CN115206489A - Meditation training method and device based on nerve feedback system and electronic equipment - Google Patents
Meditation training method and device based on nerve feedback system and electronic equipment Download PDFInfo
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Abstract
The invention provides a meditation training method, a meditation training device and electronic equipment based on a nerve feedback system, and relates to the technical field of electroencephalogram signal processing, wherein the meditation training method comprises the steps of matching audio data in a constructed audio resource library based on acquired personalized information of a user and a meditation index before intervention to obtain target audio data; playing target audio data according to set time, and acquiring a post-intervention electroencephalogram signal of a user through a brain loop; similarity calculation is carried out on the brain electric signal mode after intervention and the brain electric signal mode in the expert database, and a meditation index after intervention is obtained; comparing the pre-intervention meditation index with the post-intervention meditation index to obtain an index improvement degree; judging whether the current training time length meets the meditation time length or not; when the current training time length meets the meditation time length, the meditation training is finished. The method and the device can embody personalized operation, the user can independently select voice guidance, scene audio and the like, and automatic adjustment and intervention can be performed according to the meditation state of the user based on a neural feedback technology.
Description
Technical Field
The invention belongs to the technical field of electroencephalogram signal processing, and particularly relates to a meditation training method and device based on a nerve feedback system and electronic equipment.
Background
Meditation is a mental connection mode aiming at improving physical and mental regulation capacity (such as cognitive function and emotion regulation capacity), and generally, meditation can help people to improve mental capacity and improve corresponding health problems. But different people can generate different physiological responses such as electroencephalogram, electrocardio, respiration and the like when meditating. The effect of each meditation may vary even for the same person.
The existing meditation training is lack of feedback indexes, so that individuals often cannot measure the state change of the individuals before and after the meditation training and cannot judge the continuous effect of the meditation.
Therefore, a meditation training method, a meditation training device and an electronic device based on a neural feedback system are provided.
Disclosure of Invention
The present specification provides a meditation training method, apparatus and electronic device based on a neuro-feedback system, which provides a user with more accurate and more effective meditation training.
The present specification provides a meditation training method based on a neural feedback system, comprising:
acquiring an intervention forebrain electrical signal of a user through a brain loop;
similarity calculation is carried out on the pre-intervention electroencephalogram signal mode and electroencephalogram signal modes in an expert database to obtain a pre-intervention meditation index;
matching audio data in the constructed audio resource library based on the acquired personalized information of the user and the pre-intervention meditation index to obtain target audio data; the personalized information of the user comprises a meditation duration;
playing the target audio data according to set time, and acquiring the post-intervention electroencephalogram signal of the user through a brain loop;
carrying out similarity calculation on the post-intervention electroencephalogram signal mode and the electroencephalogram signal mode in an expert database to obtain the post-intervention meditation index;
comparing the pre-intervention meditation index with the post-intervention meditation index to obtain index improvement degree;
judging whether the current training duration satisfies the meditation duration;
when the current training duration satisfies the meditation duration, the meditation training is ended.
Optionally, the calculating the similarity between the pre-intervention electroencephalogram signal pattern and electroencephalogram signal patterns in an expert database to obtain the pre-intervention meditation index includes:
preprocessing the pre-intervention electroencephalogram signal of each electrode through high-pass filtering and low-pass filtering;
extracting target energies of the pre-interventional brain electrical signals of each electrode, the target energies including an energy of θ, an energy of low α, an energy of high α, an energy of β, an energy of γ;
determining a two-dimensional electroencephalogram characteristic of the user based on the target energy;
and inputting the two-dimensional electroencephalogram characteristics into a hierarchical clustering algorithm model to obtain the pre-intervention meditation index.
Optionally, the building an audio resource library includes:
acquiring a plurality of audio data and the evoked scores of a plurality of meditation experts;
determining a meditation index for the audio data based on an average of the evoked scores of several meditation experts;
determining labels of the audio data based on the meditation indexes and attributes of the audio data to form a one-to-many data label relationship;
and integrating a plurality of one-to-many data label relations to obtain an audio resource library.
Optionally, before the determining whether the current training duration satisfies the meditation duration, the method includes:
judging whether the index improvement degree exceeds an index improvement standard or not;
marking the target audio data as invalid once when the index improvement degree does not exceed the index improvement criterion;
and returning to match audio data in the constructed audio resource library based on the intervened meditation index to obtain new target audio data until the meditation training is finished when the current training duration meets the meditation duration.
Optionally, after the pre-intervention meditation index is compared with the post-intervention meditation index to obtain an index improvement degree, the method further includes:
uploading the pre-intervention electroencephalograms and the post-intervention electroencephalograms to a cloud;
outputting an interventional meditation evaluation report through the cloud.
The present specification provides a meditation training device based on a neurofeedback system, comprising:
the signal acquisition module is used for acquiring an intervention forebrain electrical signal of a user through a brain loop;
the first calculation module is used for calculating the similarity between the pre-intervention electroencephalogram signal mode and the electroencephalogram signal mode in an expert database to obtain the pre-intervention meditation index;
the matching audio module is used for matching audio data in the constructed audio resource library based on the acquired personalized information of the user and the pre-intervention meditation index to obtain target audio data; the personalized information of the user comprises a meditation duration;
the intervention signal module is used for playing the target audio data according to set time and acquiring an intervention electroencephalogram signal of the user through a cerebral loop;
the second calculation module is used for calculating the similarity between the post-intervention electroencephalogram signal mode and the electroencephalogram signal mode in an expert database to obtain the post-intervention meditation index;
the comparison index module is used for comparing the pre-intervention meditation index with the post-intervention meditation index to obtain an index improvement degree;
a first judgment module for judging whether the current training duration satisfies the meditation duration;
a training ending module for ending the meditation training when the current training duration satisfies the meditation duration.
Optionally, the first computing module includes:
the preprocessing unit is used for preprocessing the pre-intervention electroencephalogram signal of each electrode through high-pass filtering and low-pass filtering;
an extraction energy unit for extracting target energy of the pre-interventional electroencephalogram signal of each electrode, the target energy including energy of θ, energy of low α, energy of high α, energy of β, energy of γ;
the characteristic determining unit is used for determining the two-dimensional electroencephalogram characteristics of the user based on the target energy;
and the input analysis unit is used for inputting the two-dimensional electroencephalogram characteristics into a hierarchical clustering algorithm model to obtain the pre-intervention meditation index.
Optionally, the constructing an audio resource library includes:
acquiring a plurality of amounts of audio data and induction scores of a plurality of meditation experts;
determining a meditation index of the audio data based on an average of the induction scores of several meditation experts;
determining labels of the audio data based on the meditation indexes and attributes of the audio data to form a one-to-many data label relationship;
and integrating a plurality of one-to-many data label relations to obtain an audio resource library.
Optionally, before the first determining module, the method includes:
the second judgment module is used for judging whether the index improvement degree exceeds an index improvement standard or not;
a marking module for marking the target audio data as invalid once when the index improvement degree does not exceed the index improvement criterion;
and the circulating module is used for returning the matched audio data in the constructed audio resource library based on the intervened meditation index to obtain new target audio data until the meditation training is finished when the current training duration meets the meditation duration.
The intervention signal unit is used for playing the target audio data according to set time based on the acquired personalized information of the user and acquiring a post-intervention electroencephalogram signal of the user through a cerebral loop; the personalized information of the user comprises a meditation duration;
the index calculation unit is used for calculating the similarity between the post-intervention electroencephalogram signal mode and the electroencephalogram signal mode in an expert database to obtain a post-intervention meditation index;
and the comparison signal unit is used for comparing the intervened meditation index with the intervened meditation index to obtain the index improvement degree.
Optionally, after the training ending module, the method further includes:
uploading the pre-intervention electroencephalograms and the post-intervention electroencephalograms to a cloud;
outputting an intervention meditation evaluation report through the cloud.
The present specification also provides an electronic device, wherein the electronic device includes:
a processor; and (c) a second step of,
a memory storing computer executable instructions that, when executed, cause the processor to perform any of the methods described above.
The present specification also provides a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement any of the above methods.
In the specification, through a neural feedback intervention technology, electroencephalogram signals are collected and recorded, corresponding voice guidance and scene audio are matched, the meditation can be conveniently and deeply known, and the meditation training effect can be evaluated, so that the effect of a brain-computer interface in the field of meditation is expanded, and accurate and effective meditation training is provided for users.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram illustrating a principle of a meditation training method based on a neurofeedback system according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a meditation training device based on a nerve feedback system according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present specification;
fig. 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
Detailed Description
The following description is presented to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments described below are by way of example only, and other obvious variations will occur to those skilled in the art. The underlying principles of the invention, as defined in the following description, may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.
Exemplary embodiments of the present invention are described more fully below with reference to FIGS. 1-4. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept to those skilled in the art. The same reference numerals denote the same or similar elements, components, or portions in the drawings, and thus, a repetitive description thereof will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
The described features, structures, characteristics, or other details of the present invention are provided to enable those skilled in the art to fully understand the embodiments in the present specification. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flowcharts shown in the figures are illustrative only and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The term "and/or" and/or "includes all combinations of any one or more of the associated listed items.
Fig. 1 is a schematic diagram of a nerve feedback system-based meditation training method provided in an embodiment of the present disclosure, where the method may include:
s110: acquiring an intervention forebrain electrical signal of a user through a brain loop;
in the specific implementation manner of the description, through brain ring devices like NeuroSky, muse, emot i ve and the like, a dry electrode is used for detecting scalp electroencephalograms of a user, then a Thi nkGear ASI C module sensor is used for amplifying, digitizing and filtering the signals, intervention pre-electroencephalograms of the user are obtained, and the signals are uploaded to a cloud server through bluetooth for storage.
S120: carrying out similarity calculation on the pre-intervention electroencephalogram signal mode and the electroencephalogram signal mode in an expert database to obtain the pre-intervention meditation index;
in a specific embodiment of the present specification, the step S120 includes:
preprocessing the pre-interventional electroencephalogram signal of each electrode through high-pass filtering and low-pass filtering;
extracting target energies of the pre-interventional brain electrical signals of each electrode, the target energies including an energy of θ, an energy of low α, an energy of high α, an energy of β, an energy of γ;
determining a two-dimensional electroencephalogram characteristic of the user based on the target energy;
and inputting the two-dimensional electroencephalogram characteristics into a hierarchical clustering algorithm model to obtain the pre-intervention meditation index.
In a specific embodiment of the present specification, the pre-interventional brain electrical signals of each electrode are pre-processed by 1Hz high-pass filtering and 58Hz low-pass filtering; extracting energy of frequency bands theta (4-8 Hz), low alpha (8-10 Hz), high alpha (10-12 Hz), beta (13-30 Hz), and gamma (30-42 Hz) of the pre-intervention brain electrical signal of each electrode through Fourier transform; obtaining a user two-dimensional electroencephalogram characteristic C x T, wherein C refers to the number of electrodes, and T refers to the number of frequency bands; inputting the two-dimensional electroencephalogram characteristics of the user into a Hierarchical clustering algorithm model (Hierarchical cluster analysis) to obtain the pre-intervention meditation index.
S130: matching audio data in the constructed audio resource library based on the acquired personalized information of the user and the pre-intervention meditation index to obtain target audio data; the personalized information of the user comprises a meditation duration;
in the specific implementation mode of the specification, the personalized information of the user comprises voice gender, meditation duration, meditation course, audio scene, audio parameter information and the like selected by the user, and the meditation course comprises a relaxation course and a concentration course. And matching the audio data in the audio resource library according to the personalized information selected by the user and the pre-intervention meditation index to obtain target audio data.
In a specific embodiment of the present specification, the constructing an audio resource library includes:
acquiring a plurality of amounts of audio data and induction scores of a plurality of meditation experts;
determining a meditation index of the audio data based on an average of the induction scores of several meditation experts;
determining labels of the audio data based on the meditation indexes and attributes of the audio data to form a one-to-many data label relationship;
and integrating a plurality of one-to-many data label relations to obtain an audio resource library.
In the embodiment of the present specification, each audio is scored by several meditation experts, the induced meditation degree of the audio, that is, the induced score is evaluated, and the average value of the induced scores of the several meditation experts is obtained and is further increased by ten times, so that the meditation index of the audio data is obtained. In addition, the inherent attributes of the audio data, including voice gender, audio scene, etc., are combined with the meditation index and the inherent attribute to determine the label of the audio data, so as to form a one-to-many data label relationship, that is, one audio data corresponds to the labels "female", "natural", "relax".
In the embodiment of the present specification, in the matching process, if a plurality of audio data matching the pre-intervention meditation index exist in the audio repository, one of the audio data is randomly played.
S140: playing the target audio data according to set time, and acquiring the post-intervention electroencephalogram signal of the user through a brain loop;
in the specific embodiment of the specification, the time is set to be 30 seconds optimally, the influence of different audio time lengths on the meditation state is tested, the time is shorter than 30 seconds, and the audio is not enough to adjust the meditation state of the user; the time is longer than 30 seconds, the working efficiency is low, and the user experience is influenced.
S150: similarity calculation is carried out on the brain electric signal mode after intervention and brain electric signal modes in an expert database to obtain the meditation index after intervention;
in a specific embodiment of the present specification, the post-intervention electroencephalogram signal of each electrode is pre-processed by 1Hz high-pass filtering and 58Hz low-pass filtering; extracting, by Fourier transform, energies of post-intervention brain electrical signals of each electrode in frequency bands of theta (4-8 Hz), low alpha (8-10 Hz), high alpha (10-12 Hz), beta (13-30 Hz), and gamma (30-42 Hz); obtaining a user two-dimensional electroencephalogram characteristic C x T, wherein C refers to the number of electrodes, and T refers to the number of frequency bands; inputting the two-dimensional electroencephalogram characteristics of the user into a Hierarchical clustering algorithm model (Hierarchical cluster analysis) to obtain the post-intervention meditation index.
S160: comparing the pre-intervention meditation index with the post-intervention meditation index to obtain index improvement degree;
in the specific embodiment of the present specification, the pre-intervention meditation index and the post-intervention meditation index are compared to obtain the index improvement degree. The index improvement degree is in direct proportion to the improvement effect, namely, the higher the index improvement degree is, the better the improvement effect is.
S170: judging whether the current training duration satisfies the meditation duration;
s180: when the current training duration meets the meditation duration, the meditation training is ended.
In a specific embodiment of the present specification, the step S170 is preceded by:
judging whether the index improvement degree exceeds an index improvement standard or not;
marking the target audio data as invalid once when the index improvement degree does not exceed the index improvement criterion;
and returning to match audio data in the constructed audio resource library based on the intervened meditation index to obtain new target audio data until the meditation training is finished when the current training duration meets the meditation duration.
In a specific embodiment of the present specification, when the index improvement degree exceeds the index improvement criterion, the target audios are continuously played until the current training duration satisfies the meditation duration.
In this specification embodiment, when the audio data is marked invalid twice, this audio data will no longer be used as a meditation for the current user.
In a specific embodiment of the present specification, after the step S160, the method further includes:
uploading the pre-intervention electroencephalography signals and the post-intervention electroencephalography signals to a cloud;
outputting an intervention meditation evaluation report through the cloud.
In the specific implementation mode of the specification, the meditation intervention evaluation report comprises time variation graphs of activity, calm, relaxation and admission, time variation of low alpha (8-10 Hz) and high alpha (10-12 Hz), state variation before and after a meditation course and the effect of the meditation intervention training.
In the specification, the electroencephalogram signals are collected and recorded through a neural feedback intervention technology, and the corresponding voice guidance and scene audio are matched, so that the meditation can be conveniently and deeply known, and the meditation training effect can be evaluated, the effect of the brain-computer interface in the field of the meditation can is expanded, and more accurate and more effective meditation training can be provided for users.
Fig. 2 is a schematic diagram of a meditation training device based on a neurofeedback system according to an embodiment of the present disclosure, and the device may include:
the signal acquisition module 10 is used for acquiring an intervention forebrain electrical signal of a user through a brain loop;
the first calculation module 20 is used for calculating the similarity between the pre-intervention electroencephalogram signal mode and the electroencephalogram signal mode in an expert database to obtain the pre-intervention meditation index;
a matching audio module 30 for matching audio data in the constructed audio resource library based on the acquired personalized information of the user and the pre-intervention meditation index to obtain target audio data; the personalized information of the user comprises a meditation duration;
the intervention signal module 40 is used for playing the target audio data according to set time and acquiring an intervention electroencephalogram signal of the user through a cerebral loop;
a second calculating module 50, configured to perform similarity calculation between the post-intervention electroencephalogram signal pattern and electroencephalogram signal patterns in an expert database, so as to obtain the post-intervention meditation index;
a comparison index module 60 for comparing the pre-intervention meditation index with the post-intervention meditation index to obtain an index improvement degree;
a first judging module 70 for judging whether the current training period satisfies the meditation period;
a training end module 80 for ending the meditation training when the current training session meets the meditation session.
