Closed-loop neural feedback training method and device based on tactile-motion electroencephalogram signals

Abstract

The invention particularly relates to a closed-loop neural feedback training method and device based on tactile-motion electroencephalogram signals. The method comprises the steps that the current finger somatosensory time discrimination threshold and electroencephalogram data of a tested individual are collected; performing neural feedback training on the tested individual by using a closed-loop neural feedback system and an electroencephalogram amplifier; and collecting the current finger somatosensory time discrimination threshold and the electroencephalogram data of the tested individual again, and processing the behavioral data of the finger somatosensory time discrimination threshold of the tested individual and the electroencephalogram characteristic data of the tested individual to obtain an effect after neural feedback training. According to the method, the primary somatosensory cortex damage is utilized, fewer index tactile electroencephalogram characteristic indexes constrained by cognitive competence are extracted, a patient can observe signals related to the electroencephalogram characteristic indexes of the brain in real time in the closed-loop neural feedback training process, self-adjustment is carried out, and the accuracy of the patient is improved. Therefore, the basic nerve mechanism of the human body is more effectively changed, and the early Alzheimer's disease is controlled and intervened.

Description

Technical Field

[0001] This invention relates to the fields of neuroscience and brain-computer interface technology, and more specifically, to a closed-loop neurofeedback training method and device based on tactile-motor EEG signals. Background Technology

[0002] Alzheimer's disease (AD) is an irreversible neurodegenerative disease. Recent research has divided its progression into three stages: Subjective cognitive decline (SCD), Mild cognitive impairment (MCI), and dementia (AD). Generally, there are no effective treatments for AD; only early warning, early diagnosis, and early intervention can effectively slow its onset and progression.

[0003] The primary somatosensory cortex of AD patients is damaged, but this damage is often masked by the main early manifestation of AD—cognitive decline. In the early, mild cognitive impairment stage of AD, patients' tactile time perception ability differs from that of healthy individuals.

[0004] Electroencephalography (EEG) is a well-established and practical tool for studying brain function, psychology, and psychiatry. EEG data can be collected at a relatively low cost in laboratory and mobile environments (such as at home or school), while maintaining excellent temporal resolution and being quite robust to motion and noise compared to other neuroimaging modalities.

[0005] Neurofeedback (NFB) is a form of biofeedback that uses brain activity as a training indicator. Through a brain-computer interface, it presents brain activity as feedback signals to the individual, allowing them to learn these signals, self-regulate, and ultimately, more effectively change their cognition and behavior. This is an online feedback psychophysiological process based on the fundamental neural mechanisms of the brain. Because the primary early symptom of Alzheimer's disease (AD) is cognitive decline, previous interventions using neurofeedback for early AD have primarily focused on improving cognitive and memory functions.

[0006] In daily life, humans perceive the external world through multiple senses, including sight, hearing, and touch, thus engaging in various activities in an orderly manner. Among these, touch, being the earliest developed sense in the human body and the only sensory system integrating perception and movement, is also a hot research topic in neuroscience. In tactile interaction research, compared to vision and hearing, the information processing of the tactile channel is less constrained by cognitive abilities, meaning it is more objective and should be considered a factor in early warning and intervention for Alzheimer's disease (AD). Within tactile perception, the human fingers are the most sensitive tactile sensory organs and have the closest connection to the brain.

[0007] Current methods for early intervention in Alzheimer's disease have the following technical limitations:

[0008] 1. Cognitive Training and Rehabilitation: Some cognitive training and rehabilitation programs aim to improve cognitive abilities and promote adaptive brain function. This may include memory training, problem-solving, and attention exercises. However, these training programs are more subjective and often have a longer training period due to their lack of specificity.

[0009] 2. Drug Treatment: Some medications are used to manage the symptoms of Alzheimer's disease, although they do not cure the disease. These medications may include cholinesterase inhibitors (such as donepezil) and NMDA receptor antagonists (such as memantine). The main function of these medications is to improve the balance of neurotransmitters to some extent and alleviate symptoms. However, because it is drug treatment, some harm to the body is unavoidable.

