CN114652330B - Method, device and equipment for evaluating meditation training based on historical electroencephalogram signals - Google Patents
Method, device and equipment for evaluating meditation training based on historical electroencephalogram signals Download PDFInfo
- Publication number
- CN114652330B CN114652330B CN202210129776.8A CN202210129776A CN114652330B CN 114652330 B CN114652330 B CN 114652330B CN 202210129776 A CN202210129776 A CN 202210129776A CN 114652330 B CN114652330 B CN 114652330B
- Authority
- CN
- China
- Prior art keywords
- electroencephalogram
- signals
- historical
- standard
- curve
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
- A61B5/372—Analysis of electroencephalograms
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/742—Details of notification to user or communication with user or patient ; user input means using visual displays
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/742—Details of notification to user or communication with user or patient ; user input means using visual displays
- A61B5/743—Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Surgery (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Physics & Mathematics (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Psychiatry (AREA)
- Psychology (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The invention relates to the technical field of signal processing, in particular to a meditation training method, a meditation training device and meditation training equipment based on historical electroencephalogram signals. The evaluation method comprises the steps of collecting historical electroencephalograms before current meditation training, calculating the degree that the historical electroencephalograms can continuously reach an electroencephalogram reference signal, and then combining the degree that the historical electroencephalograms continuously reach the standard when scoring the effect of the current meditation training to obtain the evaluation result of the current meditation training. The evaluation results obtained by the invention can not only represent the effect of the current meditation training but also represent the stability of the meditation training.
Description
Technical Field
The invention relates to the technical field of signal processing, in particular to a meditation training method, a meditation training device and meditation training equipment based on historical electroencephalogram signals.
Background
The meditation training can improve the sleeping and the concentration of people, and the quality of life of people can be improved by carrying out the meditation training for a long time. The meditation training performed by the user each time can represent the whole meditation training effect of the user, and in the prior art, only the electroencephalogram signal of the current meditation training is considered and the historical electroencephalogram signal is ignored when the meditation training of the user is evaluated, so that the comprehensive evaluation on the meditation training effect cannot be performed.
In summary, the prior art cannot comprehensively evaluate the effect of meditation training.
Thus, there is a need for improvements and enhancements in the art.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method, a device and equipment for evaluating meditation training based on historical electroencephalogram signals, and solves the problem that the effect of meditation training cannot be comprehensively evaluated in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for evaluating meditation training based on historical brain electrical signals, comprising:
acquiring various historical electroencephalograms, wherein the various historical electroencephalograms are electroencephalograms generated in meditation training of a user to be evaluated in sequence at different time;
calculating the number of signals corresponding to electroencephalogram reference signals which are continuously matched in each history electroencephalogram signal generated in sequence to obtain the number of signals reaching the standard, wherein the electroencephalogram reference signals are electroencephalogram signals generated by users reaching the standard for meditation training;
acquiring a current electroencephalogram corresponding to current meditation training of a user to be evaluated;
and obtaining an evaluation result of the meditation training currently performed by the user to be evaluated according to the number of the standard-reaching signals and the current electroencephalogram signals.
In one implementation, the calculating a signal quantity corresponding to a brain electrical reference signal in each of the sequentially generated historical brain electrical signals, which is a brain electrical signal generated by a user who meets the criterion of meditation training, to obtain a criterion signal quantity includes:
drawing each historical electroencephalogram curve corresponding to each historical electroencephalogram signal according to each historical electroencephalogram signal, wherein the abscissa corresponding to each historical electroencephalogram curve is the time for generating the electroencephalogram signal, and the ordinate is the intensity of the electroencephalogram signal;
acquiring an electroencephalogram reference curve corresponding to the electroencephalogram reference signal;
according to the time of generating each historical electroencephalogram signal, marking a serial number for each historical electroencephalogram curve;
calculating a curve which is matched with the electroencephalogram reference curve in each historical electroencephalogram curve and corresponds to the electroencephalogram reference curve to obtain a standard-reaching curve;
according to the standard reaching curve, obtaining a serial number corresponding to the standard reaching curve, and marking as a standard reaching serial number;
counting the number of serial numbers corresponding to the continuous serial numbers in the standard-reaching serial numbers;
and obtaining the number of the standard signals according to the number of the serial numbers.
In one implementation, the calculating a curve corresponding to the electroencephalogram reference curve in each historical electroencephalogram curve to obtain a standard-reaching curve includes:
calculating the similarity between each historical electroencephalogram curve and the electroencephalogram reference curve;
and counting the historical electroencephalogram curves corresponding to the similarity greater than the set value to obtain standard-reaching curves.
In one implementation manner, the obtaining an evaluation result of the current meditation training of the user to be evaluated according to the number of the standard signals and the current electroencephalogram signal comprises:
obtaining an additional score corresponding to the number of the standard signals according to the number of the standard signals;
calculating the similarity of the current electroencephalogram signal and the electroencephalogram reference signal to obtain the current signal similarity;
and obtaining an evaluation result of the meditation training currently performed by the user to be evaluated according to the current signal similarity and the additional score.
In one implementation manner, the obtaining of the evaluation result of the current meditation training of the user to be evaluated according to the current signal similarity and the additional score includes:
obtaining an evaluation score according to the current signal similarity;
adding the additional score to the evaluation score to obtain an evaluation result of the meditation training currently performed by the user to be evaluated;
and displaying the evaluation result on a terminal of a user to be evaluated.
