Closed-loop neurofeedback method and device based on haptic memory electroencephalogram signal conditioning

Abstract

The present invention relates to the technical fields of human-computer interaction and brain informatics, and in particular to a closed-loop neurofeedback method and device based on haptic memory electroencephalogram signal conditioning. The method comprises: collecting electroencephalogram signals of a subject, and performing real-time online data preprocessing; designing a haptic memory stimulation task, activating the haptic memory of the subject, and recording a corresponding electroencephalogram signal response; performing feature extraction and analysis on a corresponding electroencephalogram signal to obtain an electroencephalogram feature related to haptic memory; and on the basis of a haptic memory electroencephalogram feature signal, designing a closed-loop neurofeedback system, the closed-loop neurofeedback system monitoring the electroencephalogram signals of the subject in real time, performing real-time stimulation on the basis of a preset haptic stimulation task, and recording a corresponding electroencephalogram response. In the technical solution of the present invention, the haptic memory electroencephalogram feature signal is combined with the closed-loop neurofeedback system, thus providing new ideas and methods for the diagnosis and treatment of haptic memory-related diseases.

Description

Closed-loop neurofeedback method and device based on haptic memory eeg signal regulation TECHNICAL FIELD

[0001] The present application relates to the field of human-computer interaction and brain informatics technology, in particular to a closed-loop neurofeedback method and device based on haptic memory eeg 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 eeg 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 eeg signals, such as changes in eeg patterns related to specific memory tasks, and how to provide personalized and dynamic feedback based on these changes.

[0004] And haptic memory as an important physiological indicator can be used to evaluate and regulate the cognitive state of the user. Haptic memory involves the encoding, storage and retrieval process of the brain to haptic information, and its related eeg feature signals can reflect the user's memory and cognitive function state. Therefore, a new type of closed-loop neurofeedback system is needed, which can combine haptic brain network signals to achieve more accurate and effective neural regulation and intervention.

[0005] However, there are several major drawbacks in the application of existing technology in 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 specificity and is difficult to adapt to large individual differences. Lack of real-time and dynamic adjustment capability: Existing solutions often lack sufficient real-time processing of 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.

[0007] SUMMARY

[0008] 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.

[0009] 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:

[0010] S101: Collecting the electroencephalogram signals of the subject and performing real-time online data preprocessing;

[0011] S102: Designing a haptic memory stimulation task to activate the subject's haptic memory and recording the corresponding electroencephalogram signal response;

[0012] S103: Extracting and analyzing the corresponding electroencephalogram signals to obtain electroencephalogram features related to haptic memory;

[0013] S104: Based on the electroencephalogram feature signals of haptic memory, designing a closed-loop neurofeedback system, which monitors the electroencephalogram signals of the subject in real time, stimulates in real time according to the preset haptic stimulation task, and records the electroencephalogram response.

[0014] Further, the method further comprises:

[0015] 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.

[0016] Further, the method further comprises:

[0017] S106: Experimental verification is performed to evaluate the effectiveness and feasibility of the closed-loop neurofeedback system, and the closed-loop neurofeedback system is adjusted and improved.

[0018] Further, in step S101, the electroencephalogram device is used to collect the electroencephalogram signal of the subject.

[0019] Further, in step S101, the data preprocessing includes filtering and denoising.

[0020] Further, in step S102, the tactile memory stimulation task includes a tactile memory task.

[0021] Further, in step S103, the extracted features of the electroencephalogram signal include time domain features, frequency domain features and spatial domain features.

[0022] According to another embodiment of the present application, a closed-loop neurofeedback device based on tactile memory electroencephalogram signal regulation is provided, comprising:

[0023] A data acquisition unit is configured to collect the electroencephalogram signal of the subject and perform real-time online data preprocessing;

[0024] 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 signal response;

[0025] A feature acquisition unit is configured to extract and analyze the corresponding electroencephalogram signal, and obtain the electroencephalogram features related to the tactile memory;

[0026] 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 signal 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.