[0010] 3. EEG-based behavioral therapy: Also known as open-loop neurofeedback, this involves professionals collecting and analyzing the patient's EEG data to provide training methods. This is more objective than ordinary cognitive training, but the analysis mainly focuses on features related to memory and cognition, and its efficiency is less than that of closed-loop neurofeedback training. Summary of the Invention

[0011] This invention provides a closed-loop neurofeedback training method and device based on tactile-motor EEG signals, which at least solves the technical problem of poor efficiency in existing closed-loop neurofeedback training.

[0012] According to an embodiment of the present invention, a closed-loop neurofeedback training method based on tactile-motor electroencephalogram (EEG) signals is provided, comprising the following steps:

[0013] S101: Collect the current finger somatosensory time discrimination threshold and EEG data of the individual subject;

[0014] S102: Neurofeedback training was conducted on the subjects using a closed-loop neurofeedback system and an EEG amplifier.

[0015] S103: Collect the current finger somatosensory time discrimination threshold and EEG data of the individual subjects again. By processing the behavioral data of the subject's finger somatosensory time discrimination threshold and the subject's EEG characteristic data, the effect of neurofeedback training is obtained.

[0016] Furthermore, in steps S101 and S103, a constant current stimulator and an EEG amplifier are used to collect the subject's current finger somatosensory time discrimination threshold and EEG data.

[0017] Furthermore, in steps S101 and S103, a constant current electrical stimulator is used to collect square wave electrical pulses emitted from the surface electrode on the distal phalanx of the right index finger of the subject. The stimulation intensity for each subject starts at 2 mA and increases in increments of 0.5 mA. The electrical stimulation intensity is 1.5 times the minimum intensity felt by the subject in 10 consecutive stimulations.

[0018] Further, in steps S101 and S103, the subject is given two consecutive electrical stimuli, with the time interval between the two electrical stimuli starting at a 0 ms interval and then gradually increasing in 10 ms increments to provide paired stimuli; in three consecutive paired stimuli, the time interval at which the participant first identifies the paired stimuli as being temporally separate is recorded; the somatosensory time discrimination threshold of the finger is defined as the average of the three identified stimulus time intervals.

[0019] Furthermore, in steps S101 and S103, the EEG data is divided into segments in which two stimuli are identified as being temporally separated and segments in which two stimuli are not identified as being temporally separated. The two types of data are used to calculate the corresponding two features using CSP, and the corresponding two features are input into the support vector machine to construct a model that identifies two stimuli as being temporally separated.

[0020] Furthermore, in step S102, a closed-loop neurofeedback system written in MATLAB and an EEG amplifier are used to perform neurofeedback training.

[0021] Furthermore, in step S102, the subject is instructed to imagine the previously electrically stimulated hand movements to make the tree on the display as big and blooming as possible.

[0022] Further, in step S102:

[0023] First, an EEG amplifier was used to collect EEG signals in real time from the subjects.

[0024] Next, the EEG data collected in real time from the subjects will be preprocessed: 50Hz notch filtering and 0.1-40Hz bandpass filtering will be applied to remove power line interference and obtain data in the desired frequency band.

[0025] Next, the data is downsampled at 250Hz;

[0026] Finally, the data is used for feature extraction using CSP, and then input into the constructed model for classification.

[0027] Furthermore, in step S102, throughout the training, the more EEG data the subject generates that identifies two stimuli as being temporally separated, the taller the cherry blossom tree will gradually grow until it blooms.

[0028] According to another embodiment of the present invention, a closed-loop neurofeedback training device based on tactile-motor electroencephalogram (EEG) signals is provided, comprising:

[0029] The data acquisition unit is used to collect the current finger somatosensory time discrimination threshold and EEG data of the individual subject.

[0030] The feedback training unit is used to conduct neurofeedback training on subjects using a closed-loop neurofeedback system and an EEG amplifier.

[0031] The data processing unit is used to collect the current finger somatosensory time discrimination threshold and EEG data of the individual subject again. By processing the behavioral data of the subject's finger somatosensory time discrimination threshold and the subject's EEG characteristic data, the effect of neurofeedback training is obtained.