In one implementation, the method further comprises:
calculating the number of signals which are continuously not matched with the electroencephalogram reference signals in each historical electroencephalogram signal generated in sequence to obtain the number of abnormal signals;
when the number of the abnormal signals is larger than a set value, obtaining an evaluation result of the current meditation training of the user to be evaluated according to the number of the abnormal signals, the number of the signals reaching the standard and the current electroencephalogram signal;
and displaying the evaluation result on a terminal of a teacher, wherein the teacher is a person for guiding the meditation training of the user to be evaluated.
In one implementation, when the number of abnormal signals is greater than a set value, obtaining an evaluation result of meditation training currently performed by a user to be evaluated according to the number of abnormal signals, the number of standard signals and the current electroencephalogram signal, includes:
obtaining an abnormal score corresponding to the abnormal signal quantity according to the abnormal signal quantity;
obtaining an additional score corresponding to the number of the standard signals according to the number of the standard signals;
obtaining an evaluation score corresponding to the current electroencephalogram signal according to the current electroencephalogram signal and the electroencephalogram reference signal;
and adding the additional score to the evaluation score to subtract the abnormal score to obtain an evaluation result.
In a second aspect, the present invention further provides an apparatus of a method for evaluating meditation training based on historical brain electrical signals, wherein the apparatus includes the following components:
the historical signal acquisition module is used for acquiring various historical electroencephalograms, and each historical electroencephalogram is an electroencephalogram generated in meditation training performed by a user to be evaluated in sequence at different time;
the calculating part is used for calculating the number of signals corresponding to electroencephalogram reference signals which are continuously matched in each history electroencephalogram signal generated in sequence to obtain the number of signals reaching the standard, and the electroencephalogram reference signals are electroencephalogram signals generated by users reaching the standard in meditation training;
the current signal acquisition module is used for acquiring a current electroencephalogram signal corresponding to the current meditation training of the user to be evaluated;
and the evaluation mechanism is used for obtaining the evaluation result of the meditation training currently performed by the user to be evaluated according to the number of the standard-reaching signals and the current electroencephalogram signal.
In one implementation, the calculation portion includes:
the first curve drawing module is used for drawing each historical electroencephalogram curve corresponding to each historical electroencephalogram signal according to each historical electroencephalogram signal, the abscissa corresponding to each historical electroencephalogram curve is the time for generating the electroencephalogram signal, and the ordinate is the intensity of the electroencephalogram signal;
the second curve drawing module is used for acquiring an electroencephalogram reference curve corresponding to the electroencephalogram reference signal;
the marking module is used for marking serial numbers for the historical electroencephalogram curves according to the time of generation of the historical electroencephalogram signals;
the curve calculation module is used for calculating a curve which is matched with the electroencephalogram reference curve in each historical electroencephalogram curve to obtain a standard-reaching curve;
the standard serial number calculation module is used for obtaining a serial number corresponding to the standard curve according to the standard curve and marking the serial number as a standard serial number;
the statistical module is used for counting the number of serial numbers corresponding to the continuous serial numbers in the standard-reaching serial numbers;
and the quantity calculating module is used for obtaining the quantity of the signals reaching the standard according to the quantity of the serial numbers.
In one implementation, the curve calculation module includes:
the first similarity calculation unit is used for calculating the similarity between each historical electroencephalogram curve and the electroencephalogram reference curve;
and the standard-reaching curve statistical unit is used for counting the historical electroencephalogram curve corresponding to the similarity greater than the set value to obtain a standard-reaching curve.
In one implementation, the evaluation mechanism includes:
the extra score calculating module is used for obtaining an extra score corresponding to the number of the signals reaching the standard according to the number of the signals reaching the standard;
the second similarity calculation unit is used for calculating the similarity between the current electroencephalogram signal and the electroencephalogram reference signal to obtain the similarity of the current signal;
and the evaluation module is used for obtaining the evaluation result of the meditation training currently performed by the user to be evaluated according to the current signal similarity and the additional score.
In one implementation, the evaluation module includes:
the score calculating unit is used for obtaining an evaluation score according to the current signal similarity;
the evaluation unit is used for adding the evaluation score and the additional score to obtain an evaluation result of the meditation training currently performed by the user to be evaluated;
and the display unit is used for displaying the evaluation result on the terminal of the user to be evaluated.
In one implementation, the apparatus further comprises:
the abnormal signal quantity calculation module is used for calculating the quantity of signals which are continuously not matched with the electroencephalogram reference signals in each historical electroencephalogram signal generated in sequence to obtain the quantity of abnormal signals;
the evaluation result calculation module is used for obtaining the evaluation result of the meditation training currently performed by the user to be evaluated according to the number of the abnormal signals, the number of the standard signals and the current electroencephalogram signal when the number of the abnormal signals is larger than a set value;
and the display module is used for displaying the evaluation result on a terminal of a teacher, and the teacher is a person for guiding the meditation training of the user to be evaluated.
In one implementation, the evaluation result calculation module includes:
the abnormal score calculating unit is used for obtaining an abnormal score corresponding to the abnormal signal quantity according to the abnormal signal quantity;
an extra score calculating unit, configured to obtain an extra score corresponding to the number of the signals reaching the standard according to the number of the signals reaching the standard;
the evaluation score calculation unit is used for obtaining an evaluation score corresponding to the current electroencephalogram signal according to the current electroencephalogram signal and the electroencephalogram reference signal;
and the evaluation result calculation unit is used for adding the evaluation score and the extra score to subtract the abnormal score to obtain an evaluation result.
