techniques.

[0059] The main purposes of the application include:

[0060] Improving personalization and precision: By deeply analyzing the brain electrical characteristic signals related to tactile memory, the invention aims to provide more personalized neurofeedback related to somatosensory aspects. Enhancing real-time and dynamic adjustment capabilities: The invention is committed to realizing real-time monitoring and analysis of brain electrical signals, and dynamically adjusting feedback strategies according to changes in these signals, so as to provide more effective cognitive function training and rehabilitation. Supporting the improvement of complex cognitive functions: The invention focuses on developing technologies that can accurately identify and utilize brain electrical characteristic signals related to complex cognitive tasks to support the training and rehabilitation of high-level cognitive functions such as memory and learning.

[0061] By solving these shortcomings of the prior art, the invention aims to provide a more efficient, accurate and user-friendly closed-loop neurofeedback technology for converting audiovisual signals into tactile signals to promote the improvement of cognitive functions and neural rehabilitation.

[0062] The technical solution of the invention is based on the research of a closed-loop neurofeedback system for tactile memory brain electrical characteristic signals. First, the brain electrical signals are collected and preprocessed, including filtering, denoising and other steps, to ensure the accuracy of the data. Then, a tactile memory stimulation task is designed to stimulate the tactile memory of the subjects and record the corresponding brain electrical signal response. Next, the characteristic extraction and analysis of the response brain electrical signals are carried out to obtain the characteristics related to tactile memory. Based on these characteristics, a closed-loop neurofeedback system is designed to monitor the brain electrical signals of the subjects in real time, stimulate them in real time according to the preset tactile stimulation task, and record their brain electrical responses. By analyzing the brain electrical responses and adjusting the stimulation parameters according to the preset rules, the closed-loop regulation of the brain neural activity of the subjects is realized to enhance their tactile memory ability. Finally, experimental verification and optimization are carried out to evaluate the effectiveness and feasibility of the system, and the system is adjusted and improved to improve its performance and reliability. In summary, the technical solution of the invention combines tactile memory brain electrical characteristic signals with a closed-loop neurofeedback system, providing new ideas and methods for the diagnosis and treatment of tactile memory-related diseases.

[0063] See Figure 4 , regarding the overall process of the closed-loop neurofeedback task, the pre-test and post-test use the tactile n-back experiment as the control group, and the middle part provides index feedback according to the pre-test tactile memory brain electrical signals. Specifically, the technical solution of the invention is described in detail as follows:

[0064] Data collection and preprocessing: The electroencephalogram (EEG) device is used to collect the brain electrical signals of the subjects, and real-time online data preprocessing is carried out, including denoising, filtering and other steps, to ensure the accuracy and reliability of the subsequent analysis.

[0065] Tactile memory stimulation design: Design a tactile memory stimulation task, such as a tactile memory task, to activate the tactile memory of the subjects and record the corresponding brain electrical signal response, as shown in Figure 5 .

[0066] Feature extraction and analysis: Feature extraction and analysis of EEG signals, including time-domain features, frequency-domain features and spatial-domain features, to obtain EEG features related to tactile memory.

[0067] Design of closed-loop neurofeedback system: See Figure 6 Based on the EEG characteristics of tactile memory, a closed-loop neurofeedback system was designed. This system monitors the subject's EEG signals in real time, provides real-time stimulation according to a preset tactile stimulation task, and records the subject's EEG response.

[0068] Feedback signal analysis and regulation: The study analyzes the subject's EEG response and adjusts stimulation parameters according to preset rules. By continuously monitoring EEG signals and adjusting stimulation parameters, a closed-loop regulation of the subject's brain neural activity is achieved to enhance tactile memory ability. EEG signals are adjusted in real time to address individual differences.

[0069] Experimental verification and optimization: Conduct experimental verification to evaluate the effectiveness and feasibility of the closed-loop neurofeedback system, and adjust and improve the system to enhance its performance and reliability.

