CN113729709B - Nerve feedback device, nerve feedback method, and computer-readable storage medium - Google Patents
Nerve feedback device, nerve feedback method, and computer-readable storage medium Download PDFInfo
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- A—HUMAN NECESSITIES
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
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- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
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- Y02D30/00—Reducing energy consumption in communication networks
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Abstract
The invention discloses a nerve feedback device, comprising: the brain electricity acquisition device comprises a wearable device and a signal conversion device, wherein the signal conversion device is integrated with the wearable device, the signal conversion device is electrically connected with the wearable device in a connection mode, and the brain electricity acquisition device is in wireless communication connection with the mobile terminal in a connection mode. The invention also discloses a nerve feedback method and a computer readable storage medium. By applying the nerve feedback method to the nerve feedback equipment, the use cost of the nerve feedback equipment can be reduced, and the training feedback efficiency and the treatment effect of the nerve feedback equipment can be improved.
Description
Technical Field
The present invention relates to the field of neuroscience, and in particular, to a nerve feedback device, a nerve feedback method, and a computer readable storage medium.
Background
Neurofeedback technology (Neurofeedback) is a non-invasive, safe, reliable cognitive therapeutic approach for the treatment and amelioration of common mental disorders. The technology has been rapidly developed in recent decades due to the advantages of no need of medication or surgery and no side effects. Among the many applications of this technique, the most widespread are the treatment of neurological disorders such as attention deficit, hyperactivity disorder (ADHD), post-traumatic stress disorder (PTSD) and relaxation training.
However, some of the nerve feedback devices based on brain electricity on the market at present are mainly scientific research and medical treatment, and the devices have huge volume, complicated use and high treatment cost, and a common family is difficult to bear the whole treatment course, so that the previous treatment can be abandoned; the other part of nerve feedback equipment is mainly entertainment and relaxation, and is convenient to use, low in cost and capable of being owned by common families, but the function is too simple, can not reach strict medical treatment standards far, and can only be used as common entertainment equipment.
Disclosure of Invention
The invention provides a nerve feedback device, a nerve feedback method and a computer readable storage medium, and aims to provide a nerve feedback device with low cost and medical function.
To achieve the above object, the present invention provides a nerve feedback device including: the brain electricity acquisition device comprises a wearable device and a signal conversion device, wherein the signal conversion device is integrated with the wearable device, the signal conversion device is electrically connected with the wearable device in a connection mode, and the signal conversion device is in wireless communication connection with the mobile terminal in a connection mode.
Optionally, the wearable device includes a plurality of electrodes, the electrodes are dry electrodes or gel electrodes, and the mobile terminal includes a signal analysis processing module and a real-time feedback module.
In addition, in order to achieve the above object, the present invention also provides a nerve feedback method applied to an electroencephalogram acquisition apparatus, the nerve feedback method comprising:
collecting brain electrical nerve signals;
and converting the brain electrical nerve signals into brain electrical digital signals, and sending the brain electrical digital signals to a mobile terminal.
Optionally, the step of converting the electrical brain nerve signal into an electrical brain digital signal includes:
and performing signal processing on the electroencephalogram nerve signals, and converting the processed electroencephalogram nerve signals into electroencephalogram digital signals, wherein the signal processing comprises at least one of signal amplification, signal filtering, downsampling and baseline drift removal.
In addition, in order to achieve the above object, the present invention also provides a nerve feedback method applied to a mobile terminal, the nerve feedback method comprising:
receiving an electroencephalogram digital signal sent by electroencephalogram acquisition equipment, and analyzing the electroencephalogram digital signal to generate an analysis result;
and executing feedback operation corresponding to the analysis result according to the analysis result.
Optionally, the step of analyzing the electroencephalogram digital signal to generate an analysis result includes:
correcting the electroencephalogram digital signal, determining a signal type corresponding to the corrected electroencephalogram digital signal, and generating an analysis result corresponding to the signal type according to the signal type, wherein the correction comprises at least one of signal filtering and baseline drift removal.
Optionally, the step of determining the signal type corresponding to the corrected electroencephalogram digital signal includes:
receiving input task information, and determining a learning model corresponding to the task information;
and determining the signal type corresponding to the corrected electroencephalogram digital signal according to the learning model.