[0027] Further, the device further comprises:

[0028] 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 signal and adjust the stimulation parameters to regulate the brain neural activity of the subject in a closed loop.

[0029] Further, the device further comprises:

[0030] 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.

[0031] A storage medium, the storage medium stores a program file capable of realizing the above-mentioned any one closed-loop neurofeedback method based on tactile memory electroencephalogram signal regulation.

[0032] A processor for running a program, wherein the program performs the closed-loop neurofeedback method based on the somatosensory memory EEG signal adjustment when running.

[0033] The closed-loop neurofeedback method and device based on the somatosensory memory EEG signal adjustment in the embodiment of the present application, through in-depth analysis of the EEG characteristic signal related to somatosensory memory, aims to provide more personalized neurofeedback related to somatosensory. The present 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 present 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 present application combines the somatosensory memory EEG characteristic signal with the closed-loop neurofeedback system, providing new ideas and methods for the diagnosis and treatment of somatosensory memory related diseases. BRIEF DESCRIPTION OF DRAWINGS

[0034] Fig. 1 is a flow chart of the closed-loop neurofeedback method based on the somatosensory memory EEG signal adjustment of the present application;

[0035] Fig. 2 is a preferred flow chart of the closed-loop neurofeedback method based on the somatosensory memory EEG signal adjustment of the present application;

[0036] Fig. 3 is a preferred flow chart of the closed-loop neurofeedback method based on the somatosensory memory EEG signal adjustment of the present application;

[0037] Fig. 4 is a whole flow chart of the present application;

[0038] Fig. 5 is a working memory flow chart in the present application;

[0039] Fig. 6 is a closed-loop neurofeedback training flow chart in the present application;

[0040] Fig. 7 is a pre-test task accuracy statistical chart in the present application;

[0041] Fig. 8 is a neurofeedback visual regulation chart in the present application;

[0042] Fig. 9 is a module chart of the closed-loop neurofeedback device based on the somatosensory memory EEG signal adjustment of the present application;

[0043] Fig. 10 is a preferred module chart of the closed-loop neurofeedback device based on the somatosensory memory EEG signal adjustment of the present application;

[0044] Fig. 11 is a preferred module chart of the closed-loop neurofeedback device based on the somatosensory memory EEG signal adjustment of the present application. DETAILED DESCRIPTION

[0045] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work should fall within the protection scope 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-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological 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 that 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 the clearly listed steps or units, but can include other steps or units not clearly listed or inherent to the process, method, product, or device.

[0047] Embodiment 1

[0048] According to an embodiment of the present application, a closed-loop neurofeedback method based on tactile memory electroencephalogram signal regulation is provided, referring to FIG. 1, comprising the following steps:

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

[0050] S102: Designing a tactile memory stimulation task to activate the tactile memory of the subject and recording the corresponding electroencephalogram signal response;

[0051] S103: Extracting and analyzing the corresponding electroencephalogram signal to obtain the electroencephalogram features related to tactile memory;

[0052] S104: Based on the electroencephalogram feature signal of tactile memory, designing a closed-loop neurofeedback system, real-time monitoring the electroencephalogram signal of the subject by the closed-loop neurofeedback system, real-time stimulation according to the preset tactile stimulation task, and recording the electroencephalogram response.

[0053] The closed-loop neurofeedback method based on 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 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 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.

[0054] The method further comprises the following steps, as shown in FIG. 2:

[0055] S105: Analyze the EEG response of the subject, and adjust the stimulation parameters according to the preset rules. Through continuous monitoring of EEG signals and adjusting stimulation parameters, the brain neural activity of the subject is closed-loop regulated.

[0056] The method further comprises the following steps, as shown in FIG. 3:

[0057] S106: Perform experimental verification to evaluate the effect 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 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 to accurately extract and analyze these signals through advanced signal processing and machine learning technology.

[0059] The main purposes of the application include:

[0060] Improve individualization and accuracy: By in-depth analysis of EEG characteristic signals 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 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.