In a third aspect, the present invention further provides a terminal device, wherein the terminal device includes a memory, a processor, and a program stored in the memory and operable on the processor for evaluating meditation training based on historical brain electrical signals, and when the processor executes the program for evaluating meditation training based on historical brain electrical signals, the processor implements the steps of the method for evaluating meditation training based on historical brain electrical signals.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a program for evaluating meditation training based on historical brain electrical signals is stored, and when the program for evaluating meditation training based on historical brain electrical signals is executed by a processor, the steps of the method for evaluating meditation training based on historical brain electrical signals are implemented.
Has the advantages that: the evaluation method comprises the steps of collecting historical electroencephalograms before current meditation training, calculating the degree that the historical electroencephalograms can continuously reach an electroencephalogram reference signal, and then combining the degree that the historical electroencephalograms continuously reach the standard when scoring the effect of the current meditation training to obtain the evaluation result of the current meditation training. The evaluation results obtained by the present invention can not only characterize the effect of the current meditation training but also the stability of the meditation training.
Drawings
FIG. 1 is an overall flow chart of the present invention;
fig. 2 is a schematic block diagram of an internal structure of a terminal device according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is clearly and completely described below by combining the embodiment and the attached drawings of the specification. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Researches show that the meditation training can improve the sleeping and the concentration of people, and the quality of life of people can be improved by carrying out the meditation training for a long time. The meditation training performed by the user each time can represent the whole meditation training effect of the user, and in the prior art, only the electroencephalogram signal of the current meditation training is considered and the historical electroencephalogram signal is ignored when the meditation training of the user is evaluated, so that the comprehensive evaluation on the meditation training effect cannot be performed.
In order to solve the technical problems, the invention provides a method, a device and equipment for evaluating meditation training based on historical electroencephalogram signals, and solves the problem that the effect of meditation training cannot be comprehensively evaluated in the prior art. In specific implementation, the historical electroencephalogram signals are acquired firstly, the number of the historical electroencephalogram signals continuously reaching the electroencephalogram reference signals is calculated, and the number of the continuously reaching signals is taken into consideration when the current meditation training is evaluated so as to obtain a final evaluation result.
For example, ten historical electroencephalograms generated by meditation training performed on the first day to the tenth day of the user are collected, wherein five historical electroencephalograms on the third day to the seventh day are matched with the electroencephalogram reference signal (namely, the meditation training effect on the third day to the seventh day is good). And when evaluating the meditation training of the eleventh day, acquiring the current electroencephalogram signals during the meditation training of the eleventh day, and obtaining a comprehensive evaluation result of the current meditation training according to the current electroencephalogram signals and the five coincided quantities.
Exemplary method
The method for evaluating meditation training based on historical electroencephalogram signals of the embodiment can be applied to terminal equipment, and the terminal equipment can be terminal products with an audio playing function, such as televisions, computers, mobile phones and the like. In the present embodiment, as shown in fig. 1, the method for evaluating meditation training based on historical electroencephalogram signals specifically includes the following steps:
s100, obtaining each historical electroencephalogram signal, wherein each historical electroencephalogram signal is an electroencephalogram signal generated in meditation training of a user to be evaluated in sequence at different time.
The historical brain electrical signals in this embodiment are the brain electrical signals generated by all meditation trainings prior to the current meditation trainings. For example, the historical electroencephalogram signals can be electroencephalogram signals generated by meditation training in the morning, noon and afternoon of a day.
S200, calculating the number of signals corresponding to electroencephalogram reference signals which are continuously matched in the historical electroencephalogram signals generated in sequence to obtain the number of signals reaching the standard, wherein the electroencephalogram reference signals are electroencephalogram signals generated by users reaching the standard in meditation training.
Step S200 includes steps S201, S202, S203, S204, S205, S206, S207, S208 as follows:
s201, drawing each historical electroencephalogram curve corresponding to each historical electroencephalogram signal according to each historical electroencephalogram signal, wherein the abscissa corresponding to each historical electroencephalogram curve is the time for generating the electroencephalogram signal, and the ordinate is the intensity of the electroencephalogram signal.
And S202, acquiring an electroencephalogram reference curve corresponding to the electroencephalogram reference signal.
S203, marking serial numbers for the historical electroencephalogram curves according to the time of generation of the historical electroencephalogram signals.
For example, the historical electroencephalogram curve corresponding to the first day meditation training is marked as 1, the historical electroencephalogram curve corresponding to the second day meditation training is marked as 2, and the historical electroencephalogram curve corresponding to the N-1 th day meditation training is marked as N-1. The curves are labeled to facilitate distinguishing which of the subsequent benchmarking curves are generated at adjacent times, even if the number of curves that reach the benchmarking consecutively is obtained.
And S204, calculating the similarity between each historical electroencephalogram curve and the electroencephalogram reference curve.
In the embodiment, each historical electroencephalogram curve and each electroencephalogram reference curve are drawn in the same coordinate system, and the length of each historical electroencephalogram curve along the curve trend direction and the curvature of each historical electroencephalogram curve on each point are calculated. And calculating the length of the electroencephalogram reference curve along the curve trend direction and the curvature of the electroencephalogram reference curve on each point. And calculating the length difference and curvature difference between each historical electroencephalogram curve and the electroencephalogram reference curve. The reciprocal of the sum of the difference in length plus the difference in curvature is taken as the degree of similarity.