[0070] The key points and areas to be protected in this invention are:

[0071] This invention utilizes high-precision electroencephalography (EEG) technology to accurately capture brain activity related to tactile perception, and employs a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for deep analysis of these signals to identify specific brainwave patterns. Based on these analysis results, the system provides real-time, personalized tactile feedback signals, aiming to improve the user's cognitive function and neurorehabilitation outcomes. Furthermore, the system includes a user feedback and system optimization mechanism, which collects user feedback on tactile stimuli to optimize the deep learning model, further improving system accuracy and user satisfaction. The intended protection points of this invention include a unique closed-loop system design and a tactile feedback modulation mechanism, which constitute the core technology and innovation of this invention, aiming to provide users with more precise and personalized neurorehabilitation support.

[0072] Compared with the prior art, the advantages of the present invention are:

[0073] Traditional neurorehabilitation systems primarily rely on visual and auditory signals, but this invention breaks this limitation by introducing tactile signals. This not only increases the dimensionality and richness of the data but also enables the neurorehabilitation system to more comprehensively understand and respond to the user's perceptual state. By analyzing touch-related EEG activity through deep learning, this invention can generate more precise and personalized tactile feedback signals. This is more able to meet the specific needs of different users than traditional one-size-fits-all, impersonal feedback methods, thereby improving the effectiveness of neurorehabilitation.

[0074] The present application is proved to be feasible through experiments, simulations and uses. Specifically, the present application proves that there are obvious differences between tasks through pre-test experiments of behavior of dozens of subjects. Statistical significance tests (such as p value) can be used to make statistical results of significant difference effects, as shown in Figure 7 The pre-test task accuracy statistical chart is shown in FIG. 1. Therefore, it is determined that the experiment is feasible for subsequent neurofeedback regulation of tactile memory electroencephalogram signals. In addition, in the subsequent neurofeedback training, the alpha frequency band power of the prefrontal lobe electrode is used for brain visual regulation, and it can be observed that the circle becomes larger with the electroencephalogram signal, as shown in Figure 8 The neurofeedback visual regulation chart is shown in FIG. 2.

[0075] Alternative solutions for the electroencephalogram signal acquisition device can include developing more portable and low-cost devices, or using non-contact technology to improve user comfort. In terms of tactile devices, in addition to traditional vibration feedback, the use of electrical stimulation as a new feedback method can be explored to meet the needs of different users. Data processing algorithms can also be optimized by introducing the latest machine learning techniques to improve the accuracy and efficiency of the system. In addition, the application range of the present application can be extended to the fields of games and entertainment, virtual reality, etc., to enhance user engagement by providing immersive experiences.

[0076] Embodiment 2

[0077] According to another embodiment of the present application, a closed-loop neurofeedback device based on tactile memory electroencephalogram signal regulation is provided, as shown in Figure 9 , comprising:

[0078] The data acquisition unit 201 is configured to acquire the electroencephalogram signal of the subject and perform real-time online data preprocessing.

[0079] The task design unit 202 is configured to design a tactile memory stimulation task, activate the tactile memory of the subject, and record the corresponding electroencephalogram signal response.

[0080] The feature acquisition unit 203 is configured to extract and analyze the corresponding electroencephalogram signal to obtain the electroencephalogram feature related to the tactile memory.

[0081] The real-time stimulation unit 204 is configured to design a closed-loop neurofeedback system based on the electroencephalogram feature signal of the tactile memory, monitor the electroencephalogram signal of the subject in real time by the closed-loop neurofeedback system, stimulate in real time according to the pre-set tactile stimulation task, and record the electroencephalogram response.

[0082] The closed-loop neurofeedback device based on the haptic memory EEG signal regulation in the embodiment of the application, through in-depth analysis of the EEG characteristic signal related to haptic memory, aims to provide more personalized neurofeedback related to somatosensory. The application is committed to realizing real-time monitoring and analysis of EEG signals, and dynamically adjusting the feedback strategy according to the changes of these signals, so as to provide more effective cognitive function training and rehabilitation treatment. The application focuses on developing technologies that can accurately identify and utilize EEG characteristic signals related to complex cognitive tasks to support the training and rehabilitation of high-level cognitive functions such as memory and learning. The technical scheme of the application combines haptic memory EEG characteristic signals with a closed-loop neurofeedback system, providing new ideas and methods for the diagnosis and treatment of haptic memory-related diseases.