[0061] 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.

[0062] The technical solution of the present application is based on the research of a closed-loop neurofeedback system for tactile memory EEG feature signals. 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 feature signals with a closed-loop neurofeedback system, providing new ideas and methods for the diagnosis and treatment of tactile memory-related diseases.

[0063] Referring to FIG. 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 EEG signal. Specifically, the technical solution of the present application is described in detail as follows:

[0064] 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.

[0065] Tactile memory stimulation design: a tactile memory stimulation task, such as a tactile memory task, is designed to activate the tactile memory of the subjects and record the corresponding EEG signal response, as shown in FIG. 5.

[0066] 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.

[0067] Closed-loop neurofeedback system design: referring to FIG. 6, based on the EEG feature signals 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.

[0068] 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 subject's brain neural activity is realized to enhance the tactile memory ability, and the brain electrical signals are adjusted in real time according to the individual differences.

[0069] 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.

[0070] The key points and points to be protected of the present application are:

[0071] The present application accurately 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 perform deep analysis on 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 user's cognitive function and neural rehabilitation effect. In addition, the system also includes a user feedback and system optimization mechanism, which collects user feedback on tactile stimulation to optimize the deep learning model, 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.

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

[0073] 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 more suitable for the specific needs of different users than the traditional one-size-fits-all, non-personalized feedback method, thereby improving the effect of neural rehabilitation.

[0074] The present application has been proven to be feasible through experiments, simulations, and use. Specifically, the present application has been proven to be feasible through the collection of behavioral pre-test experiments of dozens of subjects, which showed significant differences between tasks. Statistical significance tests (such as p-value) can produce significant difference effect statistics. Figure 7 is a pre-test task accuracy statistical graph, so it is determined that the experiment is feasible for subsequent neural feedback regulation of tactile memory brain electrical signals. In addition, in the subsequent neural feedback training, the alpha band power of the prefrontal electrode is used for brain visual regulation, and it can be observed that the circular brain electrical wave signal becomes larger. Figure 8 is a neural feedback visual regulation graph.

[0075] Alternatives to the brain electrical signal collection device can include developing more portable, low-cost devices, or using non-contact techniques to improve user comfort. In terms of haptic devices, in addition to traditional vibration feedback, the use of electrical stimulation as a new feedback method can be explored to adapt to 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 haptic memory brain electrical signal regulation is provided, as shown in FIG. 9, comprising:

[0078] A data acquisition unit 201 is configured to collect the brain electrical signals of the subject and perform real-time online data preprocessing;

[0079] A task design unit 202 is configured to design a haptic memory stimulation task, activate the haptic memory of the subject, and record the corresponding brain electrical signal response;

[0080] A feature acquisition unit 203 is configured to extract and analyze the corresponding brain electrical signals to obtain brain electrical features related to haptic memory;

[0081] A real-time stimulation unit 204 is configured to design a closed-loop neurofeedback system based on the brain electrical features of haptic memory, monitor the brain electrical signals of the subject in real time by the closed-loop neurofeedback system, stimulate in real time according to the preset haptic stimulation task, and record the brain electrical response.

[0082] The closed-loop neurofeedback device based on haptic memory brain electrical signal regulation in the embodiment of the present application aims to provide more personalized neurofeedback related to somatosensory by in-depth analysis of brain electrical features related to haptic memory. The present application 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 treatment. The present application focuses on developing technologies that can accurately identify and utilize brain electrical feature signals related to complex cognitive tasks to support the training and rehabilitation of memory, learning and other high-level cognitive functions. The technical solution of the present application combines haptic memory brain electrical feature signals with a closed-loop neurofeedback system to provide new ideas and methods for the diagnosis and treatment of haptic memory-related diseases.