S205, counting the historical electroencephalogram curve corresponding to the similarity greater than the set value to obtain a standard-reaching curve.
When the similarity in the step S204 is larger than the set value, the historical electroencephalogram curve is very consistent with the electroencephalogram reference curve, and the curve is the standard curve.
And S206, obtaining a serial number corresponding to the standard-reaching curve according to the standard-reaching curve, and marking as a standard-reaching serial number.
And S207, counting the number of the serial numbers corresponding to the continuous serial numbers in the standard serial numbers.
For example, the historical electroencephalogram curves marked as 1, 3, 4, 5, and 7 are standard-reaching curves, but only the serial numbers 3, 4, and 5 are consecutive, so that the number of the serial numbers corresponding to the standard-reaching serial numbers is 3.
And S208, obtaining the number of the signals reaching the standard according to the number of the serial numbers.
The number of serial numbers in this embodiment is the number of signals that reach the standard.
S300, acquiring the current electroencephalogram signal corresponding to the current meditation training of the user to be evaluated.
S400, obtaining the evaluation result of the meditation training currently performed by the user to be evaluated according to the number of the signals reaching the standard and the current electroencephalogram signal.
Step S400 includes steps S401, S402, S403, S404, S405 as follows:
s401, obtaining an additional score corresponding to the number of the signals reaching the standard according to the number of the signals reaching the standard.
The greater the number of signals that are up to standard, the higher the corresponding additional score. In this embodiment, the number N of signals up to standard and the additional fraction S satisfy the following relation:
S＝a N
wherein a is a constant greater than 1.
S402, calculating the similarity of the current electroencephalogram signal and the electroencephalogram reference signal to obtain the similarity of the current signal.
The current signal similarity is calculated in the same manner as in step S204.
And S403, obtaining an evaluation score according to the current signal similarity.
The greater the current signal similarity, the higher the evaluation score.
And S404, adding the additional score to the evaluation score to obtain an evaluation result of the meditation training currently performed by the user to be evaluated.
S405, displaying the evaluation result on the terminal of the user to be evaluated.
When the additional score of the user to be evaluated is not zero, an encouraging tone is sent out on the terminal of the user, and the prompting tone can also inform the user that the current training mode is right, so that the user continues to adopt the current training mode for meditation training.
In this embodiment, the number of signals that do not reach the standard continuously in the historical electroencephalogram signals is further calculated to obtain an evaluation result according to the number of signals, and the method specifically includes the following steps S501, S502, S503, S504, S505, and S506:
s501, calculating the number of signals which are continuously not matched with the electroencephalogram reference signals in the historical electroencephalogram signals generated in sequence to obtain the number of abnormal signals.
For example, historical electroencephalograms on days 8, 9, 10, 12, and 15 do not coincide with an electroencephalogram reference signal, and the number of consecutive abnormal signals is three signals on days 8, 9, and 10.
S502, obtaining an abnormal score corresponding to the abnormal signal quantity according to the abnormal signal quantity.
The greater the number of consecutive anomaly signals, the higher the corresponding anomaly score. The number N 'of abnormal signals and the abnormal score S' satisfy the following relational expression:
S′＝b N′
wherein b is a constant greater than 1 and b is less than S = a N A in (1).
S503, obtaining an additional score corresponding to the number of the signals reaching the standard according to the number of the signals reaching the standard.
The additional score of step S503 is the same as the additional score of step S401.
S504, obtaining an evaluation score corresponding to the current electroencephalogram signal according to the current electroencephalogram signal and the electroencephalogram reference signal.
And S505, adding the additional score to the evaluation score and subtracting the abnormal score to obtain an evaluation result.
S506, the evaluation result is displayed on the terminal of the teacher who is a person guiding the meditation training of the user to be evaluated.
In this embodiment, let S' = b N′ B is less than S = a N The a in (1) is to weaken the strike of the historical meditation training with poor performance to the user. In addition, the evaluation result obtained by combining the abnormal score S' is only fed back to the teacher, so that the attack on the training enthusiasm of the user can be reduced, and the teacher can reasonably guide the user to perform meditation training according to the real score.
In conclusion, the historical electroencephalogram signals before the current meditation training are collected, the degree that the historical electroencephalogram signals can continuously reach the electroencephalogram reference signals is calculated, and then the evaluation result of the current meditation training is obtained by combining the degree that the historical electroencephalogram signals continuously reach the standard when the effect of the current meditation training is scored. The evaluation results obtained by the invention can not only represent the effect of the current meditation training but also represent the stability of the meditation training.
Exemplary devices
The embodiment also provides a device of the method for evaluating meditation training based on historical brain electrical signals, which comprises the following components:
the historical signal acquisition module is used for acquiring various historical electroencephalograms, and the various historical electroencephalograms are electroencephalograms which are sequentially generated by the user to be evaluated in meditation training at different times;
the calculating part is used for calculating the number of signals corresponding to electroencephalogram reference signals which are continuously matched in each history electroencephalogram signal generated in sequence to obtain the number of signals reaching the standard, and the electroencephalogram reference signals are electroencephalogram signals generated by users reaching the standard in meditation training;
the current signal acquisition module is used for acquiring a current electroencephalogram signal corresponding to the current meditation training of the user to be evaluated;
and the evaluation mechanism is used for obtaining the evaluation result of the meditation training currently performed by the user to be evaluated according to the number of the standard-reaching signals and the current electroencephalogram signal.
