CN113729709A - Neurofeedback apparatus, neurofeedback method, and computer-readable storage medium - Google Patents
Neurofeedback apparatus, neurofeedback method, and computer-readable storage medium Download PDFInfo
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- 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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Abstract
The invention discloses a nerve feedback device, which comprises: brain electricity collection equipment, mobile terminal, wherein, brain electricity collection equipment includes wearing equipment and signal conversion equipment, signal conversion equipment is integrated wearing equipment, signal conversion equipment with connected mode between the wearing equipment is the electricity and connects, brain electricity collection equipment with mobile terminal's connected mode is wireless communication and connects. The invention also discloses a neural feedback method and a computer readable storage medium. By applying the neural feedback method to the neural feedback equipment, the use cost of the neural feedback equipment can be reduced, and the training feedback efficiency and the treatment effect of the neural feedback equipment are improved.
Description
Technical Field
The present invention relates to the field of neuroscience, and in particular, to a neurofeedback apparatus, a neurofeedback method, and a computer-readable storage medium.
Background
Neurofeedback technology (Neurofeedback) is a non-invasive, safe, reliable cognitive treatment for the treatment and amelioration of common psychiatric disorders. The technology has been rapidly developed in recent decades due to the advantages of no need of taking medicine or operation and no side effects. The technology is most widely used for treating neurological disorders such as Attention Deficit Hyperactivity Disorder (ADHD), post-traumatic stress disorder (PTSD), and relaxation training.
However, some of the electroencephalogram-based nerve feedback devices on the market mainly adopt scientific research and medical treatment, and the devices have the disadvantages of large volume, tedious use and high treatment cost, and are difficult for common families to undertake the whole treatment course, so that the previous treatment can be abandoned; the other part of nerve feedback equipment is mainly used for entertainment and relaxation, is convenient to use and low in cost, can be owned by a common family, but is simple in function, far less than the strict medical treatment standard, 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 low-cost nerve feedback device with a medical function.
To achieve the above object, the present invention provides a neurofeedback apparatus including: brain electricity collection equipment, mobile terminal, wherein, brain electricity collection equipment includes wearing equipment and signal conversion equipment, signal conversion equipment is integrated wearing equipment, signal conversion equipment with connected mode between the wearing equipment is the electricity and connects, signal conversion equipment with mobile terminal's connected mode is wireless communication and connects.
Optionally, the wearable device comprises a plurality of electrodes, 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 addition, in order to achieve the above object, the present invention further provides a neurofeedback method, which is applied to an electroencephalogram acquisition device, and the neurofeedback method includes:
collecting electroencephalogram neural signals;
and converting the electroencephalogram neural signals into electroencephalogram digital signals, and sending the electroencephalogram digital signals to a mobile terminal.
Optionally, the step of converting the electroencephalogram neural signal into an electroencephalogram digital signal includes:
and performing signal processing on the electroencephalogram neural signals, and converting the processed electroencephalogram neural signals into electroencephalogram digital signals, wherein the signal processing comprises at least one of signal amplification, signal filtering, down-sampling and baseline drift removal.
In addition, to achieve the above object, the present invention also provides a neurofeedback method, which is applied to a mobile terminal, and the neurofeedback method includes:
receiving an electroencephalogram digital signal sent by an electroencephalogram acquisition device, 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 analytic 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 a 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 further provides a neurofeedback method, where the neurofeedback method is applied to a neurofeedback device, the neurofeedback device includes an electroencephalogram acquisition device and a mobile terminal, and the neurofeedback method includes:
the electroencephalogram acquisition equipment is used for acquiring electroencephalogram neural signals; converting the electroencephalogram neural signals into electroencephalogram digital signals, and sending the electroencephalogram digital signals to a mobile terminal;
the mobile terminal is used for receiving the electroencephalogram digital signals sent by the electroencephalogram acquisition equipment and analyzing the electroencephalogram 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 neurofeedback apparatus including a memory, a processor, and a neurofeedback program stored on the memory and executable on the processor, wherein: the neurofeedback program when executed by the processor implements the steps of the neurofeedback method as described above.
In addition, to achieve the above object, the present invention also provides a computer readable storage medium having a neurofeedback program stored thereon, which when executed by a processor implements the steps of the neurofeedback method as described above.
The neural feedback device integrates the signal conversion device and the wearable device together in an electric connection mode, so that the size of the electroencephalogram acquisition device is greatly reduced, the electroencephalogram acquisition device can be conveniently worn on a human body, meanwhile, the connection configuration time between the signal conversion device and the wearable device is reduced, and the installation efficiency is improved; by means of wireless communication connection of the signal conversion equipment and the mobile terminal, the size and the weight of the nerve feedback equipment are greatly reduced, a user can use and store the nerve feedback equipment conveniently, electroencephalogram signals can be monitored and trained and fed back at low cost, the cost of the user for using the nerve feedback equipment is greatly reduced, and meanwhile, the treatment effect of the nerve feedback equipment is guaranteed.
Drawings
FIG. 1 is a schematic diagram of the configuration of a neurofeedback device of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of the neural feedback method of the present invention;
FIG. 3 is a schematic flow chart of a neural feedback method according to a second embodiment of the present invention;
fig. 4 is a flowchart illustrating a fifth embodiment of the neurofeedback method of the present invention.
FIG. 1 illustrates in numbered detail:
| reference numerals | Name (R) |
| 100 | |
| 1 | |
| 2 | |
| 3 | |
| 10 | Electroencephalogram acquisition equipment |
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, fig. 1 is a schematic structural diagram of a neural feedback device of the present invention, the neural feedback device 100 includes an electroencephalogram acquisition device 10 and a mobile terminal 3, wherein 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 in an electrically connected manner, and the signal conversion device 2 is connected with the mobile terminal 3 in a wireless communication manner, wherein 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, 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 wearable device may be an electroencephalogram cap, signal conversion devices may be integrated at any position of the electroencephalogram cap, such as the upper part, the lower part, the left part, the right part, and the like, and the wearable device and the signal conversion devices are powered by a battery container integrated on the wearable device, so that the wearable device and the signal conversion devices are integrated, the size of the corresponding device in the conventional nerve feedback device is greatly reduced, and a user can wear the wearable device and the signal conversion devices on the head without feeling heavy, wherein the wearable device may be made of a material with certain toughness and elasticity, such as a silica gel material, a Thermoplastic polyurethane elastomer (TPU), and a plurality of electrodes are distributed on the wearable device, and 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, the two electrodes have good conductivity and are comfortable to wear, so that the trouble that a user needs to paint conductive paste after wearing the electroencephalogram acquisition equipment is avoided, the complicated process that the electroencephalogram acquisition equipment needs to be detached and cleaned is omitted, and the sanitary standard of the treatment process is 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 a computer, a tablet, a mobile phone, a television and other devices, and the mobile terminal 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, correction and analysis on the electroencephalogram digital signals, the real-time feedback module executes corresponding feedback operations on a user according to the results analyzed by the signal analysis processing module, and the feedback operations are not limited to visual modes such as auditory, visual and tactile modes.
The above is only a preferred embodiment of the neural feedback device, and is not intended to limit the scope of the present invention, and all equivalent structural changes made by using the contents of the present specification and the drawings, or any other related technical fields directly/indirectly under the spirit of the present invention are included in the scope of the present invention.
The neural feedback device integrates the signal conversion device and the wearable device together in an electric connection mode, so that the size of the electroencephalogram acquisition device is greatly reduced, the electroencephalogram acquisition device can be conveniently worn on a human body, meanwhile, the connection configuration time between the signal conversion device and the wearable device is reduced, and the installation efficiency is improved; by means of wireless communication connection of the signal conversion equipment and the mobile terminal, the size and the weight of the nerve feedback equipment are greatly reduced, a user can use and store the nerve feedback equipment conveniently, electroencephalogram signals can be monitored and trained and fed back at low cost, the cost of the user for using the nerve feedback equipment is greatly reduced, and meanwhile, 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 neural feedback method of the present invention, in this embodiment, the neural feedback method is applied to an electroencephalogram acquisition device, and the neural feedback method includes:
step S10, collecting electroencephalogram neural signals;
in this embodiment, the neural feedback method may be applied to an electroencephalogram acquisition device, and the electroencephalogram acquisition device may include a wearable device and a signal conversion device, wherein the wearable device is worn on a head of a human body and is responsible for acquiring electroencephalogram neural signals generated by the human body, and specifically, the acquisition of the electroencephalogram neural signals may be realized by sensing brain waves and myoelectric signals of the human body through a plurality of electrodes on the wearable device. The traditional electroencephalogram acquisition equipment with the medical effect is high in price and large in size, if a patient wants to treat certain psychogenic diseases by adopting the equipment, the patient needs to frequently go to a hospital or a medical institution with medical qualifications, so that expensive medical expenses need to be paid, and precious time and energy of the user including time for going to and going back and forth and time for installing the electroencephalogram acquisition equipment are also spent.
Step S20, converting the electroencephalogram neural signals into electroencephalogram digital signals, and sending the electroencephalogram digital signals to a mobile terminal;
the signal conversion equipment in the electroencephalogram acquisition equipment can convert the analog signal of the electroencephalogram neural signal into an electroencephalogram digital signal which can be directly read and analyzed by a mobile terminal.
Specifically, can be in real time through the wire transmission to the signal conversion equipment of integration on wearing equipment after wearing equipment gathers the EEG neural signal through the multielectrode, signal conversion equipment can be with this kind of analog signal conversion EEG digital signal at receiving EEG neural signal, and can carry out wireless transmission's mode through the wireless communication module among the signal conversion equipment and send the EEG digital signal that the conversion was accomplished to mobile terminal, wherein, wireless transmission can be 5G, the mode of mobile communication techniques such as 4G, also can be WIFI, bluetooth transmission, modes such as infrared transmission, do not do the restriction here, also can send EEG digital signal to mobile terminal through wired transmission's mode in addition. The mobile terminal can be a personal computer, a tablet computer, a television, a mobile phone and other devices.
Specifically, the step of converting the electroencephalogram neural signal into an electroencephalogram digital signal includes:
step a, performing signal processing on the electroencephalogram neural signals, and converting the processed electroencephalogram neural signals into electroencephalogram digital signals, wherein the signal processing comprises at least one of signal amplification, signal filtering, down-sampling and baseline drift removal.
The electroencephalogram acquisition equipment acquires effective electroencephalogram neural signals, and meanwhile, other irrelevant interference wave band signals and invalid electroencephalogram neural signals can be inevitably received, so that the electroencephalogram acquisition equipment is required to perform a series of signal processing operations on the acquired various wave band signals, including one or more methods of signal amplification, signal filtration, down sampling and baseline wandering removal, namely, only the effective electroencephalogram wave bands and the electromyogram wave bands of a human body are reserved after the signals of different wave bands are identified as far as possible, other interference wave bands and invalid electroencephalogram neural signals are eliminated, and particularly: 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 can be filtered by a band-pass filter. When the filtering operation is carried out, the correction processing of baseline drift removal can be carried out on the electroencephalogram neural signals, namely, the electroencephalogram neural signals which obviously fluctuate unstably and jump suddenly on the baseline are filtered, and only effective electroencephalogram neural signals are reserved as far as possible. The signal processing mode is beneficial to the fact that the mobile terminal only analyzes and processes the effective electroencephalogram digital signals, and the efficiency of analyzing and processing the electroencephalogram signals is improved.
As shown in fig. 3, fig. 3 is a schematic flow chart of a neural feedback method according to a second embodiment of the present invention, in this embodiment, the neural feedback method is applied to a mobile terminal, and the neural feedback method includes:
step S30, receiving the electroencephalogram digital signal sent by the 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 analytic 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 mobile terminal can read and analyze the electroencephalogram digital signals through a built-in or neural feedback program installation mode when receiving the electroencephalogram digital signals sent by the electroencephalogram acquisition equipment, and can further correct all the received digital signals when reading and analyzing the electroencephalogram digital signals, including filtering the digital signals and removing baseline drift, so that the aim of more comprehensively removing interference signals and invalid signals is fulfilled.
The real-time characteristics of the electroencephalogram digital signal can be extracted through a signal analysis and processing module of the mobile terminal, so that the signal type corresponding to the electroencephalogram digital signal can be determined, for example, a signal related to hyperactivity, that is, the specific state of a user corresponding to the characteristics of each part of the electroencephalogram digital signal can be identified through determining the signal type, wherein the specific state can be divided into a normal state and an abnormal state, wherein the normal state can be a natural and relaxed mental state of a human body, the abnormal state can be a passive emotional state such as tension, depression, anger, sadness and the like, and the specific symptom corresponding to a mental disease and appearing in a nerve feedback treatment process, for example, an inattentive symptom appearing in the attention deficit disease, a small action symptom appearing in the hyperactivity disease and difficult control, continuous or intermittent pain appearing in the post-traumatic stress disorder disease, Desperation, apathy, anxiety and other symptoms, and drug addiction diseases which can not be controlled automatically, mental disorder and other symptoms. After various specific states corresponding to various characteristics of the electroencephalogram digital signal are analyzed, 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 a text, code or other form, for example, the analysis results are texts: post-traumatic stress disorder presents with symptoms of anxiety, again for example, the result is resolved into the code: 1, what is available in the neurofeedback system as code 1 is a mental disorder arising from drug addiction. It should be noted that each parsing result is associated with a corresponding time parameter, i.e. it can be determined at which point of time the user presents what symptoms, or at which time period the user continues what symptoms.
The whole analysis process can be completed in a common mobile terminal without other equipment, and a professional analysis chip is not required to be installed in the analysis equipment, so that the application cost of the neurofeedback 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, the user can be trained and fed back through the mobile terminal, the training feedback is not limited to visual, auditory and tactile modes such as picture output, video, audio, vibration and light flashing, for example, when the user has symptoms of frequent small actions during treatment of hyperactivity, music which the user is interested in is played, the user is reminded to sing through a loudspeaker, the user is helped to concentrate on attention, and therefore the occurrence frequency of the small actions is reduced. In addition, the training feedback can be performed on the user in a mode that the mobile terminal is in wireless connection with other auxiliary feedback equipment. By means of the training feedback, accurate treatment of the user can be achieved.
Further, a third embodiment of the present invention is proposed based on the second embodiment of the present invention, in which the step of determining the 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 electroencephalogram digital signal according to the learning model.
After the electroencephalogram digital signal is further corrected by the mobile terminal, task information input by a user or a doctor according to the common mental condition or mental disease of the user is received and obtained, for example, if the user is a post-traumatic stress disorder patient, the 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 formed by obtaining a large amount of electroencephalogram signal data of the same type of patients or users corresponding to the same treatment requirement, establishing a data model by using the continuously obtained electroencephalogram signal data and state characteristic data or symptom characteristic data reflected by the user, carrying out a large amount of machine learning and training on the data model, and updating and perfecting the new electroencephalogram signal data and characteristic data of the user by using the learning model, the Machine learning method includes, but is not limited to, a neural network, a SVM (Support Vector Machine), an LDA (Linear Discriminant Analysis), federal learning, and the like.
After the corresponding learning model is determined, the corrected electroencephalogram digital signal is judged according to the learning model, the signal type corresponding to the electroencephalogram digital signal is judged, namely the electroencephalogram digital signal is judged to be matched with the electroencephalogram signal data corresponding to which mental state or mental symptom in the learning model, and after the signal type is determined, an analysis result corresponding to the electroencephalogram signal can be generated.
In the embodiment, the learning model obtained through the machine learning mode is obtained, the electroencephalogram signal of the user can be continuously identified by the updated and perfect learning model, the identification accuracy can be ensured, meanwhile, the data volume is not so large and complicated by using the current task information corresponding to the learning model, and the corresponding mental state or symptom can be more concentrated in identification, so that the identification efficiency is improved, and the treatment requirements of more and more complicated mental states or mental disease symptoms of people at present can be continuously met.
Further, a fourth embodiment of the neural feedback method is proposed based on the third embodiment of the neural feedback method, and in this embodiment, 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 electroencephalogram digital signal according to all the learning models.
If the mobile terminal cannot identify the signal type corresponding to the corrected electroencephalogram digital signal in the learning model determined according to the task information, that is, the current learning model does not have the electroencephalogram digital signal, which usually indicates that the user may not only have the mental disease, all learning models stored in the neural feedback system are obtained, and then the signal type corresponding to the corrected electroencephalogram digital signal is determined according to all 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 defect disease is used at the beginning, but the electroencephalogram digital signal which cannot be identified by the model appears in the treatment process, then the learning models corresponding to other mental diseases can be called at this moment, if the pain analysis result is identified as' through the learning model corresponding to the post-traumatic stress disorder disease at last, then, the user can be preliminarily considered to have the post-traumatic stress disorder disease, and the two models, namely the learning model corresponding to the attention deficit disease and the learning model corresponding to the post-traumatic stress disorder disease, are called to jointly identify and analyze the electroencephalogram digital signals of the user.
The actual mental diseases of the user can be comprehensively identified and analyzed by calling all the learning models, the condition that the user delays disease treatment because the user is unaware of or hides the mental condition of the user is avoided, and meanwhile, one or more corresponding learning models can be called to carry out special training feedback treatment on the user after the actual mental diseases of the user are determined, so that the effect of symptomatic treatment 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 electroencephalogram digital signal;
and h, executing feedback operation corresponding to the prediction result according to the prediction result.
After the electroencephalogram digital signals are analyzed by the mobile terminal, the analyzed electroencephalogram digital signals can be predicted according to the learning model corresponding to the current task information or all the learning models, namely, the states and symptoms which are possibly generated next by the user can be reasonably predicted based on various learning models to generate a predicted result, and corresponding feedback operations in the forms of hearing, vision, touch and the like are executed according to the predicted result, so that the possibility of the occurrence of poor predicted results can be reduced, and some negative states or symptoms of the user can be relieved or disappear over time, so that the electroencephalogram digital signals are prevented from happening in the bud, and the treatment effect and efficiency are greatly improved.
As shown in fig. 4, fig. 4 is a flowchart illustrating a fifth embodiment of a neural feedback method according to the present invention based on a flowchart illustrating a first embodiment and a second embodiment of the neural feedback method, in this embodiment, the neural feedback method is applied to a neural feedback device, the neural feedback device includes an electroencephalogram acquisition device and a mobile terminal, and the neural feedback method includes:
s100, electroencephalogram acquisition equipment is used for acquiring electroencephalogram neural signals; converting the electroencephalogram neural signals into electroencephalogram digital signals, and sending the electroencephalogram digital signals to a mobile terminal;
s200, the mobile terminal is used for 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.
For the development of the specific scheme, reference may be made to the various embodiments of the above neural feedback method, which are not described herein again.
Further, a sixth embodiment of the neural feedback method of the present invention is proposed based on the second embodiment of the neural feedback method of 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 among 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 can automatically store user information input by the user or a doctor in association with all electroencephalogram digital signals and analysis results of the user in the treatment process, establish a user file or update the user file in the original user file, and convert all electroencephalogram digital signals, analysis results and user information stored in association with the user in the treatment process into visual chart information, so that the user or the doctor can clearly analyze and evaluate the mental health of the user, and the user can conveniently and timely know the mental health of the user.
In addition, data such as electroencephalogram digital signals and 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, and therefore the treatment effect is improved.
In addition, the present invention also provides a neurofeedback apparatus, which includes a memory, a processor and a neurofeedback program stored in the memory and operable on the processor, and when the processor executes the neurofeedback program, the processor implements the steps of the neurofeedback method according to the above embodiment.
The specific implementation of the neural feedback device of the present invention is substantially the same as that of each embodiment of the neural feedback method, and is not repeated herein.
Furthermore, the present invention also provides a computer-readable storage medium, which is characterized in that the computer-readable storage medium includes a neurofeedback program, and the neurofeedback program, when executed by a processor, implements the steps of the neurofeedback method according to the above embodiments.
The specific implementation of the computer-readable storage medium of the present invention is substantially the same as the embodiments of the neural feedback method, and is not repeated herein.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a neural feedback device to execute the method according to the embodiments of the present invention.
In the present invention, the terms "first", "second", "third", "fourth" and "fifth" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance, and those skilled in the art can understand the specific meanings of the above terms in the present invention according to specific situations.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean 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 invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer 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, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although the embodiment of the present invention has been shown and described, the scope of the present invention is not limited thereto, it should be understood that the above embodiment is illustrative and not to be construed as limiting the present invention, and that those skilled in the art can make changes, modifications and substitutions to the above embodiment within the scope of the present invention, and that these changes, modifications and substitutions should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A neurofeedback device, comprising: brain electricity collection equipment, mobile terminal, wherein, brain electricity collection equipment includes wearing equipment and signal conversion equipment, signal conversion equipment is integrated wearing equipment, signal conversion equipment with connected mode between the wearing equipment is the electricity and connects, signal conversion equipment with mobile terminal's connected mode is wireless communication and connects.
2. The neurofeedback device of claim 1, wherein the wearable device comprises a plurality of electrodes, the electrodes are dry electrodes or gel electrodes, and the mobile terminal comprises a signal analysis processing module and a real-time feedback module.
3. A neural feedback method is characterized by being applied to an electroencephalogram acquisition device, and comprising the following steps:
collecting electroencephalogram neural signals;
and converting the electroencephalogram neural signals into electroencephalogram digital signals, and sending the electroencephalogram digital signals to a mobile terminal.
4. The neurofeedback method of claim 3, wherein the step of converting the brain electrical neural signal into a brain electrical digital signal comprises:
and performing signal processing on the electroencephalogram neural signals, and converting the processed electroencephalogram neural signals into electroencephalogram digital signals, wherein the signal processing comprises at least one of signal amplification, signal filtering, down-sampling and baseline drift removal.
5. A neural feedback method is applied to a mobile terminal, and comprises the following steps:
receiving an electroencephalogram digital signal sent by an electroencephalogram acquisition device, 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.
6. The neurofeedback method of claim 5, wherein the step of interpreting the electroencephalogram digital signal to generate an interpreted result comprises:
correcting the electroencephalogram digital signal, determining a signal type corresponding to the corrected electroencephalogram digital signal, and generating an analytic 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.
7. The neurofeedback method of claim 6, wherein the step of determining the signal type corresponding to the corrected electroencephalogram digital signal comprises:
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.
8. A neural feedback method is applied to a neural feedback device, the neural feedback device comprises an electroencephalogram acquisition device and a mobile terminal, and the neural feedback method comprises the following steps:
the electroencephalogram acquisition equipment is used for acquiring electroencephalogram neural signals; converting the electroencephalogram neural signals into electroencephalogram digital signals, and sending the electroencephalogram digital signals to a mobile terminal;
the mobile terminal is used for receiving the electroencephalogram digital signals sent by the electroencephalogram acquisition equipment and analyzing the electroencephalogram digital signals to generate analysis results; and executing feedback operation corresponding to the analysis result according to the analysis result.
9. A neurofeedback device comprising a memory, a processor, and a neurofeedback program stored on the memory and executable on the processor, wherein: the neurofeedback program when executed by the processor implements the steps of the neurofeedback method of any of claims 3 to 8.
10. A computer-readable storage medium, having a neurofeedback program stored thereon, which when executed by a processor, implements the steps of the neurofeedback method of any of claims 3-8.
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