[0083] As shown in FIG. 10, the device further comprises:

[0084] The closed-loop adjustment unit 205 is configured to analyze the brain electrical response of the subject and adjust the stimulation parameters according to preset rules, and continuously monitor the brain electrical signals and adjust the stimulation parameters to perform closed-loop adjustment on the brain neural activity of the subject.

[0085] The device further comprises:

[0086] The adjustment and improvement unit 206 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.

[0087] Specifically, the present application aims to solve the problems encountered in the use of haptic memory-related brain electrical 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 present application proposes a more refined closed-loop neurofeedback technology based on haptic signals. It focuses on using haptic memory-related brain electrical characteristic signals to accurately extract and analyze these signals through advanced signal processing and machine learning techniques.

[0088] The main purposes of the present application include:

[0089] Improve individualization and accuracy: By deeply analyzing the brain electrical characteristic signals related to haptic memory, the present application aims to provide more individualized neurofeedback related to somatosensory. Enhance real-time and dynamic adjustment capability: The present application is committed to realizing real-time monitoring and analysis of brain electrical signals, and dynamically adjusting feedback strategies 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 present application 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 memory, learning and other advanced cognitive functions.

[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 haptic signals to promote the improvement of cognitive functions and neural rehabilitation.

[0091] The technical solution of the present application is based on the closed-loop neural feedback system of the tactile memory electroencephalogram feature signal. First, the electroencephalogram signal is collected and preprocessed, including filtering, denoising and other steps, to ensure the accuracy of the data. Then, the tactile memory stimulation task is designed to stimulate the tactile memory of the subjects and record the corresponding electroencephalogram signal response. Next, the features of the response electroencephalogram signal are extracted and analyzed to obtain the features related to tactile memory. Based on these features, a closed-loop neural feedback system is designed to monitor the electroencephalogram signal of the subjects in real time, stimulate them in real time according to the preset tactile stimulation task, and record their electroencephalogram response. By analyzing the electroencephalogram response 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 the tactile memory ability. Finally, experimental verification and optimization are carried out to evaluate the effect 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 the tactile memory electroencephalogram feature signal with the closed-loop neural feedback system to provide new ideas and methods for the diagnosis and treatment of tactile memory related diseases.

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

[0093] Data collection and preprocessing: The electroencephalogram (EEG) device is used to collect the electroencephalogram signal 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.

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

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

[0096] Closed-loop neural feedback system design: Referring to FIG. 6, based on the electroencephalogram feature signal of tactile memory, a closed-loop neural feedback system is designed. The system monitors the electroencephalogram signal of the subjects in real time, stimulates them in real time according to the preset tactile stimulation task, and records their electroencephalogram response.

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

[0098] Experimental verification and optimization: Conduct experimental verification to evaluate the effectiveness and feasibility of the closed-loop neurofeedback system, and make adjustments and improvements to 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 conduct deep analysis on 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 user's cognitive function and neurorehabilitation effect. In addition, the system also includes a user feedback and system optimization mechanism, which collects user feedback on tactile stimulation to optimize the deep learning model, 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 perspective of tactile, which constitute the technical core and innovation point of the present application, aiming to provide more accurate and personalized neurorehabilitation support for users.

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

[0102] Traditional neurorehabilitation 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 data, but also enables the neurorehabilitation 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 neurorehabilitation.

[0103] The present application has been proven to be feasible through experiments, simulations, and use. Specifically, the present application has been proven to be feasible through the collection of behavioral pre-test experiments of dozens of subjects, which showed significant differences between tasks. Statistical significance tests (such as p-value) can produce significant difference effect statistics. Figure 7 is a pre-test task accuracy statistical chart, thus determining that the experiment is feasible for subsequent neurofeedback regulation of tactile memory brain electrical signals. In addition, in the subsequent neurofeedback training, the alpha band power of the prefrontal electrode was used for brain visual regulation, and it was observed that the circular brain electrical wave signal became larger. Figure 8 is a neurofeedback visual regulation diagram.