Based on the above embodiments, the present invention further provides a terminal device, and a schematic block diagram thereof may be as shown in fig. 2. The terminal equipment comprises a processor, a memory, a network interface, a display screen and a temperature sensor which are connected through a system bus. Wherein the processor of the terminal device is configured to provide computing and control capabilities. The memory of the terminal equipment comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the terminal device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to implement a method of evaluating meditation training based on historical brain electrical signals. The display screen of the terminal equipment can be a liquid crystal display screen or an electronic ink display screen, and the temperature sensor of the terminal equipment is arranged in the terminal equipment in advance and used for detecting the operating temperature of the internal equipment.
It will be understood by those skilled in the art that the block diagram of fig. 2 is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation to the terminal device to which the solution of the present invention is applied, and a specific terminal device may include more or less components than those shown in the figure, or may combine some components, or have different arrangements of components.
In one embodiment, a terminal device is provided, the terminal device comprises a memory, a processor and a program stored in the memory and operable on the processor for evaluating meditation training based on historical brain electrical signals, and when the processor executes the program for evaluating meditation training based on historical brain electrical signals, the following operation instructions are realized:
acquiring various historical electroencephalograms, wherein the various historical electroencephalograms are electroencephalograms generated by a user to be evaluated in different time meditation training in sequence;
calculating the number of signals corresponding to the electroencephalogram reference signals which are continuously matched in each historical electroencephalogram signal generated in sequence to obtain the number of standard signals, wherein the electroencephalogram reference signals are electroencephalogram signals generated by users who can reach the standards for meditation training;
acquiring a current electroencephalogram corresponding to current meditation training of a user to be evaluated;
and obtaining an evaluation result of the meditation training currently performed by the user to be evaluated according to the number of the standard-reaching signals and the current electroencephalogram signals.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
In summary, the present invention discloses a method, an apparatus and a device for evaluating meditation training based on historical electroencephalogram signals, the method comprising: acquiring historical electroencephalograms, wherein the historical electroencephalograms are electroencephalograms sequentially generated by a user to be evaluated in meditation training at different times; calculating the number of signals corresponding to electroencephalogram reference signals which are continuously matched in each history electroencephalogram signal generated in sequence to obtain the number of signals reaching the standard, wherein the electroencephalogram reference signals are electroencephalogram signals generated by users reaching the standard for meditation training; acquiring a current electroencephalogram corresponding to current meditation training of a user to be evaluated; and obtaining an evaluation result of the meditation training currently performed by the user to be evaluated according to the number of the standard-reaching signals and the current electroencephalogram signals. The evaluation results obtained by the invention can not only represent the effect of the current meditation training but also represent the stability of the meditation training.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (6)
1. A method of assessing meditation training based on historical brain electrical signals, comprising:
acquiring various historical electroencephalograms, wherein the various historical electroencephalograms are electroencephalograms generated in meditation training of a user to be evaluated in sequence at different time;
calculating the number of signals corresponding to electroencephalogram reference signals which are continuously matched in each history electroencephalogram signal generated in sequence to obtain the number of signals reaching the standard, wherein the electroencephalogram reference signals are electroencephalogram signals generated by users reaching the standard for meditation training;
acquiring a current electroencephalogram corresponding to current meditation training of a user to be evaluated;
obtaining an evaluation result of the current meditation training of the user to be evaluated according to the number of the standard-reaching signals and the current electroencephalogram signals;
the calculating the number of signals corresponding to the electroencephalogram reference signals which are continuously matched in each historical electroencephalogram signal generated in sequence to obtain the number of signals reaching the standard, wherein the electroencephalogram reference signals are electroencephalogram signals generated by users reaching the standard in meditation training, and the calculating method comprises the following steps:
drawing each historical electroencephalogram curve corresponding to each historical electroencephalogram signal according to each historical electroencephalogram signal, wherein the abscissa corresponding to each historical electroencephalogram curve is the time for generating the electroencephalogram signal, and the ordinate is the intensity of the electroencephalogram signal;
acquiring an electroencephalogram reference curve corresponding to the electroencephalogram reference signal;
according to the time of generating each historical electroencephalogram signal, marking a serial number for each historical electroencephalogram curve;
calculating a curve which is matched with the electroencephalogram reference curve in each historical electroencephalogram curve and corresponds to the electroencephalogram reference curve to obtain a standard-reaching curve;
according to the standard reaching curve, obtaining a serial number corresponding to the standard reaching curve, and marking as a standard reaching serial number;
counting the number of serial numbers corresponding to the continuous serial numbers in the standard-reaching serial numbers;
obtaining the number of signals reaching the standard according to the number of the serial numbers;
the obtaining of the evaluation result of the meditation training currently performed by the user to be evaluated according to the number of the standard-reaching signals and the current electroencephalogram signals comprises the following steps:
obtaining an additional score corresponding to the number of the standard signals according to the number of the standard signals;
calculating the similarity of the current electroencephalogram signal and the electroencephalogram reference signal to obtain the current signal similarity;
obtaining an evaluation score according to the current signal similarity;
adding the additional score to the evaluation score to obtain an evaluation result of the meditation training currently performed by the user to be evaluated;
displaying the evaluation result on a terminal of a user to be evaluated;
further comprising:
calculating the number of signals which are continuously not matched with the electroencephalogram reference signals in each historical electroencephalogram signal generated in sequence to obtain the number of abnormal signals;
obtaining an abnormal score corresponding to the abnormal signal quantity according to the abnormal signal quantity;
obtaining an additional score corresponding to the number of the standard signals according to the number of the standard signals;
obtaining an evaluation score corresponding to the current electroencephalogram signal according to the current electroencephalogram signal and the electroencephalogram reference signal;
adding the additional score to the evaluation score and subtracting the abnormal score to obtain an evaluation result;
and displaying the evaluation result on a terminal of a teacher, wherein the teacher is a person for guiding the meditation training of the user to be evaluated.