In addition, in order to achieve the above object, the present invention also provides a nerve feedback method applied to a nerve feedback device including an electroencephalogram acquisition device and a mobile terminal, the nerve feedback method including:
the electroencephalogram acquisition equipment is used for acquiring electroencephalogram nerve signals; converting the brain electrical nerve signal into an brain electrical digital signal, and sending the brain electrical digital signal to a mobile terminal;
the mobile terminal is used for receiving the brain electricity digital signals sent by the brain electricity acquisition equipment and analyzing the brain electricity digital signals to generate analysis results; and executing feedback operation corresponding to the analysis result according to the analysis result.
In addition, to achieve the above object, the present invention also provides a nerve feedback device including a memory, a processor, and a nerve feedback program stored on the memory and executable on the processor, wherein: the biofeedback procedure, when executed by the processor, implements the steps of the biofeedback method as described above.
In addition, in order to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a nerve feedback program which, when executed by a processor, implements the steps of the nerve feedback method as described above.
The nerve feedback equipment integrates the signal conversion equipment and the wearing equipment in an electric connection mode, so that the volume of the electroencephalogram acquisition equipment is greatly reduced, the electroencephalogram acquisition equipment can be worn on a human body very lightly, meanwhile, the connection configuration time between the signal conversion equipment and the wearing equipment is shortened, and the installation efficiency is improved; the signal conversion equipment and the mobile terminal are in wireless communication connection, so that the volume and the weight of the nerve feedback equipment are greatly reduced, a user can use and store the nerve feedback equipment very conveniently, monitoring and training feedback of brain electrical signals can be realized at very low cost, the cost of using the nerve feedback equipment by the user is greatly reduced, and the treatment effect of the nerve feedback equipment is guaranteed.
Drawings
FIG. 1 is a schematic diagram of a nerve feedback device of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of the nerve feedback method of the present invention;
FIG. 3 is a flow chart of a second embodiment of the nerve feedback method of the present invention;
fig. 4 is a flowchart of a fifth embodiment of the nerve feedback method according to the present invention.
Figure 1 reference numerals illustrate:
| reference numerals | Name of the name |
| 100 | Nerve feedback device |
| 1 | Wearing equipment |
| 2 | Signal conversion equipment |
| 3 | Mobile terminal |
| 10 | Electroencephalogram acquisition equipment |
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, fig. 1 is a schematic structural diagram of a nerve feedback device of the present invention, where the nerve feedback device 100 includes an electroencephalogram acquisition device 10 and a mobile terminal 3, where the electroencephalogram acquisition device includes a wearable device 1 and a signal conversion device 2, the signal conversion device 2 is integrated on the wearable device 1 by an electrical connection manner, and the signal conversion device 2 is connected with the mobile terminal 3 by a wireless communication manner, where the wireless communication manner is not limited to bluetooth communication, wiFi (wireless communication technology), mobile communication technology, infrared transmission, and the like.
Specifically, the wearable device comprises a plurality of electrodes, wherein the electrodes are dry electrodes or gel electrodes, and the mobile terminal comprises a signal analysis processing module and a real-time feedback module.
In this embodiment, the wearing device may be an electroencephalogram cap, the signal conversion device may be integrated at any position such as the upper part, the lower part, the left part and the right part of the electroencephalogram cap, and the wearing device and the signal conversion device are powered through a battery container integrated on the wearing device, so that the integration of the wearing device and the signal conversion device is realized, the volume of a corresponding device in a traditional nerve feedback device is greatly reduced, and a user can wear the wearing device and the signal conversion device on the head without feel heavy, wherein the wearing device may use materials with certain toughness and elasticity, such as a silica gel material, a TPU (Thermoplastic polyurethanes), a thermoplastic polyurethane elastomer), a plurality of electrodes are distributed on the wearing device, and the electroencephalogram nerve signals are collected through the plurality of electrodes, and the electroencephalogram nerve signals belong to analog signals. Preferably, the electrode can be a disposable or reusable dry electrode or gel electrode, and the two electrodes have good conductive performance and are comfortable to wear, so that the trouble that conductive paste is required to be coated after a user wears the electroencephalogram acquisition equipment is avoided, the complicated process of removing the electroencephalogram acquisition equipment and cleaning is omitted, and meanwhile, the sanitary standard of the treatment process is also improved. In addition, the number of the plurality of electrodes on the electroencephalogram acquisition equipment can be 8-32, preferably 32, and for the distribution of the plurality of electrodes, the positioning standard of the international 10-20 system is used, so that the richness and the accuracy of acquiring the electroencephalogram signals are ensured. In addition, the mobile terminal is not limited to devices such as a computer, a tablet, a mobile phone and a television, and comprises a signal analysis processing module and a real-time feedback module, wherein the signal analysis processing module is used for performing analysis operations such as reading, correcting and analyzing on an electroencephalogram digital signal, the real-time feedback module is used for performing corresponding feedback operations on a user according to a result analyzed by the signal analysis processing module, and the feedback operations are not limited to visual modes such as hearing, vision and touch.