[0104] Alternatives to the brain electrical signal acquisition device can include developing more portable, low-cost devices, or using non-contact techniques to improve user comfort. In terms of haptic devices, in addition to traditional vibration feedback, the use of electrical stimulation as a new feedback method can be explored to accommodate 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 haptic memory brain electrical signal regulation of any one of the above.

[0107] Embodiment 4

[0108] A processor for running a program, wherein the program performs the closed-loop neurofeedback method based on the haptic memory brain electrical signal regulation of any one of the above when the program is running.

[0109] The above-mentioned embodiment numbers of the present application are only for description, not representing the pros and cons of the embodiments.

[0110] In the above-mentioned embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant 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 can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed to multiple units. According to actual needs, part or all of the units can be selected to achieve the purpose of the present embodiment scheme.

[0113] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, 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 function unit.

[0114] If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or the part that contributes to the prior art, or all 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 several 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 methods in 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 media that can store program codes.

[0115] The above is only the preferred embodiment of the present application, and it should be pointed out 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 haptic memory electroencephalographic signal regulation, characterized in that, The method comprises the following steps: S101: collecting the brain electrical signals of the subject and performing real-time online data preprocessing; S102: designing a tactile memory stimulation task to activate the tactile memory of the subject and recording the corresponding brain electrical signal response; S103: extracting and analyzing the corresponding brain electrical signals to obtain brain electrical features related to tactile memory; S104: based on the brain electrical feature signals of tactile memory, designing a closed-loop neurofeedback system to monitor the brain electrical signals of the subject in real time, stimulating in real time according to the preset tactile stimulation task, and recording the brain electrical response.

2. The closed-loop neurofeedback method based on tactile memory electroencephalographic signal regulation according to claim 1, characterized in that, The method further comprises: S105: analyzing the brain electrical response of the subject and adjusting the stimulation parameters according to the preset rules, and continuously monitoring the brain electrical signals and adjusting the stimulation parameters to close-loop regulate the brain neural activity of the subject.

3. The closed-loop neurofeedback method based on tactile memory EEG signal regulation according to claim 2, characterized in that, The method further comprises: 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.

4. The closed-loop neurofeedback method based on tactile memory electroencephalographic signal regulation of claim 1, wherein, In step S101, the brain electrical signals of the subject are collected by an electroencephalogram device.

5. The closed-loop neurofeedback method based on tactile memory electroencephalographic signal regulation according to claim 1, characterized in that, In step S101, the data preprocessing includes filtering and denoising.

6. The closed-loop neurofeedback method based on tactile memory electroencephalographic signal regulation 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 electroencephalographic signal regulation of claim 1, wherein, In step S103, the extracted features of the brain electrical signals include time domain features, frequency domain features and spatial domain features.

8. A closed-loop neurofeedback device based on tactile memory electroencephalographic signal regulation, characterized by, It comprises: a data acquisition unit for collecting the brain electrical signals of the subject and performing real-time online data preprocessing; a task design unit for designing a tactile memory stimulation task to activate the tactile memory of the subject and recording the corresponding brain electrical signal response; a feature acquisition unit for extracting and analyzing the corresponding brain electrical signals to obtain brain electrical features related to tactile memory; a real-time stimulation unit for designing a closed-loop neurofeedback system based on the brain electrical feature signals of tactile memory, monitoring the brain electrical signals of the subject in real time by the closed-loop neurofeedback system, stimulating in real time according to the preset tactile stimulation task, and recording the brain electrical response.

9. The closed-loop neurofeedback device based on tactile memory electroencephalographic signal regulation according to claim 8, characterized in that, The device further comprises: a closed-loop regulation unit for analyzing the brain electrical response of the subject and adjusting the stimulation parameters according to the preset rules, and continuously monitoring the brain electrical signals and adjusting the stimulation parameters to close-loop regulate the brain neural activity of the subject.

10. The closed-loop neurofeedback device based on tactile memory electroencephalographic signal regulation according to claim 9, characterized in that, The device further comprises: an adjustment and improvement unit 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.

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