Method and apparatus for tactile-motion electroencephalogram signal-based closed-loop neurofeedback training

Abstract

The present invention specifically relates to a method and apparatus for tactile-motion electroencephalogram signal-based closed-loop neurofeedback training. The method comprises: collecting the current finger somatosensory temporal discrimination threshold and electroencephalogram data of a test individual; using a closed-loop neurofeedback system and an electroencephalogram amplifier to perform neurofeedback training on the test individual; collecting the current finger somatosensory temporal discrimination threshold and electroencephalogram data of the test individual again, and by means of processing behavioral data of the finger somatosensory temporal discrimination threshold of the test individual and electroencephalogram feature data of the test individual, acquiring an effect after the neurofeedback training. By utilizing the impairment of a primary somatosensory cortex to extract tactile electroencephalogram feature indicators that are less constrained by cognitive abilities, the present invention enables a patient, during the closed-loop neurofeedback training, to observe in real time signals of the patient's brain-related electroencephalogram feature indicators and perform self-regulation, thereby more effectively changing the patient's own basic neural mechanisms, and achieving the control and intervention of early Alzheimer's disease.

Description

Closed-loop neurofeedback training method and device based on tactile-motor electroencephalogram signals TECHNICAL FIELD

[0001] The present application relates to the technical field of neuroscience and brain-computer interface, in particular, relates to a closed-loop neurofeedback training method and device based on tactile-motor electroencephalogram signals. BACKGROUND

[0002] Alzheimer's disease (AD) is an irreversible neurodegenerative disease, and the latest research has divided its evolution rule into three stages: subjective cognitive decline (SCD), mild cognitive impairment (MCI) and Alzheimer's disease (AD). Generally speaking, there is basically no effective treatment method for AD when the disease progresses to AD, and only early warning, early diagnosis and early intervention can effectively delay the occurrence and development of the disease.

[0003] The primary somatosensory cortex of AD patients is damaged, but this damage is often covered up by the main manifestation of AD in the early stage, i.e., cognitive decline of patients. In the mild cognitive impairment stage of the early stage of AD, the tactile temporal perception ability of patients is different from that of healthy people.

[0004] Electroencephalogram (EEG) is a mature and practical tool for studying brain function, psychology and psychiatry. Electroencephalogram can be collected at relatively low cost in the laboratory and mobile environment (such as at home or school), and compared with other neuroimaging modes, electroencephalogram also maintains excellent time resolution and is quite robust to movement and noise.

[0005] Neurofeedback (NFB) is a form of biofeedback that uses brain activity as a training indicator. Through brain-computer interface, brain activity is presented to individuals as a feedback signal, allowing individuals to learn brain signals in this process, self-regulate, and thus more effectively change the online feedback psychophysiological process of the basic neural mechanism of their cognition and behavior. Also because of the main manifestation of AD in the early stage, i.e., cognitive decline of patients, in the past, neurofeedback was mainly used to improve the cognitive memory function of patients when early intervention was made on AD.

[0006] People in daily life through visual, auditory, tactile and other sensory information to perceive the external world, so that orderly engaged in a variety of activities. Among them, the sense of touch as the earliest human development, is the only sensory system of perception and action in one, is also one of the research focus of brain science. In the tactile interaction research, compared with visual and auditory, the information processing of the tactile channel is less constrained by cognitive ability, that is, it is more objective, and should be one of the factors of AD early warning and early intervention. In tactile perception, the human finger is the most sensitive tactile perception organ, and the connection with the brain is the closest.

[0007] The current early intervention method for Alzheimer's disease has the following technical defects:

[0008] 1. Cognitive training and rehabilitation: Some cognitive training and rehabilitation programs aim to improve cognitive ability and promote brain adaptability. This may include memory training, problem solving and attention exercises. However, these training methods are more subjective, and the training period is relatively long due to insufficient targeting.

[0009] 2. Drug treatment: Some drugs are used to manage the symptoms of Alzheimer's disease, although they cannot cure the disease. These drugs may include cholinesterase inhibitors (such as donepezil) and NMDA receptor antagonists (such as memantine). The main effect of these drugs is to improve the balance of neurotransmitters to some extent and reduce symptoms. Because it is drug treatment, harm to the human body is inevitable.

[0010] 3. EEG-based behavioral therapy: This is also known as open-loop neurofeedback, which aims to collect brain waves of patients by professionals and analyze brain wave data. Through these analysis data, some training methods are provided, which are more objective than ordinary cognitive training. However, during analysis, the main focus is still on memory and cognitive-related features, and the efficiency is also poor compared to closed-loop neurofeedback training.

[0011] SUMMARY

[0012] The embodiment of the present application provides a closed-loop neurofeedback training method and device based on tactile-motor electroencephalogram signals, which at least solves the technical problem of poor efficiency of existing closed-loop neurofeedback training.

[0013] According to an embodiment of the present application, a closed-loop neurofeedback training method based on tactile-motor electroencephalogram signals is provided, comprising the following steps:

[0014] S101: Collect the current finger somatosensory temporal discrimination threshold of the subject and the electroencephalogram data;

[0015] S102: Use a closed-loop neurofeedback system and an electroencephalogram amplifier to perform neurofeedback training on the subject.

[0016] S103: Re-collect the current finger somatosensory temporal discrimination threshold of the subject and the EEG data, and obtain the effect after the neurofeedback training by processing the behavioral data of the finger somatosensory temporal discrimination threshold of the subject and the EEG characteristic data of the subject.

[0017] Further, in steps S101 and S103, the current finger somatosensory temporal discrimination threshold of the subject and the EEG data are collected by using a constant current stimulator and an EEG amplifier.

[0018] Further, in steps S101 and S103, the square wave electrical pulses emitted by the surface electrodes on the distal phalanx of the right index finger of the subject are collected by using a constant current stimulator, and the stimulation intensity of each subject starts from 2 mA and increases by 0.5 mA; the electrical stimulation intensity is 1.5 times the minimum intensity felt by the subject in 10 consecutive stimulations.

[0019] Further, in steps S101 and S103, two consecutive electrical stimulations are given to the subject, and the time interval between the two electrical stimulations starts from a 0 ms stimulation interval, and then the electrical stimulation interval is gradually increased by 10 ms to provide paired stimulations; in the three consecutive paired stimulations, the time interval at which the subject first recognizes that the stimulations are separated in time is recorded; the somatosensory temporal discrimination threshold of the finger is defined as the average of the three recognized stimulation time intervals.

[0020] Further, in steps S101 and S103, the EEG data is divided into a segment in which two stimulations are recognized to be separated in time and a segment in which two stimulations are not recognized to be separated in time, and the two kinds of data are used to calculate corresponding two kinds of features by using CSP, and the corresponding two kinds of features are input into a support vector machine to construct a model for recognizing that two stimulations are separated in time.

[0021] Further, in step S102, the closed-loop neurofeedback system written by MTALAB and the EEG amplifier are used for neurofeedback training.

[0022] Further, in step S102, the subject is guided to imagine the hand movement stimulated by the electrical stimulation before to make the tree in the display as large and blooming as possible.

[0023] Further, in step S102:

[0024] First, the EEG data of the subject is collected by using an EEG amplifier to collect real-time EEG signals;

[0025] Then the brain electrical data collected in real time from the subject individual is preprocessed: 50Hz notch filtering and 0.1-40Hz band-pass filtering to remove power frequency interference and obtain the desired frequency band data.

[0026] Then the data is down-sampled by 250Hz.

[0027] Finally, the data is extracted by CSP for feature extraction, and then input into the constructed model for classification.

[0028] Further, in step S102, the more brain electrical data that the subject individual generates to identify that the two stimuli are separated in time during the entire training, the cherry tree will gradually grow until it blooms.

[0029] According to another embodiment of the present application, a closed-loop neurofeedback training device based on tactile-motor electroencephalogram signals is provided, comprising:

[0030] A data acquisition unit is configured to acquire the current finger tactile temporal discrimination threshold of the subject individual and brain electrical data.

[0031] A feedback training unit is configured to perform neurofeedback training on the subject individual by using a closed-loop neurofeedback system and a brain electrical amplifier.