Optionally, the first computing module includes:
the preprocessing unit is used for preprocessing the pre-interventional electroencephalogram signals of each electrode through high-pass filtering and low-pass filtering;
an extraction energy unit for extracting target energy of the pre-interventional electroencephalogram signal of each electrode, the target energy including energy of θ, energy of low α, energy of high α, energy of β, energy of γ;
the determining characteristic unit is used for determining the two-dimensional electroencephalogram characteristics of the user based on the target energy;
and the input analysis unit is used for inputting the two-dimensional electroencephalogram characteristics into a hierarchical clustering algorithm model to obtain the pre-intervention meditation index.
Optionally, the constructing an audio resource library includes:
acquiring a plurality of audio data and the evoked scores of a plurality of meditation experts;
determining a meditation index of the audio data based on an average of the induction scores of several meditation experts;
determining labels of the audio data based on the meditation indexes and attributes of the audio data to form a one-to-many data label relationship;
and integrating a plurality of one-to-many data label relations to obtain an audio resource library.
Optionally, before the first determining module, the method includes:
the second judgment module is used for judging whether the index improvement degree exceeds an index improvement standard or not;
a marking module for marking the target audio data as invalid once when the index improvement degree does not exceed the index improvement criterion;
and the circulating module is used for returning the matched audio data in the constructed audio resource library based on the intervened meditation index to obtain new target audio data until the meditation training is finished when the current training duration meets the meditation duration.
Optionally, after the training ending module, the method further includes:
uploading the pre-intervention electroencephalograms and the post-intervention electroencephalograms to a cloud;
outputting an interventional meditation evaluation report through the cloud.
The functions of the apparatus in the embodiment of the present invention have been described in the above method embodiments, so that reference may be made to the related descriptions in the foregoing embodiments for details that are not described in the present embodiment, and further details are not described herein.
Based on the same inventive concept, the embodiment of the specification further provides the electronic equipment.
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details not disclosed in the embodiments of the electronic device of the present invention, reference may be made to the above-described embodiments of the method or apparatus.
Fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure. An electronic device 300 according to this embodiment of the invention is described below with reference to fig. 3. The electronic device 300 shown in fig. 3 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 3, electronic device 300 is embodied in the form of a general purpose computing device. The components of electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one memory unit 320, a bus 330 that couples various system components including the memory unit 320 and the processing unit 310, a display unit 340, and the like.
Wherein the storage unit stores program code executable by the processing unit 310 to cause the processing unit 310 to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned processing method section of the present specification. For example, the processing unit 310 may perform the steps as shown in fig. 1.
The storage unit 320 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM) 3201 and/or a cache storage unit 3202, and may further include a read only memory unit (ROM) 3203.
The storage unit 320 may also include a program/utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 300 may also communicate with one or more external devices 400 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 300, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 300 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 350. Also, the electronic device 300 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 360. Network adapter 360 may communicate with other modules of electronic device 300 via bus 330. It should be appreciated that although not shown in FIG. 3, other hardware and/or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, to name a few.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to implement the above-described method of the invention, namely: such as the method shown in fig. 1.
Fig. 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
A computer program implementing the method shown in fig. 1 may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments consistent with the present invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website, or provided on a carrier signal, or provided in any other form.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The present invention is not limited to the above embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A meditation training method based on a nerve feedback system is characterized by comprising the following steps:
acquiring an intervention forebrain electrical signal of a user through a brain loop;
similarity calculation is carried out on the pre-intervention electroencephalogram signal mode and electroencephalogram signal modes in an expert database to obtain a pre-intervention meditation index;
matching audio data in the constructed audio resource library based on the acquired personalized information of the user and the pre-intervention meditation index to obtain target audio data; the personalized information of the user comprises a meditation duration;
playing the target audio data according to set time, and acquiring the post-intervention electroencephalogram signal of the user through a brain loop;
carrying out similarity calculation on the post-intervention electroencephalogram signal mode and the electroencephalogram signal mode in an expert database to obtain the post-intervention meditation index;
comparing the pre-intervention meditation index with the post-intervention meditation index to obtain an index improvement degree;
judging whether the current training duration satisfies the meditation duration;
when the current training duration meets the meditation duration, the meditation training is ended.
2. The neurofeedback system-based meditation training method of claim 1, wherein the calculating the degree of similarity of the pre-intervention electroencephalogram pattern to electroencephalogram patterns in an expert database to obtain the pre-intervention meditation index comprises:
preprocessing the pre-intervention electroencephalogram signal of each electrode through high-pass filtering and low-pass filtering;
extracting target energies of the pre-intervention brain electrical signals of each electrode, the target energies including an energy of θ, an energy of low α, an energy of high α, an energy of β, an energy of γ;
determining a two-dimensional electroencephalogram characteristic of the user based on the target energy;
and inputting the two-dimensional electroencephalogram characteristics into a hierarchical clustering algorithm model to obtain the pre-intervention meditation index.
3. The meditation training method based on neurofeedback system as claimed in claim 1, wherein the constructing of the audio resource library comprises:
acquiring a plurality of amounts of audio data and induction scores of a plurality of meditation experts;
determining a meditation index for the audio data based on an average of the evoked scores of several meditation experts;
determining labels of the audio data based on the meditation indexes and attributes of the audio data to form a one-to-many data label relationship;
and integrating a plurality of one-to-many data label relations to obtain an audio resource library.
4. The meditation training method based on the neurofeedback system as set forth in claim 1, wherein the determining whether the current training session satisfies the meditation session before comprises:
judging whether the index improvement degree exceeds an index improvement standard or not;
marking the target audio data as invalid once when the index improvement degree does not exceed the index improvement criterion;
and returning to match audio data in the constructed audio resource library based on the intervened meditation index to obtain new target audio data until the meditation training is finished when the current training duration meets the meditation duration.
5. The neurofeedback system-based meditation training method of claim 4, wherein after comparing the pre-intervention meditation index with the post-intervention meditation index to obtain an index improvement, further comprising:
uploading the pre-intervention electroencephalography signals and the post-intervention electroencephalography signals to a cloud;
outputting an intervention meditation evaluation report through the cloud.
6. A meditation training device based on a nerve feedback system, comprising:
the signal acquisition module is used for acquiring an intervention forebrain electrical signal of a user through a brain loop;
the first calculation module is used for calculating the similarity between the pre-intervention electroencephalogram signal mode and an electroencephalogram signal mode in an expert database to obtain a pre-intervention meditation index;
the matching audio module is used for matching audio data in the constructed audio resource library based on the acquired personalized information of the user and the pre-intervention meditation index to obtain target audio data; the personalized information of the user comprises a meditation duration;
the intervention signal module is used for playing the target audio data according to set time and acquiring an intervention electroencephalogram signal of the user through a brain loop;
the second calculation module is used for calculating the similarity between the post-intervention electroencephalogram signal mode and the electroencephalogram signal mode in an expert database to obtain the post-intervention meditation index;
the comparison index module is used for comparing the pre-intervention meditation index with the post-intervention meditation index to obtain an index improvement degree;
a first judging module for judging whether the current training duration satisfies the meditation duration;
and the training ending module is used for ending the meditation training when the current training time length meets the meditation time length.
7. The meditation training device based on neurofeedback system as claimed in claim 6, wherein the first calculating module comprises:
the preprocessing unit is used for preprocessing the pre-interventional electroencephalogram signals of each electrode through high-pass filtering and low-pass filtering;
an extraction energy unit for extracting target energy of the pre-interventional electroencephalogram signal of each electrode, the target energy including energy of θ, energy of low α, energy of high α, energy of β, energy of γ;
the determining characteristic unit is used for determining the two-dimensional electroencephalogram characteristics of the user based on the target energy;
and the input analysis unit is used for inputting the two-dimensional electroencephalogram characteristics into a hierarchical clustering algorithm model to obtain the pre-intervention meditation index.
8. The neurofeedback system-based meditation training device of claim 6, wherein said constructing an audio repository comprises:
acquiring a plurality of amounts of audio data and induction scores of a plurality of meditation experts;
determining a meditation index for the audio data based on an average of the evoked scores of several meditation experts;
determining labels of the audio data based on the meditation indexes and attributes of the audio data to form a one-to-many data label relationship;
and integrating a plurality of one-to-many data label relations to obtain an audio resource library.
9. An electronic device, wherein the electronic device comprises:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-5.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-5.
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| CN115662575A (en) * | 2022-12-29 | 2023-01-31 | 深圳市心流科技有限公司 | Dynamic image generation and playing method based on meditation training |
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