2. The method for evaluating meditation training based on historical brain electrical signals as claimed in claim 1, wherein the calculating a curve matching the brain electrical reference curve in each historical brain electrical curve to obtain a standard reaching curve comprises:
calculating the similarity between each historical electroencephalogram curve and the electroencephalogram reference curve;
and counting the historical electroencephalogram curves corresponding to the similarity greater than the set value to obtain standard-reaching curves.
3. An apparatus of a method for evaluating meditation training based on historical brain electrical signals, the apparatus comprising:
the historical signal acquisition module is used for acquiring various historical electroencephalograms, and each historical electroencephalogram is an electroencephalogram generated in meditation training performed by a user to be evaluated in sequence at different time;
the calculating part is used for calculating the number of signals corresponding to electroencephalogram reference signals which are continuously matched in each history electroencephalogram signal generated in sequence to obtain the number of signals reaching the standard, and the electroencephalogram reference signals are electroencephalogram signals generated by users reaching the standard in meditation training;
the current signal acquisition module is used for acquiring a current electroencephalogram signal corresponding to the current meditation training of the user to be evaluated;
the evaluation mechanism is used for obtaining the evaluation result of the meditation training currently performed by the user to be evaluated according to the number of the standard-reaching signals and the current electroencephalogram signal;
the calculation section includes:
the first curve drawing module is used for drawing each historical electroencephalogram curve corresponding to each historical electroencephalogram signal according to each historical electroencephalogram signal, the abscissa corresponding to each historical electroencephalogram curve is the time for generating the electroencephalogram signal, and the ordinate is the intensity of the electroencephalogram signal;
the second curve drawing module is used for acquiring an electroencephalogram reference curve corresponding to the electroencephalogram reference signal;
the marking module is used for marking serial numbers for the historical electroencephalogram curves according to the time of generation of the historical electroencephalogram signals;
the curve calculation module is used for calculating a curve which is matched with the electroencephalogram reference curve in each historical electroencephalogram curve to obtain a standard-reaching curve;
the standard serial number calculation module is used for obtaining a serial number corresponding to the standard curve according to the standard curve and marking the serial number as a standard serial number;
the counting module is used for counting the number of the serial numbers corresponding to the continuous serial numbers in the standard serial numbers;
the quantity calculation module is used for obtaining the quantity of the signals reaching the standard according to the quantity of the serial numbers;
the evaluation mechanism includes:
the extra score calculating module is used for obtaining an extra score corresponding to the number of the standard signals according to the number of the standard signals;
the second similarity calculation unit is used for calculating the similarity between the current electroencephalogram signal and the electroencephalogram reference signal to obtain the similarity of the current signal;
the evaluation module is used for obtaining the evaluation result of the meditation training currently performed by the user to be evaluated according to the current signal similarity and the additional score;
the evaluation module comprises:
the score calculating unit is used for obtaining an evaluation score according to the current signal similarity;
the evaluation unit is used for adding the evaluation score and the additional score to obtain an evaluation result of the meditation training currently performed by the user to be evaluated;
the display unit is used for displaying the evaluation result on a terminal of a user to be evaluated;
the device further comprises:
the abnormal signal quantity calculation module is used for calculating the quantity of signals which are continuously not matched with the electroencephalogram reference signals in each historical electroencephalogram signal generated in sequence to obtain the quantity of abnormal signals;
the evaluation result calculation module is used for obtaining the evaluation result of the meditation training currently performed by the user to be evaluated according to the number of the abnormal signals, the number of the standard signals and the current electroencephalogram signal when the number of the abnormal signals is larger than a set value;
the display module is used for displaying the evaluation result on a terminal of a teacher, and the teacher is a person for guiding the meditation training of the user to be evaluated;
the evaluation result calculation module comprises:
the abnormal score calculating unit is used for obtaining an abnormal score corresponding to the abnormal signal quantity according to the abnormal signal quantity;
an extra score calculating unit, configured to obtain an extra score corresponding to the number of the signals reaching the standard according to the number of the signals reaching the standard;
the evaluation score calculation unit is used for obtaining an evaluation score corresponding to the current electroencephalogram signal according to the current electroencephalogram signal and the electroencephalogram reference signal;
and the evaluation result calculation unit is used for adding the additional score to the evaluation score and subtracting the abnormal score to obtain an evaluation result.