[0083] Among them, referring to Figure 10 , the device further comprises:

[0084] The closed-loop regulation unit 205 is used for analyzing the EEG response of the subject and adjusting the stimulation parameters according to the preset rules, and continuously monitoring the EEG signals and adjusting the stimulation parameters to close-loop regulate the brain neural activity of the subject.

[0085] Among them, referring to Figure 11 , the device further comprises:

[0086] The adjustment and improvement unit 206 is used for experimental verification, evaluation of the effect and feasibility of the closed-loop neurofeedback system, and adjustment and improvement of the closed-loop neurofeedback system.

[0087] Specifically, the application is committed to solving the problems encountered in using haptic memory-related EEG characteristic signals to improve the application efficiency and effect of the closed-loop neurofeedback system in the fields of cognitive function training, rehabilitation treatment and human-computer interaction. Therefore, based on the technical background and existing implementation scheme, the application proposes a more refined closed-loop neurofeedback technology based on haptic signals. It focuses on using EEG characteristic signals related to haptic memory, accurately extracting and analyzing these signals through advanced signal processing and machine learning techniques.

[0088] The main purposes of the application include:

[0089] Improve individualization and accuracy: by in-depth analysis of the EEG characteristic signal related to haptic memory, the application aims to provide more personalized neurofeedback related to somatosensory. Enhance real-time and dynamic adjustment capability: the application is committed to realizing real-time monitoring and analysis of EEG signals, and dynamically adjusting the feedback strategy according to the changes of these signals, so as to provide more effective cognitive function training and rehabilitation treatment. Support the improvement of complex cognitive functions: the application focuses on developing technologies that can accurately identify and utilize EEG characteristic signals related to complex cognitive tasks to support the training and rehabilitation of high-level cognitive functions such as memory and learning.

[0090] By solving these shortcomings of the prior art, the present application aims to provide a more efficient, accurate and user-friendly closed-loop neurofeedback technology for converting audiovisual signals into tactile signals to promote the improvement of cognitive function and neural rehabilitation.

[0091] The technical solution of the present application is based on the research of a closed-loop neurofeedback system for tactile memory EEG features. First, the EEG signal is collected and preprocessed, including filtering, denoising and other steps, to ensure the accuracy of the data. Then, a tactile memory stimulation task is designed to stimulate the tactile memory of the subjects and record the corresponding EEG signal response. Next, the features of the response EEG signal are extracted and analyzed to obtain the features related to tactile memory. Based on these features, a closed-loop neurofeedback system is designed to monitor the EEG signal of the subjects in real time, stimulate them in real time according to the preset tactile stimulation task, and record their EEG response. By analyzing the EEG response and adjusting the stimulation parameters according to the preset rules, the closed-loop regulation of the subjects' brain neural activity is achieved to enhance the tactile memory ability. Finally, experimental verification and optimization are carried out to evaluate the effectiveness and feasibility of the system, and the system is adjusted and improved to improve its performance and reliability. In summary, the technical solution of the present application combines tactile memory EEG features with a closed-loop neurofeedback system, providing new ideas and methods for the diagnosis and treatment of tactile memory-related diseases.

[0092] Referring to Figure 4 , regarding the overall process of the closed-loop neurofeedback task, the pre-test and post-test use the tactile n-back experiment as the control group, and the index feedback is based on the pre-test tactile memory EEG signal. Specifically, the technical solution of the present application is described in detail as follows:

[0093] Data collection and preprocessing: EEG (Electroencephalogram) equipment is used to collect the EEG signals of the subjects, and real-time online data preprocessing is performed, including denoising, filtering and other steps, to ensure the accuracy and reliability of subsequent analysis.

[0094] Tactile memory stimulation design: a tactile memory stimulation task is designed, such as a tactile memory task, to activate the tactile memory of the subjects and record the corresponding EEG signal response, as shown in Figure 5 .

[0095] Feature extraction and analysis: the EEG signal is extracted and analyzed for features, including time domain features, frequency domain features and spatial domain features, to obtain EEG features related to tactile memory.

[0096] Closed-loop neurofeedback system design: referring to Figure 6 , based on the EEG features of tactile memory, a closed-loop neurofeedback system is designed. This system monitors the EEG signal of the subjects in real time, stimulates them in real time according to the preset tactile stimulation task, and records their EEG response.

[0097] Feedback signal analysis and adjustment: analyze the brain electrical response of the subject, and adjust the stimulation parameters according to the preset rules. By continuously monitoring the brain electrical signals and adjusting the stimulation parameters, the closed-loop adjustment of the brain neural activity of the subject is realized to enhance the tactile memory ability, and the brain electrical signals are adjusted in real time according to the individual differences.

[0098] Experimental verification and optimization: perform experimental verification to evaluate the effect and feasibility of the closed-loop neural feedback system, and adjust and improve the system to improve its performance and reliability.

[0099] The key points and points to be protected of the present application are:

[0100] The present application precisely captures the brain electrical activity related to tactile perception through high-precision electroencephalogram (EEG) technology, and uses the combination of convolutional neural network (CNN) and recurrent neural network (RNN) to deeply analyze these signals to identify specific brain electrical patterns. According to the analysis results, the system provides feedback through real-time and personalized tactile feedback signals, aiming to improve the cognitive function and neural rehabilitation effect of the user. In addition, the system also includes a user feedback and system optimization mechanism to optimize the deep learning model by collecting user feedback on tactile stimulation, further improving the accuracy of the system and user satisfaction. The points to be protected of the present application include the unique closed-loop system design and the adjustment mechanism of tactile feedback from the aspect of tactile, which constitute the technical core and innovation point of the present application, aiming to provide more accurate and personalized neural rehabilitation support for users.

[0101] Compared with the prior art, the advantages of the present application are:

[0102] Traditional neural rehabilitation systems mainly rely on visual and auditory signals, while the present application breaks this limitation and introduces the use of tactile signals. This not only increases the dimension and richness of the data, but also enables the neural rehabilitation system to more comprehensively understand and respond to the user's perception state. Through deep learning analysis of tactile-related brain electrical activity, the present application can generate more accurate and personalized tactile feedback signals. This is better than the traditional one-size-fits-all, non-personalized feedback method, which can better meet the specific needs of different users, thereby improving the effect of neural rehabilitation.

[0103] The present application has been proven to be feasible through experiments, simulations, and use, specifically, the present application has obvious differences between pre-test experiments and prediction tasks through the collection of behavior of dozens of subjects, and significant difference test (such as p value) can be used to make significant difference effect statistical results such as Figure 7The correct rate of the pre-test task is shown in the graph, so it is determined that the experiment is feasible for subsequent neurofeedback regulation of tactile memory electroencephalogram signals. In addition, in the subsequent neurofeedback training, the alpha frequency band power of the frontal lobe electrode is used for brain visual regulation, and it can be observed that the circle becomes larger with the electroencephalogram signal. For example Figure 8 The neurofeedback visual regulation diagram.

[0104] Alternatives to the electroencephalogram signal acquisition device can include developing more portable, low-cost devices, or using non-contact technology to improve user comfort. In terms of tactile devices, in addition to traditional vibration feedback, the use of electrical stimulation as a new feedback method can be explored to meet the needs of different users. Data processing algorithms can also be optimized by introducing the latest machine learning techniques to improve the accuracy and efficiency of the system. In addition, the application range of the present application can be extended to the fields of games and entertainment, virtual reality, etc., to enhance user engagement by providing immersive experiences.

[0105] Embodiment 3

[0106] A storage medium, the storage medium stores a program file capable of realizing the closed-loop neurofeedback method based on the tactile memory electroencephalogram signal regulation described above.

[0107] Embodiment 4

[0108] A processor for running a program, wherein the program executes the closed-loop neurofeedback method based on the tactile memory electroencephalogram signal regulation described above when the program is running.

[0109] The above-mentioned embodiment numbers of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0110] In the above-mentioned embodiments of the present application, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0111] In several embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented by other ways. Among them, the system embodiments described above are only schematic, for example, the division of units can be a logical function division, and actual implementation can have another division way, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the coupling or direct coupling or communication connection between the displayed or discussed each other can be through some interface, indirect coupling or communication connection between units or modules, which can be electrical or other forms.

[0112] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present application.

[0113] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0114] If the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the prior art that essentially contributes or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method of each embodiment of the present application. The foregoing storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various program code storage media.

[0115] The above is only the preferred embodiment of the present application. It should be noted that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A closed-loop neurofeedback method based on tactile memory EEG signal modulation, characterized in that, Includes the following steps: S101: Collect the EEG signals of the subjects and perform real-time online data preprocessing; S102: Design a tactile memory stimulation task to activate the subject's tactile memory and record the corresponding EEG signal response; S103: Extract and analyze the corresponding EEG signals to obtain EEG features related to tactile memory; S104: Based on the EEG characteristic signals of tactile memory, a closed-loop neurofeedback system is designed. This closed-loop neurofeedback system monitors the subject's EEG signals in real time, provides real-time stimulation according to a preset tactile stimulation task, and records the subject's EEG response.

2. The closed-loop neurofeedback method based on tactile memory EEG signal modulation according to claim 1, characterized in that, The method further includes: S105: Analyze the subject's EEG response and adjust the stimulation parameters according to preset rules. By continuously monitoring the EEG signals and adjusting the stimulation parameters, the subject's brain neural activity is regulated in a closed loop.

3. The closed-loop neurofeedback method based on tactile memory EEG signal modulation according to claim 2, characterized in that, The method further includes: S106: Conduct experimental verification to evaluate the effectiveness and feasibility of the closed-loop neurofeedback system, and adjust and improve the closed-loop neurofeedback system.

4. The closed-loop neurofeedback method based on tactile memory EEG signal modulation according to claim 1, characterized in that, In step S101, the subject's brain signals are collected using an electroencephalogram (EEG) device.

5. The closed-loop neurofeedback method based on tactile memory EEG signal modulation according to claim 1, characterized in that, In step S101, data preprocessing includes filtering and noise reduction.

6. The closed-loop neurofeedback method based on tactile memory EEG signal modulation according to claim 1, characterized in that, In step S102, the tactile memory stimulation task includes a tactile memory task.

7. The closed-loop neurofeedback method based on tactile memory EEG signal modulation according to claim 1, characterized in that, In step S103, the features extracted from the EEG signal include time-domain features, frequency-domain features, and spatial-domain features.

8. A closed-loop neurofeedback device based on tactile memory EEG signal modulation, characterized in that, include: The data acquisition unit is used to collect the electroencephalogram (EEG) signals of the subjects and perform real-time online data preprocessing. The task design unit is used to design tactile memory stimulation tasks, activate the subject's tactile memory, and record the corresponding EEG signal responses. The feature acquisition unit is used to extract and analyze the corresponding EEG signals to obtain EEG features related to tactile memory. A real-time stimulation unit is used to design a closed-loop neurofeedback system based on the EEG characteristic signals of tactile memory. This closed-loop neurofeedback system monitors the subject's EEG signals in real time, provides real-time stimulation according to a preset tactile stimulation task, and records the subject's EEG response.

9. The closed-loop neurofeedback device based on tactile memory EEG signal modulation according to claim 8, characterized in that, The device further includes: The closed-loop regulation unit is used to analyze the subject's EEG response and adjust the stimulation parameters according to preset rules. By continuously monitoring the EEG signals and adjusting the stimulation parameters, the subject's brain neural activity is regulated in a closed loop.

10. The closed-loop neurofeedback device based on tactile memory EEG signal modulation according to claim 9, characterized in that, The device further includes: The adjustment and improvement unit is used for experimental verification, to evaluate the effectiveness and feasibility of the closed-loop neurofeedback system, and to adjust and improve the closed-loop neurofeedback system.

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