The above embodiments of the nerve feedback device of the present invention are only preferred embodiments, and therefore, the scope of the present invention is not limited by the above embodiments, and all the equivalent structural changes made by the descriptions of the present invention and the accompanying drawings or the direct/indirect application in other related technical fields are included in the scope of the present invention.
The nerve feedback equipment integrates the signal conversion equipment and the wearing equipment in an electric connection mode, so that the volume of the electroencephalogram acquisition equipment is greatly reduced, the electroencephalogram acquisition equipment can be worn on a human body very lightly, meanwhile, the connection configuration time between the signal conversion equipment and the wearing equipment is shortened, and the installation efficiency is improved; the signal conversion equipment and the mobile terminal are in wireless communication connection, so that the volume and the weight of the nerve feedback equipment are greatly reduced, a user can use and store the nerve feedback equipment very conveniently, monitoring and training feedback of brain electrical signals can be realized at very low cost, the cost of using the nerve feedback equipment by the user is greatly reduced, and the treatment effect of the nerve feedback equipment is guaranteed.
As shown in fig. 2, fig. 2 is a schematic flow chart of a first embodiment of a nerve feedback method according to the present invention, where the nerve feedback method is applied to an electroencephalogram acquisition apparatus, and the nerve feedback method includes:
step S10, acquiring brain electrical nerve signals;
in this embodiment, the nerve feedback method may be applied to an electroencephalogram acquisition device, where the electroencephalogram acquisition device may include a wearing device and a signal conversion device, where the wearing device is worn on a head of a human body and is responsible for acquiring an electroencephalogram nerve signal generated by the human body, and specifically, the acquisition of the electroencephalogram nerve signal may be achieved by sensing an electroencephalogram wave and an electromyographic signal of the human body through a plurality of electrodes on the wearing device. For traditional electroencephalogram acquisition equipment with medical effect, the price is high, the volume is large, if a patient wants to treat some mental diseases by adopting the equipment, the patient needs to go to a hospital or a medical institution with medical qualification frequently, so that not only is the expensive medical cost paid, but also precious time and energy of a user are spent, including the time on a round trip and the time for installing the electroencephalogram acquisition equipment, if the nerve feedback method is applied to the electroencephalogram acquisition equipment, some large equipment or expensive parts in the traditional electroencephalogram acquisition equipment with medical effect can be saved, the running cost of the electroencephalogram acquisition equipment is reduced, and the electroencephalogram acquisition equipment with the nerve feedback method has the characteristics and effects of low cost, high convenience and easy wearing.
Step S20, converting the brain electrical nerve signals into brain electrical digital signals, and sending the brain electrical digital signals to a mobile terminal;
the signal conversion equipment in the electroencephalogram acquisition equipment can convert an analog signal of an electroencephalogram nerve signal into an electroencephalogram digital signal which can be directly read and analyzed by the mobile terminal.
Specifically, after the wearable device collects the brain electrical nerve signals through the multiple electrodes, the brain electrical nerve signals are transmitted to the signal conversion device integrated on the wearable device in real time through the lead, the signal conversion device can convert the analog signals into brain electrical digital signals after receiving the brain electrical nerve signals, and the brain electrical digital signals after being converted can be transmitted to the mobile terminal in a wireless transmission mode through a wireless communication module in the signal conversion device, wherein the wireless transmission can be in a mobile communication technology mode such as 5G and 4G, can also be in a mode such as WIFI, bluetooth transmission and infrared transmission, is not limited, and can also transmit the brain electrical digital signals to the mobile terminal in a wired transmission mode. The mobile terminal can be a personal computer, a tablet personal computer, a television, a mobile phone and other devices.
Specifically, the step of converting the electrical brain nerve signal into an electrical brain digital signal includes:
and a step a of performing signal processing on the brain electrical nerve signals, and converting the processed brain electrical nerve signals into brain electrical digital signals, wherein the signal processing comprises at least one of signal amplification, signal filtering, downsampling and baseline drift removal.
The electroencephalogram acquisition equipment is inevitably capable of receiving other band signals which are not interfered and invalid electroencephalogram signals while acquiring the valid electroencephalogram signals, so that the electroencephalogram acquisition equipment is required to perform a series of signal processing operations on the acquired various band signals, wherein the methods comprise one or more of signal amplification, signal filtering, downsampling and baseline drift removal, namely only the valid electroencephalogram band and the myoelectric band of a human body are reserved as far as possible after the signals of different bands are identified, and other interference bands and invalid electroencephalogram signals are eliminated, and specifically: the 50HZ electromechanical signals can be filtered by a band-stop filter of the signal conversion equipment, and then the signals of other irrelevant frequency bands are filtered by a band-pass filter. And when the filtering operation is performed, correction processing of removing baseline drift can be performed on the brain electrical nerve signals, namely, the brain electrical nerve signals which obviously fluctuate up and down at the baseline and jump suddenly are filtered, and only effective brain electrical nerve signals are reserved as far as possible. The signal processing mode is beneficial to the next mobile terminal to analyze and process only effective brain electrical digital signals, and improves the efficiency of analyzing and processing brain electrical signals.
As shown in fig. 3, fig. 3 is a schematic flow chart of a second embodiment of the nerve feedback method of the present invention, in this embodiment, the nerve feedback method is applied to a mobile terminal, and the nerve feedback method includes:
step S30, receiving an electroencephalogram digital signal sent by electroencephalogram acquisition equipment, and analyzing the electroencephalogram digital signal to generate an analysis result;
specifically, the step of analyzing the electroencephalogram digital signal to generate an analysis result includes:
and b, correcting the electroencephalogram digital signal, determining a signal type corresponding to the corrected electroencephalogram digital signal, and generating an analysis result corresponding to the signal type according to the signal type, wherein the correction comprises at least one of signal filtering and baseline drift removal.
When the mobile terminal receives the brain electricity digital signals sent by the brain electricity acquisition equipment, the brain electricity digital signals can be read and analyzed in a mode of embedding or installing a nerve feedback program, and when the brain electricity digital signals are read and analyzed, correction processing can be further carried out on all received digital signals, including filtering and baseline drift removal processing on the digital signals, so that the purpose of the method is to more comprehensively eliminate interference signals and invalid signals.
The signal analysis and processing module of the mobile terminal can extract the characteristics of the real-time electroencephalogram digital signal, so as to determine the signal type corresponding to the electroencephalogram digital signal, for example, the signal related to the hyperactivity disorder can be determined, namely, the specific state of a user corresponding to the characteristics of each part of the electroencephalogram digital signal can be identified by determining the signal type, wherein the specific state can be divided into a normal state and an abnormal state, the normal state can be a natural and relaxed mental state of a human body, the abnormal state can be a negative emotion state such as stress, depression, anger, sadness and the like, and the specific symptoms corresponding to the mental diseases and occurring in the nerve feedback treatment process can be also be the symptoms such as the symptoms of uncontrollable attention deficit disorder, the minor motion symptoms of the hyperactivity disorder disease, the persistent or intermittent pain, the despair of the symptoms such as apathy, the cold and the anxiety and the like, the symptoms such as the anxiety disorder and the symptoms such as the incapacitation and the mental disorder. After analyzing various specific states corresponding to the characteristics of each part of the electroencephalogram digital signal, the mobile terminal automatically generates analysis results corresponding to various specific symptoms, wherein the analysis results can be sent to a real-time feedback module of the mobile terminal in text, code or other forms, for example, the analysis results are text: post-traumatic stress disorder presents with anxiety symptoms, and for example, the analysis results are the codes: 1, the content of which the code 1 can be obtained in the nerve feedback system is the mental disorder of drug addiction. It should be noted that, each analysis result is associated with a corresponding time parameter, i.e. it can be determined at which time point what symptom appears to the user, or which time period lasts for what symptom.
The whole analysis process can be completed in a general mobile terminal, other equipment is not needed, a professional analysis chip is not needed to be installed in analysis equipment, the application cost of the nerve feedback technology is greatly reduced, and the analysis efficiency is higher.
And step S40, executing feedback operation corresponding to the analysis result according to the analysis result.
After the analysis result is generated, training feedback can be carried out on the user through the mobile terminal, the training feedback is not limited to visual, auditory and tactile modes such as pictures, videos, audios, vibration, lamp light flickering and the like, for example, when the user is in the treatment of the symptom of frequent small actions, music interesting to the user is played, the user is reminded to sing through a loudspeaker, the user is helped to concentrate on, and therefore the occurrence frequency of the small actions is reduced. In addition, training feedback can be performed on the user in a mode that the mobile terminal is connected with other auxiliary feedback equipment in a wireless mode. By means of the training feedback mode, accurate treatment of a user can be achieved.
Further, a third embodiment of the present invention is provided based on the second embodiment of the present invention, in which the step of determining a signal type corresponding to the corrected electroencephalogram digital signal includes:
step c, receiving input task information and determining a learning model corresponding to the task information;
and d, determining the signal type corresponding to the corrected brain electrical digital signal according to the learning model.
After the mobile terminal further corrects the electroencephalogram digital signal, task information input by a user or a doctor according to the common mental condition or mental disease of the user is received and acquired, for example, the user is a post-traumatic stress disorder patient, then task information corresponding to the post-traumatic stress disorder disease can be input, after the task information is determined, a learning model corresponding to the task information is searched and determined, wherein the learning model is a learning model formed by establishing a data model by continuously acquiring electroencephalogram data and state feature data or symptom feature data reflected by the user after acquiring a large number of similar patients or electroencephalogram data of the user corresponding to the same therapeutic requirement, and performing a large number of machine learning and training on the data model, wherein the learning model can be updated and perfected by acquiring new user electroencephalogram data and feature data, and the machine learning method comprises, but is not limited by a neural network, SVM (Support Vector Machine, a support vector machine), LDA (Linear Discriminant Analysis ), federal learning and the like.
After the corresponding learning model is determined, the corrected electroencephalogram digital signals are judged according to the learning model, the signal types corresponding to the electroencephalogram digital signals are judged, namely, the matching of the electroencephalogram digital signals and the electroencephalogram data corresponding to any mental state or mental symptom in the learning model is judged, and after the signal types are determined, the analysis results corresponding to the electroencephalogram signals can be generated.
In this embodiment, the learning model obtained by the machine learning mode is obtained, so that the complete learning model can be updated continuously to identify the electroencephalogram signal of the user, the accuracy of identification can be ensured, meanwhile, the current task information is used for corresponding to the learning model, the data size is not so great, and the user can be more focused on identifying the corresponding mental state or symptom, so that the identification efficiency is improved, and the treatment requirement of the current more and more complex mental state or mental disease symptom of people can be adapted continuously.
Further, a fourth embodiment of the present invention is a method for nerve feedback according to the third embodiment of the present invention, wherein after the step of determining the learning model corresponding to the task information, the method further includes:
step e, if the signal type corresponding to the corrected electroencephalogram digital signal cannot be determined according to the learning model, acquiring all preset learning models;
and f, determining the signal type corresponding to the corrected brain electrical digital signal according to all the learning models.
If the mobile terminal cannot identify the signal type corresponding to the corrected electroencephalogram digital signal according to the task information, that is, the current learning model does not have the electroencephalogram digital signal, the situation often indicates that a user may not only have the mental disease, so all learning models stored in the nerve feedback system are acquired, and then the signal type corresponding to the corrected electroencephalogram digital signal is determined according to all the learning models, that is, the signal type corresponding to the electroencephalogram digital signal is determined to be matched with the signal data in which learning model, for example, the learning model corresponding to the attention deficit disease is initially used, but the electroencephalogram digital signal which cannot be identified by the model appears in the treatment process, then the learning model corresponding to other mental diseases can be called at the moment, and if the learning model corresponding to the stress disorder disease is finally identified as a painful analysis result through the post-traumatic stress disorder disease, the user can be primarily considered to have the post-traumatic stress disorder disease, and then the learning model corresponding to the attention deficit disorder disease and the learning model corresponding to the post-traumatic stress disorder disease are called, and the two brain digital signals of the user can be jointly identified.
By calling all the learning models, the actual mental diseases of the user can be comprehensively identified and analyzed, the situation that the user delays disease treatment due to unknowing or hiding the mental conditions of the user is avoided, and meanwhile, after the actual mental diseases of the user are determined, the corresponding one or more learning models can be called to carry out special training feedback treatment on the user, so that the symptomatic treatment effect is realized, and the treatment efficiency is improved.
In another embodiment, after the step of analyzing the electroencephalogram digital signal, the method further includes:
step g, generating a prediction result according to the analyzed brain electricity digital signal;
and h, executing feedback operation corresponding to the prediction result according to the prediction result.
After the mobile terminal analyzes the electroencephalogram digital signals, the analyzed electroencephalogram digital signals can be predicted according to a learning model corresponding to the current task information or all learning models, namely, the possible states and symptoms of the user can be reasonably predicted based on various learning models, predicted results are generated, and then feedback operations in the forms of corresponding hearing, vision, touch and the like are executed according to the predicted results, so that the possibility of occurrence of bad predicted results can be reduced, the possibility of occurrence of negative states or symptoms of the user can be relieved or eliminated over time, the situation is prevented, and the treatment effect and efficiency are greatly improved.
As shown in fig. 4, fig. 4 is a schematic flow chart of a fifth embodiment of the nerve feedback method of the present invention formed on the basis of schematic flow charts corresponding to the first embodiment and the second embodiment of the nerve feedback method of the present invention, in this embodiment, the nerve feedback method is applied to a nerve feedback device, the nerve feedback device includes an electroencephalogram acquisition device and a mobile terminal, and the nerve feedback method includes:
step S100, an electroencephalogram acquisition device is used for acquiring an electroencephalogram nerve signal; converting the brain electrical nerve signal into an brain electrical digital signal, and sending the brain electrical digital signal to a mobile terminal;
step S200, the mobile terminal is used for receiving an electroencephalogram digital signal sent by the electroencephalogram acquisition equipment and analyzing the electroencephalogram digital signal to generate an analysis result; and executing feedback operation corresponding to the analysis result according to the analysis result.
The specific embodiments of the neural feedback method may be referred to for the description of the specific embodiments, and will not be repeated herein.
Further, a sixth embodiment of the nerve feedback method according to the present invention is proposed based on the second embodiment of the nerve feedback method according to the present invention, and in the present embodiment, after step S40, the method includes:
step i, acquiring input user information, and establishing a mapping relation between the user information, the electroencephalogram digital signal and the analysis result;
and j, converting the mapping relation into chart information corresponding to the mapping relation, and outputting the chart information.
After the user finishes using the nerve feedback equipment, the nerve feedback system automatically correlates and stores the user information input by the user or doctor with all the brain electrical digital signals and analysis results of the user in the treatment process, establishes a user file or updates the user file in the original user file, and converts all the brain electrical digital signals, analysis results and user information which are correlated and stored by the user in the treatment process into visual chart information, so that the user or doctor can analyze and evaluate the mental health of the user clearly, and the user can know the mental health conveniently and timely.
In addition, the data such as the brain electrical digital signals and the analysis results in the treatment process can be input into the corresponding learning model to help the original learning model to be perfected and updated, so that the treatment effect is improved.
In addition, the invention also provides a nerve feedback device, which comprises a memory, a processor and a nerve feedback program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the nerve feedback method according to the embodiment when executing the nerve feedback program.
The specific implementation of the nerve feedback device of the present invention is substantially the same as the above embodiments of the nerve feedback method, and will not be described herein.
Furthermore, the present invention also proposes a computer readable storage medium, characterized in that the computer readable storage medium comprises a neural feedback program, which when executed by a processor implements the steps of the neural feedback method as described in the above embodiments.
The specific implementation of the computer readable storage medium of the present invention is basically the same as the embodiments of the neural feedback method described above, and will not be repeated here.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a neuro-feedback device to perform the method according to the embodiments of the present invention.
In the present invention, the terms "first", "second", "third", "fourth", "fifth" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance, and the specific meaning of the above terms in the present invention will be understood by those of ordinary skill in the art depending on the specific circumstances.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, the scope of the present invention is not limited thereto, and it should be understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications and substitutions of the above embodiments may be made by those skilled in the art within the scope of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. A nerve feedback device, the nerve feedback device comprising: the brain electricity acquisition device comprises a wearable device and a signal conversion device, wherein the signal conversion device is integrated in the wearable device, the signal conversion device is electrically connected with the wearable device in a connection mode, and the signal conversion device is connected with the mobile terminal in a wireless communication mode;
the electroencephalogram acquisition equipment is used for sending an electroencephalogram digital signal to the mobile terminal, is also used for acquiring an electroencephalogram nerve signal and converting the electroencephalogram nerve signal into an electroencephalogram digital signal;
the mobile terminal equipment is used for analyzing the electroencephalogram digital signal, extracting the signal type of the electroencephalogram digital signal, generating an analysis result to execute feedback operation, wherein the signal type is used for identifying the specific state of a user corresponding to each part of characteristics of the electroencephalogram digital signal;
the step of analyzing the electroencephalogram digital signal to generate an analysis result comprises the following steps:
correcting the electroencephalogram digital signal, determining a signal type corresponding to the corrected electroencephalogram digital signal, and generating an analysis result corresponding to the signal type according to the signal type, wherein the correction comprises at least one of signal filtering and baseline drift removal, and the analysis result comprises a time parameter, wherein the time parameter is a time point at which symptoms appear and a time period in which the symptoms are continuous;
the step of determining the signal type corresponding to the corrected brain electrical digital signal comprises the following steps:
receiving input task information, and determining a learning model corresponding to the task information;
determining the signal type corresponding to the corrected brain electrical digital signal according to the learning model,
if the signal type corresponding to the corrected electroencephalogram digital signal cannot be determined according to the learning model, acquiring all preset learning models;
and determining the signal type corresponding to the corrected electroencephalogram digital signal according to all the learning models.
2. The nerve feedback device of claim 1, wherein the wearable device comprises a plurality of electrodes, the electrodes being dry electrodes or gel electrodes, the mobile terminal comprising a signal analysis processing module and a real-time feedback module.
3. A nerve feedback method, wherein the nerve feedback method is applied to an electroencephalogram acquisition device, the nerve feedback method comprising:
collecting brain electrical nerve signals;
converting the brain electrical nerve signal into a brain electrical digital signal, and sending the brain electrical digital signal to a mobile terminal, so that the mobile terminal analyzes the brain electrical digital signal, extracts the signal type of the brain electrical digital signal to generate an analysis result, and executes a feedback operation corresponding to the analysis result according to the analysis result, wherein the signal type is used for identifying the specific state of a user corresponding to each part of characteristics of the brain electrical digital signal;
the electroencephalogram acquisition equipment comprises wearing equipment and signal conversion equipment, wherein the signal conversion equipment is integrated in the wearing equipment, the signal conversion equipment is electrically connected with the wearing equipment in a connection mode, the signal conversion equipment is in wireless communication connection with the mobile terminal, and the electroencephalogram acquisition equipment and the mobile terminal jointly form nerve feedback equipment;
the step of extracting the signal type of the electroencephalogram digital signal to generate an analysis result comprises the following steps:
correcting the electroencephalogram digital signal, determining a signal type corresponding to the corrected electroencephalogram digital signal, and generating an analysis result corresponding to the signal type according to the signal type, wherein the correction comprises at least one of signal filtering and baseline drift removal, and the analysis result comprises a time parameter, wherein the time parameter is a time point at which symptoms appear and a time period in which the symptoms are continuous;
the step of determining the signal type corresponding to the corrected brain electrical digital signal comprises the following steps:
receiving input task information, and determining a learning model corresponding to the task information;
determining the signal type corresponding to the corrected brain electrical digital signal according to the learning model,
if the signal type corresponding to the corrected electroencephalogram digital signal cannot be determined according to the learning model, acquiring all preset learning models;
and determining the signal type corresponding to the corrected electroencephalogram digital signal according to all the learning models.
4. The nerve feedback method of claim 3, wherein the step of converting the electrical brain nerve signal to an electrical brain digital signal comprises:
and performing signal processing on the electroencephalogram nerve signals, and converting the processed electroencephalogram nerve signals into electroencephalogram digital signals, wherein the signal processing comprises at least one of signal amplification, signal filtering, downsampling and baseline drift removal.
5. A nerve feedback method, wherein the nerve feedback method is applied to a mobile terminal, and the nerve feedback method comprises:
receiving an electroencephalogram digital signal sent by electroencephalogram acquisition equipment, extracting a signal type of the electroencephalogram digital signal to generate an analysis result, wherein the signal type is used for generating the analysis result to execute feedback operation, and the signal type is also used for identifying a specific state of a user corresponding to each part of characteristics of the electroencephalogram digital signal;
executing feedback operation corresponding to the analysis result according to the analysis result;
the mobile terminal is in wireless communication connection with signal conversion equipment of the electroencephalogram acquisition equipment to receive an electroencephalogram digital signal sent by the electroencephalogram acquisition equipment, the electroencephalogram acquisition equipment comprises wearing equipment and signal conversion equipment, the signal conversion equipment is integrated in the wearing equipment, the signal conversion equipment is electrically connected with the wearing equipment in a connection mode, the electroencephalogram acquisition equipment is used for acquiring an electroencephalogram nerve signal and converting the electroencephalogram nerve signal into an electroencephalogram digital signal, and the electroencephalogram acquisition equipment and the mobile terminal jointly form a nerve feedback equipment;
the step of extracting the signal type of the electroencephalogram digital signal to generate an analysis result comprises the following steps:
correcting the electroencephalogram digital signal, determining a signal type corresponding to the corrected electroencephalogram digital signal, and generating an analysis result corresponding to the signal type according to the signal type, wherein the correction comprises at least one of signal filtering and baseline drift removal;
the step of determining the signal type corresponding to the corrected brain electrical digital signal comprises the following steps:
receiving input task information, and determining a learning model corresponding to the task information;
determining the signal type corresponding to the corrected brain electrical digital signal according to the learning model,
if the signal type corresponding to the corrected electroencephalogram digital signal cannot be determined according to the learning model, acquiring all preset learning models;
and determining the signal type corresponding to the corrected electroencephalogram digital signal according to all the learning models.
6. A nerve feedback method, wherein the nerve feedback method is applied to a nerve feedback device, the nerve feedback device comprises an electroencephalogram acquisition device and a mobile terminal, and the nerve feedback method comprises:
the electroencephalogram acquisition equipment is used for acquiring electroencephalogram nerve signals; converting the brain electrical nerve signal into an brain electrical digital signal, and sending the brain electrical digital signal to a mobile terminal;
the mobile terminal is used for receiving the brain electricity digital signals sent by the brain electricity acquisition equipment, extracting the signal types of the brain electricity digital signals and generating analysis results; executing feedback operation corresponding to the analysis result according to the analysis result, wherein the signal type is used for identifying the specific state of a user corresponding to each part of characteristics of the electroencephalogram digital signal;
the electroencephalogram acquisition equipment comprises wearing equipment and signal conversion equipment, wherein the signal conversion equipment is integrated in the wearing equipment, the signal conversion equipment is electrically connected with the wearing equipment in a connection mode, and the signal conversion equipment is connected with the mobile terminal in a wireless communication mode;
the step of extracting the signal type of the electroencephalogram digital signal to generate an analysis result comprises the following steps:
correcting the electroencephalogram digital signal, determining a signal type corresponding to the corrected electroencephalogram digital signal, and generating an analysis result corresponding to the signal type according to the signal type, wherein the correction comprises at least one of signal filtering and baseline drift removal, and the analysis result comprises a time parameter, wherein the time parameter is a time point at which symptoms appear and a time period in which the symptoms are continuous;
the step of determining the signal type corresponding to the corrected brain electrical digital signal comprises the following steps:
receiving input task information, and determining a learning model corresponding to the task information;
determining the signal type corresponding to the corrected brain electrical digital signal according to the learning model,
if the signal type corresponding to the corrected electroencephalogram digital signal cannot be determined according to the learning model, acquiring all preset learning models;
and determining the signal type corresponding to the corrected electroencephalogram digital signal according to all the learning models.
7. A nerve feedback device comprising a memory, a processor, and a nerve feedback program stored on the memory and executable on the processor, wherein: the biofeedback procedure when executed by the processor implements the steps of the biofeedback method of any of claims 3 to 6.
8. A computer readable storage medium, characterized in that it has stored thereon a nerve feedback program, which when executed by a processor, implements the steps of the nerve feedback method according to any one of claims 3 to 6.
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