4. The apparatus of the method for evaluating meditation training based on historical brain electrical signals of claim 3, wherein the curve calculation module comprises:
the first similarity calculation unit is used for calculating the similarity between each historical electroencephalogram curve and the electroencephalogram reference curve;
and the standard-reaching curve counting unit is used for counting the historical electroencephalogram curve corresponding to the similarity greater than the set value to obtain a standard-reaching curve.
5. A terminal device, characterized in that the terminal device comprises a memory, a processor and a program for evaluating meditation training based on historical brain electrical signals, which is stored in the memory and can be run on the processor, and the processor, when executing the program for evaluating meditation training based on historical brain electrical signals, realizes the steps of the method for evaluating meditation training based on historical brain electrical signals according to any one of claims 1-2.
6. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a program for evaluating meditation training based on historical brain electrical signals, which program, when executed by a processor, implements the steps of the method for evaluating meditation training based on historical brain electrical signals as claimed in any one of claims 1-2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210129776.8A CN114652330B (en) | 2022-02-11 | 2022-02-11 | Method, device and equipment for evaluating meditation training based on historical electroencephalogram signals |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210129776.8A CN114652330B (en) | 2022-02-11 | 2022-02-11 | Method, device and equipment for evaluating meditation training based on historical electroencephalogram signals |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114652330A CN114652330A (en) | 2022-06-24 |
| CN114652330B true CN114652330B (en) | 2023-03-24 |
Family
ID=82027168
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210129776.8A Active CN114652330B (en) | 2022-02-11 | 2022-02-11 | Method, device and equipment for evaluating meditation training based on historical electroencephalogram signals |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114652330B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160053326A (en) * | 2014-11-03 | 2016-05-13 | 재단법인대구경북과학기술원 | Smart Meditation Apparatus For Improving Concentration and Operation Method Thereof |
| CN105595997A (en) * | 2016-03-10 | 2016-05-25 | 西安科技大学 | Driving fatigue electroencephalogram monitoring method based on stepped fatigue determination |
| CN106560158A (en) * | 2016-11-23 | 2017-04-12 | 深圳创达云睿智能科技有限公司 | Zen meditation feedback training method and device based on electroencephalogram |
| CN107049306A (en) * | 2017-03-31 | 2017-08-18 | 王晓路 | A kind of detection treatment system based on meditation degree |
| WO2019077421A1 (en) * | 2017-10-17 | 2019-04-25 | Gaurav Gupta | A system and method for customised meditation |
| CN110558959A (en) * | 2019-09-06 | 2019-12-13 | 江苏华康信息技术有限公司 | HRV signal analysis method for meditation training based on extreme value energy decomposition method |
| WO2020121299A1 (en) * | 2018-12-09 | 2020-06-18 | The Medical Research, Infrastructure and Health Services Fund of the Tel Aviv Medical Center | Stress disorder training |
| CN112512423A (en) * | 2018-05-26 | 2021-03-16 | 感官人工智能公司 | Method and apparatus for wearable device with EEG and biometric sensors |
| CN112905015A (en) * | 2021-03-08 | 2021-06-04 | 华南理工大学 | Meditation training method based on brain-computer interface |
| CN113552946A (en) * | 2021-07-21 | 2021-10-26 | 浙江强脑科技有限公司 | Meditation training method, device, terminal and medium based on intelligent wearable device |
Family Cites Families (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11471091B2 (en) * | 2010-07-29 | 2022-10-18 | Kulangara Sivadas | Mind strength trainer |
| US9983670B2 (en) * | 2012-09-14 | 2018-05-29 | Interaxon Inc. | Systems and methods for collecting, analyzing, and sharing bio-signal and non-bio-signal data |
| CA2935813C (en) * | 2013-01-08 | 2021-12-21 | Interaxon Inc. | Adaptive brain training computer system and method |
| WO2014172775A1 (en) * | 2013-04-22 | 2014-10-30 | Personal Neuro Devices Inc. | Methods and devices for brain activity monitoring supporting mental state development and training |
| US9712736B2 (en) * | 2015-12-15 | 2017-07-18 | Intel Coprporation | Electroencephalography (EEG) camera control |
| US10188345B2 (en) * | 2016-02-12 | 2019-01-29 | Fitbit, Inc. | Method and apparatus for providing biofeedback during meditation exercise |
| US10632277B2 (en) * | 2016-04-20 | 2020-04-28 | The Staywell Company, Llc | Virtual reality guided meditation in a wellness platform |
| CA3071442A1 (en) * | 2017-08-18 | 2019-02-21 | Bodhi Neuro Tech, Inc. | Systems and methods for meditation enhancement |
| GB201714471D0 (en) * | 2017-09-08 | 2017-10-25 | Virtually Live (Switzerland) Gmbh | Training Aid |
| EP3790463A1 (en) * | 2018-06-29 | 2021-03-17 | Mybrain Technologies | Multiclass classification method for the estimation of eeg signal quality |
| US20200069209A1 (en) * | 2018-07-26 | 2020-03-05 | Christopher Keane | Neurofeedback brain wave synchrony training techniques, systems, and methods |
| CN109044351A (en) * | 2018-08-03 | 2018-12-21 | 周文芳 | Logical thinking training system and method based on EEG feedback |