[0032] A data processing unit is configured to acquire the current finger tactile temporal discrimination threshold of the subject individual and brain electrical data again, and obtain the effect of neurofeedback training by processing the behavioral data of the finger tactile temporal discrimination threshold of the subject and the brain electrical feature data of the subject.

[0033] A storage medium stores a program file capable of implementing any one of the above-mentioned closed-loop neurofeedback training methods based on tactile-motor electroencephalogram signals.

[0034] A processor is configured to run a program, wherein the program performs any one of the above-mentioned closed-loop neurofeedback training methods based on tactile-motor electroencephalogram signals when the program is running.

[0035] The closed-loop neurofeedback training method and device based on tactile-motor electroencephalogram signals in the embodiment of the present application extracts the tactile electroencephalogram feature index, which is less restricted by cognitive ability, by using the primary somatosensory cortex damage, which is another defect of AD patients that has been ignored in the past. BRIEF DESCRIPTION OF DRAWINGS

[0036] Fig. 1 is a flow chart of the closed-loop neurofeedback training method based on the somatosensory-motor electroencephalogram signal according to the present application;

[0037] Fig. 2 is a flow chart of the overall experiment according to the present application;

[0038] Fig. 3 is a module diagram of the closed-loop neurofeedback training device based on the somatosensory-motor electroencephalogram signal according to the present application. DETAILED DESCRIPTION

[0039] In order to make the person skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with 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. Based on the embodiments 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.

[0040] 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 have to be used to 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 have to be limited to only 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.

[0041] Embodiment 1

[0042] According to an embodiment of the present application, a closed-loop neurofeedback training method based on a somatosensory-motor electroencephalogram signal is provided, referring to Fig. 1, comprising the following steps:

[0043] S101: Collecting the current finger somatosensory temporal discrimination threshold and electroencephalogram data of the subject individual;

[0044] S102: Using a closed-loop neurofeedback system and an electroencephalogram amplifier to perform neurofeedback training on the subject individual;

[0045] S103: Collecting the current finger somatosensory temporal discrimination threshold and electroencephalogram data of the subject individual again, and obtaining the effect of neurofeedback training by processing the behavioral data of the finger somatosensory temporal discrimination threshold of the subject and the electroencephalogram characteristic data of the subject.

[0046] The closed-loop neurofeedback training method based on the tactile-motor electroencephalogram signal in the embodiment of the application utilizes the fact that the primary somatosensory cortex of the AD patient is damaged, extracts the tactile electroencephalogram characteristic index which is less restricted by cognitive ability, and utilizes the electroencephalogram characteristic index, so that the patient can observe the signal of the brain electroencephalogram characteristic index in real time during the closed-loop neurofeedback training process, and can self-regulate and effectively change the basic neural mechanism, thereby achieving the control and intervention of the early Alzheimer's disease.

[0047] Specifically, the application provides a closed-loop neurofeedback training technology based on the tactile-motor electroencephalogram characteristic signal, which is mainly used for improving the tactile time perception ability of the fingers of the subject, supplementing the intervention method for the early Alzheimer's disease patient in the past, and providing a new way and tool for the control and intervention of the early Alzheimer's disease. The training technology is used to train the subject (referred to as the subject), improve the tactile time perception ability of the fingers of the subject due to the damage of the primary somatosensory cortex of the AD, and provide a new way and tool for the control and intervention of the early Alzheimer's disease.

[0048] The closed-loop neurofeedback training technology based on the tactile-motor electroencephalogram characteristic signal is used to utilize the fact that the primary somatosensory cortex of the AD patient is damaged, extract the tactile electroencephalogram characteristic index which is less restricted by cognitive ability, and utilize the electroencephalogram characteristic index, so that the patient can observe the signal of the brain electroencephalogram characteristic index in real time during the closed-loop neurofeedback training process, and can self-regulate and effectively change the basic neural mechanism, thereby achieving the control and intervention of the early Alzheimer's disease.

[0049] The basic content of the technical scheme of the application is as follows:

[0050] First, the current finger somatosensory time discrimination threshold and the electroencephalogram data of the subject are collected by using the constant current stimulator (Digitimer DS7A) and the electroencephalogram amplifier Grael of Neuroscan, then the closed-loop neurofeedback system written by MTALAB and the electroencephalogram amplifier Grael are used for neurofeedback training. The subject can observe the electroencephalogram characteristic signal in real time, and can adjust the neural mechanism in real time, and can self-train. Finally, the current finger somatosensory time discrimination threshold and the electroencephalogram data of the subject are collected by using the constant current stimulator (DS7A) and the electroencephalogram amplifier Grael of Neuroscan, and the effect after the neurofeedback training is obtained by processing the behavioral data of the finger somatosensory time discrimination threshold and the electroencephalogram characteristic data of the subject. The subject can adjust the training strategy through the neurofeedback training effect, and the whole training is more effective.

[0051] The technical solutions of the present application are described in detail as follows:

[0052] The closed-loop neurofeedback training technology based on the haptic-motor EEG characteristic signal of the present application (Figure 2) mainly includes the following steps:

[0053] Step one, wearing EEG cap for the subject and explaining the training process;

[0054] Step two, pre-test + pre-training;

[0055] Step three, closed-loop neurofeedback training;

[0056] Step four, post-test.

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

[0058] Let the subject sit in front of the display, adjust the sitting posture of the subject to make the subject as comfortable as possible. Put on the 34-lead EEG cap for the subject, and reduce the impedance of each electrode to below 10 kilo-ohms. Then explain the whole training process to the subject.

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

[0060] Measure the electrical stimulation intensity of the subject: use the constant current stimulator (Digitimer DS7A) to emit square wave electrical pulses on the surface electrode on the distal phalanx of the subject's right index finger. The stimulation intensity of each subject starts from 2mA and increases by 0.5mA. The electrical stimulation intensity used is 1.5 times the minimum intensity felt by the subject in 10 consecutive stimulations;

[0061] Measure the subject's finger's sensory temporal discrimination threshold: give the subject two consecutive electrical stimulations, and gradually increase the electrical stimulation interval by 10 milliseconds to provide paired stimuli. In the three consecutive paired stimuli, record the time interval at which the participant first recognizes the stimuli as separated in time. The finger's sensory temporal discrimination threshold is defined as the average of the three recognized stimulus time intervals. Divide the EEG data into two types: one in which the subject recognizes that the two stimuli are separated in time, and one in which the subject does not recognize that the two stimuli are separated in time. Calculate the corresponding two features using CSP (Common Spatial Patterns), and input the corresponding two features into a support vector machine to construct a model that can recognize that the two stimuli are separated in time;

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

[0063] (1) Guide the subject to do the following:

[0064] The subject is guided to make the tree in the display as big as possible and make it bloom by imagining the movement of the hand (the hand that has been electrically stimulated before), in the process, the body is not allowed to move, and the blinking of the eyes is reduced as much as possible.

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

[0066] Firstly, the EEG data of the subject is collected in real time by using the EEG amplifier Grael of Neuroscan; then the EEG data collected in real time from the subject is preprocessed: 50Hz notch filtering and 0.1-40Hz band-pass filtering are performed to remove power frequency interference and obtain the data of the frequency band we want, then the data is down-sampled at 250Hz to improve the system response speed while ensuring the accuracy of EEG data analysis; finally, the data is extracted by CSP, and then input into the model constructed in step two for classification. In the whole training, the more EEG data that can identify the two stimuli separated in time generated by the subject, the higher the cherry blossom tree will grow until it blooms.

[0067] Step four is the same as step two, the model trained in this way will change with the performance of the subject, and then the subject's finger somatosensory temporal discrimination threshold can be improved more targeted.

[0068] The key points and points to be protected of the present application are:

[0069] It is found that the somatosensory temporal discrimination threshold can be used as a new index for early diagnosis and intervention of Alzheimer's disease;

[0070] The EEG data that the subject can identify two electrical stimuli and cannot identify two electrical stimuli are extracted by CSP, and a two-classification model is constructed by CSP, and real-time feedback is given to the subject in the real-time neural feedback stage, so that the subject can better train according to his own brain neural activity;

[0071] The somatosensory temporal discrimination threshold of the subject is improved by using the closed-loop neural feedback training technology based on tactile-motor EEG feature signals.

[0072] Compared with the prior art, the present application has the following advantages:

[0073] Compared with the best training method at present, the present application proposes a new training index-somatosensory temporal discrimination threshold, and a more effective intervention method-closed-loop neural feedback training based on tactile-motor EEG feature signals, to improve the somatosensory temporal discrimination threshold of the subject, and to supplement the intervention method for early patients with Alzheimer's disease in the past, to provide a new way and tool for the control and intervention of early Alzheimer's disease.

[0074] Example 2

[0075] According to another embodiment of the present application, a closed-loop neurofeedback training device based on tactile-motor EEG signals is provided, as shown in Fig. 3, comprising:

[0076] a data acquisition unit 201 for acquiring the current finger somatosensory temporal discrimination threshold and EEG data of the subject;

[0077] a feedback training unit 202 for performing neurofeedback training on the subject by using a closed-loop neurofeedback system and an EEG amplifier;

[0078] a data processing unit 203 for re-acquiring the current finger somatosensory temporal discrimination threshold and EEG data of the subject, and obtaining the effect of neurofeedback training by processing the behavioral data of the finger somatosensory temporal discrimination threshold of the subject and the EEG feature data of the subject.

[0079] The closed-loop neurofeedback training device based on tactile-motor EEG signals in the embodiment of the present application extracts the tactile EEG feature index which is less restricted by cognitive ability by using the previously ignored defect of AD patients, i.e., impairment of the primary somatosensory cortex.

[0080] Specifically, the present application provides a closed-loop neurofeedback training technology based on tactile-motor EEG feature signals, which is mainly used for improving the tactile temporal perception ability of the fingers of the subject and supplementing the previous intervention method for early AD patients, thereby providing a new approach and tool for the control and intervention of early AD.

[0081] The closed-loop neurofeedback training technology based on tactile-motor EEG feature signals is used to extract the tactile EEG feature index which is less restricted by cognitive ability by using the previously ignored defect of AD patients, i.e., impairment of the primary somatosensory cortex.

[0082] The basic content of the technical solution of the present application is as follows:

[0083] First, the current finger somatosensory temporal discrimination threshold of the subject is collected by using constant current stimulator (Digitimer DS7A) and the EEG amplifier Grael of Neuroscan, and then the closed-loop neurofeedback system written by MTALAB and the EEG amplifier Grael are used for neurofeedback training. The subject can observe the EEG characteristic signal in real time and adjust the neural mechanism in real time for self-training. Finally, the current finger somatosensory temporal discrimination threshold of the subject is collected by using constant current stimulator (Digitimer DS7A) and the EEG amplifier Grael of Neuroscan, and the effect of neurofeedback training is obtained by processing the behavioral data of the finger somatosensory temporal discrimination threshold of the subject and the EEG characteristic data of the subject. The subject can adjust the training strategy through the effect of neurofeedback training, so that the whole training is more effective.

[0084] The technical scheme of the present application is described in detail as follows:

[0085] The closed-loop neurofeedback training technology based on haptic-motor EEG characteristic signal (Fig. 2) of the present application mainly comprises the following steps:

[0086] Step one, wearing EEG cap and explaining training process for the subject;

[0087] Step two, pre-test + pre-training;

[0088] Step three, closed-loop neurofeedback training;

[0089] Step four, post-test.

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

[0091] The subject is asked to sit in front of the display and adjust the sitting posture of the subject to make the subject as comfortable as possible. The subject is given a 34-lead EEG cap, and the impedance of each electrode is reduced to below 10 kΩ. Then the subject is explained the whole training process.

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

[0093] The electric stimulation intensity of the subject is measured: the square wave electric pulse emitted by the surface electrode on the distal phalanx of the right index finger of the subject is measured by using constant current stimulator (Digitimer DS7A), and the stimulation intensity of each subject is started from 2mA and increased by 0.5mA. The electric stimulation intensity used is 1.5 times the minimum intensity felt by the subject in 10 consecutive stimulations;

[0094] The somatosensory temporal discrimination threshold of the finger of the test subject is measured: two continuous electric stimuli are given to the test subject, and the two electric stimuli time interval is started from 0 ms stimulus interval, and then the electric stimulus interval is gradually increased by 10 ms to provide paired stimuli. In the three consecutive paired stimuli, the time interval of the first participant who identifies that the stimuli are separated in time is recorded. The somatosensory temporal discrimination threshold of the finger is defined as the average value of the three identified stimulus time intervals. The electroencephalogram data is divided into two parts: the part in which the two stimuli are identified to be separated in time and the part in which the two stimuli are not identified to be separated in time, and the two kinds of data are calculated by CSP (Common Spatial Patterns) to obtain corresponding two kinds of features, and the two kinds of features are input into a support vector machine to construct a model capable of identifying that the two stimuli are separated in time;

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

[0096] (1) Guide the test subject to do something:

[0097] Guide the test subject to imagine the movement of the hand (the hand that has been electrically stimulated before) as much as possible to make the tree in the display grow and bloom, and in this process, the body is not allowed to move, and blinking is reduced as much as possible.

[0098] (2) Real-time processing of electroencephalogram data and feedback system

[0099] First, the electroencephalogram amplifier Grael of Neuroscan is used to collect the electroencephalogram data of the test subject in real time; then the real-time collected electroencephalogram data of the test subject is preprocessed: 50 Hz notch filtering and 0.1-40 Hz band-pass filtering are performed to remove power frequency interference and obtain the data of the frequency band we want, and then 250 Hz downsampling is performed to ensure the accuracy of electroencephalogram data analysis and improve the system response speed; finally, the data is extracted by CSP, and then input into the model constructed in step two for classification. In the whole training process, the more electroencephalogram data that the test subject produces to identify that the two stimuli are separated in time, the higher the cherry blossom tree will grow until it blooms.

[0100] Step four is the same as step two, and the model trained in this way will change with the performance of the test subject, so as to more specifically improve the somatosensory temporal discrimination threshold of the test subject.

[0101] The key points and points to be protected of the present application are:

[0102] It is found that the somatosensory temporal discrimination threshold can be used as a new early diagnosis and intervention index for Alzheimer's disease;

[0103] The subject can identify two electric stimuli and cannot identify two electric stimuli, and the electroencephalogram data and CSP are used for corresponding feature extraction, and a two-classification model is constructed by using the CSP, and real-time feedback is given to the subject in the real-time neurofeedback stage, so that the subject can better train according to the brain neural activity of the subject;

[0104] The somatosensory temporal discrimination threshold of the subject is improved by using the closed-loop neurofeedback training technology based on the somatosensory-motor electroencephalogram feature signal.

[0105] Compared with the prior art, the advantages of the present application are that:

[0106] Compared with the best training method at present, the present application proposes a new training index, a somatosensory temporal discrimination threshold, and a more effective intervention method, a closed-loop neurofeedback training based on a somatosensory-motor electroencephalogram feature signal, to improve the somatosensory temporal discrimination threshold of the subject, and to supplement the intervention method for early patients with Alzheimer's disease in the past, thereby providing a new way and tool for the control and intervention of early Alzheimer's disease.

[0107] Embodiment 3

[0108] A storage medium, the storage medium stores a program file capable of realizing the above-mentioned any one closed-loop neurofeedback training method based on a somatosensory-motor electroencephalogram signal.

[0109] Embodiment 4

[0110] A processor, the processor is used for running a program, wherein the program executes the above-mentioned any one closed-loop neurofeedback training method based on a somatosensory-motor electroencephalogram signal when the program is running.

[0111] The above-mentioned embodiment numbers of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0112] 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.

[0113] In several embodiments provided in the present application, it should be understood that the disclosed technical contents can be realized 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. In addition, 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.

[0114] 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, i.e. may be located in one place, or may be distributed to multiple units. Part or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.

[0115] In addition, each functional unit in various embodiments 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.

[0116] 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, the essential part or the whole or part of the contribution to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to make a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the various embodiments of the present application method. The foregoing storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and various program code storage media.

[0117] 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, can make a number of improvements and refinements, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A closed-loop neurofeedback training method based on somato- motor electroencephalogram signals, characterized in that, The method comprises the following steps: S101: Collect the current finger somatosensory temporal discrimination threshold and the brain electrical data of the subject; S102: Use the closed-loop neurofeedback system and the brain electrical amplifier to perform neurofeedback training on the subject; S103: Collect the current finger somatosensory temporal discrimination threshold and the brain electrical data of the subject again, and process the behavioral data of the finger somatosensory temporal discrimination threshold of the subject and the brain electrical characteristic data of the subject to obtain the effect after the neurofeedback training.

2. The closed-loop neurofeedback training method based on somato- motor electroencephalogram signals according to claim 1, characterized in that, In steps S101 and S103, the current finger somatosensory temporal discrimination threshold and the brain electrical data of the subject are collected by using a constant current stimulator and a brain electrical amplifier.

3. The closed-loop neurofeedback training method based on somato- motor electroencephalogram signals according to claim 2, characterized in that, In steps S101 and S103, the square wave electrical pulses emitted by the surface electrodes on the distal phalanx of the right index finger of the subject are collected by using the constant current stimulator, and the stimulation intensity of each subject starts from 2 mA and increases by 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 somato- motor electroencephalogram signals according to claim 2, characterized in that, In steps S101 and S103, two consecutive electrical stimulations are given to the subject, and the two electrical stimulation time intervals start from a 0 ms stimulation interval, and then the electrical stimulation interval is gradually increased by 10 ms to provide paired stimulations; in the three consecutive paired stimulations, the time interval at which the subject first recognizes that the stimulations are separated in time is recorded; the somatosensory temporal discrimination threshold of the finger is defined as the average of the three recognized stimulation time intervals.

5. The somato-vestibular galvanic skin response based closed loop neurofeedback training method as claimed in claim 2, wherein, In steps S101 and S103, the brain electrical data is divided into a segment in which two stimulations are recognized to be separated in time and a segment in which two stimulations are not recognized to be separated in time, two kinds of data are calculated by using CSP to obtain corresponding two kinds of features, and the corresponding two kinds of features are input into a support vector machine to construct a model for recognizing that two stimulations are separated in time.

6. The somato-vestibular galvanic skin response based closed loop neurofeedback training method as claimed in claim 1, wherein, In step S102, the closed-loop neurofeedback system and the brain electrical amplifier written by MTALAB are used to perform neurofeedback training.

7. The closed-loop neurofeedback training method based on somato- motor electroencephalogram signals according to claim 6, characterized in that, In step S102, the subject is guided to imagine the hand movement stimulated by the electrical stimulation before to make the tree in the display as large and blooming as possible.

8. The closed-loop neurofeedback training method based on somato- motor electroencephalogram signals according to claim 6, characterized in that, In step S102: First, the brain electrical data of the subject is collected in real time by using the brain electrical amplifier; Then, the brain electrical data collected in real time from the subject is preprocessed: 50 Hz notch filtering and 0.1-40 Hz band-pass filtering are performed to remove power frequency interference and obtain data in the desired frequency band; Then, the data is down-sampled by 250 Hz; Finally, the data is extracted by CSP, and then input into the constructed model for classification.

9. The closed-loop neurofeedback training method based on somato- motor electroencephalogram signals according to claim 6, characterized in that, In step S102, during the entire training, the more brain electrical data that the subject produces to recognize that two stimulations are separated in time, the higher the cherry blossom tree will grow until it blooms.

10. A closed-loop neurofeedback training device based on somato- motor electroencephalographic signals, characterized in that The method comprises the following steps: A data collection unit is configured to collect the current finger somatosensory temporal discrimination threshold and the brain electrical data of the subject; A feedback training unit is configured to use a closed-loop neurofeedback system and a brain electrical amplifier to perform neurofeedback training on the subject; A data processing unit is configured to collect the current finger-sense temporal discrimination threshold of the subject and electroencephalogram data of the subject again, and obtain the effect of the neurofeedback training by processing the behavioral data of the finger-sense temporal discrimination threshold of the subject and the electroencephalogram data of the subject.

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