[0032] A storage medium storing program files capable of implementing any of the above-mentioned closed-loop neurofeedback training methods based on tactile-motor EEG signals.

[0033] A processor for running a program, wherein the program executes any of the above-mentioned closed-loop neurofeedback training methods based on tactile-motor EEG signals.

[0034] The closed-loop neurofeedback training method and device based on tactile-motor EEG signals in this invention utilizes another previously overlooked defect in AD patients—damage to the primary somatosensory cortex—to extract indicators less constrained by cognitive abilities—tactile EEG characteristic indicators. Using these EEG characteristic indicators, patients can observe the signals of their own brain's relevant EEG characteristic indicators in real time during closed-loop neurofeedback training, perform self-regulation, and thus more effectively change their own basic neural mechanisms, achieving control and intervention for early Alzheimer's disease. Attached Figure Description

[0035] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0036] Figure 1 This is a flowchart of the closed-loop neurofeedback training method based on tactile-motor EEG signals according to the present invention;

[0037] Figure 2 This is a flowchart of the overall experimental process of the present invention;

[0038] Figure 3 This is a block diagram of the closed-loop neurofeedback training device based on tactile-motor EEG signals of the present invention. Detailed Implementation

[0039] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0040] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0041] Example 1

[0042] According to an embodiment of the present invention, a closed-loop neurofeedback training method based on tactile-motor EEG signals is provided, see [link to relevant documentation]. Figure 1 This includes the following steps:

[0043] S101: Collect the current finger somatosensory time discrimination threshold and EEG data of the individual subject;

[0044] S102: Neurofeedback training was conducted on the subjects using a closed-loop neurofeedback system and an EEG amplifier.

[0045] S103: Collect the current finger somatosensory time discrimination threshold and EEG data of the individual subjects again. By processing the behavioral data of the subject's finger somatosensory time discrimination threshold and the subject's EEG characteristic data, the effect of neurofeedback training is obtained.

[0046] The closed-loop neurofeedback training method based on tactile-motor EEG signals in this embodiment of the invention utilizes another previously overlooked defect in AD patients—damage to the primary somatosensory cortex—to extract indicators less constrained by cognitive abilities—tactile EEG characteristic indicators. Using these EEG characteristic indicators, patients can observe the signals of their own brain related to these EEG characteristic indicators in real time during closed-loop neurofeedback training, perform self-regulation, and thus more effectively change their own basic neural mechanisms, achieving control and intervention for early Alzheimer's disease.

[0047] Specifically, this invention proposes a closed-loop neurofeedback training technique based on tactile-motor EEG characteristic signals, primarily used to improve the tactile time perception ability of subjects' fingers, supplementing previous intervention methods for early-stage Alzheimer's disease and providing new approaches and tools for the control and intervention of early Alzheimer's disease. This invention trains subjects (hereinafter referred to as subjects) using this training technique to improve their tactile time perception ability in the primary somatosensory cortex—the fingers—damaged by AD, thereby providing new approaches and tools for the control and intervention of early Alzheimer's disease.

[0048] The closed-loop neurofeedback training technology based on tactile-motor EEG characteristic signals proposed in this invention utilizes another previously overlooked defect in AD patients—damage to the primary somatosensory cortex—to extract less cognitively constrained indicators—tactile EEG characteristic indicators. Using these EEG characteristic indicators, patients can observe the relevant EEG characteristic signals in their brains in real time during closed-loop neurofeedback training, perform self-regulation, and thus more effectively change their basic neural mechanisms, achieving control and intervention for early Alzheimer's disease.

[0049] The basic content of the technical solution of this invention is as follows:

[0050] First, a constant current stimulator (Digitimer DS7A) and Neuroscan's Grael EEG amplifier were used to collect the subject's current finger somatosensory time discrimination threshold and EEG data. Then, a closed-loop neurofeedback system written in MATLAB and the Grael EEG amplifier were used for neurofeedback training. This allowed the subject to observe their own EEG characteristic signals in real time and adjust their neural mechanisms for self-training. Finally, the DS7A constant current stimulator and Neuroscan's Grael EEG amplifier were used again to collect the subject's current finger somatosensory time discrimination threshold and EEG data. By processing the behavioral data of the subject's finger somatosensory time discrimination threshold and the subject's EEG characteristic data, the effect of the neurofeedback training was obtained. This allowed the subject to adjust their training strategy based on the neurofeedback training effect, thereby making the entire training more effective.

[0051] The technical solution of the present invention is described in detail below:

[0052] This invention discloses a closed-loop neurofeedback training technique based on tactile-motor EEG characteristic signals. Figure 2 The main steps include:

[0053] Step 1: Fit the EEG cap onto the subject and explain the training process;

[0054] Step 2: Pre-testing + pre-training;

[0055] Step 3: Closed-loop neurofeedback training;

[0056] Step 4: Post-testing.

[0057] The specific process of step one is as follows:

[0058] Have the subject sit in front of the monitor, adjusting their posture to ensure they are as comfortable as possible. Fit the subject with a 34-channel EEG cap, reducing the impedance of each electrode to below 10 kiloohms. Then explain the entire training process to the subject.

[0059] The specific process of step two is as follows:

[0060] The intensity of electrical stimulation was measured in subjects: a square wave electrical pulse was emitted from the surface electrodes on the distal phalanx of the right index finger of the subject using a constant current electrical stimulator (Digitimer DS7A). The stimulation intensity for each subject started at 2 mA and increased in increments of 0.5 mA. The intensity of electrical stimulation used was 1.5 times the minimum intensity felt by the subject in 10 consecutive stimulations.

[0061] To measure the somatosensory temporal discrimination threshold of the participants' fingers: Two consecutive electrical stimuli were administered to the participants, with the time interval between the two stimuli starting at 0 ms and then gradually increasing in 10 ms increments to provide paired stimuli. In three consecutive paired stimuli, the time interval at which the participant first identified a temporally separated pair of stimuli was recorded. The somatosensory temporal discrimination threshold of the fingers was defined as the average of the three identified stimulus intervals. EEG data was divided into segments where the participants identified two temporally separated stimuli and segments where they could not identify two temporally separated stimuli. Two types of features were calculated using Common Spatial Patterns (CSP) for each type of data. These two types of features were then input into a support vector machine to construct a model that could identify two temporally separated stimuli.

[0062] The specific process of step three is as follows:

[0063] (1) Instructing the subjects on what to do:

[0064] Subjects were instructed to imagine moving their hands (the hands that had been previously electrically stimulated) to make the trees on the display screen as big and blooming as possible, while keeping their bodies still and minimizing blinking.

[0065] (2) Real-time processing and feedback system for EEG data

[0066] First, Neuroscan's Grael EEG amplifier was used to acquire real-time EEG signals from the subjects. Next, the acquired EEG data was preprocessed: a 50Hz notch filter and a 0.1-40Hz bandpass filter were applied to remove power line interference and obtain data in the desired frequency band. Then, the data was downsampled to 250Hz to improve the system's response speed while maintaining the accuracy of the EEG data analysis. Finally, the data was used for feature extraction using CSP and then input into the model constructed in step two for classification. Throughout the training process, the more EEG data the subjects generated that could identify two stimuli that were temporally separated, the taller the cherry blossom tree would grow until it bloomed.

[0067] Step four is the same as step two. The model trained in this way will change according to the subject's performance, thereby more effectively improving the subject's finger somatosensory time discrimination threshold.

[0068] The key points and areas to be protected in this invention are:

[0069] The discovery that the somatosensory time discrimination threshold can serve as a new indicator for the diagnosis and intervention of early Alzheimer's disease;

[0070] By using EEG data from subjects who could identify two electrical stimuli and those who could not, and CSP data, corresponding features were extracted. A binary classification model was constructed using CSP. Real-time feedback was provided to subjects during the real-time neural feedback stage, so that subjects could better train according to their own brain neural activity.

[0071] We used a closed-loop neurofeedback training technique based on tactile-motor EEG signal characteristics to improve the subjects' somatosensory time discrimination threshold.

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

[0073] Compared with the best training methods currently available, this invention proposes a novel training indicator—the somatosensory time discrimination threshold—and a more effective intervention method—closed-loop neurofeedback training based on tactile-motor EEG characteristic signals—to improve the somatosensory time discrimination threshold of subjects. This complements previous intervention methods for early-stage Alzheimer's disease patients and provides new approaches and tools for the control and intervention of early Alzheimer's disease.

[0074] Example 2

[0075] According to another embodiment of the present invention, a closed-loop neurofeedback training device based on tactile-motor EEG signals is provided, see [link to documentation]. Figure 3 ,include:

[0076] Data acquisition unit 201 is used to acquire the current finger somatosensory time discrimination threshold and EEG data of the subject.

[0077] Feedback training unit 202 is used to conduct neurofeedback training on subjects using a closed-loop neurofeedback system and an EEG amplifier.

[0078] The data processing unit 203 is used to collect the current finger somatosensory time discrimination threshold and EEG data of the subject again. By processing the behavioral data of the subject's finger somatosensory time discrimination threshold and the subject's EEG characteristic data, the effect of neurofeedback training is obtained.

[0079] The closed-loop neurofeedback training device based on tactile-motor EEG signals in this embodiment of the invention utilizes another previously overlooked defect in AD patients—damage to the primary somatosensory cortex—to extract indicators less constrained by cognitive abilities—tactile EEG characteristic indicators. Using these EEG characteristic indicators, patients can observe the signals of their own brain related to these EEG characteristic indicators in real time during closed-loop neurofeedback training, perform self-regulation, and thus more effectively change their own basic neural mechanisms, achieving control and intervention for early Alzheimer's disease.

[0080] Specifically, this invention proposes a closed-loop neurofeedback training technique based on tactile-motor EEG characteristic signals, primarily used to improve the tactile time perception ability of subjects' fingers, supplementing previous intervention methods for early-stage Alzheimer's disease and providing new approaches and tools for the control and intervention of early Alzheimer's disease. This invention trains subjects (hereinafter referred to as subjects) using this training technique to improve their tactile time perception ability in the primary somatosensory cortex—the fingers—damaged by AD, thereby providing new approaches and tools for the control and intervention of early Alzheimer's disease.

[0081] The closed-loop neurofeedback training technology based on tactile-motor EEG characteristic signals proposed in this invention utilizes another previously overlooked defect in AD patients—damage to the primary somatosensory cortex—to extract less cognitively constrained indicators—tactile EEG characteristic indicators. Using these EEG characteristic indicators, patients can observe the relevant EEG characteristic signals in their brains in real time during closed-loop neurofeedback training, perform self-regulation, and thus more effectively change their basic neural mechanisms, achieving control and intervention for early Alzheimer's disease.

[0082] The basic content of the technical solution of this invention is as follows:

[0083] First, a constant current stimulator (Digitimer DS7A) and Neuroscan's Grael EEG amplifier were used to collect the subject's current finger somatosensory time discrimination threshold and EEG data. Then, a closed-loop neurofeedback system written in MATLAB and the Grael EEG amplifier were used for neurofeedback training. This allowed the subject to observe their own EEG characteristic signals in real time and adjust their neural mechanisms for self-training. Finally, the DS7A constant current stimulator and Neuroscan's Grael EEG amplifier were used again to collect the subject's current finger somatosensory time discrimination threshold and EEG data. By processing the behavioral data of the subject's finger somatosensory time discrimination threshold and the subject's EEG characteristic data, the effect of the neurofeedback training was obtained. This allowed the subject to adjust their training strategy based on the neurofeedback training effect, thereby making the entire training more effective.

[0084] The technical solution of the present invention is described in detail below:

[0085] This invention discloses a closed-loop neurofeedback training technique based on tactile-motor EEG characteristic signals. Figure 2 The main steps include:

[0086] Step 1: Fit the EEG cap onto the subject and explain the training process;

[0087] Step 2: Pre-testing + pre-training;

[0088] Step 3: Closed-loop neurofeedback training;

[0089] Step 4: Post-testing.

[0090] The specific process of step one is as follows:

[0091] Have the subject sit in front of the monitor, adjusting their posture to ensure they are as comfortable as possible. Fit the subject with a 34-channel EEG cap, reducing the impedance of each electrode to below 10 kiloohms. Then explain the entire training process to the subject.

[0092] The specific process of step two is as follows:

[0093] The intensity of electrical stimulation was measured in subjects: a square wave electrical pulse was emitted from the surface electrodes on the distal phalanx of the right index finger of the subject using a constant current electrical stimulator (Digitimer DS7A). The stimulation intensity for each subject started at 2 mA and increased in increments of 0.5 mA. The intensity of electrical stimulation used was 1.5 times the minimum intensity felt by the subject in 10 consecutive stimulations.

[0094] To measure the somatosensory temporal discrimination threshold of the participants' fingers: Two consecutive electrical stimuli were administered to the participants, with the time interval between the two stimuli starting at 0 ms and then gradually increasing in 10 ms increments to provide paired stimuli. In three consecutive paired stimuli, the time interval at which the participant first identified a temporally separated pair of stimuli was recorded. The somatosensory temporal discrimination threshold of the fingers was defined as the average of the three identified stimulus intervals. EEG data was divided into segments where the participants identified two temporally separated stimuli and segments where they could not identify two temporally separated stimuli. Two types of features were calculated using Common Spatial Patterns (CSP) for each type of data. These two types of features were then input into a support vector machine to construct a model that could identify two temporally separated stimuli.

[0095] The specific process of step three is as follows:

[0096] (1) Instructing the subjects on what to do:

[0097] Subjects were instructed to imagine moving their hands (the hands that had been previously electrically stimulated) to make the trees on the display screen appear larger and bloom as much as possible. During this process, the body was not allowed to move, and blinking was minimized.

[0098] (2) Real-time processing and feedback system for EEG data

[0099] First, Neuroscan's Grael EEG amplifier was used to acquire real-time EEG signals from the subjects. Next, the acquired EEG data was preprocessed: a 50Hz notch filter and a 0.1-40Hz bandpass filter were applied to remove power line interference and obtain data in the desired frequency band. Then, the data was downsampled to 250Hz to improve the system's response speed while maintaining the accuracy of the EEG data analysis. Finally, the data was used for feature extraction using CSP and then input into the model constructed in step two for classification. Throughout the training process, the more EEG data the subjects generated that could identify two stimuli that were temporally separated, the taller the cherry blossom tree would grow until it bloomed.

[0100] Step four is the same as step two. The model trained in this way will change according to the subject's performance, thereby more effectively improving the subject's finger somatosensory time discrimination threshold.

[0101] The key points and areas to be protected in this invention are:

[0102] The discovery that the somatosensory time discrimination threshold can serve as a new indicator for the diagnosis and intervention of early Alzheimer's disease;

[0103] By using EEG data from subjects who could identify two electrical stimuli and those who could not, and CSP data, corresponding features were extracted. A binary classification model was constructed using CSP, and real-time feedback was provided to subjects during the real-time neural feedback stage, so that subjects could better train according to their own brain neural activity.

[0104] We used a closed-loop neurofeedback training technique based on tactile-motor EEG signal characteristics to improve the subjects' somatosensory time discrimination threshold.

[0105] Compared with the prior art, the advantages of the present invention are as follows:

[0106] Compared with the best training methods currently available, this invention proposes a novel training indicator—the somatosensory time discrimination threshold—and a more effective intervention method—closed-loop neurofeedback training based on tactile-motor EEG characteristic signals—to improve the somatosensory time discrimination threshold of subjects. This complements previous intervention methods for early-stage Alzheimer's disease patients and provides new approaches and tools for the control and intervention of early Alzheimer's disease.

[0107] Example 3

[0108] A storage medium storing program files capable of implementing any of the above-mentioned closed-loop neurofeedback training methods based on tactile-motor EEG signals.

[0109] Example 4

[0110] A processor for running a program, wherein the program executes any of the above-mentioned closed-loop neurofeedback training methods based on tactile-motor EEG signals.

[0111] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0112] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The system embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of units or modules may be electrical or other forms.

[0114] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0115] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0116] If the integrated unit is implemented as 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 solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0117] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A closed-loop neurofeedback training method based on tactile-motor EEG signals, characterized in that, Includes the following steps: S101: Collect the current finger somatosensory time discrimination threshold and EEG data of the individual subject; S102: Neurofeedback training was conducted on the subjects using a closed-loop neurofeedback system and an EEG amplifier. S103: Collect the current finger somatosensory time discrimination threshold and EEG data of the individual subjects again. By processing the behavioral data of the subject's finger somatosensory time discrimination threshold and the subject's EEG characteristic data, the effect of neurofeedback training is obtained.

2. The closed-loop neurofeedback training method based on tactile-motor EEG signals according to claim 1, characterized in that, In steps S101 and S103, a constant current stimulator and an EEG amplifier are used to collect the subject's current finger somatosensory time discrimination threshold and EEG data.

3. The closed-loop neurofeedback training method based on tactile-motor EEG signals according to claim 2, characterized in that, In steps S101 and S103, a constant current electrical stimulator is used to collect square wave electrical pulses emitted from the surface electrode on the distal phalanx of the right index finger of the subject. The stimulation intensity for each subject starts at 2 mA and increases in increments of 0.5 mA. The electrical stimulation intensity is 1.5 times the minimum intensity felt by the subject in 10 consecutive stimulations.

4. The closed-loop neurofeedback training method based on tactile-motor EEG signals according to claim 2, characterized in that, In steps S101 and S103, the subject is given two consecutive electrical stimuli. The time interval between the two electrical stimuli starts with a 0 ms interval and then gradually increases in 10 ms increments to provide paired stimuli. In three consecutive paired stimuli, the time interval at which the participant first identifies the paired stimuli as being temporally separate is recorded. The somatosensory time discrimination threshold of the finger is defined as the average of the three identified stimulus time intervals.

5. The closed-loop neurofeedback training method based on tactile-motor EEG signals according to claim 2, characterized in that, In steps S101 and S103, the EEG data is divided into segments in which two stimuli are identified as being temporally separated and segments in which two stimuli are not identified as being temporally separated. The two types of data are used to calculate the corresponding two features using CSP, and the corresponding two features are input into the support vector machine to construct a model that identifies two stimuli as being temporally separated.

6. The closed-loop neurofeedback training method based on tactile-motor EEG signals according to claim 1, characterized in that, In step S102, a closed-loop neurofeedback system written in MATLAB and an EEG amplifier are used to perform neurofeedback training.

7. The closed-loop neurofeedback training method based on tactile-motor EEG signals according to claim 6, characterized in that, In step S102, the subject is instructed to imagine the previously electrically stimulated hand movements to make the tree on the display as big and blooming as possible.

8. The closed-loop neurofeedback training method based on tactile-motor EEG signals according to claim 6, characterized in that, In step S102: First, an EEG amplifier was used to collect EEG signals in real time from the subjects. Next, the EEG data collected in real time from the subjects will be preprocessed: 50Hz notch filtering and 0.1-40Hz bandpass filtering will be applied to remove power line interference and obtain data in the desired frequency band. Next, the data is downsampled at 250Hz; Finally, the data is used for feature extraction using CSP, and then input into the constructed model for classification.

9. The closed-loop neurofeedback training method based on tactile-motor EEG signals according to claim 6, characterized in that, In step S102, throughout the training, the more EEG data the subject generates that identifies two stimuli as being temporally separated, the taller the cherry blossom tree will gradually grow until it blooms.

10. A closed-loop neurofeedback training device based on tactile-motor EEG signals, characterized in that, include: The data acquisition unit is used to collect the current finger somatosensory time discrimination threshold and EEG data of the individual subject. The feedback training unit is used to conduct neurofeedback training on subjects using a closed-loop neurofeedback system and an EEG amplifier. The data processing unit is used to collect the current finger somatosensory time discrimination threshold and EEG data of the individual subject again. By processing the behavioral data of the subject's finger somatosensory time discrimination threshold and the subject's EEG characteristic data, the effect of neurofeedback training is obtained.

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