| CN109376286A (en) * | 2018-10-16 | 2019-02-22 | 深圳市太科健康科技有限公司 | A kind of entrainment instrument controlling terminal |
| US20210307684A1 (en) * | 2018-12-14 | 2021-10-07 | Brainpatch Limited | System and method for implementing a stimulation protocol |
| WO2020248008A1 (en) * | 2019-06-14 | 2020-12-17 | The University Of Adelaide | A method and system for classifying sleep related brain activity |
| CN110876626B (en) * | 2019-11-22 | 2022-08-12 | 兰州大学 | Depression detection system based on optimal lead selection of multi-lead electroencephalogram |
| CN111317469B (en) * | 2020-01-10 | 2022-12-02 | 深圳市宏智力科技有限公司 | Brain wave monitoring equipment, system and monitoring method |
| CN111292850A (en) * | 2020-01-22 | 2020-06-16 | 福建中医药大学 | ADHD children attention intelligent rehabilitation system |
-
2022
- 2022-02-11 CN CN202210129776.8A patent/CN114652330B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160053326A (en) * | 2014-11-03 | 2016-05-13 | 재단법인대구경북과학기술원 | Smart Meditation Apparatus For Improving Concentration and Operation Method Thereof |
| CN105595997A (en) * | 2016-03-10 | 2016-05-25 | 西安科技大学 | Driving fatigue electroencephalogram monitoring method based on stepped fatigue determination |
| CN106560158A (en) * | 2016-11-23 | 2017-04-12 | 深圳创达云睿智能科技有限公司 | Zen meditation feedback training method and device based on electroencephalogram |
| CN107049306A (en) * | 2017-03-31 | 2017-08-18 | 王晓路 | A kind of detection treatment system based on meditation degree |
| WO2019077421A1 (en) * | 2017-10-17 | 2019-04-25 | Gaurav Gupta | A system and method for customised meditation |
| CN112512423A (en) * | 2018-05-26 | 2021-03-16 | 感官人工智能公司 | Method and apparatus for wearable device with EEG and biometric sensors |
| WO2020121299A1 (en) * | 2018-12-09 | 2020-06-18 | The Medical Research, Infrastructure and Health Services Fund of the Tel Aviv Medical Center | Stress disorder training |
| CN110558959A (en) * | 2019-09-06 | 2019-12-13 | 江苏华康信息技术有限公司 | HRV signal analysis method for meditation training based on extreme value energy decomposition method |
| CN112905015A (en) * | 2021-03-08 | 2021-06-04 | 华南理工大学 | Meditation training method based on brain-computer interface |
| CN113552946A (en) * | 2021-07-21 | 2021-10-26 | 浙江强脑科技有限公司 | Meditation training method, device, terminal and medium based on intelligent wearable device |
Non-Patent Citations (3)
| Title |
|---|
| 冥想放松训练对老年高血压患者睡眠障碍的影响;邓冬梅等;《卫生职业教育》;20200210(第03期);161-163 * |
| 正念冥想训练对中学生自我控制能力的干预作用;孙长玉等;《中国健康心理学杂志》;20160915(第09期);1359-1363 * |
| 正念冥想训练对抑郁症患者抑郁状态及睡眠质量的临床改善效果分析;昝婷睿;《世界睡眠医学杂志》;20200228(第02期);103-105 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114652330A (en) | 2022-06-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109522400B (en) | Nuclear protection method, device, computer equipment and storage medium | |
| CN111627512A (en) | Recommendation method and device for similar medical records, electronic equipment and storage medium | |
| CN109858405A (en) | Satisfaction evaluation method, apparatus, equipment and storage medium based on micro- expression | |
| CN108091391A (en) | Illness appraisal procedure, terminal device and computer-readable medium | |
| CN115862868A (en) | Psychological assessment system, psychological assessment platform, electronic device and storage medium | |
| CN114201626B (en) | Multimedia recommendation method and device, electronic equipment and storage medium | |
| CN114652330B (en) | Method, device and equipment for evaluating meditation training based on historical electroencephalogram signals | |
| CN110471941B (en) | Method and device for automatically positioning judgment basis and electronic equipment | |
| CN114723494A (en) | Method, device, equipment and storage medium for judging validity of questionnaire processing result | |
| CN117292783A (en) | Medical image report generating system | |
| CN110852105A (en) | Time data normalization method, device, medium and electronic equipment | |
| CN112749639B (en) | Model training method and device, computer equipment and storage medium | |
| CN117521634A (en) | Data analysis method, device, electronic equipment and storage medium | |
| CN108305688A (en) | Illness appraisal procedure, terminal device and computer-readable medium | |
| CN110866146A (en) | Video recommendation method and device, computer equipment and storage medium | |
| CN115827877A (en) | Proposal auxiliary combination method, device, computer equipment and storage medium | |
| CN113990512B (en) | Abnormal data detection method and device, electronic equipment and storage medium | |
| CN115376692A (en) | Health data processing method and device, electronic equipment and storage medium | |
| EP4165646A1 (en) | Methods and systems for searching an ecg database | |
| CN110516254B (en) | Information processing method and electronic equipment | |
| CN112711579A (en) | Medical data quality detection method and device, storage medium and electronic equipment | |
| CN110807310B (en) | Method, apparatus and storage medium for evaluating X-ray film analysis results | |
| CN115907272B (en) | Method and device for evaluating brokers, electronic equipment and storage medium | |
| Marcq et al. | Standard Errors of Kernel Equating: Accounting for Bandwidth Estimation | |
| Mori et al. | Signal processing to remove spurious contributions to the assessment of respiratory mechanics |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |